ITHACA: Development and Validation of a Multivariate Predictive Model for Prognosis in Head and Neck Cancer Patients Undergoing Radiotherapy - Ambispective Study.
Conditions: Head and Neck Cancers, Head and Neck Cancers - Nasopharyngeal, Oral Cavity Cancer, Oropharyngeal Cancers, Radiotherapy Side Effects
Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Official Title of the Study: ITHACA: Development and Validation of a Multivariate Predictive Model 
for Prognosis in Head and Neck Cancer Patients Undergoing Radiotherapy - Ambispective Study.   
NCT Number: Not yet assigned 
Date of the Document: 17/10/2024 
Background and Rationale 
The importance of targeted and personalized treatment in head and neck cancers is underscored 
by the variability of tumor locations in this region and the necessity of preserving critical functions 
such as chewing, swallowing, speaking, and breathing. A personalized treatment approach that 
integrates clinical, laboratory, and instrumental data into an advanced predictive model for 
assessing survival and toxicity represents a significant advancement in managing head and neck 
cancers. 
Radiomics plays a crucial role in this approach by extracting detailed morphological and biological 
information from radiological images, allowing for a more in-depth understanding of tumor 
pathology. Similarly, dosiomics provides valuable data on radiation dose distribution, aiding in 
treatment optimization and predicting potential side effects. By integrating radiomics and 
dosiomics, this study aims to enhance treatment outcome predictions while improving the 
management of associated toxicities, particularly those that impact patients' quality of life. 
The ITHACA model seeks to incorporate a comprehensive range of data, including clinical 
parameters such as tumor stage, risk factors, and previous treatment responses, alongside 
radiological and dosimetric data. This holistic approach enables more precise predictions and 
facilitates more effective treatments tailored to individual patients. 
 
Objectives 
The study aims to develop and validate a multivariate predictive model for predicting: 
1. Cancer recurrence 
2. Survival 
3. Treatment-related toxicity in patients with head and neck cancer 
 
Materials and Methods 
Study Design 
 
Type: Multicenter ambispective observational study utilizing clinical, laboratory, and 
instrumental data. 
Study Population 

 
Inclusion: Consecutive patients with a histologically confirmed diagnosis of head and neck 
cancer. 
Study Duration 
The study is expected to last 10 years, beginning after ethics committee approval, with: 
 
Retrospective recruitment period: 2013–2023 
 
Prospective recruitment period: 2024–2034 
 
Eligibility Criteria 
Inclusion Criteria 
Participants must meet all of the following criteria: 
 
Confirmed Diagnosis: Histologically confirmed head and neck cancer. 
 
Age: ≥ 18 years (adult patients). 
 
Complete Data Availability: Clinical data must include treatment details, follow-up 
information, and outcomes. 
 
Medical History: A comprehensive record of comorbidities and relevant medical 
background. 
Exclusion Criteria 
 
Incomplete Data: Patients lacking essential clinical data for analysis. 
 
Endpoints 
Primary Endpoint 
 
Cancer Recurrence: Probability of recurrence after completing radiotherapy. 
Outcome Evaluation Methods 
 
Evaluation Timing: 3, 6, 12, 24, and 36 months post-treatment. 
 
Methods: PET-CT, MRI, and CT scans to detect signs of recurrence. 
 
Clinical Documentation: Review of medical records to confirm recurrence episodes. 
 
Event Reporting: Inclusion of events identified during follow-up or reported by clinicians. 
Secondary Endpoints 
1. Overall Survival: Duration from the start of radiotherapy until death. 
2. Disease-Free Survival: Time from completion of radiotherapy to cancer recurrence or 
death. 

3. Treatment Toxicity: Incidence, type, and severity of acute and late radiotherapy-related 
toxicities. 
Secondary Outcome Evaluation Methods 
1. Regular Follow-up: Clinical assessments and radiological investigations to track health 
status and toxicities. 
2. Data Recording: Use of clinical databases to monitor survival, disease progression, and 
toxicities. 
 
Variables and Procedures 
Clinical Variables 
 
Tumor Stage: Classified using the TNM system. 
 
Treatment Details: Radiotherapy, chemotherapy, and/or surgery specifics. 
 
Demographics: Age, sex, ethnicity. 
 
General Health & Comorbidities: Charlson Comorbidity Index or similar assessment tools. 
 
Risk Factors: Tobacco use, alcohol consumption, HPV exposure. 
 
Oncological History: Previous cancer diagnoses and prior oncological treatments. 
Radiological Variables 
 
Pre-Treatment Imaging: Tumor size, adjacent tissue involvement, presence of lymph node 
metastases. 
 
Treatment Response: Tumor and lymph node changes, metabolic response in PET-CT. 
 
Recurrence Indicators: New lesions or changes in existing lesions. 
 
Post-Treatment Effects: Anatomical and tissue alterations caused by radiotherapy or 
surgery. 
Radiomics Analysis 
Radiomics analysis involves extracting quantifiable characteristics from radiological images to 
identify patterns associated with recurrence, survival, and toxicity. 
Timing of Measurements 
 
Baseline: Collection of clinical, radiological, and radiomics variables before treatment. 
 
During Treatment: Monitoring tumor response and toxicities. 
 
Follow-Up: Evaluations at 3, 6, 12, 24, and 36 months post-treatment. 
 
Dosiomics Variables 
Dosiomics variables provide quantitative insights into the radiation dose delivered during 
treatment. These parameters help optimize planning and assess side effect risks. 

Key Aspects of Dosiomics Variables 
1. Dose Distribution: Measurement of spatial radiation dose distribution. 
2. Dose-Volumetric Parameters: Correlation of radiation dose with tissue volume. 
3. Heterogeneity Indices: Quantification of dose heterogeneity within the target area. 
4. Toxicity Risk Assessment: Evaluating potential side effects based on radiation dose and 
tissue sensitivity. 
 
References 
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prognostic model of overall survival in oropharyngeal cancer from real-world data: 
PRO.M.E.THE.O. Acta Otorhinolaryngol Ital. 2022;42(3):205-214. doi: 10.14639/0392-100X-
N1672. 
2. Yu H, Ma SJ, Farrugia M, Iovoli AJ, Wooten KE, et al. Machine Learning Incorporating Host 
Factors for Predicting Survival in Head and Neck Squamous Cell Carcinoma Patients. 
Cancers (Basel). 2021;13(18):4559. doi: 10.3390/cancers13184559. 
3. Marcu DC, Grava C, Marcu LG. Current Role of Delta Radiomics in Head and Neck Oncology. 
Int J Mol Sci. 2023;24(3):2214. doi: 10.3390/ijms24032214. 
4. Placidi L, Gioscio E, Garibaldi C, Rancati T, Fanizzi A, et al. A Multicentre Evaluation of 
Dosiomics Features Reproducibility, Stability and Sensitivity. Cancers (Basel). 
2021;13(15):3835. doi: 10.3390/cancers13153835. 
5. Ghanati H, Madathil S, Al-Tamimi M, Al Asmar Z, et al. Methodological Issues with Head 
and Neck Cancer Prognostic Risk Prediction Models. Community Dent Health. 
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