scholarly journals The applicability of artificial intelligence algorithms on multi-parametric magnetic resonance imaging for the detection of extraprostatic extension in prostate cancer

2021 ◽  
Vol 33 ◽  
pp. S143
Author(s):  
I. van den Berg ◽  
T.F.W. Soeterik ◽  
E.J.R.J. Van Der Hoeven ◽  
H.H.E. Van Melick
Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 959
Author(s):  
Jasper J. Twilt ◽  
Kicky G. van Leeuwen ◽  
Henkjan J. Huisman ◽  
Jurgen J. Fütterer ◽  
Maarten de Rooij

Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.


Author(s):  
B. Valentin ◽  
L. Schimmöller ◽  
T. Ullrich ◽  
M. Klingebiel ◽  
D. Demetrescu ◽  
...  

Abstract Objectives The aim of this study was to investigate 3 Tesla multiparametric magnetic resonance imaging (mpMRI)-based predictors for the pretherapeutic T staging of prostate cancer and their accuracy. Methods Consecutive patients with 3 Tesla mpMRI, positive systematic and MR-targeted biopsy, and subsequent radical prostatectomy (RPE) between 01/2016 and 12/2017 were included. MRI parameters such as measurable extraprostatic extension (EPE) (≥ 3 mm), length of (pseudo)capsular contact (LCC), invasion of neurovascular bundle (NVBI), and/or seminal vesicles lesion contact (SVC) or infiltration (SVI) were assessed and correlated to clinical and histopathological results. Results 136 men were included. In 76 cases, a pT2 stage was determined, in 29 cases a pT3a, and in 31 a pT3b stage. The positive and negative predictive values (PPV, NPV) for the detection of T3 by measurable EPE on MRI was 98% (CI 0.88–1) and 81% (CI 0.72–0.87). No visible NVBI was found in pT2 patients (NPV 100%; CI 0.95–1). ROC analysis for T3a prediction with LCC (AUC 0.81) showed a sensitivity of 87% and a specificity of 62% at a threshold of 12.5 mm (J = 0.485) and 93% and 58% at 11 mm (Jmax = 0.512). All patients with pT3a had a LCC > 5 mm. In case of pT3b, 29/31 patients showed a SVC (PPV 76%, CI 0.61–0.87; NPV 98%, CI 0.93–0.99), and 23/31 patients showed a SVI (PPV 100%, CI 0.86–1; NPV 93%, CI 0.87–0.96). EPE (p < 0.01), LCC (p = 0.05), and SVC (p = 0.01) were independent predictors of pT3. Conclusions MRI-measurable EPE, LCC, and SVC were reliable, independent, preoperative predictors for a histopathological T3 stage. A LCC ≥ 11 mm indicated a pT3a stage, whereas a LCC < 5 mm excluded it. On MRI, visible SVI or even SVC of the PCa lesion was reliable preoperative predictors for a pT3b stage.


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