Examining patient-reported late toxicity and its association with quality of life and unmet need for symptom management among nasopharyngeal cancer survivors: a cross-sectional survey.

Introduction: Alongside the improved survival of nasopharyngeal cancer (NPC), late radiation toxicities are alarmingly hampering survivors' quality of life. A patient-reported symptom burden survey is lacking to address the unmet need for symptom management among local NPC survivors. Methods: A single-center cross-sectional survey was conducted on 211 NPC survivors who had completed radiation therapy for three to 120 months. We employed the Chinese version M. D. Anderson Symptom Inventory - Head & Neck Module (MDASI-HN-C), Functional Assessment of Cancer Therapy - Head & Neck (FACT-HN-C), and a question extracted from the Cancer Survivors' Unmet Needs Measure (CaSUN). Results: Two hundred valid responses were collected. Participants suffered from at least four moderate to severe symptoms (mean = 4.84, SD = 4.99). The top five severe symptoms were dry mouth, mucus problems, difficulty swallowing or chewing, teeth or gum problems, and memory problems. MDASI-HN-C subscales were negatively correlated with the physical, emotional, functional, and HN-specific domains of the FACT-HN-C. The unmet need for symptom management was positively associated with symptom burden, either general symptoms (Adjusted odds ratio [ORadj] = 1.566, 95% CI = 1.282 - 1.914, p < 0.001) or top-5 symptoms (ORadj = 1.379, 95% CI = 1.185 - 1.604, p < 0.001), while negatively associated with post-RT time (ORadj = 0.981, 95% CI [0.972, 0.991], p < 0.001). Conclusion: Virtually all NPC survivors suffer from late toxicities, which interplay with survivors' perceptions intricately to affect their unmet needs for symptom management. Personalized supportive care strategies with regular assessments and stratifications are warranted.

Predictive Factors for Chemoradiation-Induced Oral Mucositis and Dysphagia in Head and Neck Cancer: A Scoping Review.

Despite advances in head and neck cancer treatment, virtually all patients experience chemoradiation-induced toxicities. Oral mucositis (OM) and dysphagia are among the most prevalent and have a systemic impact on patients, hampering treatment outcome and harming quality of life. Accurate prediction of severe cases is crucial for improving management strategies and, ultimately, patient outcomes. This scoping review comprehensively maps the reported predictors and critically evaluates the performance, methodology, and reporting of predictive models for these conditions. A total of 174 studies were identified from database searches, with 73 reporting OM predictors, 97 reporting dysphagia predictors, and 4 reporting both OM and dysphagia predictors. These predictors included patient demographics, tumor classification, chemoradiotherapy regimen, radiation dose to organs-at-risk, genetic factors, and results of clinical laboratory tests. Notably, many studies only conducted univariate analysis or focused exclusively on certain predictor types. Among the included studies, numerous predictive models were reported: eight for acute OM, five for acute dysphagia, and nine for late dysphagia. The area under the receiver operating characteristic curve (AUC) ranged between 0.65 and 0.81, 0.60 and 0.82, and 0.70 and 0.85 for acute oral mucositis, acute dysphagia, and late dysphagia predictive models, respectively. Several areas for improvement were identified, including the need for external validation with sufficiently large sample sizes, further standardization of predictor and outcome definitions, and more comprehensive reporting to facilitate reproducibility.

Integrating CT-based radiomic model with clinical features improves long-term prognostication in high-risk prostate cancer.

Objective: High-risk prostate cancer (PCa) is often treated by prostate-only radiotherapy (PORT) owing to its favourable toxicity profile compared to whole-pelvic radiotherapy. Unfortunately, more than 50% patients still developed disease progression following PORT. Conventional clinical factors may be unable to identify at-risk subgroups in the era of precision medicine. In this study, we aimed to investigate the prognostic value of pre-treatment planning computed tomography (pCT)-based radiomic features and clinical attributes to predict 5-year progression-free survival (PFS) in high-risk PCa patients following PORT. Materials and methods: A total of 176 biopsy-confirmed PCa patients who were treated at the Hong Kong Princess Margaret Hospital were retrospectively screened for eligibility. Clinical data and pCT of one hundred eligible high-risk PCa patients were analysed. Radiomic features were extracted from the gross-tumour-volume (GTV) with and without applying Laplacian-of-Gaussian (LoG) filter. The entire patient cohort was temporally stratified into a training and an independent validation cohort in a ratio of 3:1. Radiomics (R), clinical (C) and radiomic-clinical (RC) combined models were developed by Ridge regression through 5-fold cross-validation with 100 iterations on the training cohort. A model score was calculated for each model based on the included features. Model classification performance on 5-year PFS was evaluated in the independent validation cohort by average area-under-curve (AUC) of receiver-operating-characteristics (ROC) curve and precision-recall curve (PRC). Delong's test was used for model comparison. Results: The RC combined model which contains 6 predictive features (tumour flatness, root-mean-square on fine LoG-filtered image, prostate-specific antigen serum concentration, Gleason score, Roach score and GTV volume) was the best-performing model (AUC = 0.797, 95%CI = 0.768-0.826), which significantly outperformed the R-model (AUC = 0.795, 95%CI = 0.774-0.816) and C-model (AUC = 0.625, 95%CI = 0.585-0.665) in the independent validation cohort. Besides, only the RC model score significantly classified patients in both cohorts into progression and progression-free groups regarding their 5-year PFS (p< 0.05). Conclusion: Combining pCT-based radiomic and clinical attributes provided superior prognostication value regarding 5-year PFS in high-risk PCa patients following PORT. A large multi-centre study will potentially aid clinicians in implementing personalised treatment for this vulnerable subgroup in the future.

Precision Measurements to Assess Baseline Status and Efficacy of Healthy Living Medicine.

Precision medicine recently has gained popularity, calling for more individualized approaches to prevent and/or reduce chronic-disease risk and to reduce non-communicable diseases such as cardiovascular disease (CVD). Encompassed under Precision medicine initiatives is the concept of healthy living medicine (HLM), which emphasizes the promotion of lifestyle and behavioral practices including physical activity and healthy dietary pattern. Precision measurements have the potential to improve the understanding of how risk factors influence disease trajectory, and further inform on how to precisely tailor clinical strategies to manage risk factors to prevent disease manifestation, and refine therapies according the patient's demographic, environment, and disease etiology. The purpose of this review is to summarize the application of established and emerging measurements that may be used in HLM to manage and optimize care in CVD prevention.