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Hanna MG, Ardon O. Digital pathology systems enabling quality patient care. Genes Chromosomes Cancer 2023; 62:685-697. [PMID: 37458325 PMCID: PMC11265285 DOI: 10.1002/gcc.23192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/27/2023] [Accepted: 07/06/2023] [Indexed: 09/20/2023] Open
Abstract
Pathology laboratories are undergoing digital transformations, adopting innovative technologies to enhance patient care. Digital pathology systems impact clinical, education, and research use cases where pathologists use digital technologies to perform tasks in lieu of using glass slides and a microscope. Pathology professional societies have established clinical validation guidelines, and the US Food and Drug Administration have also authorized digital pathology systems for primary diagnosis, including image analysis and machine learning systems. Whole slide images, or digital slides, can be viewed and navigated similar to glass slides on a microscope. These modern tools not only enable pathologists to practice their routine clinical activities, but can potentially enable digital computational discovery. Assimilation of whole slide images in pathology clinical workflow can further empower machine learning systems to support computer assisted diagnostics. The potential enrichment these systems can provide is unprecedented in the field of pathology. With appropriate integration, these clinical decision support systems will allow pathologists to increase the delivery of quality patient care. This review describes the digital pathology transformation process, applicable clinical use cases, incorporation of image analysis and machine learning systems in the clinical workflow, as well as future technologies that may further disrupt pathology modalities to deliver quality patient care.
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Affiliation(s)
- Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Orly Ardon
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Caldonazzi N, Rizzo PC, Eccher A, Girolami I, Fanelli GN, Naccarato AG, Bonizzi G, Fusco N, d'Amati G, Scarpa A, Pantanowitz L, Marletta S. Value of Artificial Intelligence in Evaluating Lymph Node Metastases. Cancers (Basel) 2023; 15:cancers15092491. [PMID: 37173958 PMCID: PMC10177013 DOI: 10.3390/cancers15092491] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/23/2023] [Accepted: 04/25/2023] [Indexed: 05/15/2023] Open
Abstract
One of the most relevant prognostic factors in cancer staging is the presence of lymph node (LN) metastasis. Evaluating lymph nodes for the presence of metastatic cancerous cells can be a lengthy, monotonous, and error-prone process. Owing to digital pathology, artificial intelligence (AI) applied to whole slide images (WSIs) of lymph nodes can be exploited for the automatic detection of metastatic tissue. The aim of this study was to review the literature regarding the implementation of AI as a tool for the detection of metastases in LNs in WSIs. A systematic literature search was conducted in PubMed and Embase databases. Studies involving the application of AI techniques to automatically analyze LN status were included. Of 4584 retrieved articles, 23 were included. Relevant articles were labeled into three categories based upon the accuracy of AI in evaluating LNs. Published data overall indicate that the application of AI in detecting LN metastases is promising and can be proficiently employed in daily pathology practice.
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Affiliation(s)
- Nicolò Caldonazzi
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37134 Verona, Italy
| | - Paola Chiara Rizzo
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37134 Verona, Italy
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, 37126 Verona, Italy
| | - Ilaria Girolami
- Department of Pathology, Lehrkrankenhaus der Paracelsus Medizinischen Privatuniversität, Provincial Hospital of Bolzano (SABES-ASDAA), 39100 Bolzano-Bozen, Italy
| | - Giuseppe Nicolò Fanelli
- Division of Pathology, Department of Translational Research, New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy
| | - Antonio Giuseppe Naccarato
- Division of Pathology, Department of Translational Research, New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy
| | - Giuseppina Bonizzi
- Division of Pathology, IEO, Europefan Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
| | - Nicola Fusco
- Division of Pathology, IEO, Europefan Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Giulia d'Amati
- Department of Radiology, Oncology and Pathology, Sapienza, University of Rome, 00185 Rome, Italy
| | - Aldo Scarpa
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37134 Verona, Italy
| | - Liron Pantanowitz
- Department of Pathology, University of Michigan, Ann Arbor, MI 48104, USA
| | - Stefano Marletta
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37134 Verona, Italy
- Department of Pathology, Pederzoli Hospital, 37019 Peschiera del Garda, Italy
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Kaushal RK, Yadav S, Sahay A, Karnik N, Agrawal T, Dave V, Singh N, Shah A, Desai SB. Validation of Remote Digital Pathology based diagnostic reporting of Frozen Sections from home. J Pathol Inform 2023; 14:100312. [PMID: 37214151 PMCID: PMC10192998 DOI: 10.1016/j.jpi.2023.100312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/07/2023] [Accepted: 04/12/2023] [Indexed: 05/24/2023] Open
Abstract
Background Despite the promising applications of whole-slide imaging (WSI) for frozen section (FS) diagnosis, its adoption for remote reporting is limited. Objective To assess the feasibility and performance of home-based remote digital consultation for FS diagnosis. Material & Method Cases accessioned beyond regular working hours (5 pm-10 pm) were reported simultaneously using optical microscopy (OM) and WSI. Validation of WSI for FS diagnosis from a remote site, i.e. home, was performed by 5 pathologists. Cases were scanned using a portable scanner (Grundium Ocus®40) and previewed on consumer-grade computer devices through a web-based browser (http://grundium.net). Clinical data and diagnostic reports were shared through a google spreadsheet. The diagnostic concordance, inter- and intra-observer agreement for FS diagnosis by WSI versus OM, and turnaround time (TAT), were recorded. Results The overall diagnostic accuracy for OM and WSI (from home) was 98.2% (range 97%-100%) and 97.6% (range 95%-99%), respectively, when compared with the reference standard. Almost perfect inter-observer (k = 0.993) and intra-observer (k = 0.987) agreement for WSI was observed by 4 pathologists. Pathologists used consumer-grade laptops/desktops with an average screen size of 14.58 inches (range = 12.3-17.7 inches) and a network speed of 64 megabits per second (range: 10-90 Mbps). The mean diagnostic assessment time per case for OM and WSI was 1:48 min and 5:54 min, respectively. Mean TAT of 27.27 min per case was observed using WSI from home. Seamless connectivity was observed in approximately 75% of cases. Conclusion This study validates the role of WSI for remote FS diagnosis for its safe and efficient adoption in clinical use.
