Construction and Clinical Validation of a Predictive Model for Postoperative Adjuvant Therapy in Hepatocellular Carcinoma Based on Whole-Slide Digital Pathological Images and Deep Learning
Phase: NA
Conditions: Hepatocellular Carcinoma (HCC), Artificial Intelligent, Adjuvant Chemoradiotherapy, TACE, Lenvatinib, Liver Surgery
Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University

 
 
Research Title:  Construction and Clinical Validation of a Predictive 
Model for Postoperative Adjuvant Therapy in Hepatocellular Carcinoma 
Based on Whole-Slide Digital Pathological Images and Deep Learning 
 
 
 
 
 
 
NCT Number:               Under Review  
 
 
 
 
 
 
Document date:              2025/10/24 
 
 

Research Protocol 
1. Research Background 
Hepatocellular Carcinoma (HCC) is a common malignant tumor worldwide, ranking sixth in 
incidence and third in mortality globally, causing approximately 480,000 deaths each year. China 
accounts for more than 45% of global cases, bearing an extremely heavy disease burden¹. Radical 
surgical resection is the main means for early-stage liver cancer patients to achieve long-term 
survival, but the 5-year postoperative recurrence rate is as high as 50%-70%, which severely 
limits patient prognosis². Postoperative adjuvant therapy has become a key strategy to delay 
recurrence and improve survival. Among them, Transarterial Chemoembolization (TACE) and 
Tyrosine Kinase Inhibitors (TKIs, such as sorafenib, lenvatinib, etc.) have been widely used in the 
treatment of patients at high risk of recurrence. 
 
TACE induces ischemic necrosis by locally embolizing tumor-feeding blood vessels combined with 
chemotherapy drug perfusion, and is suitable for intermediate-stage liver cancer; while 
multi-target TKI drugs can systematically inhibit signaling pathways such as Vascular Endothelial 
Growth Factor Receptor (VEGFR) and Platelet-derived Growth Factor Receptor (PDGFR), exerting 
anti-angiogenic and anti-tumor proliferation effects, and have become the standard treatment 
for advanced liver cancer. However, both treatment methods have significant limitations: the 
objective response rate of TACE is usually only 50%-60%, some patients cannot benefit and may 
suffer from liver function damage⁶; although TKIs can prolong RFS by 3-5 months in postoperative 
high-risk patients, the treatment efficiency in unselected populations is less than 20%, and the 
incidence of grade 3-4 adverse reactions (such as hypertension, hand-foot skin reaction, 
proteinuria) is over 50%, leading to 20% of patients discontinuing treatment due to intolerance to 
toxic and side effects. Currently, clinically, there is a lack of efficient and reliable biomarker 
systems to identify potential beneficiary populations, resulting in treatment decisions still relying 
on clinical experience (such as traditional pathological features based on tumor size, vascular 
invasion, etc.), with limited individualization, leading to waste of medical resources and 
additional treatment burden on patients. 
 
Recent studies have shown that the Tumor Immune Microenvironment (TIME) is the key 
biological basis affecting the sensitivity of TACE and TKI treatment. The compositional 
characteristics of TIME, including immune cell infiltration (such as CD8⁺ T cells, Tregs, CTL, 
M2-type tumor-associated macrophages), expression of immune checkpoint molecules (such as 
Programmed Cell Death Ligand 1, PD-L1) and spatial structure, are closely related to treatment 
response. For example, immune-inflammatory TIME (high CD8⁺ T cell density, formation of 
tertiary lymphoid structures) may be positively correlated with tumor necrosis after TACE and TKI 
treatment response; while immune-exempt or desert phenotypes often indicate treatment 
resistance. Nevertheless, current TIME assessment mostly relies on high-resolution technologies 
such as Multiplex Immunohistochemistry (mIHC) and spatial transcriptomics. Although these 
methods can finely depict the microenvironment, they have application bottlenecks such as high 
cost, complex operation, high requirements for sample quality and low standardization, which 
limit their clinical promotion. 
 

The breakthrough of artificial intelligence, especially deep learning technology, has provided a 
new path for mining deep pathological information in routine HE-stained Whole Slide Imaging 
(WSI). As a routine data for postoperative pathological diagnosis (each HCC patient will generate 
HE-stained WSI after surgery without additional sampling or testing), the cell morphology and 
tissue structure details contained in WSI have been confirmed to map the core characteristics of 
TIME. For example, through Convolutional Neural Network (CNN) and Vision Transformer (ViT) 
architectures, morphological patterns related to CD8⁺ T cell infiltration density and PD-L1 
expression can be automatically identified, realizing cross-modal prediction of "HE morphology 
→ immune status". Recent studies have confirmed that deep learning models based on HE-WSI 
can accurately predict microsatellite instability (MSI, AUC 0.88) in colorectal cancer, PD-L1 
expression (AUC 0.80) in non-small cell lung cancer and Tumor Mutation Burden (TMB, AUC 0.91); 
in the field of liver cancer, there are also studies predicting postoperative recurrence risk (AUC 
0.82) and immune cell infiltration in liver cancer tissue (AUC 0.78) through WSI deep learning. 
However, no research has focused on the clinical pain point of "predicting the efficacy of 
postoperative adjuvant therapy (TACE/TKI)", and there is a lack of multi-center large-sample 
clinical validation. 
 
Therefore, constructing a prediction model for postoperative adjuvant therapy of hepatocellular 
carcinoma based on HE whole-slide images and deep learning algorithms not only helps to 
analyze the mechanism of TIME in treatment response, but also breaks through the limitations of 
existing detection technologies such as high cost, long cycle and dependence on special platforms, 
promoting the development of liver cancer adjuvant therapy decisions towards precision and 
inclusiveness. This project intends to integrate multi-center clinicopathological data and artificial 
intelligence algorithms to establish a digital pathological model suitable for predicting the efficacy 
of TACE and TKI, and conduct strict clinical validation, in order to provide a scientific and reliable 
decision-making tool for the selection of postoperative adjuvant therapy for hepatocellular 
carcinoma. 
 
2. Research Objectives 
This study aims to construct and validate a prediction model for postoperative adjuvant therapy 
of hepatocellular carcinoma based on whole-slide digital pathological images and deep learning. 
By mining the morphological characteristics of the tumor immune microenvironment contained 
in HE-stained sections, an AI algorithm system that can accurately predict patients' response to 
different postoperative adjuvant therapy regimens (including surgery alone, surgery combined 
with TACE, surgery combined with TACE and TKI, etc.) will be developed. The study will establish 
an end-to-end prediction model from HE morphological features to treatment response, and 
verify its clinical utility through multi-center retrospective and prospective studies, ultimately 
providing an individualized and precise decision support tool for postoperative adjuvant therapy 
of hepatocellular carcinoma, improving patient prognosis and the efficiency of medical resource 
utilization. 
 
3. Research Design and Methods 
3.1 Study Design and Research Objects 
This study is a comprehensive research combining diagnostic trials and interventional studies, 

divided into two parts and multiple stages, aiming to develop and validate an artificial 
intelligence-based prediction system for postoperative adjuvant therapy of hepatocellular 
carcinoma (HCC). The overall research follows the standardized process for the development of 
Artificial Intelligence Medical Devices (Software as a Medical Device, SaMD), including 
retrospective data collection, model development and training, retrospective validation, 
prospective observational validation, and final prospective interventional research. 
 
