Abstract
Tumour budding has been described as an independent prognostic feature in several tumour types. We report for the first time the relationship between tumour budding and survival evaluated in patients with muscle invasive bladder cancer. A machine learning-based methodology was applied to accurately quantify tumour buds across immunofluorescence labelled whole slide images from 100 muscle invasive bladder cancer patients. Furthermore, tumour budding was found to be correlated to TNM (p = 0.00089) and pT (p = 0.0078) staging. A novel classification and regression tree model was constructed to stratify all stage II, III, and IV patients into three new staging criteria based on disease specific survival. For the stratification of non-metastatic patients into high or low risk of disease specific death, our decision tree model reported that tumour budding was the most significant feature (HR = 2.59, p = 0.0091), and no clinical feature was utilised to categorise these patients. Our findings demonstrate that tumour budding, quantified using automated image analysis provides prognostic value for muscle invasive bladder cancer patients and a better model fit than TNM staging.
Highlights
Muscle-invasive bladder cancers (MIBC) are classified as having grown into or through the muscle layers of the bladder wall[1] are phenotypically heterogeneous
We evaluate whether Tumour budding (TB) can more precisely predict MIBC patients at a low or medium risk of disease specific death than current clinical guidelines based on Tumour Node Metastasis (TNM) staging
We found that the incorporation of TB features improved the accuracy of the prediction of disease specific survival (DSS) compared to standard TNM staging alone
Summary
Muscle-invasive bladder cancers (MIBC) are classified as having grown into or through the muscle layers of the bladder wall[1] are phenotypically heterogeneous. Current clinical guidelines for both prognosis and treatment strategies are based on Tumour Node Metastasis (TNM) staging system; of which MIBC cancers are comprised of pT2–pT4 tumours, with or without nodal and distant metastasis[2]. We evaluate whether TB can more precisely predict MIBC patients at a low or medium risk of disease specific death than current clinical guidelines based on TNM staging. To this end, we introduce an image analysis solution for the detection of tumour buds, which following from our previous works[27,28], is based on the automated detection of both the nuclei and the segmentation and classification of cancer cell clusters. Survival analysis provided evidence of the prognostic value of TB quantification in MIBC patients and showed that a combination of clinicopathological features with TB predicted patient survival more precisely than the standard TNM staging
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