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Affiliation(s)
- Rajiv Kumar Kaushal
- Corresponding author at: Department of Pathology, Tata Memorial Hospital, Homi Bhabha National Institute, Dr Ernest Borges Marg, Parel, Mumbai 400 012, India.
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Mastrosimini MG, Eccher A, Nottegar A, Montin U, Scarpa A, Pantanowitz L, Girolami I. elcome@123WSI validation studies in breast and gynecological pathology. Pathol Res Pract 2022; 240:154191. [DOI: 10.1016/j.prp.2022.154191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/22/2022] [Accepted: 10/25/2022] [Indexed: 11/06/2022]
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Kantasiripitak C, Laohawetwanit T, Apornvirat S, Niemnapa K. Validation of whole slide imaging for frozen section diagnosis of lymph node metastasis: A retrospective study from a tertiary care hospital in Thailand. Ann Diagn Pathol 2022; 60:151987. [PMID: 35700561 DOI: 10.1016/j.anndiagpath.2022.151987] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/23/2022] [Accepted: 06/03/2022] [Indexed: 11/01/2022]
Abstract
BACKGROUND The use of whole slide imaging (WSI) for frozen section (FS) diagnosis is helpful, particularly in the context of pathologist shortages. However, there is minimal data on such usage in resource-limited settings. This study aims to validate the use of WSI for FS diagnosis of lymph node metastasis using a low-cost virtual microscope scanner with consumer-grade laptops at a tertiary care hospital in Thailand. METHODS FS slides were retrieved for which the clinical query was to evaluate lymph node metastasis. They were digitized by a virtual microscope scanner (MoticEasyScan, Hong Kong) using up to 40× optical magnification. Three observers with different pathology experience levels diagnosed each slide, reviewing glass slides (GS) followed by digital slides (DS) after two weeks of a wash out period. WSI and GS diagnoses were compared. The time used for scanning and diagnosis of each slide was recorded. RESULTS 295 FS slides were retrieved and digitized. The first-time successful scanning rate was 93.6 %. The mean scanning time was 2 min per slide. Both intraobserver agreement and interobserver agreement of WSI and GS diagnoses were high (Cohen's K; kappa value >0.84). The time used for DS diagnosis decreased as the observer's experience with WSI increased. CONCLUSIONS Despite varying pathological experiences, observers using WSI provided accurate FS diagnoses of lymph node metastasis. The time required for DS diagnoses decreased with additional observer's experience with WSI. Therefore, a WSI system containing low-cost scanners and consumer-grade laptops could be used for FS services in hospital laboratories lacking pathologists.
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Affiliation(s)
| | - Thiyaphat Laohawetwanit
- Division of Pathology, Thammasat University Hospital, Pathum Thani, Thailand; Division of Pathology, Chulabhorn International College of Medicine, Thammasat University, Pathum Thani, Thailand.
