A Machine Learning-Based Risk Prediction Model for Head and Neck Cancerous Lesions: A Multidimensional Feature Study Integrating Demographics and Clinical Symptomatology
Conditions: Hypopharyngeal Cancer, Laryngeal Cancer, Head and Neck Cancer
Sponsor: The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School

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Machine Learning-Based Risk Prediction for Head
and Neck Cancerous Lesions: A Multidimensional
Feature Study Based on Demographics and Clinical
Symptomatology
Principal Investigator: Chuanyao Lin
Department: Department of Otorhinolaryngology-Head and Neck Surgery
Contact Person: Junyi Wang
Contact Phone: 13309609232
Study Period: April 2026 to November 2030
Version: 4.0
Version Date: January 26, 2026
Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University
Medical School

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Project Title
Machine Learning-Based Risk Prediction for Head and Neck Cancerous
Lesions: A Multidimensional Feature Study Based on Demographics and
Clinical Symptomatology
Study
Objective
This study aims to utilize machine learning algorithms based on multi-center
clinical data to develop and validate a clinical prediction model for assessing
the risk of head and neck cancerous lesions.
Study Design
Study Process: (1) A retrospective cohort study design is utilized for the
preliminary construction and internal performance evaluation of the model.
(2) A prospective observational cohort study design is employed to evaluate
the generalization ability and clinical applicability of the final model.
Total Number
of Cases
11,000 (Retrospective study: 8,000 cases; Prospective study: 3,000 cases)
Patient
Selection
Inclusion Criteria: Patients presenting at the Department of
Otorhinolaryngology-Head and Neck Surgery, Nanjing Drum Tower
Hospital, who undergo laryngoscopy, sign the informed consent form, and
possess complete clinical data.
Exclusion Criteria: Patients with incomplete clinical data.
Treatment
Regimen
None

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Outcome
Assessment
Efficacy Evaluation Indicators (Primary and Secondary Efficacy
Indicators): As this is an observational predictive modeling study that does
not involve interventions, there are no traditional efficacy or safety indicators.
Safety Evaluation Indicators: As this is an observational predictive modeling
study that does not involve interventions, there are no traditional efficacy or
safety indicators.
Statistical
Methods
The Mann-Whitney U test is utilized to analyze the test results between the
non-diseased group and the diseased group.
Study Period
April 2026 to November 2030

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I. Study Background
Head and neck malignancies (primarily cancerous lesions of the oral cavity, pharynx,
and larynx) are the sixth most common cancer globally. In recent years, their
incidence has continued to climb, posing a severe public health challenge [1]. Due to
their deep anatomical locations, early clinical symptoms (such as persistent
hoarseness, foreign body sensation in the throat, and non-healing oral ulcers) are
insidious and lack specificity. Clinically, these symptoms are highly similar to those
of benign conditions such as chronic inflammation, easily leading to missed diagnoses,
misdiagnoses, and delayed treatment [2]. Epidemiological surveys show that over
60% of patients have already progressed to a locally advanced stage or present with
cervical lymph node metastasis at the time of initial diagnosis. Advanced-stage cancer
patients not only face complex treatment strategies, significant surgical trauma, and
heavy financial burdens, but they also suffer severe sequelae, such as permanent
impairment of speech and swallowing functions, leading to a precipitous drop in
quality of life and a significantly reduced five-year survival rate [3].
Currently, the clinical diagnostic pathway for head and neck cancers relies heavily on
a series of invasive and resource-intensive examinations. Electronic laryngoscopy can
visually assess mucosal lesions, but the procedure easily triggers the gag reflex and
other discomforts, resulting in poor patient tolerance, and it relies heavily on the
specialist's operating experience [4]. Imaging technologies such as Computed
Tomography (CT) and Magnetic Resonance Imaging (MRI) are vital adjunctive
diagnostic tools [5] that provide valuable anatomical information, but they involve
high equipment costs and expensive examination fees. The accessibility of such
examinations as primary screening tools for head and neck malignancies in grassroots
medical institutions is limited [6]. Furthermore, histopathological biopsy, the gold
standard for diagnosis, is an invasive procedure that is not only technically demanding
but also causes physical and psychological distress to patients, carrying potential risks
of
complications
[7].
These
combined
factors
constrain
the
feasibility
of

