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Background/Aim: Heterotopic ossification (HO) is a common complication following total hip arthroplasty. Various prophylactic treatments have been proposed, including radiotherapy (RT). This review summarizes the evidence from meta-analyses on the efficacy of RT in preventing hip HO. Materials and Methods: A literature search was conducted on PubMed. The quality of the metaanalyses was assessed using the AMSTAR-2 tool. Results: Seven meta-analyses were included. One meta-analysis reported a significant reduction in HO occurrence after RT compared to the control group. Comparing RT and nonsteroidal anti-inflammatory drugs, one and two meta-analyses showed significantly greater efficacy of RT in preventing severe HO and better outcomes in patients receiving drugs, respectively. Regarding RT settings, the postoperative and preoperative RT were each supported by one meta-analysis. Furthermore, two meta-analyses showed an advantage of multi-fractionated RT over single fraction RT. The overall confidence rate of the meta-analyses was moderate, low, and critically low in one, three, and three meta-analyses, respectively. Conclusion: RT is a confirmed prophylactic intervention for HO. However, the precise optimization of timing, dosage, and fractionation requires elucidation. Future research should focus on the development of predictive models through large-scale data collection and advanced analytics to refine individualized treatment strategies and assess RT comparative effectiveness with drugs. [ABSTRACT FROM AUTHOR]

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Simple Summary: The differential diagnosis between uterine leiomyosarcomas and leiomyomas based on imaging represents one of the major challenges for gynecologists and radiologists. Currently, only histological examination can definitively resolve doubts in suspicious cases. The purpose of this study is to develop a machine learning model that can support clinical decision making. One of the proposed approaches, i.e., using the general linear model (GLM) classifier, has been patented by our team and has demonstrated good performance in retrospective analyses, with predicted area under the curve (AUC), sensitivity, and specificity on the test set ranging from 0.78 to 0.82, from 0.78 to 0.89, and from 0.67 to 0.87, respectively. The next step will involve validation at other medical centers and its prospective application. Background: The accurate discrimination of uterine leiomyosarcomas and leiomyomas in a pre-operative setting remains a current challenge. To date, the diagnosis is made by a pathologist on the excised tumor. The aim of this study was to develop a machine learning algorithm using radiomic data extracted from contrast-enhanced computed tomography (CECT) images that could accurately distinguish leiomyosarcomas from leiomyomas. Methods: Pre-operative CECT images from patients submitted to surgery with a histological diagnosis of leiomyoma or leiomyosarcoma were used for the region of interest identification and radiomic feature extraction. Feature extraction was conducted using the PyRadiomics library, and three feature selection methods combined with the general linear model (GLM), random forest (RF), and support vector machine (SVM) classifiers were built, trained, and tested for the binary classification task (malignant vs. benign). In parallel, radiologists assessed the diagnosis with or without clinical data. Results: A total of 30 patients with leiomyosarcoma (mean age 59 years) and 35 patients with leiomyoma (mean age 48 years) were included in the study, comprising 30 and 51 lesions, respectively. Out of nine machine learning models, the three feature selection methods combined with the GLM and RF classifiers showed good performances, with predicted area under the curve (AUC), sensitivity, and specificity ranging from 0.78 to 0.97, from 0.78 to 1.00, and from 0.67 to 0.93, respectively, when compared to the results obtained from experienced radiologists when blinded to the clinical profile (AUC = 0.73 95%CI = 0.62–0.84), as well as when the clinical data were consulted (AUC = 0.75 95%CI = 0.65–0.85). Conclusions: CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions. [ABSTRACT FROM AUTHOR]

