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Reitsam NG, Grosser B, Steiner DF, Grozdanov V, Wulczyn E, L'Imperio V, Plass M, Müller H, Zatloukal K, Muti HS, Kather JN, Märkl B. Converging deep learning and human-observed tumor-adipocyte interaction as a biomarker in colorectal cancer. COMMUNICATIONS MEDICINE 2024; 4:163. [PMID: 39147895 PMCID: PMC11327259 DOI: 10.1038/s43856-024-00589-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 08/05/2024] [Indexed: 08/17/2024] Open
Abstract
BACKGROUND Tumor-Adipose-Feature (TAF) as well as SARIFA (Stroma AReactive Invasion Front Areas) are two histologic features/biomarkers linking tumor-associated adipocytes to poor outcomes in colorectal cancer (CRC) patients. Whereas TAF was identified by deep learning (DL) algorithms, SARIFA was established as a human-observed histopathologic biomarker. METHODS To study the overlap between TAF and SARIFA, we performed a systematic pathological review of TAF based on all published image tiles. Additionally, we analyzed the presence/absence of TAF in SARIFA-negative CRC cases to elucidate the biologic and prognostic role of a direct tumor-adipocyte contact. TCGA-CRC gene expression data is investigated to assess the association of FABP4 (fatty-acid binding protein 4) and CD36 (fatty-acid translocase) with both TAF and CRC prognosis. RESULTS By investigating the TAF/SARIFA overlap, we show that many TAF patches correspond to the recently described SARIFA-phenomenon. Even though there is a pronounced morphological and biological overlap, there are differences in the concepts. The presence of TAF in SARIFA-negative CRCs is not associated with poor outcomes in this cohort, potentially highlighting the importance of a direct tumor-adipocyte interaction. Upregulation of FABP4 and CD36 gene expression seem both linked to a poor prognosis in CRC. CONCLUSIONS By proving the substantial overlap between human-observed SARIFA and DL-based TAF as morphologic biomarkers, we demonstrate that linking DL-based image features to independently developed histopathologic biomarkers is a promising tool in the identification of clinically and biologically meaningful biomarkers. Adipocyte-tumor-cell interactions seem to be crucial in CRC, which should be considered as biomarkers for further investigations.
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Affiliation(s)
- Nic G Reitsam
- Pathology, Medical Faculty, University of Augsburg, Augsburg, Germany.
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany.
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany.
| | - Bianca Grosser
- Pathology, Medical Faculty, University of Augsburg, Augsburg, Germany
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany
| | | | | | - Ellery Wulczyn
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Vincenzo L'Imperio
- Department of Medicine and Surgery, Pathology, University of Milano-Bicocca, IRCCS (Scientific Institute for Research, Hospitalization and Healthcare) Fondazione San Gerardo dei Tintori, Monza, Italy
| | - Markus Plass
- Medical University of Graz, Diagnostic and Research Institute of Pathology, Graz, Austria
| | - Heimo Müller
- Medical University of Graz, Diagnostic and Research Institute of Pathology, Graz, Austria
| | - Kurt Zatloukal
- Medical University of Graz, Diagnostic and Research Institute of Pathology, Graz, Austria
| | - Hannah S Muti
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus Dresden, Dresden, Germany
| | - Jakob N Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- Pathology & 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
| | - Bruno Märkl
- Pathology, Medical Faculty, University of Augsburg, Augsburg, Germany
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany
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Sui Z, Li Z, Sun W. Exploit Spatially Resolved Transcriptomic Data to Infer Cellular Features from Pathology Imaging Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.05.606654. [PMID: 39149252 PMCID: PMC11326158 DOI: 10.1101/2024.08.05.606654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Digital pathology is a rapidly advancing field where deep learning methods can be employed to extract meaningful imaging features. However, the efficacy of training deep learning models is often hindered by the scarcity of annotated pathology images, particularly images with detailed annotations for small image patches or tiles. To overcome this challenge, we propose an innovative approach that leverages paired spatially resolved transcriptomic data to annotate pathology images. We demonstrate the feasibility of this approach and introduce a novel transfer-learning neural network model, STpath (Spatial Transcriptomics and pathology images), designed to predict cell type proportions or classify tumor microenvironments. Our findings reveal that the features from pre-trained deep learning models are associated with cell type identities in pathology image patches. Evaluating STpath using three distinct breast cancer datasets, we observe its promising performance despite the limited training data. STpath excels in samples with variable cell type proportions and high-resolution pathology images. As the influx of spatially resolved transcriptomic data continues, we anticipate ongoing updates to STpath, evolving it into an invaluable AI tool for assisting pathologists in various diagnostic tasks.
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Parvaiz A, Nasir ES, Fraz MM. From Pixels to Prognosis: A Survey on AI-Driven Cancer Patient Survival Prediction Using Digital Histology Images. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1728-1751. [PMID: 38429563 PMCID: PMC11300721 DOI: 10.1007/s10278-024-01049-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/30/2023] [Accepted: 12/20/2023] [Indexed: 03/03/2024]
Abstract
Survival analysis is an integral part of medical statistics that is extensively utilized to establish prognostic indices for mortality or disease recurrence, assess treatment efficacy, and tailor effective treatment plans. The identification of prognostic biomarkers capable of predicting patient survival is a primary objective in the field of cancer research. With the recent integration of digital histology images into routine clinical practice, a plethora of Artificial Intelligence (AI)-based methods for digital pathology has emerged in scholarly literature, facilitating patient survival prediction. These methods have demonstrated remarkable proficiency in analyzing and interpreting whole slide images, yielding results comparable to those of expert pathologists. The complexity of AI-driven techniques is magnified by the distinctive characteristics of digital histology images, including their gigapixel size and diverse tissue appearances. Consequently, advanced patch-based methods are employed to effectively extract features that correlate with patient survival. These computational methods significantly enhance survival prediction accuracy and augment prognostic capabilities in cancer patients. The review discusses the methodologies employed in the literature, their performance metrics, ongoing challenges, and potential solutions for future advancements. This paper explains survival analysis and feature extraction methods for analyzing cancer patients. It also compiles essential acronyms related to cancer precision medicine. Furthermore, it is noteworthy that this is the inaugural review paper in the field. The target audience for this interdisciplinary review comprises AI practitioners, medical statisticians, and progressive oncologists who are enthusiastic about translating AI-driven solutions into clinical practice. We expect this comprehensive review article to guide future research directions in the field of cancer research.
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Affiliation(s)
- Arshi Parvaiz
- National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Esha Sadia Nasir
- National University of Sciences and Technology (NUST), Islamabad, Pakistan
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Märkl B, Reitsam NG, Grochowski P, Waidhauser J, Grosser B. The SARIFA biomarker in the context of basic research of lipid-driven cancers. NPJ Precis Oncol 2024; 8:165. [PMID: 39085485 PMCID: PMC11291993 DOI: 10.1038/s41698-024-00662-2] [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: 02/27/2024] [Accepted: 07/19/2024] [Indexed: 08/02/2024] Open
Abstract
SARIFA was very recently introduced as a histomorphological biomarker with strong prognostic power for colorectal, gastric, prostate, and pancreatic cancer. It is characterized by the direct contact between tumor cells and adipocytes due to a lack of stromal reaction. This can be easily evaluated on routinely available H&E-slides with high interobserver agreement. SARIFA also reflects a specific tumor biology driven by metabolic reprogramming. Tumor cells in SARIFA-positive tumors benefit from direct interaction with adipocytes as an external source of lipids. Numerous studies have shown that lipid metabolism is crucial in carcinogenesis and cancer progression. We found that the interaction between tumor cells and adipocytes was not triggered by obesity, as previously assumed. Instead, we believe that this is due to an immunological mechanism. Knowledge about lipid metabolism in cancer from basic experiments can be transferred to develop strategies targeting this reprogramed metabolism.
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Affiliation(s)
- Bruno Märkl
- Pathology, Medical Faculty Augsburg, University of Augsburg, Augsburg, Germany.
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany.
- WERA Comprehensive Cancer Center, Augsburg, Germany.
| | - Nic G Reitsam
- Pathology, Medical Faculty Augsburg, University of Augsburg, Augsburg, Germany
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany
- WERA Comprehensive Cancer Center, Augsburg, Germany
| | - Przemyslaw Grochowski
- Pathology, Medical Faculty Augsburg, University of Augsburg, Augsburg, Germany
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany
- WERA Comprehensive Cancer Center, Augsburg, Germany
| | - Johanna Waidhauser
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany
- WERA Comprehensive Cancer Center, Augsburg, Germany
- Hematology and Oncology, Medical Faculty Augsburg, University of Augsburg, Augsburg, Germany
| | - Bianca Grosser
- Pathology, Medical Faculty Augsburg, University of Augsburg, Augsburg, Germany
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany
- WERA Comprehensive Cancer Center, Augsburg, Germany
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Ma Y, Nenkov M, Chen Y, Gaßler N. The Role of Adipocytes Recruited as Part of Tumor Microenvironment in Promoting Colorectal Cancer Metastases. Int J Mol Sci 2024; 25:8352. [PMID: 39125923 PMCID: PMC11313311 DOI: 10.3390/ijms25158352] [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: 06/10/2024] [Revised: 07/15/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024] Open
Abstract
Adipose tissue dysfunction, which is associated with an increased risk of colorectal cancer (CRC), is a significant factor in the pathophysiology of obesity. Obesity-related inflammation and extracellular matrix (ECM) remodeling promote colorectal cancer metastasis (CRCM) by shaping the tumor microenvironment (TME). When CRC occurs, the metabolic symbiosis of tumor cells recruits adjacent adipocytes into the TME to supply energy. Meanwhile, abundant immune cells, from adipose tissue and blood, are recruited into the TME, which is stimulated by pro-inflammatory factors and triggers a chronic local pro-inflammatory TME. Dysregulated ECM proteins and cell surface adhesion molecules enhance ECM remodeling and further increase contractibility between tumor and stromal cells, which promotes epithelial-mesenchymal transition (EMT). EMT increases tumor migration and invasion into surrounding tissues or vessels and accelerates CRCM. Colorectal symbiotic microbiota also plays an important role in the promotion of CRCM. In this review, we provide adipose tissue and its contributions to CRC, with a special emphasis on the role of adipocytes, macrophages, neutrophils, T cells, ECM, and symbiotic gut microbiota in the progression of CRC and their contributions to the CRC microenvironment. We highlight the interactions between adipocytes and tumor cells, and potential therapeutic approaches to target these interactions.
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Affiliation(s)
| | | | | | - Nikolaus Gaßler
- Section Pathology of the Institute of Forensic Medicine, Jena University Hospital, Friedrich Schiller University Jena, Am Klinikum 1, 07747 Jena, Germany (M.N.)
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Mazaki J, Umezu T, Saito A, Katsumata K, Fujita K, Hashimoto M, Kobayashi M, Udo R, Kasahara K, Kuwabara H, Ishizaki T, Matsubayashi J, Nagao T, Hazama S, Suzuki N, Nagano H, Tanaka T, Tsuchida A, Nagakawa Y, Kuroda M. Novel AI Combining CNN and SVM to Predict Colorectal Cancer Prognosis and Mutational Signatures from HE Images. Mod Pathol 2024:100562. [PMID: 39019345 DOI: 10.1016/j.modpat.2024.100562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 06/11/2024] [Accepted: 07/09/2024] [Indexed: 07/19/2024]
Abstract
Reducing recurrence following radical resection of colon cancer without over- or under-treatment remains a challenge. Postoperative adjuvant chemotherapy (Adj) is currently administered based solely on pathological tumor, node, and metastasis (TNM) stage. However, prognosis can vary significantly among patients with the same disease stage. Therefore, novel classification systems in addition to the TNM are necessary to inform decision-making regarding postoperative treatment strategies, especially stage II and III disease, and to minimize overtreatment and undertreatment with Adj. We developed a prognostic prediction system for colorectal cancer by using a combined convolutional neural network (CNN) and support vector machine (SVM) approach to extract features from hematoxyling and eosin staining (HE) images. We combined the TNM and our AI-based classification system into a TNM-AI (mTNM-AI) classification system with high discriminative power for recurrence-free survival (RFS). Furthermore, the cancer cell population recognized by this system as low risk of recurrence exhibited the mutational signature SBS87 as a genetic phenotype. The novel AI-based classification system developed here is expected to play an important role in prognostic prediction and personalized treatment selection in oncology.
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Affiliation(s)
- Junichi Mazaki
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan
| | - Tomohiro Umezu
- Department of Molecular Pathology, Tokyo Medical University, Tokyo, Japan
| | - Akira Saito
- Department of Molecular Pathology, Tokyo Medical University, Tokyo, Japan; Department of AI Applied Quantitative Clinical Science, Tokyo Medical University, Tokyo, Japan
| | - Kenji Katsumata
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan
| | - Koji Fujita
- Department of Molecular Pathology, Tokyo Medical University, Tokyo, Japan
| | | | | | - Ryutaro Udo
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan
| | - Kenta Kasahara
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan
| | - Hiroshi Kuwabara
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan
| | - Tetsuo Ishizaki
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan
| | - Jun Matsubayashi
- Department of Anatomic Pathology, Tokyo Medical University, Tokyo, Japan
| | - Toshitaka Nagao
- Department of Anatomic Pathology, Tokyo Medical University, Tokyo, Japan
| | - Shoichi Hazama
- Department of Gastroenterological, Breast and Endocrine Surgery, Graduate School of Medicine, Yamaguchi University, Yamaguchi, Japan; Department of Surgery, Shunan Hospital, Yamaguchi, Japan
| | - Nobuaki Suzuki
- Department of Gastroenterological, Breast and Endocrine Surgery, Graduate School of Medicine, Yamaguchi University, Yamaguchi, Japan
| | - Hiroaki Nagano
- Department of Gastroenterological, Breast and Endocrine Surgery, Graduate School of Medicine, Yamaguchi University, Yamaguchi, Japan
| | - Takashi Tanaka
- Department of Gastrointestinal Surgery, Obihiro Memorial Hospital, Hokaido, Japan
| | - Akihiko Tsuchida
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan
| | - Yuichi Nagakawa
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan.
| | - Masahiko Kuroda
- Department of Molecular Pathology, Tokyo Medical University, Tokyo, Japan; Department of AI Applied Quantitative Clinical Science, Tokyo Medical University, Tokyo, Japan.
