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AI for Head Neck Cancer Treated With Adaptive RadioTherapy (RadiomicART)

NCT05081531 · Status: ACTIVE_NOT_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 50

Last updated 2026-04-30

No results posted yet for this study

Summary

Current clinical management algorithms for squamous cell carcinoma of head and neck (HNSCC) involve the use of surgery and / or radiotherapy (RT) depending on the stage of the disease at diagnosis. Radical RT, exclusive or in combination with systemic therapy, represents an effective therapeutic option according to the international guidelines. Despite the recent technological advancements in the field of RT, about 30-50% of patients will develop locoregional failure after primary treatment . Moreover, although the development of Intensity modulated radiation therapy (IMRT) and Volumetric modulated arc therapy (VMAT) techniques allowed a greater sparing of dose on healthy tissues, radiation-induced toxicity still represents a relevant concern, impacting on quality of life. The continuous effort of personalized medicine has the goal of improving patient's outcome, in terms of both disease's control and pattern of toxicity. Advanced imaging modalities appear to play an essential role in the customization of the radiation treatment as shown through the use of Adaptive Radiotherapy (ART) and radiomic. With ART we mean the adaptation of tumor volumes and surrounding organs at risk (OARs) to the shrinkage and patient emaciation during RT treatment. Adaptive radiotherapy (ART) includes techniques that allow knowledge of patient-specific anatomical variations informed by Image-guided radiotherapies (IGRTs) to feedback into the plan and dose-delivery optimization during the treatment course. Radiomic is the extraction of quantitative features from medical images to characterize tumor pathology or heterogeneity. Radiomic features extracted from medical images can be used as input features to create a machine learning model able to predict survival, and to guide treatment thanks to its predictive value in view of therapy personalization.

The combination of both ART and radiomic analysis could potentially be considered a further advance in the personalization of oncological treatments, and in particular for radiation treatments. For this reason, the investigators designed the present research project with the aim to prospectively evaluate a machine learning-based radiomic approach to predict outcome and toxicity of HNSCC patients treated with ART by mean of CT, MRI and PET-scan.

Conditions

Interventions

RADIATION

Adaptive Radiotherapy

All the patients will be treated with VMAT technique in its RapidArc form. A simultaneous integrated boost (SIB) technique will be used. The GTV will encompass the tumor delineated on CT scan, adjusted for MRI and PET scans. Patients will be treated with a total dose of 66 Gy, 60 Gy and 54 Gy on PTV1, PTV2 and PTV3, respectively, delivered in 30 fractions, 5 fractions per week.

Sponsors & Collaborators

  • Istituto Clinico Humanitas

    lead OTHER

Study Design

Allocation
NA
Purpose
TREATMENT
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-10-05
Primary Completion
2026-05-31
Completion
2026-05-31

Countries

  • Italy

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05081531 on ClinicalTrials.gov