Comparison of Machine Leaning Models for Prediction of Acute Pain Severity and On-Treatment Opioid Utilization in Oral Cavity and Oropharyngeal Cancer Patients Receiving Radiation Therapy: Exploratory Analysis from a Large-Scale Retrospective Cohort.

Background: Acute pain is a common and debilitating symptom experienced by oral cavity and oropharyngeal cancer (OC/OPC) patients undergoing radiation therapy (RT). Uncontrolled pain can result in opioid overuse and increased risks of long-term opioid dependence. The specific aim of this exploratory analysis was the prediction of severe acute pain and opioid use in the acute on-treatment setting, to develop risk-stratification models for pragmatic clinical trials. Materials and Methods: A retrospective study was conducted on 900 OC/OPC patients treated with RT during 2017 to 2023. Clinical data including demographics, tumor data, pain scores and medication data were extracted from patient records. On-treatment pain intensity scores were assessed using a numeric rating scale (0-none, 10-worst) and total opioid doses were calculated using morphine equivalent daily dose (MEDD) conversion factors. Analgesics efficacy was assessed based on the combined pain intensity and the total required MEDD. ML models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Model (GBM) were developed and validated using ten-fold cross-validation. Performance of models were evaluated using discrimination and calibration metrics. Feature importance was investigated using bootstrap and permutation techniques. Results: For predicting acute pain intensity, the GBM demonstrated superior area under the receiver operating curve (AUC) (0.71), recall (0.39), and F1 score (0.48). For predicting the total MEDD, LR outperformed other models in the AUC (0.67). For predicting the analgesics efficacy, SVM achieved the highest specificity (0.97), and best calibration (ECE of 0.06), while RF and GBM achieved the same highest AUC, 0.68. RF model emerged as the best calibrated model with ECE of 0.02 for pain intensity prediction and 0.05 for MEDD prediction. Baseline pain scores and vital signs demonstrated the most contributed features for the different predictive models. Conclusion: These ML models are promising in predicting end-of-treatment acute pain and opioid requirements and analgesics efficacy in OC/OPC patients undergoing RT. Baseline pain score, vital sign changes were identified as crucial predictors. Implementation of these models in clinical practice could facilitate early risk stratification and personalized pain management. Prospective multicentric studies and external validation are essential for further refinement and generalizability.

A Scoping Review to Explore the Potential Benefits of Nutrition Interventions for Latino/a Adult Cancer Survivors in the US.

Despite evidence for the role of healthy diets in preventing cancer, little is known about how nutrition can support positive health outcomes after a cancer diagnosis for Latino/a cancer survivors in the United States (U.S.). The purpose of this scoping review is to understand the potential benefits of nutrition interventions in supporting healthy survivorship among Latino/a cancer survivors in the U.S. A team compiled, evaluated, and summarized the available evidence. Potentially relevant studies were identified from a comprehensive search of peer-reviewed databases and the gray literature. Eligible studies included Latino/a adult cancer survivors with a nutrition education, dietary change, or behavioral intervention; and a nutrition-related health outcome. Data were extracted and summarized using tables. The review included 10 randomized controlled trials, with samples or subsamples of Latino/a cancer survivors. Interventions mostly focused on breast cancer survivors. The results showed some evidence that dietary behaviors, like fruit and vegetable intake, were related to positive outcomes, like a decreased risk of cancer (through changes in DNA methylation), decreased risk breast cancer recurrence (through changes in inflammatory biomarkers), or improved perception of health status. The findings highlight a need for community-engaged and culturally relevant nutrition interventions for Latino/a adults, especially for rural communities; and innovative intervention approaches, including m/ehealth approaches with long-term follow-up.

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review.

Background/objective: Pain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer. Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including "Cancer", "Pain", "Pain Management", "Analgesics", "Opioids", "Artificial Intelligence", "Machine Learning", "Deep Learning", and "Neural Networks" published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies. Results: This systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients' pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%). Conclusion: Implementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

The Heart of the Matter: Immune Checkpoint Inhibitors and Immune-Related Adverse Events on the Cardiovascular System.

Cancer remains a prominent global cause of mortality, second only to cardiovascular disease. The past decades have witnessed substantial advancements in anti-cancer therapies, resulting in improved outcomes. Among these advancements, immunotherapy has emerged as a promising breakthrough, leveraging the immune system to target and eliminate cancer cells. Despite the remarkable potential of immunotherapy, concerns have arisen regarding associations with adverse cardiovascular events. This review examines the complex interplay between immunotherapy and cardiovascular toxicity and provides an overview of immunotherapy mechanisms, clinical perspectives, and potential biomarkers for adverse events, while delving into the intricate immune responses and evasion mechanisms displayed by cancer cells. The focus extends to the role of immune checkpoint inhibitors in cancer therapy, including CTLA-4, PD-1, and PD-L1 targeting antibodies. This review underscores the multifaceted challenges of managing immunotherapy-related cardiovascular toxicity. Risk factors for immune-related adverse events and major adverse cardiac events are explored, encompassing pharmacological, treatment-related, autoimmune, cardiovascular, tumor-related, social, genetic, and immune-related factors. The review also advocates for enhanced medical education and risk assessment tools to identify high-risk patients for preventive measures. Baseline cardiovascular evaluations, potential prophylactic strategies, and monitoring of emerging toxicity symptoms are discussed, along with the potential of adjunct anti-inflammatory therapies.

New synthetic pharmacotherapeutic approaches to the treatment of moderate-to-severe plaque psoriasis in adults.

INTRODUCTION: Psoriasis is a chronic inflammatory and immune-mediated condition affecting 3.2% of the United States population. There are many options for psoriasis treatment including topicals, oral systemic agents, and biologics. A greater understanding of the pathophysiology of psoriasis has led to an increase in the therapeutic options for treatment. AREAS COVERED: In this review, we outline the novel synthetic agents for moderate-to-severe plaque psoriasis and discuss a strategy for implementing these agents in clinical practice. A literature search was performed using PubMed to identify articles relevant to the topic published before October 2022. EXPERT OPINION: Topicals are first-line for the treatment of moderate-to-severe plaque psoriasis, most commonly including topical steroids, vitamin D analogs, and topical calcineurin inhibitors. While new topical agents have favorable properties, they are not always effective and adherence to topical agents is poor. Biologics are safe and effective, but patients often prefer oral therapy as opposed to injectable medications. Additionally, anti-drug antibodies can reduce effectiveness of biologics over time. Oral medications are preferred, but we now have a high bar for efficacy and safety. Cost is also a barrier for many patients. Recent development of new synthetic treatment options is promising, and we recommend that providers consider these agents as they develop holistic and individualized treatment plans for their patients.