1
|
Wang YL, Gao S, Xiao Q, Li C, Grzegorzek M, Zhang YY, Li XH, Kang Y, Liu FH, Huang DH, Gong TT, Wu QJ. Role of artificial intelligence in digital pathology for gynecological cancers. Comput Struct Biotechnol J 2024; 24:205-212. [PMID: 38510535 PMCID: PMC10951449 DOI: 10.1016/j.csbj.2024.03.007] [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: 12/28/2023] [Revised: 03/08/2024] [Accepted: 03/09/2024] [Indexed: 03/22/2024] Open
Abstract
The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract quantitative information from digital histopathology images as a rapid growth in oncology data. Gynecological cancers are major diseases affecting women's health worldwide. They are characterized by high mortality and poor prognosis, underscoring the critical importance of early detection, treatment, and identification of prognostic factors. This review highlights the various clinical applications of AI in gynecological cancers using digitized histopathology slides. Particularly, deep learning models have shown promise in accurately diagnosing, classifying histopathological subtypes, and predicting treatment response and prognosis. Furthermore, the integration with transcriptomics, proteomics, and other multi-omics techniques can provide valuable insights into the molecular features of diseases. Despite the considerable potential of AI, substantial challenges remain. Further improvements in data acquisition and model optimization are required, and the exploration of broader clinical applications, such as the biomarker discovery, need to be explored.
Collapse
Affiliation(s)
- Ya-Li Wang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Information Center, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Song Gao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qian Xiao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Chen Li
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Marcin Grzegorzek
- Institute for Medical Informatics, University of Luebeck, Luebeck, Germany
| | - Ying-Ying Zhang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiao-Han Li
- Department of Pathology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ye Kang
- Department of Pathology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Fang-Hua Liu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Dong-Hui Huang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ting-Ting Gong
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qi-Jun Wu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
- NHC Key Laboratory of Advanced Reproductive Medicine and Fertility (China Medical University), National Health Commission, Shenyang, China
| |
Collapse
|
2
|
Wang Z, Peng H, Wan J, Song A. Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm. Med Mol Morphol 2024:10.1007/s00795-024-00399-8. [PMID: 39088070 DOI: 10.1007/s00795-024-00399-8] [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: 03/11/2024] [Accepted: 07/15/2024] [Indexed: 08/02/2024]
Abstract
The aim of this study is to establish a deep learning (DL) model to predict the pathological type of gastric adenocarcinoma cancer based on whole-slide images(WSIs). We downloaded 356 histopathological images of gastric adenocarcinoma (STAD) patients from The Cancer Genome Atlas database and randomly divided them into the training set, validation set and test set (8:1:1). Additionally, 80 H&E-stained WSIs of STAD were collected for external validation. The CLAM tool was used to cut the WSIs and further construct the model by DL algorithm, achieving an accuracy of over 90% in identifying and predicting histopathological subtypes. External validation results demonstrated the model had a certain generalization ability. Moreover, DL features were extracted from the model to further investigate the differences in immune infiltration and patient prognosis between the two subtypes. The DL model can accurately predict the pathological classification of STAD patients, and provide certain reference value for clinical diagnosis. The nomogram combining DL-signature, gene-signature and clinical features can be used as a prognostic classifier for clinical decision-making and treatment.
Collapse
Affiliation(s)
- Zhihui Wang
- Department of Ultrasound Imaging, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430101, Hubei, China
| | - Hui Peng
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430101, Hubei, China
| | - Jie Wan
- Department of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430101, Hubei, China
| | - Anping Song
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430101, Hubei, China.
