Search Results (92)

Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence (AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), offers potential solutions yet is challenged by high false positive rates. Purpose: The Oncology Contouring Copilot (OCC) system is developed to leverage oncologist expertise for precise tumor contouring using textual descriptions, aiming to increase the efficiency of oncological workflows by combining the strengths of AI with human oversight. Methods: Our OCC system initially identifies nodule candidates from CT scans. Employing Language Vision Models (LVMs) like GPT-4V, OCC then effectively reduces false positives with clinical descriptive texts, merging textual and visual data to automate tumor delineation, designed to elevate the quality of oncology care by incorporating knowledge from experienced domain experts. Results: The deployment of the OCC system resulted in a 35.0% reduction in the false discovery rate, a 72.4% decrease in false positives per scan, and an F1-score of 0.652 across our dataset for unbiased evaluation. Conclusions: OCC represents a significant advance in oncology care, particularly through the use of the latest LVMs, improving contouring results by (1) streamlining oncology treatment workflows by optimizing tumor delineation and reducing manual processes; (2) offering a scalable and intuitive framework to reduce false positives in radiotherapy planning using LVMs; (3) introducing novel medical language vision prompt techniques to minimize LVM hallucinations with ablation study; and (4) conducting a comparative analysis of LVMs, highlighting their potential in addressing medical language vision challenges. Full article

Background and Objectives: Small-cell lung cancer (SCLC) is an exceedingly aggressive neoplasm distinguished by an unfavorable prognosis. Recent studies have confirmed chemo-immunotherapy as the conventional first treatment for extensive-stage small-cell lung cancer (ES-SCLC), but the impact of treatment duration remains unclear. The goal of this study was to find out how the length of treatment affected progression-free survival (PFS) and overall survival (OS) in patients with ES-SCLC who were receiving first-line atezolizumab plus chemotherapy. Materials and Methods: This retrospective multicenter study comprised 82 patients from six oncology centers in Turkey between 2017 and 2024. Patients were categorized into two categories according to the quantity of chemotherapy cycles they had undergone: standard treatment (≤4 cycles) and extended treatment (≥5 cycles). For the purpose of analyzing survival outcomes and related clinical determinants, as well as the demographic structures and features of the patients, both univariate and multivariate Cox regression models were utilized. Results: The median number of atezolizumab cycles was 8 (1–63). OS was 29.46 months after 15.8 months of follow-up, while PFS was 10.63 months. When comparing the two groups, we found no statistically significant differences in either PFS (p = 0.952) or OS (p = 0.374). Significant associations with OS were seen in the standard therapy group for both ECOG PS 1 (p = 0.028). Thoracic radiation considerably decreased progression risk (HR = 0.41, p = 0.031) in the extended group. Conclusions: While prolonging chemo-immunotherapy beyond four cycles did not significantly improve survival, the selected patient subgroups may benefit from personalized approaches. Thoracic radiotherapy emerged as a key modifier of outcome. Full article

Modern oncology increasingly relies on personalized strategies that aim to customize medical interventions, using both tumor biology and clinical features to enhance efficacy and minimize adverse effects. In recent years, precision medicine has been implemented as part of systemic therapies; however, its integration into radiation therapy (RT) is still a work in progress. Conventional RT treatment plans are based on the Linear Quadratic (LQ) model and utilize standardized alpha and beta ratios (α/β), which ignore the high variability in terms of treatment response between and within patients. Recent advances in radiobiology, as well as general medical technologies, have also driven a shift toward more tailored approaches, including in RT. This review provides an overview of current knowledge and future perspectives for the personalization of RT, highlighting the role of tumor and patient-specific biomarkers, advanced imaging techniques, and novel therapeutic approaches. As an alternative to conventional RT modalities, hadron therapy and Flash RT are discussed as innovative approaches with the potential to improve tumor targeting while sparing normal tissues. Furthermore, the synergistic combination of RT with immunotherapy is discussed as a potential strategy to support antitumor immune responses and overcome resistance. By integrating biological insights, technological innovation, and clinical expertise, personalized radiation therapy may significantly advance the precision oncology paradigm. Full article

