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Objective To train and to test for prostate zonal segmentation an existing algorithm already trained for whole-gland segmentation. Methods The algorithm, combining model-based and deep learning–based approaches, was trained for zonal segmentation using the NCI-ISBI-2013 dataset and 70 T2-weighted datasets acquired at an academic centre. Test datasets were randomly selected among examinations performed at this centre on one of two scanners (General Electric, 1.5 T; Philips, 3 T) not used for training. Automated segmentations were corrected by two independent radiologists. When segmentation was initiated outside the prostate, images were cropped and segmentation repeated. Factors influencing the algorithm’s mean Dice similarity coefficient (DSC) and its precision were assessed using beta regression. Results Eighty-two test datasets were selected; one was excluded. In 13/81 datasets, segmentation started outside the prostate, but zonal segmentation was possible after image cropping. Depending on the radiologist chosen as reference, algorithm’s median DSCs were 96.4/97.4%, 91.8/93.0% and 79.9/89.6% for whole-gland, central gland and anterior fibromuscular stroma (AFMS) segmentations, respectively. DSCs comparing radiologists’ delineations were 95.8%, 93.6% and 81.7%, respectively. For all segmentation tasks, the scanner used for imaging significantly influenced the mean DSC and its precision, and the mean DSC was significantly lower in cases with initial segmentation outside the prostate. For central gland segmentation, the mean DSC was also significantly lower in larger prostates. The radiologist chosen as reference had no significant impact, except for AFMS segmentation. Conclusions The algorithm performance fell within the range of inter-reader variability but remained significantly impacted by the scanner used for imaging. Key Points • Median Dice similarity coefficients obtained by the algorithm fell within human inter-reader variability for the three segmentation tasks (whole gland, central gland, anterior fibromuscular stroma) . • The scanner used for imaging significantly impacted the performance of the automated segmentation for the three segmentation tasks . • The performance of the automated segmentation of the anterior fibromuscular stroma was highly variable across patients and showed also high variability across the two radiologists .

Individuals undergoing lung resections experience persistent postoperative pain and are at high risk of chronic postoperative opioid use. This study aims to identify factors associated with chronic opioid use after minimally invasive lung resections (MILR). This is a retrospective cohort study of individuals who underwent MILR from March 2019 to May 2022 at a single academic institution. The primary outcome was chronic opioid usage, defined as use at least 30 days after surgery. Postoperative pain was managed with a standardized multi-modal pain-control regimen utilizing opioids only as needed. Prescription patterns and dispensing data of opioids at 30-, 60-, and 90-days postoperatively informed usage. Univariate analysis and multivariable logistic regressions (MVLR) were performed. 376 patients were included, 38.6% male, 88.8% white, and a mean age of 64.6 years. A total of 248 (66%) underwent anatomical lung resections. 16.5% used opioids at 30 days, 10.1% at 60 days, and 8.5% at 90 days. In the multivariable model, morphine milligram equivalents (MMEs) of opioids on the day before discharge showed a statistically significant association with chronic opioid usage. Age, sex, length of stay, and surgery type were not associated. A 10-unit increase in MMEs increased odds of use at 30-days by 21% (OR 1.21, 95%CI 1.11-1.32, p < 0.001), 20% at 60-days (OR 1.20, 95%CI 1.09 1.32, p < 0.001) and 18% at 90-days (OR 1.18, 95%CI 1.06-1.30, p = 0.002). Higher pre-discharge MMEs are associated with an increased likelihood of chronic opioid usage. Future studies should focus on whether preemptive early outpatient intercostal nerve blocks or cryoablations can decrease chronic narcotic usage in high-risk patients.