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Hilgers L, Ghaffari Laleh N, West NP, Westwood A, Hewitt KJ, Quirke P, Grabsch HI, Carrero ZI, Matthaei E, Loeffler CML, Brinker TJ, Yuan T, Brenner H, Brobeil A, Hoffmeister M, Kather JN. Automated curation of large-scale cancer histopathology image datasets using deep learning. Histopathology 2024; 84:1139-1153. [PMID: 38409878 DOI: 10.1111/his.15159] [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: 09/19/2023] [Revised: 12/29/2023] [Accepted: 02/09/2024] [Indexed: 02/28/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient. Manually sorting and labelling whole-slide images (WSIs) is a very time-consuming process, hindering the direct application of AI on the collected tissue samples from large cohorts. In this study we addressed this issue by developing a deep-learning (DL)-based method for automatic curation of large pathology datasets with several slides per patient. METHODS We collected multiple large multicentric datasets of colorectal cancer histopathological slides from the United Kingdom (FOXTROT, N = 21,384 slides; CR07, N = 7985 slides) and Germany (DACHS, N = 3606 slides). These datasets contained multiple types of tissue slides, including bowel resection specimens, endoscopic biopsies, lymph node resections, immunohistochemistry-stained slides, and tissue microarrays. We developed, trained, and tested a deep convolutional neural network model to predict the type of slide from the slide overview (thumbnail) image. The primary statistical endpoint was the macro-averaged area under the receiver operating curve (AUROCs) for detection of the type of slide. RESULTS In the primary dataset (FOXTROT), with an AUROC of 0.995 [95% confidence interval [CI]: 0.994-0.996] the algorithm achieved a high classification performance and was able to accurately predict the type of slide from the thumbnail image alone. In the two external test cohorts (CR07, DACHS) AUROCs of 0.982 [95% CI: 0.979-0.985] and 0.875 [95% CI: 0.864-0.887] were observed, which indicates the generalizability of the trained model on unseen datasets. With a confidence threshold of 0.95, the model reached an accuracy of 94.6% (7331 classified cases) in CR07 and 85.1% (2752 classified cases) for the DACHS cohort. CONCLUSION Our findings show that using the low-resolution thumbnail image is sufficient to accurately classify the type of slide in digital pathology. This can support researchers to make the vast resource of existing pathology archives accessible to modern AI models with only minimal manual annotations.
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Affiliation(s)
- Lars Hilgers
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Narmin Ghaffari Laleh
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Nicholas P West
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Alice Westwood
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Katherine J Hewitt
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Philip Quirke
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Heike I Grabsch
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
- Department of Pathology, GROW - Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Zunamys I Carrero
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Emylou Matthaei
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Chiara M L Loeffler
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Titus J Brinker
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tanwei Yuan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alexander Brobeil
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
- Tissue Bank, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University 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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Hu Q, Rizvi AA, Schau G, Ingale K, Muller Y, Baits R, Pretzer S, BenTaieb A, Gordhamer A, Nussenzveig R, Cole A, Leavitt MO, Jones RD, Joshi RP, Beaubier N, Stumpe MC, Nagpal K. Development and validation of a deep learning-based microsatellite instability predictor from prostate cancer whole-slide images. NPJ Precis Oncol 2024; 8:88. [PMID: 38594360 PMCID: PMC11004110 DOI: 10.1038/s41698-024-00560-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 02/22/2024] [Indexed: 04/11/2024] Open
Abstract
Microsatellite instability-high (MSI-H) is a tumor-agnostic biomarker for immune checkpoint inhibitor therapy. However, MSI status is not routinely tested in prostate cancer, in part due to low prevalence and assay cost. As such, prediction of MSI status from hematoxylin and eosin (H&E) stained whole-slide images (WSIs) could identify prostate cancer patients most likely to benefit from confirmatory testing to evaluate their eligibility for immunotherapy and need for Lynch syndrome testing. Prostate biopsies and surgical resections from prostate cancer patients referred to our institution were analyzed. MSI status was determined by next-generation sequencing. Patients sequenced before a cutoff date formed an algorithm development set (n = 4015, MSI-H 1.8%) and a paired validation set (n = 173, MSI-H 19.7%) that consisted of two serial sections from each sample, one stained and scanned internally and the other at an external site. Patients sequenced after the cutoff date formed a temporally independent validation set (n = 1350, MSI-H 2.3%). Attention-based multiple instance learning models were trained to predict MSI-H from H&E WSIs. The predictor achieved area under the receiver operating characteristic curve values of 0.78 (95% CI [0.69-0.86]), 0.72 (95% CI [0.63-0.81]), and 0.72 (95% CI [0.62-0.82]) on the internally prepared, externally prepared, and temporal validation sets, respectively, showing effective predictability and generalization to both external staining/scanning processes and temporally independent samples. While MSI-H status is significantly correlated with Gleason score, the model remained predictive within each Gleason score subgroup.
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Affiliation(s)
- Qiyuan Hu
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Abbas A Rizvi
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Geoffery Schau
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Kshitij Ingale
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Yoni Muller
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Rachel Baits
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Sebastian Pretzer
- Work done while at Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Aïcha BenTaieb
- Work done while at Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Abigail Gordhamer
- PathNet, Inc, 5100 Talley Rd Suite 300, Little Rock, AR, 72204, USA
- DDx Foundation, 2889 W Ashton Blvd. Suite 300, Lehi, UT, 84043, USA
| | - Roberto Nussenzveig
- PathNet, Inc, 5100 Talley Rd Suite 300, Little Rock, AR, 72204, USA
- DDx Foundation, 2889 W Ashton Blvd. Suite 300, Lehi, UT, 84043, USA
| | - Adam Cole
- PathNet, Inc, 5100 Talley Rd Suite 300, Little Rock, AR, 72204, USA
- DDx Foundation, 2889 W Ashton Blvd. Suite 300, Lehi, UT, 84043, USA
| | - Matthew O Leavitt
- PathNet, Inc, 5100 Talley Rd Suite 300, Little Rock, AR, 72204, USA
- DDx Foundation, 2889 W Ashton Blvd. Suite 300, Lehi, UT, 84043, USA
- Lumea, 2889 Ashton Blvd #300, Lehi, UT, 84043, USA
| | - Ryan D Jones
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Rohan P Joshi
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Nike Beaubier
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Martin C Stumpe
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Kunal Nagpal
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA.
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Godson L, Alemi N, Nsengimana J, Cook GP, Clarke EL, Treanor D, Bishop DT, Newton-Bishop J, Gooya A, Magee D. Immune subtyping of melanoma whole slide images using multiple instance learning. Med Image Anal 2024; 93:103097. [PMID: 38325154 DOI: 10.1016/j.media.2024.103097] [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: 12/16/2022] [Revised: 01/15/2024] [Accepted: 01/25/2024] [Indexed: 02/09/2024]
Abstract
Determining early-stage prognostic markers and stratifying patients for effective treatment are two key challenges for improving outcomes for melanoma patients. Previous studies have used tumour transcriptome data to stratify patients into immune subgroups, which were associated with differential melanoma specific survival and potential predictive biomarkers. However, acquiring transcriptome data is a time-consuming and costly process. Moreover, it is not routinely used in the current clinical workflow. Here, we attempt to overcome this by developing deep learning models to classify gigapixel haematoxylin and eosin (H&E) stained pathology slides, which are well established in clinical workflows, into these immune subgroups. We systematically assess six different multiple instance learning (MIL) frameworks, using five different image resolutions and three different feature extraction methods. We show that pathology-specific self-supervised models using 10x resolution patches generate superior representations for the classification of immune subtypes. In addition, in a primary melanoma dataset, we achieve a mean area under the receiver operating characteristic curve (AUC) of 0.80 for classifying histopathology images into 'high' or 'low immune' subgroups and a mean AUC of 0.82 in an independent TCGA melanoma dataset. Furthermore, we show that these models are able to stratify patients into 'high' and 'low immune' subgroups with significantly different melanoma specific survival outcomes (log rank test, P< 0.005). We anticipate that MIL methods will allow us to find new biomarkers of high importance, act as a tool for clinicians to infer the immune landscape of tumours and stratify patients, without needing to carry out additional expensive genetic tests.
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Affiliation(s)
- Lucy Godson
- School of Computing, University of Leeds, Woodhouse, Leeds, LS2 9JT, United Kingdom.
| | - Navid Alemi
- School of Computing, University of Leeds, Woodhouse, Leeds, LS2 9JT, United Kingdom
| | - Jérémie Nsengimana
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom
| | - Graham P Cook
- Leeds Institute of Medical Research, University of Leeds School of Medicine, St. James's University Hospital, Leeds, United Kingdom
| | - Emily L Clarke
- Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom; Division of Pathology and Data Analytics, Leeds Institute of Cancer and Pathology, University of Leeds, Beckett Street, Leeds, LS9 7TF, United Kingdom
| | - Darren Treanor
- Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom; Division of Pathology and Data Analytics, Leeds Institute of Cancer and Pathology, University of Leeds, Beckett Street, Leeds, LS9 7TF, United Kingdom; 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
| | - D Timothy Bishop
- Leeds Institute of Medical Research, University of Leeds School of Medicine, St. James's University Hospital, Leeds, United Kingdom
| | - Julia Newton-Bishop
- Division of Pathology and Data Analytics, Leeds Institute of Cancer and Pathology, University of Leeds, Beckett Street, Leeds, LS9 7TF, United Kingdom
| | - Ali Gooya
- School of Computing, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
| | - Derek Magee
- School of Computing, University of Leeds, Woodhouse, Leeds, LS2 9JT, United Kingdom
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Mehrotra S, Sharma S, Pandey RK. A journey from omics to clinicomics in solid cancers: Success stories and challenges. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:89-139. [PMID: 38448145 DOI: 10.1016/bs.apcsb.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.
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5
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Lo CM, Jiang JK, Lin CC. Detecting microsatellite instability in colorectal cancer using Transformer-based colonoscopy image classification and retrieval. PLoS One 2024; 19:e0292277. [PMID: 38271352 PMCID: PMC10810505 DOI: 10.1371/journal.pone.0292277] [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: 03/25/2023] [Accepted: 09/15/2023] [Indexed: 01/27/2024] Open
Abstract
Colorectal cancer (CRC) is a major global health concern, with microsatellite instability-high (MSI-H) being a defining characteristic of hereditary nonpolyposis colorectal cancer syndrome and affecting 15% of sporadic CRCs. Tumors with MSI-H have unique features and better prognosis compared to MSI-L and microsatellite stable (MSS) tumors. This study proposed establishing a MSI prediction model using more available and low-cost colonoscopy images instead of histopathology. The experiment utilized a database of 427 MSI-H and 1590 MSS colonoscopy images and vision Transformer (ViT) with different feature training approaches to establish the MSI prediction model. The accuracy of combining pre-trained ViT features was 84% with an area under the receiver operating characteristic curve of 0.86, which was better than that of DenseNet201 (80%, 0.80) in the experiment with support vector machine. The content-based image retrieval (CBIR) approach showed that ViT features can obtain a mean average precision of 0.81 compared to 0.79 of DenseNet201. ViT reduced the issues that occur in convolutional neural networks, including limited receptive field and gradient disappearance, and may be better at interpreting diagnostic information around tumors and surrounding tissues. By using CBIR, the presentation of similar images with the same MSI status would provide more convincing deep learning suggestions for clinical use.
