Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.

Purpose: Taste alteration (TA) is a frequent acute side effect of radiation treatment in HNSCC patients. Principal aim of our study was to investigate dosimetric parameters in relation to patient-assessed taste impairment in a prospective cohort treated with intensity-modulated radiotherapy. Methods: All patients with locally advanced HNSCC and amenable to radical treatment were included. Chemotherapy-induced taste alteration scale (CITAS), EORTC QLQ-C30 and QLQ-HN43 questionnaires at baseline (T0), 3 weeks (T1) and 3 months (T2) after radiotherapy conclusion were used to assess taste impairment. Base of tongue, submandibular glands (SG), parotid glands (PG) and taste buds, along with anterior and medium third of the tongue, were considered as organs at risk and thus delineated according to consensus guidelines. The mean dose to the above-mentioned structures was correlated with patient-reported outcomes. Results: Between September 2019 and November 2020, 33 patients were recruited, 31 of which analyzed. 71% had oropharyngeal carcinoma, mostly HPV-related (60%). All were treated with tomotherapy. 77.4% had concurrent cisplatin. Mean scores of general taste alterations, global health status and dry mouth and sticky saliva were assessed. The mean doses to the anterior third, medium third and base of the tongue were 23.85, 35.50 and 47.67 Gy, respectively. Taste buds received 32.72 Gy; right and left parotid 25 and 23 Gy; right and left submandibular glands 47.8 and 39.4 Gy. At univariate analysis, dysgeusia correlated with SG mean dose (95% CI 0–0.02 p = 0.05) and PG mean dose (95% CI 0–0.02 p = 0.05); dry mouth with mean dose to anterior (95% CI 0.03–1.47 p = 0.04) and medium third (95% CI 0.02–0.93 p = 0.04) of the tongue, to taste buds (95% CI 0.06–0.96 p = 0.03) and to SGs (95% CI 0.06–0.63 p = 0.02); pain mouth with mean dose to taste buds (95% CI 0–0.02 p = 0.04), to SGs (95% CI 0–0.03 p = 0.03) and to base tongue (95% CI 0–0.02 p = 0.02). Conclusions: Our analysis supports the influence of dose distribution on the development of TA in HNSCC patients. The contribution of dose to taste buds and tongue subvolumes remains unclear and worthy of further investigation.

Introduction: The main goal of radiotherapy (RT) is to deliver a precise dose to the target while sparing the surrounding normal tissue and minimizing side effects. Appropriate patient immobilization is crucial, especially for head and neck cancer (HNC) and Brain Cancer (BC). Conventional closed-face masks (CFMs), while effective in minimizing head motion, can cause significant discomfort, anxiety, and claustrophobia. Open-face masks (OFMs) have been developed to increase patient comfort while providing precise immobilization. Methods: Following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) extension for scoping reviews and the Arskey and O’Malley framework, an electronic search of EMBASE, PubMed, SCOPUS, and Web of Science was conducted to identify original studies reporting the use and description of OFMs in clinical practice up to April 2024. The inclusion criteria were English-language articles focusing on OFMs for HNC and BC patients undergoing RT. Results: Of 618 titles, 19 articles fulfilled the selection criteria. Most studies were comparative (n = 13) or observational (n = 6). The articles were categorized by treatment site, resulting in three groups: BC (n = 14, 68.4%), HNC (n = 4, 21.4%), and mixed (n = 2, 10.5%), which includes both BC and HNC. Of note, 82.4% (n = 16) of the included studies were published from 2020 onwards, emphasizing the recent adoption of OFM in clinical practice. Conclusions: The reviewed studies show that OFMs, in combination with SGRT, offer significant advantages in terms of patient comfort and positioning accuracy in HNC and BC treatments. Reproducibility in the sub-millimeter and sub-degree range can be achieved, which supports the use of OFMs in clinical practice. Future research should explore innovative combinations of immobilization and monitoring to further improve RT outcomes and ensure precise treatment while increasing patient comfort.

Purpose: This systematic review aims to analyze the literature on the application of AI in predicting patient outcomes and treatment-related toxicity in those undergoing SBRT or SRS across heterogeneous tumor sites. Materials and methods: Our review conformed to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. PubMed, EMBASE and Scopus were systematically searched for English-language human studies evaluating AI for outcome and toxicity prediction in patients undergoing SBRT or SRS for solid tumors. Search terms included (“Stereotactic Body Radiotherapy” OR “SBRT” OR “Stereotactic Radiosurgery” OR “SRS” OR “Stereotactic Ablative Radiotherapy” OR “SABR”) AND (“Artificial Intelligence” OR “AI” OR “Machine Learning” OR “Deep Learning” OR “Radiomics”) AND (“Response Prediction” OR “Response to Treatment” OR “Outcome Prediction”) AND (“Toxicity” OR “Side Effects” OR “Treatment Toxicities” OR “Adverse Events”). Results: The search yielded 29 eligible retrospective studies, published between 2020 and 2025. Eight studies addressed early-stage primary lung cancer, highlighting the potential of AI-based models in predicting radiation-induced pneumonitis, fibrosis and local control. Five studies investigated AI models for predicting hepatobiliary toxicity following SBRT for liver tumors. Sixteen studies involved SRS-treated patients with brain metastases or benign intracranial neoplasms (e.g., arteriovenous malformations, vestibular schwannomas, meningiomas), exploring AI algorithms for predicting treatment response and radiation-induced changes. In the results, AI might have been exploited to both reaffirm already known clinical predictors and to identify novel imaging, dosimetric or biological biomarkers. Examples include predicting radiation pneumonitis in lung cancer, residual liver function in hepatic tumors and local recurrence in brain metastases, thus supporting tailored treatment decisions. Conclusions: Combining AI with SBRT could greatly enhance personalized cancer care by predicting patient-specific outcomes and toxicity. AI models analyze complex datasets, including imaging and clinical data, to identify patterns that traditional methods may miss, thus enabling more accurate risk stratification and reducing variability in treatment planning. With further research and clinical validation, this integration could make radiotherapy safer, more effective and contribute to advancement in precision oncology.

The incidence of Head and Neck cancer is rapidly growing, now representing the sixth most common non-skin cancer worldwide