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Tafavvoghi M, Bongo LA, Shvetsov N, Busund LTR, Møllersen K. Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review. J Pathol Inform 2024; 15:100363. [PMID: 38405160 PMCID: PMC10884505 DOI: 10.1016/j.jpi.2024.100363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/24/2023] [Accepted: 01/23/2024] [Indexed: 02/27/2024] Open
Abstract
Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development of accurate and robust deep learning models. In this scoping review, we identified the publicly available datasets of breast H&E-stained whole-slide images (WSIs) that can be used to develop deep learning algorithms. We systematically searched 9 scientific literature databases and 9 research data repositories and found 17 publicly available datasets containing 10 385 H&E WSIs of breast cancer. Moreover, we reported image metadata and characteristics for each dataset to assist researchers in selecting proper datasets for specific tasks in breast cancer computational pathology. In addition, we compiled 2 lists of breast H&E patches and private datasets as supplementary resources for researchers. Notably, only 28% of the included articles utilized multiple datasets, and only 14% used an external validation set, suggesting that the performance of other developed models may be susceptible to overestimation. The TCGA-BRCA was used in 52% of the selected studies. This dataset has a considerable selection bias that can impact the robustness and generalizability of the trained algorithms. There is also a lack of consistent metadata reporting of breast WSI datasets that can be an issue in developing accurate deep learning models, indicating the necessity of establishing explicit guidelines for documenting breast WSI dataset characteristics and metadata.
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Affiliation(s)
- Masoud Tafavvoghi
- Department of Community Medicine, Uit The Arctic University of Norway, Tromsø, Norway
| | - Lars Ailo Bongo
- Department of Computer Science, Uit The Arctic University of Norway, Tromsø, Norway
| | - Nikita Shvetsov
- Department of Computer Science, Uit The Arctic University of Norway, Tromsø, Norway
| | | | - Kajsa Møllersen
- Department of Community Medicine, Uit The Arctic University of Norway, Tromsø, Norway
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2
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van Dooijeweert C, Flach RN, Ter Hoeve ND, Vreuls CPH, Goldschmeding R, Freund JE, Pham P, Nguyen TQ, van der Wall E, Frederix GWJ, Stathonikos N, van Diest PJ. Clinical implementation of artificial-intelligence-assisted detection of breast cancer metastases in sentinel lymph nodes: the CONFIDENT-B single-center, non-randomized clinical trial. NATURE CANCER 2024:10.1038/s43018-024-00788-z. [PMID: 38937624 DOI: 10.1038/s43018-024-00788-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 05/29/2024] [Indexed: 06/29/2024]
Abstract
Pathologists' assessment of sentinel lymph nodes (SNs) for breast cancer (BC) metastases is a treatment-guiding yet labor-intensive and costly task because of the performance of immunohistochemistry (IHC) in morphologically negative cases. This non-randomized, single-center clinical trial (International Standard Randomized Controlled Trial Number:14323711) assessed the efficacy of an artificial intelligence (AI)-assisted workflow for detecting BC metastases in SNs while maintaining diagnostic safety standards. From September 2022 to May 2023, 190 SN specimens were consecutively enrolled and allocated biweekly to the intervention arm (n = 100) or control arm (n = 90). In both arms, digital whole-slide images of hematoxylin-eosin sections of SN specimens were assessed by an expert pathologist, who was assisted by the 'Metastasis Detection' app (Visiopharm) in the intervention arm. Our primary endpoint showed a significantly reduced adjusted relative risk of IHC use (0.680, 95% confidence interval: 0.347-0.878) for AI-assisted pathologists, with subsequent cost savings of ~3,000 €. Secondary endpoints showed significant time reductions and up to 30% improved sensitivity for AI-assisted pathologists. This trial demonstrates the safety and potential for cost and time savings of AI assistance.
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Affiliation(s)
- C van Dooijeweert
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - R N Flach
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - N D Ter Hoeve
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - C P H Vreuls
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - R Goldschmeding
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - J E Freund
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - P Pham
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - T Q Nguyen
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - E van der Wall
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - G W J Frederix
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - N Stathonikos
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - P J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands.
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3
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van Diest PJ, Flach RN, van Dooijeweert C, Makineli S, Breimer GE, Stathonikos N, Pham P, Nguyen TQ, Veta M. Pros and cons of artificial intelligence implementation in diagnostic pathology. Histopathology 2024; 84:924-934. [PMID: 38433288 DOI: 10.1111/his.15153] [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] [Received: 11/15/2023] [Revised: 12/29/2023] [Accepted: 01/19/2024] [Indexed: 03/05/2024]
Abstract
The rapid introduction of digital pathology has greatly facilitated development of artificial intelligence (AI) models in pathology that have shown great promise in assisting morphological diagnostics and quantitation of therapeutic targets. We are now at a tipping point where companies have started to bring algorithms to the market, and questions arise whether the pathology community is ready to implement AI in routine workflow. However, concerns also arise about the use of AI in pathology. This article reviews the pros and cons of introducing AI in diagnostic pathology.
