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Chang J, Hatfield B. Advancements in computer vision and pathology: Unraveling the potential of artificial intelligence for precision diagnosis and beyond. Adv Cancer Res 2024; 161:431-478. [PMID: 39032956 DOI: 10.1016/bs.acr.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
Abstract
The integration of computer vision into pathology through slide digitalization represents a transformative leap in the field's evolution. Traditional pathology methods, while reliable, are often time-consuming and susceptible to intra- and interobserver variability. In contrast, computer vision, empowered by artificial intelligence (AI) and machine learning (ML), promises revolutionary changes, offering consistent, reproducible, and objective results with ever-increasing speed and scalability. The applications of advanced algorithms and deep learning architectures like CNNs and U-Nets augment pathologists' diagnostic capabilities, opening new frontiers in automated image analysis. As these technologies mature and integrate into digital pathology workflows, they are poised to provide deeper insights into disease processes, quantify and standardize biomarkers, enhance patient outcomes, and automate routine tasks, reducing pathologists' workload. However, this transformative force calls for cross-disciplinary collaboration between pathologists, computer scientists, and industry innovators to drive research and development. While acknowledging its potential, this chapter addresses the limitations of AI in pathology, encompassing technical, practical, and ethical considerations during development and implementation.
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Affiliation(s)
- Justin Chang
- Virginia Commonwealth University Health System, Richmond, VA, United States
| | - Bryce Hatfield
- Virginia Commonwealth University Health System, Richmond, VA, United States.
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2
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Hanna MG, Brogi E. Future Practices of Breast Pathology Using Digital and Computational Pathology. Adv Anat Pathol 2023; 30:421-433. [PMID: 37737690 DOI: 10.1097/pap.0000000000000414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
Pathology clinical practice has evolved by adopting technological advancements initially regarded as potentially disruptive, such as electron microscopy, immunohistochemistry, and genomic sequencing. Breast pathology has a critical role as a medical domain, where the patient's pathology diagnosis has significant implications for prognostication and treatment of diseases. The advent of digital and computational pathology has brought about significant advancements in the field, offering new possibilities for enhancing diagnostic accuracy and improving patient care. Digital slide scanning enables to conversion of glass slides into high-fidelity digital images, supporting the review of cases in a digital workflow. Digitization offers the capability to render specimen diagnoses, digital archival of patient specimens, collaboration, and telepathology. Integration of image analysis and machine learning-based systems layered atop the high-resolution digital images offers novel workflows to assist breast pathologists in their clinical, educational, and research endeavors. Decision support tools may improve the detection and classification of breast lesions and the quantification of immunohistochemical studies. Computational biomarkers may help to contribute to patient management or outcomes. Furthermore, using digital and computational pathology may increase standardization and quality assurance, especially in areas with high interobserver variability. This review explores the current landscape and possible future applications of digital and computational techniques in the field of breast pathology.
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Affiliation(s)
- Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
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3
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Rashmi R, Prasad K, Udupa CBK. Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review. J Med Syst 2021; 46:7. [PMID: 34860316 PMCID: PMC8642363 DOI: 10.1007/s10916-021-01786-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/21/2021] [Indexed: 12/24/2022]
Abstract
Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope.
