Pleural invasion of peripheral cT1 lung cancer by deep learning analysis of thoracoscopic images: a retrospective pilot study.

Abstract

Background: Sublobar resection for small peripheral non-small cell lung cancer (NSCLC) (≤2 cm) became one of the standard procedures. Retrospective studies demonstrated that pathological pleural invasion (pPL) is associated with a higher risk of local recurrence during sublobar resection. If pPL can be properly assessed intraoperatively, converting to lobectomy may reduce the risk of local recurrence associated with sublobar resection. The study objective was to develop a deep learning algorithm predicting pPL from thoracoscopic images.

Methods: Among consecutive patients who underwent radical thoracoscopic surgery for cT1N0M0 NSCLC (TNM 8th) from 5/2020 to 3/2022, 80 patients with pleural surface changes due to tumor (excluding cTis/1mi or peritumoral adhesions) were included. A tumor recognition deep learning model using the ResNet50 architecture was constructed from images and the focus was visualized using gradient-weighted class activation mapping (Grad-CAM). Among images in which a tumor is visible, the presence of pPL was predicted (trained on 64, validated on 16). Predictive ability was compared with the surgeons' intraoperative evaluation using McNemar's test.

Results: Among 80 patients (age 69±10 years, 42.5% female, tumor diameter 20±7 mm), pPL was found in 22 patients. Compared to the pPL- group, the pPL+ group was significantly older, with larger solid diameter, more pure solid nodules, and higher SUV max. Among the 422,873 images extracted from all 80 videos, 2,074 images showed tumors, of which 608 images were pPL+. The tumor recognition algorithm had an image-level accuracy of 0.78 and F1 score of 0.60. The pPL model had a patient-level accuracy of 0.69, while the accuracy of thoracic surgeons was 0.75 (P=0.32).

Conclusions: Deep learning analysis of thoracoscopic images of lung cancer surgery showed the possibility of prediction of pPL to a comparable degree to surgeons.