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Huang X, Wang Q, He J, Ban C, Zheng H, Chen H, Zhu X. Fast Multiphoton Microscopic Imaging Joint Image Super-Resolution for Automated Gleason Grading of Prostate Cancers. JOURNAL OF BIOPHOTONICS 2024:e202400233. [PMID: 39262127 DOI: 10.1002/jbio.202400233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 08/07/2024] [Accepted: 08/08/2024] [Indexed: 09/13/2024]
Abstract
Gleason grading system is dependable for quantifying prostate cancer. This paper introduces a fast multiphoton microscopic imaging method via deep learning for automatic Gleason grading. Due to the contradiction between multiphoton microscopy (MPM) imaging speed and quality, a deep learning architecture (SwinIR) is used for image super-resolution to address this issue. The quality of low-resolution image is improved, which increased the acquisition speed from 7.55 s per frame to 0.24 s per frame. A classification network (Swin Transformer) was introduced for automated Gleason grading. The classification accuracy and Macro-F1 achieved by training on high-resolution images are respectively 90.9% and 90.9%. For training on super-resolution images, the classification accuracy and Macro-F1 are respectively 89.9% and 89.9%. It shows that super-resolution image can provide a comparable performance to high-resolution image. Our results suggested that MPM joint image super-resolution and automatic classification methods hold the potential to be a real-time clinical diagnostic tool for prostate cancer diagnosis.
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Affiliation(s)
- Xinpeng Huang
- Institute of Laser and Optoelectronics Technology, Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, China
| | - Qianqiong Wang
- Institute of Laser and Optoelectronics Technology, Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, China
| | - Jia He
- Institute of Laser and Optoelectronics Technology, Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, China
| | - Chaoran Ban
- Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Hua Zheng
- Institute of Laser and Optoelectronics Technology, Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, China
| | - Hong Chen
- Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Xiaoqin Zhu
- Institute of Laser and Optoelectronics Technology, Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, China
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Ramamurthy K, Varikuti AR, Gupta B, Aswani N. A deep learning network for Gleason grading of prostate biopsies using EfficientNet. BIOMED ENG-BIOMED TE 2022; 68:187-198. [PMID: 36332194 DOI: 10.1515/bmt-2022-0201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 10/23/2022] [Indexed: 11/06/2022]
Abstract
Abstract
Objectives
The most crucial part in the diagnosis of cancer is severity grading. Gleason’s score is a widely used grading system for prostate cancer. Manual examination of the microscopic images and grading them is tiresome and consumes a lot of time. Hence to automate the Gleason grading process, a novel deep learning network is proposed in this work.
Methods
In this work, a deep learning network for Gleason grading of prostate cancer is proposed based on EfficientNet architecture. It applies a compound scaling method to balance the dimensions of the underlying network. Also, an additional attention branch is added to EfficientNet-B7 for precise feature weighting.
Result
To the best of our knowledge, this is the first work that integrates an additional attention branch with EfficientNet architecture for Gleason grading. The proposed models were trained using H&E-stained samples from prostate cancer Tissue Microarrays (TMAs) in the Harvard Dataverse dataset.
Conclusions
The proposed network was able to outperform the existing methods and it achieved an Kappa score of 0.5775.