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Affiliation(s)
- R Rashmi
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Keerthana Prasad
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India
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4
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Hardy NP, Mac Aonghusa P, Neary PM, Cahill RA. Intraprocedural Artificial Intelligence for Colorectal Cancer Detection and Characterisation in Endoscopy and Laparoscopy. Surg Innov 2021; 28:768-775. [PMID: 33634722 PMCID: PMC8647474 DOI: 10.1177/1553350621997761] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
In this article, we provide an evidence-based primer of current tools and evolving concepts in the area of intraprocedural artificial intelligence (AI) methods in colonoscopy and laparoscopy as a 'procedure companion', with specific focus on colorectal cancer recognition and characterisation. These interventions are both likely beneficiaries from an impending rapid phase in technical and technological evolution. The domains where AI is most likely to impact are explored as well as the methodological pitfalls pertaining to AI methods. Such issues include the need for large volumes of data to train AI systems, questions surrounding false positive rates, explainability and interpretability as well as recent concerns surrounding instabilities in current deep learning (DL) models. The area of biophysics-inspired models, a potential remedy to some of these pitfalls, is explored as it could allow our understanding of the fundamental physiological differences between tissue types to be exploited in real time with the help of computer-assisted interpretation. Right now, such models can include data collected from dynamic fluorescence imaging in surgery to characterise lesions by their biology reducing the number of cases needed to build a reliable and interpretable classification system. Furthermore, instead of focussing on image-by-image analysis, such systems could analyse in a continuous fashion, more akin to how we view procedures in real life and make decisions in a manner more comparable to human decision-making. Synergistical approaches can ensure AI methods usefully embed within practice thus safeguarding against collapse of this exciting field of investigation as another 'boom and bust' cycle of AI endeavour.
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Affiliation(s)
- Niall P Hardy
- UCD Centre for Precision Surgery, School of
Medicine, University College Dublin, Dublin, Ireland
| | | | - Peter M Neary
- Department of Surgery, University College
Cork, University Hospital Waterford, Waterford, Ireland
| | - Ronan A Cahill
- UCD Centre for Precision Surgery, School of
Medicine, University College Dublin, Dublin, Ireland
- Department of Surgery, Mater Misericordiae University
Hospital, Dublin, Ireland
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5
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Yousif M, van Diest PJ, Laurinavicius A, Rimm D, van der Laak J, Madabhushi A, Schnitt S, Pantanowitz L. Artificial intelligence applied to breast pathology. Virchows Arch 2021; 480:191-209. [PMID: 34791536 DOI: 10.1007/s00428-021-03213-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 09/12/2021] [Accepted: 09/27/2021] [Indexed: 12/12/2022]
Abstract
The convergence of digital pathology and computer vision is increasingly enabling computers to perform tasks performed by humans. As a result, artificial intelligence (AI) is having an astoundingly positive effect on the field of pathology, including breast pathology. Research using machine learning and the development of algorithms that learn patterns from labeled digital data based on "deep learning" neural networks and feature-engineered approaches to analyze histology images have recently provided promising results. Thus far, image analysis and more complex AI-based tools have demonstrated excellent success performing tasks such as the quantification of breast biomarkers and Ki67, mitosis detection, lymph node metastasis recognition, tissue segmentation for diagnosing breast carcinoma, prognostication, computational assessment of tumor-infiltrating lymphocytes, and prediction of molecular expression as well as treatment response and benefit of therapy from routine H&E images. This review critically examines the literature regarding these applications of AI in the area of breast pathology.
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Affiliation(s)
- Mustafa Yousif
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
- Department of Pathology, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Arvydas Laurinavicius
- Department of Pathology, Pharmacology and Forensic Medicine, Faculty of Medicine, Vilnius University, and National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - David Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, and Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, USA
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, USA
| | - Stuart Schnitt
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, USA
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6
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Improving DCIS diagnosis and predictive outcome by applying artificial intelligence. Biochim Biophys Acta Rev Cancer 2021; 1876:188555. [PMID: 33933557 DOI: 10.1016/j.bbcan.2021.188555] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 04/22/2021] [Accepted: 04/24/2021] [Indexed: 11/21/2022]
Abstract
Breast ductal carcinoma in situ (DCIS) is a preinvasive lesion that is considered to be a precursor to invasive breast cancer. Nevertheless, not all DCIS will progress to invasion. Current histopathological classification systems are unable to predict which cases will or will not progress, and therefore many women with DCIS may be overtreated. Artificial intelligence (AI) image-based analysis methods have potential to identify and analyze novel features that may facilitate tumor identification, prediction of disease outcome and response to treatment. Indeed, these methods prove promising for accurately identifying DCIS lesions, and show potential clinical utility in the therapeutic stratification of DCIS patients. Here, we review how AI techniques in histopathology may aid diagnosis and clinical decisions in regards to DCIS, and how such techniques could be incorporated into clinical practice.
