A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans
Year: 2021
Type: article
Study design: Observational Study
Abstract: The aim of this study is to evaluate whether NIS radiomics can distinguish lung adenocarcinomas from granulomas on non-contrast CT scans, and also to improve the performance of Lung-RADS by reclassifying benign nodules that were initially assessed as suspicious. The screening or standard diagnostic non-contrast CT scans of 362 patients was divided into training (St, N = 145), validation (Sv, N = 145), and independent validation (Siv, N = 62) sets from different institutions. Nodules were identified and manually segmented on CT images by a radiologist. A series of 264 features relating to the edge sharpness transition from the inside to the outside of the nodule were extracted. The top 10 features were used to train a linear discriminant analysis (LDA) machine learning classifier on St. In conjunction with the LDA classifier, NIS radiomics classified nodules with an AUC of 0.82 ± 0.04, 0.77, and 0.71 respectively on St, Sv, and Siv. We evaluated the ability of the NIS classifier to determine the proportion of the patients in Sv that were identified initially as suspicious by Lung-RADS but were reclassified as benign by applying the NIS scores. The NIS classifier was able to correctly reclassify 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS alone on Sv.
Source: Cancers
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,376
Funders: U.S. Department of Defense, National Institutes of Health, Clinical and Translational Science Collaborative of Cleveland, School of Medicine, Case Western Reserve University, National Cancer Institute, National Center for Research Resources, National Center for Advancing Translational Sciences, DOD Peer Reviewed Cancer Research Program
Awards: RadxTools for assessing tumor treatment response on imaging, Lung cancer in East Africa and the relationship to HIV-1 infection: epidemiology, molecular characterization and imaging, Pathology Image Informatics Platform for visualization, analysis and management, CHIRP Computerized Histologic Risk Predictor (CHiRP) for Early Stage Lung Cancers, G406353596, HistoTools: A suite of digital pathology tools for quality control, annotation and dataset identification, MR Fingerprinting and Computerized Decision Support for Prostate Cancer, Computerized histologic image predictor of cancer outcome, Quantitative Histomorphometric Risk Classifier (QuHbIC) in HPV + Oropharyngeal Carcinoma, NOVEL CT-DERIVED RADIOMIC BIOMARKERS FOR LUNG NODULE CHARACTERIZATION IN THE SCREENING POPULATION, QUANTITATIVE VESSEL TORTUOSITY AS BIOMARKER FOR PREDICTING AND MONITORING RESPONSE TO IMMUNOTHERAPY FOR NON-SMALL CELL LUNG CANCER FROM ROUTINE CT SCANS, Novel Radiomics for Predicting Response to Immunotherapy for Lung Cancer, Oral Cavity Quantitative Histomorphometric Risk Classifier (OHbIC) in Oral Cavity Squamous Cell Carcinoma (OC-SCC)
OpenAlex ID: W3172196475