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Affiliation(s)
- Karthik Ramamurthy
- Centre for Cyber Physical Systems, School of Electronics Engineering, Vellore Institute of Technology , Chennai , India
| | - Abinash Reddy Varikuti
- School of Computer Science Engineering, Vellore Institute of Technology , Chennai , India
| | - Bhavya Gupta
- School of Computer Science Engineering, Vellore Institute of Technology , Chennai , India
| | - Nehal Aswani
- School of Electronics Engineering, Vellore Institute of Technology , Chennai , India
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Janssen BV, Theijse R, van Roessel S, de Ruiter R, Berkel A, Huiskens J, Busch OR, Wilmink JW, Kazemier G, Valkema P, Farina A, Verheij J, de Boer OJ, Besselink MG. Artificial Intelligence-Based Segmentation of Residual Tumor in Histopathology of Pancreatic Cancer after Neoadjuvant Treatment. Cancers (Basel) 2021; 13:cancers13205089. [PMID: 34680241 PMCID: PMC8533716 DOI: 10.3390/cancers13205089] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 10/07/2021] [Accepted: 10/09/2021] [Indexed: 12/31/2022] Open
Abstract
Simple Summary The use of neoadjuvant therapy (NAT) in patients with pancreatic ductal adenocarcinoma (PDAC) is increasing. Objective quantification of the histopathological response to NAT may be used to guide adjuvant treatment and compare the efficacy of neoadjuvant regimens. However, current tumor response scoring (TRS) systems suffer from interobserver variability, originating from subjective definitions, the sometimes challenging histology, and response heterogeneity throughout the tumor bed. This study investigates if artificial intelligence-based segmentation of residual tumor burden in histopathology of PDAC after NAT may offer a more objective and reproducible TRS solution. Abstract Background: Histologic examination of resected pancreatic cancer after neoadjuvant therapy (NAT) is used to assess the effect of NAT and may guide the choice for adjuvant treatment. However, evaluating residual tumor burden in pancreatic cancer is challenging given tumor response heterogeneity and challenging histomorphology. Artificial intelligence techniques may offer a more reproducible approach. Methods: From 64 patients, one H&E-stained slide of resected pancreatic cancer after NAT was digitized. Three separate classes were manually outlined in each slide (i.e., tumor, normal ducts, and remaining epithelium). Corresponding segmentation masks and patches were generated and distributed over training, validation, and test sets. Modified U-nets with varying encoders were trained, and F1 scores were obtained to express segmentation accuracy. Results: The highest mean segmentation accuracy was obtained using modified U-nets with a DenseNet161 encoder. Tumor tissue was segmented with a high mean F1 score of 0.86, while the overall multiclass average F1 score was 0.82. Conclusions: This study shows that artificial intelligence-based assessment of residual tumor burden is feasible given the promising obtained F1 scores for tumor segmentation. This model could be developed into a tool for the objective evaluation of the response to NAT and may potentially guide the choice for adjuvant treatment.
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Affiliation(s)
- Boris V. Janssen
- Department of Surgery, Amsterdam UMC, Cancer Center Amsterdam, University of Amsterdam, 1081 HV Amsterdam, The Netherlands; (B.V.J.); (R.T.); (S.v.R.); (O.R.B.)
- Department of Pathology, Amsterdam UMC, Cancer Center Amsterdam, University of Amsterdam, 1081 HV Amsterdam, The Netherlands; (P.V.); (A.F.); (J.V.); (O.J.d.B.)
| | - Rutger Theijse
- Department of Surgery, Amsterdam UMC, Cancer Center Amsterdam, University of Amsterdam, 1081 HV Amsterdam, The Netherlands; (B.V.J.); (R.T.); (S.v.R.); (O.R.B.)
- Department of Pathology, Amsterdam UMC, Cancer Center Amsterdam, University of Amsterdam, 1081 HV Amsterdam, The Netherlands; (P.V.); (A.F.); (J.V.); (O.J.d.B.)
| | - Stijn van Roessel
- Department of Surgery, Amsterdam UMC, Cancer Center Amsterdam, University of Amsterdam, 1081 HV Amsterdam, The Netherlands; (B.V.J.); (R.T.); (S.v.R.); (O.R.B.)
| | - Rik de Ruiter
- SAS Institute Besloten Vennootschap, 1272 PC Huizen, The Netherlands; (R.d.R.); (A.B.); (J.H.)
| | - Antonie Berkel
- SAS Institute Besloten Vennootschap, 1272 PC Huizen, The Netherlands; (R.d.R.); (A.B.); (J.H.)
