Transfer learning approach based on computed tomography images for predicting late xerostomia after radiotherapy in patients with oropharyngeal cancer - Fanizzi, Annarita; Scognamillo, Giovanni; Nestola, Alessandra; Bambace, Santa; Bove, Samantha; Comes, Maria Colomba; Cristofaro, Cristian; Didonna, Vittorio; Di Rito, Alessia; Errico, Angelo; Palermo, Loredana; Tamborra, Pasquale; et al. (Frontiers in Medicine, 2022)
Although the latest breakthroughs in radiotherapy (RT) techniques have improved the adverse event rate, these techniques are still associated with substantial toxicity, including xerostomia. Imaging biomarkers can be useful to predict the toxicity risk related to each individual patient. Our preliminary work aimed to develop a radiomic-based support tool exploiting pre-treatment CT images to predict late xerostomia risk at 3rd months after RT in oropharyngeal cancer (OPC) patients. We performed a multicenter data collection. We enrolled 61 patients referred to three care centers of Apulia, Italy, among which 22 patients experienced at least mild xerostomia 3 months after the end of the RT cycle. Pre-treatment CT images, clinical and dose features, and pleasure habits were collected. We proposed a transfer learning approach to extract quantitative imaging features from CT images by means of a pre-trained convolutional neural network architecture. An optimal feature subset was then identified to train an SVM classifier. To evaluate the robustness of the proposed model with respect to different manual contouring practices on CTs, we repeated the same image analysis pipeline on ‘fake’ parotid contours. The best performances were achieved by the model exploiting the radiomic features only. On the independent test, the model reached a median AUC, accuracy, sensitivity and specificity values of 81.17%, 83.33%, 71.43%, and 90.91%, respectively. The model was robust with respect to diverse manual parotid contouring procedures. Radiomic analysis could help to develop a valid support tool for clinicians in planning radiotherapy treatment, by providing a risk score of the toxicity development for each individual patient, thus improving the quality of life of the same patient, without compromising patient care.