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Shi ZX, Li CF, Zhao LF, Sun ZQ, Cui LM, Xin YJ, Wang DQ, Kang TR, Jiang HJ. Computed tomography radiomic features and clinical factors predicting the response to first transarterial chemoembolization in intermediate-stage hepatocellular carcinoma. Hepatobiliary Pancreat Dis Int 2024; 23:361-369. [PMID: 37429785 DOI: 10.1016/j.hbpd.2023.06.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 04/24/2023] [Indexed: 07/12/2023]
Abstract
BACKGROUND According to clinical practice guidelines, transarterial chemoembolization (TACE) is the standard treatment modality for patients with intermediate-stage hepatocellular carcinoma (HCC). Early prediction of treatment response can help patients choose a reasonable treatment plan. This study aimed to investigate the value of the radiomic-clinical model in predicting the efficacy of the first TACE treatment for HCC to prolong patient survival. METHODS A total of 164 patients with HCC who underwent the first TACE from January 2017 to September 2021 were analyzed. The tumor response was assessed by modified response evaluation criteria in solid tumors (mRECIST), and the response of the first TACE to each session and its correlation with overall survival were evaluated. The radiomic signatures associated with the treatment response were identified by the least absolute shrinkage and selection operator (LASSO), and four machine learning models were built with different types of regions of interest (ROIs) (tumor and corresponding tissues) and the model with the best performance was selected. The predictive performance was assessed with receiver operating characteristic (ROC) curves and calibration curves. RESULTS Of all the models, the random forest (RF) model with peritumor (+10 mm) radiomic signatures had the best performance [area under ROC curve (AUC) = 0.964 in the training cohort, AUC = 0.949 in the validation cohort]. The RF model was used to calculate the radiomic score (Rad-score), and the optimal cutoff value (0.34) was calculated according to the Youden's index. Patients were then divided into a high-risk group (Rad-score > 0.34) and a low-risk group (Rad-score ≤ 0.34), and a nomogram model was successfully established to predict treatment response. The predicted treatment response also allowed for significant discrimination of Kaplan-Meier curves. Multivariate Cox regression identified six independent prognostic factors for overall survival, including male [hazard ratio (HR) = 0.500, 95% confidence interval (CI): 0.260-0.962, P = 0.038], alpha-fetoprotein (HR = 1.003, 95% CI: 1.002-1.004, P < 0.001), alanine aminotransferase (HR = 1.003, 95% CI: 1.001-1.005, P = 0.025), performance status (HR = 2.400, 95% CI: 1.200-4.800, P = 0.013), the number of TACE sessions (HR = 0.870, 95% CI: 0.780-0.970, P = 0.012) and Rad-score (HR = 3.480, 95% CI: 1.416-8.552, P = 0.007). CONCLUSIONS The radiomic signatures and clinical factors can be well-used to predict the response of HCC patients to the first TACE and may help identify the patients most likely to benefit from TACE.
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Affiliation(s)
- Zhong-Xing Shi
- Department of Interventional Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Chang-Fu Li
- Department of Digestive Medicine, Daqing Longnan Hospital, Daqing 163453, China
| | - Li-Feng Zhao
- Department of Radiology, Daqing Longnan Hospital, Daqing 163453, China
| | - Zhong-Qi Sun
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Li-Ming Cui
- Department of Interventional Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Yan-Jie Xin
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Dong-Qing Wang
- Department of Interventional Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Tan-Rong Kang
- Department of Interventional Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Hui-Jie Jiang
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China.
