A Combined-Radiomics Approach of CT Images to Predict Response to Anti-PD-1 Immunotherapy in NSCLC: A Retrospective Multicenter Study
ObjectiveBased on non-contrast-enhanced (NCE)/contrast-enhanced (CE) computed tomography (CT) images, we try to identify a combined-radiomics model and evaluate its predictive capacity regarding response to anti-PD1 immunotherapy of patients with non-small-cell lung cancer (NSCLC).Methods131 patient...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-01-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2021.688679/full |
| _version_ | 1798034362251345920 |
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| author | Minghao Wu Minghao Wu Yanyan Zhang Jianing Zhang Yuwei Zhang Yina Wang Feng Chen Yahong Luo Shuai He Yulin Liu Qian Yang Yanying Li Hong Wei Hong Zhang Nian Lu Sicong Wang Yan Guo Zhaoxiang Ye Ying Liu |
| author_facet | Minghao Wu Minghao Wu Yanyan Zhang Jianing Zhang Yuwei Zhang Yina Wang Feng Chen Yahong Luo Shuai He Yulin Liu Qian Yang Yanying Li Hong Wei Hong Zhang Nian Lu Sicong Wang Yan Guo Zhaoxiang Ye Ying Liu |
| author_sort | Minghao Wu |
| collection | DOAJ |
| description | ObjectiveBased on non-contrast-enhanced (NCE)/contrast-enhanced (CE) computed tomography (CT) images, we try to identify a combined-radiomics model and evaluate its predictive capacity regarding response to anti-PD1 immunotherapy of patients with non-small-cell lung cancer (NSCLC).Methods131 patients with NSCLC undergoing anti-PD1 immunotherapy were retrospectively enrolled from 7 institutions. Using largest lesion (LL) and target lesions (TL) approaches, we performed a radiomics analysis based on pretreatment NCE-CT (NCE-radiomics) and CE-CT images (CE-radiomics), respectively. Meanwhile, a combined-radiomics model based on NCE-CT and CE-CT images was constructed. Finally, we developed their corresponding nomograms incorporating clinical factors. ROC was used to evaluate models’ predictive performance in the training and testing set, and a DeLong test was employed to compare the differences between different models.ResultsFor TL approach, both NCE-radiomics and CE-radiomics performed poorly in predicting response to immunotherapy. For LL approach, NCE-radiomics nomograms and CE-radiomics nomograms incorporating with clinical factor of distant metastasis all showed satisfactory results, reflected by the AUCs in the training (AUC=0.84, 95% CI: 0.75-0.92; AUC=0.77, 95% CI: 0.67-0.87) and test sets (AUC=0.78, 95% CI: 0.64-0.92, AUC=0.73, 95% CI: 0.57-0.88), respectively. Compared with the NCE-radiomics nomograms, the combined-radiomics nomogram showed incremental predictive capacity in the training set (AUC=0.85, 95% CI: 0.77-0.92) and test set (AUC=0.81, 95% CI: 0.67-0.94), respectively, but no statistical difference (P=0.86, P=0.79).ConclusionCompared with radiomics based on single NCE or CE-CT images, the combined-radiomics model has potential advantages to identify patients with NSCLC most likely to benefit from immunotherapy, and may effectively improve more precise and individualized decision support. |
| first_indexed | 2024-04-11T20:43:14Z |
| format | Article |
| id | doaj.art-2ab12d354dc04366832611dea5deae86 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-11T20:43:14Z |
| publishDate | 2022-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-2ab12d354dc04366832611dea5deae862022-12-22T04:04:07ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-01-011110.3389/fonc.2021.688679688679A Combined-Radiomics Approach of CT Images to Predict Response to Anti-PD-1 Immunotherapy in NSCLC: A Retrospective Multicenter StudyMinghao Wu0Minghao Wu1Yanyan Zhang2Jianing Zhang3Yuwei Zhang4Yina Wang5Feng Chen6Yahong Luo7Shuai He8Yulin Liu9Qian Yang10Yanying Li11Hong Wei12Hong Zhang13Nian Lu14Sicong Wang15Yan Guo16Zhaoxiang Ye17Ying Liu18Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaDepartment of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaDepartment of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaDepartment of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaDepartment of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaDepartment of Medical Oncology, 1st Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Radiology, 