Intraoperative multi-layer perceptron-based artificial intelligence-assisted pure-vision and hyperspectral imaging reclassifies sleeve-resection candidates as eligible for lobectomy after neoadjuvant chemoimmunotherapy: A multicenter retrospective cohort and prospective exploratory study

Intelligent Medicine · Published 2026-07-01 · DOI 10.1016/j.imed.2026.07.008

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Authors (15)

Hao Yin, Xiangyang Yu, Tianru Zang, Shuozhi Li, Rongkui Luo, Ruijun Ni, Zhuoyang Fan, Feihu Zhu, Qun Wang, Huan Zhang, Yue Jin, Fenghao Sun, Mingxiang Feng, Lijie Tan, Ming Li

Abstract

Background: Centrally located non-small cell lung cancer (NSCLC) traditionally requires sleeve resection or pneumonectomy. We investigated whether neoadjuvant chemoimmunotherapy (NeoCIT) could enable conversion to standard lobectomy and evaluated an artificial intelligence (AI)-assisted pure-vision and hyperspectral imaging (HSI) system for intraoperative margin assessment. Methods: This multicenter cohort study enrolled patients from six tertiary referral centers in China between January 2019 and December 2025. The retrospective cohort (n = 56, January 2019–December 2024) and prospective exploratory cohort (n = 28, January–December 2025) included adults with histologically confirmed centrally located NSCLC, who were initially indicated for sleeve resection and received NeoCIT followed by surgical resection. Primary endpoints were event-free survival (EFS) and overall survival (OS), analyzed using Kaplan-Meier methods and multivariable Cox regression. The prospective cohort additionally underwent intraoperative bronchial margin assessment using an AI-assisted system combining HSI (600–950 nm and 16 spectral bands) with a multi-layer perceptron-based deep learning classifier for real-time pixel-wise tissue classification. Results: In the retrospective cohort, 57.1% of patients achieved major pathologic response (MPR) or pathologic complete response (pCR) and 71.4% of them transitioned from sleeve lobectomy to lobectomy. The postoperative complication rate was significantly lower in the lobectomy group (10.0%) compared to the sleeve lobectomy group (38.5%, P = 0.031). Two-year EFS was 69.9% for lobectomy versus 80.2% for sleeve resection (HR = 0.76, P = 0.665), with similar OS (84.0 vs. 90.9%, HR = 0.68, P = 0.713). Multivariable analysis identified pCR/MPR, age, Eastern Cooperative Oncology Group status, and pathological nodal stage after neoadjuvant therapy stage as independent prognostic factors. For the prospective cohort, the AI-assisted pure-vision and HSI system accurately assessed negative margins in real-time, showing high sensitivity (100%) and specificity (96.2%) compared to frozen-section pathology. Conclusion: NeoCIT safely enables surgical conversion from sleeve resection to lobectomy in centrally located NSCLC without compromising oncologic outcomes, and AI-assisted HSI provides accurate real-time intraoperative bronchial margin assessment.

Abstract from DOAJ. Public domain (CC0 1.0).

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Publication details

Year
2026

Citation

Yin, H., Yu, X., Zang, T., et al. (2026). Intraoperative multi-layer perceptron-based artificial intelligence-assisted pure-vision and hyperspectral imaging reclassifies sleeve-resection candidates as eligible for lobectomy after neoadjuvant chemoimmunotherapy: A multicenter retrospective cohort and prospective exploratory study. Intelligent Medicine. https://doi.org/10.1016/j.imed.2026.07.008

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