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implementing large-scale, accessible early screening in communities and high-risk
populations, leading to an inefficient allocation of medical resources and a substantial
proportion of missed and delayed diagnoses in early-stage cases.
Given this background, clinical practice urgently requires the development of a simple,
reliable, and clinically applicable primary screening risk stratification tool based on
routine clinical variables, demographic data, and symptom questionnaires through
multidimensional analysis. This tool would enable simple preliminary screening of
high-risk populations for head and neck cancer, thereby optimizing prevention and
treatment strategies and improving patient prognosis [8]. In recent years, the rapid
development of machine learning technology has provided a new methodological
approach to achieving this goal. Compared to traditional statistical models, machine
learning can handle multidimensional, non-linear clinical data and automatically
identify complex interactions and hidden patterns among variables, thereby building
predictive models with superior discriminatory performance [6]. Therefore, this study
plans to systematically integrate easily accessible clinical data, including demographic
characteristics, lifestyle indicators, and clinical manifestations. Utilizing machine
learning algorithms such as Random Forest and XGBoost, we aim to develop and
validate a highly accurate and generalizable risk prediction model for head and neck
cancerous lesions. This model will facilitate preliminary screening in the early stages
of the disease, guide high-risk individuals to undergo timely specialist examinations,
and promote multidisciplinary joint diagnosis and treatment, effectively improving
patients' quality of life and preventing complications [9].
II. Study Objectives
1. Primary Objective This study aims to utilize machine learning algorithms based on
multi-center clinical data to develop a low-cost, non-invasive primary screening risk
stratification tool to identify individuals at high risk for head and neck malignancies
and optimize early diagnostic and treatment pathways.

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2. Secondary Objectives
Identify and quantify the independent and interactive effects of key demographic
information, lifestyle and clinical characteristics, and laryngoscopy reports on the risk
of developing head and neck cancerous lesions.
Systematically compare the predictive performance of various mainstream machine
learning algorithms (e.g., Logistic Regression, Random Forest, Gradient Boosting
Machine) within this study cohort to select the optimal modeling strategy.
Apply
model
interpretability
techniques,
such
as
SHAP
(SHapley
Additive
exPlanations), to analyze the final primary screening risk stratification tool, elucidate
its decision-making rationale, and enhance the clinical transparency and credibility of
the model.
Evaluate the clinical applicability of the model using Decision Curve Analysis (DCA)
to verify its clinical efficacy in assisting early screening and managing high-risk
populations.
Evaluate the reliability and validity of a composite multidimensional prediction model
incorporating laryngoscopy results compared to purely symptomatological and
demographic models, to optimize screening protocols for patients with head and neck
cancerous lesions.
III. Study Design Types, Principles, and Trial Procedures
1. Study Design This study adopts a multi-center, observational study design,
adhering to the TRIPOD reporting guidelines, and is divided into two sequential
phases.
Phase One (Model Development & Internal Validation):
Study Design Type: Retrospective cohort study

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Data Source: Nanjing Drum Tower Hospital, Shanghai Ninth People's Hospital,
Changzhou No. 4 People's Hospital, and Cixi People's Hospital (2018–2025).
Sample Size: 8,000 cases. The data will be split at a 7:3 ratio into a training set
(n=5,600) and an internal validation set (n=2,400) for preliminary model construction
and internal performance evaluation.
Phase Two (External Model Validation):
Study Design Type: Prospective observational cohort study
Data Source: Nanjing Drum Tower Hospital, Shanghai Ninth People's Hospital,
Changzhou No. 4 People's Hospital, and Cixi People's Hospital et al.(2026–2030).
Sample Size: 3,000 cases (competitive enrollment across centers).
Objective: To evaluate the generalizability and temporal stability of the model in new
populations.
2. Study Centers and Sample Size This study will be conducted across four centers:
Nanjing Drum Tower Hospital, Shanghai Ninth People's Hospital, Changzhou No. 4
People's Hospital, and Cixi People's Hospital. The retrospective development cohort
plans to randomly enroll 8,000 outpatient cases meeting the inclusion/exclusion
criteria (competitive enrollment per center). The prospective validation cohort plans
to consecutively enroll 3,000 outpatient patients meeting the inclusion/exclusion
criteria (competitive enrollment per center).
3. Study Procedures This study will be systematically carried out in the following
steps:
Step 1: Data Extraction and Preprocessing

