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Multicenter Study Journal Article Research Support, Non-U.S. Gov't Ppublish 15 citations

PubMed: 37865396 DOI: 10.1136/jitc-2023-007369

Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB-IV NSCLC (LCDigital-IO Study): a multicenter retrospective study.

J Immunother Cancer 11(10)

Authors: Shaowei Wu, Weijie Zhan, Lan Liu, Daipeng Xie, Lintong Yao, Henian Yao, Guoqing Liao, Luyu Huang, Yubo Zhou, Peimeng You, Zekai Huang, Qiaxuan Li, Bin Xu, Siyun Wang, Guangyi Wang, Dong-Kun Zhang, Guibin Qiao, Lawrence Wing-Chi Chan, Michael Lanuti, Haiyu Zhou

Affiliations

  • Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China.
  • Department of Radiology, Jiangxi Cancer Hospital, Nanchang, People's Republic of China.
  • Shantou University Medical College, Shantou, China.
  • Guangdong Medical University, Zhanjiang, China.
  • Department of Thoracic Surgery, Cancer Hospital of Shantou University Medical College, Shantou, China.
  • Department of Surgery, Competence Center of Thoracic Surgery, Charité Universitätsmedizin Berlin, Berlin, Germany.
  • Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China.
  • Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China.
  • Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
  • Department of Thoracic Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China.

Abstract

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.

MeSH

Cells, Progression-Free Survival, Retrospective Studies, Neoplasms, Carcinoma, Non-Small-Cell Lung, Predictive Value Of Tests, Biomarkers, Immunotherapy, Biomarkers, Tumor, Therapeutics

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