Artificial intelligence to identify genetic alterations in conventional histopathology-Reference-Cited by-同舟云学术

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Artificial intelligence to identify genetic alterations in conventional histopathology

Published:2022-04-21 Issue:4 Volume:257 Page:430-444
ISSN:0022-3417
Container-title:The Journal of Pathology
language:en
Short-container-title:The Journal of Pathology

Author:

Cifci Didem¹^ORCID,Foersch Sebastian²,Kather Jakob Nikolas¹³⁴^ORCID

Affiliation:

1. Department of Medicine III University Hospital RWTH Aachen Aachen Germany

2. Institute of Pathology University Medical Center of the Johannes Gutenberg‐University Mainz Germany

3. Pathology and Data Analytics Leeds Institute of Medical Research at St James's, University of Leeds Leeds UK

4. Medical Oncology National Center for Tumor Diseases, University Hospital Heidelberg Heidelberg Germany

Funder

Bundesministerium für Gesundheit

Deutsche Krebshilfe

Publisher

Wiley

Subject

Pathology and Forensic Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/path.5898

Reference164 articles.

1. Standardized Assessment of the Tumor-Stroma Ratio in Colorectal Cancer: Interobserver Validation and Reproducibility of a Potential Prognostic Factor

2. Type, Density, and Location of Immune Cells Within Human Colorectal Tumors Predict Clinical Outcome

3. Deep-Learning–Based Characterization of Tumor-Infiltrating Lymphocytes in Breast Cancers From Histopathology Images and Multiomics Data

4. A digital score of tumour‐associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma

5. Assessing Tumor-infiltrating Lymphocytes in Solid Tumors: A Practical Review for Pathologists and Proposal for a Standardized Method From the International Immunooncology Biomarkers Working Group: Part 1: Assessing the Host Immune Response, TILs in Invasive Breast Carcinoma and Ductal Carcinoma In Situ, Metastatic Tumor Deposits and Areas for Further Research

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