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Role of artificial intelligence in haematolymphoid diagnostics

https://doi.org/10.1111/his.15327
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
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Histologic transformation of chronic lymphocytic leukemia/small lymphocytic lymphoma
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Machine learning‐based classification of diffuse large B‐cell lymphoma patients by eight gene expression profiles
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Explainability and causability in digital pathology
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Classification of digital pathological images of non‐Hodgkin's lymphoma subtypes based on the fusion of transfer learning and principal component analysis
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Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in chronic lymphocytic leukemia
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Artificial intelligence to identify genetic alterations in conventional histopathology
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Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma
inferredcites →https://doi.org/10.1007/s12308-009-0036-xopenalex-snapshot
Ki-67 as a prognostic marker in mantle cell lymphoma—consensus guidelines of the pathology panel of the European MCL Network
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Natural Language Processing in Pathology
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623MO Machine learning-based prediction of germinal center, MYC/BCL2 double protein expressor status, and MYC rearrangement from whole slide images in DLBCL patients
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Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer
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Automated analysis of fluorescence in situ hybridization on fixed, paraffin-embedded whole tissue sections in B-cell lymphoma
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Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma
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The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms
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Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms
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A guide to artificial intelligence for cancer researchers
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AI-based pathology predicts origins for cancers of unknown primary
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Foundation models for generalist medical artificial intelligence
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Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy
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Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis
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Pan-cancer image-based detection of clinically actionable genetic alterations
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Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
inferredcites →https://doi.org/10.1053/j.gastro.2020.06.021openalex-snapshot
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inferredcites →https://doi.org/10.1093/ajcp/aqaa001openalex-snapshot
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inferredcites →https://doi.org/10.1093/annonc/mdp495openalex-snapshot
Prognostic impact of proliferative index determined by quantitative image analysis and the International Prognostic Index in patients with mantle cell lymphoma
inferredcites →https://doi.org/10.1093/bfgp/elac038openalex-snapshot
Deep learning-based classifier of diffuse large B-cell lymphoma cell-of-origin with clinical outcome
inferredcites →https://doi.org/10.1111/joim.13030openalex-snapshot
Artificial intelligence as the next step towards precision pathology
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Multisite clinical cross-validation and variant interpretation of a next generation sequencing panel for lymphoid cancer prognostication
inferredcites →https://doi.org/10.1182/blood-2003-05-1545openalex-snapshot
Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray
inferredcites →https://doi.org/10.1182/blood-2007-10-117010openalex-snapshot
Ki-67 predicts outcome in advanced-stage mantle cell lymphoma patients treated with anti-CD20 immunochemotherapy: results from randomized trials of the European MCL Network and the German Low Grade Lymphoma Study Group
inferredcites →https://doi.org/10.1182/blood.2022015520openalex-snapshot
Lymph node excisions provide more precise lymphoma diagnoses than core biopsies: a French Lymphopath network survey
inferredcites →https://doi.org/10.1182/blood.2022015851openalex-snapshot
The International Consensus Classification of Mature Lymphoid Neoplasms: a report from the Clinical Advisory Committee
inferredcites →https://doi.org/10.1182/bloodadvances.2020001949openalex-snapshot
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inferredcites →https://doi.org/10.1186/s12885-020-07198-1openalex-snapshot
Novel bioinformatic classification system for genetic signatures identification in diffuse large B-cell lymphoma
inferredcites →https://doi.org/10.1186/s12911-015-0235-6openalex-snapshot
Classification of follicular lymphoma: the effect of computer aid on pathologists grading
inferredcites →https://doi.org/10.1186/s12920-024-01796-9openalex-snapshot
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inferredcites →https://doi.org/10.1186/s12967-019-1951-yopenalex-snapshot
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inferredcites →https://doi.org/10.1186/s13059-021-02592-9openalex-snapshot
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inferredcites →https://doi.org/10.1200/cci.18.00025openalex-snapshot
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inferredcites →https://doi.org/10.1200/jco.18.01314openalex-snapshot
Molecular High-Grade B-Cell Lymphoma: Defining a Poor-Risk Group That Requires Different Approaches to Therapy
inferredcites →https://doi.org/10.1200/jco.2015.63.8387openalex-snapshot
Prognostic Value of Ki-67 Index, Cytology, and Growth Pattern in Mantle-Cell Lymphoma: Results From Randomized Trials of the European Mantle Cell Lymphoma Network
inferredcites →https://doi.org/10.1200/jco.2016.71.2083openalex-snapshot
Impact of Expert Pathologic Review of Lymphoma Diagnosis: Study of Patients From the French Lymphopath Network
inferredcites →https://doi.org/10.3233/thc-218031openalex-snapshot
Research on the classification of lymphoma pathological images based on deep residual neural network
inferredcites →https://doi.org/10.3322/caac.21442openalex-snapshot
Cancer statistics, 2018
inferredcites →https://doi.org/10.3389/fimmu.2022.919012openalex-snapshot
Machine Learning Models for the Diagnosis and Prognosis Prediction of High-Grade B-Cell Lymphoma
inferredcites →https://doi.org/10.3389/fonc.2023.1157646openalex-snapshot
A prognostic model based on gene expression parameters predicts a better response to bortezomib-containing immunochemotherapy in diffuse large B-cell lymphoma
inferredcites →https://doi.org/10.3390/biomedinformatics4010017openalex-snapshot
Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data
inferredcites →https://doi.org/10.3390/cancers13102419openalex-snapshot
Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images
inferredcites →https://doi.org/10.3390/cancers13215463openalex-snapshot
Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas
inferredcites →https://doi.org/10.3390/cancers15215205openalex-snapshot
Image-Based Deep Learning Detection of High-Grade B-Cell Lymphomas Directly from Hematoxylin and Eosin Images
inferredcites →https://doi.org/10.3390/make2040035openalex-snapshot
Artificial Intelligence Analysis of the Gene Expression of Follicular Lymphoma Predicted the Overall Survival and Correlated with the Immune Microenvironment Response Signatures
inferredcites →https://doi.org/10.3390/make3030036openalex-snapshot
Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy
inferredcites →https://doi.org/10.4103/2153-3539.120747openalex-snapshot
Inter-reader variability in follicular lymphoma grading: Conventional and digital reading
inferredcites →https://doi.org/10.4103/jpi.jpi_14_18openalex-snapshot
Machine Learning Provides an Accurate Classification of Diffuse Large B-Cell Lymphoma from Immunohistochemical Data
inferredcites →https://doi.org/10.4103/jpi.jpi_30_19openalex-snapshot
Artificial Intelligence-Driven Structurization of Diagnostic Information in Free-Text Pathology Reports
inferredcites →https://doi.org/10.48550/arxiv.1811.02668openalex-snapshot
Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning
inferredcites →https://doi.org/10.5045/br.2016.51.2.127openalex-snapshot
Prognostic relevance of the Ki-67 proliferation index in patients with mantle cell lymphoma

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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From Prediction to Intervention: Artificial Intelligence for Adaptive Response and Toxicity Modeling in Cellular Therapies for Hematologic Malignancies
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The Intersection of Pathological Analysis and Biology: Advances in Diagnosis and Biotechnology
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