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Data Collection: Retrospective data will be extracted from the Electronic Medical
Record (EMR) systems of the four participating centers strictly according to the
inclusion and exclusion criteria. Data collected includes:
Demographics: Age (years), Gender, Education level (middle school or below / high
school / college or above), Occupation (manual labor / non-manual labor / retired).
Lifestyle: Smoking index (pack-years = packs per day × years smoked), Alcohol
consumption (yes/no; frequency/amount), History of betel nut chewing, Preference
for spicy/hot food.
Medical History: HPV infection status (serum HPV16/18 antibodies or oropharyngeal
swab PCR results), Reflux gastritis (gastroscopy or clinical diagnosis), Diabetes,
Immunosuppressive status.
Symptom Characteristics: Type of chief complaint (hoarseness, odynophagia, foreign
body sensation, neck mass, etc.), Duration (< 2 weeks / ≥2 weeks), Presence of
progressive worsening.
Laryngeal Questionnaires: Reflux Symptom Index (RSI), Voice Handicap Index-10
(VHI-10).
Electronic Laryngoscopy: Laryngoscopy result report (normal / inflammation /
suspicious neoplasm / definite space-occupying lesion; unilateral/bilateral; vocal cord
involvement).
Data Cleaning & Preprocessing: Clean the collected data and handle missing values
using multiple imputation methods. Apply one-hot encoding for categorical variables
and standardize continuous variables in preparation for machine learning modeling.
Data Grouping: Based on laryngoscopy reports and subsequent surgical biopsy
pathology reports, patients will be divided into a diseased group and a non-diseased
group for downstream data processing and analysis.

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Step 2: Feature Engineering and Dataset Splitting
Feature Selection: On the retrospective training set, feature selection methods such as
LASSO regression will be utilized to identify predictor variables most relevant to the
outcome, optimizing the model and preventing overfitting.
Dataset Splitting: The complete retrospective cohort data will be randomly divided
into a training set and an internal testing set at a 7:3 ratio.
Step 3: Machine Learning Model Development and Internal Validation
Model Training: Simultaneously train multiple machine learning algorithms on the
training set, including but not limited to Logistic Regression, Random Forest,
XGBoost, and Support Vector Machines (SVM).
Hyperparameter Tuning: Employ five-fold cross-validation and grid search techniques
to find the optimal hyperparameter combinations for each algorithm.
Internal Performance Evaluation: Evaluate the performance of each candidate model
on the locked internal testing set. Primary evaluation metrics include the Area Under
the ROC Curve (AUC), Accuracy, Recall, F1-score, and Calibration curves.
Step 4: Model Interpretation and Finalization
Model Interpretation: For the best-performing model, utilize model interpretability
techniques like SHAP to quantify the contribution of each feature to individual
predictions, generating global and individual-level explanation plots.
Model Finalization: Comprehensively consider the model's discrimination, calibration,
and clinical interpretability to finalize the prediction model.
Step 5: Prospective External Validation and Clinical Utility Evaluation

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Prospective Validation: Prospectively and consecutively collect new case data from
two (or more) centers within a predefined timeframe to form an independent external
validation cohort.
Performance Reproduction Evaluation: Validate the final model on this cohort to
evaluate its generalization performance.
Clinical Utility Evaluation: Employ Decision Curve Analysis (DCA) to assess the
clinical net benefit of the model across different decision thresholds, clarifying its
practical value in assisting clinical decision-making.
IV. Case Selection
1. Sample Size This study will be conducted across four centers: Nanjing Drum
Tower Hospital, Shanghai Ninth People's Hospital, Changzhou No. 4 People's
Hospital, and Cixi People's Hospital. The retrospective development cohort plans to
randomly enroll 8,000 outpatient cases meeting the inclusion and exclusion criteria
(competitive enrollment per center). The prospective validation cohort plans to
consecutively enroll 3,000 outpatient patients meeting the inclusion and exclusion
criteria (competitive enrollment per center).
2. Study Groups:
Positive (Diseased Group): Laryngoscopy finding of a suspicious lesion + subsequent
pathological confirmation of Head and Neck Squamous Cell Carcinoma (HNSCC).
Negative (Non-Diseased Group): Negative laryngoscopy results or solely benign
lesions (e.g., vocal cord polyps, pharyngeal/laryngeal cysts) with no confirmed
malignancy over a follow-up period of at least 6 months.