Part 1: Identification of Liver Cancer Tumor Microenvironment and Adjuvant Therapy 
Prediction Model - Retrospective Study 
(1) Construction and Validation of Tumor Microenvironment Identification Model: 
Development and Training Phase: Public datasets (such as TCGA) and retrospective datasets from 
our center (n=1500, 400 cases have been collected) will be used as the training set. The core task 
of this phase is to develop a deep learning model (such as an architecture based on Vision 
Transformer or ResNet) to automatically identify, segment and quantify key tumor 
microenvironment features from H&E whole-slide digital pathological images (Whole Slide 
Images, WSIs), including but not limited to the degree of immune cell infiltration, stromal ratio, 
vascular invasion, necrotic areas, etc. All images will be independently blindly annotated by at 
least two senior pathologists, and disputes will be resolved through consensus or arbitration by a 
third senior pathologist to form the gold standard. 
External Validation Phase: Retrospective WSI datasets from 8-10 cooperative medical centers in 
China will be used to initially verify the generalization ability of the model. This phase aims to 
evaluate the robustness of the model under different scanning equipment, different sectioning 
and staining processes, and different pathologists' interpretation habits. 
 
(2) Construction and Multi-level Clinical Validation of Treatment Response Prediction Model: 
This part is the core of the research, aiming to construct a classification model that can predict 
patients' response to different postoperative adjuvant therapy regimens (surgery alone, surgery + 
TACE, surgery + TACE + TKI) based on the model output of the first part (i.e., quantified tumor 
microenvironment features) combined with necessary clinical variables (such as TNM stage, 
Child-Pugh classification of liver function, AFP level, etc.). This part adopts a strict hierarchical 
validation strategy: 
Model Training Phase: Retrospective data from our center (n=1500) will be used. Patients' 
inclusion must meet: ① Pathologically confirmed as HCC; ② Underwent radical resection; ③ 
Have complete postoperative adjuvant therapy and follow-up data. Definition of treatment 
response: The primary endpoint is Relapse-Free Survival (RFS), and the secondary endpoints 
include Overall Survival (OS) and Objective Response Rate (ORR). Training will be conducted 
through multi-class machine learning algorithms (such as gradient boosting trees, deep neural 
networks, etc.), and the category of treatment regimen that the patient is most likely to benefit 
from will be output. 
Internal Validation Phase: An independent validation set (n≈500, no overlap with the training 
set) will be reserved from the center's data by random division to initially evaluate the 
discriminative performance of the model, and hyperparameter tuning will be performed to 
prevent overfitting. 
External Validation Phase 1 (Retrospective Validation and Model Calibration): Retrospective data 

from multiple centers nationwide (8-10 centers) (n=3000) will be collected. The main purposes of 
this phase are: ① Verify the model performance on a larger-scale and more heterogeneous 
external data; ② Calibrate or conduct limited iterative optimization of the model according to 
the validation results to form the final finalized prediction model. 
External Validation Phase 2 (Large-scale Retrospective Validation): Using the finalized model, 
validation will be performed on a new, ultra-large-scale (n=10,000) retrospective dataset (from 
3-5 centers) to further consolidate its evidence level and provide a solid basis for prospective 
studies. 
 
(3) Research Objects: The research objects are HCC patients who received radical surgical 
resection. The study will construct multiple datasets for model development, training and 
validation, and the construction of all datasets will follow strict inclusion and exclusion criteria. 
① Patient Inclusion and Exclusion Criteria 
Inclusion Criteria: ① Histopathologically confirmed as hepatocellular carcinoma; ② Aged 
18-75 years; ③ Underwent radical resection of primary liver cancer (R0 resection); ④ 
Possessed complete, digitally scannable H&E-stained paraffin-embedded tissue sections (glass 
slides) after surgery; ⑤ Had complete and accessible clinicopathological data and follow-up 
data (including but not limited to: age, gender, HBsAg status, cirrhotic background, AFP level, 
PIVKA-II level, tumor size and number, CNLC stage, TNM stage (AJCC 8th edition), BCLC stage, 
vascular invasion, degree of differentiation, postoperative adjuvant therapy regimen and detailed 
medication/operation records, recurrence and survival data). 
Exclusion Criteria: ① Received anti-tumor treatment (such as TACE, targeted therapy, 
immunotherapy, radiotherapy, etc.) at other non-cooperating centers before surgery, making it 
impossible to obtain initial imaging and clinical data; ② Complicated with primary malignant 
tumors of other organs; ③ Positive postoperative surgical margin (R1 or R2 resection); ④ Poor 
quality of tissue sections (such as severe fading, folding, damage, insufficient tissue) that cannot 
be digitally scanned or reliably analyzed; ⑤ Severe lack of clinical or follow-up data. 
 
② Definition and Source of Datasets 
The datasets of this study will adopt a multi-center, multi-stage data collection strategy, and be 
strictly divided according to purposes to ensure that the training, validation and test sets are 
independent of each other with no risk of data leakage. 
1500 eligible surgical patients from our center will be included and randomly divided into a 
model training set (n=1000 cases) and an internal validation set (n=500 cases) at a ratio of 2:1, 
which will be used for model training and preliminary model validation and optimization 
respectively. Currently, 400 eligible patients have been collected from our center, and the 
remaining 1100 cases will be continuously extracted from the center's historical medical record 
database according to the inclusion and exclusion criteria to make up the number. 
External validation adopts a multi-center collaboration model. ① External Validation Set 1 is a 
retrospective multi-center validation and calibration set, which will include 3000 retrospective 
data from 8-10 medical centers in China. It is used to verify the generalization ability of the model 
among different institutions and calibrate or conduct limited iterative optimization of the initial 
model. ② External Validation Set 2 is a large-scale retrospective test set, which will include 
10,000 retrospective data from 3-5 large medical centers in China to conduct the final test of the 

model, fully evaluate its effectiveness and stability. During this period, the locked model will be 
used for pure forward validation of this dataset without any parameter adjustment. 
 
Part 2: Clinical Validation of Liver Cancer Adjuvant Therapy Prediction Model - Prospective 
Study 
(1) Prospective Observational Study: 1000 eligible postoperative HCC patients will be 
prospectively and consecutively enrolled in 10-15 centers. Their WSI images will be collected and 
input into the model to obtain the predicted treatment regimen, but no intervention will be 
made on the actual clinical decision during this study phase. Researchers will record the model's 
prediction results and actual clinical decisions, and observe the patients' actual prognosis 
through follow-up to evaluate the consistency between the model's prediction and the actual 
outcome, as well as the performance attenuation in the real-world environment. 
 
(2) Prospective Interventional Study: This is the final validation step of the research. A 
prospective, randomized controlled or pragmatic clinical trial will be carried out in conjunction 
with 3-5 centers, with approximately 600 patients enrolled. Patients will be randomly divided into 
two groups: ① Model-assisted Decision-making Group: Clinicians will formulate treatment 
plans for patients according to the optimal treatment regimen predicted by the model; ② 
Standard Treatment Group: Treatment plans will be formulated entirely by clinicians based on 
existing guidelines and clinical experience. The primary comparison is the RFS of patients in the 
two groups, so as to provide the highest level of evidence-based medical evidence to prove the 
clinical utility of model-assisted decision-making and the value of improving prognosis. 
 
