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: The nipple is a potential source of pathogens because its lactiferous ducts act as direct conduits from the nipple–areolar complex to the breast parenchyma. Our previous studies identified breast microbiota as a factor in postoperative complications following immediate breast reconstruction using silicone implants and acellular dermal matrix. This study aimed to investigate the correlation between preoperative nipple swab microbiota and the incidence of surgical site infections (SSIs) after autologous breast reconstruction. Methods: We conducted a retrospective chart review of patients who underwent autologous breast reconstruction following total mastectomy. Preoperative nipple swab cultures were obtained. Patient demographics, surgical characteristics, and complication rates were compared between culture-positive and culture-negative groups. Microbiological data, including antibiotic‑resistance profiles, were collected. Results: Among 39 reconstructed breasts, 18 (46.9%) had positive preoperative nipple cultures. The mean duration of drain placement was significantly longer in the culture‑positive group (14.39±3.96 days) than in the culture‑negative group (12.14±2.76 days, P=0.045). Methicillin‑susceptible Staphylococcus epidermidis accounted for 55.0% of isolates. Of the four SSIs observed, three occurred in patients with positive preoperative cultures. Conclusions Although pathogen strains differed between preoperative and postoperative settings, obtaining preoperative nipple microflora cultures and determining antibiotic‑resistance profiles can guide immediate antibiotic selection for SSIs and enhance postoperative management.

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: The nipple is a potential source of pathogens because its lactiferous ducts act as direct conduits from the nipple–areolar complex to the breast parenchyma. Our previous studies identified breast microbiota as a factor in postoperative complications following immediate breast reconstruction using silicone implants and acellular dermal matrix. This study aimed to investigate the correlation between preoperative nipple swab microbiota and the incidence of surgical site infections (SSIs) after autologous breast reconstruction. Methods: We conducted a retrospective chart review of patients who underwent autologous breast reconstruction following total mastectomy. Preoperative nipple swab cultures were obtained. Patient demographics, surgical characteristics, and complication rates were compared between culture-positive and culture-negative groups. Microbiological data, including antibiotic‑resistance profiles, were collected. Results: Among 39 reconstructed breasts, 18 (46.9%) had positive preoperative nipple cultures. The mean duration of drain placement was significantly longer in the culture‑positive group (14.39±3.96 days) than in the culture‑negative group (12.14±2.76 days, P=0.045). Methicillin‑susceptible Staphylococcus epidermidis accounted for 55.0% of isolates. Of the four SSIs observed, three occurred in patients with positive preoperative cultures. Conclusions Although pathogen strains differed between preoperative and postoperative settings, obtaining preoperative nipple microflora cultures and determining antibiotic‑resistance profiles can guide immediate antibiotic selection for SSIs and enhance postoperative management.

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: The nipple is a potential source of pathogens because its lactiferous ducts act as direct conduits from the nipple–areolar complex to the breast parenchyma. Our previous studies identified breast microbiota as a factor in postoperative complications following immediate breast reconstruction using silicone implants and acellular dermal matrix. This study aimed to investigate the correlation between preoperative nipple swab microbiota and the incidence of surgical site infections (SSIs) after autologous breast reconstruction. Methods: We conducted a retrospective chart review of patients who underwent autologous breast reconstruction following total mastectomy. Preoperative nipple swab cultures were obtained. Patient demographics, surgical characteristics, and complication rates were compared between culture-positive and culture-negative groups. Microbiological data, including antibiotic‑resistance profiles, were collected. Results: Among 39 reconstructed breasts, 18 (46.9%) had positive preoperative nipple cultures. The mean duration of drain placement was significantly longer in the culture‑positive group (14.39±3.96 days) than in the culture‑negative group (12.14±2.76 days, P=0.045). Methicillin‑susceptible Staphylococcus epidermidis accounted for 55.0% of isolates. Of the four SSIs observed, three occurred in patients with positive preoperative cultures. Conclusions Although pathogen strains differed between preoperative and postoperative settings, obtaining preoperative nipple microflora cultures and determining antibiotic‑resistance profiles can guide immediate antibiotic selection for SSIs and enhance postoperative management.

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.