| | - Sompon Apornvirat
- Division of Pathology, Thammasat University Hospital, Pathum Thani, Thailand; Division of Pathology, Chulabhorn International College of Medicine, Thammasat University, Pathum Thani, Thailand
| | - Kongkot Niemnapa
- Advanced Digital Simulation Center, Chulabhorn International College of Medicine, Thammasat University, Pathum Thani, Thailand
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Girolami I, Neri S, Eccher A, Brunelli M, Hanna M, Pantanowitz L, Hanspeter E, Mazzoleni G. Frozen section telepathology service: Efficiency and benefits of an e-health policy in South Tyrol. Digit Health 2022; 8:20552076221116776. [PMID: 35923756 PMCID: PMC9340333 DOI: 10.1177/20552076221116776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 07/13/2022] [Indexed: 12/03/2022] Open
Abstract
Objective/Background Telepathology has been widely adopted to allow intraoperative pathology
examinations to be performed remotely and for obtaining second opinion
teleconsultation. In the Italian northern region of South Tyrol, the
widespread geographical distances and consequent cost for the health system
of having a travelling pathologist cover intraoperative consultations in
peripheral hospitals was a key driver for the implementation of a
telepathology system. Methods In 2010, four Menarini D-Sight whole slide scanners to digitize entire
pathology slides were placed in the peripheral hospitals of Merano,
Bressanone, Brunico, and in the hub hospital of Bolzano. Digital
workstations were also installed to allow pathologists to remotely perform
intraoperative consultations with digital slides. This study reviews the
outcome after 12 years of telepathology for this intended clinical use. Results After an initial validation phase with 100 cases which yielded a sensitivity
of 65% (CI 43–84%) and specificity of 100% (CI 95–100%), there were 2058
intraoperative consultations handled by telepathology. The cases evaluated
were mainly breast sentinel lymph nodes, followed by urological,
gynecological and general surgical pathology frozen section specimens. There
were no false-positive cases and 165 (8%) false-negative cases, yielding an
overall sensitivity and specificity of 65% (CI 61–69%) and 100% (CI
99–100%), respectively. Conclusion Telepathology is reliable for remote intraoperative diagnosis and, despite
technical issues and initial acquaintance issues, proved beneficial for
patient care in satellite hospitals, improved standardization, promoted
innovation, and resulted in cost savings for the health system.
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Affiliation(s)
- Ilaria Girolami
- Department of Pathology, Provincial Hospital of Bolzano (SABES-ASDAA), Bolzano-Bozen, Italy
| | - Stefania Neri
- Department of Pathology, Provincial Hospital of Bolzano (SABES-ASDAA), Bolzano-Bozen, Italy
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
| | - Matteo Brunelli
- Department of Diagnostics and Public Health, University and Hospital Trust of Verona, Verona, Italy
| | - Mattew Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Liron Pantanowitz
- Department of Pathology & Clinical Labs, University of Michigan, Ann Arbor, MI, USA
| | - Esther Hanspeter
- Department of Pathology, Provincial Hospital of Bolzano (SABES-ASDAA), Bolzano-Bozen, Italy
| | - Guido Mazzoleni
- Department of Pathology, Provincial Hospital of Bolzano (SABES-ASDAA), Bolzano-Bozen, Italy
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Kim YG, Song IH, Lee H, Kim S, Yang DH, Kim N, Shin D, Yoo Y, Lee K, Kim D, Jung H, Cho H, Lee H, Kim T, Choi JH, Seo C, Han SI, Lee YJ, Lee YS, Yoo HR, Lee Y, Park JH, Oh S, Gong G. Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer. Cancer Res Treat 2020; 52:1103-1111. [PMID: 32599974 PMCID: PMC7577824 DOI: 10.4143/crt.2020.337] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 06/29/2020] [Indexed: 11/26/2022] Open
Abstract
Purpose Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of SLNs by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin–stained frozen tissue sections of SLNs in breast cancer patients. Materials and Methods A total of 297 digital slides were obtained from frozen SLN sections, which include post–neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for 6 weeks with two P40 GPUs. The algorithms were assessed in terms of the area under receiver operating characteristic curve (AUC). Results The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. Conclusion In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative SLN biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting.
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Affiliation(s)
- Young-Gon Kim
- Department of Biomedical Engineering, Asan Institute of Life Science, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - In Hye Song
- Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hyunna Lee
- Health Innovation Big Data Center, Asan Institute for Life Science, Asan Medical Center, Seoul, Korea
| | - Sungchul Kim
- Department of Biomedical Engineering, Asan Institute of Life Science, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Dong Hyun Yang
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | | | | | - Kyowoon Lee
- Department of Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, Korea
| | - Dahye Kim
- Image Laboratory, School of Computer Science and Engineering, ChungAng University, Seoul, Korea
| | | | | | | | - Taeu Kim
- Department of Business Management and Convergence Software, Sogang University, Seoul, Korea
| | - Jong Hyun Choi
- Data Science & Business Analytics Lab, School of Industrial Management Engineering, College of Engineering, Korea University, Seoul, Korea
| | | | - Seong Il Han
- Software Graduate Program, School of Computing, College of Engineering, Korea Advanced Institute of Science and Technology, Seoul, Korea
| | - Young Je Lee
- Department of Biomedical Engineering, Yonsei University, Seoul, Korea
| | - Young Seo Lee
- Department of Social Studies Education, College of Education, Ewha Womans University, Seoul, Korea
| | - Hyung-Ryun Yoo
- Department of Math, University of Kwangwoon, Seoul, Korea
| | - Yongju Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Jeong Hwan Park
- Department of Pathology, Seoul National University College of Medicine and SMG-SNU Boramae Medical Center, Seoul, Korea
| | - Sohee Oh
- Department of Biostatistics, Seoul National University College of Medicine and SMG-SNU Boramae Medical Center, Seoul, Korea
| | - Gyungyub Gong
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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