This review aims to provide a comprehensive overview of the current evidence on the use of MRI-guided SBRT for oligometastases in gynaecological cancers and its potential benefits. Following PRISMA guidelines, we conducted a systematic review to identify studies focusing on MRI-guided SBRT for oligometastatic gynaecological cancer. Inclusion criteria specified English-language studies that reported clinical outcomes and toxicity data. Data extraction included study design, tumour characteristics, type of MRI-Linac utilized, treatment protocols, clinical outcomes, and toxicity profiles. Of the 475 identified articles, three studies met the inclusion criteria, encompassing 45 patients with oligometastatic gynaecological cancers, predominantly ovarian, treated with 0.35–1.5 T MRI-Linac SBRT. Doses ranged from 30 to 50 Gy over five fractions. Focusing on actuarial outcome, all the patients were alive and maintained local control at 3 months from SBRT. The toxicity profile was available in all the studies, reporting only one Grade 3 event (gastro-intestinal), underscoring a favourable safety profile. Despite some technical challenges, such as extended treatment times and limited MRI-Linac availability, MRI-guided SBRT appears to be a promising modality for oligometastatic ovarian cancer, potentially delaying the need for systemic therapy and chemotherapy line change. Larger studies are required to validate these findings and better establish the advantages relative to the use of this innovative approach.

In radiation therapy, the planning target volume (PTV) is a geometrical concept used to account for the variations of target position during treatment in terms of systematic (Σ) and random (σ) errors. It ensures that the prescribed radiation dose is delivered to the target with a high degree of confidence. We report the results of a multi-institutional study investigating PTV margin calculation for head-and-neck cancer patients across four radiotherapy departments in Bangladesh, considering the specific challenges and resource constraints of the local context. Forty patients from four different Institutions were enrolled in this multicentric prospective study. All patients underwent planning computed tomography (CT) simulation on supine position, using a head-shoulder thermoplastic mask for immobilization. Contrast-enhanced CT images were acquired with a 3–5 mm slice thickness. All institutions used their locally developed treatment planning technique and inverse planning objectives for intensity-modulated radiotherapy (IMRT) or volumetric modulated arc therapy (VMAT) optimization, together with their imaging protocols (electronic portal imaging device (EPID)-based megavolt (MV), EPID-based kilovolt (kV) or cone-beam CT imaging (CBCT)). For each patient, shifts in the lateral (LR), antero-posterior (AP) and supero-inferior (SI) directions were recorded for each daily fraction. PTV margins in each direction and three-dimensional isotropic margins (PTV3D) were calculated using the Stroom and the van Herk’s equations. The comparisons among institutions were performed using the non-parametric statistical Kruskal-Wallis test, followed by a Dunn’s test. Results were compared with those published in literature for head-and-neck cancer radiotherapy. 1400 setup images from CBCT or EPID were analysed. Using the van Herk recipe, in order to ensure ≥ 95

OBJECTIVE:To stratify the treatment outcomes of patients with oligometastatic gynecologic cancer receiving stereotactic body radiation therapy using an un-supervised clustering machine-learning method. METHODS:This multi-centric study was based on a cohort of 172 patients receiving curative-intent stereotactic body radiation therapy for oligometastatic uterine tumors, yielding a total of 268 lesions. The following clinical and dosimetric variables were collected: age, number of lesions per patient, type of lesion (lymph nodes vs parenchyma), lesion burden (number of treated lesions per patient), treatment site of lesion, number of fractions, total dose, biologically effective dose, and planning target volume. An un-supervised clustering method based on the K-means algorithm was used to identify clusters of lesions. The groups of lesions were compared in terms of local control, distant-metastases-free survival, and overall survival. RESULTS:The optimal number of clusters was found to be equal to 3. The analysis of variance indicated that the variables contributing the most to the separation of the clusters were the planning target volume, the biologically effective dose, and the type of lesion. Significant differences were found between the 3 groups of lesions in terms of local control (p =.002). At 2 years, local control was 84.6%, 74.7%, 47.5% for the 3 clusters that were "a posteriori" named as high-control, medium-control, and low-control, respectively. Distant-metastases-free survival was also found to be significantly different (p =.04) at 2 years, with values of 27.0%, 22.2%, 48.0% for the high-control, medium-control, and low-control, respectively. No differences were found for the overall survival (p =.22). CONCLUSIONS:In this study, un-supervised machine-learning partitioned oligometastatic lesions into 3 clusters associated with different treatment responses. A prospective validation is needed for prediction purposes.

