@article{doi:10.1200/JCO.2026.44.16\_suppl.8019,
author = {Li, Yin and Wang, Yunlong and Pan, Tao and Ding, Bowen and Zhu, Xin and Han, Yuchen and Cheng, Xinghua },
title = {Rapid multi-task intraoperative diagnosis of lung cancer via deep neural network-driven label-free femtosecond laser imaging (FLI).},
journal = {Journal of Clinical Oncology},
volume = {44},
number = {16\_suppl},
pages = {8019-8019},
year = {2026},
doi = {10.1200/JCO.2026.44.16\_suppl.8019},

note ={PMID: },

URL = {https://ascopubs.org/doi/abs/10.1200/JCO.2026.44.16_suppl.8019},
eprint = {https://ascopubs.org/doi/pdf/10.1200/JCO.2026.44.16_suppl.8019}
,
abstract = { 8019Background: Rapid and accurate intraoperative pathological diagnosis is critical for guiding surgical margins and preserving lung function during lung cancer surgery. Current frozen-section (FS) analysis is time-consuming and prone to artifacts. Femtosecond label-free imaging (FLI) enables high-resolution, non-destructive tissue visualization without staining or freezing, offering a promising platform for real-time assessment. Methods: We developed FastLung, an integrated FLI+AI platform for multi-task diagnosis. Fresh, unprocessed tissue samples (326 paired tumor-normal specimens) from surgical resections were imaged using multimodal FLI. FastLung employs a self-supervised deep learning model trained on ~4 million image patches, with formalin-fixed paraffin-embedded (FFPE) histopathology as ground truth. The system provides malignancy probability maps and categorical outputs within 5 minutes. Results: FastLung achieved high diagnostic performance across key intraoperative tasks: benign vs. malignant (AUC = 0.9854 ± 0.01), invasive vs. minimally invasive adenocarcinoma (AUC = 0.9573 ± 0.03), and adenocarcinoma vs. squamous cell carcinoma (AUC = 0.9892 ± 0.009). It significantly reduced diagnostic turnaround time (5–10 min vs. 20–30 min for FS, 60–80\% faster) while maintaining accuracy comparable to or exceeding FS. In core needle biopsies (n = 94), accuracy reached 95.8\%. The platform also demonstrated consistent performance across operators and time, supporting reliable integration into surgical workflow. Conclusions: FastLung combines label-free FLI with deep learning to deliver rapid, accurate, and reproducible intraoperative diagnosis of lung cancer, outperforming frozen sections in speed and multidimensional assessment. Its robust performance in both resection and biopsy specimens highlights its potential to improve surgical decision-making and extend toward pan-cancer diagnostic applications. }
}