Long non-coding RNAs (lncRNA) have recently been identified as key regulators of oxidative stress in several malignancies. The level of reactive oxygen species (ROS) must be constantly regulated to maintain cancer cell proliferation and chemoresistance and to prevent apoptosis. This review will discuss how lncRNAs alter the ROS level in cancer cells. We will first describe the role of lncRNAs in the nuclear factor like 2 (Nrf-2) coordinated antioxidant response of cancer cells. Secondly, we show how lncRNAs can promote the Warburg effect in cancer cells, thus shifting the cancer cell’s “building blocks” towards molecules important in oxidative stress regulation. Lastly, we explain the role that lncRNAs play in ROS-induced cancer cell apoptosis and proliferation.

Background. Our objective was to evaluate the rate of reemployment after having curative radiotherapy for head and neck cancer and to identify the barriers of successful return to work in this patient population. Methods. The study was retrospective, cross-sectional, analytical and cohort type, it included 52 consecutive patients attending follow-up visits after curative radiotherapy for head and neck cancer at a single tertiary center. Participants completed a survey to assess demographics, tumor characteristics, lifestyle choices and quality of life questionnaires. We analyzed the rate of reemployment and the association with different parameters. Results. At the time of the survey, the successful return to work rate among head and neck cancer survivors was 55.8%. Most of the returners, 93%, started to work within 14 months from the time of the diagnosis. Educational level, work type and tumor stage were predictive for successful return to work. The main barriers for reemployment were physical symptoms such as fatigue, speech disorders and eating problems. Conclusions. Head and neck cancer survivors who manage to return to work have a better quality of life and global health status than those who could not return. The most vulnerable patients are those with lower education, doing manual work and with advanced stage cancer. Optimized radiotherapy planning to reduce radiation induced late effects in conjunction with coordinated rehabilitation programs are needed to facilitate head and neck cancer survivors’ reemployment.

Background/Objectives: Financial toxicity (FT) refers to the financial burden directly or indirectly caused by a patient’s medical care. Patients with head and neck cancer (HNC) are particularly vulnerable to FT due to lower rates of return to work and higher out-of-pocket payments (OOPP). In this cross-sectional study, we assessed the amount and types of OOPP, as well as the prevalence of FT, in HNC patients who had completed curative radiotherapy. Methods: We included HNC patients who underwent curative-intent radiotherapy at four private clinics in Romania, within 12 months of completing treatment. Participants completed a 25-item questionnaire capturing sociodemographic information, insurance status, income, and OOPP. To assess subjective FT, we used the validated nine-item Financial Index of Toxicity (FIT), which measures three FT domains: financial stress, financial strain, and lost productivity. Each domain and the total score range from 0 to 100, with higher scores indicating greater financial toxicity. Descriptive statistics were used to summarize patient characteristics. Pearson’s chi-square, t-tests, and one-way ANOVA were used to assess statistical associations, with a significance threshold of p < 0.05. Results: Among 113 patients (mean age: 59), the majority were male (74.3%) and married (74.3%), with 40% having completed university or higher education. The most frequent tumor sites were the oropharynx (29 cases), larynx (22), and oral cavity (21). Concurrent chemoradiation was the most common treatment modality (47%). The mean total FT score was 18.8. Overall, 39.8% of patients experienced financial toxicity, and 29.2% scored above the mean in financial stress. Moderate financial strain (score > 21) was reported by 39.8% of participants, and approximately one-third reported loss of productivity. Transportation and nutritional supplements were the most common OOPP categories. Notably, 42% of patients spent at least 400 euros—equivalent to Romania’s monthly minimum income—on transportation during radiotherapy. FT was significantly associated with employment and marital status, but not with tumor site or treatment type. Conclusions: Among Romanian HNC patients treated with curative radiotherapy, we found substantial OOPP, particularly for transportation and nutritional supplements. While overall FT levels were moderate, divorced patients and those retired due to other chronic conditions were the most vulnerable to financial distress. Financial toxicity can directly affect treatment adherence, survival, and quality of life. By integrating financial counseling, social support, and broader coverage of treatment-related expenses, healthcare systems can mitigate FT for these patients.

The employment of artificial intelligence (AI) and machine learning (ML) in cancer management is well documented. The diversity and complexity of models, approaches, endpoints and anatomies makes it difficult to draw pertinent conclusions regarding the usefulness of ML in various applications. Given that clinical trials, particularly randomized ones, are the gold standard when testing new techniques, the current work aims to collate trials that investigated the effectiveness of ML in head and neck cancer (HNC). A systematic search of Medline/PubMed and Web of Science databases was performed to identify clinical trials reporting the use of AI / ML in HNC for either of the following clinical aspects: detection / classification, image segmentation and treatment response / dose distribution prediction. Of the 2395 identified studies, 42 met the eligibility criteria. Most studies confirmed the usefulness of ML in HNC clinical applications through reported parameters: accuracy, area under the curve, specificity, sensitivity, dice coefficient. Deep learning models utilizing multiple layered neural networks were often the choice as they outperform machine learning in predictive model development. However, traditional algorithms (KNN, SVM, logistic regression) often showed comparable results with deep learning. The most commonly used radiomic features were related to image texture irrespective of the model's application. Clinical trial results show a supportive role for ML in HNC management, whether diagnostic/staging- or treatment-related. The best performing applications are enhancing deep learning algorithms applied to imaging with clinical data for a more comprehensive and better-integrated approach to personalized treatment.

The advantages of intensity modulated radiotherapy (IMRT) in target coverage and organ at risk (OAR) sparing come with a low- and intermediate-dose bath effect. To control this unwanted dose dumping, planners use helping structures in the plan optimization. We hypothesized that using an anatomic definition of the posterior neck would result in reduced doses to the posterior neck muscles without compromising target coverage or increasing doses to organs at risk. We randomly selected twelve head and neck cancer patients treated with volume modulated arc therapy (VMAT). For each case, we generated 3 plans with different optimization methods: first, with a dummy volume behind the spinal vertebrae; second, a volume with the posterior neck and upper shoulder muscles according to anatomic criteria; and third, a plan without any helping structure. There was no statistically significant difference between the 3 plans in terms of coverage, conformity index or homogeneity index. All the OAR dose constraints were respected, and there was no significant increase of the dose to these organs when the anatomic definition for the posterior neck was used. However, the posterior neck muscles and upper shoulder muscles received significantly lower mean doses, 31.9 Gy vs 36.8 Gy vs 37.6 Gy, p = 0.0004. Using an anatomically defined posterior neck avoidance volume in the plan optimization significantly reduces doses to the posterior neck and upper shoulder muscles, potentially with less severe neck fibrosis, without compromising target volume coverage or increasing doses to other organs at risk.