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Affiliation(s)
- Chung-Ming Lo
- Graduate Institute of Library, Information and Archival Studies, National Chengchi University, Taipei, Taiwan
| | - Jeng-Kai Jiang
- Department of Surgery, Division of Colon and Rectal Surgery, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chun-Chi Lin
- Department of Surgery, Division of Colon and Rectal Surgery, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
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Narykov O, Zhu Y, Brettin T, Evrard YA, Partin A, Shukla M, Xia F, Clyde A, Vasanthakumari P, Doroshow JH, Stevens RL. Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models. Cancers (Basel) 2023; 16:50. [PMID: 38201477 PMCID: PMC10777918 DOI: 10.3390/cancers16010050] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/01/2023] [Accepted: 12/07/2023] [Indexed: 01/12/2024] Open
Abstract
Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions. To better characterize the interaction between cancer and drugs, we investigate the feasibility of integrating computationally derived features of molecular mechanisms of action into prediction models. Specifically, we add docking scores of drug molecules and target proteins in combination with cancer gene expressions and molecular drug descriptors for building response models. The results demonstrate a marginal improvement in drug response prediction performance when adding docking scores as additional features, through tests on large drug screening data. We discuss the limitations of the current approach and provide the research community with a baseline dataset of the large-scale computational docking for anti-cancer drugs.
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Affiliation(s)
- Oleksandr Narykov
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Yitan Zhu
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Thomas Brettin
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Yvonne A. Evrard
- Leidos Biomedical Research, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA;
| | - Alexander Partin
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Maulik Shukla
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Fangfang Xia
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Austin Clyde
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
- Department of Computer Science, The University of Chicago, Chicago, IL 60637, USA
| | - Priyanka Vasanthakumari
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - James H. Doroshow
- Developmental Therapeutics Branch, National Cancer Institute, Bethesda, MD 20892, USA;
| | - Rick L. Stevens
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
- Department of Computer Science, The University of Chicago, Chicago, IL 60637, USA
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Tavolara TE, Su Z, Gurcan MN, Niazi MKK. One label is all you need: Interpretable AI-enhanced histopathology for oncology. Semin Cancer Biol 2023; 97:70-85. [PMID: 37832751 DOI: 10.1016/j.semcancer.2023.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 09/06/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023]
Abstract
Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that require only one overall label per hematoxylin and eosin (H&E) slide with no tissue-level annotations. We present a structured review of these methods organized by their degree of verifiability and by commonly recurring application areas in oncological characterization. First, we discuss morphological markers (tumor presence/absence, metastases, subtypes, grades) in which AI-identified regions of interest (ROIs) within whole slide images (WSIs) verifiably overlap with pathologist-identified ROIs. Second, we discuss molecular markers (gene expression, molecular subtyping) that are not verified via H&E but rather based on overlap with positive regions on adjacent tissue. Third, we discuss genetic markers (mutations, mutational burden, microsatellite instability, chromosomal instability) that current technologies cannot verify if AI methods spatially resolve specific genetic alterations. Fourth, we discuss the direct prediction of survival to which AI-identified histopathological features quantitatively correlate but are nonetheless not mechanistically verifiable. Finally, we discuss in detail several opportunities and challenges for these one-label-per-slide methods within oncology. Opportunities include reducing the cost of research and clinical care, reducing the workload of clinicians, personalized medicine, and unlocking the full potential of histopathology through new imaging-based biomarkers. Current challenges include explainability and interpretability, validation via adjacent tissue sections, reproducibility, data availability, computational needs, data requirements, domain adaptability, external validation, dataset imbalances, and finally commercialization and clinical potential. Ultimately, the relative ease and minimum upfront cost with which relevant data can be collected in addition to the plethora of available AI methods for outcome-driven analysis will surmount these current limitations and achieve the innumerable opportunities associated with AI-driven histopathology for the benefit of oncology.
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Affiliation(s)
- Thomas E Tavolara
- Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Ziyu Su
- Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Metin N Gurcan
- Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - M Khalid Khan Niazi
- Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
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Saillard C, Dubois R, Tchita O, Loiseau N, Garcia T, Adriansen A, Carpentier S, Reyre J, Enea D, von Loga K, Kamoun A, Rossat S, Wiscart C, Sefta M, Auffret M, Guillou L, Fouillet A, Kather JN, Svrcek M. Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides. Nat Commun 2023; 14:6695. [PMID: 37932267 PMCID: PMC10628260 DOI: 10.1038/s41467-023-42453-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 10/11/2023] [Indexed: 11/08/2023] Open
Abstract
Mismatch Repair Deficiency (dMMR)/Microsatellite Instability (MSI) is a key biomarker in colorectal cancer (CRC). Universal screening of CRC patients for MSI status is now recommended, but contributes to increased workload for pathologists and delayed therapeutic decisions. Deep learning has the potential to ease dMMR/MSI testing and accelerate oncologist decision making in clinical practice, yet no comprehensive validation of a clinically approved tool has been conducted. We developed MSIntuit, a clinically approved artificial intelligence (AI) based pre-screening tool for MSI detection from haematoxylin-eosin (H&E) stained slides. After training on samples from The Cancer Genome Atlas (TCGA), a blind validation is performed on an independent dataset of 600 consecutive CRC patients. Inter-scanner reliability is studied by digitising each slide using two different scanners. MSIntuit yields a sensitivity of 0.96-0.98, a specificity of 0.47-0.46, and an excellent inter-scanner agreement (Cohen's κ: 0.82). By reaching high sensitivity comparable to gold standard methods while ruling out almost half of the non-MSI population, we show that MSIntuit can effectively serve as a pre-screening tool to alleviate MSI testing burden in clinical practice.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Diana Enea
- Department of Pathology, Saint-Antoine Hospital - Sorbonne Université, AP-HP, Paris, France
| | | | | | | | | | | | | | | | | | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- Department of Medicine I, University Hospital Dresden, Dresden, Germany
| | - Magali Svrcek
- Department of Pathology, Saint-Antoine Hospital - Sorbonne Université, AP-HP, Paris, France
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Cussenot O, Cancel-Tassin G, Rao SR, Woodcock DJ, Lamb AD, Mills IG, Hamdy FC. Aligning germline and somatic mutations in prostate cancer. Are genetics changing practice? BJU Int 2023; 132:472-484. [PMID: 37410655 DOI: 10.1111/bju.16120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
OBJECTIVE To review the current status of germline and somatic (tumour) genetic testing for prostate cancer (PCa), and its relevance for clinical practice. METHODS A narrative synthesis of various molecular profiles related to their clinical context was carried out. Current guidelines for genetic testing and its feasibility in clinical practice were analysed. We report the main identified genetic sequencing results or functional genomic scores for PCa published in the literature or obtained from the French PROGENE study. RESULTS The molecular alterations observed in PCa are mostly linked to disruption of the androgen receptor (AR) pathway or DNA repair deficiency. The main known germline mutations affect the BReast CAncer gene 2 (BRCA2) and homeobox B13 (HOXB13) genes, whereas AR and tumour protein p53 (TP53) are the genes with most frequent somatic alterations in tumours from men with metastatic PCa. Molecular tests are now available for detecting some of these germline or somatic alterations and sometimes recommended by guidelines, but their utilisation must combine rationality and feasibility. They can guide specific therapies, notably for the management of metastatic disease. Indeed, following androgen deprivation, targeted therapies for PCa currently include poly-(ADP-ribose)-polymerase (PARP) inhibitors, immune checkpoint inhibitors, and prostate-specific membrane antigen (PSMA)-guided radiotherapy. The genetic tests currently approved for targeted therapies remain limited to the detection of BRCA1 and BRCA2 mutation and DNA mismatch repair deficiency, while large panels are recommended for germline analyses, not only for inherited cancer predisposing syndrome, but also for metastatic PCa. CONCLUSIONS Further consensus aligning germline with somatic molecular analysis in metastatic PCa is required, including genomics scars, emergent immunohistochemistry, or functional pre-screen imaging. With rapid advances in knowledge and technology in the field, continuous updating of guidelines to help the clinical management of these individuals, and well-conducted studies to evaluate the benefits of genetic testing are needed.
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Affiliation(s)
- Olivier Cussenot
- Centre de Recherche sur les Pathologies Prostatiques et Urologiques (CeRePP), Paris, France
- GRC 5 Predictive Onco-Urology, Sorbonne University, Paris, France
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Geraldine Cancel-Tassin
- Centre de Recherche sur les Pathologies Prostatiques et Urologiques (CeRePP), Paris, France
- GRC 5 Predictive Onco-Urology, Sorbonne University, Paris, France
| | - Srinivasa R Rao
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Dan J Woodcock
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Alastair D Lamb
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Ian G Mills
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Freddie C Hamdy
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
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10
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Li Y, Du P, Zeng H, Wei Y, Fu H, Zhong X, Ma X. Integrative models of histopathological images and multi-omics data predict prognosis in endometrial carcinoma. PeerJ 2023; 11:e15674. [PMID: 37583914 PMCID: PMC10424667 DOI: 10.7717/peerj.15674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 06/11/2023] [Indexed: 08/17/2023] Open
Abstract
Objective This study aimed to predict the molecular features of endometrial carcinoma (EC) and the overall survival (OS) of EC patients using histopathological imaging. Methods The patients from The Cancer Genome Atlas (TCGA) were separated into the training set (n = 215) and test set (n = 214) in proportion of 1:1. By analyzing quantitative histological image features and setting up random forest model verified by cross-validation, we constructed prognostic models for OS. The model performance is evaluated with the time-dependent receiver operating characteristics (AUC) over the test set. Results Prognostic models based on histopathological imaging features (HIF) predicted OS in the test set (5-year AUC = 0.803). The performance of combining histopathology and omics transcends that of genomics, transcriptomics, or proteomics alone. Additionally, multi-dimensional omics data, including HIF, genomics, transcriptomics, and proteomics, attained the largest AUCs of 0.866, 0.869, and 0.856 at years 1, 3, and 5, respectively, showcasing the highest discrepancy in survival (HR = 18.347, 95% CI [11.09-25.65], p < 0.001). Conclusions The results of this experiment indicated that the complementary features of HIF could improve the prognostic performance of EC patients. Moreover, the integration of HIF and multi-dimensional omics data might ameliorate survival prediction and risk stratification in clinical practice.