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Wang Y, Liu Z, Xu H, Yang D, Jiang J, Asayo H, Yang Z. MRI-based radiomics model and nomogram for predicting the outcome of locoregional treatment in patients with hepatocellular carcinoma. BMC Med Imaging 2023; 23:67. [PMID: 37254089 DOI: 10.1186/s12880-023-01030-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 05/23/2023] [Indexed: 06/01/2023] Open
Abstract
BACKGROUND Prediction of locoregional treatment response is important for further therapeutic strategy in patients with hepatocellular carcinoma. This study aimed to investigate the role of MRI-based radiomics and nomogram for predicting the outcome of locoregional treatment in patients with hepatocellular carcinoma. METHODS The initial postoperative MRI after locoregional treatment in 100 patients with hepatocellular carcinoma was retrospectively analysed. The outcome was evaluated according to mRECIST at 6 months. We delineated the tumour volume of interest on arterial phase, portal venous phase and T2WI. The radiomics features were selected by using the independent sample t test or nonparametric Mann‒Whitney U test and the least absolute shrinkage and selection operator. The clinical variables were selected by using univariate analysis and multivariate analysis. The radiomics model and combined model were constructed via multivariate logistic regression analysis. A nomogram was constructed that incorporated the Rad score and selected clinical variables. RESULTS Fifty patients had an objective response, and fifty patients had a nonresponse. Nine radiomics features in the arterial phase were selected, but none of the portal venous phase or T2WI radiomics features were predictive of the treatment response. The best radiomics model showed an AUC of 0.833. Two clinical variables (hCRP and therapy method) were selected. The AUC of the combined model was 0.867. There was no significant difference in the AUC between the combined model and the best radiomics model (P = 0.573). Decision curve analysis demonstrated the nomogram has satisfactory predictive value. CONCLUSIONS MRI-based radiomics analysis may serve as a promising and noninvasive tool to predict outcome of locoregional treatment in HCC patients, which will facilitate the individualized follow-up and further therapeutic strategies guidance.
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Affiliation(s)
- Yuxin Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Zhenhao Liu
- Department of Radiology, Affiliated Hospital of Changzhi Institute of Traditional Chinese Medicine, Changzhi, 046099, China
| | - Hui Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Dawei Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Jiahui Jiang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Himeko Asayo
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
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de Andrade Natal R, Adur J, Cesar CL, Vassallo J. Tumor extracellular matrix: lessons from the second-harmonic generation microscopy. SURGICAL AND EXPERIMENTAL PATHOLOGY 2021. [DOI: 10.1186/s42047-021-00089-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
AbstractExtracellular matrix (ECM) represents more than a mere intercellular cement. It is physiologically active in cell communication, adhesion and proliferation. Collagen is the most abundant protein, making up to 90% of ECM, and 30% of total protein weight in humans. Second-harmonic generation (SHG) microscopy represents an important tool to study collagen organization of ECM in freshly unfixed tissues and paraffin-embedded tissue samples. This manuscript aims to review some of the applications of SHG microscopy in Oncologic Pathology, mainly in the study of ECM of epithelial tumors. It is shown how collagen parameters measured by this technique can aid in the differential diagnosis and in prognostic stratification. There is a tendency to associate higher amount, lower organization and higher linearity of collagen fibers with tumor progression and metastasizing. These represent complex processes, in which matrix remodeling plays a central role, together with cancer cell genetic modifications. Integration of studies on cancer cell biology and ECM are highly advantageous to give us a more complete picture of these processes. As microscopic techniques provide topographic information allied with biologic characteristics of tissue components, they represent important tools for a more complete understanding of cancer progression. In this context, SHG has provided significant insights in human tumor specimens, readily available for Pathologists.
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Kirchberger-Tolstik T, Ryabchykov O, Bocklitz T, Dirsch O, Settmacher U, Popp J, Stallmach A. Nondestructive molecular imaging by Raman spectroscopy vs. marker detection by MALDI IMS for an early diagnosis of HCC. Analyst 2021; 146:1239-1252. [PMID: 33313629 DOI: 10.1039/d0an01555e] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide with a steadily increasing mortality rate. Fast diagnosis at early stages of HCC is of key importance for the improvement of patient survival rates. In this regard, we combined two imaging techniques with high potential for HCC diagnosis in order to improve the prediction of liver cancer. In detail, Raman spectroscopic imaging and matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI IMS) were applied for the diagnosis of 36 HCC tissue samples. The data were analyzed using multivariate methods, and the results revealed that Raman spectroscopy alone showed a good capability for HCC tumor identification (sensitivity of 88% and specificity of 80%), which could not be improved by combining the Raman data with MALDI IMS. In addition, it could be shown that the two methods in combination can differentiate between well-, moderately- and poorly-differentiated HCC using a linear classification model. MALDI IMS not only classified the HCC grades with a sensitivity of 100% and a specificity of 80%, but also showed significant differences in the expression of glycerophospholipids and fatty acyls during HCC differentiation. Furthermore, important differences in the protein, lipid and collagen compositions of differentiated HCC were detected using the model coefficients of a Raman based classification model. Both Raman and MALDI IMS, as well as their combination showed high potential for resolving concrete questions in liver cancer diagnosis.