(3) Research Objects: The research objects are HCC patients who received radical surgical 
resection. In the prospective study, multiple datasets will be constructed for model optimization 
and validation, and the construction of all datasets will follow strict inclusion and exclusion 
criteria. 
① Patient Inclusion and Exclusion Criteria 
Inclusion Criteria: ① Histopathologically confirmed as hepatocellular carcinoma; ② Aged 
18-75 years; ③ Underwent radical resection of primary liver cancer (R0 resection); ④ 
Possessed complete, digitally scannable H&E-stained paraffin-embedded tissue sections (glass 
slides) after surgery; ⑤ Had complete and accessible clinicopathological data and follow-up 
data (including but not limited to: age, gender, HBsAg status, cirrhotic background, AFP level, 
PIVKA-II level, tumor size and number, CNLC stage, TNM stage (AJCC 8th edition), BCLC stage, 
vascular invasion, degree of differentiation, postoperative adjuvant therapy regimen and detailed 
medication/operation records, recurrence and survival data); ⑥ Patients must sign the 
informed consent form for the prospective study. 
Exclusion Criteria: ① Received anti-tumor treatment (such as TACE, targeted therapy, 
immunotherapy, radiotherapy, etc.) at other non-cooperating centers before surgery, making it 
impossible to obtain initial imaging and clinical data; ② Complicated with primary malignant 
tumors of other organs; ③ Positive postoperative surgical margin (R1 or R2 resection); ④ Poor 
quality of tissue sections (such as severe fading, folding, damage, insufficient tissue) that cannot 
be digitally scanned or reliably analyzed; ⑤ Severe lack of clinical or follow-up data. 
 

② Definition and Source of Datasets 
The datasets of this study will adopt a multi-center, multi-stage data collection strategy, and be 
strictly divided according to purposes to ensure that the training, validation and test sets are 
independent of each other with no risk of data leakage. 
The prospective cohort adopts a multi-center collaboration model: ① Prospective 
Observational Study Cohort: 1000 patients will be consecutively enrolled in 10-15 centers, their 
glass slides will be collected for digital scanning and input into the model, but no intervention will 
be exercised, only the model's prediction results and actual clinical outcomes will be recorded to 
evaluate the model's performance in the real clinical environment. ② Prospective 
Interventional Study Cohort: A randomized controlled trial will be carried out in 3-5 centers, with 
a plan to enroll 600 patients. With the patients' informed consent, they will be randomly divided 
into an intervention group (AI model-assisted decision-making) and a control group (standard 
clinical decision-making). For patients in the intervention group, clinicians will refer to the 
optimal treatment regimen predicted by the model to formulate plans; the control group will 
proceed according to the routine process, and then the clinical outcomes of the two groups will 
be compared to provide high-level evidence for the clinical utility of the model. 
 
3.2 AI System Deployment and Intervention 
To address clinical challenges such as high heterogeneity of postoperative treatment response 
and difficulty in efficacy prediction of hepatocellular carcinoma, this study intends to construct 
the HepMoE (Hepatocellular Mixture-of-Experts) multi-modal intelligent decision-making system. 
The system takes whole-slide digital pathology (H&E and multiple IHC stains: CK7, CK19, Ki-67) as 
the core data source, combines clinical and pathological structured information, and relies on a 
Mixture-of-Experts multi-modal deep learning framework driven by a pathology foundation 
model to realize individualized efficacy prediction and prognostic stratification of different 
adjuvant treatment regimens after liver cancer surgery. 
The system integrates the capabilities of the latest Pathology Foundation Model and makes full 
use of the self-supervised representation learning results on large-scale whole-slide corpora. 
Through transfer and fine-tuning mechanisms, HepMoE can efficiently extract cell-level and 
tissue-level phenotypic features from H&E and IHC multi-stained images, capturing tumor 
morphology, immune microenvironment and molecular phenotypic heterogeneity. 
The model adopts Stain-specific Expert Networks to perform feature decoupling and fine-grained 
modeling for different staining channels (H&E, CK7, CK19, Ki-67) respectively, and a Gated Fusion 
Module dynamically integrates multi-modal inputs to balance the contributions of morphological 
and immune features. On the basis of considering clinical variables (such as TNM stage, 
Child-Pugh classification, AFP level, etc.) and text information in pathological reports, it realizes 
individualized treatment benefit estimation and adjuvant treatment response prediction. Finally, 
HepMoE will form a precise treatment assistance system integrating pathology foundation model 
representation, interpretable decision-making and clinical integration, providing an intelligent 
and promotable decision support platform for postoperative adjuvant therapy of hepatocellular 
carcinoma. 
 
3.3 Evaluation Index System (Algorithm Performance, Clinical Utility, System Stability) 
This study comprehensively evaluates the comprehensive value of the AI model from three 

aspects: algorithm performance, clinical utility, and system stability. 
 
Algorithm performance evaluation is mainly conducted during the model development and 
validation phase, using quantitative indicators to objectively assess the accuracy and robustness 
of the tumor microenvironment identification model and the treatment response prediction 
model. The evaluation of the tumor microenvironment identification model includes the 
following indicators: based on pixel-level analysis, the Dice Similarity Coefficient (DSC) is used to 
assess the overlap between the segmentation results and expert annotations. Meanwhile, the 
Intersection over Union (IoU) is calculated as a supplementary indicator. Based on regional-level 
analysis, Precision, Recall, and F1-Score are used to evaluate the recognition performance of 
specific microenvironment components. The evaluation of the treatment response prediction 
model adopts the following indicators: the Area Under the Receiver Operating Characteristic 
Curve (AUC-ROC) is used to assess the binary classification discrimination ability of the model, 
and the macro-average and micro-average AUC values are calculated. Accuracy, Precision, Recall, 
and F1-Score are calculated through the confusion matrix to evaluate the performance of 
multi-classification tasks. Calibration Curve and Brier Score are used to assess the accuracy of 
predicted probabilities. 
 
Clinical utility evaluation is carried out during the prospective study phase, focusing on assessing 
the actual impact of the model on clinical decisions and patient prognosis. The evaluation of 
primary clinical endpoints includes: Relapse-Free Survival (RFS), defined as the time from 
randomization to tumor recurrence or death from any cause. Survival analysis is performed using 
the Kaplan-Meier method, the Log-rank test is used to compare differences between groups, and 
the Hazard Ratio (HR) and its 95% confidence interval are calculated. Overall Survival (OS), 
defined as the time from randomization to death from any cause, is analyzed using the same 
statistical method. The evaluation of secondary clinical endpoints includes: the rate of treatment 
decision 
change, 
which 
calculates 
the 
proportion 
of 
inconsistencies 
between 
AI 
recommendations and clinicians' initial plans; physician compliance, which assesses the 
proportion of physicians in the intervention group who ultimately follow AI recommendations; 
Decision Curve Analysis (DCA) is used to evaluate the clinical net benefit of the model, and the 
standardized net benefit value under different threshold probabilities is calculated; a Likert scale 
(1-5 points) is used to assess changes in physicians' decision-making confidence. 
 
System stability evaluation focuses on the operational performance and reliability of the AI 
system in real clinical environments. Technical performance indicators include: inference speed, 
which records the average time and standard deviation for processing a single whole-slide image; 
system availability, calculated by the formula: Availability = (1 - Downtime / Total Operating Time) 
× 100%; concurrent processing capability, which tests the performance of the system when 
processing multiple requests simultaneously; API response time, which monitors the 
communication delay with the hospital information system interface. Usability evaluation adopts 
the System Usability Scale (SUS), which includes 10 items scored on a 5-point Likert scale, and the 
total score is converted to a percentage for evaluation. At the same time, a task completion rate 
test is conducted to record the success rate of users completing specific operations within a 
specified time. Reliability evaluation is carried out by long-term monitoring of the following 

indicators: Mean Time Between Failures (MTBF), which records the average time the system 
operates normally continuously; Mean Time To Repair (MTTR), which records the average time 
required for the system to recover from a failure; error rate statistics, which classify and record 
various errors occurring during system operation and their frequency. 
 
