Current nutritional guidelines for stomach cancer survivors (SCS) mainly focus on the influence of surgical resection of the stomach, with limited guidance regarding a wider range of food options. We aimed to investigate the factors associated with healthier dietary changes in Korean adult SCS. This cross-sectional study assessed dietary pattern change after cancer treatment for 11 food categories, using a self-administered questionnaire. A ‘healthier dietary change’ was operationally defined as reduced consumption of red and processed meat, grains, salt, and burnt food, and increased consumption of poultry, fish, vegetables, fruits, legumes, and dairy products. Among a total of 624 SCS, approximately 60% of participants reported healthier directional dietary changes in three or more food categories, while 9.1% reported no changes. There was no significant difference in dietary habit changes between surgery types. Multivariable adjusted analysis showed that elderly and long-term survivors were inversely associated with a healthier dietary change. SCS with higher educational achievement and income were more likely to make healthier changes in their intake of processed meat, vegetables, fruits, burnt food, or salt. SCS with higher levels of fear of cancer recurrence, anxiety, or depression were more likely to follow healthier dietary changes for fish, meat, fruits, grains, or burnt food. Change in dietary pattern varied across different food items and was associated with various characteristics of SCS. It is crucial to repeatedly provide SCS with information about healthier dietary patterns, considering their sociodemographic, clinical, and psychological characteristics.

Abstract: Cancer survivors are at an increased risk for cardiovascular disease (CVD). However, the association between particulate matter (PM) and CVD risk among cancer survivors (alive >5 years since diagnosis) is unclear. We investigated the risk of CVD among 40,899 cancer survivors within the Korean National Health Insurance Service database. Exposure to PM was determined by assessing yearly average PM levels obtained from the Air Korea database from 2008 to 2011. PMs with sizes <2.5 (PM2.5), <10 (PM10), or 2.5-10 (PM2.5-10) μm in diameter were compared, with each PM level exposure further divided into quintiles. Patients were followed up from January 2012 to date of CVD event, death, or December 2017, whichever came earliest. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for CVD were calculated using Cox proportional hazards regression by PM exposure levels. Compared with cancer survivors in the lowest quintile of PM2.5 exposure, those within the highest quintile had a greater risk for CVD (aHR 1.31, 95% CI 1.07-1.59). Conversely, increasing PM10 and PM2.5-10 levels were not associated with increased CVD risk (p for trend 0.078 and 0.361, respectively). Cancer survivors who reduce PM2.5 exposure may reduce their risk of developing CVD.

Purpose: Metastatic spinal cord compression (MSCC) is a severe complication of cancer that can lead to irreversible neurological impairment, necessitating prompt recognition and intervention. This retrospective, single-center study aimed to determine the prognostic factors and survival rates among patients presenting with MSCC secondary to lung cancer. Methods and Materials: We identified 74 patients with epidural metastases-related spinal cord compression and a history of lung cancer, through the electronic database of Medway Maritime Hospital in United Kingdom (UK), spanning the period from April 2016 to September 2021. Among them, 39 were below 55 years old, while 35 were aged 55 years or older, whereas 24 patients were diagnosed with small cell lung cancer (SCLC), and 50 patients had non-small cell lung cancer (NSCLC). Results: The median overall survival (OS) was 5.5 months, with 52 out of 74 patients dying within 6 months of the diagnosis of MSCC. For the entire cohort, the statistically significant variables on multi-variate analysis were: cancer type (NSCLC had improved OS), number of involved vertebrae (one to two vertebrae involvement had improved OS) and time taken to develop motor deficits (≤10 days to develop motor deficits had worsened OS). For the NSCLC cohort, the statistically significant variables on multivariate analysis were: molecular alterations (patients with epidermal growth factor receptor (EGFR) mutation), pre-treatment ambulatory status, Eastern Cooperative Oncology Group (ECOG) performance status, and time taken to develop motor deficits. Conclusions: Within the entire cohort, patients diagnosed with NSCLC and spinal metastases affecting one to two vertebrae exhibited enhanced OS. Within the NSCLC subgroup, those with EGFR mutations, who were ambulatory, and possessed an ECOG performance status of 1-2, demonstrated improved OS. In both the entire cohort and NSCLC subgroup, the development of motor deficits within a period of ≤10 days was associated with poorer OS.

Glioblastoma (GBM) is a very aggressive malignant brain tumor with the vast majority of patients surviving less than 12 months (Short-term survivors [STS]). Only around 2% of patients survive more than 36 months (Long-term survivors [LTS]). Studying these extreme survival groups might help in better understanding GBM biology. This work aims at exploring application of machine learning methods in predicting survival groups(STS, LTS). We used age and gene expression profiles belonging to 249 samples from publicly available datasets. 10 Machine learning methods have been implemented and compared for their performances. Hyperparameter tuned random forest model performed best with accuracy of 80% (AUC of 74% and F1_score of 85%). The performance of this model is validated on external test data of 16 samples. The model predicted the true survival group for 15 samples achieving an accuracy of 93.75%. This classification model is deployed as a web tool GlioSurvML. The top 1500 features which retained classification efficiency (Accuracy of 80%, AUC of 74%) were studied for enriched pathways and disease-causal biomarker associations using the HumanPSD^TM database. We identified 199 genes as possible biomarkers of GBM and/or similar diseases (like Glioma, astrocytoma, and others). 57 of these genes are shown to be differentially expressed across survival groups and/or have impact on survival. This work demonstrates the application of machine learning methods in predicting survival groups of GBM.

Background: Increasing number of long-term gastrointestinal (GI) cancer survivors highlights the importance of understanding factors that contribute to their health-related quality of life (HRQoL). We investigated the risk factors of HRQoL, in-cluding demographics, clinical characteristics, and social and behavioral determinants of health (SBDH). Methods: Adult GI cancer survivors (n = 3,201) in the BRFSS surveys from 2014-2021 (except for 2015) were analyzed. Unadjusted/adjusted logistic regression was used. Results: The majority were female (54%) and White (78%), with a median age of 67. Survivors who were 65 years or older, diagnosed with colorectal cancer, or who had fewer comorbidities were more likely to report significantly better HRQoL. Significant social factors of poor HRQoL were being unmarried, racial and ethnic minorities, low socioeconomic status, and poor health care access. Significant behavioral factors of poor HRQoL were lack of physical activity, heavy alcohol consumption, and current smoking, with lack of physical activity being the most significant factor. Conclusions: The SBDH have a critical role in HRQoL. Future studies are warranted to develop a tailored survivorship intervention, such as physical rehabilitation and to ex-plore machine learning/artificial intelligence predictive models to identify cancer sur-vivors at a high risk of developing poor HRQoL.

Athletes living with and beyond cancer can continue to train and, in some cases, compete during treatment. Following cancer treatment, athletes can return to competitive sport but need to learn to adapt their physical strength and training to lingering effects of cancer. It is critical for oncology healthcare providers to use the principles of assess, refer and advise to exercise oncology programs that are appropriate for the individual. Managing side effects of treatment is key to being able to train during and immediately following cancer treatment. Keen attention to fatigue is important at any point in the cancer spectrum to avoid overtraining and optimize the effects of training.