AI model predicts invasiveness of lung cancer on CT scans

Radiologists in Beijing, China, have developed a joint deep learning and radiomics AI model that can flag how invasive tumors may be in patients with lung cancer, according to a study published April 9 in Radiology. The approach could ultimately help clinicians determine which patients with suspected disease are candidates for surgery, noted led authors Zhengsong Pan and Ge Hu, PhD, of Peking Union Medical College Hospital. “These models could assist in the preoperative care of patients with lung adenocarcinoma,” the group wrote. Lung adenocarcinoma is the most common primary lung cancer seen in the U.S. Tumors manifest as ground-glass nodules (GGNs) on CT scans. Deciding whether the lesions are preinvasive, minimally invasive, or invasive, however, is a significant challenge, and these determinations drive the timing of surgery, according to the authors. To address this challenge, the group developed their deep-learning and radiomics-based approach, which classifies GGNs into preinvasive (atypical adenomatous hyperplasia or adenocarcinoma in situ), minimally invasive, or invasive adenocarcinoma. Imaging data used in the study included CT images of a total of 4,929 nodules from 4,483 patients (mean age, 50.1 years old). The researchers divided these images into training (n = 3,384), validation (n = 579), and internal (n = 966) test sets. An external test set included a total of 361 GGNs from 281 patients. In brief, the researchers first constructed three conventiona...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: CT Artificial Intelligence Thoracic Imaging Source Type: news