Background: Radiotherapy is frequently used to treat head and neck Squamous cell carcinomas (HNSCC). Treatment outcomes being highly uncertain, there is a significant need for robust predictive tools to improvise treatment decision-making and better understand HNSCC by recognizing hidden patterns in data. We conducted this study to identify if Machine Learning (ML) could accurately predict outcomes and identify new prognostic variables in HNSCC. Method: Retrospective data of 311 HNSCC patients treated with radiotherapy between 2013 and 2018 at our center and having a follow-up of at least three months' duration were collected. Binary-classification prediction models were developed for: Choice of Initial Treatment, Residual disease, Locoregional Recurrence, Distant Recurrence, and Development of New Primary. Clinical data were pre-processed using Imputation, Feature selection, Minority Oversampling, and Feature scaling algorithms. A method to retain original characteristics of dataset in testing samples while performing minority oversampling is illustrated. The classification comparison was performed using Random Forest (RF), Kernel Support Vector Machine (KSVM), and XGBoost classification algorithms for each model. Results: For the choice of the initial treatment model, the testing accuracy was 84.58% using RF. The distant recurrence, locoregional recurrence, new-primary, and residual models had a testing accuracy (using KSVM) of 95.12%, 77.55%, 98.61%, and 92.25%, respectively. The important clinical determinants were identified using Shapely Values for each classification model, and the mean area under the curve (AUC) for the receiver operating curve was plotted. Conclusion: ML was able to predict several clinically relevant outcomes, and with additional clinical validation, could facilitate recognition of novel prognostic factors in HNSCC.

Radiation exposure can cause gut dysbiosis and there is a positive correlation between gut microbial imbalance and radiation-induced side effects in cancer patients. However, the influence of radiation on the gut-brain axis (GBA) and its neurological consequences are not well understood. Therefore, this study aimed to investigate the impact of pelvic irradiation on gut microbiota and the brain. Sprague Dawley rats were irradiated with a single dose of 6 Gy, and faecal samples were collected at different time points (7 and 12-days post-irradiation) for microbial analysis. Behavioural, histological, and gene expression analysis were performed to assess the effect of microbial dysbiosis on the brain. The findings indicated alterations in microbial diversity, disrupted intestinal morphology and integrity, neuronal death-related brain changes, neuroinflammation and reduced locomotor activity. Hippocampal gene expression analysis also showed a reduced expression of neural plasticity-related genes. Overall, this study demonstrated that pelvic irradiation affects gut microbiota, intestinal morphology, integrity, brain neuronal maturation, neural plasticity gene expression, and behaviour.

Background: In August 2017, the European Commission awarded the “European Study on Clinical Diagnostic Reference Levels (DRL) for X-ray Medical Imaging” project to the European Society of Radiology to provide up-to-date Diagnostic Reference Levels based on clinical indications. This work aimed to conduct an extensive literature review by analyzing the most recent studies published and the data provided by the National Competent Authorities to understand the current situation regarding Diagnostic Reference Levels based on clinical indications for Radiation Therapy Computed Tomography. Objective: To review the literature on established DRLs and methodologies for establishing Diagnostic reference levels in radiation therapy planning computed tomography (RTCT). Methods: Eligibility criteria: A cohort study (observational design) reporting DRLs in adult patients undergoing computed tomography (CT) for radiation therapy for the region head and neck or pelvis were included. The comprehensive literature searches for the relevant studies published between 2000 and 2021 were performed using PubMed, Scopus, CINHAL, Web of Science, and ProQuest. Results: Three hundred fifty-six articles were identified through an extensive literature search. Sixty-eight duplicate reports were removed. The title and abstract of 288 studies were assessed and excluded if they did not meet the inclusion criteria. Sixteen of 288 articles were selected for full-text screening (studies conducted between 2000 and 2021). Five articles were included in the review after the full-text screening. Conclusions: A globally approved standard protocol that includes scanning techniques, dose measurement method, and DRL percentile needs to be established to make a valuable and accurate comparison with international DRLs.

Background: In planning radiotherapy treatments, computed tomography (CT) has become a crucial tool. CT scans involve exposure to ionizing radiation, which can increase the risk of cancer and other adverse health effects in patients. Ionizing radiation doses for medical exposure must be kept “As Low As Reasonably Achievable”. Very few articles on guidelines for radiotherapy-computed tomography scans are available. This paper reviews the current literature on radiation dose optimization based on the effective dose and diagnostic reference level (DRL) for head, neck, and pelvic CT procedures used in radiation therapy planning. This paper explores the strategies used to optimize radiation doses, and high-quality images for diagnosis and treatment planning. Methods: A cross-sectional study was conducted on 300 patients with head, neck, and pelvic region cancer in our institution. The DRL, effective dose, volumetric CT dose index (CTDI_vol), and dose-length product (DLP) for the present and optimized protocol were calculated. DRLs were proposed for the DLP using the 75th percentile of the distribution. The DLP is a measure of the radiation dose received by a patient during a CT scan and is calculated by multiplying the CT dose index (CTDI) by the scan length. To calculate a DRL from a DLP, a large dataset of DLP values obtained from a specific imaging procedure must be collected and can be used to determine the median or 75th-percentile DLP value for each imaging procedure. Results: Significant variations were found in the DLP, CTDI_vol, and effective dose when we compared both the standard protocol and the optimized protocol. Also, the optimized protocol was compared with other diagnostic and radiotherapy CT scan studies conducted by other centers. As a result, we found that our institution’s DRL was significantly low. The optimized dose protocol showed a reduction in the CTDI_vol (70% and 63%), DLP (60% and 61%), and effective dose (67% and 62%) for both head, neck, and pelvic scans. Conclusions: Optimized protocol DRLs were proposed for comparison purposes.

Purpose: The study aimed at evaluating the Effectiveness of Comprehensive Intervention Programme (CIP) on Quality of life (QOL), fatigue, self-efficacy, and psychosocial distress among Head and Neck Cancer (HNC) patients receiving radiotherapy treatment. Methods: Single-centre non-RCT time series study was conducted among 134 HNC patients (67 observed, followed by 67 subjected to CIP). FACT- H&N, FACT-F, Cancer Behavior Inventory and psychosocial distress scales were used to assess QOL, fatigue, self-efficacy, and psychosocial distress respectively. CIP was provided to the intervention arm twice a week during the course of radiation therapy along with the standard care; the control arm received only standard care. Data were collected before commencing radiotherapy, and post-test assessments were carried out at the end of radiotherapy treatment, and at 3 and 6 months after completion of radiotherapy. Results: Repeated measures ANOVA revealed a statistically significant improvement with CIP in QOL (F (1.917) = 454.103, p = 0.001), fatigue (F (2.106) = 183.775, p = 0.001), self-efficacy (F (2.429) = 190.861, p = 0.001), and psychosocial distress (F (2.288) = 290.105, p = 0.001) in the intervention arm. Conclusion: The CIP implemented to address multitude of issues in HNC patients receiving radiotherapy, proved to be effective in reducing the impact of treatment on QOL, fatigue, self-efficacy and psychosocial distress in HNC patients receiving radiotherapy.

Oral squamous cell carcinoma (OSCC) is a common form of cancer, with 390,000 new cases estimated for 2022. OSCC has a poor prognosis, largely due to a high recurrence rate and resistance to therapy. Cancer cells develop resistance to standard therapy owing to various factors, such as genetic predispositions, alterations in the apoptotic pathway coupled with DNA repair pathways, drug efflux, and drug detoxification. This review is aimed at exploring the crucial role of paraoxonase 2 (PON2) in conferring resistance to chemotherapy and radiotherapy in OSCC cells. PON2, an antioxidant enzyme, protects cancer cells from the oxidative stress caused by these treatments. By influencing apoptotic pathways and DNA repair mechanisms, PON2 can reduce the effectiveness of therapy. This review is an attempt to explore the complex molecular mechanisms modulated by PON2, such as the mitigation of oxidative stress, enhancement of DNA repair, apoptosis regulation, drug efflux modulation, and drug detoxification, which decrease treatment efficacy.