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Affiliation(s)
- Yueyi Li
- Department of Targeting Therapy & Immunology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Peixin Du
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hao Zeng
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yuhao Wei
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Haoxuan Fu
- Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Xi Zhong
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xuelei Ma
- Department of Targeting Therapy & Immunology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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11
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Lo CM, Yang YW, Lin JK, Lin TC, Chen WS, Yang SH, Chang SC, Wang HS, Lan YT, Lin HH, Huang SC, Cheng HH, Jiang JK, Lin CC. Modeling the survival of colorectal cancer patients based on colonoscopic features in a feature ensemble vision transformer. Comput Med Imaging Graph 2023; 107:102242. [PMID: 37172354 DOI: 10.1016/j.compmedimag.2023.102242] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 05/05/2023] [Accepted: 05/07/2023] [Indexed: 05/14/2023]
Abstract
The prognosis of patients with colorectal cancer (CRC) mostly relies on the classic tumor node metastasis (TNM) staging classification. A more accurate and convenient prediction model would provide a better prognosis and assist in treatment. From May 2014 to December 2017, patients who underwent an operation for CRC were enrolled. The proposed feature ensemble vision transformer (FEViT) used ensemble classifiers to benefit the combinations of relevant colonoscopy features from the pretrained vision transformer and clinical features, including sex, age, family history of CRC, and tumor location, to establish the prognostic model. A total of 1729 colonoscopy images were enrolled in the current retrospective study. For the prediction of patient survival, FEViT achieved an accuracy of 94 % with an area under the receiver operating characteristic curve of 0.93, which was better than the TNM staging classification (90 %, 0.83) in the experiment. FEViT reduced the limited receptive field and gradient disappearance in the conventional convolutional neural network and was a relatively effective and efficient procedure. The promising accuracy of FEViT in modeling survival makes the prognosis of CRC patients more predictable and practical.
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Affiliation(s)
- Chung-Ming Lo
- Graduate Institute of Library, Information and Archival Studies, National Chengchi University, Taipei, Taiwan
| | - Yi-Wen Yang
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jen-Kou Lin
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tzu-Chen Lin
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wei-Shone Chen
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shung-Haur Yang
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Surgery, National Yang Ming Chiao Tung University Hospital, Yilan, Taiwan
| | - Shih-Ching Chang
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Huann-Sheng Wang
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yuan-Tzu Lan
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Hung-Hsin Lin
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Sheng-Chieh Huang
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Hou-Hsuan Cheng
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jeng-Kai Jiang
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chun-Chi Lin
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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Kim S, Lee JH, Park EJ, Lee HS, Baik SH, Jeon TJ, Lee KY, Ryu YH, Kang J. Prediction of Microsatellite Instability in Colorectal Cancer Using a Machine Learning Model Based on PET/CT Radiomics. Yonsei Med J 2023; 64:320-326. [PMID: 37114635 PMCID: PMC10151228 DOI: 10.3349/ymj.2022.0548] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/11/2023] [Accepted: 03/20/2023] [Indexed: 04/29/2023] Open
Abstract
PURPOSE We investigated the feasibility of preoperative 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomics with machine learning to predict microsatellite instability (MSI) status in colorectal cancer (CRC) patients. MATERIALS AND METHODS Altogether, 233 patients with CRC who underwent preoperative FDG PET/CT were enrolled and divided into training (n=139) and test (n=94) sets. A PET-based radiomics signature (rad_score) was established to predict the MSI status in patients with CRC. The predictive ability of the rad_score was evaluated using the area under the receiver operating characteristic curve (AUROC) in the test set. A logistic regression model was used to determine whether the rad_score was an independent predictor of MSI status in CRC. The predictive performance of rad_score was compared with conventional PET parameters. RESULTS The incidence of MSI-high was 15 (10.8%) and 10 (10.6%) in the training and test sets, respectively. The rad_score was constructed based on the two radiomic features and showed similar AUROC values for predicting MSI status in the training and test sets (0.815 and 0.867, respectively; p=0.490). Logistic regression analysis revealed that the rad_score was an independent predictor of MSI status in the training set. The rad_score performed better than metabolic tumor volume when assessed using the AUROC (0.867 vs. 0.794, p=0.015). CONCLUSION Our predictive model incorporating PET radiomic features successfully identified the MSI status of CRC, and it also showed better performance than the conventional PET image parameters.
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Affiliation(s)
- Soyoung Kim
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jae-Hoon Lee
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
| | - Eun Jung Park
- Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hye Sun Lee
- Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea
| | - Seung Hyuk Baik
- Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Tae Joo Jeon
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Kang Young Lee
- Department of Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Young Hoon Ryu
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jeonghyun Kang
- Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
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Mitteldorf C, Tronnier M. Dermatopathology - Current status and development in German-speaking dermatology. J Dtsch Dermatol Ges 2023; 21:393-397. [PMID: 36999575 DOI: 10.1111/ddg.15047] [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: 12/11/2022] [Accepted: 02/08/2023] [Indexed: 04/01/2023]
Abstract
Dermatopathology has been an integral part of dermatology for more than 100 years and is essential for high quality patient care. In German-speaking countries, dermatologists can acquire an additional qualification in dermatopathology after appropriate further training. For many years, dermatopathological diagnostics has advanced far beyond morphology. Immunohistochemistry and molecular pathology are nowadays an essential part and a prerequisite for the preservation of our discipline. Due to the increasing implementation of digitalization and artificial intelligence, dermatopathology is forward-looking and offers an attractive working environment for young colleagues. Dermatopathology is also indispensable for research, and this fact should also be taken into account by creating academic positions and professorships in the future.
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Affiliation(s)
- Christina Mitteldorf
- Clinic for Dermatology, Venereology and Allergology, University Medical Center Göttingen, Göttingen, Germany
| | - Michael Tronnier
- Clinic for Dermatology, Venerology and Allergology, Helios Klinikum Hildesheim, Hildesheim, Germany
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Cabral BP, Braga LAM, Syed-Abdul S, Mota FB. Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers. Curr Oncol 2023; 30:3432-3446. [PMID: 36975473 PMCID: PMC10047823 DOI: 10.3390/curroncol30030260] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/07/2023] [Accepted: 03/11/2023] [Indexed: 03/18/2023] Open
Abstract
Cancer significantly contributes to global mortality, with 9.3 million annual deaths. To alleviate this burden, the utilization of artificial intelligence (AI) applications has been proposed in various domains of oncology. However, the potential applications of AI and the barriers to its widespread adoption remain unclear. This study aimed to address this gap by conducting a cross-sectional, global, web-based survey of over 1000 AI and cancer researchers. The results indicated that most respondents believed AI would positively impact cancer grading and classification, follow-up services, and diagnostic accuracy. Despite these benefits, several limitations were identified, including difficulties incorporating AI into clinical practice and the lack of standardization in cancer health data. These limitations pose significant challenges, particularly regarding testing, validation, certification, and auditing AI algorithms and systems. The results of this study provide valuable insights for informed decision-making for stakeholders involved in AI and cancer research and development, including individual researchers and research funding agencies.
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Affiliation(s)
| | - Luiza Amara Maciel Braga
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110, Taiwan
- Correspondence: (S.S.-A.); (F.B.M.)
| | - Fabio Batista Mota
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil
- Correspondence: (S.S.-A.); (F.B.M.)
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15
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Blake N, Gaifulina R, Griffin LD, Bell IM, Rodriguez-Justo M, Thomas GMH. Deep Learning Applied to Raman Spectroscopy for the Detection of Microsatellite Instability/MMR Deficient Colorectal Cancer. Cancers (Basel) 2023; 15:cancers15061720. [PMID: 36980606 PMCID: PMC10046611 DOI: 10.3390/cancers15061720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 03/16/2023] Open
Abstract
Defective DNA mismatch repair is one pathogenic pathway to colorectal cancer. It is characterised by microsatellite instability which provides a molecular biomarker for its detection. Clinical guidelines for universal testing of this biomarker are not met due to resource limitations; thus, there is interest in developing novel methods for its detection. Raman spectroscopy (RS) is an analytical tool able to interrogate the molecular vibrations of a sample to provide a unique biochemical fingerprint. The resulting datasets are complex and high-dimensional, making them an ideal candidate for deep learning, though this may be limited by small sample sizes. This study investigates the potential of using RS to distinguish between normal, microsatellite stable (MSS) and microsatellite unstable (MSI-H) adenocarcinoma in human colorectal samples and whether deep learning provides any benefit to this end over traditional machine learning models. A 1D convolutional neural network (CNN) was developed to discriminate between healthy, MSI-H and MSS in human tissue and compared to a principal component analysis–linear discriminant analysis (PCA–LDA) and a support vector machine (SVM) model. A nested cross-validation strategy was used to train 30 samples, 10 from each group, with a total of 1490 Raman spectra. The CNN achieved a sensitivity and specificity of 83% and 45% compared to PCA–LDA, which achieved a sensitivity and specificity of 82% and 51%, respectively. These are competitive with existing guidelines, despite the low sample size, speaking to the molecular discriminative power of RS combined with deep learning. A number of biochemical antecedents responsible for this discrimination are also explored, with Raman peaks associated with nucleic acids and collagen being implicated.
