The radiomic-clinical model using the SHAP method for assessing the treatment response of whole-brain radiotherapy: a multicentric study
Objective To develop and validate a pretreatment magnetic resonance imaging (MRI)–based radiomic-clinical model to assess the treatment response of whole-brain radiotherapy (WBRT) by using SHapley Additive exPlanations (SHAP), which is derived from game theory, and can explain the output of differen...
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| Veröffentlicht in: | European radiology 2022-12, Vol.32 (12), p.8737-8747 |
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| container_title | European radiology |
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| creator | Wang, Yixin Lang, Jinwei Zuo, Joey Zhaoyu Dong, Yaqin Hu, Zongtao Xu, Xiuli Zhang, Yongkang Wang, Qinjie Yang, Lizhuang Wong, Stephen T. C. Wang, Hongzhi Li, Hai |
| description | Objective
To develop and validate a pretreatment magnetic resonance imaging (MRI)–based radiomic-clinical model to assess the treatment response of whole-brain radiotherapy (WBRT) by using SHapley Additive exPlanations (SHAP), which is derived from game theory, and can explain the output of different machine learning models.
Methods
We retrospectively enrolled 228 patients with brain metastases from two medical centers (184 in the training cohort and 44 in the validation cohort). Treatment responses of patients were categorized as a non-responding group vs. a responding group according to the Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. For each tumor, 960 features were extracted from the MRI sequence. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. A support vector machine (SVM) model incorporating clinical factors and radiomic features wase used to construct the radiomic-clinical model. SHAP method explained the SVM model by prioritizing the importance of features, in terms of assessment contribution.
Results
Three radiomic features and three clinical factors were identified to build the model. Radiomic-clinical model yielded AUCs of 0.928 (95%CI 0.901–0.949) and 0.851 (95%CI 0.816–0.886) for assessing the treatment response in the training cohort and validation cohort, respectively. SHAP summary plot illustrated the feature’s value affected the feature’s impact attributed to model, and SHAP force plot showed the integration of features’ impact attributed to individual response.
Conclusion
The radiomic-clinical model with the SHAP method can be useful for assessing the treatment response of WBRT and may assist clinicians in directing personalized WBRT strategies in an understandable manner.
Key Points
• Radiomic-clinical model can be useful for assessing the treatment response of WBRT.
• SHAP could explain and visualize radiomic-clinical machine learning model in a clinician-friendly way. |
| doi_str_mv | 10.1007/s00330-022-08887-0 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2674753102</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2740744595</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-92793947dae5834c135cdc33629d6df9e0c50c2be03531d05967a0ee77f8ca9e3</originalsourceid><addsrcrecordid>eNp9kU1rFTEUhoNY7If-ARcScOMm9kw-JhN3pWgrFCpY1yE3OdObMjO5JjOU-w_82aZOreLCVQLned9z4CHkdQPvGwB9WgCEAAacM-i6TjN4Ro4aKThroJPP__ofkuNS7gDANFK_IIdCtbrrlDkiP262SLMLMY3RMz_EKXo30DEFHOhS4nRL50p8vTz7QkectynQPmXqSsHyNJ0zunnEaaYZyy5NBWnq6f02Dcg22cVp3VDR7Hb7D9TRcRnm6GsiR0_LvIT9S3LQu6Hgq8f3hHz79PHm_JJdXV98Pj-7Yl5oNTPDtRFG6uBQdUL6RigfvBAtN6ENvUHwCjzfIAglmgDKtNoBotZ9551BcULerb27nL4vWGY7xuJxGNyEaSmWt1rqGgVe0bf_oHdpyVO9znItQUupjKoUXymfUykZe7vLcXR5bxuwD57s6slWT_aXJws19OaxetmMGJ4iv8VUQKxAqaPpFvOf3f-p_Qll-58o</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2740744595</pqid></control><display><type>article</type><title>The radiomic-clinical model using the SHAP method for assessing the treatment response of whole-brain radiotherapy: a multicentric study</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Wang, Yixin ; Lang, Jinwei ; Zuo, Joey Zhaoyu ; Dong, Yaqin ; Hu, Zongtao ; Xu, Xiuli ; Zhang, Yongkang ; Wang, Qinjie ; Yang, Lizhuang ; Wong, Stephen T. C. ; Wang, Hongzhi ; Li, Hai</creator><creatorcontrib>Wang, Yixin ; Lang, Jinwei ; Zuo, Joey Zhaoyu ; Dong, Yaqin ; Hu, Zongtao ; Xu, Xiuli ; Zhang, Yongkang ; Wang, Qinjie ; Yang, Lizhuang ; Wong, Stephen T. C. ; Wang, Hongzhi ; Li, Hai</creatorcontrib><description>Objective
To develop and validate a pretreatment magnetic resonance imaging (MRI)–based radiomic-clinical model to assess the treatment response of whole-brain radiotherapy (WBRT) by using SHapley Additive exPlanations (SHAP), which is derived from game theory, and can explain the output of different machine learning models.
