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

Autor:	Behmanesh B; Department of Nuclear Physics, Urmia University, Oroumieh, Iran., Abdi-Saray A; Department of Nuclear Physics, Urmia University, Oroumieh, Iran., Deevband MR; Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Amoui M; Department of Nuclear Medicine, School of Medicine, Shohada-e Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Haghighatkhah HR; Department of Radiology and Medical Imaging Center, School of Medicine, Shohada-e Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Shalbaf A; Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Jazyk:	angličtina
Zdroj:	Journal of medical signals and sensors [J Med Signals Sens] 2024 Oct 16; Vol. 14, pp. 28. Date of Electronic Publication: 2024 Oct 16 (Print Publication: 2024).
DOI:	10.4103/jmss.jmss_54_23
Abstrakt:	Background: In this study, we want to evaluate the response to Lutetium-177 ( 177 Lu)-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 177 Lu-DOTATATE for patients with NETs. Competing Interests: There are no conflicts of interest. (Copyright: © 2024 Journal of Medical Signals & Sensors.)
Databáze:	MEDLINE
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