A Generative Model-based System for Predicting Survival and Guiding Treatment Decisions in Patients With Unresectable Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization in Combination With Immunotherapy and Targeted Therapy
Conditions: Transcatheter Arterial Chemoembolization, Unresectable Hepatocellular Carcinoma
Sponsor: Zhongda Hospital

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Research protocol 
Project Title: Generative model-based system for survival prediction and treatment decision-
making in unresectable hepatocellular carcinoma receiving TACE combined with 
immunotherapy plus targeted therapy 
Departments: Interventional and Vascular Surgery 
Unit address/zip code: No. 87, Dingjiaqiao, Gulou District, Nanjing City, Jiangsu Province / 
210009 
Project Leader: Lu Jian 
Phone/E-mail：15850654644/lujian43307131@126.com 
Competent authority: Interventional Vascular Surgery 
 
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Version date: May 1, 2025 
Zhongda Hospital Affiliated to Southeast University 
 
 

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Research project design document 
Project justification  
1. Current status and challenges in prognosis prediction of unresectable liver cancer 
Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide, and the 
treatment of patients with intermediate and advanced stages is particularly challenging. The burden of liver 
cancer in China is heavy, accounting for about half of the world's new cases and deaths every year, and about 
70% of patients have lost the opportunity for surgery when they are diagnosed. In recent years, transcatheter 
arterial chemoembolization (TACE) combined with targeted and immunotherapy has become a key treatment 
modality for unresectable HCC. This strategy exerts dual anti-tumor effects through a synergistic 
mechanism: TACE induces avascular necrosis and releases high concentrations of chemotherapeutic drugs 
by local embolization of the tumor-supplying arteries; Immune checkpoint inhibitors such as PD-1/PD-L1 
activate systemic anti-tumor immune responses by reversing the immunosuppressive state of the tumor 
microenvironment. Two phase III randomized controlled trials, EMERALD-1 and LEAP-012, showed that 
TACE in combination with durvalumab and bevacizumab or pembrolizumab and lenvatinib significantly 
extended median progression-free survival (PFS) to 15.0 months and 14.6 months, significantly better than 
8.2 months and 10.0 months with conventional TACE therapy. 
However, there is significant clinical heterogeneity in this combination, with studies showing that only 
about 30% to 40% of patients achieve a significant response, and nearly 60% of patients fail to achieve 
substantial survival benefit. This difference in efficacy is closely related to tumor molecular heterogeneity, 
immune microenvironment characteristics, and host liver function status. Imaging evaluation is the main 
method of efficacy evaluation, but the widely used clinical efficacy evaluation criteria for solid tumors 
(RECIST v1.1) are mainly based on tumor diameter changes, which has limitations in evaluating the 
dynamic efficacy of TACE combined with target-immune therapy. This criterion is a static "time slice" 
analysis, which is difficult to capture the dynamic evolution of the tumor microenvironment during 
treatment. 
Radiomics and deep learning technologies provide a new way to predict the survival of liver cancer. 
Radiomics constructs a quantitative prediction model by extracting the texture, morphology and functional 
characteristics of CT/MRI images through high-throughput. Deep learning models, such as convolutional 
neural networks (CNNs), use end-to-end learning to mine deep features directly from raw images, avoiding 
the limitations of artificial feature design. However, existing deep learning models still face three challenges: 
(1) poor interpretability; (2) Insufficient vertical information integration, mostly relying on single-point data; 