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Offermans K, Reitsam NG, Simons CCJM, Grosser B, Zimmermann J, Grabsch HI, Märkl B, van den Brandt PA. The relationship between Stroma AReactive Invasion Front Areas (SARIFA), Warburg-subtype and survival: results from a large prospective series of colorectal cancer patients. Cancer Metab 2024; 12:21. [PMID: 38992781 PMCID: PMC11241902 DOI: 10.1186/s40170-024-00349-z] [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: 01/02/2024] [Accepted: 06/30/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND Stroma AReactive Invasion Front Areas (SARIFA) is a recently identified haematoxylin & eosin (H&E)based histopathologic biomarker in gastrointestinal cancers, including colorectal cancer (CRC), defined as direct contact between tumour cells and adipocytes at the tumour invasion front. The current study aimed at validating the prognostic relevance of SARIFA in a large population-based CRC series as well as at investigating the relationship between SARIFA-status and previously established Warburg-subtypes, both surrogates of the metabolic state of the tumour cells. METHODS SARIFA-status (positive versus negative) was determined on H&E slides of 1,727 CRC specimens. Warburg-subtype (high versus moderate versus low) data was available from our previous study. The associations between SARIFA-status, Warburg-subtype, clinicopathological characteristics and CRC-specific as well as overall survival were investigated. RESULTS 28.7% (n=496) CRC were SARIFA-positive. SARIFA-positivity was associated with more advanced disease stage, higher pT category, and more frequent lymph node involvement (all p<0.001). SARIFA-positivity was more common in Warburg-high CRC. 44.2% (n=219) of SARIFA-positive CRCs were Warburg-high compared to 22.8% (n=113) being Warburg-low and 33.1% (n=164) being Warburg-moderate (p<0.001). In multivariable-adjusted analysis, patients with SARIFA-positive CRCs had significantly poorer CRC-specific (HRCRC-specific 1.65; 95% CI 1.41-1.93) and overall survival (HRoverall survival 1.46; 95% CI 1.28-1.67) independent of clinically known risk factors and independent of Warburg-subtype. Combining the SARIFA-status and the Warburg-subtype to a combination score (SARIFA-negative/Warburg-high versus SARIFA-positive/Warburg-low versus SARIFA-positive/Warburg-high, and so on) did not improve the survival prediction compared to the use of SARIFA-status alone (SARIFA-negative + Warburg-high: HRCRC-specific 1.08; 95% CI 0.84-1.38; SARIFA-positive + Warburg-low: HRCRC-specific 1.79; 95% CI 1.32-2.41; SARIFA-positive + Warburg-high: HRCRC-specific 1.58; 95% CI 1.23-2.04). CONCLUSIONS Our current study is the by far largest external validation of SARIFA-positivity as a novel independent negative prognostic H&E-based biomarker in CRC. In addition, our study shows that SARIFA-positivity is associated with the Warburg-high subtype. Further research is warranted to provide a more mechanistic understanding of the underlying tumour biology. Based on our data, we conclude SARIFA-status should be implemented in pathologic routine practice to stratify CRC patients.
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Affiliation(s)
- Kelly Offermans
- Department of Epidemiology, GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Nic G Reitsam
- Pathology, Medical Faculty, University of Augsburg, Augsburg, Germany
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany
| | - Colinda C J M Simons
- Department of Epidemiology, GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Bianca Grosser
- Pathology, Medical Faculty, University of Augsburg, Augsburg, Germany
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany
| | | | - Heike I Grabsch
- Department of Pathology, GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands.
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
| | - Bruno Märkl
- Pathology, Medical Faculty, University of Augsburg, Augsburg, Germany.
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany.
| | - Piet A van den Brandt
- Department of Epidemiology, GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands.
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University Medical Center+, Maastricht, the Netherlands.
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Volinsky-Fremond S, Horeweg N, Andani S, Barkey Wolf J, Lafarge MW, de Kroon CD, Ørtoft G, Høgdall E, Dijkstra J, Jobsen JJ, Lutgens LCHW, Powell ME, Mileshkin LR, Mackay H, Leary A, Katsaros D, Nijman HW, de Boer SM, Nout RA, de Bruyn M, Church D, Smit VTHBM, Creutzberg CL, Koelzer VH, Bosse T. Prediction of recurrence risk in endometrial cancer with multimodal deep learning. Nat Med 2024; 30:1962-1973. [PMID: 38789645 PMCID: PMC11271412 DOI: 10.1038/s41591-024-02993-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 04/11/2024] [Indexed: 05/26/2024]
Abstract
Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopathology-based endometrial cancer tailored outcome risk), a multimodal deep learning prognostic model using hematoxylin and eosin-stained, whole-slide images and tumor stage as input, on 2,072 patients from eight EC cohorts including the PORTEC-1/-2/-3 randomized trials. HECTOR demonstrated C-indices in internal (n = 353) and two external (n = 160 and n = 151) test sets of 0.789, 0.828 and 0.815, respectively, outperforming the current gold standard, and identified patients with markedly different outcomes (10-year distant recurrence-free probabilities of 97.0%, 77.7% and 58.1% for HECTOR low-, intermediate- and high-risk groups, respectively, by Kaplan-Meier analysis). HECTOR also predicted adjuvant chemotherapy benefit better than current methods. Morphological and genomic feature extraction identified correlates of HECTOR risk groups, some with therapeutic potential. HECTOR improves on the current gold standard and may help delivery of personalized treatment in EC.
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Affiliation(s)
| | - Nanda Horeweg
- Department of Radiation Oncology, Leiden University Medical Center, Leiden, The Netherlands
| | - Sonali Andani
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
- Department of Pathology and Molecular Pathology, University Hospital, University of Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Jurriaan Barkey Wolf
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Maxime W Lafarge
- Department of Pathology and Molecular Pathology, University Hospital, University of Zurich, Zurich, Switzerland
| | - Cor D de Kroon
- Department of Gynecology and Obstetrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Gitte Ørtoft
- Department of Gynecology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Estrid Høgdall
- Department of Pathology, Herlev University Hospital, Herlev, Denmark
| | - Jouke Dijkstra
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jan J Jobsen
- Department of Radiation Oncology, Medisch Spectrum Twente, Enschede, The Netherlands
| | | | - Melanie E Powell
- Department of Clinical Oncology, Barts Health NHS Trust, London, UK
| | - Linda R Mileshkin
- Department of Medical Oncology, Peter MacCallum Cancer Center, Melbourne, Victoria, Australia
| | - Helen Mackay
- Department of Medical Oncology and Hematology, Odette Cancer Center Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Alexandra Leary
- Department Medical Oncology, Gustave Roussy Institute, Villejuif, France
| | - Dionyssios Katsaros
- Department of Surgical Sciences, Gynecologic Oncology, Città della Salute and S Anna Hospital, University of Turin, Turin, Italy
| | - Hans W Nijman
- Department of Obstetrics and Gynecology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Stephanie M de Boer
- Department of Radiation Oncology, Leiden University Medical Center, Leiden, The Netherlands
| | - Remi A Nout
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Marco de Bruyn
- Department of Obstetrics and Gynecology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - David Church
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford NIHR Comprehensive Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Vincent T H B M Smit
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Carien L Creutzberg
- Department of Radiation Oncology, Leiden University Medical Center, Leiden, The Netherlands
| | - Viktor H Koelzer
- Department of Pathology and Molecular Pathology, University Hospital, University of Zurich, Zurich, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Tjalling Bosse
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.
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Grochowski P, Grosser B, Sommer F, Probst A, Waidhauser J, Schenkirsch G, Reitsam NG, Märkl B. The Concept of Stroma AReactive Invasion Front Areas (SARIFA) as a new prognostic biomarker for lipid-driven cancers holds true in pancreatic ductal adenocarcinoma. BMC Cancer 2024; 24:768. [PMID: 38926671 PMCID: PMC11210040 DOI: 10.1186/s12885-024-12519-9] [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: 01/22/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) is a 'difficult-to-treat' entity. To forecast its prognosis, we introduced a new biomarker, SARIFA (stroma areactive invasion front areas), which are areas at the tumour invasion front lacking desmoplastic stroma reaction upon malignant invasion in the surrounding tissue, leading to direct contact between tumour cells and adipocytes. SARIFA showed its significance in gastric and colorectal carcinoma, revealing lipid metabolism alternations that promote tumour progression. METHODS We reviewed the SARIFA status of 166 PDAC cases on all available H&E-stained tumour slides from archival Whipple-resection specimens. SARIFA positivity was defined as SARIFA detection in at least 66% of the available slides. To investigate alterations in tumour metabolism and microenvironment, we performed immunohistochemical staining for FABP4, CD36 and CD68. To verify and quantify a supposed delipidation of adipocytes, adipose tissue was digitally morphometrised. RESULTS In total, 53 cases (32%) were classified as SARIFA positive and 113 (68%) as SARIFA negative. Patients with SARIFA-positive PDAC showed a significantly worse overall survival compared with SARIFA-negative cases (median overall survival: 11.0 months vs. 22.0 months, HR: 1.570 (1.082-2.278), 95% CI, p = 0.018), which was independent from other prognostic markers (p = 0.014). At the invasion front of SARIFA-positive PDAC, we observed significantly higher expression of FABP4 (p < 0.0001) and higher concentrations of CD68+ macrophages (p = 0.031) related to a higher risk of tumour progression. CD36 staining showed no significant expression differences. The adipocyte areas at the invasion front were significantly smaller, with mean values of 4021 ± 1058 µm2 and 1812 ± 1008 µm2 for the SARIFA-negative and -positive cases, respectively (p < 0.001). CONCLUSIONS SARIFA is a promising prognostic biomarker for PDAC. Its assessment is characterised by simplicity and low effort. The mechanisms behind SARIFA suggest a tumour-promoting increased lipid metabolism and altered immune background, both showing new therapeutic avenues.
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Affiliation(s)
| | - Bianca Grosser
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Florian Sommer
- General, Visceral and Transplantation Surgery, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Andreas Probst
- Gastroenterology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Johanna Waidhauser
- Hematology and Oncology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | | | - Nic G Reitsam
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Bruno Märkl
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
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Cai C, Zhou Y, Jiao Y, Li L, Xu J. Prognostic Analysis Combining Histopathological Features and Clinical Information to Predict Colorectal Cancer Survival from Whole-Slide Images. Dig Dis Sci 2024:10.1007/s10620-024-08501-x. [PMID: 38837111 DOI: 10.1007/s10620-024-08501-x] [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: 01/09/2024] [Accepted: 05/13/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND Colorectal cancer (CRC) is a malignant tumor within the digestive tract with both a high incidence rate and mortality. Early detection and intervention could improve patient clinical outcomes and survival. METHODS This study computationally investigates a set of prognostic tissue and cell features from diagnostic tissue slides. With the combination of clinical prognostic variables, the pathological image features could predict the prognosis in CRC patients. Our CRC prognosis prediction pipeline sequentially consisted of three modules: (1) A MultiTissue Net to delineate outlines of different tissue types within the WSI of CRC for further ROI selection by pathologists. (2) Development of three-level quantitative image metrics related to tissue compositions, cell shape, and hidden features from a deep network. (3) Fusion of multi-level features to build a prognostic CRC model for predicting survival for CRC. RESULTS Experimental results suggest that each group of features has a particular relationship with the prognosis of patients in the independent test set. In the fusion features combination experiment, the accuracy rate of predicting patients' prognosis and survival status is 81.52%, and the AUC value is 0.77. CONCLUSION This paper constructs a model that can predict the postoperative survival of patients by using image features and clinical information. Some features were found to be associated with the prognosis and survival of patients.
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Affiliation(s)
- Chengfei Cai
- School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
- College of Information Engineering, Taizhou University, Taizhou, 225300, China.
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
| | - Yangshu Zhou
- Department of Pathology, Zhujiang Hospital of Southern Medical University, Guangzhou, 510280, China
| | - Yiping Jiao
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Liang Li
- Department of Pathology, Nanfang Hospital of Southern Medical University, Guangzhou, 510515, China
| | - Jun Xu
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China
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11
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Xu H, Usuyama N, Bagga J, Zhang S, Rao R, Naumann T, Wong C, Gero Z, González J, Gu Y, Xu Y, Wei M, Wang W, Ma S, Wei F, Yang J, Li C, Gao J, Rosemon J, Bower T, Lee S, Weerasinghe R, Wright BJ, Robicsek A, Piening B, Bifulco C, Wang S, Poon H. A whole-slide foundation model for digital pathology from real-world data. Nature 2024; 630:181-188. [PMID: 38778098 PMCID: PMC11153137 DOI: 10.1038/s41586-024-07441-w] [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: 11/30/2023] [Accepted: 04/19/2024] [Indexed: 05/25/2024]
Abstract
Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles1-3. Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context4. Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer architecture for pretraining gigapixel pathology slides. To scale GigaPath for slide-level learning with tens of thousands of image tiles, GigaPath adapts the newly developed LongNet5 method to digital pathology. To evaluate Prov-GigaPath, we construct a digital pathology benchmark comprising 9 cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data6. With large-scale pretraining and ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best method on 18 tasks. We further demonstrate the potential of Prov-GigaPath on vision-language pretraining for pathology7,8 by incorporating the pathology reports. In sum, Prov-GigaPath is an open-weight foundation model that achieves state-of-the-art performance on various digital pathology tasks, demonstrating the importance of real-world data and whole-slide modelling.
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Affiliation(s)
- Hanwen Xu
- Microsoft Research, Redmond, WA, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | | | | | | | | | | | | | | | | | - Yu Gu
- Microsoft Research, Redmond, WA, USA
| | - Yanbo Xu
- Microsoft Research, Redmond, WA, USA
| | - Mu Wei
- Microsoft Research, Redmond, WA, USA
| | | | | | - Furu Wei
- Microsoft Research, Redmond, WA, USA
| | | | | | | | | | | | - Soohee Lee
- Providence Research Network, Renton, WA, USA
| | | | | | | | - Brian Piening
- Providence Genomics, Portland, OR, USA
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Carlo Bifulco
- Providence Genomics, Portland, OR, USA.
