Nasha Zhang1, Jia Wu2, Jinming Yu3, Hui Zhu1, Ming Yang4, Ruijiang Li5. 1. Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong Province, China; Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA. 2. Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA. 3. Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong Province, China. 4. Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong Province, China. Electronic address: aaryoung@yeah.net. 5. Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA. Electronic address: rli2@stanford.edu.
Abstract
BACKGROUND: Immune checkpoint inhibitors have dramatically changed the landscape of therapeutic management of non-small-cell lung cancer (NSCLC). Tumor mutation burden (TMB) is an important biomarker of the response to cancer immunotherapy. We investigated the relationship between TMB and the imaging, histologic, and genetic features in NSCLC. MATERIALS AND METHODS: We evaluated the associations between the semantic imaging features (7 quantitative or semiquantitative imaging features and 13 qualitative features that reflect the tumor characteristics) and TMB and built an imaging signature for TMB using logistic regression. Finally, we integrated the imaging signature, histologic type, and TP53 genotype into a composite model. RESULTS: Among 89 patients, 37 (41.6%) had low TMB and 52 (58.4%) had high TMB. Tumors with high TMB were more prevalent in squamous cell carcinoma (P = .017) and those with a TP53 mutation (P < .0001). The absence of concavity was significantly associated with higher TMB (P = .008). An imaging signature containing 5 features, including concavity, border definition, spiculation, thickened adjacent bronchovascular bundle and size, achieved good discrimination between tumors with low and high TMB (area under the curve [AUC], 0.79; 95% confidence interval [CI], 0.69-0.89). The composite model integrating the imaging signature, histologic type, and TP53 genotype improved the classification (AUC, 0.89; 95% CI, 0.82-0.95) compared with the imaging signature alone using the DeLong test (P = .012). The composite model achieved a high sensitivity of 95% and a specificity of 62%. CONCLUSION: Specific computed tomography features are associated with TMB in NSCLC, and the integration of imaging, histologic, and genetic information might allow for accurate prediction of TMB.
BACKGROUND: Immune checkpoint inhibitors have dramatically changed the landscape of therapeutic management of non-small-cell lung cancer (NSCLC). Tumor mutation burden (TMB) is an important biomarker of the response to cancer immunotherapy. We investigated the relationship between TMB and the imaging, histologic, and genetic features in NSCLC. MATERIALS AND METHODS: We evaluated the associations between the semantic imaging features (7 quantitative or semiquantitative imaging features and 13 qualitative features that reflect the tumor characteristics) and TMB and built an imaging signature for TMB using logistic regression. Finally, we integrated the imaging signature, histologic type, and TP53 genotype into a composite model. RESULTS: Among 89 patients, 37 (41.6%) had low TMB and 52 (58.4%) had high TMB. Tumors with high TMB were more prevalent in squamous cell carcinoma (P = .017) and those with a TP53 mutation (P < .0001). The absence of concavity was significantly associated with higher TMB (P = .008). An imaging signature containing 5 features, including concavity, border definition, spiculation, thickened adjacent bronchovascular bundle and size, achieved good discrimination between tumors with low and high TMB (area under the curve [AUC], 0.79; 95% confidence interval [CI], 0.69-0.89). The composite model integrating the imaging signature, histologic type, and TP53 genotype improved the classification (AUC, 0.89; 95% CI, 0.82-0.95) compared with the imaging signature alone using the DeLong test (P = .012). The composite model achieved a high sensitivity of 95% and a specificity of 62%. CONCLUSION: Specific computed tomography features are associated with TMB in NSCLC, and the integration of imaging, histologic, and genetic information might allow for accurate prediction of TMB.