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Kosaraju SC, Hao J, Koh HM, Kang M. Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis. Methods 2020; 179:3-13. [PMID: 32442672 DOI: 10.1016/j.ymeth.2020.05.012] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/01/2020] [Accepted: 05/14/2020] [Indexed: 01/15/2023] Open
Abstract
Digitizing whole-slide imaging in digital pathology has led to the advancement of computer-aided tissue examination using machine learning techniques, especially convolutional neural networks. A number of convolutional neural network-based methodologies have been proposed to accurately analyze histopathological images for cancer detection, risk prediction, and cancer subtype classification. Most existing methods have conducted patch-based examinations, due to the extremely large size of histopathological images. However, patches of a small window often do not contain sufficient information or patterns for the tasks of interest. It corresponds that pathologists also examine tissues at various magnification levels, while checking complex morphological patterns in a microscope. We propose a novel multi-task based deep learning model for HIstoPatholOgy (named Deep-Hipo) that takes multi-scale patches simultaneously for accurate histopathological image analysis. Deep-Hipo extracts two patches of the same size in both high and low magnification levels, and captures complex morphological patterns in both large and small receptive fields of a whole-slide image. Deep-Hipo has outperformed the current state-of-the-art deep learning methods. We assessed the proposed method in various types of whole-slide images of the stomach: well-differentiated, moderately-differentiated, and poorly-differentiated adenocarcinoma; poorly cohesive carcinoma, including signet-ring cell features; and normal gastric mucosa. The optimally trained model was also applied to histopathological images of The Cancer Genome Atlas (TCGA), Stomach Adenocarcinoma (TCGA-STAD) and TCGA Colon Adenocarcinoma (TCGA-COAD), which show similar pathological patterns with gastric carcinoma, and the experimental results were clinically verified by a pathologist. The source code of Deep-Hipo is publicly available athttp://dataxlab.org/deep-hipo.
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Affiliation(s)
| | - Jie Hao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
| | - Hyun Min Koh
- Department of Pathology, Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea.
| | - Mingon Kang
- Department of Computer Science, University of Nevada, Las Vegas, NV, USA.
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8
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Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions. BIOMED RESEARCH INTERNATIONAL 2020; 2020:4671349. [PMID: 32258124 PMCID: PMC7064857 DOI: 10.1155/2020/4671349] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 01/05/2020] [Accepted: 01/22/2020] [Indexed: 11/24/2022]
Abstract
Breast cancer is the most diagnosed cancer among women around the world. The development of computer-aided diagnosis tools is essential to help pathologists to accurately interpret and discriminate between malignant and benign tumors. This paper proposes the development of an automated proliferative breast lesion diagnosis based on machine-learning algorithms. We used Tabu search to select the most significant features. The evaluation of the feature is based on the dependency degree of each attribute in the rough set. The categorization of reduced features was built using five machine-learning algorithms. The proposed models were applied to the BIDMC-MGH and Wisconsin Diagnostic Breast Cancer datasets. The performance measures of the used models were evaluated owing to five criteria. The top performing models were AdaBoost and logistic regression. Comparisons with others works prove the efficiency of the proposed method for superior diagnosis of breast cancer against the reviewed classification techniques.