- Department of Oncology, Tongji Hospital Sino-French New City Branch, Caidian District, No.288 Xintian Avenue, Wuhan, 430101, Hubei, China.
| |
Collapse
|
3
|
Bergstrom EN, Abbasi A, Díaz-Gay M, Galland L, Ladoire S, Lippman SM, Alexandrov LB. Deep Learning Artificial Intelligence Predicts Homologous Recombination Deficiency and Platinum Response From Histologic Slides. J Clin Oncol 2024:JCO2302641. [PMID: 39083703 DOI: 10.1200/jco.23.02641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 04/23/2024] [Accepted: 05/28/2024] [Indexed: 08/02/2024] Open
Abstract
PURPOSE Cancers with homologous recombination deficiency (HRD) can benefit from platinum salts and poly(ADP-ribose) polymerase inhibitors. Standard diagnostic tests for detecting HRD require molecular profiling, which is not universally available. METHODS We trained DeepHRD, a deep learning platform for predicting HRD from hematoxylin and eosin (H&E)-stained histopathological slides, using primary breast (n = 1,008) and ovarian (n = 459) cancers from The Cancer Genome Atlas (TCGA). DeepHRD was compared with four standard HRD molecular tests using breast (n = 349) and ovarian (n = 141) cancers from multiple independent data sets, including platinum-treated clinical cohorts with RECIST progression-free survival (PFS), complete response (CR), and overall survival (OS) endpoints. RESULTS DeepHRD predicted HRD from held-out H&E-stained breast cancer slides in TCGA with an AUC of 0.81 (95% CI, 0.77 to 0.85). This performance was confirmed in two independent primary breast cancer cohorts (AUC, 0.76 [95% CI, 0.71 to 0.82]). In an external platinum-treated metastatic breast cancer cohort, samples predicted as HRD had higher complete CR (AUC, 0.76 [95% CI, 0.54 to 0.93]) with 3.7-fold increase in median PFS (14.4 v 3.9 months; P = .0019) and hazard ratio (HR) of 0.45 (P = .0047). There were no significant differences in nonplatinum treatment outcome by predicted HRD status in three breast cancer cohorts, including CR (AUC, 0.39) and PFS (HR, 0.98, P = .95) in taxane-treated metastatic breast cancer. Through transfer learning to high-grade serous ovarian cancer, DeepHRD-predicted HRD samples had better OS after first-line (HR, 0.46; P = .030) and neoadjuvant (HR, 0.49; P = .015) platinum therapy in two cohorts. CONCLUSION DeepHRD can predict HRD in breast and ovarian cancers directly from routine H&E slides across multiple external cohorts, slide scanners, and tissue fixation variables. When compared with molecular testing, DeepHRD classified 1.8- to 3.1-fold more patients with HRD, which exhibited better OS in high-grade serous ovarian cancer and platinum-specific PFS in metastatic breast cancer.
Collapse
Affiliation(s)
- Erik N Bergstrom
- Moores Cancer Center, UC San Diego, La Jolla, CA
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA
- Department of Bioengineering, UC San Diego, La Jolla, CA
| | - Ammal Abbasi
- Moores Cancer Center, UC San Diego, La Jolla, CA
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA
- Department of Bioengineering, UC San Diego, La Jolla, CA
| | - Marcos Díaz-Gay
- Moores Cancer Center, UC San Diego, La Jolla, CA
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA
- Department of Bioengineering, UC San Diego, La Jolla, CA
| | - Loïck Galland
- Department of Medical Oncology, Centre Georges-François Leclerc, Dijon, France
- Platform of Transfer in Biological Oncology, Centre Georges-François Leclerc, Dijon, France
- University of Burgundy-Franche Comté, France
- Centre de Recherche INSERM LNC-UMR1231, Dijon, France
| | - Sylvain Ladoire
- Department of Medical Oncology, Centre Georges-François Leclerc, Dijon, France
- Platform of Transfer in Biological Oncology, Centre Georges-François Leclerc, Dijon, France
- University of Burgundy-Franche Comté, France
- Centre de Recherche INSERM LNC-UMR1231, Dijon, France
| | | | - Ludmil B Alexandrov
- Moores Cancer Center, UC San Diego, La Jolla, CA
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA
- Department of Bioengineering, UC San Diego, La Jolla, CA
- Sanford Stem Cell Institute, University of California San Diego, La Jolla, CA
| |
Collapse
|
4
|
Prassas I, Clarke B, Youssef T, Phlamon J, Dimitrakopoulos L, Rofaeil A, Yousef GM. Computational pathology: an evolving concept. Clin Chem Lab Med 2024; 0:cclm-2023-1124. [PMID: 38646706 DOI: 10.1515/cclm-2023-1124] [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: 10/24/2023] [Accepted: 04/10/2024] [Indexed: 04/23/2024]
Abstract