IntroductionStereotactic arrhythmia radioablation (STAR) has emerged as an effective non-invasive treatment for refractory ventricular arrhythmias, but its ablative single-fraction regimen may also be relevant beyond electrophysiologic indications. We report the application of a STAR-inspired 25 Gy single-fraction cardiac radioablation approach for right-ventricular metastasis from Ewing sarcoma, presented as a CARE-compliant case report.Case presentationA 26-year-old man with a history of limb Ewing sarcoma developed an incidentally detected right-ventricular mass during routine staging. After partial surgical debulking, multimodal imaging confirmed a 17 × 15 mm residual intracavitary lesion. Because complete surgical resection was not technically feasible, frameless stereotactic body radiotherapy (SBRT) was delivered in a single 25 Gy fraction using STAR workflow principles. Treatment was completed without complication. At 12-month MRI the lesion was reduced (8 × 10 mm), and subsequent imaging confirmed persistent local cardiac control. At 31 months after STAR, the patient remained free of cardiac symptoms and signs of cardiac disease progression.ConclusionAdapting the STAR single-fraction regimen for oncologic cardiac targets is feasible and provided durable control with no observed cardiac toxicity. This experience supports further evaluation of STAR-inspired SBRT for unresectable cardiac tumors.

This study aimed to predict the treatment outcomes and survival of patients with locally advanced cervical cancer (LACC) receiving chemoradiotherapy (CRT) using an unsupervised clustering machine learning method. This retrospective study was based on a cohort of 152 consecutive patients. Treatment consisted of definitive CRT, combining external beam radiotherapy to the pelvis with intracavitary brachytherapy to achieve a total equivalent dose of 85–90 Gy at the tumor site. Patient-related data including age, body mass index, standard blood tests and complete blood count were recorded before CRT. Various inflammatory indices were analyzed, including the neutrophil–lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), leukocyte–lymphocyte ratio (LLR), systemic immune inflammation index (SII), and aspartate aminotransferase (AST) to neutrophil ratio index (ANRI). Based on these covariates, an unsupervised clustering method based on the agglomerative hierarchical clustering (AHC) algorithm was used to identify clusters of patients. The groups of patients were compared in terms of local control (LC), disease-free survival (DFS), distant metastases-free survival (DMFS), and overall survival (OS). A Cox proportional hazard regression analysis was performed to evaluate the relationship between the clusters and the clinical outcomes. Clustering analysis reported an optimal number of clusters equal to two. Analysis of variance indicated that the variables contributing most to the separation of the clusters were SII, LLR, ANRI, PLR, NLR, hemoglobin, and white cells count. Significant differences were found between the two groups of lesions in terms of LC (p < 0.001), DFS (p = 0.019), and OS (p = 0.017). At 2 years, LC, DFS, and OS were 93.5

