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3. Ovarian cancer identification technology based on deep learning and second harmonic generation imaging.

Author

Kang, Bingzi, Chen, Siyu, Wang, Guangxing, Huang, Yuhang, Wu, Han, He, Jiajia, Li, Xiaolu, Xi, Gangqin, Wu, Guizhu, and Zhuo, Shuangmu

Abstract

Ovarian cancer is among the most common gynecological cancers and the eighth leading cause of cancer‐related deaths among women worldwide. Surgery is among the most important options for cancer treatment. During surgery, a biopsy is generally required to screen for lesions; however, traditional case examinations are time consuming and laborious and require extensive experience and knowledge from pathologists. Therefore, this study proposes a simple, fast, and label‐free ovarian cancer diagnosis method that combines second harmonic generation (SHG) imaging and deep learning. Unstained fresh human ovarian tissues were subjected to SHG imaging and accurately characterized using the Pyramid Vision Transformer V2 (PVTv2) model. The results showed that the SHG imaged collagen fibers could quantify ovarian cancer. In addition, the PVTv2 model could accurately differentiate the 3240 SHG images obtained from our imaging collection into benign, normal, and malignant images, with a final accuracy of 98.4%. These results demonstrate the great potential of SHG imaging techniques combined with deep learning models for diagnosing the diseased ovarian tissues. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Prognostic value of tumor necrosis based on the evaluation of frequency in invasive breast cancer

Author

Chen, Jianhua, primary, Li, Zhijun, additional, Han, Zhonghua, additional, Kang, Deyong, additional, Ma, Jianli, additional, Yi, Yu, additional, Fu, Fangmeng, additional, Guo, Wenhui, additional, Zheng, Liqin, additional, Xi, Gangqin, additional, He, Jiajia, additional, Qiu, Lida, additional, Li, Lianhuang, additional, Zhang, Qingyuan, additional, Wang, Chuan, additional, and Chen, Jianxin, additional

Published
2023
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10. Additional file 2 of Prognostic value of tumor necrosis based on the evaluation of frequency in invasive breast cancer

Author

Chen, Jianhua, Li, Zhijun, Han, Zhonghua, Kang, Deyong, Ma, Jianli, Yi, Yu, Fu, Fangmeng, Guo, Wenhui, Zheng, Liqin, Xi, Gangqin, He, Jiajia, Qiu, Lida, Li, Lianhuang, Zhang, Qingyuan, Wang, Chuan, and Chen, Jianxin

Abstract

Additional file 2: Figure S1. Emission spectra of myoepithelium, TN and tumor cells obtained with an excitation wavelength of 810 nm. Figure S2. Types of TN1. A Lesions surrounded by invasive breast cancer cells. B Lesions in duct. M: myoepithelium (white arrow). Figure S3. Types of TN2. A Lesions surrounded possibly DCIS with regressive changes. B Lesions in the stroma. Figure S4. Types of TN4. A Lesions surrounded by myoepithelium and tumor cells. B Lesions surrounded by collagen and tumor cells. C Lesions surrounded by tumor cells but with collagen fibers. M: myoepithelium (white arrow). Figure S5. 5-year DFS of patients with IBC stratified by tumor size ≤ 2 cm (A and B) and > 2 cm (C and D) in training and validation sets. Figure S6. Correlation analysis between individual TNs and 5-year DFS for three sets. Figure S7. Comparison of TN classification by MPM or H&E. In (A) and (C), TNs were classified as TN4 by MPM, because necrotic lesions were surrounded by collagen and tumor cells, while the corresponding TNs were classified as TN1 by H&E, because necrotic lesions on H&E images were only surrounded by tumor cells (B and D). White arrow: collagen, blue arrow: tumor cells.

Published
2023
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11. Additional file 1 of Prognostic value of tumor necrosis based on the evaluation of frequency in invasive breast cancer

Author

Chen, Jianhua, Li, Zhijun, Han, Zhonghua, Kang, Deyong, Ma, Jianli, Yi, Yu, Fu, Fangmeng, Guo, Wenhui, Zheng, Liqin, Xi, Gangqin, He, Jiajia, Qiu, Lida, Li, Lianhuang, Zhang, Qingyuan, Wang, Chuan, and Chen, Jianxin

Abstract

Additional file 1: Table S1. Characteristics of patients in the training and validation sets. Table S2. Quantified patient-specific data based on the training set. Table S3. Clinicopathological variables of patients stratified by the presence/absence classification and TN classification in training and validation set. Table S4. Distribution of the four types of TN in training and validation sets. Table S5. Recurrence patterns of patients stratified by the presence/absence classification and TN classification. Table S6 (part 1). Univariate and multivariate Cox regression analysis of association of risk factors with 5-year DFS in patients with necrosis in the training set (only independent prognosticators were included in multivariate analysis). Table S6 (part 2). Univariate and multivariate Cox regression analysis of association of risk factors with 5-year DFS in patients with necrosis in the training set (all prognosticators, except endocrine therapy and targeted therapy strongly correlated with the molecular subtype, were included in multivariate analysis). Table S6 (part 3). Univariate and multivariate Cox regression analysis of association of risk factors with 5-year DFS in patients with necrosis in the training set (all prognosticators were included in multivariate analysis).