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Affiliation(s)
- Nathan Blake
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
| | - Riana Gaifulina
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
| | - Lewis D. Griffin
- Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Ian M. Bell
- Spectroscopy Products Division, Renishaw PLC, Wotton-under-Edge GL12 8JR, UK
| | - Manuel Rodriguez-Justo
- Department of Research Pathology, Cancer Institute, University College London, London WC1E 6DD, UK
| | - Geraint M. H. Thomas
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
- Correspondence: ; Tel.: +44-20-3549-5456
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16
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Sei S, Ahadova A, Keskin DB, Bohaumilitzky L, Gebert J, von Knebel Doeberitz M, Lipkin SM, Kloor M. Lynch syndrome cancer vaccines: A roadmap for the development of precision immunoprevention strategies. Front Oncol 2023; 13:1147590. [PMID: 37035178 PMCID: PMC10073468 DOI: 10.3389/fonc.2023.1147590] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 03/09/2023] [Indexed: 04/11/2023] Open
Abstract
Hereditary cancer syndromes (HCS) account for 5~10% of all cancer diagnosis. Lynch syndrome (LS) is one of the most common HCS, caused by germline mutations in the DNA mismatch repair (MMR) genes. Even with prospective cancer surveillance, LS is associated with up to 50% lifetime risk of colorectal, endometrial, and other cancers. While significant progress has been made in the timely identification of germline pathogenic variant carriers and monitoring and early detection of precancerous lesions, cancer-risk reduction strategies are still centered around endoscopic or surgical removal of neoplastic lesions and susceptible organs. Safe and effective cancer prevention strategies are critically needed to improve the life quality and longevity of LS and other HCS carriers. The era of precision oncology driven by recent technological advances in tumor molecular profiling and a better understanding of genetic risk factors has transformed cancer prevention approaches for at-risk individuals, including LS carriers. MMR deficiency leads to the accumulation of insertion and deletion mutations in microsatellites (MS), which are particularly prone to DNA polymerase slippage during DNA replication. Mutations in coding MS give rise to frameshift peptides (FSP) that are recognized by the immune system as neoantigens. Due to clonal evolution, LS tumors share a set of recurrent and predictable FSP neoantigens in the same and in different LS patients. Cancer vaccines composed of commonly recurring FSP neoantigens selected through prediction algorithms have been clinically evaluated in LS carriers and proven safe and immunogenic. Preclinically analogous FSP vaccines have been shown to elicit FSP-directed immune responses and exert tumor-preventive efficacy in murine models of LS. While the immunopreventive efficacy of "off-the-shelf" vaccines consisting of commonly recurring FSP antigens is currently investigated in LS clinical trials, the feasibility and utility of personalized FSP vaccines with individual HLA-restricted epitopes are being explored for more precise targeting. Here, we discuss recent advances in precision cancer immunoprevention approaches, emerging enabling technologies, research gaps, and implementation barriers toward clinical translation of risk-tailored prevention strategies for LS carriers. We will also discuss the feasibility and practicality of next-generation cancer vaccines that are based on personalized immunogenic epitopes for precision cancer immunoprevention.
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Affiliation(s)
- Shizuko Sei
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Rockville, MD, United States
- *Correspondence: Shizuko Sei, ; Steven M. Lipkin, ; Matthias Kloor,
| | - Aysel Ahadova
- Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
| | - Derin B. Keskin
- Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA, United States
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States
- Broad Institute of The Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, United States
- Department of Computer Science, Metropolitan College, Boston University, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Lena Bohaumilitzky
- Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
| | - Johannes Gebert
- Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
| | - Magnus von Knebel Doeberitz
- Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
| | - Steven M. Lipkin
- Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medical College, New York, NY, United States
- *Correspondence: Shizuko Sei, ; Steven M. Lipkin, ; Matthias Kloor,
| | - Matthias Kloor
- Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
- *Correspondence: Shizuko Sei, ; Steven M. Lipkin, ; Matthias Kloor,
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Rodríguez-Justo M. [Future and present of anatomic pathology: challenges and opportunities]. REVISTA ESPANOLA DE PATOLOGIA : PUBLICACION OFICIAL DE LA SOCIEDAD ESPANOLA DE ANATOMIA PATOLOGICA Y DE LA SOCIEDAD ESPANOLA DE CITOLOGIA 2023; 56:1-3. [PMID: 36599595 DOI: 10.1016/j.patol.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022]
Affiliation(s)
- Manuel Rodríguez-Justo
- Jefe Clínico y Profesor de Onco-patología, University College London, Cancer Institute, Londres, Reino Unido.
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18
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He X, Liu X, Zuo F, Shi H, Jing J. Artificial intelligence-based multi-omics analysis fuels cancer precision medicine. Semin Cancer Biol 2023; 88:187-200. [PMID: 36596352 DOI: 10.1016/j.semcancer.2022.12.009] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 12/16/2022] [Accepted: 12/29/2022] [Indexed: 01/02/2023]
Abstract
With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.
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Affiliation(s)
- Xiujing He
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, PR China
| | - Xiaowei Liu
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, PR China
| | - Fengli Zuo
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, PR China
| | - Hubing Shi
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, PR China
| | - Jing Jing
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, PR China.
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Couture HD. Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review. J Pers Med 2022; 12:2022. [PMID: 36556243 PMCID: PMC9784641 DOI: 10.3390/jpm12122022] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/26/2022] [Accepted: 12/05/2022] [Indexed: 12/12/2022] Open
Abstract
Molecular and genomic properties are critical in selecting cancer treatments to target individual tumors, particularly for immunotherapy. However, the methods to assess such properties are expensive, time-consuming, and often not routinely performed. Applying machine learning to H&E images can provide a more cost-effective screening method. Dozens of studies over the last few years have demonstrated that a variety of molecular biomarkers can be predicted from H&E alone using the advancements of deep learning: molecular alterations, genomic subtypes, protein biomarkers, and even the presence of viruses. This article reviews the diverse applications across cancer types and the methodology to train and validate these models on whole slide images. From bottom-up to pathologist-driven to hybrid approaches, the leading trends include a variety of weakly supervised deep learning-based approaches, as well as mechanisms for training strongly supervised models in select situations. While results of these algorithms look promising, some challenges still persist, including small training sets, rigorous validation, and model explainability. Biomarker prediction models may yield a screening method to determine when to run molecular tests or an alternative when molecular tests are not possible. They also create new opportunities in quantifying intratumoral heterogeneity and predicting patient outcomes.
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Zheng Z, Bian C, Wang H, Su J, Meng L, Xin Y, Jiang X. Prediction of immunotherapy efficacy and immunomodulatory role of hypoxia in colorectal cancer. Ther Adv Med Oncol 2022; 14:17588359221138383. [PMID: 36425871 PMCID: PMC9679351 DOI: 10.1177/17588359221138383] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 10/26/2022] [Indexed: 11/26/2023] Open
Abstract
Immunotherapy has been used in the clinical treatment of colorectal cancer (CRC); however, most patients fail to achieve satisfactory survival benefits. Biomarkers with high specificity and sensitivity are being increasingly developed to predict the efficacy of CRC immunotherapy. In addition to DNA alteration markers, such as microsatellite instability/mismatch repair and tumor mutational burden, immune cell infiltration and immune checkpoints (ICs), epigenetic changes and no-coding RNA, and gut microbiomes all show potential predictive ability. Recently, the hypoxic tumor microenvironment (TME) has been identified as a key factor mediating CRC immune evasion and resistance to treatment. Hypoxia-inducible factor-1α is the central transcription factor in the hypoxia response that drives the expression of a vast number of survival genes by binding to the hypoxia response element in cancer and immune cells in the TME. Hypoxia regulates angiogenesis, immune cell infiltration and activation, expression of ICs, and secretion of various immune molecules in the TME and is closely associated with the immunotherapeutic efficacy of CRC. Currently, various agents targeting hypoxia have been found to improve the TME and enhance the efficacy of immunotherapy. We reviewed current markers commonly used in CRC to predict therapeutic efficacy and the mechanisms underlying hypoxia-induced angiogenesis and tumor immune evasion. Exploring the mechanisms by which hypoxia affects the TME will assist the discovery of new immunotherapeutic predictive biomarkers and development of more effective combinations of agents targeting hypoxia and immunotherapy.
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Affiliation(s)
- Zhuangzhuang Zheng
- Department of Radiation Oncology, the First Hospital of Jilin University, Changchun China
- Jilin Provincial Key Laboratory of Radiation Oncology & Therapy, Changchun, China
- NHC Key Laboratory of Radiobiology, School of Public Health of Jilin University, Changchun, China
| | - Chenbin Bian
- Department of Radiation Oncology, the First Hospital of Jilin University, Changchun China
- Jilin Provincial Key Laboratory of Radiation Oncology & Therapy, Changchun, China
- NHC Key Laboratory of Radiobiology, School of Public Health of Jilin University, Changchun, China
| | - Huanhuan Wang
- Department of Radiation Oncology, the First Hospital of Jilin University, Changchun China
- Jilin Provincial Key Laboratory of Radiation Oncology & Therapy, Changchun, China
- NHC Key Laboratory of Radiobiology, School of Public Health of Jilin University, Changchun, China
| | - Jing Su
- Department of Radiation Oncology, the First Hospital of Jilin University, Changchun China
- Jilin Provincial Key Laboratory of Radiation Oncology & Therapy, Changchun, China
- NHC Key Laboratory of Radiobiology, School of Public Health of Jilin University, Changchun, China
| | - Lingbin Meng
- Department of Hematology and Medical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Ying Xin
- Key Laboratory of Pathobiology, Ministry of Education, Jilin University, 126 Xinmin Street, Changchun 130021, China
| | - Xin Jiang
- Department of Radiation Oncology, the First Hospital of Jilin University, 71 Xinmin Street, Changchun 130021, China
- Jilin Provincial Key Laboratory of Radiation Oncology & Therapy, Changchun, China
- NHC Key Laboratory of Radiobiology, School of Public Health of Jilin University, Changchun, China
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21
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Volovat SR, Augustin I, Zob D, Boboc D, Amurariti F, Volovat C, Stefanescu C, Stolniceanu CR, Ciocoiu M, Dumitras EA, Danciu M, Apostol DGC, Drug V, Shurbaji SA, Coca LG, Leon F, Iftene A, Herghelegiu PC. Use of Personalized Biomarkers in Metastatic Colorectal Cancer and the Impact of AI. Cancers (Basel) 2022; 14:cancers14194834. [PMID: 36230757 PMCID: PMC9562853 DOI: 10.3390/cancers14194834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 09/18/2022] [Accepted: 09/29/2022] [Indexed: 12/09/2022] Open
Abstract
Colorectal cancer is a major cause of cancer-related death worldwide and is correlated with genetic and epigenetic alterations in the colonic epithelium. Genetic changes play a major role in the pathophysiology of colorectal cancer through the development of gene mutations, but recent research has shown an important role for epigenetic alterations. In this review, we try to describe the current knowledge about epigenetic alterations, including DNA methylation and histone modifications, as well as the role of non-coding RNAs as epigenetic regulators and the prognostic and predictive biomarkers in metastatic colorectal disease that can allow increases in the effectiveness of treatments. Additionally, the intestinal microbiota’s composition can be an important biomarker for the response to strategies based on the immunotherapy of CRC. The identification of biomarkers in mCRC can be enhanced by developing artificial intelligence programs. We present the actual models that implement AI technology as a bridge connecting ncRNAs with tumors and conducted some experiments to improve the quality of the model used as well as the speed of the model that provides answers to users. In order to carry out this task, we implemented six algorithms: the naive Bayes classifier, the random forest classifier, the decision tree classifier, gradient boosted trees, logistic regression and SVM.