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(3) The models are mostly constructed based on the baseline status of patients, ignoring the dynamic changes 
of tumor burden and immune landscape in the treatment cycle, which is difficult to guide treatment 
adjustment in real time and does not meet the needs of precision medicine. Although the traditional 
longitudinal prediction model can incorporate dynamic changes, it needs to systematically collect follow-up 
data for multiple visits. In the real world, patient follow-up is often irregular, and there are medical referrals 
and referrals in other places, resulting in a large number of missing or uneven longitudinal imaging data, and 
it is difficult to align the information obtained at different time points and different devices. In addition, such 
models wait for patients to respond to treatment, which has a prediction lag. The LILAC system (Learning-
based Inference of Longitudinal imAge Changes) represents an important breakthrough in this direction, 
which uses a self-supervised time-series deep learning algorithm to analyze small changes in a series of MRI 
scans to achieve dynamic monitoring of disease progression. The proof-of-concept study showed that LILAC 
was significantly superior to the traditional method in the prediction of embryonic development time 
sequence and geriatric cognitive score. In pediatric glioma studies, the time-series deep learning model 
improved the F1-score of 1-year recurrence prediction by 58.5% by analyzing serial postoperative MRI 
images, and the prediction performance increased with the increase of historical scans (plateau after 3-6 
scans). However, the clinical translation of longitudinal models faces fundamental difficulties: patients with 
advanced HCC are often unable to complete the full process of imaging follow-up due to disease progression 
or adverse reactions, resulting in a lack of high-quality time series data; Data heterogeneity is caused by 
differences in image acquisition protocols in different centers. Ethics and privacy concerns limit multi-center 
data sharing. Therefore, there is an urgent need to develop new prediction paradigms that do not rely on real 
post-treatment images. Breakthroughs in Generative Artificial Intelligence provide a revolutionary solution 
to this. 
The core strength of generative models is their ability to learn the latent distribution of data and 
generate high-fidelity new samples. This field has achieved technological evolution from generative 
adversarial networks (GANs), variational autoencoders (VAEs) to diffusion models. In recent years, the 
proposed diffusion model has demonstrated better image generation quality than the previous model in a 
number of studies through the iterative process of forward noise addition and reverse noise reconstruction. In 
medicine, generative models were initially used primarily for data amplification and image synthesis (e.g., to 
generate images of rare diseases to alleviate sample imbalances), but recent advances have expanded into the 
areas of treatment response simulation and disease course prediction. Compared with traditional prediction 
models, generative models have shown unique potential in predicting curative effects: they can capture the 
spatiotemporal dynamics of tumor growth through the hidden spatial representation learning ability of 

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diffusion models. Based on this, the model can generate virtual images that simulate the optimal treatment 
response according to the initial state of the patient's tumor and the characteristics of the treatment plan in 
the absence of data. This provides clinicians with an intuitive tool for predicting efficacy, which helps to 
improve the robustness of survival prediction and provides a potential reference for subsequent treatment 
decisions. 
2. The entry point and significance of this study 
The entry point of this study is to propose "generative longitudinal prediction": only pre-treatment 
images are needed to generate high-fidelity post-treatment image predictions, which effectively avoids the 
clinical dilemma of obtaining real longitudinal follow-up data. This paradigm shift not only solves the 
problem of longitudinal data scarcity, but also develops a new approach to treatment simulation based on 
digital twins. Clinicians can intuitively evaluate the possible efficacy of different regimens through the 
virtual multi-time point images generated by the model before treatment, providing a visual basis for 
individualized treatment decisions. This study integrates generative AI and dynamic risk models to achieve: 
1. From static assessment to dynamic deduction; 2. Survival prediction is moved forward; 3. Individualized 
optimization of treatment plan. It is expected that we can provide more accurate and individualized treatment 
decision support for patients with advanced liver cancer without relying on longitudinal data, and ultimately 
improve the survival prognosis and quality of life. 