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA.
| | - Sheng Wang
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
- Department of Surgery, University of Washington, Seattle, WA, USA.
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12
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Reitsam NG, Grosser B, Enke JS, Mueller W, Westwood A, West NP, Quirke P, Märkl B, Grabsch HI. Stroma AReactive Invasion Front Areas (SARIFA): a novel histopathologic biomarker in colorectal cancer patients and its association with the luminal tumour proportion. Transl Oncol 2024; 44:101913. [PMID: 38593584 PMCID: PMC11024380 DOI: 10.1016/j.tranon.2024.101913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 02/06/2024] [Accepted: 02/13/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Stroma AReactive Invasion Front Areas (SARIFA) is a novel prognostic histopathologic biomarker measured at the invasive front in haematoxylin & eosin (H&E) stained colon and gastric cancer resection specimens. The aim of the current study was to validate the prognostic relevance of SARIFA-status in colorectal cancer (CRC) patients and investigate its association with the luminal proportion of tumour (PoT). METHODS We established the SARIFA-status in 164 CRC resection specimens. The relationship between SARIFA-status, clinicopathological characteristics, recurrence-free survival (RFS), cancer-specific survival (CSS), and PoT was investigated. RESULTS SARIFA-status was positive in 22.6% of all CRCs. SARIFA-positivity was related to higher pT, pN, pTNM stage and high grade of differentiation. SARIFA-positivity was associated with shorter RFS independent of known prognostic factors analysing all CRCs (RFS: hazard ratio (HR) 2.6, p = 0.032, CSS: HR 2.4, p = 0.05) and shorter RFS and CSS analysing only rectal cancers. SARIFA-positivity, which was measured at the invasive front, was associated with PoT-low (p = 0.009), e.g., higher stroma content, and lower vessel density (p = 0.0059) measured at the luminal tumour surface. CONCLUSION Here, we validated the relationship between SARIFA-status and prognosis in CRC patients and provided first evidence for a potential prognostic relevance in the subgroup of rectal cancer patients. Interestingly, CRCs with different SARIFA-status also showed histological differences measurable at the luminal tumour surface. Further studies to better understand the relationship between high luminal intratumoural stroma content and absence of a stroma reaction at the invasive front (SARIFA-positivity) are warranted and may inform future treatment decisions in CRC patients.
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Affiliation(s)
- N G Reitsam
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - B Grosser
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - J S Enke
- Nuclear Medicine, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - W Mueller
- Gemeinschaftspraxis Pathologie, Starnberg, Germany
| | - A Westwood
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's University, University of Leeds, Leeds, UK
| | - N P West
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's University, University of Leeds, Leeds, UK
| | - P Quirke
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's University, University of Leeds, Leeds, UK
| | - B Märkl
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany.
| | - H I Grabsch
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's University, University of Leeds, Leeds, UK; Department of Pathology, GROW - Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands.
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13
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Sinicrope FA, Nelson GD, Saberzadeh-Ardestani B, Segovia DI, Graham RP, Wu C, Hagen CE, Shivji S, Savage P, Buchanan DD, Jenkins MA, Phipps AI, Swallow C, LeMarchand L, Gallinger S, Grant RC, Pai RK, Sinicrope SN, Yan D, Shanmugam K, Conner J, Cyr DP, Kirsch R, Banerjee I, Alberts SR, Shi Q, Pai RK. Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence. CANCER RESEARCH COMMUNICATIONS 2024; 4:1344-1350. [PMID: 38709069 PMCID: PMC11114095 DOI: 10.1158/2767-9764.crc-24-0031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 03/21/2024] [Accepted: 05/02/2024] [Indexed: 05/07/2024]
Abstract
Deep learning may detect biologically important signals embedded in tumor morphologic features that confer distinct prognoses. Tumor morphologic features were quantified to enhance patient risk stratification within DNA mismatch repair (MMR) groups using deep learning. Using a quantitative segmentation algorithm (QuantCRC) that identifies 15 distinct morphologic features, we analyzed 402 resected stage III colon carcinomas [191 deficient (d)-MMR; 189 proficient (p)-MMR] from participants in a phase III trial of FOLFOX-based adjuvant chemotherapy. Results were validated in an independent cohort (176 d-MMR; 1,094 p-MMR). Association of morphologic features with clinicopathologic variables, MMR, KRAS, BRAFV600E, and time-to-recurrence (TTR) was determined. Multivariable Cox proportional hazards models were developed to predict TTR. Tumor morphologic features differed significantly by MMR status. Cancers with p-MMR had more immature desmoplastic stroma. Tumors with d-MMR had increased inflammatory stroma, epithelial tumor-infiltrating lymphocytes (TIL), high-grade histology, mucin, and signet ring cells. Stromal subtype did not differ by BRAFV600E or KRAS status. In p-MMR tumors, multivariable analysis identified tumor-stroma ratio (TSR) as the strongest feature associated with TTR [HRadj 2.02; 95% confidence interval (CI), 1.14-3.57; P = 0.018; 3-year recurrence: 40.2% vs. 20.4%; Q1 vs. Q2-4]. Among d-MMR tumors, extent of inflammatory stroma (continuous HRadj 0.98; 95% CI, 0.96-0.99; P = 0.028; 3-year recurrence: 13.3% vs. 33.4%, Q4 vs. Q1) and N stage were the most robust prognostically. Association of TSR with TTR was independently validated. In conclusion, QuantCRC can quantify morphologic differences within MMR groups in routine tumor sections to determine their relative contributions to patient prognosis, and may elucidate relevant pathophysiologic mechanisms driving prognosis. SIGNIFICANCE A deep learning algorithm can quantify tumor morphologic features that may reflect underlying mechanisms driving prognosis within MMR groups. TSR was the most robust morphologic feature associated with TTR in p-MMR colon cancers. Extent of inflammatory stroma and N stage were the strongest prognostic features in d-MMR tumors. TIL density was not independently prognostic in either MMR group.
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Affiliation(s)
- Frank A. Sinicrope
- Departments of Medicine and Oncology, Rochester, Minnesota
- Gastrointestinal Research Unit, Mayo Clinic, Rochester, Minnesota
| | - Garth D. Nelson
- Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, Minnesota
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | | | - Diana I. Segovia
- Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, Minnesota
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Rondell P. Graham
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Christina Wu
- Division of Medical Oncology, Mayo Clinic, Phoenix, Arizona
| | - Catherine E. Hagen
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Sameer Shivji
- Department of Pathology, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Paul Savage
- Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Dan D. Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia
- Genetic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Mark A. Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Amanda I. Phipps
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington, Seattle, Washington
| | - Carol Swallow
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
| | - Loic LeMarchand
- Department of Epidemiology, University of Hawaii, Honolulu, Hawaii
| | - Steven Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Robert C. Grant
- Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Reetesh K. Pai
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | | | | | | | - James Conner
- Department of Pathology, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - David P. Cyr
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
| | - Richard Kirsch
- Department of Pathology, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Imon Banerjee
- Department of Radiology and Machine Intelligence in Medicine and Imaging Center (MI-2), Mayo Clinic Arizona, Phoenix, Arizona
| | | | - Qian Shi
- Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, Minnesota
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Rish K. Pai
- Department of Pathology and Laboratory Medicine, Mayo Clinic, Arizona
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14
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McGenity C, Clarke EL, Jennings C, Matthews G, Cartlidge C, Freduah-Agyemang H, Stocken DD, Treanor D. Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy. NPJ Digit Med 2024; 7:114. [PMID: 38704465 PMCID: PMC11069583 DOI: 10.1038/s41746-024-01106-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 04/12/2024] [Indexed: 05/06/2024] Open
Abstract
Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical practice is essential. Growing numbers of studies using AI for digital pathology have been reported over recent years. The aim of this work is to examine the diagnostic accuracy of AI in digital pathology images for any disease. This systematic review and meta-analysis included diagnostic accuracy studies using any type of AI applied to whole slide images (WSIs) for any disease. The reference standard was diagnosis by histopathological assessment and/or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model, with additional subgroup analyses also performed. Of 2976 identified studies, 100 were included in the review and 48 in the meta-analysis. Studies were from a range of countries, including over 152,000 whole slide images (WSIs), representing many diseases. These studies reported a mean sensitivity of 96.3% (CI 94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4). There was heterogeneity in study design and 99% of studies identified for inclusion had at least one area at high or unclear risk of bias or applicability concerns. Details on selection of cases, division of model development and validation data and raw performance data were frequently ambiguous or missing. AI is reported as having high diagnostic accuracy in the reported areas but requires more rigorous evaluation of its performance.
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Affiliation(s)
- Clare McGenity
- University of Leeds, Leeds, UK.
- Leeds Teaching Hospitals NHS Trust, Leeds, UK.
| | - Emily L Clarke
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Charlotte Jennings
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | | | | | | | | | - Darren Treanor
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Department of Clinical Pathology and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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15
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Yilmaz F, Brickman A, Najdawi F, Yakirevich E, Egger R, Resnick MB. Advancing Artificial Intelligence Integration Into the Pathology Workflow: Exploring Opportunities in Gastrointestinal Tract Biopsies. J Transl Med 2024; 104:102043. [PMID: 38431118 DOI: 10.1016/j.labinv.2024.102043] [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: 10/30/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024] Open
Abstract
This review aims to present a comprehensive overview of the current landscape of artificial intelligence (AI) applications in the analysis of tubular gastrointestinal biopsies. These publications cover a spectrum of conditions, ranging from inflammatory ailments to malignancies. Moving beyond the conventional diagnosis based on hematoxylin and eosin-stained whole-slide images, the review explores additional implications of AI, including its involvement in interpreting immunohistochemical results, molecular subtyping, and the identification of cellular spatial biomarkers. Furthermore, the review examines how AI can contribute to enhancing the quality and control of diagnostic processes, introducing new workflow options, and addressing the limitations and caveats associated with current AI platforms in this context.
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Affiliation(s)
- Fazilet Yilmaz
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Arlen Brickman
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Fedaa Najdawi
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Evgeny Yakirevich
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | | | - Murray B Resnick
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island.
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16
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Lotter W, Hassett MJ, Schultz N, Kehl KL, Van Allen EM, Cerami E. Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions. Cancer Discov 2024; 14:711-726. [PMID: 38597966 PMCID: PMC11131133 DOI: 10.1158/2159-8290.cd-23-1199] [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: 10/12/2023] [Revised: 01/29/2024] [Accepted: 02/28/2024] [Indexed: 04/11/2024]
Abstract
Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of the field, with a specific focus on clinical integration. AI applications are structured according to cancer type and clinical domain, focusing on the four most common cancers and tasks of detection, diagnosis, and treatment. These applications encompass various data modalities, including imaging, genomics, and medical records. We conclude with a summary of existing challenges, evolving solutions, and potential future directions for the field. SIGNIFICANCE AI is increasingly being applied to all aspects of oncology, where several applications are maturing beyond research and development to direct clinical integration. This review summarizes the current state of the field through the lens of clinical translation along the clinical care continuum. Emerging areas are also highlighted, along with common challenges, evolving solutions, and potential future directions for the field.
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Affiliation(s)
- William Lotter
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael J. Hassett
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nikolaus Schultz
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center; New York, NY, USA
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kenneth L. Kehl
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Eliezer M. Van Allen
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ethan Cerami
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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17
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Ding Z, Han L, Zhang Q, Hu J, Li L, Qian X. Membrane Trafficking-Related Genes Predict Tumor Immune Microenvironment and Prognosis in Colorectal Cancer. Biochem Genet 2024; 62:1413-1427. [PMID: 37615899 DOI: 10.1007/s10528-023-10498-3] [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: 04/29/2023] [Accepted: 08/08/2023] [Indexed: 08/25/2023]
Abstract
Colorectal cancer (CRC) is a heterogeneous disease with varying clinical outcomes. The identification of distinct subgroups of CRC patients based on molecular profiling can aid in better understanding the disease and improving patient outcomes. This study aimed to investigate the potential of membrane trafficking-related genes (MTRGs) in sub-grouping colorectal cancer patients based on their overall survival and immune microenvironments. Consensus clustering analysis identified two distinct clusters with different expression profiles of membrane trafficking-related genes. The patients in cluster 1 had a significantly better overall survival than those in cluster 2. Furthermore, the immune microenvironments in the two clusters were also found to be significantly different, with cluster 1 having a higher immune score and more immune cells present. Functional analysis of differentially expressed genes between the two clusters revealed that MTRGs were involved in immune response and metabolic processes, and a risk signature model based on MTRGs was established to predict the prognosis of CRC patients. These findings suggest that MTRGs play a crucial role in the immune microenvironment and overall survival of CRC patients and may provide a potential target for personalized therapy.
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Affiliation(s)
- Zhou Ding
- Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University, Clinical Cancer Institute of Nanjing University, Nanjing, China
| | - Lu Han
- Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Qun Zhang
- Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University, Clinical Cancer Institute of Nanjing University, Nanjing, China
| | - Jing Hu
- Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University, Clinical Cancer Institute of Nanjing University, Nanjing, China
| | - Li Li
- Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University, Clinical Cancer Institute of Nanjing University, Nanjing, China
| | - Xiaoping Qian
- Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University, Clinical Cancer Institute of Nanjing University, Nanjing, China.
- Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China.
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18
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Fang Y, Xing X, Wang S, Walsh S, Yang G. Post-COVID highlights: Challenges and solutions of artificial intelligence techniques for swift identification of COVID-19. Curr Opin Struct Biol 2024; 85:102778. [PMID: 38364679 DOI: 10.1016/j.sbi.2024.102778] [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: 09/15/2023] [Revised: 01/22/2024] [Accepted: 01/26/2024] [Indexed: 02/18/2024]
Abstract
Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to develop cost-effective, non-invasive, and rapid AI-based tools. These tools were intended to alleviate the burden on healthcare systems, control the rapid spread of the virus, and enhance intervention outcomes, all in response to this unprecedented global crisis. As we transition into a post-COVID era, we retrospectively evaluate these proposed studies and offer a review of the techniques employed in AI diagnostic models, with a focus on the solutions proposed for different challenges. This review endeavors to provide insights into the diverse solutions designed to address the multifaceted challenges that arose during the pandemic. By doing so, we aim to prepare the AI community for the development of AI tools tailored to address public health emergencies effectively.