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Affiliation(s)
- Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Rachel N Flach
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Oncological Urology, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Seher Makineli
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Surgical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Gerben E Breimer
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Nikolas Stathonikos
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Paul Pham
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Tri Q Nguyen
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mitko Veta
- Department of Oncological Urology, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
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4
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Irmakci I, Nateghi R, Zhou R, Vescovo M, Saft M, Ross AE, Yang XJ, Cooper LAD, Goldstein JA. Tissue Contamination Challenges the Credibility of Machine Learning Models in Real World Digital Pathology. Mod Pathol 2024; 37:100422. [PMID: 38185250 PMCID: PMC10960671 DOI: 10.1016/j.modpat.2024.100422] [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: 04/28/2023] [Revised: 11/13/2023] [Accepted: 12/15/2023] [Indexed: 01/09/2024]
Abstract
Machine learning (ML) models are poised to transform surgical pathology practice. The most successful use attention mechanisms to examine whole slides, identify which areas of tissue are diagnostic, and use them to guide diagnosis. Tissue contaminants, such as floaters, represent unexpected tissue. Although human pathologists are extensively trained to consider and detect tissue contaminants, we examined their impact on ML models. We trained 4 whole-slide models. Three operate in placenta for the following functions: (1) detection of decidual arteriopathy, (2) estimation of gestational age, and (3) classification of macroscopic placental lesions. We also developed a model to detect prostate cancer in needle biopsies. We designed experiments wherein patches of contaminant tissue are randomly sampled from known slides and digitally added to patient slides and measured model performance. We measured the proportion of attention given to contaminants and examined the impact of contaminants in the t-distributed stochastic neighbor embedding feature space. Every model showed performance degradation in response to one or more tissue contaminants. Decidual arteriopathy detection--balanced accuracy decreased from 0.74 to 0.69 ± 0.01 with addition of 1 patch of prostate tissue for every 100 patches of placenta (1% contaminant). Bladder, added at 10% contaminant, raised the mean absolute error in estimating gestational age from 1.626 weeks to 2.371 ± 0.003 weeks. Blood, incorporated into placental sections, induced false-negative diagnoses of intervillous thrombi. Addition of bladder to prostate cancer needle biopsies induced false positives, a selection of high-attention patches, representing 0.033 mm2, and resulted in a 97% false-positive rate when added to needle biopsies. Contaminant patches received attention at or above the rate of the average patch of patient tissue. Tissue contaminants induce errors in modern ML models. The high level of attention given to contaminants indicates a failure to encode biological phenomena. Practitioners should move to quantify and ameliorate this problem.
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Affiliation(s)
- Ismail Irmakci
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Ramin Nateghi
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Rujoi Zhou
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Mariavittoria Vescovo
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Madeline Saft
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Ashley E Ross
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Ximing J Yang
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Lee A D Cooper
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Jeffery A Goldstein
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois.
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Greenberg A, Samueli B, Farkash S, Zohar Y, Ish-Shalom S, Hagege RR, Hershkovitz D. Algorithm-assisted diagnosis of Hirschsprung's disease - evaluation of robustness and comparative image analysis on data from various labs and slide scanners. Diagn Pathol 2024; 19:26. [PMID: 38321431 PMCID: PMC10845737 DOI: 10.1186/s13000-024-01452-x] [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: 11/05/2023] [Accepted: 01/25/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Differences in the preparation, staining and scanning of digital pathology slides create significant pre-analytic variability. Algorithm-assisted tools must be able to contend with this variability in order to be applicable in clinical practice. In a previous study, a decision support algorithm was developed to assist in the diagnosis of Hirschsprung's disease. In the current study, we tested the robustness of this algorithm while assessing for pre-analytic factors which may affect its performance. METHODS The decision support algorithm was used on digital pathology slides obtained from four different medical centers (A-D) and scanned by three different scanner models (by Philips, Hamamatsu and 3DHISTECH). A total of 192 cases and 1782 slides were used in this study. RGB histograms were constructed to compare images from the various medical centers and scanner models and highlight the differences in color and contrast. RESULTS The algorithm was able to correctly identify ganglion cells in 99.2% of cases, from all medical centers (All scanned by the Philips slide scanner) as well as 95.5% and 100% of the slides scanned by the 3DHISTECH and Hamamatsu brand slide scanners, respectively. The total error rate for center D was lower than the other medical centers (3.9% vs 7.1%, 10.8% and 6% for centers A-C, respectively), the vast majority of errors being false positives (3.45% vs 0.45% false negatives). The other medical centers showed a higher rate of false negatives in relation to false positives (6.81% vs 0.29%, 9.8% vs 1.2% and 5.37% vs 0.63% for centers A-C, respectively). The total error rates for the Philips, Hamamatsu and 3DHISTECH brand scanners were 3.9%, 3.2% and 9.8%, respectively. RGB histograms demonstrated significant differences in pixel value distribution between the four medical centers, as well as between the 3DHISTECH brand scanner when compared to the Philips and Hamamatsu brand scanners. CONCLUSIONS The results reported in this paper suggest that the algorithm-based decision support system has sufficient robustness to be applicable for clinical practice. In addition, the novel method used in its development - Hierarchial-Contexual Analysis (HCA) may be applicable to the development of algorithm-assisted tools in other diseases, for which available datasets are limited. Validation of any given algorithm-assisted support system should nonetheless include data from as many medical centers and scanner models as possible.
