ArticleScientific reports2019
Automated and Manual Quantification of Tumour Cellularity in Digital Slides for Tumour Burden Assessment.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
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- Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections.Scientific reports · 2020Trial
- AI-based tumor cellularity assessment in digital pathology: A review of methods, datasets, and clinical translation.Journal of pathology informatics · 2026Review
- [Artificial intelligence in molecular pathology : Introduction and overview].Pathologie (Heidelberg, Germany) · 2026Review
- Pan-cancer atlas of cellular architecture reveals a nearest-neighbour distance-associated biomechanical-immune axis involving CD4+ memory T cells.Clinical and translational medicine · 2026Article
- The role of radiomics in predicting the response to neoadjuvant chemotherapy for breast cancer.Cancer biology & medicine · 2026Review
- Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor-Infiltrating Lymphocyte Datasets.Journal of imaging informatics in medicine · 2024Review
- Analytical Validation and Performance Evaluation of Amplicon-Based Next-Generation Sequencing Assays for DetectingCancers · 2024Article
- Algorithm-assisted diagnosis of Hirschsprung's disease - evaluation of robustness and comparative image analysis on data from various labs and slide scanners.Diagnostic pathology · 2024Article
- Article
- Standardized Pre-clinical Surgical Animal Model Protocol to Investigate the Cellular and Molecular Mechanisms of Ischemic Flap Healing.Biological procedures online · 2024Article
- Improving Tumor-Infiltrating Lymphocytes Score Prediction in Breast Cancer with Self-Supervised Learning.Life (Basel, Switzerland) · 2024Article
- Navigating Quality Assessment Hurdles in Clinical Laboratory Services: A Comprehensive Review in Resource-Limited Settings.Risk management and healthcare policy · 2024Review
- Accurate, repeatable, and geometrically precise diffusion-weighted imaging on a 0.35 T magnetic resonance imaging-guided linear accelerator.Physics and imaging in radiation oncology · 2023Article
- Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology.NPJ breast cancer · 2023Review
- Global development of artificial intelligence in cancer field: a bibliometric analysis range from 1983 to 2022.Frontiers in oncology · 2023Article
- Application of Deep Learning in Histopathology Images of Breast Cancer: A Review.Micromachines · 2022Review
- The Significance of External Quality Assessment Schemes for Molecular Testing in Clinical Laboratories.Cancers · 2022Review
- Cold Atmospheric Plasma Does Not Affect Stellate Cells Phenotype in Pancreatic Cancer Tissue in Ovo.International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The residual cancer burden index is an important quantitative measure used for assessing treatment response following neoadjuvant therapy for breast cancer. It has shown to be predictive of overall survival and is composed of two key metrics: qualitative assessment of lymph nodes and the percentage of invasive or in situ tumour cellularity (TC) in the tumour bed (TB). Currently, TC is assessed through eye-balling of routine histopathology slides estimating the proportion of tumour cells within the TB. With the advances in production of digitized slides and increasing availability of slide scanners in pathology laboratories, there is potential to measure TC using automated algorithms with greater precision and accuracy. We describe two methods for automated TC scoring: 1) a traditional approach to image analysis development whereby we mimic the pathologists' workflow, and 2) a recent development in artificial intelligence in which features are learned automatically in deep neural networks using image data alone. We show strong agreements between automated and manual analysis of digital slides. Agreements between our trained deep neural networks and experts in this study (0.82) approach the inter-rater agreements between pathologists (0.89). We also reveal properties that are captured when we apply deep neural network to whole slide images, and discuss the potential of using such visualisations to improve upon TC assessment in the future.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.