All evaluation results will form a comprehensive assessment report to provide data support for 
model optimization and clinical application. A special database will be established for the data 
collected during the evaluation to support subsequent in-depth analysis and research. Through a 
systematic evaluation system, it is ensured that the artificial intelligence model meets the clinical 
application standards in terms of accuracy, practicality, and reliability. 
 
3.4 Sample Size Estimation (Explaining the Sources of Parameters and Calculation Tools for 
Sample Size Calculation) 
This study covers multiple phases including retrospective model development and prospective 
clinical validation. Each phase has different objectives, and the basis and methods for sample size 
estimation also vary. This section details the basis, parameter sources, and methods for sample 
size calculation in each part, and clearly presents the key calculation formulas. 
 
 3.4.1 Sample Size for the Retrospective Study Phase: 
(1) Sample size for model training and validation: Training set (n = 1,000) and internal validation 
set (n = 500). 
Estimation basis: The sample size at this phase mainly follows the empirical rules and feasibility 
principles in the field of machine learning. For complex deep learning models based on WSI, a 
large amount of annotated data is required to avoid overfitting. Common experience shows that 
1,000–5,000 samples are usually needed per category. This study aims for 4 treatment response 
categories, and setting 1,000 cases can balance model performance and data collection feasibility, 
which is consistent with the scale of existing top-tier pathological AI research. Internal validation 
set: Usually accounting for 20%–30% of the training set, a scale of 500 cases can provide an 
estimate with a small standard error for model performance (such as AUC) and meet the needs of 
hyperparameter tuning. 
 
(2) External Validation Set 1 (n = 3,000) 
Estimation basis: This phase is used for preliminary evaluation of model calibration and 
generalization ability, requiring a sufficient sample size to detect whether there is a clinically 
significant decline in model performance. The following formula is used for estimation: 
Where: Z₁₋α/₂: Z-value corresponding to the confidence level (Z₀.₉₇₅ = 1.96 when α = 0.05); p: 
Expected model accuracy (e.g., p ≈ 0.80 when AUC = 0.85); d: Allowable error (set to 0.03). 
Substitute for calculation: 
Setting 3,000 cases can provide more sufficient statistical power, supporting subgroup analysis 
and subsequent model fine-tuning. 
 
(3) External Validation Set 2 (n = 10,000) 
Estimation basis: This phase conducts the final test on the locked model, requiring high-precision 
estimation of performance indicators (such as AUC). The sample size is inferred using the width of 

the AUC confidence interval: 
Where:  ;  
Assuming AUC = 0.80 and the desired 95% confidence interval width ≤ 0.04 (i.e., ±0.02), 
approximately 8,600 cases are needed as calculated by PASS software. Setting 10,000 cases 
retains a sufficient margin to ensure high credibility of the results. 
 
3.4.2 Sample Size for the Prospective Study Phase 
(1) Sample size for prospective observational validation (n = 1,000) 
Estimation basis: This phase evaluates the strength of the association (C-index) between model 
predictions and real clinical prognosis, using the sample size formula for survival analysis. Taking 
Relapse-Free Survival (RFS) as the primary endpoint, the following formula and parameters are 
used: 
Sample size calculation formula (Log-Rank test): 
Where: HR: Hazard ratio (assumed to be 0.7); π₁, π₂: Proportions of samples in the two 
groups (50% each); P(E): Expected event rate; Z₁₋α/₂ = 1.96 (α = 0.05, two-sided); Z₁₋β = 
1.28 (β = 0.10, 90% test power) 
Assuming the 12-month RFS rate in the control group is 60% and the corresponding HR in the 
model-consistent group is 0.7, a total of 256 events are expected. As simulated by PASS 2021 
software, 1,000 samples can observe more than 300 events within a 24-month study period (12 
months of enrollment + 12 months of follow-up), meeting the test power requirements and 
supporting subgroup analysis. 
 
(2) Sample size for prospective interventional study 
Estimation basis: This phase is a randomized controlled trial (RCT), and the sample size needs to 
be strictly calculated. Taking RFS as the primary endpoint, the design is as follows: 
Sample size formula (Log-Rank test) is adopted: 
Parameter settings: Baseline event rate: The 12-month RFS rate in the standard treatment group 
(Arm B) is 65%. Effect size: The 12-month RFS rate in the AI-assisted decision-making group (Arm 
A) is absolutely increased by 10% (to 75%), corresponding to a Hazard Ratio (HR) = 0.67. 
Significance level (α): 0.05. Test power (1-β): 80% (common standard for RCTs). Accrual Time: 
18 months. Follow-up time: 12 months. Attrition rate: 10%. 
As calculated by PASS 2021 software, the required total number of events is 200. Considering the 
attrition rate, the required total sample size is 600 cases (300 cases per group). This number can 
detect the clinical difference with HR = 0.67 and meet the power requirements under follow-up 
and attrition conditions. 
 
3.5 Bias Control and Fairness 
To ensure the scientificity and reliability of the research results, this project adopts a multi-level 
and systematic strategy to strictly control various potential biases in the research. 
 
During the study design phase, to address selection bias, clear and standardized inclusion and 
exclusion criteria are adopted. In multi-center studies, all centers are required to adopt a unified 
patient screening process, and the possibility of selection bias is evaluated by comparing the 
basic characteristics of enrolled and non-enrolled patients. For the prospective interventional 

study, central randomization (block randomization, stratified by center) is used to assign patients 
to the AI-assisted decision-making group or the standard treatment group to ensure the balance 
of baseline characteristics between groups. 
 
During the data collection and processing phase, strict blinding principles are implemented to 
reduce information bias and measurement bias. In the pathological image annotation phase, at 
least two senior pathologists are invited to conduct independent annotations without knowing 
any clinical outcomes of the patients, and disputes are referred to a third expert for arbitration to 
construct a high-quality gold standard. During the model training phase, algorithm developers 
are ensured not to access data from the subsequent validation set to prevent overfitting caused 
by artificial tuning. In the endpoint evaluation phase, an independent Clinical Endpoint 
Adjudication Committee is established. Members of this committee are completely unaware of 
the patients' grouping information and AI prediction results, and only objectively judge 
recurrence and survival events according to established standards, thereby maximizing the 
avoidance of evaluation bias. 
 
During the algorithm development and validation phase, the following measures are taken to 
address biases specific to AI models. For spectral bias, sliced images digitized by different medical 
institutions using different brands of scanners are collected, and color normalization technology 
(such as the Macenko method) is adopted in the preprocessing phase to reduce the impact of 
staining differences. For measurement bias, the performance differences of the model among 
different populations and equipment are evaluated and corrected through multi-center and 
large-sample external validation. To prevent overfitting, in addition to dividing an independent 
validation set, techniques such as regularization and Dropout are used, and the results of 
cross-validation or Bootstrap sampling are used in model performance evaluation. 
 
In terms of fairness, a comprehensive fairness evaluation will be conducted to analyze whether 
there are significant differences in model performance among subgroups with different 
demographic (gender, age) and clinical characteristics (tumor stage, etiology, liver function 
classification). Core evaluation indicators include: inter-group AUC difference, equalized odds, 
and fairness metrics for sensitive attributes. If the model is found to have performance 
discrimination against specific subgroups, algorithm correction will be performed to ensure the 
fairness of algorithmic decisions. All fairness evaluation results are fully disclosed in the research 
report. 
 
Finally, during the statistical analysis phase, to address potential residual confounding bias, 
multivariate Cox regression models and Logistic regression models are used to statistically adjust 
for known prognostic factors (such as TNM stage, vascular invasion, AFP level, etc.) to more 
accurately estimate the independent predictive efficacy of the AI model. 
 
