BackgroundProstate cancer (PCa) diagnosis by means of multiparametric magnetic resonance imaging (mpMRI) is a current challenge for the development of computer-aided detection (CAD) tools. An innovative CAD-software (Watson Elementary™) was proposed to achieve high sensitivity and specificity, as well as to allege a correlate to Gleason grade.Aim/ObjectiveTo assess the performance of Watson Elementary™ in automated PCa diagnosis in our hospital´s database of MRI-guided prostate biopsies.MethodsThe evaluation was retrospective for 104 lesions (47 PCa, 57 benign) from 79, 64.61±6.64 year old patients using 3T T2-weighted imaging, Apparent Diffusion Coefficient (ADC) maps and dynamic contrast enhancement series. Watson Elementary™ utilizes signal intensity, diffusion properties and kinetic profile to compute a proportional Gleason grade predictor, termed Malignancy Attention Index (MAI). The analysis focused on (i) the CAD sensitivity and specificity to classify suspect lesions and (ii) the MAI correlation with the histopathological ground truth.ResultsThe software revealed a sensitivity of 46.80% for PCa classification. The specificity for PCa was found to be 75.43% with a positive predictive value of 61.11%, a negative predictive value of 63.23% and a false discovery rate of 38.89%. CAD classified PCa and benign lesions with equal probability (P 0.06, χ2 test).Accordingly, receiver operating characteristic analysis suggests a poor predictive value for MAI with an area under curve of 0.65 (P 0.02), which is not superior to the performance of board certified observers. Moreover, MAI revealed no significant correlation with Gleason grade (P 0.60, Pearson´s correlation).ConclusionThe tested CAD software for mpMRI analysis was a weak PCa biomarker in this dataset. Targeted prostate biopsy and histology remains the gold standard for prostate cancer diagnosis.

ObjectiveProstate lesions detected with multiparametric magnetic resonance imaging (mpMRI) are classified for their malignant potential according to the Prostate Imaging-Reporting And Data System (PI-RADS™2). In this study, we evaluate the diagnostic accuracy of the mpMRI with and without gadolinium, with emphasis on the added diagnostic value of the dynamic contrast enhancement (DCE).Materials and methodsThe study was retrospective for 286 prostate lesions / 213 eligible patients, n = 116/170, and 49/59% malignant for the peripheral (Pz) and transitional zone (Tz), respectively. A stereotactic MRI-guided prostate biopsy served as the histological ground truth. All patients received a mpMRI with DCE. The influence of DCE in the prediction of malignancy was analyzed by blinded assessment of the imaging protocol without DCE and the DCE separately.ResultsSignificant (CSPca) and insignificant (IPca) prostate cancers were evaluated separately to enhance the potential effects of the DCE in the detection of CSPca. The Receiver Operating Characteristics Area Under Curve (ROC-AUC), sensitivity (Se) and specificity (Spe) of PIRADS-without-DCE in the Pz was 0.70/0.47/0.86 for all cancers (IPca and CSPca merged) and 0.73/0.54/0.82 for CSPca. PIRADS-with-DCE for the same patients showed ROC-AUC/Se/Spe of 0.70/0.49/0.86 for all Pz cancers and 0.69/0.54/0.81 for CSPca in the Pz, respectively, p>0.05 chi-squared test. Similar results for the Tz, AUC/Se/Spe for PIRADS-without-DCE was 0.75/0.61/0.79 all cancers and 0.67/0.54/0.71 for CSPca, not influenced by DCE (0.66/0.47/0.81 for all Tz cancers and 0.61/0.39/0.75 for CSPca in Tz). The added Se and Spe of DCE for the detection of CSPca was 88/34% and 78/33% in the Pz and Tz, respectively.ConclusionDCE showed no significant added diagnostic value and lower specificity for the prediction of CSPca compared to the non-enhanced sequences. Our results support that gadolinium might be omitted without mitigating the diagnostic accuracy of the mpMRI for prostate cancer.

Necrotizing fasciitis is a rare, life-threatening and rapidly spreading soft-tissue infection that results in necrosis of the muscle, fascia and surrounding tissue. It can be result of a polymicrobial synergistic infection or a streptococcal infection. The authors report a case of necrotizing fasciitis occurring in the knee of a 65-year-old woman following an uneventful primary total knee arthroplasty and resulting in above-the-knee amputation. Having in mind severe infections like necrotising fasciitis, one should be aware of the possibility of such postoperative complications especially in patients with risk factors even in routine procedures like a total knee arthroplasty.

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Background: HER2 testing in breast cancer (BC), routine for >10 years, allows selection of patients (pts) for HER2-targeted therapy; however, testing quality remains a concern. While guidelines recommend assessment of HER2-positivity rates as a quality indicator, the influence of patient- or tumor-related factors on variability was unknown until we identified the effect (in order of influence) of histologic grade, hormone receptor (HR) status, histologic subtype, age, and nodal status in a large, multicenter, observational study in Germany (NIU HER2 study; Rüschoff et al., Mod Pathol 2017). Based on these variables and the statistical model developed, potential issues with HER2 testing quality in local practice may be identified. We now report interim analyses from a multicenter study in Germany (EPI HER2 BC study; NCT02666261), where data from the NIU and EPI studies were compared and the validity of the NIU study model assessed. Methods: Routine HER2 test results and patient- and tumor-related data were collected from eligible pts with BC. Factors influencing HER2-positivity rates in the EPI study were compared with those identified in the NIU study. The predictive power of the NIU study model, fitted to EPI data, was determined and assessments performed using the variable coefficients and cutoff resulting from the NIU study analysis. Attempts were also made to improve the model. Results: Analyses included 15281 (NIU) and 6019 (EPI) invasive BC samples. The distribution of relevant variables, including HER2-positivity rate (NIU: 14.4%; EPI: 13.5%), was comparable. When the NIU study model was fitted to EPI study data, all five covariates identified in the NIU analyses had a significant effect on HER2-positivity (p<0.001); the order of influence for covariates differed between studies (EPI [in order of influence]: histologic grading, histologic subtype, HR status, nodal status, and age). The relationship between HER2-positivity rate and the combined influence of covariates, visualized with the NIU study prediction profiler, was reproduced with EPI study data. The NIU study statistical model, with variable coefficients and cut-point determined in the NIU study, was used to predict the HER2-positivity of samples in EPI; if their NIU model-estimated probability of positivity was >0.1407, the resulting sensitivity, specificity, and receiver operating characteristic (ROC) area under the curve (AUC) were 0.7032, 0.6622, and 0.7259, respectively. Thus, initial validation of the NIU study model with EPI data was successful. Semiquantitative estrogen and progesterone receptor expression data were available from EPI only; their inclusion as independent continuous, rather than categorical, variables improved the model (ROC AUC = 0.7533). Conclusions: The statistical modeling approach used to analyze data from the NIU study showed that patient- or tumor-related characteristics should be considered when assessing HER2 testing quality. Our present analysis validates and improves upon this statistical model and further highlights the need to assess HER2 testing quality in BC. Comparison of calculated vs actual positivity rates may help identify centers with potential HER2 testing quality issues. Citation Format: Rüschoff J, Lebeau A, Kreipe H, Gerharz CD, Sinn P, Schildhaus H-U, Tennstedt-Schenk C, Ammann JU, Künzel C, Koch W, Untch M. Variables influencing HER2-positivity in breast cancer: Assessment and validation of a statistical model based on two multicenter noninterventional studies in Germany [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P6-03-01.