Predicting the Response of Patients Treated with 177Lu-DOTATATE Using Single-photon Emission Computed Tomography-Computed Tomography Image-based Radiomics and Clinical Features - Baharak Behmanesh; Akbar Abdi-Saray; Mohammad Reza Deevband; Mahasti Amoui; Hamid R. Haghighatkhah; Ahmad Shalbaf (Journal of Medical Signals and Sensors)
Background: In this study, we want to evaluate the response to Lutetium-177 (177Lu)-DOTATATE treatment in patients with neuroendocrine tumors (NETs) using single-photon emission computed tomography (SPECT) and computed tomography (CT), based on image-based radiomics and clinical features. Methods: The total volume of tumor areas was segmented into 61 SPECT and 41 SPECT-CT images from 22 patients with NETs. A total of 871 radiomics and clinical features were extracted from the SPECT and SPECT-CT images. Subsequently, a feature reduction method called maximum relevance minimum redundancy (mRMR) was used to select the best combination of features. These selected features were modeled using a decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) classifiers to predict the treatment response in patients. For the SPECT and SPECT-CT images, ten and eight features, respectively, were selected using the mRMR algorithm. Results: The results revealed that the RF classifier with feature selection algorithms through mRMR had the highest classification accuracies of 64% and 83% for the SPECT and SPECT-CT images, respectively. The accuracy of the classifications of DT, KNN, and SVM for SPECT-CT images is 79%, 74%, and 67%, respectively. The poor accuracy obtained from different classifications in SPECT images (≈64%) showed that these images are not suitable for predicting treatment response. Conclusions: Modeling the selected features of SPECT-CT images based on their anatomy and the presence of extensive gray levels makes it possible to predict responses to the treatment of 177Lu-DOTATATE for patients with NETs.