Purpose: To assess the effectiveness of the prostate health index (PHI) in patients with no index lesions on multiparametric magnetic resonance imaging (mpMRI) or with negative findings on past prostate biopsy if there was an index lesion on mpMRI. Materials and Methods: Patients without an index lesion on MRI or with a negative result on combined biopsy for index lesions were assessed. Patients who underwent transperineal mapping biopsy among those suspected of having prostate cancer (PCa) due to persistently elevated prostate-specific antigen (PSA) levels were analyzed. Results: Of the 291 patients, 82 (28.2%) were diagnosed with PCa. Sixty-five of 291 patients had negative finding in previous combined biopsy. In total, 226 patients did not have any index lesions. The mean age of the PCa group was 64.33±8.88 years and that of the non-cancer group was 59.88±10.26 years (p<0.001). The PHI was 46.75±28.22 in the PCa group and 37.74±17.37 in the noncancer group (p=0.001), and the prostate volume was 41.52±15.77 mL in the PCa group and 50.78±23.97 mL in the non-cancer group (p=0.001). In multivariate analysis, age (odds ratio [OR] 1.096, p<0.001), PHI (OR 1.021, p=0.005), and prostate volume (OR 0.954, p<0.001) were identified as significant factors for PCa detection. The optimal cutoff value of the PHI for PCa detection was 44.6 and the PHI density (PHID) was 0.88. Conclusions In patients with elevated PSA levels but no index lesions on mpMRI or negative biopsy findings, PHI and PHID demonstrated significant potential for improving PCa detection.

Background: Urinalysis, an essential diagnostic tool, faces challenges in terms of standardization and accuracy. The use of artificial intelligence (AI) with mobile technology can potentially solve these challenges. Therefore, we investigated the effectiveness and accuracy of an AI-based program in automatically interpreting urine test strips using mobile phone cameras, an approach that may revolutionize point-of-care testing. Methods: We developed novel urine test strips and an AI algorithm for image capture.Sample images from the Chungnam National University Sejong Hospital were collected to train a k-nearest neighbor classification algorithm to read the strips. A mobile application was developed for image capturing and processing. We assessed the accuracy, sensitivity, specificity, and ROC area under the curve for 10 parameters. Results: In total, 2,612 urine test strip images were collected. The AI algorithm demonstrated 98.7% accuracy in detecting urinary nitrite and 97.3% accuracy in detecting urinary glucose. The sensitivity and specificity were high for most parameters. However, this system could not reliably determine the specific gravity. The optimal time for capturing the test strip results was 75 secs after dipping. Conclusions The AI-based program accurately interpreted urine test strips using smartphone cameras, offering an accessible and efficient method for urinalysis. This system can be used for immediate analysis and remote testing. Further research is warranted to refine test parameters such as specific gravity to enhance accuracy and reliability.

Purpose: The original eCura system was designed to stratify the risk of lymph node metastasis (LNM) after endoscopic resection (ER) in patients with early gastric cancer (EGC).We assessed the effectiveness of a modified eCura system for reflecting the characteristics of undifferentiated-type (UD)-EGC. Materials and Methods: Six hundred thirty-four patients who underwent non-curative ER for UD-EGC and received either additional surgery (radical surgery group; n=270) or no further treatment (no additional treatment group; n=364) from 18 institutions between 2005 and 2015 were retrospectively included in this study. The eCuraU system assigned 1 point each for tumors >20 mm in size, ulceration, positive vertical margin, and submucosal invasion <500 µm; 2 points for submucosal invasion ≥500 µm; and 3 points for lymphovascular invasion. Results: LNM rates in the radical surgery group were 1.1%, 5.4%, and 13.3% for the low-(0–1 point), intermediate- (2–3 points), and high-risk (4–8 points), respectively (P-fortrend<0.001). The eCuraU system showed a significantly higher probability of identifying patients with LNM as high-risk than the eCura system (66.7% vs. 22.2%; McNemar P<0.001).In the no additional treatment group, overall survival (93.4%, 87.2%, and 67.6% at 5 years) and cancer-specific survival (99.6%, 98.9%, and 92.9% at 5 years) differed significantly among the low-, intermediate-, and high-risk categories, respectively (both P<0.001). In the high-risk category, surgery outperformed no treatment in terms of overall mortality (hazard ratio, 3.26; P=0.015). Conclusions The eCuraU system stratified the risk of LNM in patients with UD-EGC after ER. It is strongly recommended that high-risk patients undergo additional surgery.

Background: This study assessed the comparative effectiveness of sextant and extended 12-core systematic biopsy within combined biopsy for the detection of prostate cancer. Methods: Patients who underwent combined biopsy targeting lesions with a Prostate Imaging Reporting and Data System (PI-RADS) score of 3–5 were assessed. Two specialists performed all combined cognitive biopsies. Both specialists performed target biopsies with five or more cores. One performed sextant systematic biopsies, and the other performed extended 12-core systematic biopsies. A total of 550 patients were analyzed. Results: Cases requiring systematic biopsy in combined biopsy exhibited a significant association with age ≥ 65 years (odds ratio [OR], 2.32; 95% confidence interval [CI], 1.25– 4.32; P = 0.008), PI-RADS score (OR, 2.32; 95% CI, 1.25–4.32; P = 0.008), and the number of systematic biopsy cores (OR, 3.69; 95% CI, 2.11–6.44; P < 0.001). In patients with an index lesion of PI-RADS 4, an extended 12-core systematic biopsy was required (target-negative/ systematic-positive or a greater Gleason score in the systematic biopsy than in the targeted biopsy) (P < 0.001). Conclusion During combined biopsy for prostate cancer in patients with PI-RADS 3 or 5, sextant systematic biopsy should be recommended over extended 12-core systematic biopsy when an effective targeted biopsy is performed.

This study aimed to assess the prognostic role of body mass index (BMI) in patients with metastatic renal cell carcinoma (mRCC) treated with first-line immune checkpoint inhibitor (ICI)-based therapy. We searched for relevant studies in the MEDLINE, Embase, and Cochrane Library databases. The initial search yielded 599 records, of which seven articles (2,517 patients) were selected for analysis. Patients with a high BMI had a favorable overall survival (OS) based on hazard ratio (HR) (crude HR 0.69, 95% confidence interval [CI] 0.57–0.83, p<0.0001; adjusted (a)HR 0.75, 95% CI 0.59–0.95, p=0.02), but not relative risk (RR 0.88, 95% CI 0.67–1.16, p=0.37). In the subgroup analysis, patients with a high BMI had better OS in the ICI with tyrosine kinase inhibitor (TKI) subgroup (aHR 0.71, 95% CI 0.55–0.92, p=0.01), while no significant difference was found in the ICI-only subgroup (aHR 1.02, 95% CI 0.56–1.87, p=0.95). Adjusted statistics for progression-free survival (PFS) were assessable in predominantly ICI-only studies and demonstrated a favorable outcome for patients with a low BMI (aHR 1.67, 95% CI 1.14–2.45, p=0.01). In conclusion, the impact of high BMI varies depending on the treatment type, exhibiting a favorable correlation with OS within ICI with TKI subgroup, but indicating an adverse association with PFS in the ICI-only subgroup. Further research is needed to clarify the influence of BMI by stratifying patients into ICI-only and ICI with TKI treatment to provide more insights.