Predicting the Response of Patients Treated with 177Lu-DOTATATE Using Single-photon Emission Computed Tomography-Computed Tomography Image-based Radiomics and Clinical Features
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: T...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2024-10-01
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| Series: | Journal of Medical Signals and Sensors |
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| Online Access: | https://journals.lww.com/jmss/fulltext/2024/10160/predicting_the_response_of_patients_treated_with.2.aspx |
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| author | Baharak Behmanesh Akbar Abdi-Saray Mohammad Reza Deevband Mahasti Amoui Hamid R. Haghighatkhah Ahmad Shalbaf |
| author_facet | Baharak Behmanesh Akbar Abdi-Saray Mohammad Reza Deevband Mahasti Amoui Hamid R. Haghighatkhah Ahmad Shalbaf |
| author_sort | Baharak Behmanesh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c2eb8ed152c34998923f8b36b4f53b25 |
| institution | Kabale University |
| issn | 2228-7477 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Medical Signals and Sensors |
| spelling | doaj-art-c2eb8ed152c34998923f8b36b4f53b252024-11-09T06:33:10ZengWolters Kluwer Medknow PublicationsJournal of Medical Signals and Sensors2228-74772024-10-011410282810.4103/jmss.jmss_54_23Predicting the Response of Patients Treated with 177Lu-DOTATATE Using Single-photon Emission Computed Tomography-Computed Tomography Image-based Radiomics and Clinical FeaturesBaharak BehmaneshAkbar Abdi-SarayMohammad Reza DeevbandMahasti AmouiHamid R. HaghighatkhahAhmad ShalbafBackground: 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.https://journals.lww.com/jmss/fulltext/2024/10160/predicting_the_response_of_patients_treated_with.2.aspxlutetium-177-dotatateneuroendocrine tumorsradiomicssingle-photon emission computed tomographysingle-photon emission computed tomography-computed tomography |
| spellingShingle | Baharak Behmanesh Akbar Abdi-Saray Mohammad Reza Deevband Mahasti Amoui Hamid R. Haghighatkhah Ahmad Shalbaf Predicting the Response of Patients Treated with 177Lu-DOTATATE Using Single-photon Emission Computed Tomography-Computed Tomography Image-based Radiomics and Clinical Features Journal of Medical Signals and Sensors lutetium-177-dotatate neuroendocrine tumors radiomics single-photon emission computed tomography single-photon emission computed tomography-computed tomography |
| title | Predicting the Response of Patients Treated with 177Lu-DOTATATE Using Single-photon Emission Computed Tomography-Computed Tomography Image-based Radiomics and Clinical Features |
| title_full | Predicting the Response of Patients Treated with 177Lu-DOTATATE Using Single-photon Emission Computed Tomography-Computed Tomography Image-based Radiomics and Clinical Features |
| title_fullStr | Predicting the Response of Patients Treated with 177Lu-DOTATATE Using Single-photon Emission Computed Tomography-Computed Tomography Image-based Radiomics and Clinical Features |
| title_full_unstemmed | Predicting the Response of Patients Treated with 177Lu-DOTATATE Using Single-photon Emission Computed Tomography-Computed Tomography Image-based Radiomics and Clinical Features |
| title_short | Predicting the Response of Patients Treated with 177Lu-DOTATATE Using Single-photon Emission Computed Tomography-Computed Tomography Image-based Radiomics and Clinical Features |
| title_sort | predicting the response of patients treated with 177lu dotatate using single photon emission computed tomography computed tomography image based radiomics and clinical features |
| topic | lutetium-177-dotatate neuroendocrine tumors radiomics single-photon emission computed tomography single-photon emission computed tomography-computed tomography |
| url | https://journals.lww.com/jmss/fulltext/2024/10160/predicting_the_response_of_patients_treated_with.2.aspx |
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