Purpose: To evaluate the association between microscopic hematuria (MH) detected by surveillance urinalysis and cancer recurrence in nonmuscle invasive bladder cancer (NMIBC) patients. Materials and Methods: A total of 1,082 NMIBC patients who underwent transurethral resection of bladder tumor (TURB) procedures at Asan Medical Center between January 2017 and December 2019 were included. We retrospectively reviewed the follow-up data for these cases including cystoscopy, urinalysis, and urine cytology. The association between urine testing and cancer recurrence was assessed by both univariable and multivariable logistic regression analysis. Results: The study patients had a median age of 68 years (interquartile range, 60–75 years) and comprised 898 men and 184 women. Among the 1,428 TURB procedures conducted in this series, 548 of the lesions (38.4%) were diagnosed as low-grade and 880 (61.6%) as highgrade cancers. A total of 3,309 follow-up cystoscopies were conducted during the study period and were divided into high-grade (HG) (2,011 cases) and low-grade (LG) (1,298 cases) groups according to the latest TURB pathology. MH was found to have a statistically significant association with NMIBC recurrence in both the LG (odds ratio [OR], 1.57; 95% confidence interval [CI], 1.107–2.223; p=0.011) and HG (OR, 1.90; 95% CI, 1.434–2.517; p<0.001) groups. Conclusions Urinalysis during follow-up may provide important information on cancer recurrence in NMIBC patients.

Purpose: Assessing the metastasis status of the sentinel lymph nodes (SLNs) for hematoxylin and eosin–stained frozen tissue sections by pathologists is an essential but tedious and time-consuming task that contributes to accurate breast cancer staging. This study aimed to review a challenge competition (HeLP 2019) for the development of automated solutions for classifying the metastasis status of breast cancer patients. Materials and Methods: A total of 524 digital slides were obtained from frozen SLN sections: 297 (56.7%) from Asan Medical Center (AMC) and 227 (43.4%) from Seoul National University Bundang Hospital (SNUBH), South Korea. The slides were divided into training, development, and validation sets, where the development set comprised slides from both institutions and training and validation set included slides from only AMC and SNUBH, respectively. The algorithms were assessed for area under the receiver operating characteristic curve (AUC) and measurement of the longest metastatic tumor diameter. The final total scores were calculated as the mean of the two metrics, and the three teams with AUC values greater than 0.500 were selected for review and analysis in this study. Results: The top three teams showed AUC values of 0.891, 0.809, and 0.736 and major axis prediction scores of 0.525, 0.459, and 0.387 for the validation set. The major factor that lowered the diagnostic accuracy was micro-metastasis. Conclusion In this challenge competition, accurate deep learning algorithms were developed that can be helpful for making a diagnosis on intraoperative SLN biopsy. The clinical utility of this approach was evaluated by including an external validation set from SNUBH.