The initial enthusiasm about computational pathology (CP) and artificial intelligence (AI) was that they will replace pathologists entirely on the way to fully automated diagnostics. It is becoming clear that currently this is not the immediate model to pursue. On top of the legal and regulatory complexities surrounding its implementation, the majority of tested machine learning (ML)-based predictive algorithms do not display the exquisite performance needed to render them unequivocal, standalone decision makers for matters with direct implications to human health. We are thus moving into a different model of "computer-assisted diagnostics", where AI is there to provide support, rather than replacing, the pathologist. Herein we focus on the practical aspects of CP, from a pathologist perspective. There is a wide range of potential applications where CP can enhance precision of pathology diagnosis, tailor prognostic and predictive information, as well as save time. There are, however, a number of potential limitations for CP that currently hinder their wider adoption in the clinical setting. We address the key necessary steps towards clinical implementation of computational pathology, discuss the significant obstacles that hinders its adoption in the clinical context and summarize some proposed solutions. We conclude that the advancement of CP in the clinic is a promising resource-intensive endeavour that requires broad and inclusive collaborations between academia, industry, and regulatory bodies.
Collapse
Affiliation(s)
- Ioannis Prassas
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Blaise Clarke
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Timothy Youssef
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
| | - Juliana Phlamon
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
| | | | - Andrew Rofaeil
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
| | - George M Yousef
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
5
|
Wang Y, Lin W, Zhuang X, Wang X, He Y, Li L, Lyu G. Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review). Oncol Rep 2024; 51:46. [PMID: 38240090 PMCID: PMC10828921 DOI: 10.3892/or.2024.8705] [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: 07/18/2023] [Accepted: 01/05/2024] [Indexed: 01/23/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a crucial technique for extracting high‑throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has been widely used in the field of diagnosis, for the differentiation of benign and malignant ovarian cancer (OC), and for prognostic assessment, with favorable results. Notably, AI‑based radiomics has proven to be a non‑invasive, convenient and economical approach, making it an essential asset in a gynecological setting. The present study reviews the application of AI in the diagnosis, differentiation and prognostic assessment of OC. It is suggested that AI‑based multi‑omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.
Collapse
Affiliation(s)
- Yanli Wang
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Weihong Lin
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Xiaoling Zhuang
- Department of Pathology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Xiali Wang
- Department of Clinical Medicine, Quanzhou Medical College, Quanzhou, Fujian 362000, P.R. China
| | - Yifang He
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Luhong Li
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Guorong Lyu
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
- Department of Clinical Medicine, Quanzhou Medical College, Quanzhou, Fujian 362000, P.R. China
| |
Collapse
|
6
|
Ng CW, Wong KK. Deep learning-enabled breast cancer endocrine response determination from H&E staining based on ESR1 signaling activity. Sci Rep 2023; 13:21454. [PMID: 38052873 PMCID: PMC10698147 DOI: 10.1038/s41598-023-48830-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: 06/01/2023] [Accepted: 11/30/2023] [Indexed: 12/07/2023] Open
Abstract
Estrogen receptor (ER) positivity by immunohistochemistry has long been a main selection criterium for breast cancer patients to be treated with endocrine therapy. However, ER positivity might not directly correlate with activated ER signaling activity, which is a better predictor for endocrine therapy responsiveness. In this study, we investigated if a deep learning method using whole-slide H&E-stained images could predict ER signaling activity. First, ER signaling activity score was determined using RNAseq data available from each of the 1082 breast cancer samples in the TCGA Pan-Cancer dataset based on the Hallmark Estrogen Response Early gene set from the Molecular Signature Database (MSigDB). Then the processed H&E-stained images and ER signaling activity scores from a training cohort were fed into ResNet101 with three additional fully connected layers to generate a predicted ER activity score. The trained models were subsequently applied to an independent testing cohort. The result demonstrated that ER + /HER2- breast cancer patients with a higher predicted ER activity score had longer progression-free survival (p = 0.0368) than those with lower predicted ER activity score. In conclusion, a convolutional deep neural network can predict prognosis and endocrine therapy response in breast cancer patients based on whole-slide H&E-stained images. The trained models were found to robustly predict the prognosis of ER + /HER2- patients. This information is valuable for patient management, as it does not require RNA-seq or microarray data analyses. Thus, these models can reduce the cost of the diagnosis workflow if such information is required.