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Affiliation(s)
- Simona-Ruxandra Volovat
- Department of Medical Oncology-Radiotherapy, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
| | - Iolanda Augustin
- Department of Medical Oncology, AI.Trestioreanu Institute of Oncology, 022328 Bucharest, Romania
| | - Daniela Zob
- Department of Medical Oncology, AI.Trestioreanu Institute of Oncology, 022328 Bucharest, Romania
| | - Diana Boboc
- Department of Medical Oncology-Radiotherapy, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
| | - Florin Amurariti
- Department of Medical Oncology-Radiotherapy, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
| | - Constantin Volovat
- Department of Medical Oncology, “Euroclinic” Center of Oncology, 2 Vasile Conta Str., 700106 Iasi, Romania
- Correspondence: (C.V.); (C.S.)
| | - Cipriana Stefanescu
- Department of Biophysics and Medical Physics-Nuclear Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
- Correspondence: (C.V.); (C.S.)
| | - Cati Raluca Stolniceanu
- Department of Biophysics and Medical Physics-Nuclear Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
| | - Manuela Ciocoiu
- Department of Pathophysiology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Eduard Alexandru Dumitras
- Department of Pathophysiology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
- Department of Anesthesiology and Intensive Care, Regional Institute of Oncology, 700115 Iasi, Romania
| | - Mihai Danciu
- Pathology Department, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
| | | | - Vasile Drug
- Department of Gastroenterology, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
- Gastroenterology Clinic, Institute of Gastroenterology and Hepatology, ‘St. Spiridon’ Clinical Hospital, 700115 Iasi, Romania
| | - Sinziana Al Shurbaji
- Gastroenterology Clinic, Institute of Gastroenterology and Hepatology, ‘St. Spiridon’ Clinical Hospital, 700115 Iasi, Romania
| | - Lucia-Georgiana Coca
- Faculty of Computer Science, Alexandru Ioan Cuza University, 700115 Iasi, Romania
| | - Florin Leon
- Faculty of Automatic Control and Computer Engineering, Gheorghe Asachi Technical University, 700115 Iasi, Romania
| | - Adrian Iftene
- Faculty of Computer Science, Alexandru Ioan Cuza University, 700115 Iasi, Romania
| | - Paul-Corneliu Herghelegiu
- Faculty of Automatic Control and Computer Engineering, Gheorghe Asachi Technical University, 700115 Iasi, Romania
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22
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Lou J, Xu J, Zhang Y, Sun Y, Fang A, Liu J, Mur LAJ, Ji B. PPsNet: An improved deep learning model for microsatellite instability high prediction in colorectal cancer from whole slide images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:107095. [PMID: 36057226 DOI: 10.1016/j.cmpb.2022.107095] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 08/18/2022] [Accepted: 08/26/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Recent studies have shown that colorectal cancer (CRC) patients with microsatellite instability high (MSI-H) are more likely to benefit from immunotherapy. However, current MSI testing methods are not available for all patients due to the lack of available equipment and trained personnel, as well as the high cost of the assay. Here, we developed an improved deep learning model to predict MSI-H in CRC from whole slide images (WSIs). METHODS We established the MSI-H prediction model based on two stages: tumor detection and MSI classification. Previous works applied fine-tuning strategy directly for tumor detection, but ignoring the challenge of vanishing gradient due to the large number of convolutional layers. We added auxiliary classifiers to intermediate layers of pre-trained models to help propagate gradients back through in an effective manner. To predict MSI status, we constructed a pair-wise learning model with a synergic network, named parameter partial sharing network (PPsNet), where partial parameters are shared among two deep convolutional neural networks (DCNNs). The proposed PPsNet contained fewer parameters and reduced the problem of intra-class variation and inter-class similarity. We validated the proposed model on a holdout test set and two external test sets. RESULTS 144 H&E-stained WSIs from 144 CRC patients (81 cases with MSI-H and 63 cases with MSI-L/MSS) were collected retrospectively from three hospitals. The experimental results indicate that deep supervision based fine-tuning almost outperforms training from scratch and utilizing fine-tuning directly. The proposed PPsNet always achieves better accuracy and area under the receiver operating characteristic curve (AUC) than other solutions with four different neural network architectures on validation. The proposed method finally achieves obvious improvements than other state-of-the-art methods on the validation dataset with an accuracy of 87.28% and AUC of 94.29%. CONCLUSIONS The proposed method can obviously increase model performance and our model yields better performance than other methods. Additionally, this work also demonstrates the feasibility of MSI-H prediction using digital pathology images based on deep learning in the Asian population. It is hoped that this model could serve as an auxiliary tool to identify CRC patients with MSI-H more time-saving and efficiently.
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Affiliation(s)
- Jingjiao Lou
- School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong 250061, PR China
| | - Jiawen Xu
- Department of Pathology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, Shandong 250021, PR China
| | - Yuyan Zhang
- School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong 250061, PR China
| | - Yuhong Sun
- Department of Pathology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, PR China
| | - Aiju Fang
- Department of Pathology, Shandong Provincial Third Hospital, Shandong University, Jinan, Shandong 250132, PR China
| | - Jixuan Liu
- Department of Pathology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, Shandong 250021, PR China
| | - Luis A J Mur
- Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University, Aberystwyth, Wales SY23 3DZ, UK
| | - Bing Ji
- School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong 250061, PR China.
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Learn to Estimate Genetic Mutation and Microsatellite Instability with Histopathology H&E Slides in Colon Carcinoma. Cancers (Basel) 2022; 14:cancers14174144. [PMID: 36077681 PMCID: PMC9454509 DOI: 10.3390/cancers14174144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 08/21/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
Colorectal cancer is one of the most common malignancies and the third leading cause of cancer-related mortality worldwide. Identifying KRAS, NRAS, and BRAF mutations and estimating MSI status is closely related to the individualized therapeutic judgment and oncologic prognosis of CRC patients. In this study, we introduce a cascaded network framework with an average voting ensemble strategy to sequentially identify the tumor regions and predict gene mutations & MSI status from whole-slide H&E images. Experiments on a colorectal cancer dataset indicate that the proposed method can achieve higher fidelity in both gene mutation prediction and MSI status estimation. In the testing set, our method achieves 0.792, 0.886, 0.897, and 0.764 AUCs for KRAS, NRAS, BRAF, and MSI, respectively. The results suggest that the deep convolutional networks have the potential to provide diagnostic insight and clinical guidance directly from pathological H&E slides.
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24
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Rahman A, Hossain MS, Muhammad G, Kundu D, Debnath T, Rahman M, Khan MSI, Tiwari P, Band SS. Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues. CLUSTER COMPUTING 2022; 26:1-41. [PMID: 35996680 PMCID: PMC9385101 DOI: 10.1007/s10586-022-03658-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 05/10/2022] [Accepted: 06/17/2022] [Indexed: 06/15/2023]
Abstract
Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.
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Affiliation(s)
- Anichur Rahman
- Present Address: Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka Bangladesh
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Md. Sazzad Hossain
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Ghulam Muhammad
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Dipanjali Kundu
- Present Address: Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka Bangladesh
| | - Tanoy Debnath
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Muaz Rahman
- Present Address: Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka Bangladesh
| | - Md. Saikat Islam Khan
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Prayag Tiwari
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Shahab S. Band
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002 Taiwan
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25
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Wang X, Barrera C, Bera K, Viswanathan VS, Azarianpour-Esfahani S, Koyuncu C, Velu P, Feldman MD, Yang M, Fu P, Schalper KA, Mahdi H, Lu C, Velcheti V, Madabhushi A. Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors. SCIENCE ADVANCES 2022; 8:eabn3966. [PMID: 35648850 PMCID: PMC9159577 DOI: 10.1126/sciadv.abn3966] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Immune checkpoint inhibitors (ICIs) show prominent clinical activity across multiple advanced tumors. However, less than half of patients respond even after molecule-based selection. Thus, improved biomarkers are required. In this study, we use an image analysis to capture morphologic attributes relating to the spatial interaction and architecture of tumor cells and tumor-infiltrating lymphocytes (TILs) from digitized H&E images. We evaluate the association of image features with progression-free (PFS) and overall survival in non-small cell lung cancer (NSCLC) (N = 187) and gynecological cancer (N = 39) patients treated with ICIs. We demonstrated that the classifier trained with NSCLC alone was associated with PFS in independent NSCLC cohorts and also in gynecological cancer. The classifier was also associated with clinical outcome independent of clinical factors. Moreover, the classifier was associated with PFS even with low PD-L1 expression. These findings suggest that image analysis can be used to predict clinical end points in patients receiving ICI.
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Affiliation(s)
- Xiangxue Wang
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
- Corresponding author. (X.W.); (A.M.)
| | - Cristian Barrera
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Kaustav Bera
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Vidya Sankar Viswanathan
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Sepideh Azarianpour-Esfahani
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Can Koyuncu
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Priya Velu
- Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Michael D. Feldman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Yang
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Kurt A. Schalper
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Haider Mahdi
- Magee-Womens Hospital and Magee-Womens Research Institute, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Cheng Lu
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Vamsidhar Velcheti
- Department of Hematology and Oncology, NYU Langone Health, New York, NY, USA
| | - Anant Madabhushi
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
- Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH, USA
- Corresponding author. (X.W.); (A.M.)
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26
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Interpretable tumor differentiation grade and microsatellite instability recognition in gastric cancer using deep learning. J Transl Med 2022; 102:641-649. [PMID: 35177797 DOI: 10.1038/s41374-022-00742-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 01/18/2022] [Accepted: 01/22/2022] [Indexed: 12/13/2022] Open
Abstract
Gastric cancer possesses great histological and molecular diversity, which creates obstacles for rapid and efficient diagnoses. Classic diagnoses either depend on the pathologist's judgment, which relies heavily on subjective experience, or time-consuming molecular assays for subtype diagnosis. Here, we present a deep learning (DL) system to achieve interpretable tumor differentiation grade and microsatellite instability (MSI) recognition in gastric cancer directly using hematoxylin-eosin (HE) staining whole-slide images (WSIs). WSIs from 467 patients were divided into three cohorts: the training cohort with 348 annotated WSIs, the testing cohort with 88 annotated WSIs, and the integration testing cohort with 31 original WSIs without tumor contour annotation. First, the DL models comprehensibly achieved tumor differentiation recognition with an F1 values of 0.8615 and 0.8977 for poorly differentiated adenocarcinoma (PDA) and well-differentiated adenocarcinoma (WDA) classes. Its ability to extract pathological features about the glandular structure formation, which is the key to distinguishing between PDA and WDA, increased the interpretability of the DL models. Second, the DL models achieved MSI status recognition with a patient-level accuracy of 86.36% directly from HE-stained WSIs in the testing cohort. Finally, the integrated end-to-end system achieved patient-level MSI recognition from original HE staining WSIs with an accuracy of 83.87% in the integration testing cohort with no tumor contour annotation. The proposed system, therefore, demonstrated high accuracy and interpretability, which can potentially promote the implementation of artificial intelligence healthcare.