3.6 Data Governance System (Data Collection, Transmission, Storage Security Control; Full-Chain 
Data Recording) 
This study establishes a sound data governance system to ensure the quality, security, and 
compliance of data throughout the research process. The data governance system covers all links 

such as data collection, transmission, storage, processing, analysis, and archiving, forming a 
complete data management closed loop. 
 
Standardized and unified specifications are adopted for data collection. Detailed Standard 
Operating Procedures (SOPs) for data collection are formulated to clarify the definition, format, 
and collection requirements of various types of data. Clinical data uses structured electronic Case 
Report Forms (eCRFs), with logical verification and range checks set to ensure the accuracy and 
completeness of data collection. Pathological image data collection follows digital scanning 
standards, with unified scanning resolution (0.25μm/pixel) and file format (SVS). Equipment 
calibration and quality control testing are performed before each batch of scanning. Laboratory 
test data is directly collected through docking with the Laboratory Information System (LIS) to 
avoid manual transcription errors. 
 
Encrypted protection is implemented for data transmission security control. All data transmission 
adopts end-to-end encryption technology, and the TLS 1.3 protocol is used to ensure the security 
of the transmission channel. A data transmission log recording system is established to record 
information such as data transmission time, content, sender, and receiver. Data desensitization is 
performed before cross-institutional data transmission to remove direct identifiers and generalize 
indirect identifiers. Integrity verification is implemented during data transmission, and digital 
signature technology is used to prevent data tampering. 
 
A hierarchical storage strategy is adopted for data storage management. Raw data is stored on 
internal servers of each institution with physical isolation and access control implemented. 
Analytical data is stored on a dedicated research platform, and encrypted storage technology is 
used to protect data security. A data backup and recovery mechanism is established, with 
incremental backups performed daily and full backups performed weekly, and backup data is 
stored off-site. The data storage system is ISO 27001 certified, and regular security vulnerability 
scans and penetration tests are conducted. 
 
Whole-process monitoring is implemented for data processing quality control. A data quality 
evaluation indicator system is established, including dimensions such as completeness, accuracy, 
consistency, and timeliness. Automated data quality inspection tools are used to perform regular 
data quality evaluations. When data quality problems are found, root cause analysis is initiated 
and corrective and preventive actions are implemented. All data processing operations record 
complete audit logs to ensure the traceability of operations. 
 
Standardized processes are followed for data archiving and destruction. After the completion of 
the research, all research data is archived and stored in the specified format. Archived data 
includes raw data, processing process data, and final analysis data. A data access permission 
management system is established to strictly control the scope of data use. When data is 
destroyed, physical destruction or multiple overwriting technologies are used to ensure that the 
data is irrecoverable. The data archiving and destruction processes are detailedly recorded and 
relevant certification documents are retained. 
 

The organizational structure of data governance clarifies the division of responsibilities. A Data 
Governance Committee is established to formulate data management policies and standards. 
Data administrators are designated to be responsible for daily data management work. A Data 
Quality Supervision Team is established to regularly inspect the data quality. All researchers are 
required to complete data management training and pass the assessment to ensure the 
implementation of data management specifications. 
 
3.7 Adverse Events (Specific Definitions, Classification, Reporting Methods, and Handling 
Measures for Adverse Events/Severe Adverse Events) 
Adverse events defined in this study include any negative medical outcomes related to AI-assisted 
decision-making. They are specifically classified into three levels: Grade 1 Adverse Event (mild) 
refers to minor inconvenience or reversible additional examinations caused by AI 
recommendations without long-term impact; Grade 2 Adverse Event (moderate) refers to 
improper selection of treatment regimens caused by AI recommendations that require medical 
intervention for correction but do not result in permanent harm; Grade 3 Adverse Event (severe) 
refers to misdiagnosis and mistreatment caused by AI recommendations that result in permanent 
functional impairment or significantly shortened survival time. Once an adverse event occurs, the 
clinical center must report it to the study's Safety Monitoring Committee through an online 
system within 24 hours. The committee is composed of experts in hepatology, oncology, 
pathology, and medical ethics. Handling measures include immediately suspending the use of the 
AI system, organizing experts to evaluate the cause of the event, determining the correlation with 
the AI system, and formulating corrective measures. All adverse events will be completely 
recorded and included in the final research report. 
 
3.8 Statistical Analysis Plan (Methods for Handling Missing Data, Definition and Selection of 
Statistical Analysis Datasets, Statistical Methods for Comparing Primary and Secondary Outcome 
Indicators, Algorithm Stability Evaluation) 
This study will adopt rigorous statistical analysis methods to ensure the scientificity and reliability 
of the research conclusions. Statistical analysis will be completed using R language (version 4.2.0), 
Python (version 3.9), and SPSS (version 26.0) software. All statistical tests will use two-sided tests, 
with the significance level set at α = 0.05. 
 
Missing data will be handled using multiple imputation. For continuous variables, we will adopt 
the Multiple Imputation by Chained Equations (MICE) method, create 5 imputed datasets, and 
conduct combined analysis. For categorical variables, mode imputation will be used. We will test 
the missing mechanism: if the data is Missing Completely at Random (MCAR), complete case 
analysis will be adopted; if Missing at Random (MAR), multiple imputation will be used; if Missing 
Not at Random (MNAR), selection models will be employed for processing. Sensitivity analysis 
will compare the results of different missing data handling methods. 
 
Statistical analysis datasets include the following three types: The Full Analysis Set (FAS) includes 
all patients who were randomized and received at least one efficacy assessment, analyzed in 
accordance with the Intention-to-Treat (ITT) principle; the Per-Protocol Set (PPS) includes 
patients who fully followed the study protocol, excluding those with major protocol violations; 

the Safety Set (SS) includes all patients who received at least one treatment, used for safety 
evaluation. The main analysis will be based on the FAS set, and the PPS set will be used for 
sensitivity analysis to verify the robustness of the results. 
 
The following methods will be used for the analysis of primary outcome indicators: For 
categorical variables (such as treatment response groups), weighted F1-score and macro-average 
AUC will be used as the main evaluation indicators, and Cohen's Kappa coefficient will be used to 
assess the consistency between model predictions and clinical reality; for survival data (RFS and 
OS), the Kaplan-Meier method will be used to draw survival curves, the Log-rank test will be used 
to compare differences between groups, and a Cox proportional hazards regression model will be 
established to calculate the Hazard Ratio (HR) and its 95% confidence interval, adjusting for 
potential confounding factors such as age, gender, and TNM stage. 
 
The analysis of secondary outcome indicators will include: Using Decision Curve Analysis (DCA) to 
evaluate the clinical net benefit of the model, and Calibration Curve and Brier Score to assess the 
accuracy of predicted probabilities; for continuous variables, repeated measures analysis of 
variance will be used to compare differences between groups; for ordinal variables such as 
physician compliance and decision-making confidence scales, the Mann-Whitney U test will be 
used for analysis. 
 
Algorithm stability evaluation will adopt the following methods: Calculating the 95% confidence 
interval of model performance indicators through Bootstrap sampling (1000 repetitions) to assess 
the stability of the results; using Shapley Additive exPlanations (SHAP) values to analyze feature 
importance and enhance the interpretability of the model; evaluating the consistency of the 
model among different centers by calculating the Intraclass Correlation Coefficient (ICC), with ICC > 
0.75 considered good consistency. 
 
Plans for subgroup analysis and sensitivity analysis include: Conducting subgroup analysis by key 
clinical characteristics such as age (≤60 years vs >60 years), gender, TNM stage (Stage I-II vs 
Stage III-IV), and degree of liver cirrhosis, using interaction tests to assess subgroup differences; 
performing sensitivity analysis by changing statistical model parameters and using different 
missing data handling methods to verify the robustness of the results. 
 