Published
2023
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16. AutoFibroNet: A deep learning and multi‐photon microscopy‐derived automated network for liver fibrosis quantification in MAFLD.

Author

Zhan, Huiling, Chen, Siyu, Gao, Feng, Wang, Guangxing, Chen, Sui‐Dan, Xi, Gangqin, Yuan, Hai‐Yang, Li, Xiaolu, Liu, Wen‐Yue, Byrne, Christopher D., Targher, Giovanni, Chen, Miao‐Yang, Yang, Yong‐Feng, Chen, Jun, Fan, Zhiwen, Sun, Xitai, Cai, Guorong, Zheng, Ming‐Hua, and Zhuo, Shuangmu

Subjects
DEEP learning, HEPATIC fibrosis, FATTY liver, MULTILAYER perceptrons, RECEIVER operating characteristic curves
Abstract

Summary: Background: Liver fibrosis is the strongest histological risk factor for liver‐related complications and mortality in metabolic dysfunction‐associated fatty liver disease (MAFLD). Second harmonic generation/two‐photon excitation fluorescence (SHG/TPEF) is a powerful tool for label‐free two‐dimensional and three‐dimensional tissue visualisation that shows promise in liver fibrosis assessment. Aim: To investigate combining multi‐photon microscopy (MPM) and deep learning techniques to develop and validate a new automated quantitative histological classification tool, named AutoFibroNet (Automated Liver Fibrosis Grading Network), for accurately staging liver fibrosis in MAFLD. Methods: AutoFibroNet was developed in a training cohort that consisted of 203 Chinese adults with biopsy‐confirmed MAFLD. Three deep learning models (VGG16, ResNet34, and MobileNet V3) were used to train pre‐processed images and test data sets. Multi‐layer perceptrons were used to fuse data (deep learning features, clinical features, and manual features) to build a joint model. This model was then validated in two further independent cohorts. Results: AutoFibroNet showed good discrimination in the training set. For F0, F1, F2 and F3‐4 fibrosis stages, the area under the receiver operating characteristic curves (AUROC) of AutoFibroNet were 1.00, 0.99, 0.98 and 0.98. The AUROCs of F0, F1, F2 and F3‐4 fibrosis stages for AutoFibroNet in the two validation cohorts were 0.99, 0.83, 0.80 and 0.90 and 1.00, 0.83, 0.80 and 0.94, respectively, showing a good discriminatory ability in different cohorts. Conclusion: AutoFibroNet is an automated quantitative tool that accurately identifies histological stages of liver fibrosis in Chinese individuals with MAFLD. [ABSTRACT FROM AUTHOR]

Published
2023
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17. Combining the guidelines and multiphoton imaging methods to improve the prognostic value of tumor‐infiltrating lymphocytes in breast cancer.

Author

He, Jiajia, Kang, Deyong, Xu, Meifang, Han, Zhonghua, Guo, Wenhui, Fu, Fangmeng, Qiu, Lida, Zheng, Liqin, Xi, Gangqin, Wang, Wei, Ren, Wenjiao, Han, Xiahui, Tu, Haohua, Li, Lianhuang, Wang, Chuan, and Chen, Jianxin

Abstract

Multiphoton microscopy (MPM) was introduced to label‐freely obtain tumor‐infiltrating lymphocytes (TILs) images from a total of 611 patients, and the prognostic value of TILs in breast cancer was assessed by the MPM method (TILs‐MPM) and guidelines method proposed by the International Immuno‐Oncology Biomarker Working Group (TILs‐WG), respectively. Moreover, the clinical (CLI) model, TILs‐WG + TILs‐MPM model, and full model (CLI + TILs‐WG + TILs‐MPM) were developed to investigate the prognostic value of TILs. The results show that TILs‐WG performs better in estrogen receptor (ER)‐negative subgroup, and TILs‐MPM is comparable with TILs‐WG in the ER‐negative subgroup, but has the best performance in the ER‐positive subgroup. Furthermore, the TILs‐WG + TILs‐MPM model can significantly improve the prognostic power compared with the TILs‐WG model, and the full model has excellent performance, with high area under the curve (AUC) and hazard ratio (HR) in both ER‐positive, ER‐negative subgroups, and the complete cohort. Our results suggest that the combination of TILs‐WG with TILs‐MPM model can greatly improve the prognostic value of TILs. [ABSTRACT FROM AUTHOR]