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27
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Liu Q, Ding H. Application of Table Tennis Ball Trajectory and Rotation-Oriented Prediction Algorithm Using Artificial Intelligence. Front Neurorobot 2022; 16:820028. [PMID: 35645761 PMCID: PMC9131050 DOI: 10.3389/fnbot.2022.820028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 02/21/2022] [Indexed: 11/16/2022] Open
Abstract
The present work aims to accelerate sports development in China and promote technological innovation in the artificial intelligence (AI) field. After analyzing the application and development of AI, it is introduced into sports and applied to table tennis competitions and training. The principle of the trajectory prediction of the table tennis ball (TTB) based on AI is briefly introduced. It is found that the difficulty of predicting TTB trajectories lies in rotation measurement. Accordingly, the rotation and trajectory of TTB are predicted using some AI algorithms. Specifically, a TTB detection algorithm is designed based on the Feature Fusion Network (FFN). For feature exaction, the cross-layer connection network is used to strengthen the learning ability of convolutional neural networks (CNNs) and streamline network parameters to improve the network detection response. The experimental results demonstrate that the trained CNN can reach a detection accuracy of over 98%, with a detection response within 5.3 ms, meeting the requirements of the robot vision system of the table tennis robot. By comparison, the traditional Color Segmentation Algorithm has advantages in detection response, with unsatisfactory detection accuracy, especially against TTB's color changes. Thus, the algorithm reported here can immediately hit the ball with high accuracy. The research content provides a reference for applying AI to TTB trajectory and rotation prediction and has significant value in popularizing table tennis.
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Affiliation(s)
| | - Hairong Ding
- Shanghai Polytechnic University, Shanghai, China
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28
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Modern Machine Learning Practices in Colorectal Surgery: A Scoping Review. J Clin Med 2022; 11:jcm11092431. [PMID: 35566555 PMCID: PMC9100508 DOI: 10.3390/jcm11092431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/12/2022] [Accepted: 03/29/2022] [Indexed: 12/09/2022] Open
Abstract
Objective: The use of machine learning (ML) has revolutionized every domain of medicine. Surgeons are now using ML models for disease detection and outcome prediction with high precision. ML-guided colorectal surgeries are more efficient than conventional surgical procedures. The primary aim of this paper is to provide an overview of the latest research on “ML in colorectal surgery”, with its viable applications. Methods: PubMed, Google Scholar, Medline, and Cochrane library were searched. Results: After screening, 27 articles out of 172 were eventually included. Among all of the reviewed articles, those found to fit the criteria for inclusion had exclusively focused on ML in colorectal surgery, with justified applications. We identified existing applications of ML in colorectal surgery. Additionally, we discuss the benefits, risks, and safety issues. Conclusions: A better, more sustainable, and more efficient method, with useful applications, for ML in surgery is possible if we and data scientists work together to address the drawbacks of the current approach. Potential problems related to patients’ perspectives also need to be resolved. The development of accurate technologies alone will not solve the problem of perceived unreliability from the patients’ end. Confidence can only be developed within society if more research with precise results is carried out.
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29
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Farina E, Nabhen JJ, Dacoregio MI, Batalini F, Moraes FY. An overview of artificial intelligence in oncology. Future Sci OA 2022; 8:FSO787. [PMID: 35369274 PMCID: PMC8965797 DOI: 10.2144/fsoa-2021-0074] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 01/19/2022] [Indexed: 11/23/2022] Open
Abstract
Cancer is associated with significant morbimortality globally. Advances in screening, diagnosis, management and survivorship were substantial in the last decades, however, challenges in providing personalized and data-oriented care remain. Artificial intelligence (AI), a branch of computer science used for predictions and automation, has emerged as potential solution to improve the healthcare journey and to promote precision in healthcare. AI applications in oncology include, but are not limited to, optimization of cancer research, improvement of clinical practice (eg., prediction of the association of multiple parameters and outcomes - prognosis and response) and better understanding of tumor molecular biology. In this review, we examine the current state of AI in oncology, including fundamentals, current applications, limitations and future perspectives.
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Affiliation(s)
- Eduardo Farina
- Department of Radiology, Federal University of São Paulo, SP, 04021-001, Brazil; Diagnósticos da America SA (Dasa), 05425-020, Brazil
| | - Jacqueline J Nabhen
- School of Medicine, Federal University of Paraná, Curitiba, PR, 80060-000, Brazil
| | - Maria Inez Dacoregio
- School of Medicine, State University of Centro-Oeste, Guarapuava, PR, 85040-167, Brazil
| | - Felipe Batalini
- Department of Medicine, Division of Medical Oncology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Fabio Y Moraes
- Department of Oncology, Division of Radiation Oncology, Queen's University, Kingston, ON, K7L 3N6, Canada
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Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review. Int J Mol Sci 2022; 23:ijms23052462. [PMID: 35269607 PMCID: PMC8910565 DOI: 10.3390/ijms23052462] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 02/21/2022] [Indexed: 02/04/2023] Open
Abstract
Microsatellite instability (MSI)/defective DNA mismatch repair (dMMR) is receiving more attention as a biomarker for eligibility for immune checkpoint inhibitors in advanced diseases. However, due to high costs and resource limitations, MSI/dMMR testing is not widely performed. Some attempts are in progress to predict MSI/dMMR status through histomorphological features on H&E slides using artificial intelligence (AI) technology. In this study, the potential predictive role of this new methodology was reviewed through a systematic review. Studies up to September 2021 were searched through PubMed and Embase database searches. The design and results of each study were summarized, and the risk of bias for each study was evaluated. For colorectal cancer, AI-based systems showed excellent performance with the highest standard of 0.972; for gastric and endometrial cancers they showed a relatively low but satisfactory performance, with the highest standard of 0.81 and 0.82, respectively. However, analyzing the risk of bias, most studies were evaluated at high-risk. AI-based systems showed a high potential in predicting the MSI/dMMR status of different cancer types, and particularly of colorectal cancers. Therefore, a confirmation test should be required only for the results that are positive in the AI test.
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Tarabichi M, Demetter P, Craciun L, Maenhaut C, Detours V. Thyroid cancer under the scope of emerging technologies. Mol Cell Endocrinol 2022; 541:111491. [PMID: 34740746 DOI: 10.1016/j.mce.2021.111491] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 10/08/2021] [Accepted: 10/18/2021] [Indexed: 01/03/2023]
Abstract
The vast majority of thyroid cancers originate from follicular cells. We outline outstanding issues at each step along the path of cancer patient care, from prevention to post-treatment follow-up and highlight how emerging technologies will help address them in the coming years. Three directions will dominate the coming technological landscape. Genomics will reveal tumoral evolutionary history and shed light on how these cancers arise from the normal epithelium and the genomics alteration driving their progression. Transcriptomics will gain cellular and spatial resolution providing a full account of intra-tumor heterogeneity and opening a window on the microenvironment supporting thyroid tumor growth. Artificial intelligence will set morphological analysis on an objective quantitative ground laying the foundations of a systematic thyroid tumor classification system. It will also integrate into unified representations the molecular and morphological perspectives on thyroid cancer.
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Affiliation(s)
- Maxime Tarabichi
- Institute of Interdisciplinary Research (IRIBHM), Université Libre de Bruxelles, Brussels, Belgium.
| | - Pieter Demetter
- Department of Pathology, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Ligia Craciun
- Department of Pathology, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Carine Maenhaut
- Institute of Interdisciplinary Research (IRIBHM), Université Libre de Bruxelles, Brussels, Belgium.
| | - Vincent Detours
- Institute of Interdisciplinary Research (IRIBHM), Université Libre de Bruxelles, Brussels, Belgium.
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Ranasinghe R, Mathai M, Zulli A. A synopsis of modern - day colorectal cancer: Where we stand. Biochim Biophys Acta Rev Cancer 2022; 1877:188699. [DOI: 10.1016/j.bbcan.2022.188699] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 01/30/2022] [Accepted: 02/14/2022] [Indexed: 02/07/2023]
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Musa IH, Afolabi LO, Zamit I, Musa TH, Musa HH, Tassang A, Akintunde TY, Li W. Artificial Intelligence and Machine Learning in Cancer Research: A Systematic and Thematic Analysis of the Top 100 Cited Articles Indexed in Scopus Database. Cancer Control 2022; 29:10732748221095946. [PMID: 35688650 PMCID: PMC9189515 DOI: 10.1177/10732748221095946] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
INTRODUCTION Cancer is a major public health problem and a global leading cause of death where the screening, diagnosis, prediction, survival estimation, and treatment of cancer and control measures are still a major challenge. The rise of Artificial Intelligence (AI) and Machine Learning (ML) techniques and their applications in various fields have brought immense value in providing insights into advancement in support of cancer control. METHODS A systematic and thematic analysis was performed on the Scopus database to identify the top 100 cited articles in cancer research. Data were analyzed using RStudio and VOSviewer.Var1.6.6. RESULTS The top 100 articles in AI and ML in cancer received a 33 920 citation score with a range of 108 to 5758 times. Doi Kunio from the USA was the most cited author with total number of citations (TNC = 663). Out of 43 contributed countries, 30% of the top 100 cited articles originated from the USA, and 10% originated from China. Among the 57 peer-reviewed journals, the "Expert Systems with Application" published 8% of the total articles. The results were presented in highlight technological advancement through AI and ML via the widespread use of Artificial Neural Network (ANNs), Deep Learning or machine learning techniques, Mammography-based Model, Convolutional Neural Networks (SC-CNN), and text mining techniques in the prediction, diagnosis, and prevention of various types of cancers towards cancer control. CONCLUSIONS This bibliometric study provides detailed overview of the most cited empirical evidence in AI and ML adoption in cancer research that could efficiently help in designing future research. The innovations guarantee greater speed by using AI and ML in the detection and control of cancer to improve patient experience.
