scholarly journals Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Thomas Ying ◽  
Pablo Borrelli ◽  
Lars Edenbrandt ◽  
Olof Enqvist ◽  
Reza Kaboteh ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Image Analysis ◽  
Urinary Bladder ◽  
Radical Cystectomy ◽  
Cox Regression ◽  
Urinary Bladder Cancer ◽  
Muscle Volume ◽  
Analysis Tool ◽  
High Grade ◽  
Ct Studies

Abstract Background Radical cystectomy for urinary bladder cancer is a procedure associated with a high risk of complications, and poor overall survival (OS) due to both patient and tumour factors. Sarcopenia is one such patient factor. We have developed a fully automated artificial intelligence (AI)-based image analysis tool for segmenting skeletal muscle of the torso and calculating the muscle volume. Methods All patients who have undergone radical cystectomy for urinary bladder cancer 2011–2019 at Sahlgrenska University Hospital, and who had a pre-operative computed tomography of the abdomen within 90 days of surgery were included in the study. All patients CT studies were analysed with the automated AI-based image analysis tool. Clinical data for the patients were retrieved from the Swedish National Register for Urinary Bladder Cancer. Muscle volumes dichotomised by the median for each sex were analysed with Cox regression for OS and logistic regression for 90-day high-grade complications. The study was approved by the Swedish Ethical Review Authority (2020-03985). Results Out of 445 patients who underwent surgery, 299 (67%) had CT studies available for analysis. The automated AI-based tool failed to segment the muscle volume in seven (2%) patients. Cox regression analysis showed an independent significant association with OS (HR 1.62; 95% CI 1.07–2.44; p = 0.022). Logistic regression did not show any association with high-grade complications. Conclusion The fully automated AI-based CT image analysis provides a low-cost and meaningful clinical measure that is an independent biomarker for OS following radical cystectomy.

scholarly journals MP34-19 EFFECT OF HISTOLOGICAL VARIANTS ON THE OUTCOMES OF RADICAL CYSTECTOMY FOR NON-METASTATIC MUSCLE-INVASIVE URINARY BLADDER CANCER

2017 ◽  
Vol 197 (4S) ◽  
Author(s):  
Malte W. Vetterlein ◽  
Thomas Seisen ◽  
Jeffrey J. Leow ◽  
Mark A. Preston ◽  
Maxine Sun ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Urinary Bladder ◽  
Radical Cystectomy ◽  
Urinary Bladder Cancer ◽  
Histological Variants ◽  
Muscle Invasive

Utility of first order MRI-Texture analysis parameters in the prediction of histologic grade and muscle invasion in urinary bladder cancer: a preliminary study

2021 ◽  
pp. 20201114
Author(s):  
Abdul Razik ◽  
Chandan J Das ◽  
Raju Sharma ◽  
Sundeep Malla ◽  
Sanjay Sharma ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Urinary Bladder ◽  
Texture Analysis ◽  
Urinary Bladder Cancer ◽  
Histologic Grade ◽  
Low Grade ◽  
High Grade ◽  
First Order ◽  
Muscle Invasive ◽  
Muscle Invasion

Objective: To explore the utility of first-order MRI-texture analysis (TA) parameters in predicting histologic grade and muscle invasion in urinary bladder cancer (UBC). Methods: After ethical clearance, 40 patients with UBC, who were imaged on a 3.0-Tesla scanner, were retrospectively included. Using the TexRADTM platform, two readers placed freehand ROI on the sections demonstrating the largest dimension of the tumor, evaluating only one tumor per patient. Interobserver reproducibility was assessed using the intraclass correlation coefficient (ICC). Mann–Whitney U test and ROC curve analysis were used to identify statistical significance and select parameters with high class separation capacity (AUC >0.8), respectively. Pearson’s test was used to identify redundancy in the results. Results: All texture parameters showed excellent ICC. The best parameters in differentiating high and low-grade tumors were mean/ mean of positive pixels (MPP) at SSF 0 (AUC: 0.897) and kurtosis at SSF 5 (AUC: 0.828) on the ADC images. In differentiating muscle invasive from non-muscle invasive tumors, mean/ MPP at SSF 0 on the ADC images showed AUC >0.8; however, this finding resulted from the confounding effect of high-grade histology on the ADC values of muscle invasive tumors. Conclusion: MRI-TA generated few parameters which were reproducible and useful in predicting histologic grade. No independent parameters predicted muscle invasion. Advances in knowledge: There is lacuna in the literature concerning the role of MRI-TA in the prediction of histologic grade and muscle invasion in UBC. Our study generated a few first-order parameters which were useful in predicting high-grade histology.

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