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.

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.

Background: and purpose Whether pharmacologically altered high-density lipoprotein cholesterol (HDL-C) affects the risk of cardiovascular events is unknown. Recently, we have reported the Prevention of Cardiovascular Events in Asian Patients with Ischaemic Stroke at High Risk of Cerebral Haemorrhage (PICASSO) trial that demonstrated the non-inferiority of cilostazol to aspirin and superiority of probucol to non-probucol for cardiovascular prevention in ischemic stroke patients (clinicaltrials.gov: NCT01013532). We aimed to determine whether on-treatment HDL-C changes by cilostazol and probucol influence the treatment effect of each study medication during the PICASSO study. Methods: Of the 1,534 randomized patients, 1,373 (89.5%) with baseline cholesterol parameters were analyzed. Efficacy endpoint was the composite of stroke, myocardial infarction, and cardiovascular death. Cox proportional hazards regression analysis examined an interaction between the treatment effect and changes in HDL-C levels from randomization to 1 month for each study arm. Results: One-month post-randomization mean HDL-C level was significantly higher in the cilostazol group than in the aspirin group (1.08 mmol/L vs. 1.00 mmol/L, P<0.001). The mean HDL-C level was significantly lower in the probucol group than in the non-probucol group (0.86 mmol/L vs. 1.22 mmol/L, P<0.001). These trends persisted throughout the study. In both study arms, no significant interaction was observed between HDL-C changes and the assigned treatment regarding the risk of the efficacy endpoint. Conclusions Despite significant HDL-C changes, the effects of cilostazol and probucol treatment on the risk of cardiovascular events were insignificant. Pharmacologically altered HDL-C levels may not be reliable prognostic markers for cardiovascular risk.

BACKGROUND/AIMS: Gallbladder polyps (GBP) are a common clinical finding and may possess malignant potential. We conducted this study to determine whether visceral obesity is a risk factor for GBP. METHODS: We retrospectively reviewed records of subjects who received both ultrasonography and computed tomography with measurements of the areas of visceral adipose tissue and total adipose tissue (TAT) on the same day as health checkups. RESULTS: Ninety-three of 1,615 subjects (5.8%) had GBP and were compared with 186 age- and sex-matched controls. VAT (odds ratio [OR], 2.941; 95% confidence interval [CI], 1.325 to 6.529; p=0.008 for the highest quartile vs the lowest quartile) and TAT (OR, 3.568; 95% CI, 1.625 to 7.833; p=0.002 for the highest quartile vs the lowest quartile) were independent risk factors together with hypertension (OR, 2.512; 95% CI, 1.381 to 4.569; p=0.003), diabetes mellitus (OR, 2.942; 95% CI, 1.061 to 8.158; p=0.038), hepatitis B virus positivity (OR, 3.548; 95% CI, 1.295 to 9.716; p=0.014), and a higher level of total cholesterol (OR, 2.232; 95% CI, 1.043 to 4.778; p=0.039 for <200 mg/dL vs > or =240 mg/dL). Body mass index and waist circumference were not meaningful variables. CONCLUSIONS: Visceral obesity measured by VAT and TAT was associated with GBP irrespective of body mass index or waist circumference.
Adipose Tissue/ultrasonography ; Adult ; Case-Control Studies ; Cholesterol/blood ; Diabetes Complications ; Female ; Gallbladder Diseases/blood/epidemiology/*etiology ; Hepatitis B/complications ; Humans ; Hypertension/complications ; Intra-Abdominal Fat/ultrasonography ; Male ; Middle Aged ; Obesity, Abdominal/blood/*complications/ultrasonography ; Odds Ratio ; Polyps/blood/epidemiology/*etiology ; Prevalence ; Retrospective Studies ; Risk Factors

No abstract available.
Adult ; Colon, Transverse/radiography ; Colonoscopy ; Fibrosis/pathology ; Gastrointestinal Hemorrhage ; Humans ; Intestinal Mucosa/pathology/*surgery ; Male ; Pneumatosis Cystoides Intestinalis/*diagnosis/surgery ; Tomography, X-Ray Computed