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Affiliation(s)
- Ariel Greenberg
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel.
| | - Benzion Samueli
- Department of Pathology, Soroka University Medical Center, 76 Wingate Street, 8486614, Be'er Sheva, Israel
| | - Shai Farkash
- Department of Pathology, Emek Medical Center, Yitshak Rabin Boulevard 21, 1834111, Afula, Israel
| | - Yaniv Zohar
- Department of Pathology, Rambam Medical Center, 8 Haalia Hashnia, 3525408, Haifa, Israel
| | - Shahar Ish-Shalom
- Department of Pathology, Kaplan Medical Center, Pasternak St. P.O.B. 1, 76100, Rehovot, Israel
| | - Rami R Hagege
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel
| | - Dov Hershkovitz
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Ramat Aviv 69978, Tel-Aviv, Israel
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Schwen LO, Kiehl TR, Carvalho R, Zerbe N, Homeyer A. Digitization of Pathology Labs: A Review of Lessons Learned. J Transl Med 2023; 103:100244. [PMID: 37657651 DOI: 10.1016/j.labinv.2023.100244] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/18/2023] [Accepted: 08/23/2023] [Indexed: 09/03/2023] Open
Abstract
Pathology laboratories are increasingly using digital workflows. This has the potential of increasing laboratory efficiency, but the digitization process also involves major challenges. Several reports have been published describing the individual experiences of specific laboratories with the digitization process. However, a comprehensive overview of the lessons learned is still lacking. We provide an overview of the lessons learned for different aspects of the digitization process, including digital case management, digital slide reading, and computer-aided slide reading. We also cover metrics used for monitoring performance and pitfalls and corresponding values observed in practice. The overview is intended to help pathologists, information technology decision makers, and administrators to benefit from the experiences of others and to implement the digitization process in an optimal way to make their own laboratory future-proof.
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Affiliation(s)
- Lars Ole Schwen
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
| | - Tim-Rasmus Kiehl
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Rita Carvalho
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Norman Zerbe
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - André Homeyer
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
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7
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Sehring J, Dohmen H, Selignow C, Schmid K, Grau S, Stein M, Uhl E, Mukhopadhyay A, Németh A, Amsel D, Acker T. Leveraging Attention-Based Convolutional Neural Networks for Meningioma Classification in Computational Histopathology. Cancers (Basel) 2023; 15:5190. [PMID: 37958364 PMCID: PMC10647687 DOI: 10.3390/cancers15215190] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/23/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Convolutional neural networks (CNNs) are becoming increasingly valuable tools for advanced computational histopathology, promoting precision medicine through exceptional visual decoding abilities. Meningiomas, the most prevalent primary intracranial tumors, necessitate accurate grading and classification for informed clinical decision-making. Recently, DNA methylation-based molecular classification of meningiomas has proven to be more effective in predicting tumor recurrence than traditional histopathological methods. However, DNA methylation profiling is expensive, labor-intensive, and not widely accessible. Consequently, a digital histology-based prediction of DNA methylation classes would be advantageous, complementing molecular classification. In this study, we developed and rigorously assessed an attention-based multiple-instance deep neural network for predicting meningioma methylation classes using tumor methylome data from 142 (+51) patients and corresponding hematoxylin-eosin-stained histological sections. Pairwise analysis of sample cohorts from three meningioma methylation classes demonstrated high accuracy in two combinations. The performance of our approach was validated using an independent set of 51 meningioma patient samples. Importantly, attention map visualization revealed that the algorithm primarily focuses on tumor regions deemed significant by neuropathologists, offering insights into the decision-making process of the CNN. Our findings highlight the capacity of CNNs to effectively harness phenotypic information from histological sections through computerized images for precision medicine. Notably, this study is the first demonstration of predicting clinically relevant DNA methylome information using computer vision applied to standard histopathology. The introduced AI framework holds great potential in supporting, augmenting, and expediting meningioma classification in the future.
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Affiliation(s)
- Jannik Sehring
- Institute of Neuropathology, Justus Liebig University Giessen, Arndtstr. 16, D-35392 Giessen, Germany; (J.S.)
| | - Hildegard Dohmen
- Institute of Neuropathology, Justus Liebig University Giessen, Arndtstr. 16, D-35392 Giessen, Germany; (J.S.)
| | - Carmen Selignow
- Institute of Neuropathology, Justus Liebig University Giessen, Arndtstr. 16, D-35392 Giessen, Germany; (J.S.)
| | - Kai Schmid
- Institute of Neuropathology, Justus Liebig University Giessen, Arndtstr. 16, D-35392 Giessen, Germany; (J.S.)
| | - Stefan Grau
- Department of Neurosurgery, Hospital Fulda, Pacelliallee 4, D-36043 Fulda, Germany
| | - Marco Stein
- Department of Neurosurgery, University Hospital Gießen, Klinikstr. 33, D-35392 Giessen, Germany
| | - Eberhard Uhl
- Department of Neurosurgery, University Hospital Gießen, Klinikstr. 33, D-35392 Giessen, Germany
| | - Anirban Mukhopadhyay
- Department of Computer Science, Technical University of Darmstadt, Fraunhoferstraße 5, D-64283 Darmstadt, Germany
| | - Attila Németh
- Institute of Neuropathology, Justus Liebig University Giessen, Arndtstr. 16, D-35392 Giessen, Germany; (J.S.)
| | - Daniel Amsel
- Institute of Neuropathology, Justus Liebig University Giessen, Arndtstr. 16, D-35392 Giessen, Germany; (J.S.)
| | - Till Acker
- Institute of Neuropathology, Justus Liebig University Giessen, Arndtstr. 16, D-35392 Giessen, Germany; (J.S.)