Collapse
Affiliation(s)
- Chun Wai Ng
- Department of Gynecologic Oncology and Reproductive Medicine, Unit 1362, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Kwong-Kwok Wong
- Department of Gynecologic Oncology and Reproductive Medicine, Unit 1362, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
| |
Collapse
|
7
|
Bourgade R, Rabilloud N, Perennec T, Pécot T, Garrec C, Guédon AF, Delnatte C, Bézieau S, Lespagnol A, de Tayrac M, Henno S, Sagan C, Toquet C, Mosnier JF, Kammerer-Jacquet SF, Loussouarn D. Deep Learning for Detecting BRCA Mutations in High-Grade Ovarian Cancer Based on an Innovative Tumor Segmentation Method From Whole Slide Images. Mod Pathol 2023; 36:100304. [PMID: 37580018 DOI: 10.1016/j.modpat.2023.100304] [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: 03/31/2023] [Revised: 07/15/2023] [Accepted: 08/08/2023] [Indexed: 08/16/2023]
Abstract
BRCA1 and BRCA2 genes play a crucial role in repairing DNA double-strand breaks through homologous recombination. Their mutations represent a significant proportion of homologous recombination deficiency and are a reliable effective predictor of sensitivity of high-grade ovarian cancer (HGOC) to poly(ADP-ribose) polymerase inhibitors. However, their testing by next-generation sequencing is costly and time-consuming and can be affected by various preanalytical factors. In this study, we present a deep learning classifier for BRCA mutational status prediction from hematoxylin-eosin-safran-stained whole slide images (WSI) of HGOC. We constituted the OvarIA cohort composed of 867 patients with HGOC with known BRCA somatic mutational status from 2 different pathology departments. We first developed a tumor segmentation model according to dynamic sampling and then trained a visual representation encoder with momentum contrastive learning on the predicted tumor tiles. We finally trained a BRCA classifier on more than a million tumor tiles in multiple instance learning with an attention-based mechanism. The tumor segmentation model trained on 8 WSI obtained a dice score of 0.915 and an intersection-over-union score of 0.847 on a test set of 50 WSI, while the BRCA classifier achieved the state-of-the-art area under the receiver operating characteristic curve of 0.739 in 5-fold cross-validation and 0.681 on the testing set. An additional multiscale approach indicates that the relevant information for predicting BRCA mutations is located more in the tumor context than in the cell morphology. Our results suggest that BRCA somatic mutations have a discernible phenotypic effect that could be detected by deep learning and could be used as a prescreening tool in the future.
