(1) Background: Radiomics use high-throughput mining of medical imaging data to extract unique information and predict tumor behavior. Currently available clinical prediction models poorly predict treatment outcomes in pancreatic adenocarcinoma. Therefore, we used radiomic features of primary pancreatic tumors to develop outcome prediction models and compared them to traditional clinical models. (2) Methods: We extracted and analyzed radiomic data from pre-radiation contrast-enhanced CTs of 74 pancreatic cancer patients undergoing stereotactic body radiotherapy. A panel of over 800 radiomic features was screened to create overall survival and local-regional recurrence prediction models, which were compared to clinical prediction models and models combining radiomic and clinical information. (3) Results: A 6-feature radiomic signature was identified that achieved better overall survival prediction performance than the clinical model (mean concordance index: 0.66 vs. 0.54 on resampled cross-validation test sets), and the combined model improved the performance slightly further to 0.68. Similarly, a 7-feature radiomic signature better predicted recurrence than the clinical model (mean AUC of 0.78 vs. 0.66). (4) Conclusion: Overall survival and recurrence can be better predicted with models based on radiomic features than with those based on clinical features for pancreatic cancer.

Radiomic analysis has recently demonstrated versatile uses in improving diagnostic and prognostic prediction accuracy for lung cancer. However, since lung tumors are subject to substantial motion due to respiration, the stability of radiomic features over the respiratory cycle of the patient needs to be investigated to better evaluate the robustness of the inter-patient feature variability for clinical applications, and its impact in such applications needs to be assessed. A full panel of 841 radiomic features, including tumor intensity, shape, texture, and wavelet features, were extracted from individual phases of a four-dimensional (4D) computed tomography on 20 early-stage non-small-cell lung cancer (NSCLC) patients. The stability of each radiomic feature was assessed across different phase images of the same patient using the coefficient of variation (COV). The relationship between individual COVs and tumor motion magnitude was inspected. Population COVs, the mean COVs of all 20 patients, were used to evaluate feature motion stability and categorize the radiomic features into 4 different groups. The two extremes, the Very Small group (COV≤5%) and the Large group (COV>20%), each accounted for about a quarter of the features. Shape features were the most stable, with COV≤10% for all features. A clinical study was subsequently conducted using 140 early-stage NSCLC patients. Radiomic features were employed to predict the overall survival with a 500-round bootstrapping. Identical multiple regression model development process was applied, and the model performance was compared between models with and without a feature pre-selection step based on 4D COV to pre-exclude unstable features. Among the systematically tested cutoff values, feature pre-selection with 4D COV≤5% achieved the optimal model performance. The resulting 3-feature radiomic model significantly outperformed its counterpart with no 4D COV pre-selection, with P = 2.16x10 -27 in the one-tailed t-test comparing the prediction performances of the two models.

Male hypogonadism is not a risk associated with ADHD stimulant medications, but recent studies have explored this connection. Though the exact pathophysiologic connection remains unclear, we predicted that long-term use of ADHD stimulant medications could increase the risk of hypogonadism in post-pubertal males. Utilizing the national TriNetX, LLC Research Network, individuals older than 18 with a diagnosis of ADHD receiving long-term stimulant medication (> 36 monthly prescriptions) were selected for the study population. Two control groups were constructed: individuals with ADHD but no stimulant medication use, and individuals without ADHD or stimulant medication use. A diagnosis of testicular hypofunction (ICD-10: E29.1) within five years of long-term ADHD stimulant medication use was chosen as the primary outcome. After propensity score matching, 17 224 men were analyzed in each group. Of the men with long-term ADHD stimulant medication use, 1.20% were subsequently diagnosed with testicular hypofunction compared to 0.67% of individuals with ADHD but no associated medication use (RR: 1.78, 95% CI: 1.42–2.23) and 0.68% in men without an ADHD diagnosis or stimulant medication use (RR: 1.75, 95% CI: 1.39–2.19). Therefore, chronic ADHD stimulant medication use was found to be significantly associated with a subsequent diagnosis of testicular hypofunction.

3/125 and 0/30 features with CoV<0.05 respectively). Mean signal intensity ratio F1/SIM in GTV correlates strongly with corresponding changes in ROI_tis (Pearson r Z 0.88, p Z 0.0006), with no significant trend in either (t-test p Z 0.28 and p Z 0.46). Histogram width features in tumor show an increase SIM-F1 and a drop F1-F5 (e.g. Interquartile range (IQR): p Z 0.042, 1.3AE0.4 vs. 0.92AE0.19 F1/SIM vs. F5/F1 ratios, Standard Deviation (SD): p Z 0.08, 1.3AE0.5 vs. 0.93AE0.15). The only patient with pathological complete response at surgery was also only one of 10 to show SIM-F1 decrease (ratios: IQR 0.96, SD 0.92). Normalization by median ROI_tis signal further strengthened the trend (IQR p Z 0.01, SD p Z 0.005). No trends were seen in ROI_tis (IQR p Z 0.50, SD p Z 0.48). Conclusion: These results present early application for the radiomic and histogram analysis for MRgRT image quantification. PDAC may be difficult to define in TRUFI MRgRT images, highlighting the value of observed high spatial robustness of features. Tumor specific changes in histogram width were observed. These metrics, associated with tumor internal heterogeneity, show promise to elucidate clinically relevant information from the images. Preliminary link with histological response was established. Efficient normalization method was found to improve sensitivity. More work is required to understand the biological basis of the observed trends.