Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB–IV NSCLC (LCDigital-IO Study): a multicenter retrospective study
Background The predictive efficacy of current biomarker of immune checkpoint inhibitors (ICIs) is not sufficient. This study investigated the causality between radiomic biomarkers and immunotherapy response status in patients with stage IB–IV non-small cell lung cancer (NSCLC), including its biologi...
| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2023-10-01
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| Series: | Journal for ImmunoTherapy of Cancer |
| Online Access: | https://jitc.bmj.com/content/11/10/e007369.full |
| _version_ | 1797628692409614336 |
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| author | Bin Xu Lan Liu Shaowei Wu Guangyi Wang Guibin Qiao Weijie Zhan Daipeng Xie Lintong Yao Henian Yao Guoqing Liao Luyu Huang Yubo Zhou Peimeng You Zekai Huang Qiaxuan Li Siyun Wang Dong-Kun Zhang Lawrence Wing-Chi Chan Michael Lanuti Haiyu Zhou |
| author_facet | Bin Xu Lan Liu Shaowei Wu Guangyi Wang Guibin Qiao Weijie Zhan Daipeng Xie Lintong Yao Henian Yao Guoqing Liao Luyu Huang Yubo Zhou Peimeng You Zekai Huang Qiaxuan Li Siyun Wang Dong-Kun Zhang Lawrence Wing-Chi Chan Michael Lanuti Haiyu Zhou |
| author_sort | Bin Xu |
| collection | DOAJ |
| description | Background The predictive efficacy of current biomarker of immune checkpoint inhibitors (ICIs) is not sufficient. This study investigated the causality between radiomic biomarkers and immunotherapy response status in patients with stage IB–IV non-small cell lung cancer (NSCLC), including its biological context for ICIs treatment response prediction.Methods CT images from 319 patients with pretreatment NSCLC receiving immunotherapy between January 2015 and November 2021 were retrospectively collected and composed a discovery (n=214), independent validation (n=54), and external test cohort (n=51). A set of 851 features was extracted from tumorous and peritumoral volumes of interest (VOIs). The reference standard is the durable clinical benefit (DCB, sustained disease control for more than 6 months assessed via radiological evaluation). The predictive value of combined radiomic signature (CRS) for pathological response was subsequently assessed in another cohort of 98 patients with resectable NSCLC receiving ICIs preoperatively. The association between radiomic features and tumor immune landscape on the online data set (n=60) was also examined. A model combining clinical predictor and radiomic signatures was constructed to improve performance further.Results CRS discriminated DCB and non-DCB patients well in the training and validation cohorts with an area under the curve (AUC) of 0.82, 95% CI: 0.75 to 0.88, and 0.75, 95% CI: 0.64 to 0.87, respectively. In this study, the predictive value of CRS was better than programmed cell death ligand-1 (PD-L1) expression (AUC of PD-L1 subset: 0.59, 95% CI: 0.50 to 0.69) or clinical model (AUC: 0.66, 95% CI: 0.51 to 0.81). After combining the clinical signature with CRS, the predictive performance improved further with an AUC of 0.837, 0.790 and 0.781 in training, validation and D2 cohorts, respectively. When predicting pathological response, CRS divided patients into a major pathological response (MPR) and non-MPR group (AUC: 0.76, 95% CI: 0.67 to 0.81). Moreover, CRS showed a promising stratification ability on overall survival (HR: 0.49, 95% CI: 0.27 to 0.89; p=0.020) and progression-free survival (HR: 0.43, 95% CI: 0.26 to 0.74; p=0.002).Conclusion By analyzing both tumorous and peritumoral regions of CT images in a radiomic strategy, we developed a non-invasive biomarker for distinguishing responders of ICIs therapy and stratifying their survival outcome efficiently, which may support the clinical decisions on the use of ICIs in advanced as well as patients with resectable NSCLC. |
| first_indexed | 2024-03-11T10:43:40Z |
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| id | doaj.art-11ea3475d19b49ab8ececbeb1a429d48 |
| institution | Directory Open Access Journal |
| issn | 2051-1426 |
| language | English |
| last_indexed | 2024-03-11T10:43:40Z |
| publishDate | 2023-10-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | Journal for ImmunoTherapy of Cancer |
| spelling | doaj.art-11ea3475d19b49ab8ececbeb1a429d482023-11-14T07:50:08ZengBMJ Publishing GroupJournal for ImmunoTherapy of Cancer2051-14262023-10-01111010.1136/jitc-2023-007369Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB–IV NSCLC (LCDigital-IO Study): a multicenter retrospective studyBin Xu0Lan Liu1Shaowei Wu2Guangyi Wang3Guibin Qiao4Weijie Zhan5Daipeng Xie6Lintong Yao7Henian Yao8Guoqing Liao9Luyu Huang10Yubo Zhou11Peimeng You12Zekai Huang13Qiaxuan Li14Siyun Wang15Dong-Kun Zhang16Lawrence Wing-Chi Chan17Michael Lanuti18Haiyu Zhou19Department of Thoracic Surgery, Guangdong Provincial People`s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People`s Republic of ChinaDepartment of Radiology, Jiangxi Cancer Hospital, Nanchang, People`s Republic of ChinaDepartment of Thoracic Surgery, Guangdong Provincial People`s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People`s Republic of ChinaDepartment of Radiology, Guangdong Provincial People`s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People`s Republic of ChinaDepartment of Thoracic Surgery, Guangdong Provincial People`s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People`s Republic of ChinaDepartment of Thoracic Surgery, Guangdong Provincial People`s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People`s