Collapse
Affiliation(s)
- Raphaël Bourgade
- Department of Pathology, University Hospital of Nantes, Nantes, France.
| | - Noémie Rabilloud
- Laboratoire du Traitement du Signal et de l'Image - Inserm U1099, University of Rennes, Rennes, France
| | - Tanguy Perennec
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest Nantes, Saint-Herblain, France
| | - Thierry Pécot
- Facility for Artificial Intelligence and Image Analysis (FAIIA), Biosit UAR 3480 CNRS-US18 INSERM, University of Rennes, Rennes, France
| | - Céline Garrec
- Department of Medical Genetics, University Hospital of Nantes, Nantes, France
| | - Alexis F Guédon
- National Institute of Health and Medical Research, Pierre Louis Institute of Epidemiology and Public Health, Sorbonne University, Paris, France
| | - Capucine Delnatte
- Department of Medical Genetics, University Hospital of Nantes, Nantes, France
| | - Stéphane Bézieau
- Department of Medical Genetics, University Hospital of Nantes, Nantes, France
| | - Alexandra Lespagnol
- Department of Molecular Genetics and Genomics, University Hospital of Rennes, Rennes, France
| | - Marie de Tayrac
- Department of Molecular Genetics and Genomics, University Hospital of Rennes, Rennes, France
| | - Sébastien Henno
- Department of Pathology, University Hospital of Rennes, Rennes, France
| | - Christine Sagan
- Department of Pathology, University Hospital of Nantes, Nantes, France
| | - Claire Toquet
- Department of Pathology, University Hospital of Nantes, Nantes, France
| | | | - Solène-Florence Kammerer-Jacquet
- Laboratoire du Traitement du Signal et de l'Image - Inserm U1099, University of Rennes, Rennes, France; Department of Pathology, University Hospital of Rennes, Rennes, France
| | | |
Collapse
|
8
|
Breen J, Allen K, Zucker K, Adusumilli P, Scarsbrook A, Hall G, Orsi NM, Ravikumar N. Artificial intelligence in ovarian cancer histopathology: a systematic review. NPJ Precis Oncol 2023; 7:83. [PMID: 37653025 PMCID: PMC10471607 DOI: 10.1038/s41698-023-00432-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 08/01/2023] [Indexed: 09/02/2023] Open
Abstract
This study evaluates the quality of published research using artificial intelligence (AI) for ovarian cancer diagnosis or prognosis using histopathology data. A systematic search of PubMed, Scopus, Web of Science, Cochrane CENTRAL, and WHO-ICTRP was conducted up to May 19, 2023. Inclusion criteria required that AI was used for prognostic or diagnostic inferences in human ovarian cancer histopathology images. Risk of bias was assessed using PROBAST. Information about each model was tabulated and summary statistics were reported. The study was registered on PROSPERO (CRD42022334730) and PRISMA 2020 reporting guidelines were followed. Searches identified 1573 records, of which 45 were eligible for inclusion. These studies contained 80 models of interest, including 37 diagnostic models, 22 prognostic models, and 21 other diagnostically relevant models. Common tasks included treatment response prediction (11/80), malignancy status classification (10/80), stain quantification (9/80), and histological subtyping (7/80). Models were developed using 1-1375 histopathology slides from 1-776 ovarian cancer patients. A high or unclear risk of bias was found in all studies, most frequently due to limited analysis and incomplete reporting regarding participant recruitment. Limited research has been conducted on the application of AI to histopathology images for diagnostic or prognostic purposes in ovarian cancer, and none of the models have been demonstrated to be ready for real-world implementation. Key aspects to accelerate clinical translation include transparent and comprehensive reporting of data provenance and modelling approaches, and improved quantitative evaluation using cross-validation and external validations. This work was funded by the Engineering and Physical Sciences Research Council.
Collapse
Affiliation(s)
- Jack Breen
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK.