BACKGROUND:Accurate estimation of prognosis and life expectancy is essential in patients with advanced cancer, as it guides clinical decision-making and helps avoid unnecessary interventions while facilitating timely integration of palliative and supportive care. Palliative radiotherapy plays a key role within multidisciplinary management, offering effective and well-tolerated symptom relief for complications such as pain, bleeding, and obstruction, with treatment strategies closely tailored to expected survival. Although recent advances in machine learning have improved prognostic accuracy by modeling complex variable interactions, their application in palliative care settings remains limited. PURPOSE:To aid clinical decision-making, we developed a decision tree multi-classifier to predict the mortality at 3, 24, and 52 weeks following palliative radiotherapy for bone metastases. METHODS:Data from 573 adults diagnosed with metastatic cancer were analyzed. The primary endpoint was the overall survival (OS) defined as the number of months from treatment to death event. Four clinically relevant classes were defined: Class 0 (OS: ≤ 3 weeks), Class 1 (OS: 3-24 weeks), Class 2 (OS: 24-52 weeks) and Class 3 (OS ≥ 52 weeks). Candidate covariate predictors consisted of 65 clinical, dosimetric and laboratory variables. Two supervised decision tree machine-learning models were trained and validated using the Python package. A SHapley Additive exPlanations (SHAP) explanaibility analysis was performed to infer the global and local feature importance. RESULTS:The SHAP analysis selected three laboratory variables, the interleukin8, haemoglobin and lymphocytes count as the first three ranked variables representing the major impact on OS in each of the four classes and accounting for more than 80% of contribution. In all classes, higher chance of OS was associated with low values of interleukin8 (IL8) and higher values of haemoglobin (HEM) and lymphocytes count (LYMPH). Pre-treatment values of IL8 > 36.7 relocated more than 50% of patients with survival < 3 weeks and only 1.5% of patient with survival > 52 weeks. On the other hand, pre-treatment values of IL8 < 19 relocated about 92% of patients with survival > 52 weeks. Patients are then additionally separated based on the lymphocytes count (LYMPH). LYMPH values higher than 7.5 will drive the probability of survival > 52 weeks still over 90% while it drops down to 2.1% for LYMPH < 7.5. CONCLUSION:An explainable machine learning approach based on decision trees is able to predict the survival at different timing after radiotherapy in patients with advanced cancer. This approach provides an intelligible explanation of individualized risk prediction, helping clinicians to identify the best strategy for patient stratification and treatment selection.

ABSTRACT Background This study investigates the impact of radiotherapy (RT) on fatigue levels in prostate cancer (PC) patients, considering the significant physical and psychological impact of fatigue associated with cancer and its treatments. Methods PC patients undergoing a radical treatment from December 2002 to September 2022 were retrospectively evaluated. Fatigue was assessed using the Cancer Linear Analogue Scale (CLAS) across three dimensions: wellbeing (CLAS1), energy level (CLAS2), and daily activity performance (CLAS3), measured at baseline (T0), 1 month (T1), and 12 months (T2) post‐RT. Changes in CLAS scores ≥ 2 points from T0 were deemed clinically significant for RT‐induced fatigue. Results The cohort consisted of 1253 patients, with a median age of 72 years (range 45–90). Approximately 30% of patients experienced moderate or high levels of fatigue at baseline. At T1, RT‐related fatigue onset (decrease of CLAS1, 2, and 3 values) was observed in 10.8%, 14.3%, and 14.8% of patients, respectively. These figures slightly increased at T2 (12.7%, 18.8%, and 19.4%, respectively). Logistic regression identified hypofractionated RT, ADT, surgery, alcohol consumption, and higher‐grade toxicities as predictors of worsened fatigue across various dimensions. Conclusion In this extensive cohort of PC patients, approximately 30% experienced moderate to severe fatigue before initiating RT, with less than 20% reporting new or exacerbated fatigue post‐treatment. Factors including treatment‐related toxicities, hypofractionation, alcohol use, and ADT were significant contributors to fatigue. These findings underscore the complexity of managing fatigue in PC, highlighting the influence of both treatment modalities and lifestyle factors.