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8
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Swillens JEM, Nagtegaal ID, Engels S, Lugli A, Hermens RPMG, van der Laak JAWM. Pathologists' first opinions on barriers and facilitators of computational pathology adoption in oncological pathology: an international study. Oncogene 2023; 42:2816-2827. [PMID: 37587332 PMCID: PMC10504072 DOI: 10.1038/s41388-023-02797-1] [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: 11/17/2022] [Accepted: 07/26/2023] [Indexed: 08/18/2023]
Abstract
Computational pathology (CPath) algorithms detect, segment or classify cancer in whole slide images, approaching or even exceeding the accuracy of pathologists. Challenges have to be overcome before these algorithms can be used in practice. We therefore aim to explore international perspectives on the future role of CPath in oncological pathology by focusing on opinions and first experiences regarding barriers and facilitators. We conducted an international explorative eSurvey and semi-structured interviews with pathologists utilizing an implementation framework to classify potential influencing factors. The eSurvey results showed remarkable variation in opinions regarding attitude, understandability and validation of CPath. Interview results showed that barriers focused on the quality of available evidence, while most facilitators concerned strengths of CPath. A lack of consensus was present for multiple factors, such as the determination of sufficient validation using CPath, the preferred function of CPath within the digital workflow and the timing of CPath introduction in pathology education. The diversity in opinions illustrates variety in influencing factors in CPath adoption. A next step would be to quantitatively determine important factors for adoption and initiate validation studies. Both should include clear case descriptions and be conducted among a more homogenous panel of pathologists based on sub specialization.
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Affiliation(s)
- Julie E M Swillens
- Scientific Center for Quality of Healthcare (IQ Healthcare), Radboud Institute for Health Sciences (RIHS), Radboud University Medical Centre, Nijmegen, The Netherlands.
| | - Iris D Nagtegaal
- Department of Pathology, Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Sam Engels
- Scientific Center for Quality of Healthcare (IQ Healthcare), Radboud Institute for Health Sciences (RIHS), Radboud University Medical Centre, Nijmegen, The Netherlands
| | | | - Rosella P M G Hermens
- Scientific Center for Quality of Healthcare (IQ Healthcare), Radboud Institute for Health Sciences (RIHS), Radboud University Medical Centre, Nijmegen, The Netherlands
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Asif A, Rajpoot K, Graham S, Snead D, Minhas F, Rajpoot N. Unleashing the potential of AI for pathology: challenges and recommendations. J Pathol 2023; 260:564-577. [PMID: 37550878 PMCID: PMC10952719 DOI: 10.1002/path.6168] [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: 05/16/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 08/09/2023]
Abstract
Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potential to revolutionize diagnostic pipelines as well as treatment planning and overall patient care. Numerous peer-reviewed studies reporting remarkable performance across diverse tasks serve as a testimony to the potential of AI in the field. However, widespread adoption of these methods in clinical and pre-clinical settings still remains a challenge. In this review article, we present a detailed analysis of the major obstacles encountered during the development of effective models and their deployment in practice. We aim to provide readers with an overview of the latest developments, assist them with insights into identifying some specific challenges that may require resolution, and suggest recommendations and potential future research directions. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Amina Asif
- Tissue Image Analytics Centre, Department of Computer ScienceUniversity of WarwickCoventryUK
| | - Kashif Rajpoot
- School of Computer ScienceUniversity of BirminghamBirminghamUK
| | - Simon Graham
- Histofy Ltd, Birmingham Business ParkBirminghamUK
| | - David Snead
- Histofy Ltd, Birmingham Business ParkBirminghamUK
- Department of PathologyUniversity Hospitals Coventry & Warwickshire NHS TrustCoventryUK
| | - Fayyaz Minhas
- Tissue Image Analytics Centre, Department of Computer ScienceUniversity of WarwickCoventryUK
- Cancer Research CentreUniversity of WarwickCoventryUK
| | - Nasir Rajpoot
- Tissue Image Analytics Centre, Department of Computer ScienceUniversity of WarwickCoventryUK
- Histofy Ltd, Birmingham Business ParkBirminghamUK
- Cancer Research CentreUniversity of WarwickCoventryUK
- The Alan Turing InstituteLondonUK
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10
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Ahmed AA, Brychcy A, Abouzid M, Witt M, Kaczmarek E. Perception of Pathologists in Poland of Artificial Intelligence and Machine Learning in Medical Diagnosis-A Cross-Sectional Study. J Pers Med 2023; 13:962. [PMID: 37373951 DOI: 10.3390/jpm13060962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/31/2023] [Accepted: 06/04/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND In the past vicennium, several artificial intelligence (AI) and machine learning (ML) models have been developed to assist in medical diagnosis, decision making, and design of treatment protocols. The number of active pathologists in Poland is low, prolonging tumor patients' diagnosis and treatment journey. Hence, applying AI and ML may aid in this process. Therefore, our study aims to investigate the knowledge of using AI and ML methods in the clinical field in pathologists in Poland. To our knowledge, no similar study has been conducted. METHODS We conducted a cross-sectional study targeting pathologists in Poland from June to July 2022. The questionnaire included self-reported information on AI or ML knowledge, experience, specialization, personal thoughts, and level of agreement with different aspects of AI and ML in medical diagnosis. Data were analyzed using IBM® SPSS® Statistics v.26, PQStat Software v.1.8.2.238, and RStudio Build 351. RESULTS Overall, 68 pathologists in Poland participated in our study. Their average age and years of experience were 38.92 ± 8.88 and 12.78 ± 9.48 years, respectively. Approximately 42% used AI or ML methods, which showed a significant difference in the knowledge gap between those who never used it (OR = 17.9, 95% CI = 3.57-89.79, p < 0.001). Additionally, users of AI had higher odds of reporting satisfaction with the speed of AI in the medical diagnosis process (OR = 4.66, 95% CI = 1.05-20.78, p = 0.043). Finally, significant differences (p = 0.003) were observed in determining the liability for legal issues used by AI and ML methods. CONCLUSION Most pathologists in this study did not use AI or ML models, highlighting the importance of increasing awareness and educational programs regarding applying AI and ML in medical diagnosis.