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Affiliation(s)
- Yingying Fang
- National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK
| | - Xiaodan Xing
- Bioengineering Department, Imperial College London, London W12 7SL, UK
| | - Shiyi Wang
- National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK
| | - Simon Walsh
- National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK; Bioengineering Department, Imperial College London, London W12 7SL, UK; Imperial-X, Imperial College London, London W12 7SL, UK; Cardiovascular Research Centre, Royal Brompton Hospital, London SW3 6NP, UK; School of Biomedical Engineering & Imaging Sciences, King's College London, London WC2R 2LS, UK.
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19
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Liu B, Polack M, Coudray N, Quiros AC, Sakellaropoulos T, Crobach ASLP, van Krieken JHJM, Yuan K, Tollenaar RAEM, Mesker WE, Tsirigos A. Self-Supervised Learning Reveals Clinically Relevant Histomorphological Patterns for Therapeutic Strategies in Colon Cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.26.582106. [PMID: 38496571 PMCID: PMC10942268 DOI: 10.1101/2024.02.26.582106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-and-eosin-stained whole-slide images (WSIs). We trained an SSL Barlow Twins-encoder on 435 TCGA colon adenocarcinoma WSIs to extract features from small image patches. Leiden community detection then grouped tiles into histomorphological phenotype clusters (HPCs). HPC reproducibility and predictive ability for overall survival was confirmed in an independent clinical trial cohort (N=1213 WSIs). This unbiased atlas resulted in 47 HPCs displaying unique and sharing clinically significant histomorphological traits, highlighting tissue type, quantity, and architecture, especially in the context of tumor stroma. Through in-depth analysis of these HPCs, including immune landscape and gene set enrichment analysis, and association to clinical outcomes, we shed light on the factors influencing survival and responses to treatments like standard adjuvant chemotherapy and experimental therapies. Further exploration of HPCs may unveil new insights and aid decision-making and personalized treatments for colon cancer patients.
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Reitsam NG, Grozdanov V, Löffler CML, Muti HS, Grosser B, Kather JN, Märkl B. Novel biomarker SARIFA in colorectal cancer: highly prognostic, not genetically driven and histologic indicator of a distinct tumor biology. Cancer Gene Ther 2024; 31:207-216. [PMID: 37990064 PMCID: PMC10874891 DOI: 10.1038/s41417-023-00695-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 10/24/2023] [Accepted: 11/08/2023] [Indexed: 11/23/2023]
Abstract
SARIFA (Stroma AReactive Invasion Front Areas) has recently emerged as a promising histopathological biomarker for colon and gastric cancer. To elucidate the underlying tumor biology, we assessed SARIFA-status in tissue specimens from The-Cancer-Genome-Atlas (TCGA) cohorts COAD (colonic adenocarcinoma) and READ (rectal adenocarcinoma). For the final analysis, 207 CRC patients could be included, consisting of 69 SARIFA-positive and 138 SARIFA-negative cases. In this external validation cohort, H&E-based SARIFA-positivity was strongly correlated with unfavorable overall, disease-specific, and progression-free survival, partly outperforming conventional prognostic factors. SARIFA-positivity was not associated with known high-risk genetic profiles, such as BRAF V600E mutations or microsatellite-stable status. Transcriptionally, SARIFA-positive CRCs exhibited an overlap with CRC consensus molecular subtypes CMS1 and CMS4, along with distinct differential gene expression patterns, linked to lipid metabolism and increased stromal cell infiltration scores (SIIS). Gene-expression-based drug sensitivity prediction revealed a differential treatment response in SARIFA-positive CRCs. In conclusion, SARIFA represents the H&E-based counterpart of an aggressive tumor biology, demonstrating a partial overlap with CMS1/4 and also adding a further biological layer related to lipid metabolism. Our findings underscore SARIFA-status as an ideal biomarker for refined patient stratification and novel drug developments, particularly given its cost-effective assessment based on routinely available H&E slides.
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Affiliation(s)
- Nic G Reitsam
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany.
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany.
| | | | - Chiara M L Löffler
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- Department of Medicine I, University Hospital Dresden, Dresden, Germany
| | - Hannah S Muti
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus Dresden, Dresden, Germany
| | - Bianca Grosser
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany
| | - Jakob N Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- Department of Medicine I, University Hospital Dresden, Dresden, Germany
- Pathology & 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, Germany
| | - Bruno Märkl
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany
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21
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Enke JS, Groß M, Grosser B, Sipos E, Steinestel J, Löhr P, Waidhauser J, Lapa C, Märkl B, Reitsam NG. SARIFA as a new histopathological biomarker is associated with adverse clinicopathological characteristics, tumor-promoting fatty-acid metabolism, and might predict a metastatic pattern in pT3a prostate cancer. BMC Cancer 2024; 24:65. [PMID: 38216952 PMCID: PMC10785487 DOI: 10.1186/s12885-023-11771-9] [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: 10/13/2023] [Accepted: 12/17/2023] [Indexed: 01/14/2024] Open
Abstract
BACKGROUND Recently, we introduced Stroma-AReactive-Invasion-Front-Areas (SARIFA) as a novel hematoxylin-eosin (H&E)-based histopathologic prognostic biomarker for various gastrointestinal cancers, closely related to lipid metabolism. To date, no studies on SARIFA, which is defined as direct tumor-adipocyte-interaction, beyond the alimentary tract exist. Hence, the objective of our current investigation was to study the significance of SARIFA in pT3a prostate cancer (PCa) and explore its association with lipid metabolism in PCa as lipid metabolism plays a key role in PCa development and progression. METHODS To this end, we evaluated SARIFA-status in 301 radical prostatectomy specimens and examined the relationship between SARIFA-status, clinicopathological characteristics, overall survival, and immunohistochemical expression of FABP4 and CD36 (proteins closely involved in fatty-acid metabolism). Additionally, we investigated the correlation between SARIFA and biochemical recurrence-free survival (BRFS) and PSMA-positive recurrences in PET/CT imaging in a patient subgroup. Moreover, a quantitative SARIFA cut-off was established to further understand the underlying tumor biology. RESULTS SARIFA positivity occurred in 59.1% (n = 178) of pT3a PCas. Our analysis demonstrated that SARIFA positivity is strongly associated with established high-risk features, such as R1 status, extraprostatic extension, and higher initial PSA values. Additionally, we observed an upregulation of immunohistochemical CD36 expression specifically at SARIFAs (p = 0.00014). Kaplan-Meier analyses revealed a trend toward poorer outcomes, particularly in terms of BRFS (p = 0.1). More extensive tumor-adipocyte interaction, assessed as quantity-dependent SARIFA-status on H&E slides, is also significantly associated with high-risk features, such as lymph node metastasis, and seems to be associated with worse survival outcomes (p = 0.16). Moreover, SARIFA positivity appeared to be linked to more distant lymph node and bone metastasis, although statistical significance was slightly not achieved (both p > 0.05). CONCLUSIONS This is the first study to introduce SARIFA as easy-and-fast-to-assess H&E-based biomarker in locally advanced PCa. SARIFA as the histopathologic correlate of a distinct tumor biology, closely related to lipid metabolism, could pave the way to a more detailed patient stratification and to the development of novel drugs targeting lipid metabolism in pT3a PCa. On the basis of this biomarker discovery study, further research efforts on the prognostic and predictive role of SARIFA in PCa can be designed.
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Affiliation(s)
- Johanna S Enke
- Nuclear Medicine, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Matthias Groß
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Bianca Grosser
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Eva Sipos
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Julie Steinestel
- Urology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Phillip Löhr
- Hematology and Oncology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Johanna Waidhauser
- Hematology and Oncology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Constantin Lapa
- Nuclear Medicine, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Bruno Märkl
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Nic G Reitsam
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany.
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22
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Liu P, Ji L, Ye F, Fu B. AdvMIL: Adversarial multiple instance learning for the survival analysis on whole-slide images. Med Image Anal 2024; 91:103020. [PMID: 37926034 DOI: 10.1016/j.media.2023.103020] [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: 12/13/2022] [Revised: 04/05/2023] [Accepted: 10/31/2023] [Indexed: 11/07/2023]
Abstract
The survival analysis on histological whole-slide images (WSIs) is one of the most important means to estimate patient prognosis. Although many weakly-supervised deep learning models have been developed for gigapixel WSIs, their potential is generally restricted by classical survival analysis rules and fully-supervised learning requirements. As a result, these models provide patients only with a completely-certain point estimation of time-to-event, and they could only learn from the labeled WSI data currently at a small scale. To tackle these problems, we propose a novel adversarial multiple instance learning (AdvMIL) framework. This framework is based on adversarial time-to-event modeling, and integrates the multiple instance learning (MIL) that is much necessary for WSI representation learning. It is a plug-and-play one, so that most existing MIL-based end-to-end methods can be easily upgraded by applying this framework, gaining the improved abilities of survival distribution estimation and semi-supervised learning. Our extensive experiments show that AdvMIL not only could often bring performance improvement to mainstream WSI survival analysis methods at a relatively low computational cost, but also enables these methods to effectively utilize unlabeled data via semi-supervised learning. Moreover, it is observed that AdvMIL could help improving the robustness of models against patch occlusion and two representative image noises. The proposed AdvMIL framework could promote the research of survival analysis in computational pathology with its novel adversarial MIL paradigm.
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Affiliation(s)
- Pei Liu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Xiyuan Ave, Chengdu, 611731, Sichuan, China.
| | - Luping Ji
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Xiyuan Ave, Chengdu, 611731, Sichuan, China.
| | - Feng Ye
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
| | - Bo Fu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Xiyuan Ave, Chengdu, 611731, Sichuan, China.
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23
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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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24
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Yang L, Yang J, Kleppe A, Danielsen HE, Kerr DJ. Personalizing adjuvant therapy for patients with colorectal cancer. Nat Rev Clin Oncol 2024; 21:67-79. [PMID: 38001356 DOI: 10.1038/s41571-023-00834-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/26/2023] [Indexed: 11/26/2023]
Abstract
The current standard-of-care adjuvant treatment for patients with colorectal cancer (CRC) comprises a fluoropyrimidine (5-fluorouracil or capecitabine) as a single agent or in combination with oxaliplatin, for either 3 or 6 months. Selection of therapy depends on conventional histopathological staging procedures, which constitute a blunt tool for patient stratification. Given the relatively marginal survival benefits that patients can derive from adjuvant treatment, improving the safety of chemotherapy regimens and identifying patients most likely to benefit from them is an area of unmet need. Patient stratification should enable distinguishing those at low risk of recurrence and a high chance of cure by surgery from those at higher risk of recurrence who would derive greater absolute benefits from chemotherapy. To this end, genetic analyses have led to the discovery of germline determinants of toxicity from fluoropyrimidines, the identification of patients at high risk of life-threatening toxicity, and enabling dose modulation to improve safety. Thus far, results from analyses of resected tissue to identify mutational or transcriptomic signatures with value as prognostic biomarkers have been rather disappointing. In the past few years, the application of artificial intelligence-driven models to digital images of resected tissue has identified potentially useful algorithms that stratify patients into distinct prognostic groups. Similarly, liquid biopsy approaches involving measurements of circulating tumour DNA after surgery are additionally useful tools to identify patients at high and low risk of tumour recurrence. In this Perspective, we provide an overview of the current landscape of adjuvant therapy for patients with CRC and discuss how new technologies will enable better personalization of therapy in this setting.
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Affiliation(s)
- Li Yang
- Department of Gastroenterology, Sichuan University, Chengdu, China
| | - Jinlin Yang
- Department of Gastroenterology, Sichuan University, Chengdu, China
| | - Andreas Kleppe
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
- Centre for Research-based Innovation Visual Intelligence, UiT The Arctic University of Norway, Tromsø, Norway
| | - Håvard E Danielsen
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway
- Radcliffe Department of Medicine, Oxford University, Oxford, UK
| | - David J Kerr
- Radcliffe Department of Medicine, Oxford University, Oxford, UK.
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25
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Grosser B, Heyer CM, Austgen J, Sipos E, Reitsam NG, Hauser A, VanSchoiack A, Kroeppler D, Vlasenko D, Probst A, Novotny A, Weichert W, Keller G, Schlesner M, Märkl B. Stroma AReactive Invasion Front Areas (SARIFA) proves prognostic relevance in gastric carcinoma and is based on a tumor-adipocyte interaction indicating an altered immune response. Gastric Cancer 2024; 27:72-85. [PMID: 37874427 PMCID: PMC10761465 DOI: 10.1007/s10120-023-01436-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 09/28/2023] [Indexed: 10/25/2023]
Abstract
BACKGROUND Recently, we presented Stroma AReactive Invasion Front Areas (SARIFA) as a new histomorphologic negative prognostic biomarker in gastric cancer. It is defined as direct contact between tumor cells and fat cells. The aim of this study was to further elucidate the underlying genomic, transcriptional, and immunological mechanisms of the SARIFA phenomenon. METHODS To address these questions, SARIFA was classified on H&E-stained tissue sections of three cohorts: an external cohort (n = 489, prognostic validation), the TCGA-STAD cohort (n = 194, genomic and transcriptomic analysis), and a local cohort (n = 60, digital spatial profiling (whole transcriptome) and double RNA in situ hybridization/immunostaining of cytokines). RESULTS SARIFA status proved to be an independent negative prognostic factor for overall survival in an external cohort of gastric carcinomas. In TCGA-STAD cohort, SARIFA is not driven by distinct genomic alterations, whereas the gene expression analyses showed an upregulation of FABP4 in SARIFA-positive tumors. In addition, the transcriptional regulations of white adipocyte differentiation, triglyceride metabolism, and catabolism were upregulated in pathway analyses. In the DSP analysis of SARIFA-positive tumors, FABP4 and the transcriptional regulation of white adipocyte differentiation were upregulated in macrophages. Additionally, a significantly lower expression of the cytokines IL6 and TNFα was observed at the invasion front. CONCLUSIONS SARIFA proves to be a strong negative prognostic biomarker in advanced gastric cancer, implicating an interaction of tumor cells with tumor-promoting adipocytes with crucial changes in tumor cell metabolism. SARIFA is not driven by tumor genetics but is very likely driven by an altered immune response as a causative mechanism.