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Research content 
1. Research Objectives 
Main objectives: 1) To construct a generative image model based on preoperative CT/MRI; 2) the 
treatment response was evaluated in the training cohort based on the virtual image images generated by the 
model, including: disease control rate (DCR) and objective response rate (ORR), and compared with the best 
treatment response images after real treatment; 3) predict the prognosis of patients based on real pre-treatment 
imaging images (CT/MRI/DSA) and virtual post-treatment images, including progression-free survival (PFS) 
and overall survival (OS);  
Secondary objectives: 1) Integrate text information such as patient demographic data, laboratory 
examination data, and image reports/surgical records to achieve cross-modal fusion and further improve the 
performance of the model; 2) Adopt machine learning methods based on causal inference to realize the 
recommendation of diagnosis and treatment plans.  
2. Research content detail 
(1) Selection of research site: 
Zhongda Hospital Affiliated to Southeast University, General Hospital of Chinese People's Liberation 
Army (301), Third Affiliated Hospital of Naval Medical University (Oriental Hepatobiliary Surgery 
Hospital), First Affiliated Hospital of Zhengzhou University, Cancer Hospital Affiliated to Chinese Academy 
of Medical Sciences, First Affiliated Hospital of University of Science and Technology of China (Anhui 
Provincial Hospital), Jiangsu Cancer Hospital, Changzhou First People's Hospital, etc. 
(2) Selection of research subjects: 
This project is a real-world study, including retrospective training, validation and testing cohorts and 
prospective testing cohorts, all registries are derived from real cases in clinical practice, and the collected case 
data should include the following requirements: 
1. 
Unresectable hepatocellular carcinoma confirmed by histopathological diagnosis and/or clinical 
diagnosis (typical imaging features, clinical manifestations, laboratory tests, etc.) (Annex: Guidelines for the 
diagnosis and treatment of primary liver cancer (2024 edition); 
2. 
Patients who receive TACE combined with targeted immunotherapy on the basis of the diagnosis of 
unresectable hepatocellular carcinoma; 
3. 
Liver function grade Child-Pugh A or B; 
4. 
Over 18 years old, regardless of gender; 
5. 
Expected survival time≥ 3 months; 
6. 
ECOG PS score ≤2; 

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7. Meet the following laboratory examination parameters; 
1) Hematologic function: absolute neutrophil count ≥1.0×10⁹/L; Platelet count ≥ 50×10⁹ /L; 
hemoglobin ≥90 g/L; The international normalized ratio is less than 1.7 or the prothrombin time is 
prolonged by no more than 4 seconds, 
2) Liver function: alanine aminotransferase/aspartate aminotransferase does not exceed 5 times the 
upper limit of normal; total bilirubin ≤ 210 micromol/L [≤2.38 mg/dL]; Albumin ≥ 28g/L, 
3) Renal function: serum creatinine does not exceed 1.5 times the upper limit of normal. 
Exclusion Criteria: 
This project is a real-world study, including retrospective training, validation, and testing cohorts and 
prospective testing cohorts. All registries are derived from real cases in clinical practice, and the collected case 
data should include the following requirements: 
1. At the same time, other malignant tumors except HCC were combined; 
2. Moderate to severe ascites (ascites on a Child-Pugh score of 3); 
3. Received other first-line or second- and third-line systemic therapy (including any regimen of 
systemic therapy, etc.) or any local therapy (including transcatheter interventional therapy, ablation 
therapy, internal/external radiotherapy, etc.) and surgical resection or Chinese herbal medicine within 
4 weeks prior to TACE combined with targeted immunotherapy. 
4. Incomplete data, such as incomplete baseline laboratory examination data, missing or poor quality 
imaging data, no prognosis information, etc.; 
5. Severe liver dysfunction: liver diseases such as decompensated cirrhosis that seriously affect 
bilirubin levels; 
6. Severe comorbidities: such as refractory hypertension (blood pressure is still higher than 150/100 
mm Hg after optimal drug therapy), sustained arrhythmia (CTCAE standard grade 2 and above), 
atrial fibrillation of any degree, Qtc interval prolongation (more than 450 msec in men and more than 
470 msec in women), renal insufficiency, etc.;  
7. Concomitant human immunodeficiency virus infection (HIV) or acquired immunodeficiency 
disorder syndrome; 
8. pregnant or lactating women; 
9. Accompanied by acute or chronic mental disorders (including mental disorders that affect the 
enrollment, treatment intervention, and follow-up of subjects). 
(3) Sample size: 