Purpose To report the final results of the xxxxx dose-escalation study evaluating an SBRT boost for persistent disease within 4 months after in-field radiotherapy. Material/Methods Within the xxxxx trial, arms (f) and (g) were designed to evaluate an SBRT boost for persistent disease after prior in-field radiotherapy. Arm (f) included patients previously irradiated with doses <50 Gy or with persistent disease in regions previously exposed to small bowel irradiation, whereas arm (g) enrolled patients with persistence in sites receiving prior doses >50 Gy. Dose escalation proceeded through three levels up to a total dose of 35 Gy in arm (f) and 30 Gy in arm (g), respectively. Each cohort was assessed for dose-limiting adverse events, defined as any radiation-related adverse events > grade 3 occurring within 6 months after SBRT. Results Sixty-nine lesions in 57 patients were treated between 2005 and 2018. Acute adverse events were mostly grade 1–2, with one grade 3 gastrointestinal event in arm (g). Late adverse events were limited, with a single grade 3 lower gastrointestinal event in arm (f). Clinical response was available for 65 lesions, with a disease control rate of 98.4%. One-year local control was 86.5%, while 1-year DMFS, DFS, and OS were 70.8%, 63.8%, and 91.9%, respectively. Conclusion Dose escalation up to 35 Gy and 30 Gy in the two study arms was feasible and associated with acceptable adverse events and favourable local control outcomes.

Purpose 4π radiotherapy is an innovative technique in oncology that leverages non-coplanar beam arrangements to enhance tumor targeting while minimizing exposure to surrounding organs at risk (OARs). Compared to conventional techniques such as VMAT, IMRT and SBRT, this approach promises superior dosimetric results. However, its clinical advantages and potential risks, particularly increased integral dose and its implications for secondary malignancies, require thorough evaluation. Methods A comprehensive literature review was conducted using PubMed on August 1st, 2024, to identify studies examining 4π radiotherapy. Keywords included “4π radiotherapy,” “non-coplanar radiotherapy,” “VMAT,” “IMRT,” and “stereotactic body radiation therapy (SBRT).” Studies were included if they assessed dosimetric comparisons between 4π and other techniques or reported on clinical outcomes. Two independent reviewers screened and selected studies, resolving discrepancies through discussion. A total of 18 papers were included in this review. Results The review revealed that 4π radiotherapy significantly improves dosimetric parameters, including better dose conformity, reduced dose spillage, and enhanced sparing of OARs such as the heart, lungs, liver, and spinal cord. Its application across various cancers, including glioblastoma, prostate, and head-and-neck cancers, showed improved tumor coverage and potential for dose escalation without increasing toxicity. Advanced optimization techniques, such as automated planning, further enhanced treatment quality and delivery efficiency. However, concerns about increased integral dose, which could elevate the risk of secondary malignancies, particularly in long-surviving patients, were highlighted. These findings underscore the importance of careful patient selection and long-term monitoring in clinical settings. Conclusion 4π radiotherapy offers substantial potential to improve treatment outcomes by enhancing tumor coverage and sparing healthy tissues more effectively than conventional techniques. Future research should evaluate long-term clinical outcomes and develop strategies to minimize integral dose exposure, ensuring the safe and effective integration of 4π radiotherapy into routine clinical practice.

The management of large bulky tumors is very challenging. The current treatment options for effective palliation of symptoms are limited. These tumors often present a large burden at the time of diagnosis, growing along critical bony and neural structures and preventing surgical resection in most of the cases. These tumors are also known to be relatively resistant to chemotherapy, with very low response rates. In addition, conventional photon-based radiotherapy has a limited effect due to their radioresistance, the use of large treatment fields, and the impossibility of delivering high doses because of the higher risk of normal tissue toxicity. Therefore, more effective radiation treatments for palliation are needed to achieve greater local control rates. A recent approach called partial ablative radiotherapy (PART) has been shown to be potentially able to improve the effectiveness of radiotherapy. This technique is based on the ability of recent advanced delivery techniques to deliver a high “ablative” dose to the central part of the tumor, maintaining a very low and safe dose profile at the periphery to spare the surrounding organs at risk. Although this technique has been evaluated only in small studies and case reports, it showed notable treatment responses and safety profiles. The present narrative review describes the rationale for PART, the current and forthcoming state of evidence, the existing studies, and the future directions for the development of this approach, including the associated challenges.