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Affiliation(s)
- Bianca Grosser
- Pathology, Medical Faculty Augsburg, Institute of Pathology and Molecular Diagnostics, University of Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany.
| | - Christian M Heyer
- Biomedical Informatics, Data Mining and Data Analytics, Faculty of Applied Computer Science and Medical Faculty, University of Augsburg, Augsburg, Germany
| | - Johannes Austgen
- Pathology, Medical Faculty Augsburg, Institute of Pathology and Molecular Diagnostics, University of Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
| | - Eva Sipos
- Pathology, Medical Faculty Augsburg, Institute of Pathology and Molecular Diagnostics, University of Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
| | - Nic G Reitsam
- Pathology, Medical Faculty Augsburg, Institute of Pathology and Molecular Diagnostics, University of Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
| | - Andreas Hauser
- Biomedical Informatics, Data Mining and Data Analytics, Faculty of Applied Computer Science and Medical Faculty, University of Augsburg, Augsburg, Germany
| | | | | | - Dmytro Vlasenko
- General and Visceral Surgery, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Andreas Probst
- Gastroenterology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Alexander Novotny
- Department of Surgery, TUM School of Medicine, Technical University of Munich, Munich, Germany
| | - Wilko Weichert
- Institute of Pathology, TUM School of Medicine, Technical University of Munich, Munich, Germany
| | - Gisela Keller
- Institute of Pathology, TUM School of Medicine, Technical University of Munich, Munich, Germany
| | - Matthias Schlesner
- Biomedical Informatics, Data Mining and Data Analytics, Faculty of Applied Computer Science and Medical Faculty, University of Augsburg, Augsburg, Germany
| | - Bruno Märkl
- Pathology, Medical Faculty Augsburg, Institute of Pathology and Molecular Diagnostics, University of Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
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26
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Liang M, Wang R, Liang J, Wang L, Li B, Jia X, Zhang Y, Chen Q, Zhang T, Zhang C. Interpretable Inference and Classification of Tissue Types in Histological Colorectal Cancer Slides Based on Ensembles Adaptive Boosting Prototype Tree. IEEE J Biomed Health Inform 2023; 27:6006-6017. [PMID: 37871093 DOI: 10.1109/jbhi.2023.3326467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Digital pathology images are treated as the "gold standard" for the diagnosis of colorectal lesions, especially colon cancer. Real-time, objective and accurate inspection results will assist clinicians to choose symptomatic treatment in a timely manner, which is of great significance in clinical medicine. However, Manual methods suffers from long inspection cycle and serious reliance on subjective interpretation. It is also a challenging task for existing computer-aided diagnosis methods to obtain models that are both accurate and interpretable. Models that exhibit high accuracy are always more complex and opaque, while interpretable models may lack the necessary accuracy. Therefore, the framework of ensemble adaptive boosting prototype tree is proposed to predict the colorectal pathology images and provide interpretable inference by visualizing the decision-making process in each base learner. The results showed that the proposed method could effectively address the "accuracy-interpretability trade-off" issue by ensemble of m adaptive boosting neural prototype trees. The superior performance of the framework provides a novel paradigm for interpretable inference and high-precision prediction of pathology image patches in computational pathology.
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27
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McHugh K, Pai RK. Deep Learning and Colon Cancer Interpretation: Rise of the Machine. Surg Pathol Clin 2023; 16:651-658. [PMID: 37863557 DOI: 10.1016/j.path.2023.05.003] [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] [Indexed: 10/22/2023]
Abstract
The rapidly evolving development of artificial intelligence (AI) has spurred the development of numerous algorithms that augment information obtained from routine pathologic review of hematoxylin and eosin-stained slides. AI tools that predict prognosis and underlying molecular alterations have been the focus of much of the research to date. The results of these studies highlight the tremendous potential of AI to enhance our pathology reports by providing rapid predictions of key features that influence therapy and outcomes.
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Affiliation(s)
- Kelsey McHugh
- Department of Pathology and Laboratory Medicine, Mayo Clinic Arizona, 13400 East Shea Boulevard, Scottsdale, AZ 85253, USA
| | - Rish K Pai
- Department of Pathology and Laboratory Medicine, Mayo Clinic Arizona, 13400 East Shea Boulevard, Scottsdale, AZ 85253, USA.
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28
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Muti HS, Röcken C, Behrens HM, Löffler CML, Reitsam NG, Grosser B, Märkl B, Stange DE, Jiang X, Velduizen GP, Truhn D, Ebert MP, Grabsch HI, Kather JN. Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology: a retrospective multicentric study. Eur J Cancer 2023; 194:113335. [PMID: 37862795 DOI: 10.1016/j.ejca.2023.113335] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 08/15/2023] [Accepted: 09/03/2023] [Indexed: 10/22/2023]
Abstract
AIM Gastric cancer (GC) is a tumour entity with highly variant outcomes. Lymph node metastasis is a prognostically adverse biomarker. We hypothesised that GC primary tissue contains information that is predictive of lymph node status and patient prognosis and that this information can be extracted using deep learning (DL). METHODS Using three patient cohorts comprising 1146 patients, we trained and validated a DL system to predict lymph node status directly from haematoxylin and eosin-stained GC tissue sections. We investigated the concordance between the DL-based prediction from the primary tumour slides (aiN score) and the histopathological lymph node status (pN). Furthermore, we assessed the prognostic value of the aiN score alone and when combined with the pN status. RESULTS The aiN score predicted the pN status reaching area under the receiver operating characteristic curves of 0.71 in the training cohort and 0.69 and 0.65 in the two test cohorts. In a multivariate Cox analysis, the aiN score was an independent predictor of patient survival with hazard ratios of 1.5 in the training cohort and of 1.3 and 2.2 in the two test cohorts. A combination of the aiN score and the pN status prognostically stratified patients by survival with p-values <0.05 in logrank tests. CONCLUSION GC primary tumour tissue contains additional prognostic information that is accessible using the aiN score. In combination with the pN status, this can be used for personalised management of GC patients after prospective validation.
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Affiliation(s)
- Hannah S Muti
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Visceral, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus Dresden, Dresden, Germany
| | - Christoph Röcken
- Department of Pathology, University Hospital Schleswig-Holstein, Kiel, Germany
| | | | - Chiara M L Löffler
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Medicine I, University Hospital Dresden, Dresden, Germany
| | - Nic G Reitsam
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Bianca Grosser
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Bruno Märkl
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Daniel E Stange
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus Dresden, Dresden, Germany
| | - Xiaofeng Jiang
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Gregory P Velduizen
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Germany
| | - Matthias P Ebert
- Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; DKFZ-Hector Cancer Institute at the University Medical Center, Mannheim, Germany; Clinical Cooperation Unit Healthy Metabolism, Center for Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Mannheim Institute for Innate Immunoscience (MI3), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Heike I Grabsch
- Pathology & 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, the Netherlands
| | - Jakob N Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Medicine I, University Hospital Dresden, Dresden, Germany; Pathology & 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, Germany.
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Qi L, Liang JY, Li ZW, Xi SY, Lai YN, Gao F, Zhang XR, Wang DS, Hu MT, Cao Y, Xu LJ, Chan RC, Xing BC, Wang X, Li YH. Deep learning-derived spatial organization features on histology images predicts prognosis in colorectal liver metastasis patients after hepatectomy. iScience 2023; 26:107702. [PMID: 37701575 PMCID: PMC10494211 DOI: 10.1016/j.isci.2023.107702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 07/10/2023] [Accepted: 08/21/2023] [Indexed: 09/14/2023] Open
Abstract
Histopathological images of colorectal liver metastases (CRLM) contain rich morphometric information that may predict patients' outcomes. However, to our knowledge, no study has reported any practical deep learning framework based on the histology images of CRLM, and their direct association with prognosis remains largely unknown. In this study, we developed a deep learning-based framework for fully automated tissue classification and quantification of clinically relevant spatial organization features (SOFs) in H&E-stained images of CRLM. The SOFs based risk-scoring system demonstrated a strong and robust prognostic value that is independent of the current clinical risk score (CRS) system in independent clinical cohorts. Our framework enables fully automated tissue classification of H&E images of CRLM, which could significantly reduce assessment subjectivity and the workload of pathologists. The risk-scoring system provides a time- and cost-efficient tool to assist clinical decision-making for patients with CRLM, which could potentially be implemented in clinical practice.
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Affiliation(s)
- Lin Qi
- Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China
| | - Jie-ying Liang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
- Department of Medical Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Zhong-wu Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Pathology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Shao-yan Xi
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
- Department of Pathology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yu-ni Lai
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China
| | - Feng Gao
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xian-rui Zhang
- Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China
| | - De-shen Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
- Department of Medical Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Ming-tao Hu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
- Department of Medical Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yi Cao
- Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China
| | - Li-jian Xu
- Centre for Perceptual and Interactive Intelligence, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Ronald C.K. Chan
- Department of Pathology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Bao-cai Xing
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Hepatopancreatobiliary Surgery Department I, Peking University Cancer Hospital & Institute, Beijing, China
| | - Xin Wang
- Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China
| | - Yu-hong Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
- Department of Medical Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, China
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30
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Jing Y, Li C, Du T, Jiang T, Sun H, Yang J, Shi L, Gao M, Grzegorzek M, Li X. A comprehensive survey of intestine histopathological image analysis using machine vision approaches. Comput Biol Med 2023; 165:107388. [PMID: 37696178 DOI: 10.1016/j.compbiomed.2023.107388] [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: 06/08/2023] [Revised: 08/06/2023] [Accepted: 08/25/2023] [Indexed: 09/13/2023]
Abstract
Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is the third most common malignancy and the fourth leading cause of cancer death worldwide. It ranks as the second most frequent cause of cancer-related deaths in the United States and other developed countries. Histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of CRC. In order to improve the objectivity and diagnostic efficiency for image analysis of intestinal histopathology, Computer-aided Diagnosis (CAD) methods based on machine learning (ML) are widely applied in image analysis of intestinal histopathology. In this investigation, we conduct a comprehensive study on recent ML-based methods for image analysis of intestinal histopathology. First, we discuss commonly used datasets from basic research studies with knowledge of intestinal histopathology relevant to medicine. Second, we introduce traditional ML methods commonly used in intestinal histopathology, as well as deep learning (DL) methods. Then, we provide a comprehensive review of the recent developments in ML methods for segmentation, classification, detection, and recognition, among others, for histopathological images of the intestine. Finally, the existing methods have been studied, and the application prospects of these methods in this field are given.
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Affiliation(s)
- Yujie Jing
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Chen Li
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.
| | - Tianming Du
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Tao Jiang
- School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China; International Joint Institute of Robotics and Intelligent Systems, Chengdu University of Information Technology, Chengdu, China
| | - Hongzan Sun
- Shengjing Hospital of China Medical University, Shenyang, China
| | - Jinzhu Yang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Liyu Shi
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Minghe Gao
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Marcin Grzegorzek
- Institute for Medical Informatics, University of Luebeck, Luebeck, Germany; Department of Knowledge Engineering, University of Economics in Katowice, Katowice, Poland
| | - Xiaoyan Li
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China.
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Höhn J, Krieghoff-Henning E, Wies C, Kiehl L, Hetz MJ, Bucher TC, Jonnagaddala J, Zatloukal K, Müller H, Plass M, Jungwirth E, Gaiser T, Steeg M, Holland-Letz T, Brenner H, Hoffmeister M, Brinker TJ. Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning. NPJ Precis Oncol 2023; 7:98. [PMID: 37752266 PMCID: PMC10522577 DOI: 10.1038/s41698-023-00451-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 09/06/2023] [Indexed: 09/28/2023] Open
Abstract
Studies have shown that colorectal cancer prognosis can be predicted by deep learning-based analysis of histological tissue sections of the primary tumor. So far, this has been achieved using a binary prediction. Survival curves might contain more detailed information and thus enable a more fine-grained risk prediction. Therefore, we established survival curve-based CRC survival predictors and benchmarked them against standard binary survival predictors, comparing their performance extensively on the clinical high and low risk subsets of one internal and three external cohorts. Survival curve-based risk prediction achieved a very similar risk stratification to binary risk prediction for this task. Exchanging other components of the pipeline, namely input tissue and feature extractor, had largely identical effects on model performance independently of the type of risk prediction. An ensemble of all survival curve-based models exhibited a more robust performance, as did a similar ensemble based on binary risk prediction. Patients could be further stratified within clinical risk groups. However, performance still varied across cohorts, indicating limited generalization of all investigated image analysis pipelines, whereas models using clinical data performed robustly on all cohorts.
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Affiliation(s)
- Julia Höhn
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Eva Krieghoff-Henning
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christoph Wies
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty, University Heidelberg, Heidelberg, Germany
| | - Lennard Kiehl
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin J Hetz
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tabea-Clara Bucher
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jitendra Jonnagaddala
- School of Population Health, Faculty of Medicine and Health, UNSW Sydney, Kensington, NSW, Australia
| | - Kurt Zatloukal
- Diagnostic and Research Center for Molecular BioMedicine, Diagnostic & Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Heimo Müller
- Diagnostic and Research Center for Molecular BioMedicine, Diagnostic & Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Markus Plass
- Diagnostic and Research Center for Molecular BioMedicine, Diagnostic & Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Emilian Jungwirth
- Diagnostic and Research Center for Molecular BioMedicine, Diagnostic & Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Timo Gaiser
- Institute of Pathology, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany
- Institute of Applied Pathology, Speyer, Germany
| | - Matthias Steeg
- Institute of Pathology, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany
| | - Tim Holland-Letz
- Department of Biostatistics, 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
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Titus J Brinker
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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32
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Pham TD. Prediction of Five-Year Survival Rate for Rectal Cancer Using Markov Models of Convolutional Features of RhoB Expression on Tissue Microarray. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3195-3204. [PMID: 37155403 DOI: 10.1109/tcbb.2023.3274211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
The ability to predict survival in cancer is clinically important because the finding can help patients and physicians make optimal treatment decisions. Artificial intelligence in the context of deep learning has been increasingly realized by the informatics-oriented medical community as a powerful machine-learning technology for cancer research, diagnosis, prediction, and treatment. This paper presents the combination of deep learning, data coding, and probabilistic modeling for predicting five-year survival in a cohort of patients with rectal cancer using images of RhoB expression on biopsies. Using about one-third of the patients' data for testing, the proposed approach achieved 90% prediction accuracy, which is much higher than the direct use of the best pretrained convolutional neural network (70%) and the best coupling of a pretrained model and support vector machines (70%).
