Background: Mass spectrometry (MS) methods exhibit higher accuracy and comparability in measuring serum C-peptide concentrations than immunoassays. We developed and validated a novel isotope dilution-ultraperformance liquid chromatography-tandem MS (IDUPLC-MS/MS) assay to measure serum C-peptide concentrations. Methods: Sample pretreatment involved solid-phase extraction, ion-exchange solid-phase extraction, and derivatization with 6-aminoquinolyl-N-hydroxysuccinimidylcarbamate (Cayman Chemical, Ann Arbor, Michigan, USA). We used an ExionLC UPLC system (Sciex, Framingham, MA, USA) and a Sciex Triple Quad 6500 + MS/MS system (Sciex) for electrospray ionization in positive-ion mode with multiple charge states of [M+3H]3+ and multiple reaction monitoring transitions. The total run time was 50 mins, and the flow rate was 0.20 mL/min. We evaluated the precision, trueness, linearity, lower limit of quantitation (LLOQ), carryover, and matrix effects. Method comparison with electrochemiluminescence immunoassay (ECLIA) was performed in 138 clinical specimens. Results: The intra- and inter-run precision coefficients of variation were < 5% and the bias values for trueness were < 4%, which were all acceptable. The verified linear interval was 0.050–15 ng/mL, and the LLOQ was 0.050 ng/mL. No significant carryover or matrix effects were observed. The correlation between this ID-UPLC-MS/MS method and ECLIA was good (R = 0.995, slope = 1.564); however, the ECLIA showed a positive bias (51.8%). Conclusions The developed ID-UPLC-MS/MS assay shows acceptable performance in measuring serum C-peptide concentrations. This will be useful in situations requiring accurate measurement of serum C-peptide in clinical laboratories.

Background: Graft failure (GF) is a major complication of allogeneic hematopoietic cell transplantation (allo-HCT). Secondary transplantation has been recognized as a potential curative intervention. Methods: This study aimed to investigate the characteristics and outcomes of salvage transplantation by analyzing the patients who underwent a second HCT for GF following the initial allo-HCT between 1998 and 2020. Results: Overall, 23 recipients were identified, including 14 and 9 individuals with primary and secondary GF, respectively. Nine recipients underwent a second transplant from the same donor. Familial mismatched donors predominated in the second HCT (86.9%), with reduced-intensity conditioning as the prevailing approach (60.9%). Neutrophil engraftment occurred in 17 patients (73.9%) following the second HCT at a median of 17 days (range: 9–58 days) post-transplantation. However, secondary GF subsequently occurred in 5 patients, and successful engraftment following salvage transplantation was achieved in 12 (52.2%) patients. In the entire study population, the estimated 5-year probability of overall survival (OS) and treatment-related mortality (TRM) were 30.4% and 58.5%, respectively. Among patients who achieved successful engraftment following a second transplantation, the OS and TRM rates were 41.7% and 33.3%, respectively, indicating a trend toward better OS and significantly lower TRM compared to those with GF. Notably, 17 patients died, with infection being the most common cause (n = 12), irrespective of the engraftment status. Conclusion A successful engraftment following a second allo-HCT reduced the TRM; however, the OS remained suboptimal. The effective control of infectious diseases remains crucial for patients with GF, regardless of the engraftment status following salvage transplantation.

