Quantification of tumour budding, lymphatic vessel density and invasion through image analysis in colorectal cancer
By Peter David Caie, A. Turnbull, S. Farrington, A. Oniscu, D. Harrison. 2014
55 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Oncology
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Citations in the corpus
- Continuous representation of tumor microvessel density and detection of angiogenic hotspots in histological whole-slide images (2015, unknown): “A recent approach included segmentation of lymphatic vessels by proprietary software and analysis of global measures of vascularization [30].”
- Machine Learning Methods for Histopathological Image Analysis (2017, methodology): “Other diagnosis-related tasks include detection or segmentation of Region of Interest (ROI) such as tumor region in WSI [15,16], scoring of immunostaining [11,17], cancer staging [14,18], mitosis detection [19,20], gland segmentation [21–23], and detection and quantification of vascular invasion [24”