Wu K.-CChen S.-WHsieh T.-CYen K.-YLaw K.-MKuo Y.-CRUEY-FENG CHANGKao C.-H.2022-04-252022-04-25202120726694https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121286363&doi=10.3390%2fcancers13246350&partnerID=40&md5=188ea875644308e52e6b9334bc3ae306https://scholars.lib.ntu.edu.tw/handle/123456789/607451Objectives: Neoadjuvant chemoradiotherapy (NCRT) followed by surgery is the mainstay of treatment for patients with locally advanced rectal cancer. Based on baseline 18F-fluorodeoxy-glucose ([18F]-FDG)-positron emission tomography (PET)/computed tomography (CT), a new artificial intelligence model using metric learning (ML) was introduced to predict responses to NCRT. Patients and Methods: This study used the data of 236 patients with newly diagnosed rectal cancer; the data of 202 and 34 patients were for training and validation, respectively. All patients received pretreatment [18F]FDG-PET/CT, NCRT, and surgery. The treatment response was scored by Dworak tumor regression grade (TRG); TRG3 and TRG4 indicated favorable responses. The model employed ML combined with the Uniform Manifold Approximation and Projection for dimensionality reduction. A receiver operating characteristic (ROC) curve analysis was performed to assess the model’s predictive performance. Results: In the training cohort, 115 patients (57%) achieved TRG3 or TRG4 responses. The area under the ROC curve was 0.96 for the prediction of a favorable response. The sensitivity, specificity, and accuracy were 98.3%, 96.5%, and 97.5%, respectively. The sensitivity, specificity, and accuracy for the validation cohort were 95.0%, 100%, and 98.8%, respectively. Conclusions: The new ML model presented herein was used to determined that baseline 18F[FDG]-PET/CT images could predict a favorable response to NCRT in patients with rectal cancer. External validation is required to verify the model’s predictive value. ? 2021 by the authors. Licensee MDPI, Basel, Switzerland.18F-fluorodeoxyglucose positron emission tomography/computed tomographyMetric learningNeoadjuvant chemoradiotherapyRectal cancer[SDGs]SDG3journal article10.3390/cancers132463502-s2.0-85121286363