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Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs. metastatic lesion classification on staging CTs and to compare its performance with radiologists. This retrospective study developed two AI models using 297 staging CT scans (81 metastatic) with 4601 benign and 1911 metastatic lesions in PCa patients. Metastases were validated by follow-up scans, bone biopsy, or PET/CT. Segmentation AI (3DAISeg) was developed using the lesion contours delineated by a radiologist. 3DAISeg performance was evaluated with the Dice similarity coefficient, and classification AI (3DAIClass) performance on AI and radiologist contours was assessed with F1-score and accuracy. Training/validation/testing data partitions of 70:15:15 were used. A multi-reader study was performed with two junior and two senior radiologists within a subset of the testing dataset (n = 36). In 45 unseen staging CT scans (12 metastatic PCa) with 669 benign and 364 metastatic lesions, 3DAISeg detected 73.1% of metastatic (266/364) and 72.4% of benign lesions (484/669). Each scan averaged 12 extra segmentations (range: 1–31). All metastatic scans had at least one detected metastatic lesion, achieving a 100% patient-level detection. The mean Dice score for 3DAISeg was 0.53 (median: 0.59, range: 0–0.87). The F1 for 3DAIClass was 94.8% (radiologist contours) and 92.4% (3DAISeg contours), with a median false positive of 0 (range: 0–3). Using radiologist contours, 3DAIClass had PPV and NPV rates comparable to junior and senior radiologists: PPV (semi-automated approach AI 40.0% vs. Juniors 32.0% vs. Seniors 50.0%) and NPV (AI 96.2% vs. Juniors 95.7% vs. Seniors 91.9%). When using 3DAISeg, 3DAIClass mimicked junior radiologists in PPV (pure-AI 20.0% vs. Juniors 32.0% vs. Seniors 50.0%) but surpassed seniors in NPV (pure-AI 93.8% vs. Juniors 95.7% vs. Seniors 91.9%). Our lesion detection and classification AI model performs on par with junior and senior radiologists in discerning benign and metastatic lesions on staging CTs obtained for PCa. [ABSTRACT FROM AUTHOR]

To determine the rigor, generalizability, and reproducibility of published classification and detection artificial intelligence (AI) models for prostate cancer (PCa) on MRI using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines, a 42-item checklist that is considered a measure of best practice for presenting and reviewing medical imaging AI research. This review searched English literature for studies proposing PCa AI detection and classification models on MRI. Each study was evaluated with the CLAIM checklist. The additional outcomes for which data were sought included measures of AI model performance (eg, area under the curve [AUC], sensitivity, specificity, free-response operating characteristic curves), training and validation and testing group sample size, AI approach, detection versus classification AI, public data set utilization, MRI sequences used, and definition of gold standard for ground truth. The percentage of CLAIM checklist fulfillment was used to stratify studies into quartiles. Wilcoxon's rank-sum test was used for pair-wise comparisons. In all, 75 studies were identified, and 53 studies qualified for analysis. The original CLAIM items that most studies did not fulfill includes item 12 (77% no): de-identification methods; item 13 (68% no): handling missing data; item 15 (47% no): rationale for choosing ground truth reference standard; item 18 (55% no): measurements of inter- and intrareader variability; item 31 (60% no): inclusion of validated interpretability maps; item 37 (92% no): inclusion of failure analysis to elucidate AI model weaknesses. An AUC score versus percentage CLAIM fulfillment quartile revealed a significant difference of the mean AUC scores between quartile 1 versus quartile 2 (0.78 versus 0.86, P =.034) and quartile 1 versus quartile 4 (0.78 versus 0.89, P =.003) scores. Based on additional information and outcome metrics gathered in this study, additional measures of best practice are defined. These new items include disclosure of public dataset usage, ground truth definition in comparison to other referenced works in the defined task, and sample size power calculation. A large proportion of AI studies do not fulfill key items in CLAIM guidelines within their methods and results sections. The percentage of CLAIM checklist fulfillment is weakly associated with improved AI model performance. Additions or supplementations to CLAIM are recommended to improve publishing standards and aid reviewers in determining study rigor. [ABSTRACT FROM AUTHOR]

