Rapid and Precise Diagnosis of Retroperitoneal Liposarcoma with Deep-Learned Label-Free Molecular MicroscopyClick to copy article linkArticle link copied!
- Wanhui ZhouWanhui ZhouKey Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, ChinaMore by Wanhui Zhou
- Daoning LiuDaoning LiuKey laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery/Sarcoma Center, Peking University Cancer Hospital & Institute, Beijing 100142, ChinaMore by Daoning Liu
- Tinghe FangTinghe FangKey Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, ChinaMore by Tinghe Fang
- Xun ChenXun ChenKey Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, ChinaSchool of Engineering Medicine, Beihang University, Beijing 100191, ChinaMore by Xun Chen
- Hao JiaHao JiaKey Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, ChinaMore by Hao Jia
- Xiuyun TianXiuyun TianKey laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery/Sarcoma Center, Peking University Cancer Hospital & Institute, Beijing 100142, ChinaMore by Xiuyun Tian
- Chunyi Hao*Chunyi Hao*Email: [email protected]Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery/Sarcoma Center, Peking University Cancer Hospital & Institute, Beijing 100142, ChinaMore by Chunyi Hao
- Shuhua Yue*Shuhua Yue*Email: [email protected]Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, ChinaMore by Shuhua Yue
Abstract
The retroperitoneal liposarcoma (RLPS) is a rare malignancy whose only curative therapy is surgical resection. However, well-differentiated liposarcomas (WDLPSs), one of its most common types, can hardly be distinguished from normal fat during operation without an effective margin assessment method, jeopardizing the prognosis severely with a high recurrence risk. Here, we combined dual label-free nonlinear optical modalities, stimulated Raman scattering (SRS) microscopy and second harmonic generation (SHG) microscopy, to image two predominant tissue biomolecules, lipids and collagen fibers, in 35 RLPSs and 34 normal fat samples collected from 35 patients. The produced dual-modal tissue images were used for RLPS diagnosis based on deep learning. Dramatically decreasing lipids and increasing collagen fibers during tumor progression were reflected. A ResNeXt101-based model achieved 94.7% overall accuracy and 0.987 mean area under the ROC curve (AUC) in differentiating among normal fat, WDLPSs, and dedifferentiated liposarcomas (DDLPSs). In particular, WDLPSs were detected with 94.1% precision and 84.6% sensitivity superior to existing methods. The ablation experiment showed that such performance was attributed to both SRS and SHG microscopies, which increased the sensitivity of recognizing WDLPS by 16.0 and 3.6%, respectively. Furthermore, we utilized this model on RLPS margins to identify the tumor infiltration. Our method holds great potential for accurate intraoperative liposarcoma detection.
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