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Sample size estimation based on the core principles of predictive model development (TRIPOD 
statement). The core strategy follows an outcome event-driven approach, focusing on the number of target 
events (e.g., disease positivity). Using the method recommended by Riley et al., the minimum sample size was 
calculated by the R language pmsampsize package. The calculation parameters include: (1) expected model 
discrimination (e.g., AUC is preset to 0.8);(2) the estimated number of predictors in the final model (k 
according to the literature and pre-experiments); and (3) the expected effect strength of the predictor 
(conservative estimates). At the same time, considering the high-dimensional characteristics of radiomics, the 
total sample size meets the following empirical criteria: (1) the number of events ≥ (10 times k (EPV 
principle); (2) Total sample size ≥ (10 times k). The final sample size was increased by 10-20% to compensate 
for missing data and heterogeneity. Multicenter image heterogeneity was included in the sample size 
calculation, and the sample size adequacy was verified by Bootstrap sampling.  
(4) Methods of extraction and allocation of research objects: 
All subjects in this study were derived from real cases in clinical practice. 
(5) Informed consent of the study subject: 
In this study, the cases obtained from the previous process of diagnosis and treatment were used without 
the application for informed consent. 
（6） Evaluation Indicators: 
Key research indicators 
In this study, the performance of the prediction model was comprehensively evaluated by the following 
five types of indicators, and all indicators reported point estimates and 95% confidence intervals (1000 times 
repeated sampling by the Bootstrap method): 1) discrimination: the C-index (Concordance Index) was used 
to evaluate the consistency of the model's ranking of individual survival time (two patients were randomly 
selected to predict the probability that the one with longer survival time would actually survive longer); 2) 
Time-dependent AUC (time-dependent AUC) was used to evaluate the model's ability to distinguish whether 
an event occurred or not at a preset time point; 3) calibration, the difference between the predicted probability 
and the actual outcome and the probability distribution were visualized by calibration plot and density plot, 
and the mean square error of the Brier Score was calculated. 4) Decision Curve Analysis (DCA) was used to 
evaluate clinical practicability; 5) The proportion of variation explained by the Nagelkerke R² assessment 
model was used. 
Secondary Research Indicators 
1) Safety: the incidence of adverse reactions and the severity of adverse events. Adverse events were 

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graded using the National Cancer Institute (NCI) Common Adverse Event Evaluation Criteria (CTCAE) 
version 5.0, which were classified as 1 to 5 based on the severity of adverse events (AEs): 
Grade 1: mild; asymptomatic or mild; Seen clinically or diagnostically only; No treatment is 
required. 
Grade 2: moderate; Requires minor, topical, or non-invasive treatments; Age-appropriate 
instrumental limitations in activities of daily living (e.g., cooking, buying clothes, using the phone, 
managing money, etc.). 
Grade 3: Severe or medically significant but not immediately life-threatening; resulting in 
hospitalization or prolongation of hospitalization; Disability; Limitation of self-directed activities of 
daily living (e.g., bathing, dressing, undressing, eating, washing, taking medication, etc., and not being 
bedridden). 
Grade 4: life-threatening; Urgent treatment is required. 
Grade 5: AE-related deaths. 
2) Validity: 
Overall survival (OS) is defined as the time from the start of chemotherapy combined with 
immunotherapy to the patient's death from any cause, measured in "months". For subjects who were still alive 
at the time of data analysis, OS was calculated based on the date on which the subjects were last known to be 
alive;  
Progression-free survival (PFS): defined as the time from the start of chemotherapy combined with 
immunotherapy to the first disease progression or death from any cause. 
To assess response to treatment in patients with intrahepatic lesions: 
a. Objective response rate (ORR): according to RECIST 1.1 criteria, objective response rate = (CR + 
PR / CR + PR + SD+PD); Participants with CR + PR efficacy should be retested 4 weeks after the 
first evaluation. 
b. Disease control rate (DCR): according to RECIST 1.1 criteria, lesion control rate = (CR + PR + SD/ 
CR + PR + SD + PD); Subjects with CR + PR + SD efficacy should be retested 4 weeks after the first 
evaluation. 
(7) Intervention methods: 
The basic information of patients and related information of treatment plans are collected through the 
electronic medical record system, including treatment plan, dosing cycle, dosing date, dosage, frequency, 