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Affiliation(s)
- Tatiana Kirchberger-Tolstik
- Jena University Hospital, Department of Internal Medicine IV, Gastroenterology, Hepatology, Infectious Disease, Am Klinikum, 1, 07747 Jena, Germany
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Chen M, Cao J, Hu J, Topatana W, Li S, Juengpanich S, Lin J, Tong C, Shen J, Zhang B, Wu J, Pocha C, Kudo M, Amedei A, Trevisani F, Sung PS, Zaydfudim VM, Kanda T, Cai X. Clinical-Radiomic Analysis for Pretreatment Prediction of Objective Response to First Transarterial Chemoembolization in Hepatocellular Carcinoma. Liver Cancer 2021; 10:38-51. [PMID: 33708638 PMCID: PMC7923935 DOI: 10.1159/000512028] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 09/23/2020] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND The preoperative selection of patients with intermediate-stage hepatocellular carcinoma (HCC) who are likely to have an objective response to first transarterial chemoembolization (TACE) remains challenging. OBJECTIVE To develop and validate a clinical-radiomic model (CR model) for preoperatively predicting treatment response to first TACE in patients with intermediate-stage HCC. METHODS A total of 595 patients with intermediate-stage HCC were included in this retrospective study. A tumoral and peritumoral (10 mm) radiomic signature (TPR-signature) was constructed based on 3,404 radiomic features from 4 regions of interest. A predictive CR model based on TPR-signature and clinical factors was developed using multivariate logistic regression. Calibration curves and area under the receiver operating characteristic curves (AUCs) were used to evaluate the model's performance. RESULTS The final CR model consisted of 5 independent predictors, including TPR-signature (p < 0.001), AFP (p = 0.004), Barcelona Clinic Liver Cancer System Stage B (BCLC B) subclassification (p = 0.01), tumor location (p = 0.039), and arterial hyperenhancement (p = 0.050). The internal and external validation results demonstrated the high-performance level of this model, with internal and external AUCs of 0.94 and 0.90, respectively. In addition, the predicted objective response via the CR model was associated with improved survival in the external validation cohort (hazard ratio: 2.43; 95% confidence interval: 1.60-3.69; p < 0.001). The predicted treatment response also allowed for significant discrimination between the Kaplan-Meier curves of each BCLC B subclassification. CONCLUSIONS The CR model had an excellent performance in predicting the first TACE response in patients with intermediate-stage HCC and could provide a robust predictive tool to assist with the selection of patients for TACE.
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Affiliation(s)
- Mingyu Chen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China,Engineering Research Center of Cognitive Healthcare of Zhejiang Province, Hangzhou, China,Zhejiang University School of Medicine, Hangzhou, China
| | - Jiasheng Cao
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China
| | - Jiahao Hu
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China
| | - Win Topatana
- Zhejiang University School of Medicine, Hangzhou, China
| | - Shijie Li
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China
| | | | - Jian Lin
- General Surgery, Longyou People's Hospital, Quzhou, China
| | - Chenhao Tong
- General Surgery, Shaoxing People's Hospital, Shaoxing, China
| | - Jiliang Shen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China
| | - Bin Zhang
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China
| | - Jennifer Wu
- Perlmutter Cancer Center, NYU Langone Health, New York, New York, USA
| | - Christine Pocha
- Avera McKennnan Hospital and University Medical Center, Sanford School of Medicine, University of South Dakota, Sioux Falls, South Dakota, USA
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University School of Medicine, Osaka, Japan
| | - Amedeo Amedei
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Franco Trevisani
- Department of Medical and Surgical Sciences, Semeiotica Medica, Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Pil Soo Sung
- Department of Internal Medicine, College of Medicine, Eunpyeong St. Mary's Hospital, Seoul, Republic of Korea
| | - Victor M. Zaydfudim
- Department of Surgery, Section of Hepatobiliary and Pancreatic Surgery, University of Virginia, Charlottesville, Virginia, USA
| | - Tatsuo Kanda
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Xiujun Cai
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China,Engineering Research Center of Cognitive Healthcare of Zhejiang Province, Hangzhou, China,Zhejiang Research and Development Engineering Laboratory of Minimally Invasive Technology and Equipment, Hangzhou, China,*Xiujun Cai, Department of General Surgery, Sir Run-Run Shaw Hospital, No. 3 East Qingchun Road, Hangzhou 310016 (China),