Background: Graft failure (GF) is a major complication of allogeneic hematopoietic cell transplantation (allo-HCT). Secondary transplantation has been recognized as a potential curative intervention. Methods: This study aimed to investigate the characteristics and outcomes of salvage transplantation by analyzing the patients who underwent a second HCT for GF following the initial allo-HCT between 1998 and 2020. Results: Overall, 23 recipients were identified, including 14 and 9 individuals with primary and secondary GF, respectively. Nine recipients underwent a second transplant from the same donor. Familial mismatched donors predominated in the second HCT (86.9%), with reduced-intensity conditioning as the prevailing approach (60.9%). Neutrophil engraftment occurred in 17 patients (73.9%) following the second HCT at a median of 17 days (range: 9–58 days) post-transplantation. However, secondary GF subsequently occurred in 5 patients, and successful engraftment following salvage transplantation was achieved in 12 (52.2%) patients. In the entire study population, the estimated 5-year probability of overall survival (OS) and treatment-related mortality (TRM) were 30.4% and 58.5%, respectively. Among patients who achieved successful engraftment following a second transplantation, the OS and TRM rates were 41.7% and 33.3%, respectively, indicating a trend toward better OS and significantly lower TRM compared to those with GF. Notably, 17 patients died, with infection being the most common cause (n = 12), irrespective of the engraftment status. Conclusion A successful engraftment following a second allo-HCT reduced the TRM; however, the OS remained suboptimal. The effective control of infectious diseases remains crucial for patients with GF, regardless of the engraftment status following salvage transplantation.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Purpose: Recent treatments for pediatric acute lymphoblastic leukemia (ALL) are founded on risk stratification. We examined the survival rates and prognostic factors of patients over a 20-year period at a single institution. Materials and Methods: This study analyzed patients diagnosed with ALL and treated at the Pediatric Department of Samsung Medical Center (SMC). Patients were categorized into standard-risk (SR), high-risk (HR), and very high-risk (VHR) groups. The SMC protocol for the HR group underwent two changes during the study period: a modified Children’s Cancer Group (CCG)-1882 protocol was used from 2000 to 2005, the Korean multicenter HR ALL-0601 protocol from 2006 to 2014, and the Korean multicenter HR ALL-1501 protocol from 2015 to 2019. Results: Of the 460 patients, complete remission was achieved in 436 patients (94.8%). The 10-year overall survival rate (OS) was 83.8±1.9% for all patients. OS according to the SMC risk group was as follows: 95.9%±1.4% in the SR group, 83.8%±3.6% in the HR group, and 66.2%±6.9% in the VHR group. The 5-year OS within the HR group varied according to the treatment protocol: 73.9%±7.5%, in the modified CCG-1882 protocol, 83.0%±3.9%, in the 0601 protocol, and 96.2%±2.6%, in the 1501 protocol. For those aged 15 years and older, the OS was only 56.5%±13.1%. Relapse occurred in 71 patients (15.4%), and the OS after relapse was 37.7%±6.0%. Conclusion The treatment outcomes of patients with ALL improved markedly. However, there is a need to further characterize adolescents and young adult patients, as well as those who have experienced relapses.

Purpose: Hematopoietic stem cell transplantation (HSCT) has been an important method of treatment in the advance of pediatric acute lymphoblastic leukemia (ALL). The indications for HSCT are evolving and require updated establishment. In this study, we aimed to investigate the efficacy of HSCT on the treatment outcome of pediatric ALL, considering the indications for HSCT and subgroups. Materials and Methods: A retrospective analysis was conducted on ALL patients diagnosed and treated at a single center. Risk groups were categorized based on age at diagnosis, initial white blood cell count, disease lineage (B/T), and cytogenetic study results. Data on the patients’ disease status at HSCT and indications of HSCT were collected. Indications for HSCT were categorized as upfront HSCT at 1st complete remission, relapse, and refractory disease. Results: Among the 549 screened patients, a total of 418 patients were included in the study; B-cell ALL (n=379) and T-cell ALL (T-ALL) (n=39). HSCT was conducted on a total of 106 patients (25.4%), with a higher frequency as upfront HSCT in higher-risk groups and specific cytogenetics. The overall survival (OS) was significantly better when done upfront than in relapsed or refractory state in T-ALL patients (p=0.002). The KMT2A-rearranged ALL patients showed superior event-free survival (p=0.002) and OS (p=0.022) when HSCT was done as upfront treatment. Conclusion HSCT had a substantial positive effect in a specific subset of pediatric ALL. In particular, frontline HSCT for T-ALL and KMT2A-rearranged ALL offered a better prognosis than when HSCT was conducted in a relapsed or refractory setting.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Purpose: Recent treatments for pediatric acute lymphoblastic leukemia (ALL) are founded on risk stratification. We examined the survival rates and prognostic factors of patients over a 20-year period at a single institution. Materials and Methods: This study analyzed patients diagnosed with ALL and treated at the Pediatric Department of Samsung Medical Center (SMC). Patients were categorized into standard-risk (SR), high-risk (HR), and very high-risk (VHR) groups. The SMC protocol for the HR group underwent two changes during the study period: a modified Children’s Cancer Group (CCG)-1882 protocol was used from 2000 to 2005, the Korean multicenter HR ALL-0601 protocol from 2006 to 2014, and the Korean multicenter HR ALL-1501 protocol from 2015 to 2019. Results: Of the 460 patients, complete remission was achieved in 436 patients (94.8%). The 10-year overall survival rate (OS) was 83.8±1.9% for all patients. OS according to the SMC risk group was as follows: 95.9%±1.4% in the SR group, 83.8%±3.6% in the HR group, and 66.2%±6.9% in the VHR group. The 5-year OS within the HR group varied according to the treatment protocol: 73.9%±7.5%, in the modified CCG-1882 protocol, 83.0%±3.9%, in the 0601 protocol, and 96.2%±2.6%, in the 1501 protocol. For those aged 15 years and older, the OS was only 56.5%±13.1%. Relapse occurred in 71 patients (15.4%), and the OS after relapse was 37.7%±6.0%. Conclusion The treatment outcomes of patients with ALL improved markedly. However, there is a need to further characterize adolescents and young adult patients, as well as those who have experienced relapses.