Utility of CT radiomics for prediction of PD‐L1 expression in advanced lung adenocarcinomas
| العنوان: | Utility of CT radiomics for prediction of PD‐L1 expression in advanced lung adenocarcinomas |
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| المؤلفون: | Hye Jeong Lee, Jiyoung Yoon, Kyunghwa Han, Byoung Wook Choi, Hyoun Cho, Jin Hur, Young-Joo Suh |
| المصدر: | Thoracic Cancer, Vol 11, Iss 4, Pp 993-1004 (2020) Thoracic Cancer |
| بيانات النشر: | Wiley, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | 0301 basic medicine, Pulmonary and Respiratory Medicine, Adult, Male, medicine.medical_specialty, Lung Neoplasms, Computed tomography, Adenocarcinoma of Lung, Logistic regression, lcsh:RC254-282, B7-H1 Antigen, 03 medical and health sciences, 0302 clinical medicine, Radiomics, Statistical significance, medicine, Humans, Aged, Neoplasm Staging, Retrospective Studies, Aged, 80 and over, Lung, medicine.diagnostic_test, Receiver operating characteristic, business.industry, Retrospective cohort study, General Medicine, Original Articles, Middle Aged, medicine.disease, lung adenocarcinoma, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, 030104 developmental biology, medicine.anatomical_structure, Oncology, ROC Curve, programmed death ligand 1, radiomics, 030220 oncology & carcinogenesis, Adenocarcinoma, Original Article, Female, Radiology, immunotherapy, business, Tomography, X-Ray Computed, Follow-Up Studies |
| الوصف: | Background We aimed to assess if quantitative radiomic features can predict programmed death ligand 1 (PD-L1) expression in advanced stage lung adenocarcinoma. Methods This retrospective study included 153 patients who had advanced stage (>IIIA by TNM classification) lung adenocarcinoma with pretreatment thin section computed tomography (CT) images and PD-L1 expression test results in their pathology reports. Clinicopathological data were collected from electronic medical records. Visual analysis and radiomic feature extraction of the tumor from pretreatment CT were performed. We constructed two models for multivariate logistic regression analysis (one based on clinical variables, and the other based on a combination of clinical variables and radiomic features), and compared c-statistics of the receiver operating characteristic curves of each model to identify the model with the higher predictability. Results Among 153 patients, 53 patients were classified as PD-L1 positive and 100 patients as PD-L1 negative. There was no significant difference in clinical characteristics or imaging findings on visual analysis between the two groups (P > 0.05 for all). Rad-score by radiomic analysis was higher in the PD-L1 positive group than in the PD-L1 negative group with a statistical significance (-0.378 ± 1.537 vs. -1.171 ± 0.822, P = 0.0008). A prediction model that uses clinical variables and CT radiomic features showed higher performance compared to a prediction model that uses clinical variables only (c-statistic = 0.646 vs. 0.550, P = 0.0299). Conclusions Quantitative CT radiomic features can predict PD-L1 expression in advanced stage lung adenocarcinoma. A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD-L1 expression. Key points Significant findings of the study Quantitative CT radiomic features can help predict PD-L1 expression, whereas none of the qualitative imaging findings is associated with PD-L1 positivity. What this study adds A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD-L1 expression. |
| اللغة: | English |
| تدمد: | 1759-7706 1759-7714 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fbe18dec001e3f8bc57cee2272b5e016Test https://doaj.org/article/c5fdbf4615b74772bebdbcb6184c03f2Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....fbe18dec001e3f8bc57cee2272b5e016 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17597706 17597714 |
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