☆ 4.7 Review

Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers

BRIEFINGS IN BIOINFORMATICS (2023)

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Review Oncology

Overall Treatment Strategy for Patients With Metastatic NSCLC With Activating EGFR Mutations

Hidetoshi Hayashi et al.

Summary: EGFR-TKIs are important in the first-line treatment of metastatic non-small cell lung cancer patients with activating EGFR mutations. However, different mutation types have varying sensitivity to EGFR-TKIs. Osimertinib, as a third-generation EGFR-TKI, overcomes T790M-mediated resistance, but there are currently no approved targeted therapies after third-generation EGFR-TKIs. Combination of VEGF inhibitors and EGFR-TKIs or chemotherapy and EGFR-TKIs may be a potential approach in the first-line setting.

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Article Urology & Nephrology

Artificial Intelligence-based Detection of FGFR3 Mutational Status Directly from Routine Histology in Bladder Cancer: A Possible Preselection for Molecular Testing?

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Summary: In this study, an artificial intelligence system was used to predict mutations of the FGFR3 gene in bladder cancer from histological slides, and it was found to detect these mutations with high accuracy. In the future, this system could be used to preselect patients for further molecular testing.

EUROPEAN UROLOGY FOCUS (2022)

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Review Oncology

Current Trend of Artificial Intelligence Patents in Digital Pathology: A Systematic Evaluation of the Patent Landscape

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Summary: The integration of digital pathology with artificial intelligence enables faster, more accurate, and thorough diagnoses, leading to more precise personalized treatment. In the past five years, there has been an increasing trend in patent filings, primarily focusing on the digitization of pathological images and AI technologies that support the vital role of pathologists.

CANCERS (2022)

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Review Oncology

Recent Applications of Artificial Intelligence from Histopathologic Image-Based Prediction of Microsatellite Instability in Solid Cancers: A Systematic Review

Mohammad Rizwan Alam et al.

Summary: This study assessed the current status of artificial intelligence (AI) applications for microsatellite instability (MSI) prediction based on whole slide images (WSIs), and suggested a better study design. The AI models showed promising results in predicting MSI, but the limitations included small datasets and lack of external validation. Future studies with larger datasets are needed to implement AI models in routine diagnostic practice for MSI prediction.

CANCERS (2022)

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Review Oncology

Recent Application of Artificial Intelligence in Non-Gynecological Cancer Cytopathology: A Systematic Review

Nishant Thakur et al.

Summary: Artificial intelligence has shown promising results in non-gynecological cancer cytology, especially in classification and segmentation tasks. However, the lack of large-scale, well-annotated datasets is a major limitation in current studies.

CANCERS (2022)

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Article Oncology

Computational Analysis of Pathological Image Enables Interpretable Prediction for Microsatellite Instability

Jin Zhu et al.

Summary: In this study, interpretable pathological image analysis strategies are developed to assist medical experts in identifying MSI. Equipped with machine learning and image processing techniques, intelligent models diagnose MSI based on pathological images and provide interpretability at both image and feature levels.

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miR-199a-3p increases the anti-tumor activity of palbociclib in liver cancer models

Elisa Callegari et al.

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Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning

Korsuk Sirinukunwattana et al.

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Primary mismatch repair deficient IDH-mutant astrocytoma (PMMRDIA) is a distinct type with a poor prognosis

Abigail K. Suwala et al.

Summary: A novel epigenetic group of IDH-mutant gliomas with proven or suspected hereditary mismatch repair deficiency, termed primary mismatch repair-deficient IDH-mutant astrocytomas (PMMRDIA), has been identified in children, adolescents, and young adults. These tumors exhibit high-grade histological features, hypermutant genotype, and poor clinical outcomes.

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Yosep Chong et al.

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Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes

James A. Diao et al.

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ARID1A deficiency weakens BRG1-RAD21 interaction that jeopardizes chromatin compactness and drives liver cancer cell metastasis

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Hao Zeng et al.

Summary: This study utilized histopathological image features to predict molecular features and survival risk in HGSOC patients, showing the potential of integrating image features and multi-omics data to improve prognosis prediction.

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Weakly supervised learning on unannotated H&E-stained slides predicts BRAF mutation in thyroid cancer with high accuracy

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Summary: The study utilized weakly supervised learning to train a DNN model for predicting a specific mutational status without requiring regional annotations, achieving high predictive accuracy. A visualization technique was also developed to accurately highlight the most informative regions, moving towards explainable artificial intelligence.

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Constantine S. Velmahos et al.

Summary: This study utilized a convolutional neural network to extract imaging biomarkers from tumor diagnostic slides of bladder cancer in order to predict FGFR gene alterations. The predictive model was found to be proficient in identifying patients with FGFR gene aberrations, providing earlier screening options for targeted therapies.

CANCER MEDICINE (2021)

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Artificial Intelligence Can Cut Costs While Maintaining Accuracy in Colorectal Cancer Genotyping

Alec J. Kacew et al.

Summary: A study found that in first-line treatment of metastatic colorectal cancer, using high-sensitivity artificial intelligence followed by confirmatory high-specificity polymerase chain reaction or immunohistochemistry panel testing strategy resulted in the greatest project cost savings, while the strategy of using high-specificity artificial intelligence alone had the most favorable clinical impact in guiding genotype-directed treatment.

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GASTROENTEROLOGY (2020)

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CANCERS (2020)

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Haotian Liao et al.

CLINICAL AND TRANSLATIONAL MEDICINE (2020)

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Javad Noorbakhsh et al.

NATURE COMMUNICATIONS (2020)

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Recommendations for pathologic practice using digital pathology: consensus report of the Korean Society of Pathologists

Yosep Chong et al.

JOURNAL OF PATHOLOGY AND TRANSLATIONAL MEDICINE (2020)

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Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning

Hyun-Jong Jang et al.

WORLD JOURNAL OF GASTROENTEROLOGY (2020)

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A Multiple-Instance Learning-Based Convolutional Neural Network Model to Detect the IDH1 Mutation in the Histopathology Images of Glioma Tissues

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The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database

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NATURE CANCER (2020)

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Soojeong Nam et al.

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Rapid BRAF mutation tests in patients with advanced melanoma: comparison of immunohistochemistry, Droplet Digital PCR, and the Idylla Mutation Platform

Cornelis Bisschop et al.

MELANOMA RESEARCH (2018)

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Liang Cheng et al.

MODERN PATHOLOGY (2018)

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Histological subtypes of hepatocellular carcinoma are related to gene mutations and molecular tumour classification

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Identification of BRAF-Positive Cases Based on Whole-Slide Image Analysis

Vlad Popovici et al.

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E. Sanz-Garcia et al.

ANNALS OF ONCOLOGY (2017)

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Catherine H. Han et al.

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ONCOLOGY LETTERS (2016)

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BRCA2 mutation-associated breast cancers exhibit a distinguishing phenotype based on morphology and molecular profiles from tissue microarrays

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AMERICAN JOURNAL OF SURGICAL PATHOLOGY (2003)

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