As the demand for end-of-life care increases, the development of a well-structured training and certification system for palliative medicine specialists is becoming increasingly important. In South Korea, a certification system for palliative care physicians has been in place since 2019, managed by the Korean Society for Hospice and Palliative Care. To further develop this certification system and training process, this review aims to describe hospice and palliative medicine certification programs across eight countries/regions—the United States, the United Kingdom, Australia, Japan, Taiwan, Hong Kong, and South Korea—to identify key differences and draw insights for enhancing Korea's physician training and certification system. Most countries/regions recognize hospice and palliative medicine as medical subspecialty and provide standardized training and certification pathways. Training durations range from 1-year fellowships to multiyear structured programs with clinical experience. Japan’s tiered certification system offers a flexible approach based on care settings and physicians’ expertise. However, Korea’s system lacks in-depth clinical experience and government recognition, limiting its sustainability. To strengthen palliative care in Korea, it is essential to enhance training duration, expand clinical exposure, and foster multispecialty collaboration. A tiered certification system adapted to Korea’s healthcare environment and supported by government policy could improve both the quality and reach of palliative care services. These findings can inform future policy and educational reforms to ensure more effective and sustainable training of palliative care professionals in Korea.

Purpose: Radiomic models elaborate geometric and texture features of tumors extracted from imaging to develop predictors for clinical outcomes. Stereotactic body radiation therapy (SBRT) has been increasingly applied in the ablative treatment of thoracic tumors. This study aims to identify predictors of treatment responses in patients affected by early stage non-small cell lung cancer (NSCLC) or pulmonary oligo-metastases treated with SBRT and to develop an accurate machine learning model to predict radiological response to SBRT. Materials and Methods: Computed tomography (CT) images of 85 tumors (stage I–II NSCLC and pulmonary oligo-metastases) from 69 patients treated with SBRT were analyzed. Gross tumor volumes (GTV) were contoured on CT images. Patients that achieved complete response (CR) or partial response (PR) were defined as responders. One hundred ten radiomic features were extracted using PyRadiomics module based on the GTV. The association of features with response to SBRT was evaluated. A model using support vector machine (SVM) was then trained to predict response based solely on the extracted radiomics features. Receiver operating characteristic curves were constructed to evaluate model performance of the identified radiomic predictors. Results: Sixty-nine patients receiving thoracic SBRT from 2008 to 2018 were retrospectively enrolled. Skewness and root mean squared were identified as radiomic predictors of response to SBRT. The SVM machine learning model developed had an accuracy of 74.8%. The area under curves for CR, PR, and non-responder prediction were 0.86 (95% confidence interval [CI], 0.794–0.921), 0.946 (95% CI, 0.873–0.978), and 0.857 (95% CI, 0.789–0.915), respectively. Conclusion Radiomic analysis of pre-treatment CT scan is a promising tool that can predict tumor response to SBRT.