Plan for sample size re-estimation: If the interim analysis shows that the event rate of the primary 
outcome indicator deviates by more than 20% from the initial assumption, the Lan-DeMets 
method combined with the O'Brien-Fleming boundary will be used for sample size re-estimation, 
but only once, and approval from the Data Safety Monitoring Board (DSMB) is required. 
 
All statistical analyses will follow a pre-formulated statistical analysis plan, and any post-hoc 
analyses will be clearly marked as exploratory. Statistical analysis results will report both point 
estimates and 95% confidence intervals, with p-values retained to three decimal places. P-values 
will not be used as the sole basis for decision-making. 
 
 

3.9 Full-Process Quality Control of Project Data (e.g., Methods for Data Entry, Coding, and Storage; 
Measures to Improve Data Quality; Measures for Clinical Observation, Follow-up, and Ensuring 
Subject Compliance; Measures to Ensure Consistency Among Centers in Multi-Center Studies) 
This study establishes a comprehensive and systematic data quality management system covering 
the entire process of data collection, transmission, storage, processing, and analysis to ensure the 
completeness, accuracy, consistency, and reliability of the data. 
 
Standardized operating procedures are adopted for data collection quality control. Unified 
Standard Operating Procedures (SOPs) for data collection are formulated to clarify the definition, 
collection time, and methods of various data. An Electronic Data Capture (EDC) system is used for 
data entry, with logical checks and range checks set to identify outliers and inconsistent data in 
real-time. Clinical data is entered independently by two personnel, and inconsistencies are 
verified by a third party against the original data. Pathological image data collection follows 
standardized processes, using calibrated scanning equipment. Quality control tests are performed 
before each batch of scanning to ensure consistency in image resolution and color. 
 
Strict management measures are implemented for data transmission and storage security control. 
Data transmission adopts encryption protocols (SSL/TLS) to ensure the security of the 
transmission process. A hierarchical management strategy is used for data storage: raw data is 
stored on internal hospital servers with physical isolation and access control; desensitized data 
for analysis is stored on a secure cloud platform. A data backup and recovery mechanism is 
established, with incremental backups performed daily and full backups weekly, and backup data 
is stored off-site. Complete logs are kept for all data operations to ensure traceability. 
 
A multi-level review mechanism is established for data governance and quality control. A Data 
Quality Management Team is set up to conduct regular random checks on data quality and 
generate monthly data quality reports. A data quality evaluation indicator system is established, 
including key indicators such as data completeness rate, consistency rate, and timeliness rate. 
When data quality issues are identified, root cause analysis is initiated and Corrective and 
Preventive Actions (CAPA) are implemented. The entire data cleaning process is documented, 
retaining all traces of data modifications. 
 
Standardized management is implemented for clinical observation and follow-up quality control. 
Detailed SOPs for clinical observation and follow-up are formulated to unify observation 
indicators, measurement methods, and recording standards. An electronic follow-up system is 
adopted to automatically remind of follow-up time points and reduce the loss-to-follow-up rate. 
Researchers receive regular training and quality assessments to ensure uniform operational 
standards. An Endpoint Adjudication Committee is established to uniformly determine disease 
recurrence and survival status using a blinded method, ensuring the objectivity of endpoint data. 
 
Unified management measures are adopted for quality control in multi-center studies. Before the 
start of the study, researchers from all centers receive unified training to ensure consistent 
understanding of the study protocol. A centralized monitoring plan is formulated, including 
regular on-site and remote monitoring. Standardized data collection tools and definitions are 

used to ensure data consistency among all centers. A regular coordination meeting system is 
established to promptly address issues encountered by each center during data collection. 
Centralized random audits are implemented to ensure consistent implementation of the protocol 
across all centers. 
 
Data security management and quality control comply with legal and regulatory requirements. A 
data classification and grading management system is established, with encrypted storage and 
access control for sensitive data. All researchers complete data security and privacy protection 
training and sign confidentiality agreements. Regular security vulnerability scans and risk 
assessments are conducted to promptly eliminate potential security hazards. A data emergency 
response mechanism is established, and emergency plans for data security incidents are 
formulated. 
 
Through the above comprehensive and systematic quality control measures, the authenticity, 
accuracy, and completeness of the research data are ensured, providing a solid guarantee for the 
reliability of the research conclusions. All quality control activities are documented, forming a 
complete quality assurance document system. 
 
4. Sample Size Calculation 
This study covers multiple phases including retrospective model development and prospective 
clinical validation. Each phase has distinct objectives, and thus the basis and methods for sample 
size estimation vary accordingly. This section details the rationale, parameter sources, and 
methodologies for sample size calculation in each part, with clear presentation of key formulas. 
 
4.1 Sample Size for the Retrospective Study Phase 
(1) Sample size for model training and validation: Training set (n = 1,000) and internal validation 
set (n = 500). 
Rationale: The sample size at this phase primarily adheres to empirical rules and feasibility 
principles in the field of machine learning. Complex deep learning models based on Whole Slide 
Imaging (WSI) require a large volume of annotated data to avoid overfitting. Common experience 
suggests that 1,000–5,000 samples are typically needed per category. This study targets 4 
treatment response categories, and setting the training set at 1,000 cases balances model 
performance and data collection feasibility, consistent with the scale of leading pathological AI 
research. Internal validation set: Usually accounting for 20%–30% of the training set, 500 cases 
can provide estimates of model performance (e.g., AUC) with small standard errors, meeting the 
requirements for hyperparameter tuning. 
 
(2) External Validation Set 1 (n = 3,000) 
Rationale: This phase is designed for preliminary evaluation of model calibration and 
generalization ability, requiring a sufficient sample size to detect clinically significant declines in 
model performance. The following formula is used for estimation: 
Where: Z₁₋α/₂: Z-value corresponding to the confidence level (Z₀.₉₇₅ = 1.96 when α = 0.05); p: 
Expected model accuracy (e.g., p ≈ 0.80 when AUC = 0.85); d: Allowable error (set to 0.03). 
Calculation: Setting the sample size at 3,000 cases provides adequate statistical power to support 

subgroup analysis and subsequent model fine-tuning. 
 
(3) External Validation Set 2 (n = 10,000) 
Rationale: This phase involves the final testing of the locked model, requiring high-precision 
estimation of performance indicators (e.g., AUC). The sample size is derived by reversing the 
calculation based on the width of the AUC confidence interval: 
Where: Assuming an AUC of 0.80 and a desired 95% confidence interval width ≤ 0.04 (i.e., ±
0.02), PASS software calculates that approximately 8,600 cases are needed. Setting the sample 
size at 10,000 cases retains a sufficient margin to ensure high credibility of the results. 
 
4.2 Sample Size for the Prospective Study Phase 
(1) Sample size for prospective observational validation (n = 1,000) 
Rationale: This phase evaluates the strength of the association (C-index) between model 
predictions and real clinical outcomes, using sample size formulas for survival analysis. Taking 
Relapse-Free Survival (RFS) as the primary endpoint, the following formula and parameters are 
applied: 
Sample size calculation formula (Log-Rank test): 
Where: HR: Hazard ratio (assumed to be 0.7); π₁, π₂: Proportions of samples in the two 
groups (50% each); P(E): Expected event rate; Z₁₋α/₂ = 1.96 (α = 0.05, two-sided); Z₁₋β = 
1.28 (β = 0.10, 90% statistical power). 
Assuming a 12-month RFS rate of 60% in the control group and a corresponding HR of 0.7 in the 
model-consistent group, a total of 256 events are expected. Simulations using PASS 2021 
software indicate that 1,000 samples can observe over 300 events within a 24-month study 
period (12 months of enrollment + 12 months of follow-up), meeting statistical power 
requirements and supporting subgroup analysis. 
 
