Implementation of artificial intelligence in the histological assessment of pulmonary subsolid nodules.

ABSTRACT:

Background

Clinical management of subsolid nodules (SSNs) is defined by the suspicion of tumor invasiveness. We sought to develop an artificial intelligent (AI) algorithm for invasiveness assessment of lung adenocarcinoma manifesting as radiological SSNs. We investigated the performance of this algorithm in classification of SSNs related to invasiveness.

Methods

A retrospective chest computed tomography (CT) dataset of 1,589 SSNs was constructed to develop (85%) and internally test (15%) the proposed AI diagnostic tool, SSNet. Diagnostic performance was evaluated in the hold-out test set and was further tested in an external cohort of 102 SSNs. Three thoracic surgeons and three radiologists were required to evaluate the invasiveness of SSNs on both test datasets to investiga

SUBMITTER: Deng J 

PROVIDER: S-EPMC8743520 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature