1
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Kaczmarzyk JR, Saltz JH, Koo PK. Explainable AI for computational pathology identifies model limitations and tissue biomarkers. ARXIV 2024:arXiv:2409.03080v1. [PMID: 39279830 PMCID: PMC11398542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/18/2024]
Abstract
Deep learning models have shown promise in histopathology image analysis, but their opaque decision-making process poses challenges in high-risk medical scenarios. Here we introduce HIPPO, an explainable AI method that interrogates attention-based multiple instance learning (ABMIL) models in computational pathology by generating counterfactual examples through tissue patch modifications in whole slide images. Applying HIPPO to ABMIL models trained to detect breast cancer metastasis reveals that they may overlook small tumors and can be misled by non-tumor tissue, while attention maps-widely used for interpretation-often highlight regions that do not directly influence predictions. By interpreting ABMIL models trained on a prognostic prediction task, HIPPO identified tissue areas with stronger prognostic effects than high-attention regions, which sometimes showed counterintuitive influences on risk scores. These findings demonstrate HIPPO's capacity for comprehensive model evaluation, bias detection, and quantitative hypothesis testing. HIPPO greatly expands the capabilities of explainable AI tools to assess the trustworthy and reliable development, deployment, and regulation of weakly-supervised models in computational pathology.
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Affiliation(s)
- Jakub R. Kaczmarzyk
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Medical Scientist Training Program, Stony Brook University, Stony Brook, NY, USA
| | - Joel H. Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Peter K. Koo
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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2
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Behera A, Dharmalingam Jothinathan MK. Artificial intelligence transforms the future of oncology care. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2024; 125:101915. [PMID: 38762121 DOI: 10.1016/j.jormas.2024.101915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 05/09/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024]
Affiliation(s)
- Archana Behera
- Centre for Global Health Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, India
| | - Mukesh Kumar Dharmalingam Jothinathan
- Centre for Global Health Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, India.
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3
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Jaume G, Peeters T, Song AH, Pettit R, Williamson DFK, Oldenburg L, Vaidya A, de Brot S, Chen RJ, Thiran JP, Le LP, Gerber G, Mahmood F. AI-driven Discovery of Morphomolecular Signatures in Toxicology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.19.604355. [PMID: 39091765 PMCID: PMC11291055 DOI: 10.1101/2024.07.19.604355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Early identification of drug toxicity is essential yet challenging in drug development. At the preclinical stage, toxicity is assessed with histopathological examination of tissue sections from animal models to detect morphological lesions. To complement this analysis, toxicogenomics is increasingly employed to understand the mechanism of action of the compound and ultimately identify lesion-specific safety biomarkers for which in vitro assays can be designed. However, existing works that aim to identify morphological correlates of expression changes rely on qualitative or semi-quantitative morphological characterization and remain limited in scale or morphological diversity. Artificial intelligence (AI) offers a promising approach for quantitatively modeling this relationship at an unprecedented scale. Here, we introduce GEESE, an AI model designed to impute morphomolecular signatures in toxicology data. Our model was trained to predict 1,536 gene targets on a cohort of 8,231 hematoxylin and eosin-stained liver sections from Rattus norvegicus across 127 preclinical toxicity studies. The model, evaluated on 2,002 tissue sections from 29 held-out studies, can yield pseudo-spatially resolved gene expression maps, which we correlate with six key drug-induced liver injuries (DILI). From the resulting 25 million lesion-expression pairs, we established quantitative relations between up and downregulated genes and lesions. Validation of these signatures against toxicogenomic databases, pathway enrichment analyses, and human hepatocyte cell lines asserted their relevance. Overall, our study introduces new methods for characterizing toxicity at an unprecedented scale and granularity, paving the way for AI-driven discovery of toxicity biomarkers.
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Affiliation(s)
- Guillaume Jaume
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA
| | - Thomas Peeters
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Signal Processing Laboratory, EPFL, Lausanne, Switzerland
| | - Andrew H. Song
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA
| | - Rowland Pettit
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Drew F. K. Williamson
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Pathology & Laboratory Medicine, Emory University School of Medicine, Atlanta, GA
| | - Lukas Oldenburg
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Anurag Vaidya
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA
- Health Sciences and Technology, Harvard-MIT, Cambridge, MA
| | - Simone de Brot
- Institute of Animal Pathology, Vetsuisse, University of Bern, Switzerland
- COMPATH, Institute of Animal Pathology, University of Bern, Switzerland
- Bern Center for Precision Medicine, University of Bern, Switzerland
| | - Richard J. Chen
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA
| | | | - Long Phi Le
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Harvard Data Science Initiative, Harvard University, Cambridge, MA
| | - Georg Gerber
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Health Sciences and Technology, Harvard-MIT, Cambridge, MA
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA
- Harvard Data Science Initiative, Harvard University, Cambridge, MA
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4
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Cisternino F, Ometto S, Chatterjee S, Giacopuzzi E, Levine AP, Glastonbury CA. Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types. Nat Commun 2024; 15:5906. [PMID: 39003292 PMCID: PMC11246527 DOI: 10.1038/s41467-024-50317-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 07/04/2024] [Indexed: 07/15/2024] Open
Abstract
As vast histological archives are digitised, there is a pressing need to be able to associate specific tissue substructures and incident pathology to disease outcomes without arduous annotation. Here, we learn self-supervised representations using a Vision Transformer, trained on 1.7 M histology images across 23 healthy tissues in 838 donors from the Genotype Tissue Expression consortium (GTEx). Using these representations, we can automatically segment tissues into their constituent tissue substructures and pathology proportions across thousands of whole slide images, outperforming other self-supervised methods (43% increase in silhouette score). Additionally, we can detect and quantify histological pathologies present, such as arterial calcification (AUROC = 0.93) and identify missing calcification diagnoses. Finally, to link gene expression to tissue morphology, we introduce RNAPath, a set of models trained on 23 tissue types that can predict and spatially localise individual RNA expression levels directly from H&E histology (mean genes significantly regressed = 5156, FDR 1%). We validate RNAPath spatial predictions with matched ground truth immunohistochemistry for several well characterised control genes, recapitulating their known spatial specificity. Together, these results demonstrate how self-supervised machine learning when applied to vast histological archives allows researchers to answer questions about tissue pathology, its spatial organisation and the interplay between morphological tissue variability and gene expression.
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Affiliation(s)
| | - Sara Ometto
- Human Technopole, Viale Rita Levi-Montalcini 1, 20157, Milan, Italy
| | | | | | - Adam P Levine
- Research Department of Pathology, University College London, London, UK
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5
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Overkamp F. [A look into the neighboring discipline: eHealth in oncology]. CHIRURGIE (HEIDELBERG, GERMANY) 2024; 95:451-458. [PMID: 38727743 DOI: 10.1007/s00104-024-02089-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/09/2024] [Indexed: 05/16/2024]
Abstract
Digitalization is dramatically changing the entire healthcare system. Keywords such as artificial intelligence, electronic patient files (ePA), electronic prescriptions (eRp), telemedicine, wearables, augmented reality and digital health applications (DiGA) represent the digital transformation that is already taking place. Digital becomes real! This article outlines the state of research and development, current plans and ongoing uses of digital tools in oncology in the first half of 2024. The possibilities for using artificial intelligence and the use of DiGAs in oncology are presented in more detail in this overview according to their stage of development as they already show a noticeable benefit in oncology.
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Affiliation(s)
- Friedrich Overkamp
- OncoConsult Overkamp GmbH, Europaplatz 2, 10557, Berlin, Deutschland.
- onkowissen.de GmbH, Würzburg, Deutschland.
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6
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Bryant AK, Zamora‐Resendiz R, Dai X, Morrow D, Lin Y, Jungles KM, Rae JM, Tate A, Pearson AN, Jiang R, Fritsche L, Lawrence TS, Zou W, Schipper M, Ramnath N, Yoo S, Crivelli S, Green MD. Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances. Cancer Med 2024; 13:e7253. [PMID: 38899720 PMCID: PMC11187737 DOI: 10.1002/cam4.7253] [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: 08/17/2023] [Revised: 02/05/2024] [Accepted: 04/28/2024] [Indexed: 06/21/2024] Open
Abstract
PURPOSE Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology. METHODS We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology. RESULTS Artificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping. CONCLUSIONS Despite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.
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Affiliation(s)
- Alex K. Bryant
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of Radiation Oncology, Veterans Affairs Ann Arbor Healthcare SystemAnn ArborMichiganUSA
| | - Rafael Zamora‐Resendiz
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Xin Dai
- Computational Science Initiative, Brookhaven National LaboratoryUptonNew YorkUSA
| | - Destinee Morrow
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Yuewei Lin
- Computational Science Initiative, Brookhaven National LaboratoryUptonNew YorkUSA
| | - Kassidy M. Jungles
- Department of PharmacologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - James M. Rae
- Department of PharmacologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of Internal MedicineUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Akshay Tate
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Ashley N. Pearson
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Ralph Jiang
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of StatisticsUniversity of MichiganAnn ArborMichiganUSA
| | - Lars Fritsche
- Department of StatisticsUniversity of MichiganAnn ArborMichiganUSA
| | - Theodore S. Lawrence
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Weiping Zou
- Department of StatisticsUniversity of MichiganAnn ArborMichiganUSA
- Center of Excellence for Cancer Immunology and ImmunotherapyUniversity of Michigan Rogel Cancer CenterAnn ArborMichiganUSA
- Department of PathologyUniversity of MichiganAnn ArborMichiganUSA
- Graduate Program in ImmunologyUniversity of MichiganAnn ArborMichiganUSA
| | - Matthew Schipper
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of PharmacologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Nithya Ramnath
- Division of Hematology Oncology, Department of MedicineUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Division of Hematology Oncology, Department of MedicineVeterans Affairs Ann Arbor Healthcare SystemAnn ArborMichiganUSA
| | - Shinjae Yoo
- Computational Science Initiative, Brookhaven National LaboratoryUptonNew YorkUSA
| | - Silvia Crivelli
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Michael D. Green
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of Radiation Oncology, Veterans Affairs Ann Arbor Healthcare SystemAnn ArborMichiganUSA
- Graduate Program in ImmunologyUniversity of MichiganAnn ArborMichiganUSA
- Graduate Program in Cancer BiologyUniversity of MichiganAnn ArborMichiganUSA
- Department of Microbiology and ImmunologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
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7
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Xiao T, Kong S, Zhang Z, Hua D, Liu F. A review of big data technology and its application in cancer care. Comput Biol Med 2024; 176:108577. [PMID: 38739981 DOI: 10.1016/j.compbiomed.2024.108577] [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: 11/19/2023] [Revised: 05/07/2024] [Accepted: 05/07/2024] [Indexed: 05/16/2024]
Abstract
The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future.
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Affiliation(s)
- Tianyun Xiao
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Shanshan Kong
- College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China.
| | - Zichen Zhang
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Dianbo Hua
- Beijing Sitairui Cancer Data Analysis Joint Laboratory, Beijing, 101149, China
| | - Fengchun Liu
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China; Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan, Hebei, China; Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan, Hebei, China
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8
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Chen RJ, Ding T, Lu MY, Williamson DFK, Jaume G, Song AH, Chen B, Zhang A, Shao D, Shaban M, Williams M, Oldenburg L, Weishaupt LL, Wang JJ, Vaidya A, Le LP, Gerber G, Sahai S, Williams W, Mahmood F. Towards a general-purpose foundation model for computational pathology. Nat Med 2024; 30:850-862. [PMID: 38504018 PMCID: PMC11403354 DOI: 10.1038/s41591-024-02857-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 02/05/2024] [Indexed: 03/21/2024]
Abstract
Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology.
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Affiliation(s)
- Richard J Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tong Ding
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Ming Y Lu
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Drew F K Williamson
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Guillaume Jaume
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Andrew H Song
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Bowen Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrew Zhang
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Health Sciences and Technology, Harvard-MIT, Cambridge, MA, USA
| | - Daniel Shao
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Health Sciences and Technology, Harvard-MIT, Cambridge, MA, USA
| | - Muhammad Shaban
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Mane Williams
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Lukas Oldenburg
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Luca L Weishaupt
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Health Sciences and Technology, Harvard-MIT, Cambridge, MA, USA
| | - Judy J Wang
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Anurag Vaidya
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Health Sciences and Technology, Harvard-MIT, Cambridge, MA, USA
| | - Long Phi Le
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Health Sciences and Technology, Harvard-MIT, Cambridge, MA, USA
| | - Georg Gerber
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sharifa Sahai
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Systems Biology, Harvard University, Cambridge, MA, USA
| | - Walt Williams
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
- Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA.
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9
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Lu MY, Chen B, Williamson DFK, Chen RJ, Liang I, Ding T, Jaume G, Odintsov I, Le LP, Gerber G, Parwani AV, Zhang A, Mahmood F. A visual-language foundation model for computational pathology. Nat Med 2024; 30:863-874. [PMID: 38504017 PMCID: PMC11384335 DOI: 10.1038/s41591-024-02856-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 02/05/2024] [Indexed: 03/21/2024]
Abstract
The accelerated adoption of digital pathology and advances in deep learning have enabled the development of robust models for various pathology tasks across a diverse array of diseases and patient cohorts. However, model training is often difficult due to label scarcity in the medical domain, and a model's usage is limited by the specific task and disease for which it is trained. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text and, notably, over 1.17 million image-caption pairs through task-agnostic pretraining. Evaluated on a suite of 14 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving histopathology images and/or text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, and text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning.
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Affiliation(s)
- Ming Y Lu
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Bowen Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Drew F K Williamson
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Richard J Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Ivy Liang
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Tong Ding
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Guillaume Jaume
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Igor Odintsov
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Long Phi Le
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Georg Gerber
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Anil V Parwani
- Department of Pathology, Wexner Medical Center, Ohio State University, Columbus, OH, USA
| | - Andrew Zhang
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Health Sciences and Technology, Harvard-MIT, Cambridge, MA, USA
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
- Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA.
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10
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Kehl A, Aupperle-Lellbach H, de Brot S, van der Weyden L. Review of Molecular Technologies for Investigating Canine Cancer. Animals (Basel) 2024; 14:769. [PMID: 38473154 DOI: 10.3390/ani14050769] [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: 12/21/2023] [Revised: 02/09/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024] Open
Abstract
Genetic molecular testing is starting to gain traction as part of standard clinical practice for dogs with cancer due to its multi-faceted benefits, such as potentially being able to provide diagnostic, prognostic and/or therapeutic information. However, the benefits and ultimate success of genomic analysis in the clinical setting are reliant on the robustness of the tools used to generate the results, which continually expand as new technologies are developed. To this end, we review the different materials from which tumour cells, DNA, RNA and the relevant proteins can be isolated and what methods are available for interrogating their molecular profile, including analysis of the genetic alterations (both somatic and germline), transcriptional changes and epigenetic modifications (including DNA methylation/acetylation and microRNAs). We also look to the future and the tools that are currently being developed, such as using artificial intelligence (AI) to identify genetic mutations from histomorphological criteria. In summary, we find that the molecular genetic characterisation of canine neoplasms has made a promising start. As we understand more of the genetics underlying these tumours and more targeted therapies become available, it will no doubt become a mainstay in the delivery of precision veterinary care to dogs with cancer.
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Affiliation(s)
- Alexandra Kehl
- Laboklin GmbH & Co. KG, Steubenstr. 4, 97688 Bad Kissingen, Germany
- School of Medicine, Institute of Pathology, Technical University of Munich, Trogerstr. 18, 81675 München, Germany
| | - Heike Aupperle-Lellbach
- Laboklin GmbH & Co. KG, Steubenstr. 4, 97688 Bad Kissingen, Germany
- School of Medicine, Institute of Pathology, Technical University of Munich, Trogerstr. 18, 81675 München, Germany
| | - Simone de Brot
- Institute of Animal Pathology, COMPATH, University of Bern, 3012 Bern, Switzerland
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11
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Lv Q, Liu Y, Sun Y, Wu M. Insight into deep learning for glioma IDH medical image analysis: A systematic review. Medicine (Baltimore) 2024; 103:e37150. [PMID: 38363910 PMCID: PMC10869095 DOI: 10.1097/md.0000000000037150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/11/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND Deep learning techniques explain the enormous potential of medical image analysis, particularly in digital pathology. Concurrently, molecular markers have gained increasing significance over the past decade in the context of glioma patients, providing novel insights into diagnosis and more personalized treatment options. Deep learning combined with imaging and molecular analysis enables more accurate prognostication of patients, more accurate treatment plan proposals, and accurate biomarker (IDH) prediction for gliomas. This systematic study examines the development of deep learning techniques for IDH prediction using histopathology images, spanning the period from 2019 to 2023. METHOD The study adhered to the PRISMA reporting requirements, and databases including PubMed, Google Scholar, Google Search, and preprint repositories (such as arXiv) were systematically queried for pertinent literature spanning the period from 2019 to the 30th of 2023. Search phrases related to deep learning, digital pathology, glioma, and IDH were collaboratively utilized. RESULTS Fifteen papers meeting the inclusion criteria were included in the analysis. These criteria specifically encompassed studies utilizing deep learning for the analysis of hematoxylin and eosin images to determine the IDH status in patients with gliomas. CONCLUSIONS When predicting the status of IDH, the classifier built on digital pathological images demonstrates exceptional performance. The study's predictive effectiveness is enhanced with the utilization of the appropriate deep learning model. However, external verification is necessary to showcase their resilience and universality. Larger sample sizes and multicenter samples are necessary for more comprehensive research to evaluate performance and confirm clinical advantages.
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Affiliation(s)
- Qingqing Lv
- Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410008, Hunan, China
- The Key Laboratory of Carcinogenesis of the Chinese Ministry of Health, The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, 410078, Hunan, China
| | - Yihao Liu
- Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410008, Hunan, China
- The Key Laboratory of Carcinogenesis of the Chinese Ministry of Health, The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, 410078, Hunan, China
| | - Yingnan Sun
- Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410008, Hunan, China
| | - Minghua Wu
- Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410008, Hunan, China
- The Key Laboratory of Carcinogenesis of the Chinese Ministry of Health, The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, 410078, Hunan, China
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12
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El Nahhas OSM, Loeffler CML, Carrero ZI, van Treeck M, Kolbinger FR, Hewitt KJ, Muti HS, Graziani M, Zeng Q, Calderaro J, Ortiz-Brüchle N, Yuan T, Hoffmeister M, Brenner H, Brobeil A, Reis-Filho JS, Kather JN. Regression-based Deep-Learning predicts molecular biomarkers from pathology slides. Nat Commun 2024; 15:1253. [PMID: 38341402 PMCID: PMC10858881 DOI: 10.1038/s41467-024-45589-1] [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: 04/11/2023] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions' correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.
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Grants
- P30 CA008748 NCI NIH HHS
- JNK is supported by the German Federal Ministry of Health (DEEP LIVER, ZMVI1-2520DAT111) and the Max-Eder-Programme of the German Cancer Aid (grant #70113864), the German Federal Ministry of Education and Research (PEARL, 01KD2104C; CAMINO, 01EO2101; SWAG, 01KD2215A; TRANSFORM LIVER, 031L0312A), the German Academic Exchange Service (SECAI, 57616814), the German Federal Joint Committee (Transplant.KI, 01VSF21048) the European Union (ODELIA, 101057091; GENIAL, 101096312) and the National Institute for Health and Care Research (NIHR, NIHR213331) Leeds Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.
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Affiliation(s)
- Omar S M El Nahhas
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Chiara M L Loeffler
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Zunamys I Carrero
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Marko van Treeck
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Fiona R Kolbinger
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Katherine J Hewitt
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Hannah S Muti
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Mara Graziani
- University of Applied Sciences of Western Switzerland (HES-SO Valais), Rue du Technopole 3, 3960, Sierre, Valais, Switzerland
| | - Qinghe Zeng
- Centre d'Histologie, d'Imagerie et de Cytométrie (CHIC), Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, Paris, France
| | - Julien Calderaro
- Assistance Publique-Hôpitaux de Paris, Département de Pathologie, CHU Henri Mondor, F-94000, Créteil, France
| | - Nadina Ortiz-Brüchle
- Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
- Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Cologne, Germany
| | - Tanwei Yuan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alexander Brobeil
- Institute of Pathology, University Hospital Heidelberg, 69120, Heidelberg, Germany
- Tissue Bank, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Jorge S Reis-Filho
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
- Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
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13
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Ligero M, Serna G, El Nahhas OS, Sansano I, Mauchanski S, Viaplana C, Calderaro J, Toledo RA, Dienstmann R, Vanguri RS, Sauter JL, Sanchez-Vega F, Shah SP, Ramón y Cajal S, Garralda E, Nuciforo P, Perez-Lopez R, Kather JN. Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression. CANCER RESEARCH COMMUNICATIONS 2024; 4:92-102. [PMID: 38126740 PMCID: PMC10782919 DOI: 10.1158/2767-9764.crc-23-0287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 10/11/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023]
Abstract
Programmed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunotherapy response. However, quantification of PD-L1 status in pathology slides is challenging. Neither manual quantification nor a computer-based mimicking of manual readouts is perfectly reproducible, and the predictive performance of both approaches regarding immunotherapy response is limited. In this study, we developed a deep learning (DL) method to predict PD-L1 status directly from raw IHC image data, without explicit intermediary steps such as cell detection or pigment quantification. We trained the weakly supervised model on PD-L1-stained slides from the non-small cell lung cancer (NSCLC)-Memorial Sloan Kettering (MSK) cohort (N = 233) and validated it on the pan-cancer-Vall d'Hebron Institute of Oncology (VHIO) cohort (N = 108). We also investigated the performance of the model to predict response to immune checkpoint inhibitors (ICI) in terms of progression-free survival. In the pan-cancer-VHIO cohort, the performance was compared with tumor proportion score (TPS) and combined positive score (CPS). The DL model showed good performance in predicting PD-L1 expression (TPS ≥ 1%) in both NSCLC-MSK and pan-cancer-VHIO cohort (AUC 0.88 ± 0.06 and 0.80 ± 0.03, respectively). The predicted PD-L1 status showed an improved association with response to ICIs [HR: 1.5 (95% confidence interval: 1-2.3), P = 0.049] compared with TPS [HR: 1.4 (0.96-2.2), P = 0.082] and CPS [HR: 1.2 (0.79-1.9), P = 0.386]. Notably, our explainability analysis showed that the model does not just look at the amount of brown pigment in the IHC slides, but also considers morphologic factors such as lymphocyte conglomerates. Overall, end-to-end weakly supervised DL shows potential for improving patient stratification for cancer immunotherapy by analyzing PD-L1 IHC, holistically integrating morphology and PD-L1 staining intensity. SIGNIFICANCE The weakly supervised DL model to predict PD-L1 status from raw IHC data, integrating tumor staining intensity and morphology, enables enhanced patient stratification in cancer immunotherapy compared with traditional pathologist assessment.
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Affiliation(s)
- Marta Ligero
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Garazi Serna
- Molecular Oncology Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Omar S.M. El Nahhas
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Irene Sansano
- Pathology Department, Vall d'Hebron University Hospital (VHUH), Barcelona, Spain
| | - Siarhei Mauchanski
- Molecular Oncology Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Cristina Viaplana
- Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Julien Calderaro
- Assistance Publique-Hôpitaux de Paris, Département de Pathologie, CHU Henri Mondor, Créteil, France
- Université Paris-Est Créteil, Faculté de Médecine, Créteil, France
| | - Rodrigo A. Toledo
- Biomakers and Clonal Dynamics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Rodrigo Dienstmann
- Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Rami S. Vanguri
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Jennifer L. Sauter
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Sohrab P. Shah
- Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Elena Garralda
- Department of Medical Oncology, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), Barcelona, Spain
| | - Paolo Nuciforo
- Molecular Oncology Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
- Department of Medicine I, University Hospital Dresden, Dresden, Germany
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
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14
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Jiang X, Hoffmeister M, Brenner H, Muti HS, Yuan T, Foersch S, West NP, Brobeil A, Jonnagaddala J, Hawkins N, Ward RL, Brinker TJ, Saldanha OL, Ke J, Müller W, Grabsch HI, Quirke P, Truhn D, Kather JN. End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study. Lancet Digit Health 2024; 6:e33-e43. [PMID: 38123254 DOI: 10.1016/s2589-7500(23)00208-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/21/2023] [Accepted: 10/12/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Precise prognosis prediction in patients with colorectal cancer (ie, forecasting survival) is pivotal for individualised treatment and care. Histopathological tissue slides of colorectal cancer specimens contain rich prognostically relevant information. However, existing studies do not have multicentre external validation with real-world sample processing protocols, and algorithms are not yet widely used in clinical routine. METHODS In this retrospective, multicentre study, we collected tissue samples from four groups of patients with resected colorectal cancer from Australia, Germany, and the USA. We developed and externally validated a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with resected colorectal cancer. We used the model-predicted risk scores to stratify patients into different risk groups and compared survival outcomes between these groups. Additionally, we evaluated the prognostic value of these risk groups after adjusting for established prognostic variables. FINDINGS We trained and validated our model on a total of 4428 patients. We found that patients could be divided into high-risk and low-risk groups on the basis of the deep learning-based risk score. On the internal test set, the group with a high-risk score had a worse prognosis than the group with a low-risk score, as reflected by a hazard ratio (HR) of 4·50 (95% CI 3·33-6·09) for overall survival and 8·35 (5·06-13·78) for disease-specific survival (DSS). We found consistent performance across three large external test sets. In a test set of 1395 patients, the high-risk group had a lower DSS than the low-risk group, with an HR of 3·08 (2·44-3·89). In two additional test sets, the HRs for DSS were 2·23 (1·23-4·04) and 3·07 (1·78-5·3). We showed that the prognostic value of the deep learning-based risk score is independent of established clinical risk factors. INTERPRETATION Our findings indicate that attention-based self-supervised deep learning can robustly offer a prognosis on clinical outcomes in patients with colorectal cancer, generalising across different populations and serving as a potentially new prognostic tool in clinical decision making for colorectal cancer management. We release all source codes and trained models under an open-source licence, allowing other researchers to reuse and build upon our work. FUNDING The German Federal Ministry of Health, the Max-Eder-Programme of German Cancer Aid, the German Federal Ministry of Education and Research, the German Academic Exchange Service, and the EU.
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Affiliation(s)
- Xiaofeng Jiang
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Medicine III, University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Ageing Research, German Cancer Research Center, Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Ageing Research, German Cancer Research Center, Heidelberg, Germany; German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center and National Center for Tumour Diseases, Heidelberg, Germany
| | - Hannah Sophie Muti
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Tanwei Yuan
- Division of Clinical Epidemiology and Ageing Research, German Cancer Research Center, Heidelberg, Germany
| | - Sebastian Foersch
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Nicholas P West
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Alexander Brobeil
- Institute of Pathology, National Center for Tumour Diseases, University Hospital Heidelberg, Heidelberg, Germany; Tissue Bank, National Center for Tumour Diseases, University Hospital Heidelberg, Heidelberg, Germany
| | - Jitendra Jonnagaddala
- School of Population Health, Faculty of Medicine and Health, University of New South Wales Sydney, Kensington, NSW, Australia
| | - Nicholas Hawkins
- School of Medical Sciences, Faculty of Medicine and Health, University of New South Wales Sydney, Kensington, NSW, Australia
| | - Robyn L Ward
- School of Medical Sciences, Faculty of Medicine and Health, University of New South Wales Sydney, Kensington, NSW, Australia; Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia
| | - Titus J Brinker
- Digital Biomarkers for Oncology Group, German Cancer Research Center, Heidelberg, Germany
| | - Oliver Lester Saldanha
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Medicine III, University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Jia Ke
- Department of General Surgery (Colorectal Surgery), Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, and Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | | | - Heike I Grabsch
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK; Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, Netherlands
| | - Philip Quirke
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Medicine III, University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany; Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK; Medical Oncology, National Center for Tumour Diseases, University Hospital Heidelberg, Heidelberg, Germany; Department of Medicine I, University Hospital Dresden, Dresden, Germany.
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15
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Calderaro J, Ghaffari Laleh N, Zeng Q, Maille P, Favre L, Pujals A, Klein C, Bazille C, Heij LR, Uguen A, Luedde T, Di Tommaso L, Beaufrère A, Chatain A, Gastineau D, Nguyen CT, Nguyen-Canh H, Thi KN, Gnemmi V, Graham RP, Charlotte F, Wendum D, Vij M, Allende DS, Aucejo F, Diaz A, Rivière B, Herrero A, Evert K, Calvisi DF, Augustin J, Leow WQ, Leung HHW, Boleslawski E, Rela M, François A, Cha AWH, Forner A, Reig M, Allaire M, Scatton O, Chatelain D, Boulagnon-Rombi C, Sturm N, Menahem B, Frouin E, Tougeron D, Tournigand C, Kempf E, Kim H, Ningarhari M, Michalak-Provost S, Gopal P, Brustia R, Vibert E, Schulze K, Rüther DF, Weidemann SA, Rhaiem R, Pawlotsky JM, Zhang X, Luciani A, Mulé S, Laurent A, Amaddeo G, Regnault H, De Martin E, Sempoux C, Navale P, Westerhoff M, Lo RCL, Bednarsch J, Gouw A, Guettier C, Lequoy M, Harada K, Sripongpun P, Wetwittayaklang P, Loménie N, Tantipisit J, Kaewdech A, Shen J, Paradis V, Caruso S, Kather JN. Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma. Nat Commun 2023; 14:8290. [PMID: 38092727 PMCID: PMC10719304 DOI: 10.1038/s41467-023-43749-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 11/17/2023] [Indexed: 12/17/2023] Open
Abstract
Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA.
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Affiliation(s)
- Julien Calderaro
- Université Paris Est Créteil, INSERM, IMRB, F-94010, Créteil, France.
- Assistance Publique-Hôpitaux de Paris, Henri Mondor-Albert Chenevier University Hospital, Department of Pathology, Créteil, France.
- Inserm, U955, Team 18, Créteil, France.
- European Reference Network (ERN) RARE-LIVER, Créteil, France.
| | - Narmin Ghaffari Laleh
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Department of Medicine III, University Hospital RWTH Aachen, RWTH Aachen university, Aachen, Germany
| | - Qinghe Zeng
- Centre d'Histologie, d'Imagerie et de Cytométrie (CHIC), Centre de Recherche des Cordeliers, Paris, France
- Laboratoire d'Informatique Paris Descartes (LIPADE), Université Paris Cité, Paris, France
| | - Pascale Maille
- Université Paris Est Créteil, INSERM, IMRB, F-94010, Créteil, France
- Assistance Publique-Hôpitaux de Paris, Henri Mondor-Albert Chenevier University Hospital, Department of Pathology, Créteil, France
- Inserm, U955, Team 18, Créteil, France
| | - Loetitia Favre
- Université Paris Est Créteil, INSERM, IMRB, F-94010, Créteil, France
- Assistance Publique-Hôpitaux de Paris, Henri Mondor-Albert Chenevier University Hospital, Department of Pathology, Créteil, France
- Inserm, U955, Team 18, Créteil, France
| | - Anaïs Pujals
- Université Paris Est Créteil, INSERM, IMRB, F-94010, Créteil, France
- Assistance Publique-Hôpitaux de Paris, Henri Mondor-Albert Chenevier University Hospital, Department of Pathology, Créteil, France
- Inserm, U955, Team 18, Créteil, France
| | - Christophe Klein
- Centre d'Histologie, d'Imagerie et de Cytométrie (CHIC), Centre de Recherche des Cordeliers, Paris, France
- INSERM, Sorbonne Université, Université Paris Cité, Paris, France
| | - Céline Bazille
- Caen University Hospital, Department of Pathology, Caen, France
| | - Lara R Heij
- Department of Surgery and Transplantation, University Hospital RWTH Aachen, Aachen, Germany
- Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
| | - Arnaud Uguen
- CHRU Brest, Department of Pathology, Brest, 29220, France
- Univ Brest, Inserm, CHU de Brest, LBAI, UMR1227, Brest, France
| | - Tom Luedde
- Clinic for Gastroenterology, Hepatology and Infectious Diseases, University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Luca Di Tommaso
- Department of Pathology, Humanitas University, Humanitas Clinical and Research Center, IRCCS, Rozzano, Milan, Italy
| | - Aurélie Beaufrère
- Assistance Publique-Hôpitaux de Paris, Beaujon University Hospital, Department of Pathology, F-92110, Clichy, France
- Université de Paris, Inflammation Research Center, Inserm, U1149, CNRS, ERL8252, F-75018, Paris, France
| | | | | | - Cong Trung Nguyen
- Department of Pathology, E Hospital, Hanoi Medical University, Hanoi, Vietnam
| | - Hiep Nguyen-Canh
- Pathology Center, Bachmai Hospital, Hanoi, Vietnam
- Department of Human Pathology, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
| | - Khuyen Nguyen Thi
- Pathology and Molecular biology Center, National Cancer Hospital, Hanoi, Vietnam
| | - Viviane Gnemmi
- University Lille, UMR9020-U1277, Cancer Heterogeneity Plasticity and Resistance to Therapies, Lille, France
- CHU Lille, Institute of Pathology, Lille, France
| | - Rondell P Graham
- Department of Laboratory Medicine and Pathology, Mayo Clinic Rochester, Rochester, MN, USA
| | - Frédéric Charlotte
- Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière University Hospital, Department of Pathology, Paris, France
| | - Dominique Wendum
- Assistance Publique-Hôpitaux de Paris, Saint-Antoine University Hospital, Department of Pathology, Paris, France
| | - Mukul Vij
- Department of Pathology, Dr Rela Institute and Medical Centre, Bharath Institute of Higher Education and Research, Chennai, India
| | - Daniela S Allende
- Department of Hepatobiliary Pathology, Cleveland Clinic Foundation, Cleveland, OH, USA
- Robert J. Tomsich Pathology & Laboratory Medicine Institute, Cleveland Clinic, 9500 Euclid Avenue, L25, Cleveland, OH, 44195, USA
| | - Federico Aucejo
- Department of Gastrointestinal and Hepatobiliary Surgery, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Alba Diaz
- Barcelona Clinic Liver Cancer (BCLC) Group, Department of Pathology, Hospital Clínic de Barcelona, Universitat de Barcelona, Barcelona, Spain
| | - Benjamin Rivière
- Department of Pathology, Gui-de-Chauliac University Hospital, 80, avenue Augustin-Fliche, 34295, Montpellier, France
| | - Astrid Herrero
- Department of Digestive and Hepatobiliary Surgery, Gui-de-Chauliac University Hospital, 80, avenue Augustin-Fliche, 34295, Montpellier, France
| | - Katja Evert
- Institute of Pathology, University of Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
| | - Diego Francesco Calvisi
- Institute of Pathology, University of Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
| | - Jérémy Augustin
- Université Paris Est Créteil, INSERM, IMRB, F-94010, Créteil, France
- Assistance Publique-Hôpitaux de Paris, Henri Mondor-Albert Chenevier University Hospital, Department of Pathology, Créteil, France
- Inserm, U955, Team 18, Créteil, France
| | - Wei Qiang Leow
- Department of Anatomical Pathology, Singapore General Hospital, Singapore, Singapore
| | - Howard Ho Wai Leung
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | | | - Mohamed Rela
- Dr Rela Institute and Medical Centre, Bharath Institute of Higher Education and Research, Chennai, India
| | - Arnaud François
- Rouen University Hospital, Department of Pathology, Rouen, France
| | - Anthony Wing-Hung Cha
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Alejandro Forner
- Barcelona Clinic Liver Cancer (BCLC), Liver Unit, Hospital Clinic of Barcelona, IDIBAPS, CIBEREHD, Universidad de Barcelona, Barcelona, Spain
| | - Maria Reig
- Barcelona Clinic Liver Cancer (BCLC), Liver Unit, Hospital Clinic of Barcelona, IDIBAPS, CIBEREHD, Universidad de Barcelona, Barcelona, Spain
| | - Manon Allaire
- Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière University Hospital, Department of Hepatology, Paris, France
| | - Olivier Scatton
- Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière University Hospital, Department of Digestive and Hepatobiliary Surgery, Paris, France
| | - Denis Chatelain
- Centre Hospitalier Universitaire d'Amiens, Département de Pathologie, Amiens, France
| | | | - Nathalie Sturm
- Department of Pathology, University Hospital, Grenoble, France
- Translational Innovation in Medicine and Complexity, Centre National de la Recherche Scientifique UMR5525, La Tronche, France
| | - Benjamin Menahem
- Caen University Hospital, Department of Digestive and Hepatobiliary Surgery, Caen, France
| | - Eric Frouin
- Poitiers University Hospital, Department of Pathology, Poitiers, France
- LITEC, Université de Poitiers, Poitiers, France
| | - David Tougeron
- Poitiers University Hospital, Department of Hepatogastroenterology and Oncology, Poitiers, France
| | - Christophe Tournigand
- Assistance Publique-Hôpitaux de Paris, Henri Mondor-Albert Chenevier University Hospital, Department of Medical Oncology, Créteil, France
| | - Emmanuelle Kempf
- Assistance Publique-Hôpitaux de Paris, Henri Mondor-Albert Chenevier University Hospital, Department of Medical Oncology, Créteil, France
| | - Haeryoung Kim
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | | | | | - Purva Gopal
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Raffaele Brustia
- Assistance Publique-Hôpitaux de Paris, Henri Mondor University Hospital, Department of Digestive and Hepatobiliary Surgery, Créteil, France
| | - Eric Vibert
- Assistance Publique-Hôpitaux de Paris, Paul Brousse University Hospital, Department of Digestive and Hepatobiliary Surgery, Paris, France
| | - Kornelius Schulze
- Department of Internal Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Darius F Rüther
- Department of Internal Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Sören A Weidemann
- Department of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Rami Rhaiem
- Reims University Hospital, Department of Digestive and Hepatobiliary Surgery, Reims, France
| | - Jean-Michel Pawlotsky
- Université Paris Est Créteil, INSERM, IMRB, F-94010, Créteil, France
- Inserm, U955, Team 18, Créteil, France
- Assistance Publique-Hôpitaux de Paris, Paul Brousse University Hospital, Department of Digestive and Hepatobiliary Surgery, Paris, France
| | - Xuchen Zhang
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Alain Luciani
- Assistance Publique-Hôpitaux de Paris, Henri Mondor University Hospital, Department of Medical Imaging, Créteil, France
| | - Sébastien Mulé
- Assistance Publique-Hôpitaux de Paris, Henri Mondor University Hospital, Department of Medical Imaging, Créteil, France
| | - Alexis Laurent
- Assistance Publique-Hôpitaux de Paris, Henri Mondor University Hospital, Department of Digestive and Hepatobiliary Surgery, Créteil, France
| | - Giuliana Amaddeo
- Assistance Publique-Hôpitaux de Paris, Henri Mondor University Hospital, Department of Hepatology, Créteil, France
| | - Hélène Regnault
- Assistance Publique-Hôpitaux de Paris, Henri Mondor University Hospital, Department of Hepatology, Créteil, France
| | - Eleonora De Martin
- Assistance Publique-Hôpitaux de Paris, Paul Brousse University Hospital, Department of Hepatology, Paris, France
| | - Christine Sempoux
- Institute of Pathology, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Pooja Navale
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - Maria Westerhoff
- Department of Pathology University of Michigan, Ann Arbor, MI, USA
| | - Regina Cheuk-Lam Lo
- Department of Pathology, The University of Hong Kong, Pok Fu Lam, Hong Kong, China
- State Key Laboratory of Liver Research, (The University of Hong Kong), Pok Fu Lam, Hong Kong, China
| | - Jan Bednarsch
- Department of Surgery and Transplantation, University Hospital RWTH Aachen, Aachen, Germany
| | - Annette Gouw
- Department of Pathology and Medical Biology, University Medical Center Groningen, Groningen, the Netherlands
| | - Catherine Guettier
- Department of Surgery and Transplantation, University Hospital RWTH Aachen, Aachen, Germany
- Department of Pathology and Medical Biology, University Medical Center Groningen, Groningen, the Netherlands
- Assistance Publique-Hôpitaux de Paris, Paul Brousse University Hospital, Department of Pathology, Villejuif, France
| | - Marie Lequoy
- Assistance Publique-Hôpitaux de Paris, Saint Antoine University Hospital, Department of Hepatology, Paris, France
| | - Kenichi Harada
- Department of Human Pathology, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
| | - Pimsiri Sripongpun
- Gastroenterology and Hepatology Unit, Division of Internal Medicine, Faculty of Medicine, Prince of Songkla University, Hat Yai, Thailand
| | | | - Nicolas Loménie
- Laboratoire d'Informatique Paris Descartes (LIPADE), Université Paris Cité, Paris, France
| | - Jarukit Tantipisit
- Prince of Songkla University, Department of Pathology, Hat Yai, Thailand
| | - Apichat Kaewdech
- Gastroenterology and Hepatology Unit, Division of Internal Medicine, Faculty of Medicine, Prince of Songkla University, Hat Yai, Thailand
| | - Jeanne Shen
- Center for Artificial Intelligence in Medicine and Imaging, Stanford University, 1701 Page Mill Road, Palo Alto, CA, 94304, USA
- Department of Pathology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Valérie Paradis
- Assistance Publique-Hôpitaux de Paris, Beaujon University Hospital, Department of Pathology, F-92110, Clichy, France
- Université de Paris, Inflammation Research Center, Inserm, U1149, CNRS, ERL8252, F-75018, Paris, France
| | - Stefano Caruso
- Université Paris Est Créteil, INSERM, IMRB, F-94010, Créteil, France
- Assistance Publique-Hôpitaux de Paris, Henri Mondor-Albert Chenevier University Hospital, Department of Pathology, Créteil, France
- Inserm, U955, Team 18, Créteil, France
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
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16
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Tavolara TE, Su Z, Gurcan MN, Niazi MKK. One label is all you need: Interpretable AI-enhanced histopathology for oncology. Semin Cancer Biol 2023; 97:70-85. [PMID: 37832751 DOI: 10.1016/j.semcancer.2023.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 09/06/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023]
Abstract
Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that require only one overall label per hematoxylin and eosin (H&E) slide with no tissue-level annotations. We present a structured review of these methods organized by their degree of verifiability and by commonly recurring application areas in oncological characterization. First, we discuss morphological markers (tumor presence/absence, metastases, subtypes, grades) in which AI-identified regions of interest (ROIs) within whole slide images (WSIs) verifiably overlap with pathologist-identified ROIs. Second, we discuss molecular markers (gene expression, molecular subtyping) that are not verified via H&E but rather based on overlap with positive regions on adjacent tissue. Third, we discuss genetic markers (mutations, mutational burden, microsatellite instability, chromosomal instability) that current technologies cannot verify if AI methods spatially resolve specific genetic alterations. Fourth, we discuss the direct prediction of survival to which AI-identified histopathological features quantitatively correlate but are nonetheless not mechanistically verifiable. Finally, we discuss in detail several opportunities and challenges for these one-label-per-slide methods within oncology. Opportunities include reducing the cost of research and clinical care, reducing the workload of clinicians, personalized medicine, and unlocking the full potential of histopathology through new imaging-based biomarkers. Current challenges include explainability and interpretability, validation via adjacent tissue sections, reproducibility, data availability, computational needs, data requirements, domain adaptability, external validation, dataset imbalances, and finally commercialization and clinical potential. Ultimately, the relative ease and minimum upfront cost with which relevant data can be collected in addition to the plethora of available AI methods for outcome-driven analysis will surmount these current limitations and achieve the innumerable opportunities associated with AI-driven histopathology for the benefit of oncology.
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Affiliation(s)
- Thomas E Tavolara
- Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Ziyu Su
- Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Metin N Gurcan
- Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - M Khalid Khan Niazi
- Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
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17
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Chatterji S, Niehues JM, van Treeck M, Loeffler CML, Saldanha OL, Veldhuizen GP, Cifci D, Carrero ZI, Abu-Eid R, Speirs V, Kather JN. Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer. NPJ Breast Cancer 2023; 9:91. [PMID: 37940649 PMCID: PMC10632426 DOI: 10.1038/s41523-023-00599-y] [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: 05/29/2023] [Accepted: 10/27/2023] [Indexed: 11/10/2023] Open
Abstract
Breast cancer prognosis and management for both men and women are reliant upon estrogen receptor alpha (ERα) and progesterone receptor (PR) expression to inform therapy. Previous studies have shown that there are sex-specific binding characteristics of ERα and PR in breast cancer and, counterintuitively, ERα expression is more common in male than female breast cancer. We hypothesized that these differences could have morphological manifestations that are undetectable to human observers but could be elucidated computationally. To investigate this, we trained attention-based multiple instance learning prediction models for ERα and PR using H&E-stained images of female breast cancer from the Cancer Genome Atlas (TCGA) (n = 1085) and deployed them on external female (n = 192) and male breast cancer images (n = 245). Both targets were predicted in the internal (AUROC for ERα prediction: 0.86 ± 0.02, p < 0.001; AUROC for PR prediction = 0.76 ± 0.03, p < 0.001) and external female cohorts (AUROC for ERα prediction: 0.78 ± 0.03, p < 0.001; AUROC for PR prediction = 0.80 ± 0.04, p < 0.001) but not the male cohort (AUROC for ERα prediction: 0.66 ± 0.14, p = 0.43; AUROC for PR prediction = 0.63 ± 0.04, p = 0.05). This suggests that subtle morphological differences invisible upon visual inspection may exist between the sexes, supporting previous immunohistochemical, genomic, and transcriptomic analyses.
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Affiliation(s)
- Subarnarekha Chatterji
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
- Aberdeen Cancer Centre, University of Aberdeen, Aberdeen, UK
| | - Jan Moritz Niehues
- Else Kröner Fresenius Centre for Digital Health, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
- Department of Medicine III, University Hospital RWTH (Rheinisch-Westfälische Technische Hochschule) Aachen, Aachen, Germany
| | - Marko van Treeck
- Else Kröner Fresenius Centre for Digital Health, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
- Department of Medicine III, University Hospital RWTH (Rheinisch-Westfälische Technische Hochschule) Aachen, Aachen, Germany
| | - Chiara Maria Lavinia Loeffler
- Else Kröner Fresenius Centre for Digital Health, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
- Department of Medicine III, University Hospital RWTH (Rheinisch-Westfälische Technische Hochschule) Aachen, Aachen, Germany
- Department of Medicine I, University Hospital and Faculty of Medicine, Technical University of Dresden, Dresden, Germany
| | - Oliver Lester Saldanha
- Else Kröner Fresenius Centre for Digital Health, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
- Department of Medicine III, University Hospital RWTH (Rheinisch-Westfälische Technische Hochschule) Aachen, Aachen, Germany
| | - Gregory Patrick Veldhuizen
- Else Kröner Fresenius Centre for Digital Health, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
- Department of Medicine III, University Hospital RWTH (Rheinisch-Westfälische Technische Hochschule) Aachen, Aachen, Germany
| | - Didem Cifci
- Else Kröner Fresenius Centre for Digital Health, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
- Department of Medicine III, University Hospital RWTH (Rheinisch-Westfälische Technische Hochschule) Aachen, Aachen, Germany
| | - Zunamys Itzell Carrero
- Else Kröner Fresenius Centre for Digital Health, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
| | - Rasha Abu-Eid
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
- Aberdeen Cancer Centre, University of Aberdeen, Aberdeen, UK
- Institute of Dentistry, University of Aberdeen, Aberdeen, UK
| | - Valerie Speirs
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK.
- Aberdeen Cancer Centre, University of Aberdeen, Aberdeen, UK.
| | - Jakob Nikolas Kather
- Else Kröner Fresenius Centre for Digital Health, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
- Department of Medicine III, University Hospital RWTH (Rheinisch-Westfälische Technische Hochschule) Aachen, Aachen, Germany
- Department of Medicine I, University Hospital and Faculty of Medicine, Technical University of Dresden, Dresden, Germany
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds, UK
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18
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Perry C, Greenberg O, Haberman S, Herskovitz N, Gazy I, Avinoam A, Paz-Yaacov N, Hershkovitz D, Avivi I. Image-Based Deep Learning Detection of High-Grade B-Cell Lymphomas Directly from Hematoxylin and Eosin Images. Cancers (Basel) 2023; 15:5205. [PMID: 37958379 PMCID: PMC10650414 DOI: 10.3390/cancers15215205] [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: 08/02/2023] [Revised: 10/04/2023] [Accepted: 10/17/2023] [Indexed: 11/15/2023] Open
Abstract
Deep learning applications are emerging as promising new tools that can support the diagnosis and classification of different cancer types. While such solutions hold great potential for hematological malignancies, there have been limited studies describing the use of such applications in this field. The rapid diagnosis of double/triple-hit lymphomas (DHLs/THLs) involving MYC, BCL2 and/or BCL6 rearrangements is obligatory for optimal patient care. Here, we present a novel deep learning tool for diagnosing DHLs/THLs directly from scanned images of biopsy slides. A total of 57 biopsies, including 32 in a training set (including five DH lymphoma cases) and 25 in a validation set (including 10 DH/TH cases), were included. The DHL-classifier demonstrated a sensitivity of 100%, a specificity of 87% and an AUC of 0.95, with only two false positive cases, compared to FISH. The DHL-classifier showed a 92% predictive value as a screening tool for performing conventional FISH analysis, over-performing currently used criteria. The work presented here provides the proof of concept for the potential use of an AI tool for the identification of DH/TH events. However, more extensive follow-up studies are required to assess the robustness of this tool and achieve high performances in a diverse population.
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Affiliation(s)
- Chava Perry
- Hematology Division, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Orli Greenberg
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Pathology Department, Tel Aviv Sourasky Medical Center, Tel Aviv 6492601, Israel
| | - Shira Haberman
- Hematology Division, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Neta Herskovitz
- Hematology Division, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel
| | - Inbal Gazy
- Imagene AI Ltd., Tel Aviv 6721409, Israel (N.P.-Y.)
| | | | | | - Dov Hershkovitz
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Pathology Department, Tel Aviv Sourasky Medical Center, Tel Aviv 6492601, Israel
| | - Irit Avivi
- Hematology Division, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
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19
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Wagner SJ, Reisenbüchler D, West NP, Niehues JM, Zhu J, Foersch S, Veldhuizen GP, Quirke P, Grabsch HI, van den Brandt PA, Hutchins GGA, Richman SD, Yuan T, Langer R, Jenniskens JCA, Offermans K, Mueller W, Gray R, Gruber SB, Greenson JK, Rennert G, Bonner JD, Schmolze D, Jonnagaddala J, Hawkins NJ, Ward RL, Morton D, Seymour M, Magill L, Nowak M, Hay J, Koelzer VH, Church DN, Matek C, Geppert C, Peng C, Zhi C, Ouyang X, James JA, Loughrey MB, Salto-Tellez M, Brenner H, Hoffmeister M, Truhn D, Schnabel JA, Boxberg M, Peng T, Kather JN. Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study. Cancer Cell 2023; 41:1650-1661.e4. [PMID: 37652006 PMCID: PMC10507381 DOI: 10.1016/j.ccell.2023.08.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 06/18/2023] [Accepted: 08/07/2023] [Indexed: 09/02/2023]
Abstract
Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem.
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Affiliation(s)
- Sophia J Wagner
- Helmholtz Munich - German Research Center for Environment and Health, Munich, Germany; School of Computation, Information and Technology, Technical University of Munich, Munich, Germany; Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | - Daniel Reisenbüchler
- Helmholtz Munich - German Research Center for Environment and Health, Munich, Germany
| | - Nicholas P West
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Jan Moritz Niehues
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | - Jiefu Zhu
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | - Sebastian Foersch
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | | | - Philip Quirke
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Heike I Grabsch
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK; Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Piet A van den Brandt
- Department of Epidemiology, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Gordon G A Hutchins
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Susan D Richman
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Tanwei Yuan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rupert Langer
- Institute of Pathology und Molecular Pathology, Johannes Kepler University Hospital Linz, Linz, Österreich
| | - Josien C A Jenniskens
- Department of Epidemiology, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Kelly Offermans
- Department of Epidemiology, Maastricht University Medical Center+, Maastricht, the Netherlands
| | | | - Richard Gray
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Stephen B Gruber
- Center for Precision Medicine and Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Joel K Greenson
- Department of Pathology, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Gad Rennert
- Department of Community Medicine & Epidemiology, Lady Davis Carmel Medical Center, Ruth & Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel; Steve and Cindy Rasmussen Institute for Genomic Medicine, Lady Davis Carmel Medical Center and Technion Faculty of Medicine, Clalit National Cancer Control Center, Haifa, Israel
| | - Joseph D Bonner
- Department of Community Medicine & Epidemiology, Lady Davis Carmel Medical Center, Ruth & Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - Daniel Schmolze
- Center for Precision Medicine and Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Jitendra Jonnagaddala
- School of Population Health, Faculty of Medicine and Health, UNSW Sydney, Sydney, NSW, Australia
| | - Nicholas J Hawkins
- School of Medical Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, NSW, Australia
| | - Robyn L Ward
- School of Medical Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, NSW, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Dion Morton
- University Hospital Birmingham, Birmingham, UK
| | | | - Laura Magill
- University of Birmingham Clinical Trials Unit, Birmingham, UK
| | - Marta Nowak
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Jennifer Hay
- Glasgow Tissue Research Facility, University of Glasgow, Queen Elizabeth University Hospital, Glasgow, UK
| | - Viktor H Koelzer
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Oncology, University of Oxford, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, UK
| | - David N Church
- Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, UK; Oxford NIHR Comprehensive Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Christian Matek
- Helmholtz Munich - German Research Center for Environment and Health, Munich, Germany; Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Erlangen, Germany
| | - Carol Geppert
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Erlangen, Germany
| | - Chaolong Peng
- Medical School, Jianggang Shan University, Jiangxi, China
| | - Cheng Zhi
- Department of Pathology, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaoming Ouyang
- Department of Pathology, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jacqueline A James
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK; Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast, UK; The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Maurice B Loughrey
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK; Department of Cellular Pathology, Belfast Health and Social Care Trust, Belfast, UK; Centre for Public Health, Queen's University Belfast, Belfast, UK
| | - Manuel Salto-Tellez
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK; Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast, UK; Integrated Pathology Unit, Institute for Cancer Research and Royal Marsden Hospital, London, UK
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Julia A Schnabel
- Helmholtz Munich - German Research Center for Environment and Health, Munich, Germany; School of Computation, Information and Technology, Technical University of Munich, Munich, Germany; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Melanie Boxberg
- Institute of Pathology, Technical University Munich, Munich, Germany; Institute of Pathology Munich-North, Munich, Germany
| | - Tingying Peng
- Helmholtz Munich - German Research Center for Environment and Health, Munich, Germany.
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany; Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg.
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20
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Choi S, Kim S. Artificial Intelligence in the Pathology of Gastric Cancer. J Gastric Cancer 2023; 23:410-427. [PMID: 37553129 PMCID: PMC10412971 DOI: 10.5230/jgc.2023.23.e25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/09/2023] [Accepted: 07/14/2023] [Indexed: 08/10/2023] Open
Abstract
Recent advances in artificial intelligence (AI) have provided novel tools for rapid and precise pathologic diagnosis. The introduction of digital pathology has enabled the acquisition of scanned slide images that are essential for the application of AI. The application of AI for improved pathologic diagnosis includes the error-free detection of potentially negligible lesions, such as a minute focus of metastatic tumor cells in lymph nodes, the accurate diagnosis of potentially controversial histologic findings, such as very well-differentiated carcinomas mimicking normal epithelial tissues, and the pathological subtyping of the cancers. Additionally, the utilization of AI algorithms enables the precise decision of the score of immunohistochemical markers for targeted therapies, such as human epidermal growth factor receptor 2 and programmed death-ligand 1. Studies have revealed that AI assistance can reduce the discordance of interpretation between pathologists and more accurately predict clinical outcomes. Several approaches have been employed to develop novel biomarkers from histologic images using AI. Moreover, AI-assisted analysis of the cancer microenvironment showed that the distribution of tumor-infiltrating lymphocytes was related to the response to the immune checkpoint inhibitor therapy, emphasizing its value as a biomarker. As numerous studies have demonstrated the significance of AI-assisted interpretation and biomarker development, the AI-based approach will advance diagnostic pathology.
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Affiliation(s)
- Sangjoon Choi
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
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21
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Senthil Kumar K, Miskovic V, Blasiak A, Sundar R, Pedrocchi ALG, Pearson AT, Prelaj A, Ho D. Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment. Am Soc Clin Oncol Educ Book 2023; 43:e390084. [PMID: 37235822 DOI: 10.1200/edbk_390084] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Recently, a wide spectrum of artificial intelligence (AI)-based applications in the broader categories of digital pathology, biomarker development, and treatment have been explored. In the domain of digital pathology, these have included novel analytical strategies for realizing new information derived from standard histology to guide treatment selection and biomarker development to predict treatment selection and response. In therapeutics, these have included AI-driven drug target discovery, drug design and repurposing, combination regimen optimization, modulated dosing, and beyond. Given the continued advances that are emerging, it is important to develop workflows that seamlessly combine the various segments of AI innovation to comprehensively augment the diagnostic and interventional arsenal of the clinical oncology community. To overcome challenges that remain with regard to the ideation, validation, and deployment of AI in clinical oncology, recommendations toward bringing this workflow to fruition are also provided from clinical, engineering, implementation, and health care economics considerations. Ultimately, this work proposes frameworks that can potentially integrate these domains toward the sustainable adoption of practice-changing AI by the clinical oncology community to drive improved patient outcomes.
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Affiliation(s)
- Kirthika Senthil Kumar
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Vanja Miskovic
- Department of Electronics, Informatics, and Bioengineering, Politecnico di Milano, Milan, Italy
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Agata Blasiak
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Raghav Sundar
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Haematology-Oncology, National University Cancer Institute, National University Hospital
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Singapore Gastric Cancer Consortium, Singapore
- NUS Centre for Cancer Research (N2CR), National University of Singapore, Singapore
| | | | - Alexander T Pearson
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL
- University of Chicago Comprehensive Cancer Center, Chicago, IL
| | - Arsela Prelaj
- Department of Electronics, Informatics, and Bioengineering, Politecnico di Milano, Milan, Italy
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Dean Ho
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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22
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Hewitt KJ, Löffler CML, Muti HS, Berghoff AS, Eisenlöffel C, van Treeck M, Carrero ZI, El Nahhas OSM, Veldhuizen GP, Weil S, Saldanha OL, Bejan L, Millner TO, Brandner S, Brückmann S, Kather JN. Direct image to subtype prediction for brain tumors using deep learning. Neurooncol Adv 2023; 5:vdad139. [PMID: 38106649 PMCID: PMC10724115 DOI: 10.1093/noajnl/vdad139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023] Open
Abstract
Background Deep Learning (DL) can predict molecular alterations of solid tumors directly from routine histopathology slides. Since the 2021 update of the World Health Organization (WHO) diagnostic criteria, the classification of brain tumors integrates both histopathological and molecular information. We hypothesize that DL can predict molecular alterations as well as WHO subtyping of brain tumors from hematoxylin and eosin-stained histopathology slides. Methods We used weakly supervised DL and applied it to three large cohorts of brain tumor samples, comprising N = 2845 patients. Results We found that the key molecular alterations for subtyping, IDH and ATRX, as well as 1p19q codeletion, were predictable from histology with an area under the receiver operating characteristic curve (AUROC) of 0.95, 0.90, and 0.80 in the training cohort, respectively. These findings were upheld in external validation cohorts with AUROCs of 0.90, 0.79, and 0.87 for prediction of IDH, ATRX, and 1p19q codeletion, respectively. Conclusions In the future, such DL-based implementations could ease diagnostic workflows, particularly for situations in which advanced molecular testing is not readily available.
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Affiliation(s)
- Katherine J Hewitt
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, North Rhine-Westphalia, Germany
- Clinical Artificial Intelligence, Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Saxony, Germany
| | - Chiara M L Löffler
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, North Rhine-Westphalia, Germany
- Clinical Artificial Intelligence, Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Saxony, Germany
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Saxony, Germany
| | - Hannah Sophie Muti
- Clinical Artificial Intelligence, Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Saxony, Germany
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus Dresden, Dresden, Saxony, Germany
| | - Anna Sophie Berghoff
- Department of Medicine 1, Division of Oncology, Medical University of Vienna, Vienna, Vienna, Austria
| | - Christian Eisenlöffel
- Department of Pathology, St. Georg Teaching Hospital, University of Leipzig, Leipzig, Saxony, Germany
| | - Marko van Treeck
- Clinical Artificial Intelligence, Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Saxony, Germany
| | - Zunamys I Carrero
- Clinical Artificial Intelligence, Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Saxony, Germany
| | - Omar S M El Nahhas
- Clinical Artificial Intelligence, Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Saxony, Germany
| | - Gregory P Veldhuizen
- Clinical Artificial Intelligence, Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Saxony, Germany
| | - Sophie Weil
- Neurology Clinic, Department of Neurology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Baden- Württemberg, Germany
- Clinical Cooperation Unit Neuro-oncology, Department of Neurology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Baden- Württemberg, Germany
| | - Oliver Lester Saldanha
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, North Rhine-Westphalia, Germany
| | - Laura Bejan
- School of Medicine, Faculty of Medicine and Dentistry, University College London, London, Greater London, UK
| | - Thomas O Millner
- Division of Neuropathology, Queen Square Institute of Neurology, University College London, London, Greater London, UK
- Blizard Institute, Faculty of Medicine and Dentistry, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, Greater London, UK
| | - Sebastian Brandner
- Division of Neuropathology, Queen Square Institute of Neurology, University College London, London, Greater London, UK
| | - Sascha Brückmann
- Institut für Pathologie, University Hospital Carl Gustav Carus, Dresden, Saxony, Germany
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, North Rhine-Westphalia, Germany
- Clinical Artificial Intelligence, Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Saxony, Germany
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Saxony, Germany
- Pathology & Data Analytics, Faculty of Medicine and Health, Leeds Institute of Medical Research at St James’s, University of Leeds, Leeds, West Yorkshire, UK
- Department of Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Baden- Württemberg, Germany
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