BACKGROUND/AIMS: Proton pump inhibitor (PPI) is generally prescribed to prevent post endoscopic submucosal dissection (ESD) bleeding. However, there was no consensus about the effectiveness of intravenous (IV) or oral PPI. We conducted this investigation to evaluate whether oral PPI can be also safely and effectively used to prevent post-ESD bleeding by measurement of intragastric pH. MATERIALS AND METHODS: Patients were assigned in the fixed order to IV pantoprazole by 40 mg every 12 hours and oral lansoprazole fast disintegrating tablet (LFDT) by 30 mg every 8 hours. We checked intragastric pH and hemoglobin (Hb) levels at pre and post-ESD procedure. RESULTS: A total of 10 patients (LFDT group: 6 patients, IV pantoprazole group: 4 patients) were included. There was no difference of baseline Hb level between two groups (LFDT, 14.38+/-0.46 mg/dL; IV pantoprazole, 13.85+/-0.83 mg/dL; P=0.18). After 24 hours, change of Hb level was not different between LFDT (0.95+/-0.30 mg/dL) and IV pantoprazole group (0.98+/-0.45 mg/dL; P=0.96). Baseline intragastric pH was 3.72+/-0.19 with LFDT and 4.31+/-0.41 with IV pantoprazole group (P=0.18). After 24 hours, there was no significant difference of the extent of pH increase between LFDT (2.38+/-0.28) and IV pantoprazole group (2.17+/-0.21; P=0.60). CONCLUSIONS: There was no difference in both the increase of post-24 hour intragastric pH and decrease of post-24 hour Hb between LFDT and IV pantoprazole group. Oral PPI regimen may be able to replace IV PPI therapy for the prevention of post-ESD bleeding and LFDT might be superior to IV PPIs in the aspect of cost-effectiveness.
Consensus ; Hemorrhage* ; Humans ; Hydrogen-Ion Concentration ; Lansoprazole* ; Proton Pump Inhibitors ; Proton Pumps

BACKGROUND/AIMS: Proton pump inhibitor (PPI) is generally prescribed to prevent post endoscopic submucosal dissection (ESD) bleeding. However, there was no consensus about the effectiveness of intravenous (IV) or oral PPI. We conducted this investigation to evaluate whether oral PPI can be also safely and effectively used to prevent post-ESD bleeding by measurement of intragastric pH. MATERIALS AND METHODS: Patients were assigned in the fixed order to IV pantoprazole by 40 mg every 12 hours and oral lansoprazole fast disintegrating tablet (LFDT) by 30 mg every 8 hours. We checked intragastric pH and hemoglobin (Hb) levels at pre and post-ESD procedure. RESULTS: A total of 10 patients (LFDT group: 6 patients, IV pantoprazole group: 4 patients) were included. There was no difference of baseline Hb level between two groups (LFDT, 14.38+/-0.46 mg/dL; IV pantoprazole, 13.85+/-0.83 mg/dL; P=0.18). After 24 hours, change of Hb level was not different between LFDT (0.95+/-0.30 mg/dL) and IV pantoprazole group (0.98+/-0.45 mg/dL; P=0.96). Baseline intragastric pH was 3.72+/-0.19 with LFDT and 4.31+/-0.41 with IV pantoprazole group (P=0.18). After 24 hours, there was no significant difference of the extent of pH increase between LFDT (2.38+/-0.28) and IV pantoprazole group (2.17+/-0.21; P=0.60). CONCLUSIONS: There was no difference in both the increase of post-24 hour intragastric pH and decrease of post-24 hour Hb between LFDT and IV pantoprazole group. Oral PPI regimen may be able to replace IV PPI therapy for the prevention of post-ESD bleeding and LFDT might be superior to IV PPIs in the aspect of cost-effectiveness.
Consensus ; Hemorrhage* ; Humans ; Hydrogen-Ion Concentration ; Lansoprazole* ; Proton Pump Inhibitors ; Proton Pumps