Nina Xu1, Jiejie Zhou1, Xiaxia He1, Shuxin Ye1, Haiwei Miao1, Huiru Liu1, Zhongwei Chen1, Youfan Zhao1, Zhifang Pan2, Meihao Wang3. 1. Department of Radiology, The First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China. 2. Information Technology Center, The First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China. 3. Department of Radiology, The First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China. Electronic address: wzwmh@wmu.edu.cn.
Abstract
BACKGROUND: To help identify potential breast cancer (BC) candidates for immunotherapies, we aimed to develop and validate a radiology-based biomarker (radiomic score) to predict the level of tumor-infiltrating lymphocytes (TILs) in patients with BC. PATIENTS AND METHODS: This retrospective study enrolled 172 patients with histopathology-confirmed BC assigned to the training (n = 121) or testing (n = 51) cohorts. Radiomic features were extracted and selected using Analysis-Kit software. The correlation between TIL levels and clinical features and radiomic features was evaluated. The clinical features model, radiomic signature model, and combined prediction model were constructed and compared. Predictive performance was assessed by receiver operating characteristic analysis and clinical utility by implementing a nomogram. RESULTS: Seven radiomic features were selected as the best discriminators to construct the radiomic signature model, the performance of which was good in both the training and validation data sets, with an area under the curve (AUC) of 0.742 (95% confidence interval [CI], 0.642-0.843) and 0.718 (95% CI, 0.558-0.878), respectively. Estrogen receptor status and tumor diameter were confirmed to be significant features for building the clinical feature model, which had an AUC of 0.739 (95% CI, 0.632-0.846) and 0.824 (95% CI, 0.692-0.957), respectively. The combined prediction model had an AUC of 0.800 (95% CI, 0.709-0.892) and 0.842 (95% CI, 0.730-0.954), respectively. CONCLUSION: The radiomic signature could be an important predictor of the TIL level in BC, which, when validated, could be useful in identifying BC patients who can benefit from immunotherapies. The nomogram may help clinicians make decisions.
BACKGROUND: To help identify potential breast cancer (BC) candidates for immunotherapies, we aimed to develop and validate a radiology-based biomarker (radiomic score) to predict the level of tumor-infiltrating lymphocytes (TILs) in patients with BC. PATIENTS AND METHODS: This retrospective study enrolled 172 patients with histopathology-confirmed BC assigned to the training (n = 121) or testing (n = 51) cohorts. Radiomic features were extracted and selected using Analysis-Kit software. The correlation between TIL levels and clinical features and radiomic features was evaluated. The clinical features model, radiomic signature model, and combined prediction model were constructed and compared. Predictive performance was assessed by receiver operating characteristic analysis and clinical utility by implementing a nomogram. RESULTS: Seven radiomic features were selected as the best discriminators to construct the radiomic signature model, the performance of which was good in both the training and validation data sets, with an area under the curve (AUC) of 0.742 (95% confidence interval [CI], 0.642-0.843) and 0.718 (95% CI, 0.558-0.878), respectively. Estrogen receptor status and tumor diameter were confirmed to be significant features for building the clinical feature model, which had an AUC of 0.739 (95% CI, 0.632-0.846) and 0.824 (95% CI, 0.692-0.957), respectively. The combined prediction model had an AUC of 0.800 (95% CI, 0.709-0.892) and 0.842 (95% CI, 0.730-0.954), respectively. CONCLUSION: The radiomic signature could be an important predictor of the TIL level in BC, which, when validated, could be useful in identifying BC patients who can benefit from immunotherapies. The nomogram may help clinicians make decisions.
Authors: Lindsey J Mattick; Cruz M Nazario; Rosa V Rosario-Rosado; Michelle Schelske-Santos; Imar Mansilla-Rivera; Farah A Ramírez-Marrero; Jing Nie; Jo L Freudenheim Journal: Cancer Causes Control Date: 2022-01-09 Impact factor: 2.506
Authors: Laurent Dercle; Jeremy McGale; Shawn Sun; Aurelien Marabelle; Randy Yeh; Eric Deutsch; Fatima-Zohra Mokrane; Michael Farwell; Samy Ammari; Heiko Schoder; Binsheng Zhao; Lawrence H Schwartz Journal: J Immunother Cancer Date: 2022-09 Impact factor: 12.469