Background: The associations between thyroid cancer and skeletal outcomes have not been thoroughly investigated. We aimed to investigate the risk of osteoporotic fractures in patients with thyroid cancer compared to that in a matched control group. Methods: This retrospective cohort study included 2,514 patients with thyroid cancer and 75,420 matched controls from the Korean National Health Insurance Service-National Sample Cohort (NHIS-NSC, 2006–2019). The rates of osteoporotic fractures were analyzed, and associations with the levothyroxine dose were evaluated. Results: Patients with thyroid cancer had a significantly lower risk of fracture than did the control group (hazard ratio [HR], 0.81; 95% confidence interval [CI], 0.69 to 0.94; P=0.006). Patients diagnosed with thyroid cancer after the age of 50 years (older cancer group) had a significantly lower risk of fracture than did those in the control group (HR, 0.72; 95% CI, 0.6 to 0.85; P<0.001), especially those diagnosed with spinal fractures (HR, 0.66; 95% CI, 0.51 to 0.85; P=0.001). Patients in the older cancer group started osteoporosis treatment earlier than did those in the control group (65.5±7.5 years vs. 67.3±7.6 years, P<0.001). Additionally, a lower dose of levothyroxine was associated with a reduced risk of fractures. Conclusion In the clinical setting, the risk of fracture in women diagnosed with thyroid cancer after the age of 50 years was lower than that in the control group, which was caused by more proactive osteoporosis treatment in postmenopausal women with thyroid cancer.

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 associations between thyroid cancer and skeletal outcomes have not been thoroughly investigated. We aimed to investigate the risk of osteoporotic fractures in patients with thyroid cancer compared to that in a matched control group. Methods: This retrospective cohort study included 2,514 patients with thyroid cancer and 75,420 matched controls from the Korean National Health Insurance Service-National Sample Cohort (NHIS-NSC, 2006–2019). The rates of osteoporotic fractures were analyzed, and associations with the levothyroxine dose were evaluated. Results: Patients with thyroid cancer had a significantly lower risk of fracture than did the control group (hazard ratio [HR], 0.81; 95% confidence interval [CI], 0.69 to 0.94; P=0.006). Patients diagnosed with thyroid cancer after the age of 50 years (older cancer group) had a significantly lower risk of fracture than did those in the control group (HR, 0.72; 95% CI, 0.6 to 0.85; P<0.001), especially those diagnosed with spinal fractures (HR, 0.66; 95% CI, 0.51 to 0.85; P=0.001). Patients in the older cancer group started osteoporosis treatment earlier than did those in the control group (65.5±7.5 years vs. 67.3±7.6 years, P<0.001). Additionally, a lower dose of levothyroxine was associated with a reduced risk of fractures. Conclusion In the clinical setting, the risk of fracture in women diagnosed with thyroid cancer after the age of 50 years was lower than that in the control group, which was caused by more proactive osteoporosis treatment in postmenopausal women with thyroid cancer.

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 associations between thyroid cancer and skeletal outcomes have not been thoroughly investigated. We aimed to investigate the risk of osteoporotic fractures in patients with thyroid cancer compared to that in a matched control group. Methods: This retrospective cohort study included 2,514 patients with thyroid cancer and 75,420 matched controls from the Korean National Health Insurance Service-National Sample Cohort (NHIS-NSC, 2006–2019). The rates of osteoporotic fractures were analyzed, and associations with the levothyroxine dose were evaluated. Results: Patients with thyroid cancer had a significantly lower risk of fracture than did the control group (hazard ratio [HR], 0.81; 95% confidence interval [CI], 0.69 to 0.94; P=0.006). Patients diagnosed with thyroid cancer after the age of 50 years (older cancer group) had a significantly lower risk of fracture than did those in the control group (HR, 0.72; 95% CI, 0.6 to 0.85; P<0.001), especially those diagnosed with spinal fractures (HR, 0.66; 95% CI, 0.51 to 0.85; P=0.001). Patients in the older cancer group started osteoporosis treatment earlier than did those in the control group (65.5±7.5 years vs. 67.3±7.6 years, P<0.001). Additionally, a lower dose of levothyroxine was associated with a reduced risk of fractures. Conclusion In the clinical setting, the risk of fracture in women diagnosed with thyroid cancer after the age of 50 years was lower than that in the control group, which was caused by more proactive osteoporosis treatment in postmenopausal women with thyroid cancer.

