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.

Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy.

Given the high death rate caused by high-risk prostate cancer (PCa) (>40%) and the reliability issues associated with traditional prognostic markers, the purpose of this study is to investigate planning computed tomography (pCT)-based radiomics for the long-term prognostication of high-risk localized PCa patients who received whole pelvic radiotherapy (WPRT). This is a retrospective study with methods based on best practice procedures for radiomics research. Sixty-four patients were selected and randomly assigned to training (n = 45) and testing (n = 19) cohorts for radiomics model development with five major steps: pCT image acquisition using a Philips Big Bore CT simulator; multiple manual segmentations of clinical target volume for the prostate (CTVprostate) on the pCT images; feature extraction from the CTVprostate using PyRadiomics; feature selection for overfitting avoidance; and model development with three-fold cross-validation. The radiomics model and signature performances were evaluated based on the area under the receiver operating characteristic curve (AUC) as well as accuracy, sensitivity and specificity. This study's results show that our pCT-based radiomics model was able to predict the six-year progression-free survival of the high-risk localized PCa patients who received the WPRT with highly consistent performances (mean AUC: 0.76 (training) and 0.71 (testing)). These are comparable to findings of other similar studies including those using magnetic resonance imaging (MRI)-based radiomics. The accuracy, sensitivity and specificity of our radiomics signature that consisted of two texture features were 0.778, 0.833 and 0.556 (training) and 0.842, 0.867 and 0.750 (testing), respectively. Since CT is more readily available than MRI and is the standard-of-care modality for PCa WPRT planning, pCT-based radiomics could be used as a routine non-invasive approach to the prognostic prediction of WPRT treatment outcomes in high-risk localized PCa.

Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients' anatomy. However, the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians. Moreover, some potentially useful quantitative information in medical images, especially that which is not visible to the naked eye, is often ignored during clinical practice. In contrast, radiomics performs high-throughput feature extraction from medical images, which enables quantitative analysis of medical images and prediction of various clinical endpoints. Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis, demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine. However, radiomics remains in a developmental phase as numerous technical challenges have yet to be solved, especially in feature engineering and statistical modeling. In this review, we introduce the current utility of radiomics by summarizing research on its application in the diagnosis, prognosis, and prediction of treatment responses in patients with cancer. We focus on machine learning approaches, for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling. Furthermore, we introduce the stability, reproducibility, and interpretability of features, and the generalizability and interpretability of models. Finally, we offer possible solutions to current challenges in radiomics research.

Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma.

PURPOSE: Radiation dermatitis (RD) is a common, unpleasant side effect of patients receiving radiation therapy. In clinical practice, the severity of RD is graded manually through visual inspection, which is labor intensive and often leads to large interrater variations. To overcome these shortcomings, this study aimed to develop an automatic RD assessment based on deep learning (DL) techniques that could efficiently assist the RD severity classification in clinical application. METHODS AND MATERIALS: A total of 1205 photographs of the head and neck region were collected from patients with nasopharyngeal carcinoma (NPC) undergoing radiation therapy. The severity of RD in these photographs was graded by 5 qualified assessors based on the Radiation Therapy Oncology Group guidance. An end-to-end RD grading framework was developed by combining a DL-based segmentation network and a DL-based RD severity classifier, which are used for segmenting the neck region from the camera-captured photographs and grading, respectively. U-Net was used for segmentation and another convolutional neural network classifier (DenseNet-121) was applied to RD severity classification. Dice similarity coefficient was used to evaluate the performance of segmentation. Severity classification was evaluated by several metrics, including overall accuracy, precision, recall, and F1 score. RESULTS: Results of segmentation showed that the averaged dice similarity coefficients were 91.2% and 90.8% for front and side view, respectively. For RD severity classification, the overall accuracy of test photographs was 83.0%. Our method accurately classified 90.5% of grade 0, 67.2% of grade 1, 93.8% of grade 2, and 100% of above grade 2 cases. The overall prediction performance was comparable with human assessors. There was no significant difference in accuracy when using manually or automatically segmented regions (P = .683). CONCLUSIONS: We have successfully demonstrated a DL-based method for automatic assessment of RD severity in patients with NPC. This method holds great potential for efficient and effective assessing and monitoring of RD in patients with NPC.

Rationale for Mass Masking in Controlling the COVID-19 Pandemic.

The rapid spread of the coronavirus disease 2019 (COVID-19) into a global pandemic caught the world unprepared. Previously effective measures for containing disease outbreaks were overwhelmed, necessitating strict controls such as lockdowns or curfews. Among the disease control interventions, community mass masking was one of the highly controversial issues with differing opinions on its indications or effectiveness from different health authorities around the world. Regions where community mass masking was timely introduced were associated with lower transmission rates, and more effective disease control. In this article, we discuss the evidence on the effectiveness, and rationale for community mass masking to prevent the COVID-19 transmission. Areas for further research to define the role of mass masking in light of the COVID-19 pandemic will be suggested. This would help policy makers in formulating mass masking policies.

Review on the Cellular Mechanisms of Low-Level Laser Therapy Use in Oncology.

Photobiomodulation (PBM) using low-level laser therapy (LLLT) is a treatment that is increasingly used in oncology. Studies reported enhancement of wound healing with reduction in pain, tissue swelling and inflammatory conditions such as radiation dermatitis, oral mucositis, and lymphedema. However, factors such as wavelength, energy density and irradiation frequency influence the cellular mechanisms of LLLT. Moreover, the effects of LLLT vary according to cell types. Thus, controversy arose as a result of poor clinical response reported in some studies that may have used inadequately planned treatment protocols. Since LLLT may enhance tumor cell proliferation, these will also need to be considered before clinical use. This review aims to summarize the current knowledge of the cellular mechanisms of LLLT by considering its effects on cell proliferation, metabolism, angiogenesis, apoptosis and inflammation. With a better understanding of the cellular mechanisms, bridging findings from laboratory studies to clinical application can be improved.

Performance evaluation of a portable bioimpedance cardiac output monitor for measuring hemodynamic changes in athletes during a head-up tilt test.

Cardiac output (CO) monitoring is useful for sports performance training, but most methods are unsuitable as they are invasive or hinder performance. The performance of PhysioFlow (PF), a portable noninvasive transthoracic bioimpedance CO monitor, was evaluated and compared with a reference Doppler CO monitor, USCOM, using a head-up tilt (HUT) test. With ethics committee approval, 20 healthy well-trained athletes were subjected to HUT in a fixed order of 0°, 70°, 30°, and 0° for 3 min each. Simultaneous hemodynamic measurements using PF and USCOM were made 30 s after a change in HUT and analyzed using t tests, ANOVA, and mountain plots. Heart rate (HR) and stroke volume (SV) from both monitors changed according to physiological expectation of tilt, but PF measurements of SV were higher with a positive bias (PF vs. USCOM, 0°: 87.3 vs. 54.0 mL, P < 0.001; 70°: 76.5 vs. 39.5 mL, P < 0.001; 30°: 81.4 vs. 50.1 mL, P < 0.001; 0°: 88.3 vs. 57.1 mL, P < 0.001). Relative changes in SV (∆SV) after each tilt measured using PF were lower with a negative bias (PF vs. USCOM, 0° to 70°: -12.3% vs. -26.3%, P = 0.002; 70° to 30°: +6.4% vs. +31.2%, P < 0.001; 30° to 0°: +9.2% vs. +15.8%, P = 0.280). CO measurements using PF at 70° were erroneous. Compared with USCOM, PF overestimated SV measurements but underestimated the ∆SV between HUT. Accuracy of the PF deteriorated at 70°, implying a gravitational influence on its performance. These findings suggested that the suitability of PF for sports use is questionable.NEW & NOTEWORTHY The use of impedance cardiography to monitor physiological changes in sports is rarely reported. Using head-up tilt test, we evaluated a portable noninvasive impedance cardiography device (PhysioFlow) by comparing it with a reference Doppler monitor (USCOM). Accuracy in tracking hemodynamic changes deteriorated with higher tilt, implying a gravitational influence on its performance. Stroke volume measurements were overestimated, but the changes were underestimated. Despite its convenient physical features, the suitability of PhysioFlow for sports use is questionable.