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Ouellette JN, Drifka CR, Pointer KB, Liu Y, Lieberthal TJ, Kao WJ, Kuo JS, Loeffler AG, Eliceiri KW. Navigating the Collagen Jungle: The Biomedical Potential of Fiber Organization in Cancer. Bioengineering (Basel) 2021; 8:17. [PMID: 33494220 PMCID: PMC7909776 DOI: 10.3390/bioengineering8020017] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 01/10/2021] [Accepted: 01/13/2021] [Indexed: 02/07/2023] Open
Abstract
Recent research has highlighted the importance of key tumor microenvironment features, notably the collagen-rich extracellular matrix (ECM) in characterizing tumor invasion and progression. This led to great interest from both basic researchers and clinicians, including pathologists, to include collagen fiber evaluation as part of the investigation of cancer development and progression. Fibrillar collagen is the most abundant in the normal extracellular matrix, and was revealed to be upregulated in many cancers. Recent studies suggested an emerging theme across multiple cancer types in which specific collagen fiber organization patterns differ between benign and malignant tissue and also appear to be associated with disease stage, prognosis, treatment response, and other clinical features. There is great potential for developing image-based collagen fiber biomarkers for clinical applications, but its adoption in standard clinical practice is dependent on further translational and clinical evaluations. Here, we offer a comprehensive review of the current literature of fibrillar collagen structure and organization as a candidate cancer biomarker, and new perspectives on the challenges and next steps for researchers and clinicians seeking to exploit this information in biomedical research and clinical workflows.
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Affiliation(s)
- Jonathan N. Ouellette
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; (J.N.O.); (C.R.D.); (T.J.L.); (W.J.K.)
- Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI 53706, USA; (K.B.P.); (Y.L.)
| | - Cole R. Drifka
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; (J.N.O.); (C.R.D.); (T.J.L.); (W.J.K.)
- Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI 53706, USA; (K.B.P.); (Y.L.)
| | - Kelli B. Pointer
- Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI 53706, USA; (K.B.P.); (Y.L.)
| | - Yuming Liu
- Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI 53706, USA; (K.B.P.); (Y.L.)
| | - Tyler J Lieberthal
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; (J.N.O.); (C.R.D.); (T.J.L.); (W.J.K.)
| | - W John Kao
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; (J.N.O.); (C.R.D.); (T.J.L.); (W.J.K.)
- Department of Industrial and Manufacturing Systems Engineering, Faculty of Engineering, University of Hong Kong, Pokfulam, Hong Kong
| | - John S. Kuo
- Department of Neurosurgery, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA;
| | - Agnes G. Loeffler
- Department of Pathology, MetroHealth Medical Center, Cleveland, OH 44109, USA;
| | - Kevin W. Eliceiri
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; (J.N.O.); (C.R.D.); (T.J.L.); (W.J.K.)
- Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI 53706, USA; (K.B.P.); (Y.L.)
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53705, USA
- Morgridge Institute for Research, Madison, WI 53715, USA
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Wang G, Sun Y, Chen Y, Gao Q, Peng D, Lin H, Zhan Z, Liu Z, Zhuo S. Rapid identification of human ovarian cancer in second harmonic generation images using radiomics feature analyses and tree-based pipeline optimization tool. JOURNAL OF BIOPHOTONICS 2020; 13:e202000050. [PMID: 32500634 DOI: 10.1002/jbio.202000050] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 05/15/2020] [Accepted: 05/28/2020] [Indexed: 06/11/2023]
Abstract
Ovarian cancer is currently one of the most common cancers of the female reproductive organs, and its mortality rate is the highest among all types of gynecologic cancers. Rapid and accurate classification of ovarian cancer plays an important role in the determination of treatment plans and prognoses. Nevertheless, the most commonly used classification method is based on histopathological specimen examination, which is time-consuming and labor-intensive. Thus, in this study, we utilize radiomics feature extraction methods and the automated machine learning tree-based pipeline optimization tool (TOPT) for analysis of 3D, second harmonic generation images of benign, malignant and normal human ovarian tissues, to develop a high-efficiency computer-aided diagnostic model. Area under the receiver operating characteristic curve values of 0.98, 0.96 and 0.94 were obtained, respectively, for the classification of the three tissue types. Furthermore, this approach can be readily applied to other related tissues and diseases, and has great potential for improving the efficiency of medical diagnostic processes.
