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Background: Bone age (BA) assessment performed by artificial intelligence (AI) is of growing interest due to improved accuracy, precision and time efficiency in daily routine. The aim of this study was to investigate the accuracy and efficiency of a novel AI software version for automated BA assessment in comparison to the Greulich-Pyle method.
Methods: Radiographs of 514 patients were analysed in this retrospective study. Total BA was assessed independently by three blinded radiologists applying the GP method and by the AI software. Overall and gender-specific BA assessment results, as well as reading times of both approaches, were compared, while the reference BA was defined by two blinded experienced paediatric radiologists in consensus by application of the Greulich-Pyle method.
Results: Mean absolute deviation (MAD) and root mean square deviation (RSMD) were significantly lower between AI-derived BA and reference BA (MAD 0.34 years, RSMD 0.38 years) than between reader-calculated BA and reference BA (MAD 0.79 years, RSMD 0.89 years; p < 0.001). The correlation between AI-derived BA and reference BA (r = 0.99) was significantly higher than between reader-calculated BA and reference BA (r = 0.90; p < 0.001). No statistical difference was found in reader agreement and correlation analyses regarding gender (p = 0.241). Mean reading times were reduced by 87% using the AI system.
Conclusions: A novel AI software enabled highly accurate automated BA assessment. It may improve efficiency in clinical routine by reducing reading times without compromising the accuracy compared with the Greulich-Pyle method.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
Introduction: There is still an ongoing debate whether a transrectal ultrasound (TRUS) approach for prostate biopsies is associated with higher (infectious) complications rates compared to transperineal biopsies. This is especially of great interests in settings with elevated frequencies of multidrug resistant organisms (MDRO).
Materials and Methods: Between 01/2018 and 05/2019 230 patients underwent a TRUS-guided prostate biopsy at the department of Urology at University Hospital Frankfurt. Patients were followed up within the clinical routine that was not conducted earlier than 6 weeks after the biopsy. Among 230 biopsies, 180 patients took part in the follow-up. No patients were excluded. Patients were analyzed retrospectively regarding complications, infections and underlying infectious agents or needed interventions.
Results: Of all patients with follow up, 84 patients underwent a systematic biopsy (SB) and 96 a targeted biopsy (TB) after MRI of the prostate with additional SB. 74.8% of the patients were biopsy-naïve. The most frequent objective complications (classified by Clavien-Dindo) lasting longer than one day after biopsy were hematuria (17.9%, n = 32), hematospermia (13.9%, n = 25), rectal bleeding (2.8%, n = 5), and pain (2.2%, n = 4). Besides a known high MDRO prevalence in the Rhine-Main region, only one patient (0.6%) developed fever after biopsy. One patient each (0.6%) consulted a physician due to urinary retention, rectal bleeding or gross hematuria. There were no significant differences in complications seen between SB and SB + TB patients. The rate of patients who consulted a physician was significantly higher for patients with one or more prior biopsies compared to biopsy-naïve patients.
Conclusion: Complications after transrectal prostate biopsies are rare and often self-limiting. Infections were seen in <1% of all patients, regardless of an elevated local prevalence of MDROs. Severe complications (Clavien-Dindo ≥ IIIa) were only seen in 3 (1.7%) of the patients. Repeated biopsy is associated with higher complication rates in general.