A commercial MRI-based deep learning algorithm for prostate cancer detection showed greater positive predictive value, despite its lower sensitivity, potentially allowing it to assist radiologists in biopsy planning.

The importance of clinical outcome prediction models using artificial intelligence (AI) is being emphasized owing to the increasing necessity of developing a clinical decision support system (CDSS) employing AI. Therefore, in this study, we proposed a “Dr. Answer” AI software based on the clinical outcome prediction model for prostate cancer treated with radical prostatectomy. The Dr. Answer AI was developed based on a clinical outcome prediction model, with a user-friendly interface. We used 7,128 clinical data of prostate cancer treated with radical prostatectomy from three hospitals. An outcome prediction model was developed to calculate the probability of occurrence of 1) tumor, node, and metastasis (TNM) staging, 2) extracapsular extension, 3) seminal vesicle invasion, and 4) lymph node metastasis. Random forest and k-nearest neighbors algorithms were used, and the proposed system was compared with previous algorithms. Random forest exhibited good performance for TNM staging (recall value: 76.98%), while k-nearest neighbors exhibited good performance for extracapsular extension, seminal vesicle invasion, and lymph node metastasis (80.24%, 98.67%, and 95.45%, respectively). The Dr. Answer AI software consisted of three primary service structures: 1) patient information, 2) clinical outcome prediction, and outcomes according to the National Comprehensive Cancer Network guideline. The proposed clinical outcome prediction model could function as an effective CDSS, supporting the decisions of the physicians, while enabling the patients to understand their treatment outcomes. The Dr. Answer AI software for prostate cancer helps the doctors to explain the treatment outcomes to the patients, allowing the patients to be more confident about their treatment plans.

[¹⁷⁷Lu]Ludotadipep, which enables targeted delivery of beta-particle radiation to prostate tumor cells, had been suggested as a promising therapeutic option for mCRPC. From November 2020 to March 2022, a total of 30 patients were enrolled for single dose of [¹⁷⁷Lu]Ludotadipep RPT, 6 subjects in each of the 5 different activity groups of 1.9 GBq, 2.8 GBq, 3.7 GBq, 4.6 GBq, and 5.6 GBq. [¹⁷⁷Lu]Ludotadipep was administered via venous injection, and patients were hospitalized for three days to monitor for any adverse effects. Serum PSA levels were followed up at weeks 1, 2, 3, 4, 6, 8, and 12, and PSMA PET/CT with [¹⁸F]Florastamin was obtained at baseline and again at weeks 4 and 8. The subjects required positive PSMA PET/CT prior to [¹⁷⁷Lu]Ludotadipep administration. Among the 29 subjects who received [¹⁷⁷Lu]Ludotadipep, 36 treatment emergent adverse events (TEAEs) occurred in 17 subjects (58.6%) and 4 adverse drug reactions (ADRs) in 3 subjects (10.3%). Of the total 24 subjects who had full 12-week follow-up data, 16 (66.7%) showed decrease in PSA of any magnitude, and 9 (37.5%) showed a decrease in PSA by 50% or greater. A total of 5 of the 24 patients (20.8%) showed disease progression (PSA increase of 25% or higher from the baseline) at the 12th week following single dose of [¹⁷⁷Lu]Ludotadipep. These data thus far suggest that [¹⁷⁷Lu]Ludotadipep could be a promising RPT agent with low toxicity in mCRPC patients who have not been responsive to conventional treatments.

Transurethral resection of bladder tumor (TURBT) is a standard treatment for non-muscle invasive bladder cancer. However, catheter-related bladder discomfort (CRBD) often occurs due to bladder irritation caused by indwelling large-diameter urinary catheters and delays patient recovery. We investigated the efficacy of the intraoperative administration of magnesium and ketorolac in preventing early CRBD after TURBT. One hundred patients scheduled for TURBT were enrolled in this prospective, randomized, double-blind study from December 2021 to June 2022. During surgery, the experimental group (n = 48) received intravenous magnesium and ketorolac, and the control group (n = 50) received only intravenous ketorolac. The primary outcome was CRBD incidence immediately after surgery. CRBD severity and the postoperative recovery profiles were also evaluated. Compared to the control group, the experimental group had significantly less CRBD until 1 h after surgery (0 h: 31.3% vs. 52.0%, p = 0.037; 1 h: 54.2% vs. 74.0%, p = 0.041). However, the two groups did not differ in other postoperative findings, including CRBD severity. Co-administration of magnesium and ketorolac during surgery significantly decreased the incidence of early CRBD after TURBT compared to the single use of ketorolac.