(2) Sample size for prospective interventional study (n = 600) 
Rationale: This phase is a Randomized Controlled Trial (RCT), requiring rigorous sample size 
calculation. With RFS as the primary endpoint, the design is as follows: 
Sample size formula (Log-Rank test) is adopted: 
Parameter settings: Baseline event rate: 12-month RFS rate of 65% in the standard treatment 
group (Arm B); Effect size: 10% absolute increase in 12-month RFS rate (to 75%) in the AI-assisted 
decision-making group (Arm A), corresponding to a Hazard Ratio (HR) = 0.67; Significance level 
(α): 0.05; Statistical power (1-β): 80% (common standard for RCTs); Accrual time: 18 months; 
Follow-up time: 12 months; Attrition rate: 10%. 
Calculations using PASS 2021 software indicate that a total of 200 events are required. 
Considering the 10% attrition rate, the total required sample size is 600 cases (300 cases per 
group). This sample size can detect the clinical difference with an HR of 0.67 and meet statistical 
power requirements under follow-up and attrition conditions. 
 
5. Data Management and Confidentiality 
All records related to the identity of participants shall be kept confidential and shall not be 
disclosed to the public beyond the scope permitted by relevant laws and/or regulations. 
 

6. Informed Consent 
Written informed consent must be obtained from participants before conducting any study 
procedures. Participants shall be informed of the specific details related to the study, as detailed 
in the Informed Consent Form. The specific process for obtaining informed consent includes: the 
participant themselves and/or their authorized family member(s) sign the form after the 
participant has fully understood the study design and objectives. 
 
7. Adverse Event Reporting 
This study includes a prospective cohort and may involve treatment-related adverse reactions. 
The main treatments include interventional therapy (e.g., Transarterial Chemoembolization, TACE) 
and targeted drugs (e.g., lenvatinib, sorafenib, or regorafenib). 
 
7.1 Common Risks of Interventional Therapy 
(1) Postoperative syndrome: Fever, pain, nausea, and vomiting. 
(2) Liver function impairment: Transient elevation of transaminases, jaundice, ascites, and in 
severe cases, liver failure. Embolization-related complications: Cholecystitis, gastroduodenal 
erosion or ulcer, splenic infarction, etc. 
(3) Puncture-related complications: Hematoma at the puncture site, pseudoaneurysm, vascular 
injury. 
(4) Others: Renal function impairment, allergic reactions, infections, etc. 
 
7.2 Common Risks of Targeted Drug Therapy 
(1) Common adverse reactions: Hypertension, hand-foot skin reaction (swelling, desquamation, 
pain of hands and feet), diarrhea, decreased appetite, fatigue, weight loss, thyroid dysfunction, 
proteinuria, etc. 
(2) Hematological toxicity: Thrombocytopenia, leukopenia, increased risk of bleeding. 
(3) Severe but rare adverse reactions: Cardiac function impairment, arterial thromboembolism, 
gastrointestinal perforation, poor wound healing, etc. 
 
7.3 Preventive Measures 
Strictly follow standard operating procedures. Conduct a comprehensive assessment of 
participants' physical conditions before treatment. During and after treatment, closely monitor 
participants, conduct regular examinations such as blood routine, liver and kidney function tests, 
and electrocardiograms to promptly detect and manage any potential adverse reactions. Prepare 
a complete emergency plan and first-aid measures, and provide researchers with detailed 
guidelines for adverse reaction management (e.g., how to manage hypertension, skin reactions, 
etc.). 
 
If intolerable serious adverse events (SAEs) occur, the investigating physician will reduce the drug 
dose, suspend, or permanently discontinue the study treatment in accordance with the protocol, 
and provide necessary medical support to the participant. Record the event in the Case Report 
Form and immediately report it to the Ethics Committee, the Drug Clinical Trial Institution, and 
the sponsor. Report to the national and provincial drug regulatory authorities within 24 hours. 
SAEs must also be reported through the "Hospital Intranet Adverse Event and Near-Miss 

Non-Punitive Reporting System." 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Informed Consent Form for Human Research at the Second Affiliated 

Hospital of Zhejiang University School of Medicine 
Dear Patient, 
 
We invite you to participate in a clinical study titled "Construction and Clinical Validation of a 
Deep Learning-Based Predictive Model for Adjuvant Therapy in Hepatocellular Carcinoma After 
Surgery: A Randomized, Controlled, Multi-Center, Prospective Clinical Study." Before deciding 
whether to participate in this study, please carefully read the following content. It will help you 
understand the study, its purpose, procedures, duration, as well as the potential benefits, risks, 
and inconveniences that may arise from participation. 
 
The following is an introduction to the study: 
 
I. Research Background and Objectives 
Liver cancer is a common and serious disease. For early-stage liver cancer, surgical resection is 
the main treatment method, but the postoperative recurrence rate is relatively high. To reduce 
the risk of recurrence, doctors often recommend postoperative adjuvant therapy, mainly 
including two approaches: local therapy (Transarterial Chemoembolization, TACE) and oral 
targeted drugs (Tyrosine Kinase Inhibitors, TKIs, such as sorafenib, lenvatinib, etc.). 
 
However, these treatment methods are not equally effective for everyone. Some patients may 
have no obvious effect or experience side effects that prevent them from continuing treatment. 
Currently, doctors mainly rely on experience to determine which treatment is more suitable for 
each patient, lacking a more precise predictive method. 
 
Recent studies have found that the immune microenvironment within tumors can affect the 
efficacy of the above treatments. Traditional methods for detecting the immune 
microenvironment are relatively complex and costly. Now, we hope to use artificial intelligence 
technology to analyze the routine pathological sections (i.e., Hematoxylin and Eosin (HE)-stained 
sections) prepared after surgery to find clues that can predict the efficacy of TACE or TKI 
treatment. 
 
The purpose of this study is to establish an artificial intelligence predictive model. If successful, it 
may help doctors more accurately select postoperative adjuvant treatment plans for patients in 
the future, making treatment more effective and personalized. 
 
II. Specific Procedures and Processes 
This study is a comprehensive research combining diagnostic trials and interventional studies, 
divided into two main parts and multiple phases. The current phase of the study focuses on 
prospective trials, aiming to verify the clinical application efficacy of the predictive model for liver 
cancer adjuvant therapy. 
 
1. Prospective Observational Study: Continuously enroll 1,000 eligible postoperative patients with 
Hepatocellular Carcinoma (HCC) prospectively across 10-15 centers. Collect their Whole Slide 

Imaging (WSI) images and input them into the model to obtain predicted treatment plans, but no 
intervention will be made on actual clinical decisions during this phase of the study. Researchers 
will record the model's predicted results and actual clinical decisions, and observe the patients' 
actual prognosis through follow-up to evaluate the consistency between the model's predictions 
and actual outcomes, as well as the performance attenuation in real-world settings. 
 
2. Prospective Interventional Study: This is the final validation step of the research. A prospective, 
randomized controlled or pragmatic clinical trial will be conducted in collaboration with 3-5 
centers, enrolling approximately 600 patients. Patients will be randomly divided into two groups: 
① Model-Assisted Decision-Making Group: Clinicians will formulate treatment plans for patients 
based on the optimal treatment plan predicted by the model; ② Standard Treatment Group: 
Clinicians will formulate treatment plans entirely based on existing guidelines and clinical 
experience. The primary endpoint is to compare the Relapse-Free Survival (RFS) of patients in the 
two groups, thereby providing the highest level of evidence-based medical evidence to prove the 
clinical utility of model-assisted decision-making and its value in improving prognosis. 
 
