Mengmeng Jiang1, Dazhen Sun2, Yinglong Guo1, Yixian Guo1, Jie Xiao3, Lisheng Wang4, Xiuzhong Yao5. 1. Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai 200032, China; Department of Radiology, Zhongshan Hospital of Fudan University, Fudan University, No 180, Fenglin Road, Xuhui District, Shanghai 200032, China. 2. Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, and Department of Automation, Shanghai Jiao Tong University, 800 Dongchuan, RD. Minhang District, Shanghai, China. 3. Department of Nuclear Medicine, Zhongshan Hospital of Fudan University, Fudan University, Shanghai, China. 4. Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, and Department of Automation, Shanghai Jiao Tong University, 800 Dongchuan, RD. Minhang District, Shanghai, China. Electronic address: lswang@sjtu.edu.c. 5. Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai 200032, China; Department of Radiology, Zhongshan Hospital of Fudan University, Fudan University, No 180, Fenglin Road, Xuhui District, Shanghai 200032, China. Electronic address: zsyyyxz@163.com.
Abstract
RATIONALE AND OBJECTIVES: To explore the potential value of radiomic features-derived approach in assessing PD-L1 expression status in nonsmall cell lung cancer (NSCLC) patients. MATERIALS AND METHODS: A cohort of 399 stage I-IV NSCLC patients were enrolled. Tumor segmentation was performed to select essential primary lesions of NSCLC cases after PET/CT images acquisition. Features were extracted, then filtered with automatic relevance determination and minimized with LASSO model based on its relevance of PD-L1 expression status. Finally, we built predictive models with features from the CT, the PET, and the PET/CT images, respectively, for differentiating different status of specific PD-L1 types. Five-fold cross validation was practiced to evaluate the signatures' accuracy, and the receiver operating characteristic as well as the corresponding area under the curve (AUC) was reckoned for each model. RESULTS: With the total of 24 selected features which were significantly associated with PD-L1 expression levels, models based on CT-, PET-, PET/CT-derived features were built and compared. For PD-L1 (SP142) expression level over 1% prediction, models that comprised radiomic features from the CT, the PET, and the PET/CT images resulted in an AUC of 0.97, 0.61, and 0.97, respectively; models for over 50% prediction resulted with AUC of 0.80, 0.65, and 0.77. For PD-L1 (28-8) expression level prediction, predictive models of over 1% expression scored at 0.86, 0.62, and 0.85; and signatures of over 50% expression reached the score of AUCs at 0.91, 0.75, and 0.88, respectively. CONCLUSION: The radiomic-based predictive approach, especially CT-derived predictive model, may anticipate PD-L1 expression status in NSCLC patients relatively accurate. It may be helpful in guiding immunotherapy in clinical practice and deserves further analysis.
RATIONALE AND OBJECTIVES: To explore the potential value of radiomic features-derived approach in assessing PD-L1 expression status in nonsmall cell lung cancer (NSCLC) patients. MATERIALS AND METHODS: A cohort of 399 stage I-IV NSCLCpatients were enrolled. Tumor segmentation was performed to select essential primary lesions of NSCLC cases after PET/CT images acquisition. Features were extracted, then filtered with automatic relevance determination and minimized with LASSO model based on its relevance of PD-L1 expression status. Finally, we built predictive models with features from the CT, the PET, and the PET/CT images, respectively, for differentiating different status of specific PD-L1 types. Five-fold cross validation was practiced to evaluate the signatures' accuracy, and the receiver operating characteristic as well as the corresponding area under the curve (AUC) was reckoned for each model. RESULTS: With the total of 24 selected features which were significantly associated with PD-L1 expression levels, models based on CT-, PET-, PET/CT-derived features were built and compared. For PD-L1 (SP142) expression level over 1% prediction, models that comprised radiomic features from the CT, the PET, and the PET/CT images resulted in an AUC of 0.97, 0.61, and 0.97, respectively; models for over 50% prediction resulted with AUC of 0.80, 0.65, and 0.77. For PD-L1 (28-8) expression level prediction, predictive models of over 1% expression scored at 0.86, 0.62, and 0.85; and signatures of over 50% expression reached the score of AUCs at 0.91, 0.75, and 0.88, respectively. CONCLUSION: The radiomic-based predictive approach, especially CT-derived predictive model, may anticipate PD-L1 expression status in NSCLCpatients relatively accurate. It may be helpful in guiding immunotherapy in clinical practice and deserves further analysis.
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