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Affiliation(s)
- Alhassan Ali Ahmed
- Department of Bioinformatics and Computational Biology, Poznan University of Medical Sciences, 61-806 Poznan, Poland
- Doctoral School, Poznan University of Medical Sciences, 61-806 Poznan, Poland
| | - Agnieszka Brychcy
- Department of Clinical Patomorphology, Heliodor Swiecicki Clinical Hospital of the Poznan University of Medical Sciences, 61-806 Poznan, Poland
| | - Mohamed Abouzid
- Doctoral School, Poznan University of Medical Sciences, 61-806 Poznan, Poland
- Department of Physical Pharmacy and Pharmacokinetics, Poznan University of Medical Sciences, 60-806 Poznan, Poland
| | - Martin Witt
- Department of Anatomy, Rostock University Medical Centre, 18057 Rostock, Germany
- Department of Anatomy, Technische Universität Dresden, 01307 Dresden, Germany
| | - Elżbieta Kaczmarek
- Department of Bioinformatics and Computational Biology, Poznan University of Medical Sciences, 61-806 Poznan, Poland
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11
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Flach RN, Stathonikos N, Nguyen TQ, Ter Hoeve ND, van Diest PJ, van Dooijeweert C. CONFIDENT-trial protocol: a pragmatic template for clinical implementation of artificial intelligence assistance in pathology. BMJ Open 2023; 13:e067437. [PMID: 37286323 DOI: 10.1136/bmjopen-2022-067437] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/09/2023] Open
Abstract
INTRODUCTION Artificial intelligence (AI) has been on the rise in the field of pathology. Despite promising results in retrospective studies, and several CE-IVD certified algorithms on the market, prospective clinical implementation studies of AI have yet to be performed, to the best of our knowledge. In this trial, we will explore the benefits of an AI-assisted pathology workflow, while maintaining diagnostic safety standards. METHODS AND ANALYSIS This is a Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence compliant single-centre, controlled clinical trial, in a fully digital academic pathology laboratory. We will prospectively include prostate cancer patients who undergo prostate needle biopsies (CONFIDENT-P) and breast cancer patients who undergo a sentinel node procedure (CONFIDENT-B) in the University Medical Centre Utrecht. For both the CONFIDENT-B and CONFIDENT-P trials, the specific pathology specimens will be pseudo-randomised to be assessed by a pathologist with or without AI assistance in a pragmatic (bi-)weekly sequential design. In the intervention group, pathologists will assess whole slide images (WSI) of the standard hematoxylin and eosin (H&E)-stained sections assisted by the output of the algorithm. In the control group, pathologists will assess H&E WSI according to the current clinical workflow. If no tumour cells are identified or when the pathologist is in doubt, immunohistochemistry (IHC) staining will be performed. At least 80 patients in the CONFIDENT-P and 180 patients in the CONFIDENT-B trial will need to be enrolled to detect superiority, allocated as 1:1. Primary endpoint for both trials is the number of saved resources of IHC staining procedures for detecting tumour cells, since this will clarify tangible cost savings that will support the business case for AI. ETHICS AND DISSEMINATION The ethics committee (MREC NedMec) waived the need of official ethical approval, since participants are not subjected to procedures nor are they required to follow rules. Results of both trials (CONFIDENT-B and CONFIDENT-P) will be published in scientific peer-reviewed journals.
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Affiliation(s)
- Rachel N Flach
- Department of Pathology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Nikolas Stathonikos
- Department of Pathology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Tri Q Nguyen
- Department of Pathology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Natalie D Ter Hoeve
- Department of Pathology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Paul J van Diest
- Department of Pathology, University Medical Centre Utrecht, Utrecht, The Netherlands
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12
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Irmakci I, Nateghi R, Zhou R, Ross AE, Yang XJ, Cooper LAD, Goldstein JA. Tissue contamination challenges the credibility of machine learning models in real world digital pathology. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.28.23289287. [PMID: 37205404 PMCID: PMC10187357 DOI: 10.1101/2023.04.28.23289287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Machine learning (ML) models are poised to transform surgical pathology practice. The most successful use attention mechanisms to examine whole slides, identify which areas of tissue are diagnostic, and use them to guide diagnosis. Tissue contaminants, such as floaters, represent unexpected tissue. While human pathologists are extensively trained to consider and detect tissue contaminants, we examined their impact on ML models. We trained 4 whole slide models. Three operate in placenta for 1) detection of decidual arteriopathy (DA), 2) estimation of gestational age (GA), and 3) classification of macroscopic placental lesions. We also developed a model to detect prostate cancer in needle biopsies. We designed experiments wherein patches of contaminant tissue are randomly sampled from known slides and digitally added to patient slides and measured model performance. We measured the proportion of attention given to contaminants and examined the impact of contaminants in T-distributed Stochastic Neighbor Embedding (tSNE) feature space. Every model showed performance degradation in response to one or more tissue contaminants. DA detection balanced accuracy decreased from 0.74 to 0.69 +/- 0.01 with addition of 1 patch of prostate tissue for every 100 patches of placenta (1% contaminant). Bladder, added at 10% contaminant raised the mean absolute error in estimating gestation age from 1.626 weeks to 2.371 +/ 0.003 weeks. Blood, incorporated into placental sections, induced false negative diagnoses of intervillous thrombi. Addition of bladder to prostate cancer needle biopsies induced false positives, a selection of high-attention patches, representing 0.033mm2, resulted in a 97% false positive rate when added to needle biopsies. Contaminant patches received attention at or above the rate of the average patch of patient tissue. Tissue contaminants induce errors in modern ML models. The high level of attention given to contaminants indicates a failure to encode biological phenomena. Practitioners should move to quantify and ameliorate this problem.