Rhodium-105 (0.567 MeV β-, 319 keV γ, 35.4 h half-life) was produced by neutron irradiation of enriched 104Ru (>99%) over multiple decades. A method is reported to recover the previously irradiated 104Ru (trapped in HCl as RuO 4 2−) as the metal. The 104Ru was recovered in >93% yield and >98% enrichment. Neutron re-irradiation of the recycled 104Ru produced 105Rh, which was successfully radiolabeled with tetrathioethers in high yield. This recovery and recycling method for enriched 104Ru makes 105Rh production and utilization economical. • Neutron irradiated 104Ru recovered from 3 M HCl solution as 104RuO 4. • 104RuO 4 converted to 104RuCl 3 · x H 2 O and then to 104Ru metal directly under H 2. • 104RuO 4 converted to 104RuCl 3 · x H 2 O and then to RuO 2 in air and to 104Ru metal under H 2. • Greater than 93% recovery and >98% enrichment of 104Ru metal. • Neutron re-irradiation of recycled 104Ru produced 105Rh, which labeled standard tetrathioethers in high yield. [ABSTRACT FROM AUTHOR]

Abstract Positron-emitting 72As is the PET imaging counterpart for beta-emitting 77As. Its parent, no carrier added (n.c.a.) 72Se, was produced for a 72Se/72As generator by irradiating an enriched 7°Ge metal-graphite target via the 70Ge(α, 2 n)72Se reaction. Target dissolution used a fast, environmentally friendly method with 93% radioactivity recovery. Chromatographic parameters of the 72Se/72As generator were evaluated, the eluted n.c.a. 72As was characterized with a phantom imaging study, and the previously reported trithiol and aryl-dithiol ligand systems were radiolabeled with the separated n.c.a. 72As in high yield. Highlights • 72Se production by α irradiation of 70Ge. • Simple Ge metal dissolution. • 72Se/72As generator prepared and evaluated. [ABSTRACT FROM AUTHOR]

Introduction Rhenium-186g ( t 1/2 = 3.72 d) is a β − emitting isotope suitable for theranostic applications. Current production methods rely on reactor production by way of the reaction 185 Re(n,γ) 186g Re, which results in low specific activities limiting its use for cancer therapy. Production via charged particle activation of enriched 186 W results in a 186g Re product with a higher specific activity, allowing it to be used more broadly for targeted radiotherapy applications. This targets the unmet clinical need for more efficient radiotherapeutics. Methods A target consisting of highly enriched, pressed 186 WO 3 was irradiated with protons at the Los Alamos National Laboratory Isotope Production Facility (LANL-IPF) to evaluate 186g Re product yield and quality. LANL-IPF was operated in a dedicated nominal 40 MeV mode. Alkaline dissolution followed by anion exchange chromatography was used to isolate 186g Re from the target material. Phantom and radiolabeling studies were conducted with the produced 186g Re activity. Results A 186g Re batch yield of 1.38 ± 0.09 MBq/μAh or 384.9 ± 27.3 MBq/C was obtained after 16.5 h in a 205 μA average/230μA maximum current proton beam. The chemical recovery yield was 93% and radiolabeling was achieved with efficiencies ranging from 60–80%. True specific activity of 186g Re at EOB was determined via ICP-AES and amounted to 0.788 ± 0.089 GBq/μg (0.146 ± 0.017 GBq/nmol), which is approximately seven times higher than the product obtained from neutron capture in a reactor. Phantom studies show similar imaging quality to the gold standard 99m Tc. Conclusions We report a preliminary study of the large-scale production and novel anion exchange based chemical recovery of high specific activity 186g Re from enriched 186 WO 3 targets in a high-intensity proton beam with exceptional chemical recovery and radiochemical purity. [ABSTRACT FROM AUTHOR]