Molecular subtype and visceral metastasis should be considered for prediction of prognosis for patients with brain metastasis. The patients with HER2 and TNBC cancer subtypes having visceral metastasis, close surveillance could contribute to early detection of brain metastasis and may putatively lead to improved quality of life and survival.

We evaluated changes in incidence, relative survival (RS), and conditional survival (CS) of head and neck squamous cell carcinoma (HNSCC), focusing on oral tongue squamous cell carcinoma (OTSCC). Data of 74 680 HNSCC patients from 1976 to 2015 were obtained from the Surveillance, Epidemiology, and End Results database. Five anatomical sites and their subsites were analyzed. Annual percent change (APC) of incidence was calculated. RS and CS were compared across the four decades. Adjusted hazard ratios (aHRs) of RS were evaluated using multivariate regression. OTSCC incidence decreased from 1976 (APC = −0.76, P < 0.05) but has increased since 1999 (APC = 2.36, P < 0.05). During 2006-2015, the 5-year CS exceeded 90% only for OTSCC and oropharyngeal squamous cell carcinoma (OPSCC). RS improved in OTSCC (aHR = 0.697, 95% confidence interval [CI] 0.642-0.757, P < 0.001) and OPSCC (aHR = 0.669, 95% CI 0.633-0.706, P < 0.001) during the last two decades. For both OTSCC and OPSCC, improved survival was observed regardless of treatment. Incidence and survival remained unchanged for nasopharyngeal, hypopharyngeal, and laryngeal cancers during this period. In conclusion, OTSCC incidence has been increasing since the 2000s, with improving prognosis irrespective of treatment. Given its similarity to OPSCC, OTSCC may represent an emerging HNSCC, warranting further research and clinical recognition.

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Background: Cellular phones enable communication between healthcare providers and patients for prevention, diagnosis, and treatment of diseases. However, few studies have examined the userfriendliness or effectiveness of cellular phone-based medical informatics (CPBMI) for healthcare. Materials and Methods: This study investigated the use of CPBMI to identify its current status within the medical field, advantages and disadvantages, practicability, clinical effectiveness, costs, and cost-saving potential. Results: CPBMI was validated in terms of practicality and provision of medical benefits. It is critical to use CPBMI in accordance with the different features of each disease and condition. Use of CPBMI is expected to be especially useful for patients with chronic disease. Conclusions: We discussed the current status of the clinical use, benefits, and risks of CPBMI. CPBMI and information technology-based health management tools are anticipated to become useful and effective components of healthcare management in the future.

Chondrosarcomas, malignant cartilaginous neoplasms, are capable of transitioning to highly aggressive, metastatic, and treatment-refractory states, resulting in significant patient mortality. Here, we aim to uncover the transcriptional program directing such tumor progression in chondrosarcomas. We conduct weighted correlation network analysis to extract a characteristic gene module underlying chondrosarcoma malignancy. Hypoxia-inducible factor-2α (HIF-2α, encoded by EPAS1) is identified as an upstream regulator that governs the malignancy gene module. HIF-2α is upregulated in high-grade chondrosarcoma biopsies and EPAS1 gene amplification is associated with poor prognosis in chondrosarcoma patients. Using tumor xenograft mouse models, we demonstrate that HIF-2α confers chondrosarcomas the capacities required for tumor growth, local invasion, and metastasis. Meanwhile, pharmacological inhibition of HIF-2α, in conjunction with the chemotherapy agents, synergistically enhances chondrosarcoma cell apoptosis and abolishes malignant signatures of chondrosarcoma in mice. We expect that our insights into the pathogenesis of chondrosarcoma will provide guidelines for the development of molecular targeted therapeutics for chondrosarcoma.