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number of cycles, efficacy, adverse reactions, etc. Ensure that the data collected in the study can be traced 
back to the original medical records. 
3. Research Methodology 
This study is based on a multicenter retrospective cohort including a prospective test cohort to include 
patients with unresectable hepatocellular carcinoma who have received TACE in combination with target-
immune therapy. The data comes from the clinical diagnosis and treatment records of 8 tertiary hospitals 
from 2016 to 2025, and strictly follows the preset inclusion standards. 
Generative model construction: diffusion model-based treatment response simulator: input data: 
enhanced CT/MRI images at the patient's baseline, real post-treatment enhanced CT/MRI images and 
clinical features (Child-Pugh score, AFP level, treatment plan, etc.). Model architecture: The conditional 
diffusion model was used to dynamically simulate the virtual follow-up images of the best response after 
treatment with the treatment protocol (TACE embolic agent dose + target immune drug combination) as the 
generation conditions. Training mechanism: The model is optimized by comparing the differences in deep 
visual features between the generated images and the real follow-up images to ensure that the generated 
results conform to the laws of biological evolution. Quantitatively evaluate the visual fidelity of the 
generated images (FID index <25); The clinical rationality was scored by 3 interventional radiology experts 
by blind (on a 5-point scale≥ 4 points were considered qualified). 
Dynamic survival prediction framework: Capture 256-dimensional semantic feature vectors from the 
generated virtual images to characterize the dynamic changes of tumor microenvironment. The depth 
features extracted by the generative model are fused with the patient's clinical variables, and the key 
predictors are automatically identified through the gated attention mechanism. Survival risk modeling: 
Construct a Transformer-based time-series survival model to output individualized survival curves and 
survival probability predictions at key time points (12/24/36 months). 
Treatment decision support systems: causal effects deduction: quantifying the expected survival benefit 
of different treatment regimens (eg, TACE + lenvatinib + pembrolizumab versus TACE + bevacizumab + 
atezolizumab) using a counterfactual analysis framework. Generate a heat map of treatment regimen 
comparisons to visualize simulated tumor regression differences. Decision rule: When the median survival 
benefit of a regimen is expected to be more than 3 months and the confidence probability is greater than 
80%, the system preferentially recommends the regimen. 
Model validation strategy: Stratified by study center, randomly assigned to the training set (70%), 
validation set (30%), and multiple test sets.   
Survival prediction assessment: main indicators: C-index evaluation of overall survival prediction 

