Rapid and precise multifocal cutaneous tumor margin assessment using fluorescence lifetime detection and machine learning

Author:

Su Wenhua1,Zheng Dachao2,Zhou Jiacheng1ORCID,Chen Qiushu1,Chen Liwen1,Yang Yuwei1,Fei Yiyan1ORCID,Yao Haijun2,Ma Jiong134ORCID,Mi Lan13ORCID

Affiliation:

1. Department of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), School of Information Science and Technology, Fudan University 1 , Shanghai, China

2. Department of Urology, Shanghai Ninth People’s Hospital, Shanghai Jiaotong University School of Medicine 2 , Shanghai, China

3. Institute of Biomedical Engineering and Technology, Academy for Engineer and Technology, Fudan University 3 , Shanghai, China

4. Shanghai Engineering Research Center of Industrial Microorganisms, the Multiscale Research Institute of Complex Systems (MRICS), School of Life Sciences, Fudan University 4 , Shanghai, China

Abstract

The precise determination of surgical margins is essential for the management of multifocal cutaneous cancers, including extramammary Paget’s disease. This study introduces a novel strategy for precise margin identification in such tumors, employing multichannel autofluorescence lifetime decay (MALD), fluorescence lifetime imaging microscopy (FLIM), and machine learning, including confidence learning algorithms. Using FLIM, 51 unstained frozen sections were analyzed, of which 13 (25%) sections, containing 5003 FLIM patches, were used for training the residual network model (ResNet–FLIM). The remaining 38 (75%) sections, including 16 918 patches, were retained for external validation. Application of confidence learning with deep learning reduced the reliance on extensive pathologist annotation. Refined labels obtained by ResNet–FLIM were then incorporated into a support vector machine (SVM) model, which utilized fiber-optic-based MALD data. Both models exhibited substantial agreement with the pathological assessments. Of the 35 MALD-measured tissue segments, six (17%) segments were selected as the training dataset, including 900 decay profiles. The remaining 29 segments (83%), including 2406 decay profiles, were reserved for external validation. The ResNet–FLIM model achieved 100% sensitivity and specificity. The SVM–MALD model demonstrated 94% sensitivity and 83% specificity. Notably, fiber-optic-MALD allows assessing 12 sites per patient and delivering predictions within 10 min. Variations in the necessary safe margin length were observed among patients, highlighting the necessity for patient-specific approaches to determine surgical margins. This innovative approach holds potential for wide clinical application, providing a rapid and accurate margin evaluation method that significantly reduces a pathologist’s workload and improves patient outcomes through personalized medicine.

Funder

National Natural Science Foundation of China

Publisher

AIP Publishing

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