Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

γ-Radiation resistance is a major obstacle to the success of radiotherapy in colorectal cancer. Antioxidant-related factors contribute to resistance to radiation therapy and, therefore, are targets for improving the therapeutic response. In this study, we evaluated the molecular mechanisms underlying γ-radiation resistance using the colorectal cancer cell line SNUC5 and γ-radiation-resistant variant SNUC5/RR, including analyses of the role of nuclear factor erythroid 2-related factor 2 (NRF2), a transcription factor that regulates antioxidant enzymes, and related epigenetic regulators. Reactive oxygen species (ROS) levels, antioxidant enzyme expression, NRF2 expression, and nuclear translocation were higher in SNUC5/RR cells irradiated with or without 8 Gy than in SNUC5 cells. The DNA demethylase ten-eleven translocation 1 (TET1) expression and TET1 binding to the NRF2 promoter in SNUC5/RR cells were stronger than those in SNUC5 cells, indicating lower methylation of CpG islands in the NRF2 promoter.TET1 knockdown in SNUC5/RR cells suppressed NRF2 expression significantly. Additionally, histone mixed-lineage leukemia (MLL), a histone methyltransferase, was upregulated, leading to increased trimethylation of histone H3 lysine 4, whereas enhancer of zeste homolog 2 (EZH2), a histone methyltransferase, was downregulated, leading to decreased trimethylation of histone H3 lysine 27. Histone deacetylase (HDAC) and histone acetyltransferase (HAT) levels were lower and higher in SNUC5/RR cells than in SNUC5 cells, respectively. MLL and HAT knockdown in SNUC5/RR cells irradiated with or without 8 Gy decreased levels of NRF2 and heme-oxygenase 1, resulting in enhanced γ-radiation sensitivity. These findings support NRF2 as a target for improving the response to radiotherapy in patients with colorectal cancer.

Purpose: Cancer poses a significant global health challenge, demanding precise genomic testing for individualized treatment strategies. Targeted-panel sequencing (TPS) has improved personalized oncology but often lacks comprehensive coverage of crucial cancer alterations. Whole-genome sequencing (WGS) addresses this gap, offering extensive genomic testing. This study demonstrates the medical potential of WGS. Materials and Methods: This study evaluates target-enhanced WGS (TE-WGS), a clinical-grade WGS method sequencing both cancer and matched normal tissues. Forty-nine patients with various solid cancer types underwent both TE-WGS and TruSight Oncology 500 (TSO500), one of the mainstream TPS approaches. Results: TE-WGS detected all variants reported by TSO500 (100%, 498/498). A high correlation in variant allele fractions was observed between TE-WGS and TSO500 (r=0.978). Notably, 223 variants (44.8%) within the common set were discerned exclusively by TE-WGS in peripheral blood, suggesting their germline origin. Conversely, the remaining subset of 275 variants (55.2%) were not detected in peripheral blood using the TE-WGS, signifying them as bona fide somatic variants. Further, TE-WGS provided accurate copy number profiles, fusion genes, microsatellite instability, and homologous recombination deficiency scores, which were essential for clinical decision-making. Conclusion TE-WGS is a comprehensive approach in personalized oncology, matching TSO500’s key biomarker detection capabilities. It uniquely identifies germline variants and genomic instability markers, offering additional clinical actions. Its adaptability and cost-effectiveness underscore its clinical utility, making TE-WGS a valuable tool in personalized cancer treatment.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea’s cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

