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.

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.

Clear cell sarcoma (CCS) is a rare malignant soft tissue tumor originating from neural crest cells. Histologically resembling malignant melanoma but lacking cutaneous precursor lesions, CCS is characterized by a specific chromosomal translocation (t[12;22][q13;q12]). Primarily affecting young adults, this tumor typically arises in the extremities, especially the knee, foot, and ankle. To our knowledge, distal toe involvement is uncommon. Herein, we present a case initially diagnosed on magnetic resonance imaging as a superficial soft tissue tumor resembling malignant melanoma but subsequently confirmed as CCS of the great toe.

Clear cell sarcoma (CCS) is a rare malignant soft tissue tumor originating from neural crest cells. Histologically resembling malignant melanoma but lacking cutaneous precursor lesions, CCS is characterized by a specific chromosomal translocation (t[12;22][q13;q12]). Primarily affecting young adults, this tumor typically arises in the extremities, especially the knee, foot, and ankle. To our knowledge, distal toe involvement is uncommon. Herein, we present a case initially diagnosed on magnetic resonance imaging as a superficial soft tissue tumor resembling malignant melanoma but subsequently confirmed as CCS of the great toe.

Clear cell sarcoma (CCS) is a rare malignant soft tissue tumor originating from neural crest cells. Histologically resembling malignant melanoma but lacking cutaneous precursor lesions, CCS is characterized by a specific chromosomal translocation (t[12;22][q13;q12]). Primarily affecting young adults, this tumor typically arises in the extremities, especially the knee, foot, and ankle. To our knowledge, distal toe involvement is uncommon. Herein, we present a case initially diagnosed on magnetic resonance imaging as a superficial soft tissue tumor resembling malignant melanoma but subsequently confirmed as CCS of the great toe.

Schwannomas are slow-growing benign tumors originating from the Schwann cells of the peripheral nerve sheaths. Herein, we report the first documented case of a schwannoma presenting as a painful nipple mass in a 32-year-old woman. This mass initially developed six years ago following a period of breastfeeding. Breast magnetic resonance imaging (MRI) scans revealed an iso-intense mass, with an approximate size of 2.2 cm, on a T1-weighted image with internal cystic changes. The mass exhibited heterogeneously delayed enhancement and restricted diffusion.Surgical excision was performed, and the diagnosis of cutaneous plexiform nipple schwannoma was confirmed histopathologically. A literature review revealed that the MRI findings of the nipple mass in our case were consistent with the common features of a schwannoma.

Objectives: This study investigated the relationship between living arrangements and metabolic syndrome (MetS) risk in the adult population in the Republic of Korea. Methods: The samples were derived from the data collected during the second year of the seventh Korea National Health and Nutrition Examination Survey. The study targeted a total of 6,265 adults who were aged 20 years and above, and multiple logistic regression analysis was conducted. Living arrangements were classified into 4 categories: single-person households, 1-generation households, 2-generation households, and other family types. MetS was identified by the presence of at least 3 out of the 5 National Cholesterol Education Program Adult Treatment Panel III criteria. Results: For men, the odds ratio (ORs) for MetS in 1- and 2-generation households, comparedto single-person households, were 0.92 (95% confidence interval [CI], 0.55–1.54) and 0.97 (95% CI, 0.58–1.62), respectively. The OR for other types of households was 0.96 (95% CI, 0.79–1.17). For women, the OR for MetS in 1- and 2-generation households, compared to single-person households, were 1.52 (95% CI, 1.15–2.01) and 1.29 (95% CI, 1.01–1.67), respectively. Conclusion Our study suggests that a national strategy involving tailored interventions for women living in high-risk conditions is necessary to reduce the risk of MetS in Korean women.

Sebaceous carcinoma is a malignant neoplasm frequently classified as periocular or extraocular. Extraocular sebaceous carcinoma is a relatively rare tumor commonly appearing in the head and neck region. Furthermore, extraocular sebaceous carcinoma limited to the epithelium, which is called ‘extraocular sebaceous carcinoma in situ’ is extremely rare with less than 10 cases reported in the literature. Herein, we present the case of a 93-year-old female who diagnosed as extraocular sebaceous carcinoma in situ occurring concurrently with actinic keratosis. In the present case, extraocular sebaceous carcinoma in situ as identified without any connection to an adnexal component but concurrent with actinic keratosis. This case is evidence that extraocular sebaceous carcinoma can arise from the epidermis and the tumor cells of intraepidermal squamous neoplasia can differentiate toward malignant sebocytes.

Background: This study aimed to investigate the capability of an automated immunohistochemical (IHC) evaluation of hormonal receptor status in breast cancer patients compared to a well-validated quantitative reverse transcription–polymerase chain reaction (RT-qPCR) method. Methods: This study included 93 invasive breast carcinoma cases that had both standard IHC assay and Oncotype Dx assay results. The same paraffin blocks on which Oncotype Dx assay had been performed were selected. Estrogen receptor (ER) and progesterone receptor (PR) receptor status were evaluated through IHC stains using SP1 monoclonal antibody for ER, and 1E2 monoclonal antibody for PR. All ER and PR immunostained slides were scanned, and invasive tumor areas were marked. Using the QuantCenter image analyzer provided by 3DHISTECH, IHC staining of hormone receptors was measured and converted to histochemical scores (H scores). Pearson correlation coefficients were calculated between Oncotype Dx hormone receptor scores and H scores, and between Oncotype Dx scores and Allred scores. Results: H scores measured by an automated imaging system showed high concordance with RT-qPCR scores. ER concordance was 98.9% (92/93), and PR concordance was 91.4% (85/93). The correlation magnitude between automated H scores and RT-qPCR scores was high and comparable to those of Allred scores (for ER, 0.51 vs. 0.37 [p=.121], for PR, 0.70 vs. 0.72 [p=.39]). Conclusions Automated H scores showed a high concordance with quantitative mRNA expression levels measured by RT-qPCR.