AI Summary & Key Takeaways
AI InsightConclusion: Consistent LCC estimates can be obtained from MR images providing a beneficial metric for detecting the existence of EPE and for discriminating the grades of EPE.Key words: Prostate cancer, extraprostatic extension, length of capsular contact, tumor grading, multi-parametric magnetic resonance imaging
📌 3 Key Takeaways
Abstract
Backround/aim: In prostate cancer, extraprostatic extension (EPE) is an unfavorable prognostic factor, and the grade of EPE is correlated with the prognosis. This study aims to evaluate the utility of length of capsular contact (LCC ) in predicting the grade of EPE by correlating the measurements from MRI images and the measurements performed from radical prostatectomy specimens. Materials and methods: MR images and specimens of 110 tumors are analyzed retrospectively. The specimens are used as reference to validate the presence of EPE and to measure the ground truth LCC. MR images are evaluated by two radiologists to identify the presence of EPE and to predict the LCC indirectly. Reliability, accuracy, sensitivity, and specificity of the evaluations are analyzed in comparison with the findings obtained from the specimens. Results: In detection of EPE existence, the radiologists achieve almost the same performance (all AUCs = 0.73) with optimal cut-off values lead to moderate sensitivity and specificity pairs (For cut-off = 15.8 mm; Se = 0.69, Sp = 0.68 and for cut-off of 14.5 mm: Se = 0.77, Sp = 0.62). In distinguishing high-grade EPE from low-grade EPE, the radiologists accomplish very similar performances (AUCs = 0.73 and 0.72) Optimal thresholds of 20.0 mm and 18.5 mm for the readers retrospectively reveal medium sensitivity and specificity pairs (Se = 0.64, Sp = 0.67; Se = 0.64, Sp = 0.67). Conclusion: Consistent LCC estimates can be obtained from MR images providing a beneficial metric for detecting the existence of EPE and for discriminating the grades of EPE.Key words: Prostate cancer, extraprostatic extension, length of capsular contact, tumor grading, multi-parametric magnetic resonance imaging