A Non-invasive Method to Diagnose Lung Adenocarcinoma

Abstract

Purpose: To find out the CT radiomics features of differentiating lung adenocarcinoma from another lung cancer histological type.Methods: This was a historical cohort study, three independent lung cancer cohorts included. One cohort was used to evaluate the stability of radiomics features, one cohort was used to feature selection, and the last was used to construct and evaluate classification models. The research is divided into four steps: region of interest segmentation, feature extraction, feature selection, and model building and validation. The feature selection methods included the intraclass correlation coefficient, ReliefF coefficient, and Partition-Membership filter. The performance metrics of the classification model included accuracy (Acc), precision (Pre), area under curve (AUC), and kappa statistics.

Comparison of Comprehensive Morphological and Radiomics Features of Subsolid Pulmonary Nodules to Distinguish Minimally Invasive Adenocarcinomas and Invasive Adenocarcinomas in CT Scan

“…Besides, our study proved that GLDM_ low gray level emphasis was an important indicator for distinguishing MIAs from IACs in subsolid nodules; studies also proved that low gray level emphasis helps to differentiate lung adenocarcinoma from another lung cancer histological type (21) and predict NSCLC survival (22).…”

Radiomic Analysis of CT Predicts Tumor Response in Human Lung Cancer with Radiotherapy

“…Radiomic features (such as intensity, shape, texture, or wavelet), extracted from medical images, when combined with clinical parameters can make clinical decision more precise [ 11 ]. It has shown a great ability to be the biomarkers in predicting clinical events of lung cancer patients, recent examples like predicting the response of enzymes, gene and immunity therapy which are associated with lung tumor [ 12 ], evaluating the drug reaction [ 13 ], radiation pneumonitis [ 14 ], and distinguishing lung cancer histologic subtypes [ 15 ].…”

Value of pre-treatment 18F-FDG PET/CT radiomics in predicting the prognosis of stage III-IV colorectal cancer

“…Both are important factors for predicting the response of rectal cancer to nCRT [26] , [27] , and have significant correlation with clinical prognostic factors [28] . Some studies reported that the three radiomics features showed good repeatability [26] , [29] . In our study, the three radiomics features could predict PFS or OS in patients with stage III-IV CRC.…”