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accuracy; Secondary indicators: 12/24/36 months of time dependence on AUC to assess discriminant ability 
at a specific time point; Calibration analysis: The Brier score quantifies the prediction error, and the 
calibration curve evaluates the risk prediction consistency. Comparative experimental design: baseline model 
(clinical variables only); benchmark model + real follow-up images; Datum model + generate model virtual 
image. 
Reproducibility & Ethics: The model code and preprocessing process are published in the public 
repository (GitHub). The self-service resampling method (1000 times) was used to calculate the 95% 
confidence interval. 
5. The innovation of the project 
This study is a multicenter, retrospective, real-world study of unresectable hepatocellular carcinoma 
(HCC) undergoing TACE in combination with targeted immunotherapy in the project from January 2016 to 
June 2025. Baseline imaging data, previous treatments, adverse reactions, clinical outcomes, follow-up 
imaging data, longitudinal laboratory examination data, etc., were collected to construct a generative model 
and generate imaging images of the best efficacy after virtual treatment. Combine virtual and real pre-
treatment images with other clinical information for accurate prognosis prediction. 
6. Research plan and forecast progress 
2025/6-2025/12: Complete the collection, cleaning, and statistical analysis of patients' baseline images, 
efficacy evaluation, and safety data; 
2026/01-2026/06: Complete the construction of the generative model, and evaluate and predict the 
performance of the model; 
2025/06-2026/12: Write a research paper and submit it, and complete the final report. 
7. Expected research results 
(1) Construct a prognosis and treatment decision-making model based on TACE combined with targeted 
immunotherapy for unresectable liver cancer based on generative model. 
(2) Publish 2-3 high-quality original papers in journals indexed by SCI. 
(3) Apply for 1 invention patent and participate in academic exchanges at home and abroad. 
Bases and conditions of work 
1. The accumulation of research work related to the project and the research results obtained; 
2. The existing experimental conditions, the missing experimental conditions and the ways to be solved; 
3. List of personnel in the main enrollment center; 

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name 
gender 
Job title 
Units and departments 
Division of 
labor in the 
project 
Lu Jian 
man 
Deputy 
Chief 
Physician 
Zhongda Hospital Affiliated to 
Southeast University 
Primary 
enrollment 
center 
Zhang 
Xiuping 
man 
Deputy 
Chief 
Physician 
General Hospital of the Chinese 
People's Liberation Army 
Primary 
enrollment 
center 
Zhang 
Kai 
man 
Attending 
Cancer Hospital Affiliated to 
Chinese Academy of Medical 
Sciences 
Primary 
enrollment 
center 
Lu Weifu 
man 
Chief 
physician 
The First Affiliated Hospital of 
University of Science and 
Technology of China 
Primary 
enrollment 
center 
Ge 
Naijian 
man 
Chief 
physician 
The Third Affiliated Hospital of the 
Naval Medical University 
Primary 
enrollment 
center 
Wu Jun 
man 
Chief 
physician 
The First People's Hospital of 
Changzhou 
Primary 
enrollment 
center 
Wu 
Pingping 
woman 
Chief 
physician 
Jiangsu Provincial Cancer Hospital 
Primary 
enrollment 
center 
Li Zhen 
man 
Chief 
physician 
The First Affiliated Hospital of 
Zhengzhou University 
Primary 
enrollment 
center 
4. Research team; 
Key members of the project team 
(including project leader) 
name 
job title 
workplace 
Division of 
tasks 
Lu Jian 
Deputy 
Chief 
Physician 
Interventional & 
Vascular Surgery 
Schematic 
design 
Chen 
yang 
professor 
School of 
Computer 
Science, 
Southeast 
University 
Schematic 
design 
Xue 
Cheng 
professor 
School of 
Computer 
Science, 
Southeast 
University 
Algorithm 
support 

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Mei 
Junhao 
—— 
Interventional & 
Vascular Surgery 
Statistical 
analysis 
Du 
Zheyu 
—— 
School of 
Computer 
Science, 
Southeast 
University 
Code 
implementati
on 
Jia 
Kaizhi 
—— 
Interventional & 
Vascular Surgery 
Data 
Collection 
 
Budget 
The clinical research project initiated by me is based on generative models to construct a survival 
prediction and treatment decision-making system for unresectable hepatocellular carcinoma with TACE 
combined with targeted immunotherapy, without funding from any institution, organization or individual.  
 
Miscellaneous    
The applicant undertakes that the above content has taken into account all possible risks in the project. Once 
any risk arises, it will be reported in a timely manner and measures will be taken to control and resolve the 
risk to prevent the risk from expanding and spreading, and to minimize the risk. In the event of a serious 
adverse event, I will promptly report it to the Science and Technology Department, the Medical Department 
and the Hospital Ethics Committee within 24 hours.