Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Materials and Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusion The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Materials and Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusion The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

Morbid obesity requires active intervention, with treatment options including lifestyle modification, pharmacotherapy, and surgery. As the prevalence of obesity continues to rise in Korea, it is crucial for specialists and general practitioners to have a comprehensive understanding of obesity and its management. Bariatric surgery is the most effective treatment modality for obesity, leading to significant weight loss and metabolic benefits. It involves surgical alterations of normal anatomical structures to improve overall health. Therefore, selecting the appropriate procedure based on the individual characteristics of patients is crucial. This review highlights the two most commonly performed bariatric procedures worldwide, including in Korea: sleeve gastrectomy (SG) and Roux-en-Y gastric bypass (RYGB). Furthermore, it provides a comprehensive overview of the surgical techniques involved in SG and RYGB, addresses potential complications, and presents findings from key studies on the weight loss and metabolic outcomes of these surgeries. Additionally, to support clinical application, the review provides outcome data for these procedures based on studies conducted in Korean populations. In addition to SG and RYGB, this review briefly introduces other surgical and endoscopic options, as well as pharmacological treatments that are currently available or may become viable options in the near future.

Morbid obesity requires active intervention, with treatment options including lifestyle modification, pharmacotherapy, and surgery. As the prevalence of obesity continues to rise in Korea, it is crucial for specialists and general practitioners to have a comprehensive understanding of obesity and its management. Bariatric surgery is the most effective treatment modality for obesity, leading to significant weight loss and metabolic benefits. It involves surgical alterations of normal anatomical structures to improve overall health. Therefore, selecting the appropriate procedure based on the individual characteristics of patients is crucial. This review highlights the two most commonly performed bariatric procedures worldwide, including in Korea: sleeve gastrectomy (SG) and Roux-en-Y gastric bypass (RYGB). Furthermore, it provides a comprehensive overview of the surgical techniques involved in SG and RYGB, addresses potential complications, and presents findings from key studies on the weight loss and metabolic outcomes of these surgeries. Additionally, to support clinical application, the review provides outcome data for these procedures based on studies conducted in Korean populations. In addition to SG and RYGB, this review briefly introduces other surgical and endoscopic options, as well as pharmacological treatments that are currently available or may become viable options in the near future.

Morbid obesity requires active intervention, with treatment options including lifestyle modification, pharmacotherapy, and surgery. As the prevalence of obesity continues to rise in Korea, it is crucial for specialists and general practitioners to have a comprehensive understanding of obesity and its management. Bariatric surgery is the most effective treatment modality for obesity, leading to significant weight loss and metabolic benefits. It involves surgical alterations of normal anatomical structures to improve overall health. Therefore, selecting the appropriate procedure based on the individual characteristics of patients is crucial. This review highlights the two most commonly performed bariatric procedures worldwide, including in Korea: sleeve gastrectomy (SG) and Roux-en-Y gastric bypass (RYGB). Furthermore, it provides a comprehensive overview of the surgical techniques involved in SG and RYGB, addresses potential complications, and presents findings from key studies on the weight loss and metabolic outcomes of these surgeries. Additionally, to support clinical application, the review provides outcome data for these procedures based on studies conducted in Korean populations. In addition to SG and RYGB, this review briefly introduces other surgical and endoscopic options, as well as pharmacological treatments that are currently available or may become viable options in the near future.

Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Materials and Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusion The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

Morbid obesity requires active intervention, with treatment options including lifestyle modification, pharmacotherapy, and surgery. As the prevalence of obesity continues to rise in Korea, it is crucial for specialists and general practitioners to have a comprehensive understanding of obesity and its management. Bariatric surgery is the most effective treatment modality for obesity, leading to significant weight loss and metabolic benefits. It involves surgical alterations of normal anatomical structures to improve overall health. Therefore, selecting the appropriate procedure based on the individual characteristics of patients is crucial. This review highlights the two most commonly performed bariatric procedures worldwide, including in Korea: sleeve gastrectomy (SG) and Roux-en-Y gastric bypass (RYGB). Furthermore, it provides a comprehensive overview of the surgical techniques involved in SG and RYGB, addresses potential complications, and presents findings from key studies on the weight loss and metabolic outcomes of these surgeries. Additionally, to support clinical application, the review provides outcome data for these procedures based on studies conducted in Korean populations. In addition to SG and RYGB, this review briefly introduces other surgical and endoscopic options, as well as pharmacological treatments that are currently available or may become viable options in the near future.

Morbid obesity requires active intervention, with treatment options including lifestyle modification, pharmacotherapy, and surgery. As the prevalence of obesity continues to rise in Korea, it is crucial for specialists and general practitioners to have a comprehensive understanding of obesity and its management. Bariatric surgery is the most effective treatment modality for obesity, leading to significant weight loss and metabolic benefits. It involves surgical alterations of normal anatomical structures to improve overall health. Therefore, selecting the appropriate procedure based on the individual characteristics of patients is crucial. This review highlights the two most commonly performed bariatric procedures worldwide, including in Korea: sleeve gastrectomy (SG) and Roux-en-Y gastric bypass (RYGB). Furthermore, it provides a comprehensive overview of the surgical techniques involved in SG and RYGB, addresses potential complications, and presents findings from key studies on the weight loss and metabolic outcomes of these surgeries. Additionally, to support clinical application, the review provides outcome data for these procedures based on studies conducted in Korean populations. In addition to SG and RYGB, this review briefly introduces other surgical and endoscopic options, as well as pharmacological treatments that are currently available or may become viable options in the near future.

Purpose: Triple-negative breast cancer (TNBC) is a particularly challenging subtype of breast cancer, with a poorer prognosis compared to other subtypes. Unfortunately, unlike luminal-type cancers, there is no validated biomarker to predict the prognosis of patients with early-stage TNBC. Accurate biomarkers are needed to establish effective therapeutic strategies. Materials and Methods: In this study, we analyzed gene expression profiles of tumor samples from 184 TNBC patients (training cohort, n=76; validation cohort, n=108) using RNA sequencing. Results: By combining weighted gene expression, we identified a 10-gene signature (DGKH, GADD45B, KLF7, LYST, NR6A1, PYCARD, ROBO1, SLC22A20P, SLC24A3, and SLC45A4) that stratified patients by risk score with high sensitivity (92.31%), specificity (92.06%), and accuracy (92.11%) for invasive disease-free survival. The 10-gene signature was validated in a separate institution cohort and supported by meta-analysis for biological relevance to well-known driving pathways in TNBC. Furthermore, the 10-gene signature was the only independent factor for invasive disease-free survival in multivariate analysis when compared to other potential biomarkers of TNBC molecular subtypes and T-cell receptor β diversity. 10-gene signature also further categorized patients classified as molecular subtypes according to risk scores. Conclusion Our novel findings may help address the prognostic challenges in TNBC and the 10-gene signature could serve as a novel biomarker for risk-based patient care.