| | - Joost Huiskens
- SAS Institute Besloten Vennootschap, 1272 PC Huizen, The Netherlands; (R.d.R.); (A.B.); (J.H.)
| | - Olivier R. Busch
- Department of Surgery, Amsterdam UMC, Cancer Center Amsterdam, University of Amsterdam, 1081 HV Amsterdam, The Netherlands; (B.V.J.); (R.T.); (S.v.R.); (O.R.B.)
| | - Johanna W. Wilmink
- Department of Medical Oncology, Amsterdam UMC, Cancer Center Amsterdam, University of Amsterdam, 1081 HV Amsterdam, The Netherlands;
| | - Geert Kazemier
- Department of Surgery, Amsterdam UMC, Cancer Center Amsterdam, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands;
| | - Pieter Valkema
- Department of Pathology, Amsterdam UMC, Cancer Center Amsterdam, University of Amsterdam, 1081 HV Amsterdam, The Netherlands; (P.V.); (A.F.); (J.V.); (O.J.d.B.)
| | - Arantza Farina
- Department of Pathology, Amsterdam UMC, Cancer Center Amsterdam, University of Amsterdam, 1081 HV Amsterdam, The Netherlands; (P.V.); (A.F.); (J.V.); (O.J.d.B.)
| | - Joanne Verheij
- Department of Pathology, Amsterdam UMC, Cancer Center Amsterdam, University of Amsterdam, 1081 HV Amsterdam, The Netherlands; (P.V.); (A.F.); (J.V.); (O.J.d.B.)
| | - Onno J. de Boer
- Department of Pathology, Amsterdam UMC, Cancer Center Amsterdam, University of Amsterdam, 1081 HV Amsterdam, The Netherlands; (P.V.); (A.F.); (J.V.); (O.J.d.B.)
| | - Marc G. Besselink
- Department of Surgery, Amsterdam UMC, Cancer Center Amsterdam, University of Amsterdam, 1081 HV Amsterdam, The Netherlands; (B.V.J.); (R.T.); (S.v.R.); (O.R.B.)
- Correspondence: ; Tel.: +31-20-444-4444
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Abstract
PURPOSE OF REVIEW Over the last decade, major advancements in artificial intelligence technology have emerged and revolutionized the extent to which physicians are able to personalize treatment modalities and care for their patients. Artificial intelligence technology aimed at mimicking/simulating human mental processes, such as deep learning artificial neural networks (ANNs), are composed of a collection of individual units known as 'artificial neurons'. These 'neurons', when arranged and interconnected in complex architectural layers, are capable of analyzing the most complex patterns. The aim of this systematic review is to give a comprehensive summary of the contemporary applications of deep learning ANNs in urological medicine. RECENT FINDINGS Fifty-five articles were included in this systematic review and each article was assigned an 'intermediate' score based on its overall quality. Of these 55 articles, nine studies were prospective, but no nonrandomized control trials were identified. SUMMARY In urological medicine, the application of novel artificial intelligence technologies, particularly ANNs, have been considered to be a promising step in improving physicians' diagnostic capabilities, especially with regards to predicting the aggressiveness and recurrence of various disorders. For benign urological disorders, for example, the use of highly predictive and reliable algorithms could be helpful for the improving diagnoses of male infertility, urinary tract infections, and pediatric malformations. In addition, articles with anecdotal experiences shed light on the potential of artificial intelligence-assisted surgeries, such as with the aid of virtual reality or augmented reality.
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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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Editorial for: Bertoni et al. ex vivo fluorescence confocal microscopy: prostatic and periprostatic tissues atlas and evaluation of the learning curve. Virchows Arch 2020; 476:487-488. [PMID: 32006120 PMCID: PMC7156354 DOI: 10.1007/s00428-020-02763-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 01/13/2020] [Accepted: 01/23/2020] [Indexed: 11/29/2022]
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