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Turner OC, Aeffner F, Bangari DS, High W, Knight B, Forest T, Cossic B, Himmel LE, Rudmann DG, Bawa B, Muthuswamy A, Aina OH, Edmondson EF, Saravanan C, Brown DL, Sing T, Sebastian MM. Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology. Toxicol Pathol 2019; 48:277-294. [DOI: 10.1177/0192623319881401] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Toxicologic pathology is transitioning from analog to digital methods. This transition seems inevitable due to a host of ongoing social and medical technological forces. Of these, artificial intelligence (AI) and in particular machine learning (ML) are globally disruptive, rapidly growing sectors of technology whose impact on the long-established field of histopathology is quickly being realized. The development of increasing numbers of algorithms, peering ever deeper into the histopathological space, has demonstrated to the scientific community that AI pathology platforms are now poised to truly impact the future of precision and personalized medicine. However, as with all great technological advances, there are implementation and adoption challenges. This review aims to define common and relevant AI and ML terminology, describe data generation and interpretation, outline current and potential future business cases, discuss validation and regulatory hurdles, and most importantly, propose how overcoming the challenges of this burgeoning technology may shape toxicologic pathology for years to come, enabling pathologists to contribute even more effectively to answering scientific questions and solving global health issues. [Box: see text]
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Affiliation(s)
- Oliver C. Turner
- Novartis, Novartis Institutes for Biomedical Research, Preclinical Safety, East Hanover, NJ, USA
| | - Famke Aeffner
- Amgen Inc, Research, Comparative Biology and Safety Sciences, San Francisco, CA, USA
| | | | - Wanda High
- High Preclinical Pathology Consulting, Rochester, NY, USA
| | - Brian Knight
- Boehringer Ingelheim Pharmaceuticals Incorporated, Nonclinical Drug Safety, Ridgefield, CT, USA
| | | | - Brieuc Cossic
- Roche, Pharmaceutical Research and Early Development (pRED), Roche Innovation Center, Basel, Switzerland
| | - Lauren E. Himmel
- Division of Animal Care, Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | | | | | - Elijah F. Edmondson
- Pathology/Histotechnology Laboratory, Frederick National Laboratory for Cancer Research, NIH, Frederick, MD, USA
| | - Chandrassegar Saravanan
- Novartis, Novartis Institutes for Biomedical Research, Preclinical Safety, Cambridge, MA, USA
| | | | - Tobias Sing
- Novartis, Novartis Institutes for Biomedical Research, NIBR Informatics, Basel, Switzerland
| | - Manu M. Sebastian
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX, USA
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10
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Mercan E, Mehta S, Bartlett J, Shapiro LG, Weaver DL, Elmore JG. Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions. JAMA Netw Open 2019; 2:e198777. [PMID: 31397859 PMCID: PMC6692690 DOI: 10.1001/jamanetworkopen.2019.8777] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
IMPORTANCE Following recent US Food and Drug Administration approval, adoption of whole slide imaging in clinical settings may be imminent, and diagnostic accuracy, particularly among challenging breast biopsy specimens, may benefit from computerized diagnostic support tools. OBJECTIVE To develop and evaluate computer vision methods to assist pathologists in diagnosing the full spectrum of breast biopsy samples, from benign to invasive cancer. DESIGN, SETTING, AND PARTICIPANTS In this diagnostic study, 240 breast biopsies from Breast Cancer Surveillance Consortium registries that varied by breast density, diagnosis, patient age, and biopsy type were selected, reviewed, and categorized by 3 expert pathologists as benign, atypia, ductal carcinoma in situ (DCIS), and invasive cancer. The atypia and DCIS cases were oversampled to increase statistical power. High-resolution digital slide images were obtained, and 2 automated image features (tissue distribution feature and structure feature) were developed and evaluated according to the consensus diagnosis of the expert panel. The performance of the automated image analysis methods was compared with independent interpretations from 87 practicing US pathologists. Data analysis was performed between February 2017 and February 2019. MAIN OUTCOMES AND MEASURES Diagnostic accuracy defined by consensus reference standard of 3 experienced breast pathologists. RESULTS The accuracy of machine learning tissue distribution features, structure features, and pathologists for classification of invasive cancer vs noninvasive cancer was 0.94, 0.91, and 0.98, respectively; the accuracy of classification of atypia and DCIS vs benign tissue was 0.70, 0.70, and 0.81, respectively; and the accuracy of classification of DCIS vs atypia was 0.83, 0.85, and 0.80, respectively. The sensitivity of both machine learning features was lower than that of the pathologists for the invasive vs noninvasive classification (tissue distribution feature, 0.70; structure feature, 0.49; pathologists, 0.84) but higher for the classification of atypia and DCIS vs benign cases (tissue distribution feature, 0.79; structure feature, 0.85; pathologists, 0.72) and the classification of DCIS vs atypia (tissue distribution feature, 0.88; structure feature, 0.89; pathologists, 0.70). For the DCIS vs atypia classification, the specificity of the machine learning feature classification was similar to that of the pathologists (tissue distribution feature, 0.78; structure feature, 0.80; pathologists, 0.82). CONCLUSION AND RELEVANCE The computer-based automated approach to interpreting breast pathology showed promise, especially as a diagnostic aid in differentiating DCIS from atypical hyperplasia.
