Background/Objectives: Breast cancer is the most common cancer in women worldwide, requiring strategic efforts to reduce its mortality. This study aimed to develop a predictive classification model for breast cancer mortality using real-world data, including various clinical features. Methods: A total of 11,286 patients with breast cancer from the National Cancer Center were included in this study. The mortality rate of the total sample was approximately 6.2%. Propensity score matching was used to reduce bias. Several machine learning models, including extreme gradient boosting, were applied to 31 clinical features. To enhance model interpretability, we used the SHapley Additive exPlanations method. ML analyses were also performed on the samples, excluding patients who developed other cancers after breast cancer. Results: Among the ML models, the XGB model exhibited the highest discriminatory power, with an area under the curve of 0.8722 and a specificity of 0.9472. Key predictors of the mortality classification model included occurrence in other organs, age at diagnosis, N stage, T stage, curative radiation treatment, and Ki-67(%). Even after excluding patients who developed other cancers after breast cancer, the XGB model remained the best-performing, with an AUC of 0.8518 and a specificity of 0.9766. Additionally, the top predictors from SHAP were similar to the results for the overall sample. Conclusions: Our models provided excellent predictions of breast cancer mortality using real-world data from South Korea. Explainable artificial intelligence, such as SHAP, validated the clinical applicability and interpretability of these models.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea’s cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

Oncotype DX (ODX), a 21-gene assay, predicts the recurrence risk in early breast cancer; however, it has high costs and long testing times. We aimed to identify clinicopathological factors that can predict the ODX risk group and serve as alternatives to the ODX test. This retrospective study included 547 estrogen receptor-positive, human epidermal growth factor receptor 2-negative, and lymph node-negative breast cancer patients who underwent ODX testing. Based on the recurrence scores, three ODX risk categories (low: 0–15, intermediate: 16–25, and high: 26–100) were established in patients aged ≤50 years (n = 379), whereas two ODX risk categories (low: 0–25 and high: 26–100) were established in patients aged >50 years (n = 168). Factors selected for analysis included body mass index, menopausal status, type of surgery, and pathological and immunohistochemical features. The ODX risk groups showed significant association with histologic grade (p = 0.0002), progesterone receptor expression (p < 0.0001), Ki-67 (p < 0.0001), and p53 expression (p = 0.023) in patients aged ≤50 years. In patients aged >50 years, tumor size (p = 0.022), Ki-67 (p = 0.001), and p53 expression (p = 0.001) were significantly associated with the risk group. Certain clinicopathological factors can predict the ODX risk group and enable decision-making on adjuvant chemotherapy; these factors differ according to age.

Background: The increased demand for genetic testing and counseling necessitates healthcare professionals (HCPs) to improve their genetic competency through training programs. This systematic review identified HCPs’ learning needs and their perspectives on essential information for families with hereditary cancer. Methods: This review covered studies published from 2013 to 2024 across five databases. Data were analyzed using a content analysis. Results: Thirteen studies involving 332 HCPs were analyzed. Most studies focused on the learning needs of physicians caring for families affected by Hereditary Breast and Ovarian Cancer in North America and Europe. HCPs required training emphasizing practical counseling skills over the basics of genetics. Learning needs varied by profession: physicians needed training in assessing cancer risk and supporting decision-making in risk management; nurses required information on resources and the genetic care system; genetic counselors sought guidance on family communication and planning. Essential information identified for families included risk-reducing strategies, personalized cancer risk assessment, family implications, psychological issues, (cascade) genetic testing, and social concerns. Conclusions: The findings have implications for the development of training programs for HCPs, emphasizing the need for tailored training based on professions. Future research should explore the needs of HCPs caring for families with diverse hereditary cancers and cultural backgrounds.

Purpose: This study aimed to determine the risk factors for palbociclib-induced grade 4 or grade 3 neutropenia (NP) requiring dose reduction or delayed treatment in patients with HR+/HER2−metastatic breast cancer in the first 3 cycles (early grade 3/4 NP) and whether the early developing grade 3/4 NP affects progression-free survival. Methods: A retrospective study using electronic medical records was conducted on patients who received palbociclib for metastatic breast cancer between January 2018 and August 2022. The early grade 3/4 NP risk factors were evaluated with univariate and multivariable logistic regression analyses. In addition, the Kaplan-Meier method was used to estimate the median progression-free survival (PFS) to analyze the effect of early grade 3/4 NP on treatment. Results: Out of the 264 patients included in this study, 173 (65.6%) experienced early grade 3/4 NP. A total of four models were applied for multivariable analysis to identify early grade 3/4 NP-developing factors. Low baseline ANC, WBC, PLT, and BSA were significant risk factors for early grade 3/4 NP; baseline ANC < 3700/mm³, WBC < 6.30 × 10⁹/mm³, PLT < 230 × 10⁹/mm³, and BSA < 1.58 m² increased the risk by approximately 4.0-fold, 3.7–4.0-fold, 2.1-fold, and 2.0-fold, respectively. Early grade 3/4 NP did not affect PFS (p = 0.710), although patients with early grade 3/4 NP had more frequent dose reductions or treatment delays. Conclusions: Based on the results, low baseline ANC, WBC, PLT, and BSA were associated with early grade 3/4 NP. Patients with risk factors require careful monitoring, and this study is expected to help predict NP, which may appear in early treatment.