ArticleJTCVS open2026
Femtosecond label-free imaging: A rapid and reliable alternative for intraoperative pathological assessment in thoracic oncology.
Article in JTCVS open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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14 authors.
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Abstract
Objective: The study objective was to evaluate femtosecond label-free imaging combined with artificial intelligence as a rapid alternative to frozen-section pathology for intraoperative assessment in thoracic oncologic surgery. Methods: This prospective study enrolled 144 patients undergoing resection for thoracic tumors at Shanghai Zhongshan Hospital (June to December 2025). Fresh specimens (259 lung, 96 esophageal) underwent femtosecond label-free imaging scanning using ultrashort laser pulses to generate multiple nonlinear optical signals (third harmonic generation, second harmonic generation, 2-photon and 3-photon fluorescence) without sectioning or staining. Deep learning algorithms were developed for lung tumor classification and exploratory high-risk feature prediction in esophageal squamous cell carcinoma. To avoid data leakage, specimen-level splits were adopted for lung tumor classification and patient-level splits for esophageal squamous cell carcinoma high-risk feature prediction. The models were constructed based on the pretrained UNI v1 foundation model combined with an attention-based multiple-instance learning framework, with data augmentation (flipping and rotation) applied during training. Performance was evaluated against conventional histopathology using receiver operating characteristic curve analysis, with data partitioned into training, validation, and test sets at a 7:1:2 ratio. Results: Femtosecond label-free imaging demonstrated significant time advantage over frozen-section analysis (median 5.4 vs 36.3 minutes, Conclusions: Femtosecond label-free imaging provides rapid, morphologically concordant pathological assessment with significant workflow advantages over conventional frozen-section analysis. Depth-scanning capability addresses sampling limitations inherent to single-plane evaluation. Exploratory artificial intelligence models demonstrate feasibility for intraoperative prediction of high-risk pathological features in esophageal cancer; however, these results are preliminary and require validation in larger, multicenter cohorts before clinical deployment.
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