Republic of ChinaDepartment of Thoracic Surgery, Guangdong Provincial People`s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People`s Republic of ChinaDepartment of Thoracic Surgery, Guangdong Provincial People`s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People`s Republic of ChinaDepartment of Thoracic Surgery, Guangdong Provincial People`s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People`s Republic of ChinaDepartment of Thoracic Surgery, Cancer Hospital of Shantou University Medical College, Shantou, ChinaDepartment of Surgery, Competence Center of Thoracic Surgery, Charité Universitätsmedizin Berlin, Berlin, GermanyDepartment of Thoracic Surgery, Guangdong Provincial People`s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People`s Republic of ChinaDepartment of Radiology, Jiangxi Cancer Hospital, Nanchang, People`s Republic of ChinaGuangdong Medical University, Zhanjiang, ChinaDepartment of Thoracic Surgery, Guangdong Provincial People`s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People`s Republic of ChinaDepartment of Nuclear Medicine, Guangdong Provincial People`s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People`s Republic of ChinaDepartment of Thoracic Surgery, Guangdong Provincial People`s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People`s Republic of ChinaDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Thoracic Surgery, Massachusetts General Hospital, Boston, Massachusetts, USADepartment of Thoracic Surgery, Guangdong Provincial People`s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People`s Republic of ChinaBackground The predictive efficacy of current biomarker of immune checkpoint inhibitors (ICIs) is not sufficient. This study investigated the causality between radiomic biomarkers and immunotherapy response status in patients with stage IB–IV non-small cell lung cancer (NSCLC), including its biological context for ICIs treatment response prediction.Methods CT images from 319 patients with pretreatment NSCLC receiving immunotherapy between January 2015 and November 2021 were retrospectively collected and composed a discovery (n=214), independent validation (n=54), and external test cohort (n=51). A set of 851 features was extracted from tumorous and peritumoral volumes of interest (VOIs). The reference standard is the durable clinical benefit (DCB, sustained disease control for more than 6 months assessed via radiological evaluation). The predictive value of combined radiomic signature (CRS) for pathological response was subsequently assessed in another cohort of 98 patients with resectable NSCLC receiving ICIs preoperatively. The association between radiomic features and tumor immune landscape on the online data set (n=60) was also examined. A model combining clinical predictor and radiomic signatures was constructed to improve performance further.Results CRS discriminated DCB and non-DCB patients well in the training and validation cohorts with an area under the curve (AUC) of 0.82, 95% CI: 0.75 to 0.88, and 0.75, 95% CI: 0.64 to 0.87, respectively. In this study, the predictive value of CRS was better than programmed cell death ligand-1 (PD-L1) expression (AUC of PD-L1 subset: 0.59, 95% CI: 0.50 to 0.69) or clinical model (AUC: 0.66, 95% CI: 0.51 to 0.81). After combining the clinical signature with CRS, the predictive performance improved further with an AUC of 0.837, 0.790 and 0.781 in training, validation and D2 cohorts, respectively. When predicting pathological response, CRS divided patients into a major pathological response (MPR) and non-MPR group (AUC: 0.76, 95% CI: 0.67 to 0.81). Moreover, CRS showed a promising stratification ability on overall survival (HR: 0.49, 95% CI: 0.27 to 0.89; p=0.020) and progression-free survival (HR: 0.43, 95% CI: 0.26 to 0.74; p=0.002).Conclusion By analyzing both tumorous and peritumoral regions of CT images in a radiomic strategy, we developed a non-invasive biomarker for distinguishing responders of ICIs therapy and stratifying their survival outcome efficiently, which may support the clinical decisions on the use of ICIs in advanced as well as patients with resectable NSCLC.https://jitc.bmj.com/content/11/10/e007369.full |
| spellingShingle | Bin Xu Lan Liu Shaowei Wu Guangyi Wang Guibin Qiao Weijie Zhan Daipeng Xie Lintong Yao Henian Yao Guoqing Liao Luyu Huang Yubo Zhou Peimeng You Zekai Huang Qiaxuan Li Siyun Wang Dong-Kun Zhang Lawrence Wing-Chi Chan Michael Lanuti Haiyu Zhou Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB–IV NSCLC (LCDigital-IO Study): a multicenter retrospective study Journal for ImmunoTherapy of Cancer |
| title | Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB–IV NSCLC (LCDigital-IO Study): a multicenter retrospective study |
| title_full | Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB–IV NSCLC (LCDigital-IO Study): a multicenter retrospective study |
| title_fullStr | Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB–IV NSCLC (LCDigital-IO Study): a multicenter retrospective study |
| title_full_unstemmed | Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB–IV NSCLC (LCDigital-IO Study): a multicenter retrospective study |
| title_short | Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB–IV NSCLC (LCDigital-IO Study): a multicenter retrospective study |
| title_sort | pretreatment radiomic biomarker for immunotherapy responder prediction in stage ib iv nsclc lcdigital io study a multicenter retrospective study |
| url | https://jitc.bmj.com/content/11/10/e007369.full |
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