Published
2023
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19. Collagen signature as a novel biomarker to predict axillary lymph node metastasis in breast cancer using multiphoton microscopy

Author

Fang, Ye, primary, Kang, Deyong, additional, Guo, Wenhui, additional, Zhang, Qingyuan, additional, Xu, Shuoyu, additional, Huang, Xingxin, additional, Xi, Gangqin, additional, He, Jiajia, additional, Wu, Shulian, additional, Li, Lianhuang, additional, Han, Xiahui, additional, Chen, Jianhua, additional, Zheng, Liqin, additional, Wang, Chuan, additional, and Chen, Jianxin, additional

Published
2022
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21. Intratumor Graph Neural Network of Tumor-Associated Collagen Signatures from Multiphoton Microscopy Empowers Prognosis of 995 Breast Cancer Patients

Author

Qiu, Lida, primary, Kang, Deyong, additional, Wang, Chuan, additional, Guo, Wenhui, additional, Fu, Fangmeng, additional, Wu, Qingxiang, additional, Xi, Gangqin, additional, He, Jiajia, additional, Zheng, Liqin, additional, Zhang, Qingyuan, additional, Liao, Xiaoxia, additional, Li, Lianhuang, additional, Chen, Jianxin, additional, and Tu, Haohua, additional

Published
2022
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22. Label‐free detection of invasive micropapillary carcinoma of the breast using multiphoton microscopy.

Author

He, Jiajia, Kang, Deyong, Shen, Tingfeng, Zheng, Liqin, Zhan, Zhenlin, Xi, Gangqin, Ren, Wenjiao, Chen, Zhong, Qiu, Lida, Xu, Shuoyu, Li, Lianhuang, and Chen, Jianxin

Abstract

Invasive micropapillary carcinoma of the breast (IMPC) is a rare form of breast cancer with unique histological features, and is associated with high axillary lymph node metastasis and poor clinical prognosis. Thus, IMPC should be diagnosed in time to improve the treatment and management of patients. In this study, multiphoton microscopy (MPM) is used to label‐free visualize the morphological features of IMPC. Our results demonstrate that MPM images are well in agreement with hematoxylin and eosin staining and epithelial membrane antigen staining, indicating MPM is comparable to traditional histological analysis in identifying the tissue structure and cell morphology. Statistical analysis shows significant differences in the circumference and area of the glandular lumen and cancer nest between the different IMPC cell clusters with complete glandular lumen morphology, and also shows difference in collagen length, width, and orientation, indicating the invasive ability of different morphologies of IMPC may be different. [ABSTRACT FROM AUTHOR]

Published
2023
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24. Wound-like tumor periphery in human breast cancer predicts a convergent drug nonresponse

Author

Li, Lianhuang, primary, Liao, Xiaoxia, additional, Fu, Fangmeng, additional, Xi, Gangqin, additional, Kang, Deyong, additional, He, Jiajia, additional, Guo, Wenhui, additional, Qiu, Lida, additional, Han, Zhonghua, additional, Wang, Xingfu, additional, Zhan, Qingyuan, additional, You, Sixian, additional, Chen, Jianxin, additional, Wang, Chuan, additional, Boppart, Stephen A., additional, and Tu, Haohua, additional

Published
2021
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26. Nomogram model combining macro and micro tumor-associated collagen signatures obtained from multiphoton images to predict the histologic grade in breast cancer

Author

Xi, Gangqin, primary, He, Jiajia, additional, Kang, Deyong, additional, Xu, Shuoyu, additional, Guo, Wenhui, additional, Fu, Fangmeng, additional, Liu, Yulan, additional, Zheng, Liqin, additional, Qiu, Lida, additional, Li, Lianhuang, additional, Wang, Chuan, additional, and Chen, Jianxin, additional

Published
2021
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28. Additional file 1 of Computer-assisted quantification of tumor-associated collagen signatures to improve the prognosis prediction of breast cancer

Author

Xi, Gangqin, Qiu, Lida, Xu, Shuoyu, Guo, Wenhui, Fu, Fangmeng, Kang, Deyong, Zheng, Liqin, He, Jiajia, Zhang, Qingyuan, Li, Lianhuang, Wang, Chuan, and Chen, Jianxin