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3. Definition of Head and Neck Malignancy:
Anatomical Scope: Oral cavity (tongue body, gingiva, buccal mucosa, floor of the
mouth, hard palate), Oropharynx (tonsils, soft palate, base of tongue), Hypopharynx,
Larynx (supraglottis, glottis, subglottis).
Histological Type: Primary Squamous Cell Carcinoma (including subtypes like
verrucous carcinoma, basaloid squamous cell carcinoma).
Exclusions: Nasopharyngeal carcinoma (different EBV-related mechanism), Thyroid
cancer, Salivary gland adenocarcinoma, Lymphoma, Metastatic cancer, Skin cancer,
Precancerous lesions.
Gold Standard: Confirmed via pathological biopsy or postoperative specimen, with
reports issued by the pathology departments of the collaborating hospitals.
4. Inclusion Criteria Patients enrolled at the study centers must be those presenting at
the Department of Otorhinolaryngology-Head and Neck Surgery at Nanjing Drum
Tower Hospital (and collaborating centers) who have undergone a laryngoscopy. For
the retrospective study, informed consent is waived; for the prospective study,
patients must sign an informed consent form. Patients in both the retrospective and
prospective arms must have complete clinical data.
5. Exclusion Criteria Patients who refuse to sign the informed consent form (for the
prospective arm), have incomplete clinical data, or have known other head and neck
malignancies (e.g., thyroid cancer, malignant parotid gland tumors, etc.).

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V. Flow-Chart
VI. Study Quality Control and Quality Assurance
The Department of Otorhinolaryngology-Head and Neck Surgery at Nanjing Drum
Tower Hospital has established a specialized quality control group. Procedures
ranging from data sample collection and testing to data analysis will be strictly
implemented in accordance with relevant Standard Operating Procedures (SOPs). The
Principal Investigator and all relevant members have received professional skills
training.
VII. Data Safety Monitoring
A corresponding data safety monitoring plan will be formulated for the clinical study
based on the level of risk. Any personal information and data obtained about you
during the trial will be kept strictly confidential. Your information, data, and
questionnaire information will be identified by a study number/digit rather than your
name. Information that can identify your identity will not be disclosed to members
outside the research team without your permission. All research members and the

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sponsor are required to maintain confidentiality regarding your identity. Your files
will be stored in a locked data cabinet, accessible only to research personnel. To
ensure the study is conducted according to regulations, when necessary, government
regulatory authorities, monitors authorized by the sponsor, or Ethics Committee
members may, according to regulations, access relevant information concerning your
participation in the trial at the research site; however, they guarantee not to disclose
your information to other parties. Although study results may be published, your
identity will not be revealed in these publications. All detailed data of this study will
be stored at Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University
Medical School.
VIII. Ethics of Clinical Research
The clinical study will adhere to relevant regulations, including the World Medical
Association's "Declaration of Helsinki." Clinical research shall only be implemented
after the Ethics Committee has approved the trial protocol before the start of the study.
Before enrolling each subject into this study, the investigator is responsible for
providing a complete and comprehensive overview of the purpose, procedures, and
potential risks of this study to the subject or their legal representative, and for signing
a written informed consent form. Subjects should be informed of their right to
withdraw from the study at any time. The informed consent form should be retained
as a clinical research document for review. Subject personal privacy and data
confidentiality will be protected throughout the research process.
IX. Collection and Storage of Samples/Specimens
All clinical data will be managed by specialized personnel to ensure data privacy,
confidentiality, and security.
X. Study Timeline

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April 2026 – November 2029: Prospectively collect data from 3,000 patients
undergoing laryngoscopy examinations at the Otorhinolaryngology-Head and Neck
Surgery outpatient clinics of Nanjing Drum Tower Hospital, Changzhou No. 4
People's Hospital, Cixi People's Hospital, and Shanghai Ninth People's Hospital.
November 2028 – November 2029: Complete all sample collection and data analysis.
November 2029 – November 2030: Machine learning modeling, patent application,
and manuscript writing.
XII Reference
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