Automated Ki-67 labeling index assessment in prostate cancer using artificial intelligence and multiplex fluorescence immunohistochemistry.

The Ki-67 labeling index (Ki-67 LI) is a strong prognostic marker in prostate cancer, although its analysis requires cumbersome manual quantification of Ki-67 immunostaining in 200-500 tumor cells. To enable automated Ki-67 LI assessment in routine clinical practice, a framework for automated Ki-67 LI quantification, which comprises three different artificial intelligence analysis steps and an algorithm for cell-distance analysis of multiplex fluorescence immunohistochemistry (mfIHC) staining, was developed and validated in a cohort of 12,475 prostate cancers. The prognostic impact of the Ki-67 LI was tested on a tissue microarray (TMA) containing one 0.6 mm sample per patient. A 'heterogeneity TMA' containing three to six samples from different tumor areas in each patient was used to model Ki-67 analysis of multiple different biopsies, and 30 prostate biopsies were analyzed to compare a 'classical' bright field-based Ki-67 analysis with the mfIHC-based framework. The Ki-67 LI provided strong and independent prognostic information in 11,845 analyzed prostate cancers (p < 0.001 each), and excellent agreement was found between the framework for automated Ki-67 LI assessment and the manual quantification in prostate biopsies from routine clinical practice (intraclass correlation coefficient: 0.94 [95% confidence interval: 0.87-0.97]). The analysis of the heterogeneity TMA revealed that the Ki-67 LI of the sample with the highest Gleason score (area under the curve [AUC]: 0.68) was as prognostic as the mean Ki-67 LI of all six foci (AUC: 0.71 [p = 0.24]). The combined analysis of the Ki-67 LI and Gleason score obtained on identical tissue spots showed that the Ki-67 LI added significant additional prognostic information in case of classical International Society of Urological Pathology grades (AUC: 0.82 [p = 0.002]) and quantitative Gleason score (AUC: 0.83 [p = 0.018]). The Ki-67 LI is a powerful prognostic parameter in prostate cancer that is now applicable in routine clinical practice. In the case of multiple cancer-positive biopsies, the sole automated analysis of the worst biopsy was sufficient. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Accuracy of multiparametric MR imaging with PI-RADS V2 assessment in detecting infiltration of the neurovascular bundles prior to prostatectomy.

OBJECTIVES: To evaluate the accuracy of assessment of neurovascular bundle (NVB) infiltration using multiparametric magnetic resonance imaging (mpMRI) and PI-RADS V2 prior to prostatectomy. METHODS: The ethics committee approved this retrospective study with waiver of informed consent. N=198 consecutive patients with biopsy proved cancer underwent standardized mpMRI at 3T prior to surgery. NVB infiltration was assessed for each side (a total of 396). Maximum PI-RADS V2 scores were determined for the posterolateral areas adjacent to the NVBs. Imaging results were correlated with postoperative pathology and standard descriptive statistics were calculated. RESULTS: Overall T-staging sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of mpMRI were 64.4%, 89.2%, 82.4%, 76.2% and 78.3%, respectively. In 396 cases NVB infiltration was predicted with 75.3%, 94.0%, 80.2%, 92.1 % and 89.4 % sensitivity, specificity, PPV, NPV and accuracy, respectively. Analyses of 396 NVB and their adjacent PI-RADS V2 scores with pathology revealed significantly more NVB-infiltrations in suspect scores of 5 and 4 vs. uncertain scores of 3-1 (81/264 vs. 16/132, p=0.0001). Considering scores higher than 3 as a criterion of infiltration demonstrated moderate sensitivity and poor specificity (83.5% and 38.8%, respectively). Interobserver agreement of a second reading of a random sample was good (κ=0.64) for NVB infiltrations and moderate (κ=0.59) for PI-RADS V2. CONCLUSIONS: Assessment of infiltration of the neurovascular bundles using mpMRI has valuable diagnostic performance, yet PI-RADS V2 Scores demonstrate limited eligibility. Combined findings offer crucial information for the planning of prostatectomy.