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Affiliation(s)
- Ibrahim H. Musa
- Department of Software Engineering, School of Computer Science and Engineering, Southeast University, Nanjing, China
- Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing, China
| | - Lukman O. Afolabi
- Guangdong Immune Cell Therapy Engineering and Technology Research Center, Center for Protein and Cell-Based Drugs, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Ibrahim Zamit
- University of Chinese Academy of Sciences, Beijing, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Taha H. Musa
- Biomedical Research Institute, Darfur University College, Nyala, South Darfur, Sudan
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, Jiangsu Province, China
| | - Hassan H. Musa
- Faculty of Medical Laboratory Sciences, University of Khartoum, Khartoum, Sudan
| | - Andrew Tassang
- Faculty of Health Sciences, University of Buea, Cameroon
- Buea Regional Hospital, Annex, Cameroon
| | - Tosin Y. Akintunde
- Department of Sociology, School of Public Administration, Hohai University, Nanjing, China
| | - Wei Li
- Department of quality management, Children’s hospital of Nanjing Medical University, Nanjing, China
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Bustos A, Payá A, Torrubia A, Jover R, Llor X, Bessa X, Castells A, Carracedo Á, Alenda C. xDEEP-MSI: Explainable Bias-Rejecting Microsatellite Instability Deep Learning System in Colorectal Cancer. Biomolecules 2021; 11:1786. [PMID: 34944430 PMCID: PMC8699085 DOI: 10.3390/biom11121786] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 11/25/2021] [Accepted: 11/26/2021] [Indexed: 02/06/2023] Open
Abstract
The prediction of microsatellite instability (MSI) using deep learning (DL) techniques could have significant benefits, including reducing cost and increasing MSI testing of colorectal cancer (CRC) patients. Nonetheless, batch effects or systematic biases are not well characterized in digital histology models and lead to overoptimistic estimates of model performance. Methods to not only palliate but to directly abrogate biases are needed. We present a multiple bias rejecting DL system based on adversarial networks for the prediction of MSI in CRC from tissue microarrays (TMAs), trained and validated in 1788 patients from EPICOLON and HGUA. The system consists of an end-to-end image preprocessing module that tile samples at multiple magnifications and a tissue classification module linked to the bias-rejecting MSI predictor. We detected three biases associated with the learned representations of a baseline model: the project of origin of samples, the patient's spot and the TMA glass where each spot was placed. The system was trained to directly avoid learning the batch effects of those variables. The learned features from the bias-ablated model achieved maximum discriminative power with respect to the task and minimal statistical mean dependence with the biases. The impact of different magnifications, types of tissues and the model performance at tile vs patient level is analyzed. The AUC at tile level, and including all three selected tissues (tumor epithelium, mucin and lymphocytic regions) and 4 magnifications, was 0.87 ± 0.03 and increased to 0.9 ± 0.03 at patient level. To the best of our knowledge, this is the first work that incorporates a multiple bias ablation technique at the DL architecture in digital pathology, and the first using TMAs for the MSI prediction task.
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Affiliation(s)
- Aurelia Bustos
- AI Cancer Research Unit, MedBravo, 03560 Alicante, Spain;
| | - Artemio Payá
- Pathology Department, Alicante University General Hospital (HGUA), 03010 Alicante, Spain; (A.P.); (R.J.)
- Alicante Institute for Health and Biomedical Research (ISABIAL), 03010 Alicante, Spain
| | | | - Rodrigo Jover
- Pathology Department, Alicante University General Hospital (HGUA), 03010 Alicante, Spain; (A.P.); (R.J.)
- Alicante Institute for Health and Biomedical Research (ISABIAL), 03010 Alicante, Spain
| | - Xavier Llor
- Department of Medicine, Yale Cancer Center, Yale University, New Haven, CT 06511, USA;
| | - Xavier Bessa
- Gastroenterology Department, Hospital del Mar Medical Research Institute (IMIM), 08003 Barcelona, Spain;
| | - Antoni Castells
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Gastroenterology Department, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, 08036 Barcelona, Spain;
| | - Ángel Carracedo
- Fundación Pública Gallega de Medicina Genómica (GMX), 15706 Santiago de Compostela, Spain;
| | - Cristina Alenda
- Pathology Department, Alicante University General Hospital (HGUA), 03010 Alicante, Spain; (A.P.); (R.J.)
- Alicante Institute for Health and Biomedical Research (ISABIAL), 03010 Alicante, Spain
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Hu LF, Lan HR, Huang D, Li XM, Jin KT. Personalized Immunotherapy in Colorectal Cancers: Where Do We Stand? Front Oncol 2021; 11:769305. [PMID: 34888246 PMCID: PMC8649954 DOI: 10.3389/fonc.2021.769305] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 10/26/2021] [Indexed: 12/17/2022] Open
Abstract
Colorectal cancer (CRC) is the second leading cause of cancer death in the world. Immunotherapy using monoclonal antibodies, immune-checkpoint inhibitors, adoptive cell therapy, and cancer vaccines has raised great hopes for treating poor prognosis metastatic CRCs that are resistant to the conventional therapies. However, high inter-tumor and intra-tumor heterogeneity hinder the success of immunotherapy in CRC. Patients with a similar tumor phenotype respond differently to the same immunotherapy regimen. Mutation-based classification, molecular subtyping, and immunoscoring of CRCs facilitated the multi-aspect grouping of CRC patients and improved immunotherapy. Personalized immunotherapy using tumor-specific neoantigens provides the opportunity to consider each patient as an independent group deserving of individualized immunotherapy. In the recent decade, the development of sequencing and multi-omics techniques has helped us classify patients more precisely. The expansion of such advanced techniques along with the neoantigen-based immunotherapy could herald a new era in treating heterogeneous tumors such as CRC. In this review article, we provided the latest findings in immunotherapy of CRC. We elaborated on the heterogeneity of CRC patients as a bottleneck of CRC immunotherapy and reviewed the latest advances in personalized immunotherapy to overcome CRC heterogeneity.
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Affiliation(s)
- Li-Feng Hu
- Department of Colorectal Surgery, Shaoxing People’s Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, China
| | - Huan-Rong Lan
- Department of Breast and Thyroid Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Dong Huang
- Department of Colorectal Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Xue-Min Li
- Department of Hepatobiliary Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Ke-Tao Jin
- Department of Colorectal Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
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Kim BW, Choi MC, Kim MK, Lee JW, Kim MT, Noh JJ, Park H, Jung SG, Joo WD, Song SH, Lee C. Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers. Cancers (Basel) 2021; 13:cancers13225670. [PMID: 34830824 PMCID: PMC8616351 DOI: 10.3390/cancers13225670] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 11/10/2021] [Accepted: 11/10/2021] [Indexed: 12/15/2022] Open
Abstract
Simple Summary Recurrent patients with gynecologic cancer experience a difficult situation when using immune checkpoint inhibitors based on mismatch repair gene immunohistochemistry and microsatellite instability. Six machine learning algorithms were used to create predictive models with seven prospective features (four MMR IHC [MLH1, MSH2, MSH6, and PMS2], MSI, Age 60, and tumor size). The experimental results showed that the RF-based model performed best at predicting gynecologic cancer recurrence, with AUCs of 0.818 and 0.826 for the 5-fold cross-validation (CV) and 5-fold CV with 10 repetitions, respectively. This provides novel and baseline results of patients with recurrent gynecologic cancer using immune checkpoint inhibitors by using machine learning methods based on Lynch syndrome-related screening markers. Abstract To support the implementation of genome-based precision medicine, we developed machine learning models that predict the recurrence of patients with gynecologic cancer in using immune checkpoint inhibitors (ICI) based on clinical and pathologic characteristics, including Lynch syndrome-related screening markers such as immunohistochemistry (IHC) and microsatellite instability (MSI) tests. To accomplish our goal, we reviewed the patient demographics, clinical data, and pathological results from their medical records. Then we identified seven potential characteristics (four MMR IHC [MLH1, MSH2, MSH6, and PMS2], MSI, Age 60, and tumor size). Following that, predictive models were built based on these variables using six machine learning algorithms: logistic regression (LR), support vector machine (SVM), naive Bayes (NB), random forest (RF), gradient boosting (GB), and extreme gradient boosting (EGB) (XGBoost). The experimental results showed that the RF-based model performed best at predicting gynecologic cancer recurrence, with AUCs of 0.818 and 0.826 for the 5-fold cross-validation (CV) and 5-fold CV with 10 repetitions, respectively. This study provides novel and baseline results about predicting the recurrence of gynecologic cancer in patients using ICI by using machine learning methods based on Lynch syndrome-related screening markers.
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Affiliation(s)
- Byung Wook Kim
- Department of Information and Communication Engineering, Changwon National University, Changwon 51140, Korea; (B.W.K.); (M.T.K.)
| | - Min Chul Choi
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
| | - Min Kyu Kim
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon 51353, Korea
- Correspondence: (M.K.K.); (J.-W.L.)
| | - Jeong-Won Lee
- Gynecologic Cancer Center, Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
- Correspondence: (M.K.K.); (J.-W.L.)
| | - Min Tae Kim
- Department of Information and Communication Engineering, Changwon National University, Changwon 51140, Korea; (B.W.K.); (M.T.K.)
| | - Joseph J. Noh
- Gynecologic Cancer Center, Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
| | - Hyun Park
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
| | - Sang Geun Jung
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
| | - Won Duk Joo
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
| | - Seung Hun Song
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
| | - Chan Lee
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
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Zhao M, Yao S, Li Z, Wu L, Xu Z, Pan X, Lin H, Xu Y, Yang S, Zhang S, Li Y, Zhao K, Liang C, Liu Z. The Crohn's-like lymphoid reaction density: a new artificial intelligence quantified prognostic immune index in colon cancer. Cancer Immunol Immunother 2021; 71:1221-1231. [PMID: 34642778 DOI: 10.1007/s00262-021-03079-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 10/01/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND The Crohn's-like lymphoid reaction (CLR) is manifested as peritumoral lymphocytes aggregation in colon cancer, which is a major component of the host immune response to cancer. However, the lack of a unified and objective CLR evaluation standard limits its clinical application. We, therefore, developed a deep learning model for the fully automated CLR density quantification on routine hematoxylin and eosin (HE)-stained whole-slide images (WSIs) and further investigated its prognostic validity for patient stratification. METHODS The CLR density was calculated by using a deep learning method on HE-stained WSIs. A training (N = 279) and a validation (N = 194) cohorts were used to evaluate the prognostic value of CLR density for overall survival (OS). RESULT The fully automated quantified CLR density was an independent prognostic factor, with high CLR density associated with increased OS in the discovery (HR 0.58, 95% CI 0.38-0.89, P = 0.012) and validation cohort (0.45, 0.23-0.88, 0.020). Integrating CLR density into a Cox model with other risk factors showed improved prognostic capability. CONCLUSION We developed a new immune indicator (CLR density) quantified by a deep learning method to evaluate the lymphocytes aggregation in colon cancer. The CLR density was demonstrated its predictive value for OS in two independent cohorts. This approach allows for the objective and standardized quantification while reducing pathologists' workload. Therefore, this fully automated standardized method of CLR evaluation had potential clinical value.
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Affiliation(s)
- Minning Zhao
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.,Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Su Yao
- Department of Pathology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhenhui Li
- Department of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Lin Wu
- Department of Pathology, Yunnan Cancer Center, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zeyan Xu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China.,China School of Medicine, South China University of Technology, Guangzhou, China
| | - Xipeng Pan
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Huan Lin
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China.,China School of Medicine, South China University of Technology, Guangzhou, China
| | - Yao Xu
- School of Bioengineering, Chongqing University, Chongqing, China
| | - Shangqing Yang
- School of Life Science and Technology, Xidian University, Xian, China
| | - Shenyan Zhang
- Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Yong Li
- Department of General Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Ke Zhao
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China. .,China School of Medicine, South China University of Technology, Guangzhou, China.