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Wessels F, Schmitt M, Krieghoff-Henning E, Nientiedt M, Waldbillig F, Neuberger M, Kriegmair MC, Kowalewski KF, Worst TS, Steeg M, Popovic ZV, Gaiser T, von Kalle C, Utikal JS, Fröhling S, Michel MS, Nuhn P, Brinker TJ. A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma. World J Urol 2023; 41:2233-2241. [PMID: 37382622 PMCID: PMC10415487 DOI: 10.1007/s00345-023-04489-7] [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: 02/07/2023] [Accepted: 06/10/2023] [Indexed: 06/30/2023] Open
Abstract
PURPOSE To develop and validate an interpretable deep learning model to predict overall and disease-specific survival (OS/DSS) in clear cell renal cell carcinoma (ccRCC). METHODS Digitised haematoxylin and eosin-stained slides from The Cancer Genome Atlas were used as a training set for a vision transformer (ViT) to extract image features with a self-supervised model called DINO (self-distillation with no labels). Extracted features were used in Cox regression models to prognosticate OS and DSS. Kaplan-Meier for univariable evaluation and Cox regression analyses for multivariable evaluation of the DINO-ViT risk groups were performed for prediction of OS and DSS. For validation, a cohort from a tertiary care centre was used. RESULTS A significant risk stratification was achieved in univariable analysis for OS and DSS in the training (n = 443, log rank test, p < 0.01) and validation set (n = 266, p < 0.01). In multivariable analysis, including age, metastatic status, tumour size and grading, the DINO-ViT risk stratification was a significant predictor for OS (hazard ratio [HR] 3.03; 95%-confidence interval [95%-CI] 2.11-4.35; p < 0.01) and DSS (HR 4.90; 95%-CI 2.78-8.64; p < 0.01) in the training set but only for DSS in the validation set (HR 2.31; 95%-CI 1.15-4.65; p = 0.02). DINO-ViT visualisation showed that features were mainly extracted from nuclei, cytoplasm, and peritumoural stroma, demonstrating good interpretability. CONCLUSION The DINO-ViT can identify high-risk patients using histological images of ccRCC. This model might improve individual risk-adapted renal cancer therapy in the future.
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Affiliation(s)
- Frederik Wessels
- Digital Biomarkers for Oncology Group, National Centre for Tumour Diseases (NCT), German Cancer Research Centre (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Max Schmitt
- Digital Biomarkers for Oncology Group, National Centre for Tumour Diseases (NCT), German Cancer Research Centre (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Eva Krieghoff-Henning
- Digital Biomarkers for Oncology Group, National Centre for Tumour Diseases (NCT), German Cancer Research Centre (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Malin Nientiedt
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Frank Waldbillig
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Manuel Neuberger
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Maximilian C Kriegmair
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Karl-Friedrich Kowalewski
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Thomas S Worst
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Matthias Steeg
- Institute of Pathology, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Zoran V Popovic
- Institute of Pathology, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Timo Gaiser
- Institute of Pathology, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Christof von Kalle
- Department of Clinical-Translational Sciences, Berlin Institute of Health (BIH), Charité University Medicine, Berlin, Germany
| | - Jochen S Utikal
- Skin Cancer Unit, German Cancer Research Centre (DKFZ), Heidelberg, Germany
- Department of Dermatology, Venereology and Allergology, University Medical Centre Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Stefan Fröhling
- National Centre for Tumour Diseases, German Cancer Research Centre, Heidelberg, Germany
| | - Maurice S Michel
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Philipp Nuhn
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Titus J Brinker
- Digital Biomarkers for Oncology Group, National Centre for Tumour Diseases (NCT), German Cancer Research Centre (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
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Zhou J, Foroughi Pour A, Deirawan H, Daaboul F, Aung TN, Beydoun R, Ahmed FS, Chuang JH. Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality. EBioMedicine 2023; 94:104726. [PMID: 37499603 PMCID: PMC10388166 DOI: 10.1016/j.ebiom.2023.104726] [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: 01/12/2023] [Revised: 06/19/2023] [Accepted: 07/10/2023] [Indexed: 07/29/2023] Open
Abstract
BACKGROUND Colorectal cancers are the fourth most diagnosed cancer and the second leading cancer in number of deaths. Many clinical variables, pathological features, and genomic signatures are associated with patient risk, but reliable patient stratification in the clinic remains a challenging task. Here we assess how image, clinical, and genomic features can be combined to predict risk. METHODS We developed and evaluated integrative deep learning models combining formalin-fixed, paraffin-embedded (FFPE) whole slide images (WSIs), clinical variables, and mutation signatures to stratify colon adenocarcinoma (COAD) patients based on their risk of mortality. Our models were trained using a dataset of 108 patients from The Cancer Genome Atlas (TCGA), and were externally validated on newly generated dataset from Wayne State University (WSU) of 123 COAD patients and rectal adenocarcinoma (READ) patients in TCGA (N = 52). FINDINGS We first observe that deep learning models trained on FFPE WSIs of TCGA-COAD separate high-risk (OS < 3 years, N = 38) and low-risk (OS > 5 years, N = 25) patients (AUC = 0.81 ± 0.08, 5 year survival p < 0.0001, 5 year relative risk = 1.83 ± 0.04) though such models are less effective at predicting overall survival (OS) for moderate-risk (3 years < OS < 5 years, N = 45) patients (5 year survival p-value = 0.5, 5 year relative risk = 1.05 ± 0.09). We find that our integrative models combining WSIs, clinical variables, and mutation signatures can improve patient stratification for moderate-risk patients (5 year survival p < 0.0001, 5 year relative risk = 1.87 ± 0.07). Our integrative model combining image and clinical variables is also effective on an independent pathology dataset (WSU-COAD, N = 123) generated by our team (5 year survival p < 0.0001, 5 year relative risk = 1.52 ± 0.08), and the TCGA-READ data (5 year survival p < 0.0001, 5 year relative risk = 1.18 ± 0.17). Our multicenter integrative image and clinical model trained on combined TCGA-COAD and WSU-COAD is effective in predicting risk on TCGA-READ (5 year survival p < 0.0001, 5 year relative risk = 1.82 ± 0.13) data. Pathologist review of image-based heatmaps suggests that nuclear size pleomorphism, intense cellularity, and abnormal structures are associated with high-risk, while low-risk regions have more regular and small cells. Quantitative analysis shows high cellularity, high ratios of tumor cells, large tumor nuclei, and low immune infiltration are indicators of high-risk tiles. INTERPRETATION The improved stratification of colorectal cancer patients from our computational methods can be beneficial for treatment plans and enrollment of patients in clinical trials. FUNDING This study was supported by the National Cancer Institutes (Grant No. R01CA230031 and P30CA034196). The funders had no roles in study design, data collection and analysis or preparation of the manuscript.
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Affiliation(s)
- Jie Zhou
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA; Department of Genetics and Genome Sciences, UCONN Health, Farmington, CT, USA
| | | | - Hany Deirawan
- Department of Pathology, Wayne State University, Detroit, MI, USA; Department of Dermatology, Wayne State University, Detroit, MI, USA
| | - Fayez Daaboul
- Department of Pathology, Wayne State University, Detroit, MI, USA
| | - Thazin Nwe Aung
- Department of Pathology, Yale University, New Haven, CT, USA
| | - Rafic Beydoun
- Department of Pathology, Wayne State University, Detroit, MI, USA
| | | | - Jeffrey H Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA; Department of Genetics and Genome Sciences, UCONN Health, Farmington, CT, USA.
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Wu Y, Li Y, Xiong X, Liu X, Lin B, Xu B. Recent advances of pathomics in colorectal cancer diagnosis and prognosis. Front Oncol 2023; 13:1094869. [PMID: 37538112 PMCID: PMC10396402 DOI: 10.3389/fonc.2023.1094869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 06/13/2023] [Indexed: 08/05/2023] Open
Abstract
Colorectal cancer (CRC) is one of the most common malignancies, with the third highest incidence and the second highest mortality in the world. To improve the therapeutic outcome, the risk stratification and prognosis predictions would help guide clinical treatment decisions. Achieving these goals have been facilitated by the fast development of artificial intelligence (AI) -based algorithms using radiological and pathological data, in combination with genomic information. Among them, features extracted from pathological images, termed pathomics, are able to reflect sub-visual characteristics linking to better stratification and prediction of therapeutic responses. In this paper, we review recent advances in pathological image-based algorithms in CRC, focusing on diagnosis of benign and malignant lesions, micro-satellite instability, as well as prediction of neoadjuvant chemoradiotherapy and the prognosis of CRC patients.
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Affiliation(s)
- Yihan Wu
- School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
| | - Yi Li
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
- Bioengineering College, Chongqing University, Chongqing, China
| | - Xiaomin Xiong
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
- Bioengineering College, Chongqing University, Chongqing, China
| | - Xiaohua Liu
- Bioengineering College, Chongqing University, Chongqing, China
| | - Bo Lin
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
| | - Bo Xu
- School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
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Bousis D, Verras GI, Bouchagier K, Antzoulas A, Panagiotopoulos I, Katinioti A, Kehagias D, Kaplanis C, Kotis K, Anagnostopoulos CN, Mulita F. The role of deep learning in diagnosing colorectal cancer. PRZEGLAD GASTROENTEROLOGICZNY 2023; 18:266-273. [PMID: 37937113 PMCID: PMC10626379 DOI: 10.5114/pg.2023.129494] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 02/24/2023] [Indexed: 11/09/2023]
Abstract
Colon cancer is a major public health issue, affecting a growing number of individuals worldwide. Proper and early diagnosis of colon cancer is the necessary first step toward effective treatment and/or prevention of future disease relapse. Artificial intelligence and its subtypes, deep learning in particular, tend nowadays to have an expanding role in all fields of medicine, and diagnosing colon cancer is no exception. This report aims to summarize the entire application spectrum of deep learning in all diagnostic tests regarding colon cancer, from endoscopy and histologic examination to medical imaging and screening serologic tests.
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Affiliation(s)
- Dimitrios Bousis
- Department of Internal Medicine, General University Hospital of Patras, Patras, Greece
| | | | | | - Andreas Antzoulas
- Department of Surgery, General University Hospital of Patras, Patras, Greece
| | | | | | - Dimitrios Kehagias
- Department of Surgery, General University Hospital of Patras, Patras, Greece
| | | | - Konstantinos Kotis
- Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece
| | | | - Francesk Mulita
- Department of Surgery, General University Hospital of Patras, Patras, Greece
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Sun B, Chen L. Interpretable deep learning for improving cancer patient survival based on personal transcriptomes. Sci Rep 2023; 13:11344. [PMID: 37443344 PMCID: PMC10344908 DOI: 10.1038/s41598-023-38429-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 07/07/2023] [Indexed: 07/15/2023] Open
Abstract
Precision medicine chooses the optimal drug for a patient by considering individual differences. With the tremendous amount of data accumulated for cancers, we develop an interpretable neural network to predict cancer patient survival based on drug prescriptions and personal transcriptomes (CancerIDP). The deep learning model achieves 96% classification accuracy in distinguishing short-lived from long-lived patients. The Pearson correlation between predicted and actual months-to-death values is as high as 0.937. About 27.4% of patients may survive longer with an alternative medicine chosen by our deep learning model. The median survival time of all patients can increase by 3.9 months. Our interpretable neural network model reveals the most discriminating pathways in the decision-making process, which will further facilitate mechanistic studies of drug development for cancers.
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Affiliation(s)
- Bo Sun
- Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA, 90089, USA
| | - Liang Chen
- Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA, 90089, USA.
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38
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AlGhamdi R, Asar TO, Assiri FY, Mansouri RA, Ragab M. Al-Biruni Earth Radius Optimization with Transfer Learning Based Histopathological Image Analysis for Lung and Colon Cancer Detection. Cancers (Basel) 2023; 15:3300. [PMID: 37444410 DOI: 10.3390/cancers15133300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 06/13/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023] Open
Abstract
An early diagnosis of lung and colon cancer (LCC) is critical for improved patient outcomes and effective treatment. Histopathological image (HSI) analysis has emerged as a robust tool for cancer diagnosis. HSI analysis for a LCC diagnosis includes the analysis and examination of tissue samples attained from the LCC to recognize lesions or cancerous cells. It has a significant role in the staging and diagnosis of this tumor, which aids in the prognosis and treatment planning, but a manual analysis of the image is subject to human error and is also time-consuming. Therefore, a computer-aided approach is needed for the detection of LCC using HSI. Transfer learning (TL) leverages pretrained deep learning (DL) algorithms that have been trained on a larger dataset for extracting related features from the HIS, which are then used for training a classifier for a tumor diagnosis. This manuscript offers the design of the Al-Biruni Earth Radius Optimization with Transfer Learning-based Histopathological Image Analysis for Lung and Colon Cancer Detection (BERTL-HIALCCD) technique. The purpose of the study is to detect LCC effectually in histopathological images. To execute this, the BERTL-HIALCCD method follows the concepts of computer vision (CV) and transfer learning for accurate LCC detection. When using the BERTL-HIALCCD technique, an improved ShuffleNet model is applied for the feature extraction process, and its hyperparameters are chosen by the BER system. For the effectual recognition of LCC, a deep convolutional recurrent neural network (DCRNN) model is applied. Finally, the coati optimization algorithm (COA) is exploited for the parameter choice of the DCRNN approach. For examining the efficacy of the BERTL-HIALCCD technique, a comprehensive group of experiments was conducted on a large dataset of histopathological images. The experimental outcomes demonstrate that the combination of AER and COA algorithms attain an improved performance in cancer detection over the compared models.
