A deep learning approach for predicting visceral pleural invasion in cT1 lung adenocarcinoma.

ABSTRACT:

Background

Currently, patients with cT1 stage non-small cell lung cancer measuring less than 2 cm are recommended to undergo sublobar resection. However, it should be noted that there is tumor heterogeneity within these lung nodules. Potential visceral pleural invasion (VPI) is regarded as a significant factor that contributes to local recurrence and poorer prognosis after sublobar resection and postoperative upstaging of the T-stage. Currently, there are no effective techniques for preoperative or intraoperative prediction of the status of VPI in lung nodules. The primary objective of this study is to develop a machine learning model for the non-invasive prediction of VPI, thereby providing surgical decision-making support for surgeons during operations.

Methods

A total of

SUBMITTER: Liu S 

PROVIDER: S-EPMC11494600 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature