DEEP RDT
Development of deep learning models to predict therapeutic response to radiotherapy in cancer patients.
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Radiotherapy has become a keystay of treatment in multiple cancers, with proven benefits in outcomes, both as neo/adjuvant and as primary treatment. However, it is not free of adverse effects and there is variability in efficacy between patients, with 20-40% non-responders, depending on tumor characteristics.
Solution
Patient radiotherapy response predictor algorithm trained by radiological imaging of patients with localized rectal cancer treated with neo/adjuvant radiotherapy or early stage lung cancer treated with stereotactic radiotherapy.
Conventional neural models for data extraction of diagnostic computed tomography, positron emission tomography, magnetic resonance imaging, tumor biopsy histopathology, and molecular-clinical data.
Objective
Primary objective: Prediction of response to radiotherapy. RECISTO criteria evaluated at 6 months. Stratification of patients.
Other objectives: Toxicity related to radiotherapy and explicability of the model.