ArticleOncotarget2016
Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 24 citations in OpenAlex.
- Measuring multiple parameters of CD8+ tumor-infiltrating lymphocytes in human cancers by image analysis.Journal for immunotherapy of cancer · 2018Trial
- Integration of histopathological image features and multi-dimensional omics data in predicting molecular features and survival in glioblastoma.Frontiers in medicine · 2025Article
- Automated Detection and Classification of Desmoplastic Reaction at the Colorectal Tumour Front Using Deep Learning.Cancers · 2021Article
- Prognostic significance of mesothelin expression in colorectal cancer disclosed by area-specific four-point tissue microarrays.Virchows Archiv : an international journal of pathology · 2020Article
- Artificial intelligence-based morphological fingerprinting of megakaryocytes: a new tool for assessing disease in MPN patients.Blood advances · 2020Article
- A high-throughput digital script for multiplexed immunofluorescent analysis and quantification of sarcolemmal and sarcomeric proteins in muscular dystrophies.Acta neuropathologica communications · 2020Article
- Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk.PloS one · 2020Article
- Specific immune cell and lymphatic vessel signatures identified by image analysis in renal cancer.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2019Article
- Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis.Scientific reports · 2019Article
- Deep Learning for Whole Slide Image Analysis: An Overview.Frontiers in medicine · 2019Review
- A principled machine learning framework improves accuracy of stage II colorectal cancer prognosis.NPJ digital medicine · 2018Article
- Nuclear features of infiltrating urothelial carcinoma are distinguished from low-grade noninvasive papillary urothelial carcinoma by image analysis.Oncology letters · 2017Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A number of candidate histopathologic factors show promise in identifying stage II colorectal cancer (CRC) patients at a high risk of disease-specific death, however they can suffer from low reproducibility and none have replaced classical pathologic staging. We developed an image analysis algorithm which standardized the quantification of specific histopathologic features and exported a multi-parametric feature-set captured without bias. The image analysis algorithm was executed across a training set (n = 50) and the resultant big data was distilled through decision tree modelling to identify the most informative parameters to sub-categorize stage II CRC patients. The most significant, and novel, parameter identified was the 'sum area of poorly differentiated clusters' (AreaPDC). This feature was validated across a second cohort of stage II CRC patients (n = 134) (HR = 4; 95% CI, 1.5- 11). Finally, the AreaPDC was integrated with the significant features within the clinical pathology report, pT stage and differentiation, into a novel prognostic index (HR = 7.5; 95% CI, 3-18.5) which improved upon current clinical staging (HR = 4.26; 95% CI, 1.7- 10.3). The identification of poorly differentiated clusters as being highly significant in disease progression presents evidence to suggest that these features could be the source of novel targets to decrease the risk of disease specific death.
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