Methods
We retrospectively enrolled 228 patients with brain metastases from two medical centers (184 in the training cohort and 44 in the validation cohort). Treatment responses of patients were categorized as a non-responding group vs. a responding group according to the Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. For each tumor, 960 features were extracted from the MRI sequence. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. A support vector machine (SVM) model incorporating clinical factors and radiomic features wase used to construct the radiomic-clinical model. SHAP method explained the SVM model by prioritizing the importance of features, in terms of assessment contribution.
Results
Three radiomic features and three clinical factors were identified to build the model. Radiomic-clinical model yielded AUCs of 0.928 (95%CI 0.901–0.949) and 0.851 (95%CI 0.816–0.886) for assessing the treatment response in the training cohort and validation cohort, respectively. SHAP summary plot illustrated the feature’s value affected the feature’s impact attributed to model, and SHAP force plot showed the integration of features’ impact attributed to individual response.
Conclusion
The radiomic-clinical model with the SHAP method can be useful for assessing the treatment response of WBRT and may assist clinicians in directing personalized WBRT strategies in an understandable manner.
Key Points
• Radiomic-clinical model can be useful for assessing the treatment response of WBRT.
• SHAP could explain and visualize radiomic-clinical machine learning model in a clinician-friendly way.</description><identifier>ISSN: 1432-1084</identifier><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-022-08887-0</identifier><identifier>PMID: 35678859</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Brain ; Brain - diagnostic imaging ; Brain cancer ; Brain Neoplasms - diagnostic imaging ; Brain Neoplasms - radiotherapy ; Diagnostic Radiology ; Feature extraction ; Game theory ; Health care facilities ; Health services ; Humans ; Imaging ; Imaging Informatics and Artificial Intelligence ; Internal Medicine ; Interventional Radiology ; Learning algorithms ; Machine Learning ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Medicine ; Medicine & Public Health ; Metastases ; Metastasis ; Neuroimaging ; Neuroradiology ; Oncology ; Patients ; Radiation therapy ; Radiology ; Radiomics ; Retrospective Studies ; Support vector machines ; Training ; Tumors ; Ultrasound</subject><ispartof>European radiology, 2022-12, Vol.32 (12), p.8737-8747</ispartof><rights>The Author(s), under exclusive licence to European Society of Radiology 2022</rights><rights>2022. The Author(s), under exclusive licence to European Society of Radiology.</rights><rights>The Author(s), under exclusive licence to European Society of Radiology 2022.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-92793947dae5834c135cdc33629d6df9e0c50c2be03531d05967a0ee77f8ca9e3</citedby><cites>FETCH-LOGICAL-c375t-92793947dae5834c135cdc33629d6df9e0c50c2be03531d05967a0ee77f8ca9e3</cites><orcidid>0000-0001-8504-5811</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00330-022-08887-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-022-08887-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35678859$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Yixin</creatorcontrib><creatorcontrib>Lang, Jinwei</creatorcontrib><creatorcontrib>Zuo, Joey Zhaoyu</creatorcontrib><creatorcontrib>Dong, Yaqin</creatorcontrib><creatorcontrib>Hu, Zongtao</creatorcontrib><creatorcontrib>Xu, Xiuli</creatorcontrib><creatorcontrib>Zhang, Yongkang</creatorcontrib><creatorcontrib>Wang, Qinjie</creatorcontrib><creatorcontrib>Yang, Lizhuang</creatorcontrib><creatorcontrib>Wong, Stephen T. C.</creatorcontrib><creatorcontrib>Wang, Hongzhi</creatorcontrib><creatorcontrib>Li, Hai</creatorcontrib><title>The radiomic-clinical model using the SHAP method for assessing the treatment response of whole-brain radiotherapy: a multicentric study</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objective
To develop and validate a pretreatment magnetic resonance imaging (MRI)–based radiomic-clinical model to assess the treatment response of whole-brain radiotherapy (WBRT) by using SHapley Additive exPlanations (SHAP), which is derived from game theory, and can explain the output of different machine learning models.
