Risk factors for prostate cancer in men with false-negative mpMRI: A retrospective single center cohort study of image quality scores and clinical parameters.

PURPOSE: To identify predictors of prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in men with prior false-negative multiparametric MRI (mpMRI), focusing on image quality scoring systems and clinical parameters. METHODS: In this IRB-approved retrospective single-center study, patients with a negative mpMRI (PI-RADS score ≤2) and subsequent prostate biopsies were included. Histopathological results served as reference standard. Welch's t-Test was conducted to identify significant differences in image quality scores (PI-QUAL and PSHS) between patients with and without PCa/csPCA. In addition, clinical parameters (age, BMI, PSA density) and image quality scores (PI-QUAL and PSHS) were examined as potential predictors of PCa/csPCa detection after a false-negative mpMRI in uni- and multivariate analyses. RESULTS: Among 96 patients with negative mpMRI results, 44.8 % had PCa and 16.7 % had csPCa upon biopsy with histopathological confirmation. PI-QUAL scores were significantly lower in patients with PCa (p = 0.03) and csPCa (p = 0.005). PSHS scores were lower in patients with csPCa, but the difference was not statistically significant (p = 0.1). Higher age (p = 0.035) and a lower PI-QUAL score (p < 0.004) were predictors of subsequent csPCa detection upon biopsy, however, a lower PI-QUAL score was the only independent predictor of missed csPCa in false-negative mpMRIs. CONCLUSIONS: Lower image quality scores were associated with missed PCa/csPCa in patients with false-negative mpMRIs, with PI-QUAL being an independent predictor of failed csPCa detection. This highlights the importance of image quality for prostate MRI and advocats the inclusion of its measurement into the standardized report.

AI-based automated evaluation of image quality and protocol tailoring in patients undergoing MRI for suspected prostate cancer.

PURPOSE: To develop and validate an artificial intelligence (AI) application in a clinical setting to decide whether dynamic contrast-enhanced (DCE) sequences are necessary in multiparametric prostate MRI. METHODS: This study was approved by the institutional review board and requirement for study-specific informed consent was waived. A mobile app was developed to integrate AI-based image quality analysis into clinical workflow. An expert radiologist provided reference decisions. Diagnostic performance parameters (sensitivity and specificity) were calculated and inter-reader agreement was evaluated. RESULTS: Fully automated evaluation was possible in 87% of cases, with the application reaching a sensitivity of 80% and a specificity of 100% in selecting patients for multiparametric MRI. In 2% of patients, the application falsely decided on omitting DCE. With a technician reaching a sensitivity of 29% and specificity of 98%, and resident radiologists reaching sensitivity of 29% and specificity of 93%, the use of the application allowed a significant increase in sensitivity. CONCLUSION: The presented AI application accurately decides on a patient-specific MRI protocol based on image quality analysis, potentially allowing omission of DCE in the diagnostic workup of patients with suspected prostate cancer. This could streamline workflow and optimize time utilization of healthcare professionals.

Continuous and Unconstrained Tremor Monitoring in Parkinson's Disease Using Supervised Machine Learning and Wearable Sensors.

Background: Accurately assessing the severity and frequency of fluctuating motor symptoms is important at all stages of Parkinson's disease management. Contrarily to time-consuming clinical testing or patient self-reporting with uncertain reliability, recordings with wearable sensors show promise as a tool for continuously and objectively assessing PD symptoms. While wearables-based clinical assessments during standardised and scripted tasks have been successfully implemented, assessments during unconstrained activity remain a challenge. Methods: We developed and implemented a supervised machine learning algorithm, trained and tested on tremor scores. We evaluated the algorithm on a 67-hour database comprising sensor data and clinical tremor scores for 24 Parkinson patients at four extremities for periods of about 3 hours. A random 25% subset of the labelled samples was used as test data, the remainder as training data. Based on features extracted from the sensor data, a Support Vector Machine was trained to predict tremor severity. Due to the inherent imbalance in tremor scores, we applied dataset rebalancing techniques. Results: Our classifier demonstrated robust performance in detecting tremor events with a sensitivity of 0.90 on the test-portion of the resampled dataset. The overall classification accuracy was high at 0.88. Conclusion: We implemented an accurate classifier for tremor monitoring in free-living environments that can be trained even with modestly sized and imbalanced datasets. This advancement offers significant clinical value in continuously monitoring Parkinson's disease symptoms beyond the hospital setting, paving the way for personalized management of PD, timely therapeutic adjustments, and improved patient quality of life.

Clinical outcome after pencil beam scanning proton therapy and dysphagia/xerostomia NTCP calculations of proton and photon radiotherapy delivered to patients with cancer of the major salivary glands.

OBJECTIVES: The purpose of this study is to report the oncological outcome, observed toxicities and normal tissue complication probability (NTCP) calculation for pencil beam scanning (PBS) PT delivered to salivary gland tumour (SGT) patients. METHODS: We retrospectively reviewed 26 SGT patients treated with PBSPT (median dose, 67.5 Gy(RBE)) between 2005 and 2020 at our institute. Toxicities were recorded according to CTCAEv.4.1. Overall survival (OS), local control (LC), locoregional control (LRC) and distant control (DC) were estimated. For all patients, a photon plan was re-calculated in order to assess the photon/proton NTCP. RESULTS: With a median follow-up time of 46 months (range, 3-118), 5 (19%), 2 (8%), 3 (12%) and 2 (8%) patients presented after PT with distant, local, locoregional failures and death, respectively. The estimated 4 year OS, LC, LCR and DC were 90%, 90%, 87 and 77%, respectively. Grade 3 late toxicity was observed in 2 (8%) patients. The estimated 4 year late high-grade (≥3) toxicity-free survival was 78.4%. The calculated mean difference of NTCP-values after PBSPT and VMAT plans for developing Grade 2 or 3 xerostomia were 3.8 and 2.9%, respectively. For Grade 2-3 dysphagia, the grade corresponding percentages were 8.6 and 1.9%. Not using an up-front model-based approach to select patients for PT, only 40% of our patients met the Dutch eligibility criteria. CONCLUSION: Our data suggest excellent oncological outcome and low late toxicity rates for patients with SGT treated with PBSPT. NTCP calculation showed a substantial risk reduction for Grade 2 or 3 xerostomia and dysphagia in some SGT patients, while for others, no clear benefit was seen with protons, suggesting that comparative planning should be performed routinely for these patients. ADVANCES IN KNOWLEDGE: We have reported that the clinical outcome of SGT patients treated with PT and compared IMPT to VMAT for the treatment of salivary gland tumour and have observed that protons delivered significantly less dose to organs at risks and were associated with less NTCP for xerostomia and dysphagia. Noteworthy, not using an up-front model-based approach, only 40% of our patients met the Dutch eligibility criteria.