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Grignaffini F, Barbuto F, Troiano M, Piazzo L, Simeoni P, Mangini F, De Stefanis C, Onetti Muda A, Frezza F, Alisi A. The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review. Diagnostics (Basel) 2024; 14:388. [PMID: 38396427 PMCID: PMC10887838 DOI: 10.3390/diagnostics14040388] [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: 12/27/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that a workflow that combines DP and artificial intelligence (AI) applied to histopathology has potential value in supporting the diagnosis, treatment evaluation, and prognosis prediction of liver diseases. Here, we provide a systematic review of the use of this workflow in the field of hepatology. Based on the PRISMA 2020 criteria, a search of the PubMed, SCOPUS, and Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted the specifications and objectives of each study, the AI tools used, and the results obtained. From the 266 initial records identified, 25 eligible studies were selected, mainly conducted on human liver tissues. Most of the studies were performed using whole-slide imaging systems for imaging acquisition and applying different machine learning and deep learning methods for image pre-processing, segmentation, feature extractions, and classification. Of note, most of the studies selected demonstrated good performance as classifiers of liver histological images compared to pathologist annotations. Promising results to date bode well for the not-too-distant inclusion of these techniques in clinical practice.
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Affiliation(s)
- Flavia Grignaffini
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Francesco Barbuto
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Maurizio Troiano
- Research Unit of Genetics of Complex Phenotypes, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (M.T.); (C.D.S.)
| | - Lorenzo Piazzo
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Patrizio Simeoni
- National Transport Authority (NTA), D02 WT20 Dublin, Ireland;
- Faculty of Lifelong Learning, South East Technological University (SETU), R93 V960 Carlow, Ireland
| | - Fabio Mangini
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Cristiano De Stefanis
- Research Unit of Genetics of Complex Phenotypes, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (M.T.); (C.D.S.)
| | | | - Fabrizio Frezza
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Anna Alisi
- Research Unit of Genetics of Complex Phenotypes, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (M.T.); (C.D.S.)
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Brouwer NPM, Khan A, Bokhorst JM, Ayatollahi F, Hay J, Ciompi F, Simmer F, Hugen N, de Wilt JHW, Berger MD, Lugli A, Zlobec I, Edwards J, Nagtegaal ID. The Complexity of Shapes: How the Circularity of Tumor Nodules Affects Prognosis in Colorectal Cancer. Mod Pathol 2024; 37:100376. [PMID: 37926423 DOI: 10.1016/j.modpat.2023.100376] [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: 08/04/2023] [Revised: 10/04/2023] [Accepted: 10/30/2023] [Indexed: 11/07/2023]
Abstract
The current stratification of tumor nodules in colorectal cancer (CRC) staging is subjective and leads to high interobserver variability. In this study, the objective assessment of the shape of lymph node metastases (LNMs), extranodal extension (ENE), and tumor deposits (TDs) was correlated with outcomes. A test cohort and a validation cohort were included from 2 different institutions. The test cohort consisted of 190 cases of stage III CRC. Slides with LNMs and TDs were annotated and processed using a segmentation algorithm to determine their shape. The complexity ratio was calculated for every shape and correlated with outcomes. A cohort of 160 stage III CRC cases was used to validate findings. TDs showed significantly more complex shapes than LNMs with ENE, which were more complex than LNMs without ENE (P < .001). In the test cohort, patients with the highest sum of complexity ratios had significantly lower disease-free survival (P < .01). When only the nodule with the highest complexity was considered, this effect was even stronger (P < .001). This maximum complexity ratio per patient was identified as an independent prognostic factor in the multivariate analysis (hazard ratio, 2.47; P < .05). The trends in the validation cohort confirmed the results. More complex nodules in stage III CRC were correlated with significantly worse disease-free survival, even if only based on the most complex nodule. These results suggest that more complex nodules reflect more invasive tumor biology. As most of the more complex nodules were diagnosed as TDs, we suggest providing a more prominent role for TDs in the nodal stage and include an objective complexity measure in their definition.
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Affiliation(s)
- Nelleke P M Brouwer
- Department of pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Amjad Khan
- Institute of Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - John-Melle Bokhorst
- Department of pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Fazael Ayatollahi
- Department of pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jennifer Hay
- Glasgow Tissue Research Facility, University of Glasgow, Queen Elizabeth University Hospital, Glasgow, United Kingdom
| | - Francesco Ciompi
- Department of pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Femke Simmer
- Department of pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Niek Hugen
- Department of Surgery, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Johannes H W de Wilt
- Department of Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Martin D Berger
- Department of Medical Oncology, Inselspital, University Hospital of Bern, University of Bern, Bern, Switzerland
| | - Alessandro Lugli
- Institute of Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Inti Zlobec
- Institute of Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Joanne Edwards
- School of Cancer Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Iris D Nagtegaal
- Department of pathology, Radboud University Medical Center, Nijmegen, The Netherlands
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Huang YL, Liu XQ, Huang Y, Jin FY, Zhao Q, Wu QY, Ma KL. Application of cloud server-based machine learning for assisting pathological structure recognition in IgA nephropathy. J Clin Pathol 2023:jcp-2023-209215. [PMID: 38123970 DOI: 10.1136/jcp-2023-209215] [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: 09/24/2023] [Accepted: 11/26/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Machine learning (ML) models can help assisting diagnosis by rapidly localising and classifying regions of interest (ROIs) within whole slide images (WSIs). Effective ML models for clinical decision support require a substantial dataset of 'real' data, and in reality, it should be robust, user-friendly and universally applicable. METHODS WSIs of primary IgAN were collected and annotated. The H-AI-L algorithm which could facilitate direct WSI viewing and potential ROI detection for clinicians was built on the cloud server of matpool, a shared internet-based service platform. Model performance was evaluated using F1-score, precision, recall and Matthew's correlation coefficient (MCC). RESULTS The F1-score of glomerular localisation in WSIs was 0.85 and 0.89 for the initial and pretrained models, respectively, with corresponding recall values of 0.79 and 0.83, and precision scores of 0.92 and 0.97. Dichotomous differentiation between global sclerotic (GS) and other glomeruli revealed F1-scores of 0.70 and 0.91, and MCC values of 0.55 and 0.87, for the initial and pretrained models, respectively. The overall F1-score of multiclassification was 0.81 for the pretrained models. The total glomerular recall rate was 0.96, with F1-scores of 0.68, 0.56 and 0.26 for GS, segmental glomerulosclerosis and crescent (C), respectively. Interstitial fibrosis/tubular atrophy lesion similarity between the true label and model predictions was 0.75. CONCLUSIONS Our results underscore the efficacy of the ML integration algorithm in segmenting ROIs in IgAN WSIs, and the internet-based model deployment is in favour of widespread adoption and utilisation across multiple centres and increased volumes of WSIs.
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Affiliation(s)
- Yu-Lin Huang
- Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Xiao Qi Liu
- Department of Nephrology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yang Huang
- Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Feng Yong Jin
- Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Qing Zhao
- Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Qin Yi Wu
- Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Kun Ling Ma
- Department of Nephrology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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