Methods
We retrospectively enrolled 228 patients with brain metastases from two medical centers (184 in the training cohort and 44 in the validation cohort). Treatment responses of patients were categorized as a non-responding group vs. a responding group according to the Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. For each tumor, 960 features were extracted from the MRI sequence. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. A support vector machine (SVM) model incorporating clinical factors and radiomic features wase used to construct the radiomic-clinical model. SHAP method explained the SVM model by prioritizing the importance of features, in terms of assessment contribution.
Results
Three radiomic features and three clinical factors were identified to build the model. Radiomic-clinical model yielded AUCs of 0.928 (95%CI 0.901–0.949) and 0.851 (95%CI 0.816–0.886) for assessing the treatment response in the training cohort and validation cohort, respectively. SHAP summary plot illustrated the feature’s value affected the feature’s impact attributed to model, and SHAP force plot showed the integration of features’ impact attributed to individual response.
Conclusion
The radiomic-clinical model with the SHAP method can be useful for assessing the treatment response of WBRT and may assist clinicians in directing personalized WBRT strategies in an understandable manner.
Key Points
• Radiomic-clinical model can be useful for assessing the treatment response of WBRT.
• SHAP could explain and visualize radiomic-clinical machine learning model in a clinician-friendly way.</description><subject>Brain</subject><subject>Brain - diagnostic imaging</subject><subject>Brain cancer</subject><subject>Brain Neoplasms - diagnostic imaging</subject><subject>Brain Neoplasms - radiotherapy</subject><subject>Diagnostic Radiology</subject><subject>Feature extraction</subject><subject>Game theory</subject><subject>Health care facilities</subject><subject>Health services</subject><subject>Humans</subject><subject>Imaging</subject><subject>Imaging Informatics and Artificial Intelligence</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Metastases</subject><subject>Metastasis</subject><subject>Neuroimaging</subject><subject>Neuroradiology</subject><subject>Oncology</subject><subject>Patients</subject><subject>Radiation therapy</subject><subject>Radiology</subject><subject>Radiomics</subject><subject>Retrospective Studies</subject><subject>Support vector machines</subject><subject>Training</subject><subject>Tumors</subject><subject>Ultrasound</subject><issn>1432-1084</issn><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kU1rFTEUhoNY7If-ARcScOMm9kw-JhN3pWgrFCpY1yE3OdObMjO5JjOU-w_82aZOreLCVQLned9z4CHkdQPvGwB9WgCEAAacM-i6TjN4Ro4aKThroJPP__ofkuNS7gDANFK_IIdCtbrrlDkiP262SLMLMY3RMz_EKXo30DEFHOhS4nRL50p8vTz7QkectynQPmXqSsHyNJ0zunnEaaYZyy5NBWnq6f02Dcg22cVp3VDR7Hb7D9TRcRnm6GsiR0_LvIT9S3LQu6Hgq8f3hHz79PHm_JJdXV98Pj-7Yl5oNTPDtRFG6uBQdUL6RigfvBAtN6ENvUHwCjzfIAglmgDKtNoBotZ9551BcULerb27nL4vWGY7xuJxGNyEaSmWt1rqGgVe0bf_oHdpyVO9znItQUupjKoUXymfUykZe7vLcXR5bxuwD57s6slWT_aXJws19OaxetmMGJ4iv8VUQKxAqaPpFvOf3f-p_Qll-58o</recordid><startdate>20221201</startdate><enddate>20221201</enddate><creator>Wang, Yixin</creator><creator>Lang, Jinwei</creator><creator>Zuo, Joey Zhaoyu</creator><creator>Dong, Yaqin</creator><creator>Hu, Zongtao</creator><creator>Xu, Xiuli</creator><creator>Zhang, Yongkang</creator><creator>Wang, Qinjie</creator><creator>Yang, Lizhuang</creator><creator>Wong, Stephen T. C.</creator><creator>Wang, Hongzhi</creator><creator>Li, Hai</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-8504-5811</orcidid></search><sort><creationdate>20221201</creationdate><title>The radiomic-clinical model using the SHAP method for assessing the treatment response of whole-brain radiotherapy: a multicentric study</title><author>Wang, Yixin ; Lang, Jinwei ; Zuo, Joey Zhaoyu ; Dong, Yaqin ; Hu, Zongtao ; Xu, Xiuli ; Zhang, Yongkang ; Wang, Qinjie ; Yang, Lizhuang ; Wong, Stephen T. C. ; Wang, Hongzhi ; Li, Hai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-92793947dae5834c135cdc33629d6df9e0c50c2be03531d05967a0ee77f8ca9e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Brain</topic><topic>Brain - diagnostic imaging</topic><topic>Brain cancer</topic><topic>Brain Neoplasms - diagnostic imaging</topic><topic>Brain Neoplasms - radiotherapy</topic><topic>Diagnostic Radiology</topic><topic>Feature extraction</topic><topic>Game theory</topic><topic>Health care facilities</topic><topic>Health services</topic><topic>Humans</topic><topic>Imaging</topic><topic>Imaging Informatics and Artificial Intelligence</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Learning