Background: Immune checkpoint inhibitors (ICIs), administered alone or combined with chemotherapy, are the standard of care in advanced non-oncogene addicted Non-Small Cell Lung Cancer (NSCLC). Despite these treatments' success, most long-term survival benefit is restricted to approximately 20% of patients, highlighting the need to identify novel biomarkers to optimize treatment strategies. In several solid tumors, immune soluble factors, the activatory CD137+ Tcells, and the immunosuppressive cell subsets Tregs and MDSCs (PMN(Lox1+)-MDSC and M-MDSCs) correlated with responses to ICIs and clinical outcomes thus becoming appealing predictive and prognostic factors. This study investigated the role of distinct CD137+ Tcell subsets, Tregs, MDSCs, and immune-soluble factors in NSCLC patients as possible biomarkers. Methods: The levels of T cells, MDSCs and soluble factors were evaluated in 89 metastatic NSCLC patients who underwent ICIs as first- or second-line treatment. T cell analysis was performed by cytoflurimetry evaluating Tregs and different CD137+ Tcell subsets also combined with CD3+, CD8+, PD1+, and Ki67+ markers. Circulating cytokines and immune checkpoints were also evaluated by Luminex analysis. All these parameters were correlated with several clinical factors (age, sex, smoking status, PS and TPS), response to therapy, PFS , and OS. The analyses were conducted in the overall population and in patients treated with ICIs as first-line (naïve patients). Results: In both groups of patients, high levels of circulating CD137+ and CD137+PD1+ T cells (total, CD4 and CD8) and the soluble factor LAG3 positively correlated with response to therapy. In naïve patients, PMN(Lox1+)-MDSCs negatively correlated with clinical response, and a high percentage of Tregs was associated with favorable survival. Moreover, the balance between Treg/CD137+ Tcells or PMN(Lox1+)-MDSC/CD137+ Tcells was higher in non-responding patients and was associated with poor survival. CD137+ Tcells and Tregs resulted as two positive independent prognostic factors. Conclusion: High levels of CD137+, CD137+PD1+ Tcells and sLAG3 could predict the response to ICIs in NSCLC patients independently by previous therapy. Combining the evaluation of CD137+ Tcells and Tregs also as Treg/CD137+ T cells ratio it is possible to identify naive patients with longer survival. [ABSTRACT FROM AUTHOR]

Simple Summary: This study investigates how the assessment of body mass index (BMI), together with body fat and muscle mass, might influence the success of treatments for advanced cervical cancer. Unlike most research which focuses on general health and cancer stages, this research pays particular attention to the patient's weight-to-height ratio, muscle mass, and especially the condition known as sarcopenic obesity—a combination of high body fat and low muscle mass. The findings reveal that sarcopenic obesity significantly affects patient outcomes, suggesting that it could be a key factor in deciding the best course of treatment. Recognizing these body composition indicators could lead to more personalized and effective treatment plans, marking a step forward in the way we approach care for cervical cancer patients. Locally advanced cervical cancer represents a significant treatment challenge. Body composition parameters such as body mass index, sarcopenia, and sarcopenic obesity, defined by sarcopenia and BMI ≥ 30 kg/m2, have been identified as potential prognostic factors, yet their overall impact remains underexplored. This study assessed the relationship between these anthropometric parameters alongside clinical prognostic factors on the prognosis of 173 cervical cancer patients. Survival outcomes in terms of local control (LC), distant metastasis-free survival (DMFS), disease-free survival (DFS), and overall survival (OS) were analyzed using Kaplan regression methods—Meier and Cox. Older age, lower hemoglobin levels, higher FIGO (International Federation of Gynecology and Obstetrics) stages, and lower total radiation doses were significantly associated with worse outcomes. Univariate analysis showed a significant correlation between BMI and the outcomes examined, revealing that normal-weight patients show higher survival rates, which was not confirmed by the multivariate analysis. Sarcopenia was not correlated with any of the outcomes considered, while sarcopenic obesity was identified as an independent negative predictor of DFS (HR: 5.289, 95% CI: 1.298–21.546, p = 0.020) and OS (HR: 2.645, 95% CI: 1.275–5.488, p = 0.009). This study highlights the potential of sarcopenic obesity as an independent predictor of clinical outcomes. These results support their inclusion in prognostic assessments and treatment planning for patients with advanced cervical cancer. [ABSTRACT FROM AUTHOR]

Brachytherapy (BRT) plays a pivotal role in the treatment of tumors, offering precise radiation therapy directly to the affected area. However, this technique demands extensive training and skills development, posing challenges for widespread adoption and ensuring patient safety. This narrative review explored the utilization of augmented reality (AR) in BRT, seeking to summarize existing evidence, discuss key findings, limitations, and quality of research as well as outline future research directions. The review revealed promising findings regarding the integration of AR in BRT. Studies have suggested the feasibility and potential benefits of AR in education, training, intra-operative guidance, and treatment planning. However, the evidence remains limited and heterogeneous, with most studies in preliminary phases. Standardization, prospective clinical trials, patient-centered outcomes assessment, and cost-effectiveness analysis emerge as critical areas for future research. Augmented reality holds transformative potential for BRT by enhancing precision, safety, and training efficiency. To fully implement these benefits, the field requires standardized protocols, rigorous clinical trials, and in-depth patient-centered investigations. Policy-makers and healthcare providers should closely monitor developments in AR and consider its implementation in clinical practice, contingent and robust evidence, and cost-effectiveness analysis. The pro-active pursuit of evidence-based practices will contribute to optimizing patient care in BRT. [ABSTRACT FROM AUTHOR]