1st Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, ChinaDepartment of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, ChinaDepartment of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Thoracic Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Radiology, Tianjin Chest Hospital, Tianjin, China0Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Guangzhou, China1Prognostic Diagnosis, GE Healthcare China, Beijing, China1Prognostic Diagnosis, GE Healthcare China, Beijing, ChinaDepartment of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaDepartment of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaObjectiveBased on non-contrast-enhanced (NCE)/contrast-enhanced (CE) computed tomography (CT) images, we try to identify a combined-radiomics model and evaluate its predictive capacity regarding response to anti-PD1 immunotherapy of patients with non-small-cell lung cancer (NSCLC).Methods131 patients with NSCLC undergoing anti-PD1 immunotherapy were retrospectively enrolled from 7 institutions. Using largest lesion (LL) and target lesions (TL) approaches, we performed a radiomics analysis based on pretreatment NCE-CT (NCE-radiomics) and CE-CT images (CE-radiomics), respectively. Meanwhile, a combined-radiomics model based on NCE-CT and CE-CT images was constructed. Finally, we developed their corresponding nomograms incorporating clinical factors. ROC was used to evaluate models’ predictive performance in the training and testing set, and a DeLong test was employed to compare the differences between different models.ResultsFor TL approach, both NCE-radiomics and CE-radiomics performed poorly in predicting response to immunotherapy. For LL approach, NCE-radiomics nomograms and CE-radiomics nomograms incorporating with clinical factor of distant metastasis all showed satisfactory results, reflected by the AUCs in the training (AUC=0.84, 95% CI: 0.75-0.92; AUC=0.77, 95% CI: 0.67-0.87) and test sets (AUC=0.78, 95% CI: 0.64-0.92, AUC=0.73, 95% CI: 0.57-0.88), respectively. Compared with the NCE-radiomics nomograms, the combined-radiomics nomogram showed incremental predictive capacity in the training set (AUC=0.85, 95% CI: 0.77-0.92) and test set (AUC=0.81, 95% CI: 0.67-0.94), respectively, but no statistical difference (P=0.86, P=0.79).ConclusionCompared with radiomics based on single NCE or CE-CT images, the combined-radiomics model has potential advantages to identify patients with NSCLC most likely to benefit from immunotherapy, and may effectively improve more precise and individualized decision support.https://www.frontiersin.org/articles/10.3389/fonc.2021.688679/fullimmunotherapynon-small-cell lung cancerradiomicscomputed tomographyresponse prediction |
| spellingShingle | Minghao Wu Minghao Wu Yanyan Zhang Jianing Zhang Yuwei Zhang Yina Wang Feng Chen Yahong Luo Shuai He Yulin Liu Qian Yang Yanying Li Hong Wei Hong Zhang Nian Lu Sicong Wang Yan Guo Zhaoxiang Ye Ying Liu A Combined-Radiomics Approach of CT Images to Predict Response to Anti-PD-1 Immunotherapy in NSCLC: A Retrospective Multicenter Study Frontiers in Oncology immunotherapy non-small-cell lung cancer radiomics computed tomography response prediction |
| title | A Combined-Radiomics Approach of CT Images to Predict Response to Anti-PD-1 Immunotherapy in NSCLC: A Retrospective Multicenter Study |
| title_full | A Combined-Radiomics Approach of CT Images to Predict Response to Anti-PD-1 Immunotherapy in NSCLC: A Retrospective Multicenter Study |
| title_fullStr | A Combined-Radiomics Approach of CT Images to Predict Response to Anti-PD-1 Immunotherapy in NSCLC: A Retrospective Multicenter Study |
| title_full_unstemmed | A Combined-Radiomics Approach of CT Images to Predict Response to Anti-PD-1 Immunotherapy in NSCLC: A Retrospective Multicenter Study |
| title_short | A Combined-Radiomics Approach of CT Images to Predict Response to Anti-PD-1 Immunotherapy in NSCLC: A Retrospective Multicenter Study |
| title_sort | combined radiomics approach of ct images to predict response to anti pd 1 immunotherapy in nsclc a retrospective multicenter study |
| topic | immunotherapy non-small-cell lung cancer radiomics computed tomography response prediction |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2021.688679/full |
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