algorithms</topic><topic>Machine Learning</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Metastases</topic><topic>Metastasis</topic><topic>Neuroimaging</topic><topic>Neuroradiology</topic><topic>Oncology</topic><topic>Patients</topic><topic>Radiation therapy</topic><topic>Radiology</topic><topic>Radiomics</topic><topic>Retrospective Studies</topic><topic>Support vector machines</topic><topic>Training</topic><topic>Tumors</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Yixin</creatorcontrib><creatorcontrib>Lang, Jinwei</creatorcontrib><creatorcontrib>Zuo, Joey Zhaoyu</creatorcontrib><creatorcontrib>Dong, Yaqin</creatorcontrib><creatorcontrib>Hu, Zongtao</creatorcontrib><creatorcontrib>Xu, Xiuli</creatorcontrib><creatorcontrib>Zhang, Yongkang</creatorcontrib><creatorcontrib>Wang, Qinjie</creatorcontrib><creatorcontrib>Yang, Lizhuang</creatorcontrib><creatorcontrib>Wong, Stephen T. C.</creatorcontrib><creatorcontrib>Wang, Hongzhi</creatorcontrib><creatorcontrib>Li, Hai</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Yixin</au><au>Lang, Jinwei</au><au>Zuo, Joey Zhaoyu</au><au>Dong, Yaqin</au><au>Hu, Zongtao</au><au>Xu, Xiuli</au><au>Zhang, Yongkang</au><au>Wang, Qinjie</au><au>Yang, Lizhuang</au><au>Wong, Stephen T. C.</au><au>Wang, Hongzhi</au><au>Li, Hai</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The radiomic-clinical model using the SHAP method for assessing the treatment response of whole-brain radiotherapy: a multicentric study</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2022-12-01</date><risdate>2022</risdate><volume>32</volume><issue>12</issue><spage>8737</spage><epage>8747</epage><pages>8737-8747</pages><issn>1432-1084</issn><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objective
To develop and validate a pretreatment magnetic resonance imaging (MRI)–based radiomic-clinical model to assess the treatment response of whole-brain radiotherapy (WBRT) by using SHapley Additive exPlanations (SHAP), which is derived from game theory, and can explain the output of different machine learning models.
Methods
We retrospectively enrolled 228 patients with brain metastases from two medical centers (184 in the training cohort and 44 in the validation cohort). Treatment responses of patients were categorized as a non-responding group vs. a responding group according to the Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. For each tumor, 960 features were extracted from the MRI sequence. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. A support vector machine (SVM) model incorporating clinical factors and radiomic features wase used to construct the radiomic-clinical model. SHAP method explained the SVM model by prioritizing the importance of features, in terms of assessment contribution.
Results
Three radiomic features and three clinical factors were identified to build the model. Radiomic-clinical model yielded AUCs of 0.928 (95%CI 0.901–0.949) and 0.851 (95%CI 0.816–0.886) for assessing the treatment response in the training cohort and validation cohort, respectively. SHAP summary plot illustrated the feature’s value affected the feature’s impact attributed to model, and SHAP force plot showed the integration of features’ impact attributed to individual response.
Conclusion
The radiomic-clinical model with the SHAP method can be useful for assessing the treatment response of WBRT and may assist clinicians in directing personalized WBRT strategies in an understandable manner.
Key Points
• Radiomic-clinical model can be useful for assessing the treatment response of WBRT.
• SHAP could explain and visualize radiomic-clinical machine learning model in a clinician-friendly way.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>35678859</pmid><doi>10.1007/s00330-022-08887-0</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-8504-5811</orcidid></addata></record> |
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| subjects | Brain Brain - diagnostic imaging Brain cancer Brain Neoplasms - diagnostic imaging Brain Neoplasms - radiotherapy Diagnostic Radiology Feature extraction Game theory Health care facilities Health services Humans Imaging Imaging Informatics and Artificial Intelligence Internal Medicine Interventional Radiology Learning algorithms Machine Learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Medicine Medicine & Public Health Metastases Metastasis Neuroimaging Neuroradiology Oncology Patients Radiation therapy Radiology Radiomics Retrospective Studies Support vector machines Training Tumors Ultrasound |
| title | The radiomic-clinical model using the SHAP method for assessing the treatment response of whole-brain radiotherapy: a multicentric study |
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