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Affiliation(s)
- Ezgi Mercan
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle
- nowwith Seattle Children’s Hospital, Seattle, Washington
| | - Sachin Mehta
- Department of Electrical and Computer Engineering, University of Washington, Seattle
| | - Jamen Bartlett
- University of Vermont Medical Center, Burlington
- now with Southern Ohio Pathology Consultants, Cincinnati, Ohio
| | - Linda G. Shapiro
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle
| | - Donald L. Weaver
- Department of Pathology and University of Vermont Cancer Center, Larner College of Medicine, University of Vermont, Burlington
| | - Joann G. Elmore
- Division of General Internal Medicine and Health Services Research, Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles
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11
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Alagappan M, Brown JRG, Mori Y, Berzin TM. Artificial intelligence in gastrointestinal endoscopy: The future is almost here. World J Gastrointest Endosc 2018; 10:239-249. [PMID: 30364792 PMCID: PMC6198310 DOI: 10.4253/wjge.v10.i10.239] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 06/09/2018] [Accepted: 06/30/2018] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) enables machines to provide unparalleled value in a myriad of industries and applications. In recent years, researchers have harnessed artificial intelligence to analyze large-volume, unstructured medical data and perform clinical tasks, such as the identification of diabetic retinopathy or the diagnosis of cutaneous malignancies. Applications of artificial intelligence techniques, specifically machine learning and more recently deep learning, are beginning to emerge in gastrointestinal endoscopy. The most promising of these efforts have been in computer-aided detection and computer-aided diagnosis of colorectal polyps, with recent systems demonstrating high sensitivity and accuracy even when compared to expert human endoscopists. AI has also been utilized to identify gastrointestinal bleeding, to detect areas of inflammation, and even to diagnose certain gastrointestinal infections. Future work in the field should concentrate on creating seamless integration of AI systems with current endoscopy platforms and electronic medical records, developing training modules to teach clinicians how to use AI tools, and determining the best means for regulation and approval of new AI technology.
