ArticleJournal of translational medicine2014
Quantification of tumour budding, lymphatic vessel density and invasion through image analysis in colorectal cancer.
Article in Journal of translational medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 53 citations in OpenAlex.
- LVI-PathNet: Segmentation-classification pipeline for detection of lymphovascular invasion in whole slide images of lung adenocarcinoma.Journal of pathology informatics · 2024Article
- Prognostic significance of tumor budding in pancreatic carcinoma: Digitalized image approach evaluation using artificial intelligence.F1000Research · 2024Article
- Colorectal Carcinomas: Searching for New Histological Parameters Associated with Lymph Node Metastases.Medicina (Kaunas, Lithuania) · 2023Article
- Preparing pathological data to develop an artificial intelligence model in the nonclinical study.Scientific reports · 2023Article
- Prognostic significance of mesothelin expression in colorectal cancer disclosed by area-specific four-point tissue microarrays.Virchows Archiv : an international journal of pathology · 2020Article
- Article
- Artificial intelligence-based morphological fingerprinting of megakaryocytes: a new tool for assessing disease in MPN patients.Blood advances · 2020Article
- Cellular resolution in clinical MALDI mass spectrometry imaging: the latest advancements and current challenges.Clinical chemistry and laboratory medicine · 2020Review
- Article
- 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
- Intratumor heterogeneity inferred from targeted deep sequencing as a prognostic indicator.Scientific reports · 2019Article
- Automatic evaluation of tumor budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome.Diagnostic pathology · 2018Article
- Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer.Scientific reports · 2018Article
- Machine Learning Methods for Histopathological Image Analysis.Computational and structural biotechnology journal · 2018Review
- Tumour budding in rectal cancer. A comprehensive review.Contemporary oncology (Poznan, Poland) · 2018Review
- Construction and analysis of tissue microarrays in the era of digital pathology: a pilot study targeting CDX1 and CDX2 in a colon cancer cohort of 612 patients.The journal of pathology. Clinical research · 2017Article
- Malignant Colorectal Polyps; Pathological Consideration (A review).Iranian journal of pathology · 2017Review
- Article
- Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting.Oncotarget · 2016Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundTumour budding (TB), lymphatic vessel density (LVD) and lymphatic vessel invasion (LVI) have shown promise as prognostic factors in colorectal cancer (CRC) but reproducibility using conventional histopathology is challenging. We demonstrate image analysis methodology to quantify the histopathological features which could permit standardisation across institutes and aid risk stratification of Dukes B patients.
methodsMultiplexed immunofluorescence of pan-cytokeratin, D2-40 and DAPI identified epithelium, lymphatic vessels and all nuclei respectively in tissue sections from 50 patients diagnosed with Dukes A (n = 13), Dukes B (n = 29) and Dukes C (n = 8) CRC. An image analysis algorithm was developed and performed, on digitised images of the CRC tissue sections, to quantify TB, LVD, and LVI at the invasive front.
resultsTB (HR =5.7; 95% CI, 2.38-13.8), LVD (HR =5.1; 95% CI, 2.04-12.99) and LVI (HR =9.9; 95% CI, 3.57-27.98) were successfully quantified through image analysis and all were shown to be significantly associated with poor survival, in univariate analyses. LVI (HR =6.08; 95% CI, 1.17-31.41) is an independent prognostic factor within the study and was correlated to both TB (Pearson r =0.71, p <0.0003) and LVD (Pearson r =0.69, p <0.0003).
conclusionWe demonstrate methodology through image analysis which can standardise the quantification of TB, LVD and LVI from a single tissue section while decreasing observer variability. We suggest this technology is capable of stratifying a high risk Dukes B CRC subpopulation and we show the three histopathological features to be of prognostic significance.
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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.