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Affiliation(s)
- Rayed AlGhamdi
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Turky Omar Asar
- Department of Biology, College of Science and Arts at Alkamil, University of Jeddah, Jeddah, Saudi Arabia
| | - Fatmah Y Assiri
- Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Rasha A Mansouri
- Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Mahmoud Ragab
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Mathematics Department, Faculty of Science, Al-Azhar University, Naser City 11884, Cairo, Egypt
- King Abdulaziz University-University of Oxford Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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39
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Zhao M, Lau MC, Haruki K, Väyrynen JP, Gurjao C, Väyrynen SA, Dias Costa A, Borowsky J, Fujiyoshi K, Arima K, Hamada T, Lennerz JK, Fuchs CS, Nishihara R, Chan AT, Ng K, Zhang X, Meyerhardt JA, Song M, Wang M, Giannakis M, Nowak JA, Yu KH, Ugai T, Ogino S. Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data. NPJ Precis Oncol 2023; 7:57. [PMID: 37301916 PMCID: PMC10257677 DOI: 10.1038/s41698-023-00406-8] [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: 01/25/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023] Open
Abstract
Routine tumor-node-metastasis (TNM) staging of colorectal cancer is imperfect in predicting survival due to tumor pathobiological heterogeneity and imprecise assessment of tumor spread. We leveraged Bayesian additive regression trees (BART), a statistical learning technique, to comprehensively analyze patient-specific tumor characteristics for the improvement of prognostic prediction. Of 75 clinicopathologic, immune, microbial, and genomic variables in 815 stage II-III patients within two U.S.-wide prospective cohort studies, the BART risk model identified seven stable survival predictors. Risk stratifications (low risk, intermediate risk, and high risk) based on model-predicted survival were statistically significant (hazard ratios 0.19-0.45, vs. higher risk; P < 0.0001) and could be externally validated using The Cancer Genome Atlas (TCGA) data (P = 0.0004). BART demonstrated model flexibility, interpretability, and comparable or superior performance to other machine-learning models. Integrated bioinformatic analyses using BART with tumor-specific factors can robustly stratify colorectal cancer patients into prognostic groups and be readily applied to clinical oncology practice.
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Affiliation(s)
- Melissa Zhao
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Mai Chan Lau
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Koichiro Haruki
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Juha P Väyrynen
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
- Cancer and Translational Medicine Research Unit, Medical Research Center Oulu, Oulu University Hospital, and University of Oulu, Oulu, Finland
| | - Carino Gurjao
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sara A Väyrynen
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Andressa Dias Costa
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Jennifer Borowsky
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Kenji Fujiyoshi
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kota Arima
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Tsuyoshi Hamada
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jochen K Lennerz
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Reiko Nishihara
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Kimmie Ng
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Xuehong Zhang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jeffrey A Meyerhardt
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Mingyang Song
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
| | - Molin Wang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Marios Giannakis
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jonathan A Nowak
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tomotaka Ugai
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Shuji Ogino
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Cancer Immunology and Cancer Epidemiology Programs, Dana-Farber Harvard Cancer Center, Boston, MA, USA.
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40
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Azizi S, Culp L, Freyberg J, Mustafa B, Baur S, Kornblith S, Chen T, Tomasev N, Mitrović J, Strachan P, Mahdavi SS, Wulczyn E, Babenko B, Walker M, Loh A, Chen PHC, Liu Y, Bavishi P, McKinney SM, Winkens J, Roy AG, Beaver Z, Ryan F, Krogue J, Etemadi M, Telang U, Liu Y, Peng L, Corrado GS, Webster DR, Fleet D, Hinton G, Houlsby N, Karthikesalingam A, Norouzi M, Natarajan V. Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging. Nat Biomed Eng 2023:10.1038/s41551-023-01049-7. [PMID: 37291435 DOI: 10.1038/s41551-023-01049-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 05/02/2023] [Indexed: 06/10/2023]
Abstract
Machine-learning models for medical tasks can match or surpass the performance of clinical experts. However, in settings differing from those of the training dataset, the performance of a model can deteriorate substantially. Here we report a representation-learning strategy for machine-learning models applied to medical-imaging tasks that mitigates such 'out of distribution' performance problem and that improves model robustness and training efficiency. The strategy, which we named REMEDIS (for 'Robust and Efficient Medical Imaging with Self-supervision'), combines large-scale supervised transfer learning on natural images and intermediate contrastive self-supervised learning on medical images and requires minimal task-specific customization. We show the utility of REMEDIS in a range of diagnostic-imaging tasks covering six imaging domains and 15 test datasets, and by simulating three realistic out-of-distribution scenarios. REMEDIS improved in-distribution diagnostic accuracies up to 11.5% with respect to strong supervised baseline models, and in out-of-distribution settings required only 1-33% of the data for retraining to match the performance of supervised models retrained using all available data. REMEDIS may accelerate the development lifecycle of machine-learning models for medical imaging.
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Affiliation(s)
| | | | | | | | | | | | - Ting Chen
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | | | | | - Aaron Loh
- Google Research, Mountain View, CA, USA
| | | | - Yuan Liu
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - Fiona Ryan
- Georgia Institute of Technology, Computer Science, Atlanta, GA, USA
| | | | - Mozziyar Etemadi
- School of Medicine/School of Engineering, Northwestern University, Chicago, IL, USA
| | | | - Yun Liu
- Google Research, Mountain View, CA, USA
| | - Lily Peng
- Google Research, Mountain View, CA, USA
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41
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Holub P, Müller H, Bíl T, Pireddu L, Plass M, Prasser F, Schlünder I, Zatloukal K, Nenutil R, Brázdil T. Privacy risks of whole-slide image sharing in digital pathology. Nat Commun 2023; 14:2577. [PMID: 37142591 PMCID: PMC10160114 DOI: 10.1038/s41467-023-37991-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 04/11/2023] [Indexed: 05/06/2023] Open
Abstract
Access to large volumes of so-called whole-slide images-high-resolution scans of complete pathological slides-has become a cornerstone of the development of novel artificial intelligence methods in pathology for diagnostic use, education/training of pathologists, and research. Nevertheless, a methodology based on risk analysis for evaluating the privacy risks associated with sharing such imaging data and applying the principle "as open as possible and as closed as necessary" is still lacking. In this article, we develop a model for privacy risk analysis for whole-slide images which focuses primarily on identity disclosure attacks, as these are the most important from a regulatory perspective. We introduce a taxonomy of whole-slide images with respect to privacy risks and mathematical model for risk assessment and design . Based on this risk assessment model and the taxonomy, we conduct a series of experiments to demonstrate the risks using real-world imaging data. Finally, we develop guidelines for risk assessment and recommendations for low-risk sharing of whole-slide image data.
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Affiliation(s)
- Petr Holub
- BBMRI-ERIC, Graz, Austria.
- Institute of Computer Science, Masaryk University, Brno, Czech Republic.
| | - Heimo Müller
- BBMRI.at & Diagnostic & Research Center for Molecular BioMedicine, Medical University of Graz, Graz, A-8010, Austria
| | - Tomáš Bíl
- Institute of Computer Science, Masaryk University, Brno, Czech Republic
| | - Luca Pireddu
- Visual and Data-intensive Computing Group, CRS4, Pula, Italy
| | - Markus Plass
- BBMRI.at & Diagnostic & Research Center for Molecular BioMedicine, Medical University of Graz, Graz, A-8010, Austria
| | - Fabian Prasser
- Berlin Institute of Health @ Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - Kurt Zatloukal
- BBMRI.at & Diagnostic & Research Center for Molecular BioMedicine, Medical University of Graz, Graz, A-8010, Austria
| | - Rudolf Nenutil
- BBMRI.cz & Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Tomáš Brázdil
- Faculty of Informatics, Masaryk University, Brno, Czech Republic
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42
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Jiang S, Suriawinata AA, Hassanpour S. MHAttnSurv: Multi-head attention for survival prediction using whole-slide pathology images. Comput Biol Med 2023; 158:106883. [PMID: 37031509 PMCID: PMC10148238 DOI: 10.1016/j.compbiomed.2023.106883] [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: 10/17/2022] [Revised: 03/10/2023] [Accepted: 03/30/2023] [Indexed: 04/11/2023]
Abstract
Whole slide images (WSI) based survival prediction has attracted increasing interest in pathology. Despite this, extracting prognostic information from WSIs remains a challenging task due to their enormous size and the scarcity of pathologist annotations. Previous studies have utilized multiple instance learning approach to combine information from several randomly sampled patches, but this approach may not be adequate as different visual patterns may contribute unequally to prognosis prediction. In this study, we introduce a multi-head attention mechanism that allows each attention head to independently explore the utility of various visual patterns on a tumor slide, thereby enabling more comprehensive information extraction from WSIs. We evaluated our approach on four cancer types from The Cancer Genome Atlas database. Our model achieved an average c-index of 0.640, outperforming three existing state-of-the-art approaches for WSI-based survival prediction on these datasets. Visualization of attention maps reveals that the attention heads synergistically focus on different morphological patterns, providing additional evidence for the effectiveness of multi-head attention in survival prediction.
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Affiliation(s)
- Shuai Jiang
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA
| | - Arief A Suriawinata
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, 03756, USA
| | - Saeed Hassanpour
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA; Department of Computer Science, Dartmouth College, Hanover, NH, 03755, USA; Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA.
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43
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Krogue JD, Azizi S, Tan F, Flament-Auvigne I, Brown T, Plass M, Reihs R, Müller H, Zatloukal K, Richeson P, Corrado GS, Peng LH, Mermel CH, Liu Y, Chen PHC, Gombar S, Montine T, Shen J, Steiner DF, Wulczyn E. Predicting lymph node metastasis from primary tumor histology and clinicopathologic factors in colorectal cancer using deep learning. COMMUNICATIONS MEDICINE 2023; 3:59. [PMID: 37095223 PMCID: PMC10125969 DOI: 10.1038/s43856-023-00282-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 03/29/2023] [Indexed: 04/26/2023] Open
Abstract
BACKGROUND Presence of lymph node metastasis (LNM) influences prognosis and clinical decision-making in colorectal cancer. However, detection of LNM is variable and depends on a number of external factors. Deep learning has shown success in computational pathology, but has struggled to boost performance when combined with known predictors. METHODS Machine-learned features are created by clustering deep learning embeddings of small patches of tumor in colorectal cancer via k-means, and then selecting the top clusters that add predictive value to a logistic regression model when combined with known baseline clinicopathological variables. We then analyze performance of logistic regression models trained with and without these machine-learned features in combination with the baseline variables. RESULTS The machine-learned extracted features provide independent signal for the presence of LNM (AUROC: 0.638, 95% CI: [0.590, 0.683]). Furthermore, the machine-learned features add predictive value to the set of 6 clinicopathologic variables in an external validation set (likelihood ratio test, p < 0.00032; AUROC: 0.740, 95% CI: [0.701, 0.780]). A model incorporating these features can also further risk-stratify patients with and without identified metastasis (p < 0.001 for both stage II and stage III). CONCLUSION This work demonstrates an effective approach to combine deep learning with established clinicopathologic factors in order to identify independently informative features associated with LNM. Further work building on these specific results may have important impact in prognostication and therapeutic decision making for LNM. Additionally, this general computational approach may prove useful in other contexts.
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Affiliation(s)
| | | | - Fraser Tan
- Google Health, Palo Alto, California, USA
| | | | | | | | | | | | | | - Pema Richeson
- Department of Pathology, Stanford University School of Medicine, Stanford, California, USA
| | | | | | | | - Yun Liu
- Google Health, Palo Alto, California, USA
| | | | - Saurabh Gombar
- Department of Pathology, Stanford University School of Medicine, Stanford, California, USA
| | - Thomas Montine
- Department of Pathology, Stanford University School of Medicine, Stanford, California, USA
| | - Jeanne Shen
- Department of Pathology, Stanford University School of Medicine, Stanford, California, USA
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44
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Tsai PC, Lee TH, Kuo KC, Su FY, Lee TLM, Marostica E, Ugai T, Zhao M, Lau MC, Väyrynen JP, Giannakis M, Takashima Y, Kahaki SM, Wu K, Song M, Meyerhardt JA, Chan AT, Chiang JH, Nowak J, Ogino S, Yu KH. Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients. Nat Commun 2023; 14:2102. [PMID: 37055393 PMCID: PMC10102208 DOI: 10.1038/s41467-023-37179-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 03/03/2023] [Indexed: 04/15/2023] Open
Abstract
Histopathologic assessment is indispensable for diagnosing colorectal cancer (CRC). However, manual evaluation of the diseased tissues under the microscope cannot reliably inform patient prognosis or genomic variations crucial for treatment selections. To address these challenges, we develop the Multi-omics Multi-cohort Assessment (MOMA) platform, an explainable machine learning approach, to systematically identify and interpret the relationship between patients' histologic patterns, multi-omics, and clinical profiles in three large patient cohorts (n = 1888). MOMA successfully predicts the overall survival, disease-free survival (log-rank test P-value<0.05), and copy number alterations of CRC patients. In addition, our approaches identify interpretable pathology patterns predictive of gene expression profiles, microsatellite instability status, and clinically actionable genetic alterations. We show that MOMA models are generalizable to multiple patient populations with different demographic compositions and pathology images collected from distinctive digitization methods. Our machine learning approaches provide clinically actionable predictions that could inform treatments for colorectal cancer patients.