ObjectivesTo compare overall survival (OS) in locally advanced hypopharyngeal cancer treated with surgery or definitive chemoradiotherapy in the contemporary era.MethodsFrom 2010 to 2015, data for patients diagnosed with hypopharyngeal cancer (T2‐T4aM0) and treated with total pharyngectomy with lymph node dissection (surgery group) or definitive radiotherapy and chemotherapy (chemoradiotherapy group) was retrieved from the SEER database. Multivariate analyses were performed in each subgroup divided according to T category (T2‐3 or T4a).ResultsThe number of patients in the surgery and chemoradiotherapy groups was 209 and 648, respectively. Among them, the number of T4a patients was 111 and 126 in each group. Three‐year OS rate in the surgery and chemoradiotherapy groups was 37.9% and 44.1%, respectively (P = 0.178). The 3‐year OS rate for the T2‐3 patients was 46.5% and 48.7% (P = 0.598), and the 3‐year OS rate for the T4a patients was 29.9% and 26.1% in the surgery and chemoradiotherapy groups, respectively (P = 0.439). On multivariate analysis, the chemoradiotherapy group was not inferior to the surgery group in T2‐T4a patients (Hazard ratio [HR] for the chemoradiotherapy group 0.889, 95% confidence interval [CI] 0.699‐1.129, P = 0.334), in T2‐3 patients (HR 0.932, 95% CI 0.699‐1.297, P = 0.675), and in T4a patients (HR 0.880, 95% CI 0.617‐1.256, P = 0.481).ConclusionsChemoradiotherapy for locally advanced hypophagyngeal cancer showed a comparable OS rate to surgery. For patients with T4a category cancer with high possibility of preserving the laryngopharyngeal function, chemoradiotherapy may be a promising alternative treatment.

Accurate prediction of postoperative mortality is important for not only successful postoperative patient care but also for information-based shared decision-making with patients and efficient allocation of medical resources. This study aimed to create a machine-learning prediction model for 30-day mortality after a non-cardiac surgery that adapts to the manageable amount of clinical information as input features and is validated against multi-centered rather than single-centered data. Data were collected from 454,404 patients over 18 years of age who underwent non-cardiac surgeries from four independent institutions. We performed a retrospective analysis of the retrieved data. Only 12–18 clinical variables were used for model training. Logistic regression, random forest classifier, extreme gradient boosting (XGBoost), and deep neural network methods were applied to compare the prediction performances. To reduce overfitting and create a robust model, bootstrapping and grid search with tenfold cross-validation were performed. The XGBoost method in Seoul National University Hospital (SNUH) data delivers the best performance in terms of the area under receiver operating characteristic curve (AUROC) (0.9376) and the area under the precision-recall curve (0.1593). The predictive performance was the best when the SNUH model was validated with Ewha Womans University Medical Center data (AUROC, 0.941). Preoperative albumin, prothrombin time, and age were the most important features in the model for each hospital. It is possible to create a robust artificial intelligence prediction model applicable to multiple institutions through a light predictive model using only minimal preoperative information that can be automatically extracted from each hospital.

The survival benefit from radiotherapy in stage IV breast cancer has not been fully evaluated. We investigated the survival benefit of radiotherapy after surgery in de novo stage IV breast cancer. Using a population-based database (the Surveillance, Epidemiology, and End Results database 18, 2010–2013), patients diagnosed with de novo stage IV breast cancer were divided into those undergoing surgery alone (no-radiotherapy group) and those undergoing surgery followed by radiotherapy (radiotherapy group). After propensity-score matching (PSM), the cancer-specific survival (CSS) rates were estimated. Multivariate analysis was performed to evaluate the prognostic value of radiotherapy on survival. After PSM, the 3-year CSS rates in the no-radiotherapy (n = 882) and radiotherapy (n = 882) groups were 57.1% and 70.9% (P < 0.001), respectively. On multivariate analysis, radiotherapy after surgery was a significant prognosticator (hazard ratio [HR] 0.572; 95% confidence interval [CI] 0.472–0.693, P < 0.001). Regardless of surgery type and lymph node involvement, the radiotherapy group showed significantly higher CSS rates. For patients who survived six months or more, radiotherapy after surgery demonstrated favorable prognosis compared to surgery alone (HR 0.593; 95% CI 0.479–0.733, P < 0.001). In conclusion, radiotherapy after surgery increased CSS rates in de novo stage IV breast cancer compared to surgery alone.