Elderly Rectal Cancer: An Updated Review.

PURPOSE OF REVIEW: Treatment of rectal cancer patients of advanced age should be modulated by life expectancy and tolerance. Due to the rapid advance of this field, we aim to conduct an updated review of this topic. RECENT FINDINGS: The field of elderly rectal cancer has advanced a lot. This review covers all the treatment aspects of elderly rectal cancer, including the prognostic factor, surgery, radiotherapy, chemotherapy, and palliative treatment. We also provide the future aspect of the management of elderly rectal cancer. The advancement of prognostic factor research, surgery, radiotherapy, chemotherapy, and palliative treatment has made the care of elderly rectal cancer patients better. The future of these fields should focus on the definition of the elderly and the application of particle therapy.

Use of ChatGPT on Taiwan's Examination for Medical Doctors.

The study evaluates the performance of OpenAI's GPT-3 model on answering medical exam questions from Staged Senior Professional and Technical Examinations Regulations for Medical Doctors in the field of internal medicine. The study used the official API to connect the questionnaire with the ChatGPT model, and the results showed that the AI model performed reasonably well, with the highest score of 8/13 in chest medicine. However, the overall performance of the AI model was limited, with only chest medicine scoring more than 60. ChatGPT scored relatively high in Chest medicine, Gastroenterology, and general medicine. One of the limitations of the study is the use of non-English text, which may affect the model's performance as the model is primarily trained on English text.

A systematic review and meta-analysis of osteoradionecrosis following proton therapy in patients with head and neck cancer.

INTRODUCTION: Head and neck cancer ranks as the seventh most common cancer worldwide. Proton therapy is widely used in head and neck cancer. Osteoradionecrosis(ORN) is currently a commonly investigated side effect of proton therapy. A meta-analysis is needed to investigate this topic. MATERIAL/METHODS: Two authors searched three databases, including PubMed, Embase, and Cochrane Library; the search period was from inception to June 2023. The search keyword was set to be ((("osteoradionecrosis") OR ("osteonecrosis")) AND ("proton")). RESULTS: We initially collected 410 articles, and after article selections, 22 articles remained in our systematic reviews. Due to the overlapping of patient populations, 17 studies were finally included in our meta-analysis. The pooled grade 3 or more ORN rate is 0.01(95 % CI = 0.01-0.03). Subgroup analysis showed that IMPT didn't reduce grade 3 or more ORN compared with 3DCPT (p = 0.15). CONCLUSIONS: Our meta-analysis showed that severe ORN rarely occurred in proton therapy for head and neck cancer patients.

The Association between Metformin and the Cancer-Specific Mortality Rate in Nasopharyngeal Cancer Patients: Real-World Evidence.

OBJECTIVES: Nasopharyngeal cancer is a common cancer in East and South Asia. The radiotherapy and chemotherapy regimen has advanced in recent years. However, many patients still suffer from local recurrence and distant metastasis; thus, identifying medication that can be combined with standard treatment to improve the treatment outcomes in nasopharyngeal cancer patients is an unmet need. METHODS: We included nasopharyngeal cancer patients from the Taiwan National Health Insurance Database (NHIRD). The primary endpoint was set as the cancer-specific mortality rate. Metformin cohorts and non-Metformin cohorts were matched by sex, age, and the year of the index date. Propensity score matching with a ratio of 1:1 was applied. RESULTS: A total of 6078 subjects were included in the study, with 3039 patients in each group. Male participants outnumbered female participants. Most of the patients were aged 50 to 64; the mean age was 60.4 ± 10.4 years in Metformin non-users, and that of Metformin users was 59.9 ± 10.5 years. Metformin users had a lower risk of death due to nasopharyngeal cancer (adjusted HR = 0.80; 95% CI = 0.71, 0.90) than controls. CONCLUSIONS: We concluded that Metformin might be effective at reducing the cancer-specific mortality rate in nasopharyngeal cancer patients. Further randomized control trials should be completed.

Topical Prevention of Radiation Dermatitis in Breast Cancer Patients: A Network Meta-analysis of Randomized Controlled Trials.

BACKGROUND/AIM: Radiation dermatitis is a common complication of radiation therapy in breast cancer patients. Severe dermatitis may alter treatment schedules and clinical outcomes. The topical prevention strategy is the widely used option to prevent radiation dermatitis. However, the comparison between the current topical prevention strategies is insufficient. Therefore, this study aimed to investigate the topical prevention efficacy of radiation dermatitis in patients with breast cancer through a network meta-analysis. PATIENTS AND METHODS: This study followed The Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Network Meta-Analyses guidelines. A random effects model was used to compare different treatments. The treatment modality ranking was evaluated using the P-score. I2 and Cochran's Q test were used to evaluate the heterogeneity among studies. RESULTS: Forty-five studies were analyzed in this systematic review. A total of 19 studies were finally included in this meta-analysis for grade 3 or higher radiation dermatitis, which included 18 treatment arms and 2,288 patients. The forest plot showed that none of the identified regimens were superior to standard care. CONCLUSION: A more effective regimen than standard care for the prevention of grade 3 or higher radiation dermatitis in breast cancer patients was not identified. Our network meta-analysis showed that current topical prevention strategies are similarly efficacious. However, since preventing severe radiation dermatitis is an important clinical challenge, further trials should be conducted to address this issue.

The Relationship between Cancer and Dementia: An Updated Review.

The risk of cancer and dementia increases with age, raising complex questions about whether it is appropriate to continue cancer treatment in older patients. There is emerging research suggesting the association between cancer and dementia. However, the mechanistic underpinnings are still under investigation. Progress has already been made toward understanding the cognitive effects associated with cancer therapy. Such associations raise awareness about the need to establish better prevention methods and early screening in clinical practice. Additionally, recent studies have suggested possible therapeutic strategies for better preserving cognitive function and reducing the risk for dementia before patients start cancer treatment. We review the current literature and summarize the incidence and mechanisms of cognitive impairment in patients with lung cancer, breast cancer, head and neck cancer, gastric cancer, prostate cancer, colorectal cancer, and brain tumor/brain metastasis following different kinds of therapies. Possible risk factors are suggested to identify the early onset of cognitive changes in cancer patients and provide more insight into the pathophysiological process of dementia.

Can the Electronic Health Record Predict Risk of Falls in Hospitalized Patients by Using Artificial Intelligence? A Meta-analysis.

Because of an aging population worldwide, the increasing prevalence of falls and their consequent injuries are becoming a safety, health, and social-care issue among elderly people. We conducted a meta-analysis to investigate the benchmark of prediction power when using the EHR with artificial intelligence to predict risk of falls in hospitalized patients. The CHARMS guideline was used in this meta-analysis. We searched PubMed, Cochrane, and EMBASE. The pooled sensitivity and specificity were calculated, and the summary receiver operating curve was formed to investigate the predictive power of artificial intelligence models. The PROBAST table was used to assess the quality of the selected studies. A total of 132 846 patients were included in this meta-analysis. The pooled area under the curve of the collected research was estimated to be 0.78. The pooled sensitivity was 0.63 (95% confidence interval, 0.52-0.72), whereas the pooled specificity was 0.82 (95% confidence interval, 0.73-0.88). The quality of our selected studies was high, with most of them being evaluated with low risk of bias and low concern for applicability. Our study demonstrates that using the EHR with artificial intelligence to predict the risk of falls among hospitalized patients is feasible. Future clinical applications are anticipated.