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Affiliation(s)
| | | | | | | | | | | | - Jeffery A. Goldstein
- To whom correspondence should be addressed: Olson 2-455, 710 N. Fairbanks Ave, Chicago IL, 60611,
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Berbís MA, McClintock DS, Bychkov A, Van der Laak J, Pantanowitz L, Lennerz JK, Cheng JY, Delahunt B, Egevad L, Eloy C, Farris AB, Fraggetta F, García del Moral R, Hartman DJ, Herrmann MD, Hollemans E, Iczkowski KA, Karsan A, Kriegsmann M, Salama ME, Sinard JH, Tuthill JM, Williams B, Casado-Sánchez C, Sánchez-Turrión V, Luna A, Aneiros-Fernández J, Shen J. Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade. EBioMedicine 2023; 88:104427. [PMID: 36603288 PMCID: PMC9823157 DOI: 10.1016/j.ebiom.2022.104427] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 12/06/2022] [Accepted: 12/13/2022] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the practice of pathology. However, clinical integration remains challenging, with no AI algorithms to date in routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience. METHODS Perspectives were solicited using the Delphi method from 24 subject matter experts between December 2020 and February 2021 regarding the anticipated role of AI in pathology by the year 2030. The study consisted of three consecutive rounds: 1) an open-ended, free response questionnaire generating a list of survey items; 2) a Likert-scale survey scored by experts and analysed for consensus; and 3) a repeat survey of items not reaching consensus to obtain further expert consensus. FINDINGS Consensus opinions were reached on 141 of 180 survey items (78.3%). Experts agreed that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030. High consensus was reached on 100 items across nine categories encompassing the impact of AI on (1) pathology key performance indicators (KPIs) and (2) the pathology workforce and specific tasks performed by (3) pathologists and (4) AP lab technicians, as well as (5) specific AI applications and their likelihood of routine use by 2030, (6) AI's role in integrated diagnostics, (7) pathology tasks likely to be fully automated using AI, and (8) regulatory/legal and (9) ethical aspects of AI integration in pathology. INTERPRETATION This systematic consensus study details the expected short-to-mid-term impact of AI on pathology practice. These findings provide timely and relevant information regarding future care delivery in pathology and raise key practical, ethical, and legal challenges that must be addressed prior to AI's successful clinical implementation. FUNDING No specific funding was provided for this study.
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Affiliation(s)
- M. Alvaro Berbís
- Department of R&D, HT Médica, San Juan de Dios Hospital, Córdoba, Spain,Faculty of Medicine, Autonomous University of Madrid, Madrid, Spain,Corresponding author. Department of R&D, HT Médica, San Juan de Dios Hospital, Córdoba, 14011, Spain.
| | - David S. McClintock
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Andrey Bychkov
- Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, Japan
| | - Jeroen Van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | | | - Jochen K. Lennerz
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Jerome Y. Cheng
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Brett Delahunt
- Wellington School of Medicine and Health Sciences, University of Otago, Wellington, New Zealand
| | - Lars Egevad
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Catarina Eloy
- Pathology Laboratory, Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal
| | - Alton B. Farris
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA
| | - Filippo Fraggetta
- Pathology Unit, Azienda Sanitaria Provinciale Catania, Gravina Hospital, Caltagirone, Italy
| | | | - Douglas J. Hartman
- Department of Anatomic Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Markus D. Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Eva Hollemans
- Department of Pathology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | | | - Aly Karsan
- Department of Pathology & Laboratory Medicine, University of British Columbia, Michael Smith Genome Sciences Centre, Vancouver, Canada
| | - Mark Kriegsmann
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | | | - John H. Sinard
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - J. Mark Tuthill
- Department of Pathology, Henry Ford Hospital, Detroit, MI, USA
| | - Bethany Williams
- Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - César Casado-Sánchez
- Department of Plastic and Reconstructive Surgery, La Paz University Hospital, Madrid, Spain
| | - Víctor Sánchez-Turrión
- Department of General Surgery and Digestive Tract, Puerta de Hierro-Majadahonda University Hospital, Madrid, Spain
| | - Antonio Luna
- Department of Integrated Diagnostics, HT Médica, Clínica Las Nieves, Jaén, Spain
| | - José Aneiros-Fernández
- Department of R&D, HT Médica, San Juan de Dios Hospital, Córdoba, Spain,Pathology Unit, Azienda Sanitaria Provinciale Catania, Gravina Hospital, Caltagirone, Italy
| | - Jeanne Shen
- Department of Pathology and Center for Artificial Intelligence in Medicine & Imaging, Stanford University School of Medicine, Stanford, CA, USA.