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 associations between thyroid cancer and skeletal outcomes have not been thoroughly investigated. We aimed to investigate the risk of osteoporotic fractures in patients with thyroid cancer compared to that in a matched control group. Methods: This retrospective cohort study included 2,514 patients with thyroid cancer and 75,420 matched controls from the Korean National Health Insurance Service-National Sample Cohort (NHIS-NSC, 2006–2019). The rates of osteoporotic fractures were analyzed, and associations with the levothyroxine dose were evaluated. Results: Patients with thyroid cancer had a significantly lower risk of fracture than did the control group (hazard ratio [HR], 0.81; 95% confidence interval [CI], 0.69 to 0.94; P=0.006). Patients diagnosed with thyroid cancer after the age of 50 years (older cancer group) had a significantly lower risk of fracture than did those in the control group (HR, 0.72; 95% CI, 0.6 to 0.85; P<0.001), especially those diagnosed with spinal fractures (HR, 0.66; 95% CI, 0.51 to 0.85; P=0.001). Patients in the older cancer group started osteoporosis treatment earlier than did those in the control group (65.5±7.5 years vs. 67.3±7.6 years, P<0.001). Additionally, a lower dose of levothyroxine was associated with a reduced risk of fractures. Conclusion In the clinical setting, the risk of fracture in women diagnosed with thyroid cancer after the age of 50 years was lower than that in the control group, which was caused by more proactive osteoporosis treatment in postmenopausal women with thyroid cancer.

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.

Canine adenovirus type 2 (CAV-2) is a common causative agent of respiratory disease in canines. There have been no reports of CAV-2 variants isolated from raccoon dogs. This study aims to investigate the biological and genetic characteristics of a novel Korean CAV-2 variant. Madin-Darby canine kidney cells were used to isolate the CAV-2 variant from 45 fecal swab samples. Diagnostic tools such as the cytopathic effect (CPE) assay, electron microscopy, polymerase chain reaction, and immunofluorescence and hemagglutination assays were used to confirm the presence of the CAV-2 isolate. A cross-virus neutralization assay was performed to verify the novelty of this CAV variant. Genetic analysis was performed using nucleotide sequences obtained through next-generation sequencing. The isolate was confirmed to be a CAV-2 variant based on the aforementioned methods and designated CAV2232. The number of bases in the fiber and E3 genes of CAV2232 were 1,626 and 414, respectively. Phylogenetic analysis of the fiber and E3 genes confirmed that CAV2232 was classified into a different clade from the known CAV-1 and CAV-2 strains. Mice inoculated with the CAV2232 vaccine developed high virus neutralization antibody titers of 1,024 (210) against CAV2232, while mice inoculated with CAV-1 and CAV-2 vaccines had low virus neutralization antibody titers of 12.9 (23.7) and 6.5 (22.7), respectively, against CAV2232. CAV2232 isolated from wild raccoon dog feces was classified as a novel CAV-2 variant. CAV2232 may therefore be used as an antigen for new vaccine development and serological investigations.

Canine adenovirus type 2 (CAV-2) is a common causative agent of respiratory disease in canines. There have been no reports of CAV-2 variants isolated from raccoon dogs. This study aims to investigate the biological and genetic characteristics of a novel Korean CAV-2 variant. Madin-Darby canine kidney cells were used to isolate the CAV-2 variant from 45 fecal swab samples. Diagnostic tools such as the cytopathic effect (CPE) assay, electron microscopy, polymerase chain reaction, and immunofluorescence and hemagglutination assays were used to confirm the presence of the CAV-2 isolate. A cross-virus neutralization assay was performed to verify the novelty of this CAV variant. Genetic analysis was performed using nucleotide sequences obtained through next-generation sequencing. The isolate was confirmed to be a CAV-2 variant based on the aforementioned methods and designated CAV2232. The number of bases in the fiber and E3 genes of CAV2232 were 1,626 and 414, respectively. Phylogenetic analysis of the fiber and E3 genes confirmed that CAV2232 was classified into a different clade from the known CAV-1 and CAV-2 strains. Mice inoculated with the CAV2232 vaccine developed high virus neutralization antibody titers of 1,024 (210) against CAV2232, while mice inoculated with CAV-1 and CAV-2 vaccines had low virus neutralization antibody titers of 12.9 (23.7) and 6.5 (22.7), respectively, against CAV2232. CAV2232 isolated from wild raccoon dog feces was classified as a novel CAV-2 variant. CAV2232 may therefore be used as an antigen for new vaccine development and serological investigations.