PURPOSE:The results of stereotactic body radiation therapy (SBRT) for parenchymal lesions in the setting of oligometastatic ovarian cancer are reported in the context of the prospective multicenter phase 2 MITO-RT3/RAD trial (NCT04593381). METHODS AND MATERIALS:The primary endpoint was the complete response (CR) rate, secondary endpoints included local control (LC), progression-free survival, overall survival, treatment-free interval, and toxicity rates. Sample size was based on a previous study reporting an average 40.0% CR with SBRT. The study was powered to detect an improvement in the CR rate from 40.0% to 55.0%, with an α error of 0.05 (one-side) and a β error of 0.1. RESULTS:The study met its primary endpoint of a statistically significant improvement of CR. A total of 88 patients with 127 lesions were enrolled across 15 institutions from May 2019 to November 2023. CRs were observed in 71 lesions (55.9%), partial response in 37 (29.1%), stable disease in 14 (11.0%), and progressive disease in 5 lesions (4.0%). The objective response rate was 85.0%, with an overall clinical benefit rate of 96.0%. The overall 12-month LC was 81.6%, with CR lesions exhibiting a significantly higher rate than partial or not responding lesions (12-month LC: 96.3% vs 61.4%, P < .001). The 12-month actuarial rates for progression-free survival and for overall survival were 34.9% and 91.5%, respectively. The median actuarial treatment-free interval was 9 months (range, 2.5-15.4 months), whereas the 12-month actuarial rate was 44.1%. No grade 3 or higher toxicity was reported. In particular, 15 (20.5%) patients experienced mild acute toxicity (≤grade 2). There were 12 grade 1 events and 6 grade 2 events, the latter mostly represented by pain flare (N = 2). Late toxicity was reported in 4 patients (4.5%) accounting for 4 events, mostly grade 1, except for one case of moderate asthenia (grade 2). CONCLUSIONS:Parenchymal oligometastatic lesions showed a high rate of CR and encouraging long-term outcomes for patients achieving CR, including a substantial period of systemic therapy-free survival after radiation therapy. The observed toxicity was minimal, strengthening the safety of ablative SBRT as a noninvasive alternative to surgical resection for parenchymal metastases in high-risk areas.

Background/Objective: This study aimed to assess the prognostic significance of pretreatment nutritional and systemic inflammatory indices (IIs), and body composition parameters in patients with locally advanced cervical cancer (LACC) treated with chemoradiation and brachytherapy. The goal was to identify key predictors of clinical outcomes, such as local control (LC), metastasis-free survival (MFS), disease-free survival (DFS), and overall survival (OS), using machine learning techniques. Materials and methods: A retrospective analysis of 173 patients with LACC treated between 2007 and 2021 was conducted. The study utilized machine learning techniques, including LASSO regression and Classification and Regression Tree (CART) analysis, to identify significant predictors of outcomes. Clinical data, tumor-related parameters, and treatment factors, along with IIs and body composition metrics (e.g., sarcopenic obesity), were incorporated into the models. Model performance was evaluated using ROC curves and AUC values. Results: Among 173 patients, hemoglobin (Hb) levels, ECOG performance status, and total protein emerged as primary prognostic indicators across multiple endpoints. For 2-year LC, patients with Hb >11.9 g/dL had a rate of 95.1% compared to 73.6% in those with lower levels, with further stratification by ECOG status, ANRI, and total protein refining predictions. For 5-year LC, rates were 83.1% for Hb >11.5 g/dL and 43.3% for lower levels. For 2-year MFS, ECOG 0 patients had an 88.1% rate compared to 73.8% for ECOG ≥ 1. In 2-year OS, Hb > 11.9 g/dL predicted a 95.1% rate, while ≤11.9 g/dL correlated with 74.0%. IIs (ANRI, SIRI, MLR) demonstrated predictive value only within specific patient subgroups defined by the primary prognostic indicators. The model showed strong predictive accuracy, with AUCs ranging from 0.656 for 2-year MFS to 0.851 for 2-year OS. Conclusions: These findings underscore the value of integrating traditional prognostic factors with emerging markers to enhance risk stratification in LACC. The use of machine learning techniques like LASSO and CART demonstrated strong predictive capabilities, highlighting their potential to refine individualized treatment strategies. Prospective validation of these models is warranted to confirm their utility in clinical practice.