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Zhao J, Han Z, Ma Y, Liu H, Yang T. Research progress in digital pathology: A bibliometric and visual analysis based on Web of Science. Pathol Res Pract 2022; 240:154171. [DOI: 10.1016/j.prp.2022.154171] [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: 07/11/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 11/05/2022]
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15
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López-Úbeda P, Martín-Noguerol T, Aneiros-Fernández J, Luna A. Natural Language Processing in Pathology: Current Trends and Future Insights. THE AMERICAN JOURNAL OF PATHOLOGY 2022; 192:1486-1495. [PMID: 35985480 DOI: 10.1016/j.ajpath.2022.07.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/21/2022] [Accepted: 07/29/2022] [Indexed: 06/15/2023]
Abstract
Natural language processing (NLP) plays a key role in advancing health care, being key to extracting structured information from electronic health reports. In the last decade, several advances in the field of pathology have been derived from the application of NLP to pathology reports. Herein, a comprehensive review of the most used NLP methods for extracting, coding, and organizing information from pathology reports is presented, including how the development of tools is used to improve workflow. In addition, this article discusses, from a practical point of view, the steps necessary to extract data and encode natural language information for its analytical processing, ranging from preprocessing of text to its inclusion in complex algorithms. Finally, the potential of NLP-based automatic solutions for improving workflow in pathology and their further applications in the near future is highlighted.
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Affiliation(s)
| | | | | | - Antonio Luna
- MRI Unit, Radiology Department, HT Medica, Jaén, Spain
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16
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Bülow RD, Hölscher DL, Boor P. Automatische Bildanalyse und künstliche Intelligenz in der Nephropathologie. DIE NEPHROLOGIE 2022. [PMCID: PMC9360682 DOI: 10.1007/s11560-022-00598-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Hintergrund Fragestellung Material und Methoden Ergebnisse Diskussion
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Affiliation(s)
- Roman D. Bülow
- Institut für Pathologie, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Deutschland
| | - David L. Hölscher
- Institut für Pathologie, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Deutschland
| | - Peter Boor
- Institut für Pathologie, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Deutschland
- Medizinische Klinik II, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Deutschland
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17
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Elfer K, Dudgeon S, Garcia V, Blenman K, Hytopoulos E, Wen S, Li X, Ly A, Werness B, Sheth MS, Amgad M, Gupta R, Saltz J, Hanna MG, Ehinger A, Peeters D, Salgado R, Gallas BD. Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms. J Med Imaging (Bellingham) 2022; 9:047501. [PMID: 35911208 PMCID: PMC9326105 DOI: 10.1117/1.jmi.9.4.047501] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 06/28/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose: Validation of artificial intelligence (AI) algorithms in digital pathology with a reference standard is necessary before widespread clinical use, but few examples focus on creating a reference standard based on pathologist annotations. This work assesses the results of a pilot study that collects density estimates of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer biopsy specimens. This work will inform the creation of a validation dataset for the evaluation of AI algorithms fit for a regulatory purpose. Approach: Collaborators and crowdsourced pathologists contributed glass slides, digital images, and annotations. Here, "annotations" refer to any marks, segmentations, measurements, or labels a pathologist adds to a report, image, region of interest (ROI), or biological feature. Pathologists estimated sTILs density in 640 ROIs from hematoxylin and eosin stained slides of 64 patients via two modalities: an optical light microscope and two digital image viewing platforms. Results: The pilot study generated 7373 sTILs density estimates from 29 pathologists. Analysis of annotations found the variability of density estimates per ROI increases with the mean; the root mean square differences were 4.46, 14.25, and 26.25 as the mean density ranged from 0% to 10%, 11% to 40%, and 41% to 100%, respectively. The pilot study informs three areas of improvement for future work: technical workflows, annotation platforms, and agreement analysis methods. Upgrades to the workflows and platforms will improve operability and increase annotation speed and consistency. Conclusions: Exploratory data analysis demonstrates the need to develop new statistical approaches for agreement. The pilot study dataset and analysis methods are publicly available to allow community feedback. The development and results of the validation dataset will be publicly available to serve as an instructive tool that can be replicated by developers and researchers.
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Affiliation(s)
- Katherine Elfer
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics & Software Reliability, Silver Spring, Maryland, United States
- National Institutes of Health, National Cancer Institute, Division of Cancer Prevention, Cancer Prevention Fellowship Program, Bethesda, Maryland, United States
| | - Sarah Dudgeon
- Yale University Computational Biology and Bioinformatics, New Haven, Connecticut, United States
- Yale New Haven Hospital, Center for Outcomes Research and Evaluation, New Haven, Connecticut, United States
| | - Victor Garcia
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics & Software Reliability, Silver Spring, Maryland, United States
| | - Kim Blenman
- School of Medicine, Yale Cancer Center, Department of Internal Medicine, Section of Medical Oncology, New Haven, Connecticut, United States
- Yale University, School of Engineering and Applied Science, Department of Computer Science, New Haven, Connecticut, United States
| | | | - Si Wen
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics & Software Reliability, Silver Spring, Maryland, United States
| | - Xiaoxian Li
- Emory University School of Medicine, Department of Pathology and Laboratory Medicine, Atlanta, Georgia, United States
| | - Amy Ly
- Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Bruce Werness
- Inova Health System Department of Pathology, Falls Church, Virginia, United States
- Arrive Bio LLC, San Francisco, California, United States
| | - Manasi S. Sheth
- United States Food and Drug Administration (FDA), Center for Devices and Radiologic Health, Office of Product Evaluation and Quality, Office of Clinical Evidence and Analysis, Division of Biostatistics, White Oak, Maryland, United States
