A quantitative imaging biomarker for predicting disease-free-survival-associated histologic subgroups in lung adenocarci
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A quantitative imaging biomarker for predicting disease-free-survival-associated histologic subgroups in lung adenocarcinoma Lin Lu 1 & Deling Wang 2 & Lili Wang 3 & Linning E 4 & Pingzhen Guo 1 & Zhiming Li 5 & Jin Xiang 6 & Hao Yang 1 & Hui Li 2 & Shaohan Yin 2 & Lawrence H. Schwartz 1 & Chuanmiao Xie 2 & Binsheng Zhao 1 Received: 18 September 2019 / Revised: 11 December 2019 / Accepted: 17 January 2020 # European Society of Radiology 2020
Abstract Objectives Classification of histologic subgroups has significant prognostic value for lung adenocarcinoma patients who undergo surgical resection. However, clinical histopathology assessment is generally performed on only a small portion of the overall tumor from biopsy or surgery. Our objective is to identify a noninvasive quantitative imaging biomarker (QIB) for the classification of histologic subgroups in lung adenocarcinoma patients. Methods We retrospectively collected and reviewed 1313 CT scans of patients with resected lung adenocarcinomas from two geographically distant institutions who were seen between January 2014 and October 2017. Three study cohorts, the training, internal validation, and external validation cohorts, were created, within which lung adenocarcinomas were divided into two disease-free-survival (DFS)-associated histologic subgroups, the mid/poor and good DFS groups. A comprehensive machine learning– and deep learning–based analytical system was adopted to identify reproducible QIBs and help to understand QIBs’ significance. Results Intensity-Skewness, a QIB quantifying tumor density distribution, was identified as the optimal biomarker for predicting histologic subgroups. Intensity-Skewness achieved high AUCs (95% CI) of 0.849(0.813,0.881), 0.820(0.781,0.856) and 0.863(0.827,0.895) on the training, internal validation, and external validation cohorts, respectively. A criterion of IntensitySkewness ≤ 1.5, which indicated high tumor density, showed high specificity of 96% (sensitivity 46%) and 99% (sensitivity 53%) on predicting the mid/poor DFS group in the training and external validation cohorts, respectively. Conclusions A QIB derived from routinely acquired CT was able to predict lung adenocarcinoma histologic subgroups, providing a noninvasive method that could potentially benefit personalized treatment decision-making for lung cancer patients.
Lin Lu and Deling Wang contributed equally to this work. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-06663-6) contains supplementary material, which is available to authorized users. * Chuanmiao Xie [email protected] * Binsheng Zhao [email protected] 1
2
Department of Radiology, Columbia University Medical Center, 710 West 168th Street, B26, New York, NY 10032, USA Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, People’s Republic of China
3
Department of Molecular Pathology, the Af
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