Identification of Non–Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics
| العنوان: | Identification of Non–Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics |
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| المؤلفون: | Pingzhen Guo, Julia Wilkerson, Shuyan Du, Lawrence H. Schwartz, Laurent Dercle, David Leung, Amit Roy, Lin Lu, Wendy Hayes, Matthew Fronheiser, Antonio Tito Fojo, Binsheng Zhao |
| المصدر: | Clin Cancer Res |
| بيانات النشر: | American Association for Cancer Research (AACR), 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Male, Oncology, Cancer Research, medicine.medical_specialty, Lung Neoplasms, Docetaxel, Article, 030218 nuclear medicine & medical imaging, Machine Learning, 03 medical and health sciences, Clinical Trials, Phase II as Topic, 0302 clinical medicine, Gefitinib, Carcinoma, Non-Small-Cell Lung, Internal medicine, Antineoplastic Combined Chemotherapy Protocols, medicine, Carcinoma, Humans, Lung cancer, Survival rate, Randomized Controlled Trials as Topic, Retrospective Studies, business.industry, Cancer, Prognosis, medicine.disease, Survival Rate, Clinical trial, Nivolumab, Clinical Trials, Phase III as Topic, 030220 oncology & carcinogenesis, Disease Progression, Female, Tomography, X-Ray Computed, business, medicine.drug |
| الوصف: | Purpose: Using standard-of-care CT images obtained from patients with a diagnosis of non–small cell lung cancer (NSCLC), we defined radiomics signatures predicting the sensitivity of tumors to nivolumab, docetaxel, and gefitinib. Experimental Design: Data were collected prospectively and analyzed retrospectively across multicenter clinical trials [nivolumab, n = 92, CheckMate017 (NCT01642004), CheckMate063 (NCT01721759); docetaxel, n = 50, CheckMate017; gefitinib, n = 46, (NCT00588445)]. Patients were randomized to training or validation cohorts using either a 4:1 ratio (nivolumab: 72T:20V) or a 2:1 ratio (docetaxel: 32T:18V; gefitinib: 31T:15V) to ensure an adequate sample size in the validation set. Radiomics signatures were derived from quantitative analysis of early tumor changes from baseline to first on-treatment assessment. For each patient, 1,160 radiomics features were extracted from the largest measurable lung lesion. Tumors were classified as treatment sensitive or insensitive; reference standard was median progression-free survival (NCT01642004, NCT01721759) or surgery (NCT00588445). Machine learning was implemented to select up to four features to develop a radiomics signature in the training datasets and applied to each patient in the validation datasets to classify treatment sensitivity. Results: The radiomics signatures predicted treatment sensitivity in the validation dataset of each study group with AUC (95 confidence interval): nivolumab, 0.77 (0.55–1.00); docetaxel, 0.67 (0.37–0.96); and gefitinib, 0.82 (0.53–0.97). Using serial radiographic measurements, the magnitude of exponential increase in signature features deciphering tumor volume, invasion of tumor boundaries, or tumor spatial heterogeneity was associated with shorter overall survival. Conclusions: Radiomics signatures predicted tumor sensitivity to treatment in patients with NSCLC, offering an approach that could enhance clinical decision-making to continue systemic therapies and forecast overall survival. |
| تدمد: | 1557-3265 1078-0432 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::418d6d7efe9f74233e4fe0d11d49a2ca https://doi.org/10.1158/1078-0432.ccr-19-2942 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....418d6d7efe9f74233e4fe0d11d49a2ca |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15573265 10780432 |
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