Subjects
stomatognathic diseases
Abstract

Additional file 1: Supplementary Methods: 1.1 Multiphoton imaging system. 1.2 TACS-score calculation formula. 1.3 Morphological features. 1.4 Texture features. 1.5 TCMF-score calculation formula. Figures S1-S10: Fig. S1. Recruitment pathways for patients. Fig. S2. Distribution histogram of TCMF-score, TACS-score and TCMF+TACS-score. Fig. S3. Hazard ratio of 5-year recurrence for all patients according to the TCMF+TACS-score classifier in different subgroups stratified by clinical parameters. Fig. S4. Forest plot shows the concordance index (C-index) of different prediction models (TCMF, TACS, and TCMF+TACS) in the different risk combination patients. Fig. S5. Kaplan-Meier survival curves for comparing the risk stratification ability of the CLI-score under four different situations (TACS-low risk and TCMF-low risk, TACS-high risk and TCMF-low risk, TACS-low risk and TCMF-high risk, TACS-high risk and TCMF-high risk). Fig. S6. Nomogram and Calibration curves. Fig. S7. TCMF selection using LASSO cox regression analysis. Fig. S8. Schematic of extracting TACS corresponding microscopy features (TCMF). Fig. S9. Kaplan-Meier curves of DFS according to the CLI, TCMF, TACS, TCMF+TACS, and CLI+TCMF+TACS models for the low risk and high risk patients classified by the treatment guideline. Fig. S10. Representative MPM images of three patients to illustrate the extraction of TCMF. Tables S1-S11: Table S1. Univariate and multivariate Cox proportional hazards regression analyses of the association of variables with DFS in the training cohort. Table S2. Univariate and multivariate Cox proportional hazards regression analyses of the association of variables with DFS in the internal validation cohort. Table S3. Univariate and multivariate Cox proportional hazards regression analyses of the association of variables with DFS in the external validation cohort. Table S4. Risk stratification ability of TCMF+TACS-score in subgroups of the three cohorts. Table S5. Risk stratification ability of the five prediction models in three cohorts. Table S6. Prognosis performance of the five models at 5-year DFS in patients stratified by the combination of different risk factors. Table S7. The number of likely undertreated (orange-highlighted) and overtreated (purple-highlighted) patients according to the Chinese treatment guideline and four models. Table S8. Morphological features. Table S9. First order intensity histogram features. Table S10. Grey-level co-occurrence matrix-based features. Table S11. Gabor wavelet transform features.

Published
2021
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31. Collagen Signature as a Novel Biomarker to Predict Axillary Lymph Node Metastasis in Breast Cancer Using Multiphoton Microscopy

Author

Fang, Ye, primary, Guo, Wenhui, additional, Kang, Deyong, additional, Zhang, QingYuan, additional, Xu, Shuoyu, additional, Huang, Xingxin, additional, Xi, Gangqin, additional, He, Jiajia, additional, Wu, Shulian, additional, Li, Lianhuang, additional, Han, Xiahui, additional, Chen, Jianhua, additional, Zheng, Liqin, additional, Wang, Chuan, additional, and Chen, Jianxin, additional

Published
2021
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32. Large-scale tumor-associated collagen signatures identify high-risk breast cancer patients

Author

Xi, Gangqin, primary, Guo, Wenhui, additional, Kang, Deyong, additional, Ma, Jianli, additional, Fu, Fangmeng, additional, Qiu, Lida, additional, Zheng, Liqin, additional, He, Jiajia, additional, Fang, Na, additional, Chen, Jianhua, additional, Li, Jingtong, additional, Zhuo, Shuangmu, additional, Liao, Xiaoxia, additional, Tu, Haohua, additional, Li, Lianhuang, additional, Zhang, Qingyuan, additional, Wang, Chuan, additional, Boppart, Stephen A., additional, and Chen, Jianxin, additional

Published
2021
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45. Featured Cover.

Author

Zhan, Huiling, Chen, Siyu, Gao, Feng, Wang, Guangxing, Chen, Sui‐Dan, Xi, Gangqin, Yuan, Hai‐Yang, Li, Xiaolu, Liu, Wen‐Yue, Byrne, Christopher D., Targher, Giovanni, Chen, Miao‐Yang, Yang, Yong‐Feng, Chen, Jun, Fan, Zhiwen, Sun, Xitai, Cai, Guorong, Zheng, Ming‐Hua, and Zhuo, Shuangmu

Subjects
HEPATIC fibrosis, DEEP learning
Published
2023
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