III. What You Need to Do If You Participate in the Study 
① Fully understand the potential transformative value and significance of this study. 
② Agree to the study's access to all your past medical records, examination results, and 
pathological sections at this hospital. 
③ Cooperate with doctors for follow-up and undergo relevant examinations. 
④ Accept the postoperative adjuvant therapy recommended by doctors, including 
interventional therapy, targeted drug therapy, or only follow-up without adjuvant therapy after 
surgery. 
 
IV. Potential Benefits of Participating in the Study 
If you are assigned to the treatment group, you will receive the currently considered most 
beneficial active adjuvant therapy (interventional therapy or targeted drug therapy) based on the 
results of the predictive model in our study. This may reduce the risk of tumor recurrence or 
delay the time to recurrence, thereby potentially bringing survival benefits. If you are assigned to 
the observation group, you will receive the current standard postoperative management plan 
(regular follow-up and observation). This is in line with current clinical practice guidelines and will 
provide you with the most basic standard medical security. At the same time, your attending 
doctor can detect signs of recurrence earlier through the intensive follow-up of this study, 
enabling timely initiation of treatment. 
 
Regardless of which group you are assigned to, you will receive more rigorous and regular 
follow-up monitoring than routine outpatient visits (including imaging examinations and tumor 
marker tests, etc.). This helps detect any changes in your condition earlier and more accurately. 
 
The pathological tissues and clinical data you contribute will help us validate this innovative 
technology, which may promote the progress of postoperative adjuvant therapy for 
hepatocellular carcinoma and improve the postoperative survival of liver cancer patients in the 
future. 

 
V. Potential Adverse Reactions, Risks, and Risk Prevention Measures of Participating 
in the Study 
This is a prospective study that may involve treatment-related adverse reactions. The main 
treatments include interventional therapy (such as Transarterial Chemoembolization, TACE) and 
targeted drugs (such as lenvatinib, sorafenib, or regorafenib). 
 
Common Risks of Interventional Therapy 
(1) Postoperative syndrome: Fever, pain, nausea, and vomiting. 
(2) Liver function impairment: Transient elevation of transaminases, jaundice, ascites, and in 
severe cases, liver failure. Embolization-related complications: Cholecystitis, gastroduodenal 
erosion or ulcer, splenic infarction, etc. 
(3) Puncture-related complications: Hematoma at the puncture site, pseudoaneurysm, vascular 
injury. 
(4) Others: Renal function impairment, allergic reactions, infections, etc. 
 
 Common Risks of Targeted Drug Therapy 
(1) Common adverse reactions: Hypertension, hand-foot skin reaction (swelling, desquamation, 
pain of hands and feet), diarrhea, decreased appetite, fatigue, weight loss, thyroid dysfunction, 
proteinuria, etc. 
(2) Hematological toxicity: Thrombocytopenia, leukopenia, increased risk of bleeding. 
(3) Severe but rare adverse reactions: Cardiac function impairment, arterial thromboembolism, 
gastrointestinal perforation, poor wound healing, etc. 
 
Prevention Measures 
Strictly follow standard operating procedures. Conduct a comprehensive assessment of the 
participant's physical condition before treatment. During and after treatment, closely monitor 
the participant, and perform regular examinations such as blood routine, liver and kidney 
function tests, and electrocardiograms to promptly detect and manage any potential adverse 
reactions. Prepare a complete emergency plan and first-aid measures, and provide researchers 
with detailed guidelines for adverse reaction management (e.g., how to manage hypertension, 
skin reactions, etc.). 
 
If intolerable severe toxic and side effects occur, the investigating doctor will reduce the drug 
dose, suspend, or permanently discontinue the study treatment in accordance with the protocol, 
and provide necessary medical support. 
 
VI. Explanation of Costs 
Costs borne by the study: The analysis cost of the research-related predictive model. The cost of 
additional examinations beyond the standard clinical diagnosis and treatment routine as 
specified in the study protocol (such as additional blood tests or imaging examinations at specific 
visit points for research purposes). The cost of study drugs (if applicable) or study-specific 
procedures. 
 

Costs to be borne by you: The cost of radical liver cancer surgery (this part is your standard 
treatment and is not covered by the study). All medical expenses incurred during the receipt of 
adjuvant therapy (interventional or targeted), including but not limited to: hospitalization fees, 
surgical operation fees, targeted drug fees, adjuvant medication fees, routine laboratory tests, 
routine imaging examinations, etc., shall be jointly paid by you and your medical insurance in 
accordance with regular medical policies. This study does not cover your standard treatment 
costs. 
 
VII. Compensation for Participating in the Study, Including Compensation for Injuries 
Medical care and compensation for injuries related to the study: If an adverse event/injury 
directly caused by this study (not resulting from your own disease) occurs, the study sponsor will 
compensate for the medical expenses and corresponding compensation required for damages 
related to the study. You do not need to waive any legal rights. Even if you have signed this 
informed consent form, it will not affect your right to seek compensation through legal channels. 
For specific terms, application procedures, and insurance information regarding injury 
compensation, please consult your investigating doctor or nurse in detail, and they will provide 
you with more detailed explanatory documents. 
 
VIII. Alternative Options 
If you are unwilling to participate in this study, there are no alternative options, and this will not 
affect your routine clinical treatment. 
 
IX. Confidentiality of Your Personal Information 
Your medical records (including research medical records and physical and chemical examination 
reports, etc.) will be stored in the hospital in accordance with regulations. In addition to 
researchers, the Ethics Committee, monitors, auditors, drug regulatory authorities, and other 
relevant personnel who will be allowed to access your medical records, other personnel 
unrelated to the study have no right to access your medical records without permission. The 
public report of the study results will not disclose your personal identity. We will make every 
effort to protect the privacy of your personal medical data within the permitted scope. 
 
X. Termination of Participation in the Study 
Participation in this study is entirely voluntary. You may refuse to participate in this study or 
withdraw from the study at any time during the research process without giving any reason. This 
will not affect your relationship with the doctor, nor will it affect your medical care or other 
benefits. In addition, your participation in the study may be terminated for the following reasons: 
1. You fail to follow the doctor's advice. 
2. You experience a serious condition that may require treatment. 
3. The investigating doctor believes that terminating the study is in your best health and welfare 
interests. 
 
XI. Ethics Committee 
This study has been reported to the Human Research Ethics Committee of the Second Affiliated 
Hospital of Zhejiang University School of Medicine. After a comprehensive review by the 

committee, including an assessment of the risks to participants, it has been approved. During the 
study, you may contact the Human Research Ethics Committee of the Second Affiliated Hospital 
of Zhejiang University School of Medicine regarding ethical and rights issues. Telephone: 
0571-87783759 (during the day); 13757118366 (on-call doctor at night); Email address: 
HREC2013@126.com. 
 
I confirm that I have read and understood the informed consent form for this study, voluntarily 
accept the treatment methods in this study, and agree to the use of my medical data for the 
publication of this study. 
 
Participant's Signature: ______________ Contact Information: ______________ Date: 
______________ 
 
Agent's Signature (if needed): ______________ Relationship with Participant: ______________ 
Contact Information: ______________ Date: ______________ 
 
Witness's Signature (if needed): ______________ Contact Information: ______________  
Date: ______________ 
 
I confirm that I have explained the detailed information of this study to the patient, including 
their rights, potential benefits, and risks, and provided them with a signed copy of the informed 
consent form. 
 
Investigator's Signature: ______________ 
 
Contact Information (Mobile Phone): ______________ Date: ______________