CT-based machine learning model integrating intra- and peri-tumoral radiomics features for predicting occult lymph node metastasis in peripheral lung cancer.

ABSTRACT: Accurate preoperative assessment of occult lymph node metastasis (OLNM) plays a crucial role in informing therapeutic decision-making for lung cancer patients. Computed tomography (CT) is the most widely used imaging modality for preoperative work-up. The aim of this study was to develop and validate a CT-based machine learning model integrating intra-and peri-tumoral features to predict OLNM in lung cancer patients. Eligible patients with peripheral lung cancer confirmed by radical surgical excision with systematic lymphadenectomy were retrospectively recruited from January 2019 to December 2021. 1688 radiomics features were obtained from each manually segmented VOI which was composed of gross tumor volume (GTV) covering the boundary of entire tumor and three peritumoral volumes (PTV3, PT

SUBMITTER: Lu X 

PROVIDER: S-EPMC12372188 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature