Pilot Study of CT-Based Radiomics Model for Early Evaluation of Response to Immunotherapy in Patients With Metastatic Melanoma
Objective: Determine the performance of a computed tomography (CT) -based radiomics model in predicting early response to immunotherapy in patients with metastatic melanoma.Methods: This retrospective study examined 50 patients with metastatic melanoma who received immunotherapy treatment in our hos...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-08-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fonc.2020.01524/full |
| _version_ | 1819015997930078208 |
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| author | Zhi-long Wang Li-li Mao Zhi-guo Zhou Lu Si Hai-tao Zhu Xi Chen Mei-juan Zhou Ying-shi Sun Jun Guo |
| author_facet | Zhi-long Wang Li-li Mao Zhi-guo Zhou Lu Si Hai-tao Zhu Xi Chen Mei-juan Zhou Ying-shi Sun Jun Guo |
| author_sort | Zhi-long Wang |
| collection | DOAJ |
| description | Objective: Determine the performance of a computed tomography (CT) -based radiomics model in predicting early response to immunotherapy in patients with metastatic melanoma.Methods: This retrospective study examined 50 patients with metastatic melanoma who received immunotherapy treatment in our hospital with an anti-programmed cell death-1 (PD-1) agent or an inhibitor of cytotoxic T lymphocyte antigen-4 (CTLA-4). Thirty-four patients who received an anti-PD-1 agent were in the training sample and 16 patients who received a CTLA-4 inhibitor were in the validation sample. Patients with true progressive disease (PD) were in the poor response group, and those with pseudoprogression, complete response (CR), partial response (PR), or stable disease (SD) were in the good response group. CT images were examined at baseline and after the first and second cycles of treatment, and the imaging data were extracted for radiomics modeling.Results: The radiomics model based on pre-treatment, post-treatment, and delta features provided the best results for predicting response to immunotherapy. Receiver operating characteristic (ROC) analysis for good response indicated an area under the curve (AUC) of 0.882 for the training group and an AUC of 0.857 for the validation group. The sensitivity, specificity, and accuracy of model were 85.70% (6/7), 66.70% (6/9), and 75% (12/16) for predicting a good response.Conclusion: A CT-based radiomics model for metastatic melanoma has the potential to predict early response to immunotherapy and to identify pseudoprogression. |
| first_indexed | 2024-12-21T02:40:38Z |
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| id | doaj.art-ad6a9151bb0046b5917f6fc36c031c99 |
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| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-21T02:40:38Z |
| publishDate | 2020-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-ad6a9151bb0046b5917f6fc36c031c992022-12-21T19:18:41ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2020-08-011010.3389/fonc.2020.01524559765Pilot Study of CT-Based Radiomics Model for Early Evaluation of Response to Immunotherapy in Patients With Metastatic MelanomaZhi-long Wang0Li-li Mao1Zhi-guo Zhou2Lu Si3Hai-tao Zhu4Xi Chen5Mei-juan Zhou6Ying-shi Sun7Jun Guo8Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, ChinaKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Renal Cancer and Melanoma, Peking University Cancer Hospital & Institute, Beijing, ChinaSchool of Computer Science and Mathematics, University of Central Missouri, Warrensburg, MO, United StatesKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Renal Cancer and Melanoma, Peking University Cancer Hospital & Institute, Beijing, ChinaKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, ChinaSchool of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an, ChinaSchool of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an, ChinaKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, ChinaKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Renal Cancer and Melanoma, Peking University Cancer Hospital & Institute, Beijing, ChinaObjective: Determine the performance of a computed tomography (CT) -based radiomics model in predicting early response to immunotherapy in patients with metastatic melanoma.Methods: This retrospective study examined 50 patients with metastatic melanoma who received immunotherapy treatment in our hospital with an anti-programmed cell death-1 (PD-1) agent or an inhibitor of cytotoxic T lymphocyte antigen-4 (CTLA-4). Thirty-four patients who received an anti-PD-1 agent were in the training sample and 16 patients who received a CTLA-4 inhibitor were in the validation sample. Patients with true progressive disease (PD) were in the poor response group, and those with pseudoprogression, complete response (CR), partial response (PR), or stable disease (SD) were in the good response group. CT images were examined at baseline and after the first and second cycles of treatment, and the imaging data were extracted for radiomics modeling.Results: The radiomics model based on pre-treatment, post-treatment, and delta features provided the best results for predicting response to immunotherapy. Receiver operating characteristic (ROC) analysis for good response indicated an area under the curve (AUC) of 0.882 for the training group and an AUC of 0.857 for the validation group. The sensitivity, specificity, and accuracy of model were 85.70% (6/7), 66.70% (6/9), and 75% (12/16) for predicting a good response.Conclusion: A CT-based radiomics model for metastatic melanoma has the potential to predict early response to immunotherapy and to identify pseudoprogression.https://www.frontiersin.org/article/10.3389/fonc.2020.01524/fullmalignant melanomaimmunotherapyradiomicscomputed tomographypseudoprogression |
| spellingShingle | Zhi-long Wang Li-li Mao Zhi-guo Zhou Lu Si Hai-tao Zhu Xi Chen Mei-juan Zhou Ying-shi Sun Jun Guo Pilot Study of CT-Based Radiomics Model for Early Evaluation of Response to Immunotherapy in Patients With Metastatic Melanoma Frontiers in Oncology malignant melanoma immunotherapy radiomics computed tomography pseudoprogression |
| title | Pilot Study of CT-Based Radiomics Model for Early Evaluation of Response to Immunotherapy in Patients With Metastatic Melanoma |
| title_full | Pilot Study of CT-Based Radiomics Model for Early Evaluation of Response to Immunotherapy in Patients With Metastatic Melanoma |
| title_fullStr | Pilot Study of CT-Based Radiomics Model for Early Evaluation of Response to Immunotherapy in Patients With Metastatic Melanoma |
| title_full_unstemmed | Pilot Study of CT-Based Radiomics Model for Early Evaluation of Response to Immunotherapy in Patients With Metastatic Melanoma |
| title_short | Pilot Study of CT-Based Radiomics Model for Early Evaluation of Response to Immunotherapy in Patients With Metastatic Melanoma |
| title_sort | pilot study of ct based radiomics model for early evaluation of response to immunotherapy in patients with metastatic melanoma |
| topic | malignant melanoma immunotherapy radiomics computed tomography pseudoprogression |
| url | https://www.frontiersin.org/article/10.3389/fonc.2020.01524/full |
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