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Affiliation(s)
- Muthuraman Alagappan
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center, Harvard Medical, Boston, MA 02215, United States
| | - Jeremy R Glissen Brown
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center, Harvard Medical, Boston, MA 02215, United States
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center, Harvard Medical, Boston, MA 02215, United States
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12
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Olsen TG, Jackson BH, Feeser TA, Kent MN, Moad JC, Krishnamurthy S, Lunsford DD, Soans RE. Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology. J Pathol Inform 2018; 9:32. [PMID: 30294501 PMCID: PMC6166480 DOI: 10.4103/jpi.jpi_31_18] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Accepted: 08/21/2018] [Indexed: 12/21/2022] Open
Abstract
Background Artificial intelligence is advancing at an accelerated pace into clinical applications, providing opportunities for increased efficiency, improved accuracy, and cost savings through computer-aided diagnostics. Dermatopathology, with emphasis on pattern recognition, offers a unique opportunity for testing deep learning algorithms. Aims This study aims to determine the accuracy of deep learning algorithms to diagnose three common dermatopathology diagnoses. Methods Whole slide images (WSI) of previously diagnosed nodular basal cell carcinomas (BCCs), dermal nevi, and seborrheic keratoses were annotated for areas of distinct morphology. Unannotated WSIs, consisting of five distractor diagnoses of common neoplastic and inflammatory diagnoses, were included in each training set. A proprietary fully convolutional neural network was developed to train algorithms to classify test images as positive or negative relative to ground truth diagnosis. Results Artificial intelligence system accurately classified 123/124 (99.45%) BCCs (nodular), 113/114 (99.4%) dermal nevi, and 123/123 (100%) seborrheic keratoses. Conclusions Artificial intelligence using deep learning algorithms is a potential adjunct to diagnosis and may result in improved workflow efficiencies for dermatopathologists and laboratories.
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Affiliation(s)
- Thomas George Olsen
- Department of Dermatology, Boonshoft School of Medicine, Wright State University School of Medicine, Dayton, Ohio, USA.,Dermatopathology Laboratory of Central States, Dayton, Ohio, USA
| | | | | | - Michael N Kent
- Department of Dermatology, Boonshoft School of Medicine, Wright State University School of Medicine, Dayton, Ohio, USA.,Dermatopathology Laboratory of Central States, Dayton, Ohio, USA
| | - John C Moad
- Department of Dermatology, Boonshoft School of Medicine, Wright State University School of Medicine, Dayton, Ohio, USA.,Dermatopathology Laboratory of Central States, Dayton, Ohio, USA
| | - Smita Krishnamurthy
- Department of Dermatology, Boonshoft School of Medicine, Wright State University School of Medicine, Dayton, Ohio, USA.,Dermatopathology Laboratory of Central States, Dayton, Ohio, USA
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Gray E, Mitchell E, Jindal S, Schedin P, Chang YH. A METHOD FOR QUANTIFICATION OF CALPONIN EXPRESSION IN MYOEPITHELIAL CELLS IN IMMUNOHISTOCHEMICAL IMAGES OF DUCTAL CARCINOMA IN SITU. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2018; 2018:796-799. [PMID: 30364524 PMCID: PMC6196724 DOI: 10.1109/isbi.2018.8363692] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Ductal carcinoma in situ (DCIS) is breast cancer confined within mammary ducts, surrounded by an intact myoepithelial cell layer that prevents local invasion. A DCIS diagnosis confers increased lifetime risk of developing invasive breast cancer (IBC) and results in surgical excision with radiation, and possibly endocrine- or chemo-therapy. DCIS is known to be over treated, with associated co-morbidities. Biomarkers are needed that delineate patients at low risk of DCIS progression from patients requiring more aggressive treatment. Investigating the role of myoepithelial cell differentiation in barrier function is anticipated to provide insight into DCIS progression and delineate between low and high risk lesions. Here, we develop a high throughput technique to assess loss of myoepithelial differentiation markers. This method facilitates automated analysis of a clinically relevant histopathologic feature, as demonstrated by a high correlation with pathologist annotation (r = 0.959), and further, contributes analytical foundations to a multiplexed immunohistochemistry (IHC) approach.
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Affiliation(s)
- Elliot Gray
- Department of Biomedical Engineering and Computational Biology Program
| | - Elizabeth Mitchell
- Department of Cell, Developmental and Cancer Biology Oregon Health & Science University
| | - Sonali Jindal
- Department of Cell, Developmental and Cancer Biology Oregon Health & Science University
| | - Pepper Schedin
- Department of Cell, Developmental and Cancer Biology Oregon Health & Science University
| | - Young Hwan Chang
- Department of Biomedical Engineering and Computational Biology Program
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