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China.
| | - Zaiyi Liu
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China. .,Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China.
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Finkelman BS, Meindl A, LaBoy C, Griffin B, Narayan S, Brancamp R, Siziopikou KP, Pincus JL, Blanco LZ. Correlation of manual semi-quantitative and automated quantitative Ki-67 proliferative index with OncotypeDXTM recurrence score in invasive breast carcinoma. Breast Dis 2021; 41:55-65. [PMID: 34397396 DOI: 10.3233/bd-201011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Ki-67 immunohistochemistry (IHC) staining is a widely used cancer proliferation assay; however, its limitations could be improved with automated scoring. The OncotypeDXTM Recurrence Score (ORS), which primarily evaluates cancer proliferation genes, is a prognostic indicator for breast cancer chemotherapy response; however, it is more expensive and slower than Ki-67. OBJECTIVE To compare manual Ki-67 (mKi-67) with automated Ki-67 (aKi-67) algorithm results based on manually selected Ki-67 "hot spots" in breast cancer, and correlate both with ORS. METHODS 105 invasive breast carcinoma cases from 100 patients at our institution (2011-2013) with available ORS were evaluated. Concordance was assessed via Cohen's Kappa (κ). RESULTS 57/105 cases showed agreement between mKi-67 and aKi-67 (κ 0.31, 95% CI 0.18-0.45), with 41 cases overestimated by aKi-67. Concordance was higher when estimated on the same image (κ 0.53, 95% CI 0.37-0.69). Concordance between mKi-67 score and ORS was fair (κ 0.27, 95% CI 0.11-0.42), and concordance between aKi-67 and ORS was poor (κ 0.10, 95% CI -0.03-0.23). CONCLUSIONS These results highlight the limits of Ki-67 algorithms that use manual "hot spot" selection. Due to suboptimal concordance, Ki-67 is likely most useful as a complement to, rather than a surrogate for ORS, regardless of scoring method.
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Affiliation(s)
- Brian S Finkelman
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Amanda Meindl
- Department of Pathology, Great Lakes Pathologists, West Allis, WI, USA
| | - Carissa LaBoy
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Brannan Griffin
- Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Suguna Narayan
- Department of Pathology, University of Colorado Denver School of Medicine, Aurora, CO, USA
| | - Ryan Brancamp
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kalliopi P Siziopikou
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Jennifer L Pincus
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Luis Z Blanco
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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Zeng H, Chen L, Zhang M, Luo Y, Ma X. Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer. Gynecol Oncol 2021; 163:171-180. [PMID: 34275655 DOI: 10.1016/j.ygyno.2021.07.015] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 07/04/2021] [Accepted: 07/09/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVE This study used histopathological image features to predict molecular features, and combined with multi-dimensional omics data to predict overall survival (OS) in high-grade serous ovarian cancer (HGSOC). METHODS Patients from The Cancer Genome Atlas (TCGA) were distributed into training set (n = 115) and test set (n = 114). In addition, we collected tissue microarrays of 92 patients as an external validation set. Quantitative features were extracted from histopathological images using CellProfiler, and utilized to establish prediction models by machine learning methods in training set. The prediction performance was assessed in test set and validation set. RESULTS The prediction models were able to identify BRCA1 mutation (AUC = 0.952), BRCA2 mutation (AUC = 0.912), microsatellite instability-high (AUC = 0.919), microsatellite stable (AUC = 0.924), and molecular subtypes: proliferative (AUC = 0.961), differentiated (AUC = 0.952), immunoreactive (AUC = 0.941), mesenchymal (AUC = 0.918) in test set. The prognostic model based on histopathological image features could predict OS in test set (5-year AUC = 0.825) and validation set (5-year AUC = 0.703). We next explored the integrative prognostic models of image features, genomics, transcriptomics and proteomics. In test set, the models combining two omics had higher prediction accuracy, such as image features and genomics (5-year AUC = 0.834). The multi-omics model including all features showed the best prediction performance (5-year AUC = 0.911). According to risk score of multi-omics model, the high-risk and low-risk groups had significant survival differences (HR = 18.23, p < 0.001). CONCLUSIONS These results indicated the potential ability of histopathological image features to predict above molecular features and survival risk of HGSOC patients. The integration of image features and multi-omics data may improve prognosis prediction in HGSOC patients.
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Affiliation(s)
- Hao Zeng
- Department of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center, Chengdu, China
| | - Linyan Chen
- Department of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center, Chengdu, China
| | - Mingxuan Zhang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuling Luo
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center, Chengdu, China.
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Combinatorial therapy in tumor microenvironment: Where do we stand? Biochim Biophys Acta Rev Cancer 2021; 1876:188585. [PMID: 34224836 DOI: 10.1016/j.bbcan.2021.188585] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/28/2021] [Accepted: 06/23/2021] [Indexed: 01/09/2023]
Abstract
The tumor microenvironment plays a pivotal role in tumor initiation and progression by creating a dynamic interaction with cancer cells. The tumor microenvironment consists of various cellular components, including endothelial cells, fibroblasts, pericytes, adipocytes, immune cells, cancer stem cells and vasculature, which provide a sustained environment for cancer cell proliferation. Currently, targeting tumor microenvironment is increasingly being explored as a novel approach to improve cancer therapeutics, as it influences the growth and expansion of malignant cells in various ways. Despite continuous advancements in targeted therapies for cancer treatment, drug resistance, toxicity and immune escape mechanisms are the basis of treatment failure and cancer escape. Targeting tumor microenvironment efficiently with approved drugs and combination therapy is the solution to this enduring challenge that involves combining more than one treatment modality such as chemotherapy, surgery, radiotherapy, immunotherapy and nanotherapy that can effectively and synergistically target the critical pathways associated with disease pathogenesis. This review shed light on the composition of the tumor microenvironment, interaction of different components within tumor microenvironment with tumor cells and associated hallmarks, the current status of combinatorial therapies being developed, and various growing advancements. Furthermore, computational tools can also be used to monitor the significance and outcome of therapies being developed. We addressed the perceived barriers and regulatory hurdles in developing a combinatorial regimen and evaluated the present status of these therapies in the clinic. The accumulating depth of knowledge about the tumor microenvironment in cancer may facilitate further development of effective treatment modalities. This review presents the tumor microenvironment as a sweeping landscape for developing novel cancer therapies.
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Cao B, Zhang KC, Wei B, Chen L. Status quo and future prospects of artificial neural network from the perspective of gastroenterologists. World J Gastroenterol 2021; 27:2681-2709. [PMID: 34135549 PMCID: PMC8173384 DOI: 10.3748/wjg.v27.i21.2681] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/29/2021] [Accepted: 04/22/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.
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Affiliation(s)
- Bo Cao
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Ke-Cheng Zhang
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Bo Wei
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Lin Chen
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
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Evaluating mismatch repair deficiency for solid tumor immunotherapy eligibility: immunohistochemistry versus microsatellite molecular testing. Hum Pathol 2021; 115:10-18. [PMID: 34052294 DOI: 10.1016/j.humpath.2021.05.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 05/13/2021] [Accepted: 05/20/2021] [Indexed: 12/11/2022]
Abstract
While many landmark solid tumor immunotherapy studies show clinical benefits for solid tumors with high microsatellite instability (MSI-H) and mismatch repair deficiency (dMMR), the methodologies focus only on confirmatory polymerase chain reaction (PCR) testing for MSI-H. Because some tumors are either dMMR or MSI-H but not the other, clinicians must choose between two testing methods for a broad patient population. We investigated the level of correlation between MMR protein immunohistochemistry (IHC) and microsatellite PCR testing results in 62 cancer patients. Thirty-five of the 62 cases (56.5%) were MSI-H by PCR, whereas 35 (56.5%) were dMMR by IHC. MMR IHC results correlated well with MSI PCR in 32 co-positive cases (91.4%) and 24 co-negative cases (88.9%). Six discrepant cases (9.7%) were identified, among which three were MSI-H and MMR intact, and three were dMMR and microsatellite stable. The results of this study highlight the implications of dMMR/MSI testing strategies on precision oncology. Co-testing with both MMR IHC and MSI PCR may be an effective screening strategy for evaluating immunotherapy eligibility status for solid tumors.
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Gilson P, Merlin JL, Harlé A. Detection of Microsatellite Instability: State of the Art and Future Applications in Circulating Tumour DNA (ctDNA). Cancers (Basel) 2021; 13:cancers13071491. [PMID: 33804907 PMCID: PMC8037825 DOI: 10.3390/cancers13071491] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 03/15/2021] [Accepted: 03/22/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary Microsatellite instability (MSI) is a molecular fingerprint for defects in the mismatch repair system (dMMR) and is associated with higher risks of cancers. MSI/dMMR tumours are characterized by the accumulation of mutations throughout the genome, and particularly in microsatellite (MS) DNA repeat sequences. MSI stands as a major biomarker for familial cancer risk assessment, cancer prognosis, and therapeutic choices. Standard-of-care classification of MSI/dMMR tumours is most frequently achieved using immunohistochemistry or PCR-based assay directed against a set of five MS regions. However, novel molecular methods based on tumour tissue or plasma samples have been developed and could enter in the future trends of MSI testing. Here, we provide insights into these emerging approaches and discuss their advantages and limitations. Abstract Microsatellite instability (MSI) is a molecular scar resulting from a defective mismatch repair system (dMMR) and associated with various malignancies. MSI tumours are characterized by the accumulation of mutations throughout the genome and particularly clustered in highly repetitive microsatellite (MS) regions. MSI/dMMR status is routinely assessed in solid tumours for the initial screening of Lynch syndrome, the evaluation of cancer prognosis, and treatment decision-making. Currently, pentaplex PCR-based methods and MMR immunohistochemistry on tumour tissue samples are the standard diagnostic methods for MSI/dMMR. Other tissue methods such as next-generation sequencing or real-time PCR-based systems have emerged and represent viable alternatives to standard MSI testing in specific settings. The evolution of the standard molecular techniques has offered the opportunity to extend MSI determination to liquid biopsy based on the analysis of cell-free DNA (cfDNA) in plasma. This review aims at synthetizing the standard and emerging techniques used on tumour tissue samples for MSI/dMMR determination. We also provide insights into the MSI molecular techniques compatible with liquid biopsy and the potential clinical consequences for patients with solid cancers.
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Affiliation(s)
- Pauline Gilson
- Correspondence: ; Tel.: +33-(0)3-8365-6035; Fax: +33-(0)3-8365-6152
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