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Affiliation(s)
- Guangxing Wang
- School of Science, Jimei University, Xiamen, Fujian, China
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education & Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China
| | - Yang Sun
- Department of Gynecology, Fujian Cancer Hospital, Affiliated Cancer Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Youting Chen
- Department of Hepatopancreatobiliary Surgery, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Qiqi Gao
- Department of Gynecology, Fujian Cancer Hospital, Affiliated Cancer Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Dongqing Peng
- School of Science, Jimei University, Xiamen, Fujian, China
| | - Hongxin Lin
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education & Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China
| | - Zhenlin Zhan
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education & Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China
| | - Zhiyi Liu
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou Zhejiang, China
| | - Shuangmu Zhuo
- School of Science, Jimei University, Xiamen, Fujian, China
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education & Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China
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Lin H, Fan T, Sui J, Wang G, Chen J, Zhuo S, Zhang H. Recent advances in multiphoton microscopy combined with nanomaterials in the field of disease evolution and clinical applications to liver cancer. NANOSCALE 2019; 11:19619-19635. [PMID: 31599299 DOI: 10.1039/c9nr04902a] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Multiphoton microscopy (MPM) is expected to become a powerful clinical tool, with its unique advantages of being label-free, high resolution, deep imaging depth, low light photobleaching and low phototoxicity. Nanomaterials, with excellent physical and chemical properties, are biocompatible and easy to prepare and functionalize. The addition of nanomaterials exactly compensates for some defects of MPM, such as the weak endogenous signal strength, limited imaging materials, insufficient imaging depth and lack of therapeutic effects. Therefore, combining MPM with nanomaterials is a promising biomedical imaging method. Here, we mainly review the principle of MPM and its application in liver cancer, especially in disease evolution and clinical applications, including monitoring tumor progression, diagnosing tumor occurrence, detecting tumor metastasis, and evaluating cancer therapy response. Then, we introduce the latest advances in the combination of MPM with nanomaterials, including the MPM imaging of gold nanoparticles (AuNPs) and carbon dots (CDs). Finally, we also propose the main challenges and future research directions of MPM technology in HCC.
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Affiliation(s)
- Hongxin Lin
- Fujian Normal University, Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fuzhou, 350007, China.
| | - Taojian Fan
- Shenzhen Engineering Laboratory of Phosphorene and Optoelectronics and Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen, 518060, China.
| | - Jian Sui
- Department of Gastrointestinal surgery, Fujian Provincial Hospital, Fuzhou, 350000, China
| | - Guangxing Wang
- Fujian Normal University, Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fuzhou, 350007, China.
| | - Jianxin Chen
- Fujian Normal University, Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fuzhou, 350007, China.
| | - Shuangmu Zhuo
- Fujian Normal University, Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fuzhou, 350007, China.
| | - Han Zhang
- Shenzhen Engineering Laboratory of Phosphorene and Optoelectronics and Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen, 518060, China.
