Predicting the Response of Patients Treated with 177Lu-DOTATATE Using Single-photon Emission Computed Tomography–Computed Tomography Image-based Radiomics and Clinical Features

• Published in: Journal of medical signals and sensors 2024-10, Vol.14 (10), p.28-28
• Main Authors: Behmanesh, Baharak, Abdi-Saray, Akbar, Deevband, Mohammad Reza, Amoui, Mahasti, Haghighatkhah, Hamid R., Shalbaf, Ahmad
• Format: Article
• Language: English
• Subjects: lutetium-177-dotatate; neuroendocrine tumors; Original; radiomics; single-photon emission computed tomography; single-photon emission computed tomography-computed tomography
• ISSN: 2228-7477; 2228-7477
• DOI: 10.4103/jmss.jmss_54_23

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.BackgroundIn 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.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.MethodsThe 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.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.ResultsThe results revealed that the RF classifier with feature selection algorithms through mRMR had the highest classificat