| | - Katie Allen
- Leeds Institute of Medical Research at St James's, School of Medicine, University of Leeds, Leeds, UK
| | - Kieran Zucker
- Leeds Cancer Centre, St James's University Hospital, Leeds, UK
| | - Pratik Adusumilli
- Leeds Institute of Medical Research at St James's, School of Medicine, University of Leeds, Leeds, UK
- Department of Radiology, St James's University Hospital, Leeds, UK
| | - Andrew Scarsbrook
- Leeds Institute of Medical Research at St James's, School of Medicine, University of Leeds, Leeds, UK
- Department of Radiology, St James's University Hospital, Leeds, UK
| | - Geoff Hall
- Leeds Cancer Centre, St James's University Hospital, Leeds, UK
| | - Nicolas M Orsi
- Leeds Institute of Medical Research at St James's, School of Medicine, University of Leeds, Leeds, UK
| | - Nishant Ravikumar
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK
| |
Collapse
|
9
|
Küchler L, Posthaus C, Jäger K, Guscetti F, van der Weyden L, von Bomhard W, Schmidt JM, Farra D, Aupperle-Lellbach H, Kehl A, Rottenberg S, de Brot S. Artificial Intelligence to Predict the BRAF V595E Mutation in Canine Urinary Bladder Urothelial Carcinomas. Animals (Basel) 2023; 13:2404. [PMID: 37570213 PMCID: PMC10416820 DOI: 10.3390/ani13152404] [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: 06/12/2023] [Revised: 07/10/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
In dogs, the BRAF mutation (V595E) is common in bladder and prostate cancer and represents a specific diagnostic marker. Recent advantages in artificial intelligence (AI) offer new opportunities in the field of tumour marker detection. While AI histology studies have been conducted in humans to detect BRAF mutation in cancer, comparable studies in animals are lacking. In this study, we used commercially available AI histology software to predict BRAF mutation in whole slide images (WSI) of bladder urothelial carcinomas (UC) stained with haematoxylin and eosin (HE), based on a training (n = 81) and a validation set (n = 96). Among 96 WSI, 57 showed identical PCR and AI-based BRAF predictions, resulting in a sensitivity of 58% and a specificity of 63%. The sensitivity increased substantially to 89% when excluding small or poor-quality tissue sections. Test reliability depended on tumour differentiation (p < 0.01), presence of inflammation (p < 0.01), slide quality (p < 0.02) and sample size (p < 0.02). Based on a small subset of cases with available adjacent non-neoplastic urothelium, AI was able to distinguish malignant from benign epithelium. This is the first study to demonstrate the use of AI histology to predict BRAF mutation status in canine UC. Despite certain limitations, the results highlight the potential of AI in predicting molecular alterations in routine tissue sections.
Collapse
Affiliation(s)
- Leonore Küchler
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland; (C.P.); (S.R.)
| | - Caroline Posthaus
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland; (C.P.); (S.R.)
| | - Kathrin Jäger
- Laboklin GmbH & Co. KG, 97688 Bad Kissingen, Germany; (K.J.); (H.A.-L.); (A.K.)
- Institute of Pathology, Department of Comparative Experimental Pathology, School of Medicine, Technical University of Munich, 81675 Munich, Germany
| | - Franco Guscetti
- Institute of Veterinary Pathology, Vetsuisse Faculty, University of Zurich, 8057 Zurich, Switzerland;
| | | | | | | | - Dima Farra
- Veterinary Public Health Institute, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland;
| | - Heike Aupperle-Lellbach
- Laboklin GmbH & Co. KG, 97688 Bad Kissingen, Germany; (K.J.); (H.A.-L.); (A.K.)
- Institute of Pathology, Department of Comparative Experimental Pathology, School of Medicine, Technical University of Munich, 81675 Munich, Germany
| | - Alexandra Kehl
- Laboklin GmbH & Co. KG, 97688 Bad Kissingen, Germany; (K.J.); (H.A.-L.); (A.K.)
- Institute of Pathology, Department of Comparative Experimental Pathology, School of Medicine, Technical University of Munich, 81675 Munich, Germany
| | - Sven Rottenberg
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland; (C.P.); (S.R.)
- COMPATH, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland
- Bern Center for Precision Medicine, University of Bern, 3008 Bern, Switzerland
| | - Simone de Brot
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland; (C.P.); (S.R.)
- COMPATH, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland
- Bern Center for Precision Medicine, University of Bern, 3008 Bern, Switzerland
| |
Collapse
|