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Wang R, He Y, Yao C, Wang S, Xue Y, Zhang Z, Wang J, Liu X. Classification and Segmentation of Hyperspectral Data of Hepatocellular Carcinoma Samples Using 1-D Convolutional Neural Network. Cytometry A 2019; 97:31-38. [PMID: 31403260 DOI: 10.1002/cyto.a.23871] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 07/16/2019] [Accepted: 07/19/2019] [Indexed: 12/24/2022]
Abstract
Pathological diagnosis plays an important role in the diagnosis and treatment of hepatocellular carcinoma (HCC). The traditional method of pathological diagnosis of most cancers requires freezing, slicing, hematoxylin and eosin staining, and manual analysis, limiting the speed of the diagnosis process. In this study, we designed a one-dimensional convolutional neural network to classify the hyperspectral data of HCC sample slices acquired by our hyperspectral imaging system. A weighted loss function was employed to promote the performance of the model. The proposed method allows us to accelerate the diagnosis process of identifying tumor tissues. Our deep learning model achieved good performance on our data set with sensitivity, specificity, and area under receiver operating characteristic curve of 0.871, 0.888, and 0.950, respectively. Meanwhile, our deep learning model outperformed the other machine learning methods assessed on our data set. The proposed method is a potential tool for the label-free and real-time pathologic diagnosis. © 2019 International Society for Advancement of Cytometry.
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Affiliation(s)
- Rendong Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Yida He
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Cuiping Yao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Sijia Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Yuan Xue
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Zhenxi Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Jing Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Xiaolong Liu
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, People's Republic of China
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Lin H, Wei C, Wang G, Chen H, Lin L, Ni M, Chen J, Zhuo S. Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning. JOURNAL OF BIOPHOTONICS 2019; 12:e201800435. [PMID: 30868728 DOI: 10.1002/jbio.201800435] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 01/29/2019] [Accepted: 03/12/2019] [Indexed: 05/22/2023]
Abstract
In the case of hepatocellular carcinoma (HCC) samples, classification of differentiation is crucial for determining prognosis and treatment strategy decisions. However, a label-free and automated classification system for HCC grading has not been yet developed. Hence, in this study, we demonstrate the fusion of multiphoton microscopy and a deep-learning algorithm for classifying HCC differentiation to produce an innovative computer-aided diagnostic method. Convolutional neural networks based on the VGG-16 framework were trained using 217 combined two-photon excitation fluorescence and second-harmonic generation images; the resulting classification accuracy of the HCC differentiation grade was over 90%. Our results suggest that a combination of multiphoton microscopy and deep learning can realize label-free, automated methods for various tissues, diseases and other related classification problems.
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Affiliation(s)
- Hongxin Lin
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education and Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, P.R. China
| | - Chao Wei
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education and Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, P.R. China
| | - Guangxing Wang
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education and Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, P.R. China
| | - Hu Chen
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, P.R. China
| | - Lisheng Lin
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education and Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, P.R. China
| | - Ming Ni
- School of Biological Sciences and Engineering, Yachay Tech University, San Miguel de Urcuquí, Ecuador
| | - Jianxin Chen
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education and Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, P.R. China
| | - Shuangmu Zhuo
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education and Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, P.R. China
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Margin diagnosis for endoscopic submucosal dissection of early gastric cancer using multiphoton microscopy. Surg Endosc 2019; 34:408-416. [PMID: 30972623 DOI: 10.1007/s00464-019-06783-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Accepted: 04/04/2019] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND AIMS Endoscopic submucosal dissection (ESD) has become the primary option for the treatment of early gastric cancer (EGC). Thus, it is necessary to diagnose whether residual cancer cells exist in the ESD specimen margins, which can affect tumor recurrence and survival rates in the future. Multiphoton microscopy (MPM) can be suitably used for nondestructive imaging of biological tissue on a cellular level to enable real-time guidance during endoscopic therapy. Considering this, the objective of this study is to explore the practicality of MPM for the diagnosis of ESD specimen margins in the case of EGC. METHODS First, a total of 20 surgical samples was imaged using the proposed MPM technique to obtain two-photo excited fluorescence signal from the intrinsic fluorescent substances within cells and second-harmonic generation signal from collagen; these signals were used to determine MPM pathological features for margin diagnosis. Then, a double-blind study of 50 samples was conducted to evaluate the diagnosis results based on the obtained MPM pathological features. RESULTS Multiphoton microscopy can accurately identify the cytological and morphological differences between tissue in the negative and positive margin. The sensitivity, specificity, accuracy, negative predictive, and positive predictive values of MPM in the diagnosis of ESD specimen margins were 97.62, 75.00, 94.00, 95.35, and 85.71%, respectively. CONCLUSION These results indicate that MPM can be used as an effective, real-time, and label-free novel method to determine intraoperative resection margins.
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