| | - Mohamed Amgad
- Northwestern University Feinberg School of Medicine, Department of Pathology, Chicago, Illinois, United States
| | - Rajarsi Gupta
- SUNY Stony Brook Medicine, Department of Biomedical Informatics, Stony Brook, New York, United States
| | - Joel Saltz
- SUNY Stony Brook Medicine, Department of Biomedical Informatics, Stony Brook, New York, United States
- SUNY Stony Brook Medicine, Department of Pathology, Stony Brook, New York, United States
| | - Matthew G. Hanna
- Memorial Sloan Kettering Cancer Center, New York, New York, United States
| | - Anna Ehinger
- Lund University, Laboratory Medicine, Region Skåne, Department of Genetics and Pathology, Lund, Sweden
| | - Dieter Peeters
- Sint-Maarten Hospital, Department of Pathology, Mechelen, Belgium
- University of Antwerp, Department of Biomedical Sciences, Antwerp, Belgium
| | - Roberto Salgado
- Peter Mac Callum Cancer Centre, Division of Research, Melbourne, Australia
- GZA-ZNA Hospitals, Department of Pathology, Antwerp, Belgium
| | - Brandon D. Gallas
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics & Software Reliability, Silver Spring, Maryland, United States
- Address all correspondence to Brandon D. Gallas,
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General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology. Diagnostics (Basel) 2022; 12:diagnostics12051272. [PMID: 35626427 PMCID: PMC9141041 DOI: 10.3390/diagnostics12051272] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/16/2022] [Accepted: 05/18/2022] [Indexed: 11/17/2022] Open
Abstract
Integrating Artificial Intelligence (AI) tools in the tissue diagnostic workflow will benefit the pathologist and, ultimately, the patient. The generation of such AI tools has two parallel yet interconnected processes, namely the definition of the pathologist’s task to be delivered in silico, and the software development requirements. In this review paper, we demystify this process, from a viewpoint that joins experienced pathologists and data scientists, by proposing a general pathway and describing the core steps to build an AI digital pathology tool. In doing so, we highlight the importance of the collaboration between AI scientists and pathologists, from the initial formulation of the hypothesis to the final, ready-to-use product.
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Abstract
Artificial intelligence (AI) powered by the accumulating clinical and molecular data about cancer has fueled the expectation that a transformation in cancer treatments towards significant improvement of patient outcomes is at hand. However, such transformation has been so far elusive. The opacity of AI algorithms and the lack of quality annotated data being available at population scale are among the challenges to the application of AI in oncology. Fundamentally however, the heterogeneity of cancer and its evolutionary dynamics make every tumor response to therapy sufficiently different from the population, machine-learned statistical models, challenging hence the capacity of these models to yield reliable inferences about treatment recommendations that can improve patient outcomes. This article reviews the nominal elements of clinical decision-making for precision oncology and frames the utility of AI to cancer treatment improvements in light of cancer unique challenges.
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Affiliation(s)
- Youcef Derbal
- Ted Rogers School of Information Technology Management, 7984Ryerson University, Toronto, ON, Canada
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20
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OUP accepted manuscript. Bioinformatics 2022; 38:3312-3314. [DOI: 10.1093/bioinformatics/btac315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 04/21/2022] [Accepted: 05/03/2022] [Indexed: 11/12/2022] Open
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21
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Prabhudesai S, Wang NC, Ahluwalia V, Huan X, Bapuraj JR, Banovic N, Rao A. Stratification by Tumor Grade Groups in a Holistic Evaluation of Machine Learning for Brain Tumor Segmentation. Front Neurosci 2021; 15:740353. [PMID: 34690680 PMCID: PMC8526730 DOI: 10.3389/fnins.2021.740353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/01/2021] [Indexed: 11/13/2022] Open
Abstract
Accurate and consistent segmentation plays an important role in the diagnosis, treatment planning, and monitoring of both High Grade Glioma (HGG), including Glioblastoma Multiforme (GBM), and Low Grade Glioma (LGG). Accuracy of segmentation can be affected by the imaging presentation of glioma, which greatly varies between the two tumor grade groups. In recent years, researchers have used Machine Learning (ML) to segment tumor rapidly and consistently, as compared to manual segmentation. However, existing ML validation relies heavily on computing summary statistics and rarely tests the generalizability of an algorithm on clinically heterogeneous data. In this work, our goal is to investigate how to holistically evaluate the performance of ML algorithms on a brain tumor segmentation task. We address the need for rigorous evaluation of ML algorithms and present four axes of model evaluation-diagnostic performance, model confidence, robustness, and data quality. We perform a comprehensive evaluation of a glioma segmentation ML algorithm by stratifying data by specific tumor grade groups (GBM and LGG) and evaluate these algorithms on each of the four axes. The main takeaways of our work are-(1) ML algorithms need to be evaluated on out-of-distribution data to assess generalizability, reflective of tumor heterogeneity. (2) Segmentation metrics alone are limited to evaluate the errors made by ML algorithms and their describe their consequences. (3) Adoption of tools in other domains such as robustness (adversarial attacks) and model uncertainty (prediction intervals) lead to a more comprehensive performance evaluation. Such a holistic evaluation framework could shed light on an algorithm's clinical utility and help it evolve into a more clinically valuable tool.
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Affiliation(s)
- Snehal Prabhudesai
- Computer Science and Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Nicholas Chandler Wang
- Computational Medicine and Bioinformatics, Michigan Medicine, Ann Arbor, MI, United States
| | - Vinayak Ahluwalia
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
| | - Xun Huan
- Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States
| | | | - Nikola Banovic
- Computer Science and Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Arvind Rao
- Computational Medicine and Bioinformatics, Michigan Medicine, Ann Arbor, MI, United States
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
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