Purpose: This study aims to evaluate the treatment approaches and locoregional patterns for adenoid cystic carcinoma (ACC) in the breast, which is an uncommon malignant tumor with limited clinical data. Materials and Methods: A total of 93 patients diagnosed with primary ACC in the breast between 1992 and 2022 were collected from multi-institutions. All patients underwent surgical resection, including breast-conserving surgery (BCS) or total mastectomy (TM). Recurrence patterns and locoregional recurrence-free survival (LRFS) were assessed. Results: Seventy-five patients (80.7%) underwent BCS, and 71 of them (94.7%) received post-operative radiation therapy (PORT). Eighteen patients (19.3%) underwent TM, with five of them (27.8%) also receiving PORT. With a median follow-up of 50 months, the LRFS rate was 84.2% at 5 years. Local recurrence (LR) was observed in five patients (5.4%) and four cases (80%) of the LR occurred in the tumor bed. Three of LR (3/75, 4.0%) had a history of BCS and PORT, meanwhile, two of LR (2/18, 11.1%) had a history of mastectomy. Regional recurrence occurred in two patients (2.2%), and both cases had a history of PORT with (n=1) and without (n=1) irradiation of the regional lymph nodes. Partial breast irradiation (p=0.35), BCS (p=0.96) and PORT in BCS group (p=0.33) had no significant association with LRFS. Conclusion BCS followed by PORT was the predominant treatment approach for ACC of the breast and LR mostly occurred in the tumor bed. The findings of this study suggest that partial breast irradiation might be considered for PORT in primary breast ACC.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Background: There are few safe effective ways to relieve osteoarthritis (OA) pain; as a result, off-label psychotropic drug prescriptions have increased worldwide. This study examined the change in psychotropic drug prescriptions for patients with OA from 2011 to 2020 using the Korean National Health Insurance Service dataset. Methods: The study population consisted of patients with hip or knee OA aged ≥ 65 years.Psychotropic drugs included opioids, benzodiazepines, non-benzodiazepine hypnotics (Z-drugs), anti-epileptics, tricyclic antidepressants, selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs), typical antipsychotics, atypical antipsychotics, and anxiolytics. The prevalence and long-term (> 3 months) prescription rates of psychotropic drugs in OA patients were calculated. Results: The study included 1,821,158 patients with OA (mean age 71.7 years; 65.32% female).Of the cohort, 49% had comorbidities for which psychotropics were indicated. The prevalence of psychotropic prescriptions decreased from 58.2% to 52.0% in 2018 and then leveled off.The long-term prescription rate remained constant until 2018 and then increased slightly.The most commonly prescribed psychotropics were opioids and long- and short-acting benzodiazepines. The prescription rates of opioids and long-acting benzodiazepines decreased from 2011 to 2020. For those with psychiatric co-morbidities, the prescription rates of anti-epileptics and SNRIs increased, while the prescription rates of anti-epileptics, SSRIs, other antidepressants, and atypical psychotropics increased for those without such co-morbidities. The most commonly prescribed psychotropics were diazepam and alprazolam, excluding tramadol and tramadol–acetaminophen combination. For those with psychiatric co-morbidities, the prescription rates of gabapentin and fentanyl increased, while for those without such co-morbidities, the prescription rates of lorazepam, fentanyl, escitalopram and quetiapine increased. Conclusion A significant number of older Korean patients with OA were prescribed psychotropic drugs in the absence of comorbidities requiring such drugs, including drugs that have little effect on OA and unfavorable safety profiles in older adults.

Background: There are few safe effective ways to relieve osteoarthritis (OA) pain; as a result, off-label psychotropic drug prescriptions have increased worldwide. This study examined the change in psychotropic drug prescriptions for patients with OA from 2011 to 2020 using the Korean National Health Insurance Service dataset. Methods: The study population consisted of patients with hip or knee OA aged ≥ 65 years.Psychotropic drugs included opioids, benzodiazepines, non-benzodiazepine hypnotics (Z-drugs), anti-epileptics, tricyclic antidepressants, selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs), typical antipsychotics, atypical antipsychotics, and anxiolytics. The prevalence and long-term (> 3 months) prescription rates of psychotropic drugs in OA patients were calculated. Results: The study included 1,821,158 patients with OA (mean age 71.7 years; 65.32% female).Of the cohort, 49% had comorbidities for which psychotropics were indicated. The prevalence of psychotropic prescriptions decreased from 58.2% to 52.0% in 2018 and then leveled off.The long-term prescription rate remained constant until 2018 and then increased slightly.The most commonly prescribed psychotropics were opioids and long- and short-acting benzodiazepines. The prescription rates of opioids and long-acting benzodiazepines decreased from 2011 to 2020. For those with psychiatric co-morbidities, the prescription rates of anti-epileptics and SNRIs increased, while the prescription rates of anti-epileptics, SSRIs, other antidepressants, and atypical psychotropics increased for those without such co-morbidities. The most commonly prescribed psychotropics were diazepam and alprazolam, excluding tramadol and tramadol–acetaminophen combination. For those with psychiatric co-morbidities, the prescription rates of gabapentin and fentanyl increased, while for those without such co-morbidities, the prescription rates of lorazepam, fentanyl, escitalopram and quetiapine increased. Conclusion A significant number of older Korean patients with OA were prescribed psychotropic drugs in the absence of comorbidities requiring such drugs, including drugs that have little effect on OA and unfavorable safety profiles in older adults.