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Affiliation(s)
- Pei-Chen Tsai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan ROC
| | - Tsung-Hua Lee
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan ROC
| | - Kun-Chi Kuo
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan ROC
| | - Fang-Yi Su
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan ROC
| | - Tsung-Lu Michael Lee
- Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Tainan, Taiwan ROC
| | - Eliana Marostica
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Health Sciences and Technology, Harvard-Massachusetts Institute of Technology, Boston, MA, USA
| | - Tomotaka Ugai
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Melissa Zhao
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Mai Chan Lau
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Juha P Väyrynen
- Cancer and Translational Medicine Research Unit, Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Marios Giannakis
- Department of Medicine, Dana Farber Cancer Institute, Boston, MA, USA
| | | | | | - Kana Wu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Mingyang Song
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Andrew T Chan
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Jung-Hsien Chiang
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan ROC.
| | - Jonathan Nowak
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Shuji Ogino
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
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45
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Plass M, Kargl M, Kiehl TR, Regitnig P, Geißler C, Evans T, Zerbe N, Carvalho R, Holzinger A, Müller H. Explainability and causability in digital pathology. J Pathol Clin Res 2023. [PMID: 37045794 DOI: 10.1002/cjp2.322] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 02/17/2023] [Accepted: 03/16/2023] [Indexed: 04/14/2023]
Abstract
The current move towards digital pathology enables pathologists to use artificial intelligence (AI)-based computer programmes for the advanced analysis of whole slide images. However, currently, the best-performing AI algorithms for image analysis are deemed black boxes since it remains - even to their developers - often unclear why the algorithm delivered a particular result. Especially in medicine, a better understanding of algorithmic decisions is essential to avoid mistakes and adverse effects on patients. This review article aims to provide medical experts with insights on the issue of explainability in digital pathology. A short introduction to the relevant underlying core concepts of machine learning shall nurture the reader's understanding of why explainability is a specific issue in this field. Addressing this issue of explainability, the rapidly evolving research field of explainable AI (XAI) has developed many techniques and methods to make black-box machine-learning systems more transparent. These XAI methods are a first step towards making black-box AI systems understandable by humans. However, we argue that an explanation interface must complement these explainable models to make their results useful to human stakeholders and achieve a high level of causability, i.e. a high level of causal understanding by the user. This is especially relevant in the medical field since explainability and causability play a crucial role also for compliance with regulatory requirements. We conclude by promoting the need for novel user interfaces for AI applications in pathology, which enable contextual understanding and allow the medical expert to ask interactive 'what-if'-questions. In pathology, such user interfaces will not only be important to achieve a high level of causability. They will also be crucial for keeping the human-in-the-loop and bringing medical experts' experience and conceptual knowledge to AI processes.
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Affiliation(s)
- Markus Plass
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Michaela Kargl
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Tim-Rasmus Kiehl
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Peter Regitnig
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Christian Geißler
- DAI-Labor, Agent Oriented Technologies (AOT), Technische Universität Berlin, Berlin, Germany
| | - Theodore Evans
- DAI-Labor, Agent Oriented Technologies (AOT), Technische Universität Berlin, Berlin, Germany
| | - Norman Zerbe
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Rita Carvalho
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Andreas Holzinger
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
- Human-Centered AI Lab, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Heimo Müller
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
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Susič D, Syed-Abdul S, Dovgan E, Jonnagaddala J, Gradišek A. Artificial intelligence based personalized predictive survival among colorectal cancer patients. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107435. [PMID: 36842345 DOI: 10.1016/j.cmpb.2023.107435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 12/14/2022] [Accepted: 02/18/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Colorectal cancer is a major health concern. It is now the third most common cancer and the fourth leading cause of cancer mortality worldwide. The aim of this study was to evaluate the performance of machine learning algorithms for predicting survival of colorectal cancer patients 1 to 5 years after diagnosis, and identify the most important variables. METHODS A sample of 1236 patients diagnosed with colorectal cancer and 118 predictor variables has been used. The outcome of interest was a binary variable indicating whether the patient survived the number of years in question or not. 20 predictor variables were selected using mutual information score with the outcome. We implemented 11 machine learning algorithms and evaluated their performance with a 5 by 2-fold cross-validation with stratified folds and with paired Student's t-tests. We compared the results with the Kaplan-Meier estimator and Cox's proportional hazard regression. RESULTS Using the 20 most important predictor variables for each of the survival years, the logistic regression algorithm achieved an area under the receiver operating characteristic curve of 0.850 (0.014 SD, 0.840-0.860 95 % CI) for the 1-year, and 0.872 (0.014 SD, 0.861-0.882 95% CI) for the 5-year survival prediction. Using only the 5 most important predictor variables, the corresponding values are 0.793 (0.020 SD, 0.778-0.807 95% CI) and 0.794 (0.011 SD, 0.785-0.802 95% CI). The most important variables for 1-year prediction were number of R residual, M distant metastasis, overall stage, probable recurrence within 5 years, and tumour length, whereas for 5-year prediction the most important were probable recurrence within 5 years, R residual, M distant metastasis, number of positive lymph nodes, and palliative chemotherapy. Biomarkers do not appear among the top 20 most important ones. For all survival intervals, the probability of the top model agrees with the Kaplan-Meier estimate, both in the interval of one standard deviation and in the 95% confidence interval. CONCLUSIONS The findings suggest that machine learning algorithms can predict the survival probability of colorectal cancer patients and can be used to inform the patients and assist decision-making in clinical care management. In addition, this study unveils the most essential variables for estimating survival short- and long-term among patients with Colorectal cancer.
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Affiliation(s)
- David Susič
- Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia; Jožef Stefan International Postgraduate School, Jamova cesta 39, SI-1000 Ljubljana, Slovenia
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan.
| | - Erik Dovgan
- Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia
| | | | - Anton Gradišek
- Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia.
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From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. Cell 2023; 186:1772-1791. [PMID: 36905928 DOI: 10.1016/j.cell.2023.01.035] [Citation(s) in RCA: 85] [Impact Index Per Article: 85.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 01/10/2023] [Accepted: 01/26/2023] [Indexed: 03/12/2023]
Abstract
Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient outcomes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflow. We review how these techniques are applied to medical imaging and to molecular data obtained from liquid and solid tumor biopsies for cancer diagnosis, prognosis, and treatment design. We discuss key considerations in developing ML for the distinct challenges posed by imaging and molecular data. Finally, we examine ML models approved for cancer-related patient usage by regulatory agencies and discuss approaches to improve the clinical usefulness of ML.
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L’Imperio V, Wulczyn E, Plass M, Müller H, Tamini N, Gianotti L, Zucchini N, Reihs R, Corrado GS, Webster DR, Peng LH, Chen PHC, Lavitrano M, Liu Y, Steiner DF, Zatloukal K, Pagni F. Pathologist Validation of a Machine Learning-Derived Feature for Colon Cancer Risk Stratification. JAMA Netw Open 2023; 6:e2254891. [PMID: 36917112 PMCID: PMC10015309 DOI: 10.1001/jamanetworkopen.2022.54891] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/16/2023] Open
Abstract
IMPORTANCE Identifying new prognostic features in colon cancer has the potential to refine histopathologic review and inform patient care. Although prognostic artificial intelligence systems have recently demonstrated significant risk stratification for several cancer types, studies have not yet shown that the machine learning-derived features associated with these prognostic artificial intelligence systems are both interpretable and usable by pathologists. OBJECTIVE To evaluate whether pathologist scoring of a histopathologic feature previously identified by machine learning is associated with survival among patients with colon cancer. DESIGN, SETTING, AND PARTICIPANTS This prognostic study used deidentified, archived colorectal cancer cases from January 2013 to December 2015 from the University of Milano-Bicocca. All available histologic slides from 258 consecutive colon adenocarcinoma cases were reviewed from December 2021 to February 2022 by 2 pathologists, who conducted semiquantitative scoring for tumor adipose feature (TAF), which was previously identified via a prognostic deep learning model developed with an independent colorectal cancer cohort. MAIN OUTCOMES AND MEASURES Prognostic value of TAF for overall survival and disease-specific survival as measured by univariable and multivariable regression analyses. Interpathologist agreement in TAF scoring was also evaluated. RESULTS A total of 258 colon adenocarcinoma histopathologic cases from 258 patients (138 men [53%]; median age, 67 years [IQR, 65-81 years]) with stage II (n = 119) or stage III (n = 139) cancer were included. Tumor adipose feature was identified in 120 cases (widespread in 63 cases, multifocal in 31, and unifocal in 26). For overall survival analysis after adjustment for tumor stage, TAF was independently prognostic in 2 ways: TAF as a binary feature (presence vs absence: hazard ratio [HR] for presence of TAF, 1.55 [95% CI, 1.07-2.25]; P = .02) and TAF as a semiquantitative categorical feature (HR for widespread TAF, 1.87 [95% CI, 1.23-2.85]; P = .004). Interpathologist agreement for widespread TAF vs lower categories (absent, unifocal, or multifocal) was 90%, corresponding to a κ metric at this threshold of 0.69 (95% CI, 0.58-0.80). CONCLUSIONS AND RELEVANCE In this prognostic study, pathologists were able to learn and reproducibly score for TAF, providing significant risk stratification on this independent data set. Although additional work is warranted to understand the biological significance of this feature and to establish broadly reproducible TAF scoring, this work represents the first validation to date of human expert learning from machine learning in pathology. Specifically, this validation demonstrates that a computationally identified histologic feature can represent a human-identifiable, prognostic feature with the potential for integration into pathology practice.
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Affiliation(s)
- Vincenzo L’Imperio
- Department of Medicine and Surgery, Pathology, University of Milano-Bicocca, IRCCS (Scientific Institute for Research, Hospitalization and Healthcare) Fondazione San Gerardo dei Tintori, Monza, Italy
| | | | - Markus Plass
- Medical University of Graz, Diagnostic and Research Institute of Pathology, Graz, Austria
| | - Heimo Müller
- Medical University of Graz, Diagnostic and Research Institute of Pathology, Graz, Austria
| | - Nicolò Tamini
- Department of Surgery, San Gerardo Hospital, Monza, Italy
| | - Luca Gianotti
- Department of Surgery, San Gerardo Hospital, Monza, Italy
| | - Nicola Zucchini
- Department of Medicine and Surgery, Pathology, University of Milano-Bicocca, IRCCS (Scientific Institute for Research, Hospitalization and Healthcare) Fondazione San Gerardo dei Tintori, Monza, Italy
| | - Robert Reihs
- Medical University of Graz, Diagnostic and Research Institute of Pathology, Graz, Austria
| | | | | | - Lily H. Peng
- Google Health, Google LLC, Palo Alto, California
| | | | - Marialuisa Lavitrano
- Department of Medicine and Surgery, Pathology, University of Milano-Bicocca, IRCCS (Scientific Institute for Research, Hospitalization and Healthcare) Fondazione San Gerardo dei Tintori, Monza, Italy
| | - Yun Liu
- Google Health, Google LLC, Palo Alto, California
| | | | - Kurt Zatloukal
- Medical University of Graz, Diagnostic and Research Institute of Pathology, Graz, Austria
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, University of Milano-Bicocca, IRCCS (Scientific Institute for Research, Hospitalization and Healthcare) Fondazione San Gerardo dei Tintori, Monza, Italy
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Alterations in Natural Killer Cells in Colorectal Cancer Patients with Stroma AReactive Invasion Front Areas (SARIFA). Cancers (Basel) 2023; 15:cancers15030994. [PMID: 36765951 PMCID: PMC9913252 DOI: 10.3390/cancers15030994] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 01/22/2023] [Accepted: 02/02/2023] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND Recently, our group introduced Stroma AReactive Invasion Front Areas (SARIFA) as an independent prognostic predictor for a poorer outcome in colon cancer patients, which is probably based on immunologic alterations combined with a direct tumor-adipocyte interaction: the two together reflecting a distinct tumor biology. Considering it is already known that peripheral immune cells are altered in colorectal cancer (CRC) patients, this study aims to investigate the changes in lymphocyte subsets in SARIFA-positive cases and correlate these changes with the local immune response. METHODS Flow cytometry was performed to analyze B, T, and natural killer (NK) cells in the peripheral blood (PB) of 45 CRC patients. Consecutively, lymphocytes in PB, tumor-infiltrating lymphocytes (TILs), and CD56+ and CD57+ lymphocytes at the invasion front and the tumor center were compared between patients with SARIFA-positive and SARIFA-negative CRCs. RESULTS Whereas no differences could be observed regarding most PB lymphocyte populations as well as TILs, NK cells were dramatically reduced in the PB of SARIFA-positive cases. Moreover, CD56 and CD57 immunohistochemistry suggested SARIFA-status-dependent changes regarding NK cells and NK-like lymphocytes in the tumor microenvironment. CONCLUSION This study proves that our newly introduced biomarker, SARIFA, comes along with distinct immunologic alterations, especially regarding NK cells.
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Foersch S, Glasner C, Woerl AC, Eckstein M, Wagner DC, Schulz S, Kellers F, Fernandez A, Tserea K, Kloth M, Hartmann A, Heintz A, Weichert W, Roth W, Geppert C, Kather JN, Jesinghaus M. Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer. Nat Med 2023; 29:430-439. [PMID: 36624314 DOI: 10.1038/s41591-022-02134-1] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 11/17/2022] [Indexed: 01/11/2023]
Abstract
Although it has long been known that the immune cell composition has a strong prognostic and predictive value in colorectal cancer (CRC), scoring systems such as the immunoscore (IS) or quantification of intraepithelial lymphocytes are only slowly being adopted into clinical routine use and have their limitations. To address this we established and evaluated a multistain deep learning model (MSDLM) utilizing artificial intelligence (AI) to determine the AImmunoscore (AIS) in more than 1,000 patients with CRC. Our model had high prognostic capabilities and outperformed other clinical, molecular and immune cell-based parameters. It could also be used to predict the response to neoadjuvant therapy in patients with rectal cancer. Using an explainable AI approach, we confirmed that the MSDLM's decisions were based on established cellular patterns of anti-tumor immunity. Hence, the AIS could provide clinicians with a valuable decision-making tool based on the tumor immune microenvironment.
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Affiliation(s)
- Sebastian Foersch
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
| | - Christina Glasner
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Ann-Christin Woerl
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
- Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Markus Eckstein
- Institute of Pathology and Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | | | - Stefan Schulz
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Franziska Kellers
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
- Department of Pathology, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Aurélie Fernandez
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | | | - Michael Kloth
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Arndt Hartmann
- Institute of Pathology and Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Achim Heintz
- Department of General Visceral and Vascular Surgery, Marien Hospital Mainz, Mainz, Germany
| | - Wilko Weichert
- Institute of Pathology, Technical University Munich, Munich, Germany
| | - Wilfried Roth
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Carol Geppert
- Institute of Pathology and Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Moritz Jesinghaus
- Institute of Pathology, Technical University Munich, Munich, Germany
- Institute of Pathology, University Hospital Marburg, Marburg, Germany
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