Background: This study aimed to develop a predictive model for acute gastrointestinal (GI) and genitourinary (GU) toxicity in prostate cancer patients treated with salvage radiotherapy (SRT) post-prostatectomy, using machine learning techniques to identify key prognostic factors. Methods: A multicenter retrospective study analyzed 454 patients treated with SRT from three Italian radiotherapy centers. Acute toxicity was assessed using Radiation Therapy Oncology Group criteria. Predictors of grade ≥ 2 toxicity were identified through Least Absolute Shrinkage and Selection Operator (LASSO) regression and Classification and Regression Tree (CART) modeling. The analyzed variables included surgical technique, clinical target volume (CTV) to planning target volume (PTV) margins, extent of lymphadenectomy, radiotherapy technique, and androgen-deprivation therapy (ADT). Results: No patients experienced grade ≥ 4 toxicity, and grade 3 toxicity was below 1% for both GI and GU events. The primary determinant of acute toxicity was the surgical technique. Open prostatectomy was associated with significantly higher grade ≥ 2 GI (41.8%) and GU (35.9%) toxicity compared to laparoscopic/robotic approaches (18.9% and 12.2%, respectively). A CTV-to-PTV margin ≥ 10 mm further increased toxicity, particularly when combined with extensive lymphadenectomy. SRT technique and ADT were additional predictors in some subgroups. Conclusions: SRT demonstrated excellent tolerability. Surgical technique, CTV-to-PTV margin, and treatment parameters were key predictors of toxicity. These findings emphasize the need for personalized treatment strategies integrating surgical and radiotherapy factors to minimize toxicity and optimize outcomes in prostate cancer patients. Full article

Background/Objectives: Sacral chordomas are rare, locally invasive tumors that pose significant surgical and oncological challenges due to their anatomical complexity, proximity to critical structures, and resistance to conventional therapies. Methods: A literature search focused on contemporary multidisciplinary management of sacral chordoma was conducted. An illustrative case of such a multidisciplinary approach is presented. Results: Achieving optimal outcomes necessitates a multidisciplinary approach that balances en-bloc resection with negative margins and preservation of biomechanical stability and neurological function. Negative resection margins are a key determinant of long-term survival and reduced recurrence, particularly for tumors involving the upper sacrum (S1–S2). While postoperative radiation therapy provides adjunctive benefits, precision in surgical planning and execution remains paramount. Emerging technologies, such as augmented reality and 3D-printed anatomical models, are enhancing surgical precision, while the role of multidisciplinary surgical teams in improving outcomes requires further study. Conclusions: This review highlights the complexities of sacral chordoma management, focusing on surgical strategies, functional trade-offs, and future directions to optimize oncological and functional outcomes. Full article

Introduction: Interventional radiotherapy (brachytherapy, IRT, BT) has evolved with technological advancements, improving dose precision while minimizing exposure to healthy tissues. The integration of 3D-printing technology in IRT has enabled the development of patient-specific devices, optimizing treatment personalization and dosimetric accuracy. Methods: A systematic literature search was conducted in PubMed, Scopus, and Google Scholar to identify studies published between 2020 and 2024 on 3D-printing applications in IRT. The selection process resulted in 74 peer-reviewed articles categorized by radioactive source, brachytherapy technique, endpoint of the 3D-printed product, and study type. Results: The analysis highlights the growing implementation of 3D-printed devices in brachytherapy, particularly in gynecological, prostate, and skin cancers. Most studies focus on technique, including intracavitary, interstitial, and contact applications, with custom applicators and templates emerging as predominant endpoints. The majority of studies involved in vivo clinical applications, followed by in silico computational modeling and in vitro experiments. Conclusions: The upward trend in scientific publications underscores the growing attention on 3D printing for enhancing personalized brachytherapy. The increasing use of 3D-printed templates and applicators highlights their role in optimizing dose delivery and expanding personalized treatment strategies. The current research trend is shifting toward real-world data and in vivo studies to assess clinical applications, ensuring these innovations translate effectively into routine practice. The integration of 3D printing represents a major advancement in radiation oncology, with the potential to enhance treatment efficacy and patient outcomes. Future research should focus on standardizing manufacturing processes and expanding clinical validation to facilitate broader adoption. Full article

Background/Objectives: The primary aim of this study was to evaluate whether the replacement of roughly one-decade-old computed tomography (CT) scanners with new-model CT scanners were associated with an additional reduction in the radiation dose delivered to oncological patients, in a radiological setting where the optimization of protocols had already reached very low radiation doses. An exploratory secondary objective was to evaluate the potential differences in the objective image quality between the CT scans obtained before and after the installation of the new-generation CT scanners. Methods: Chest and abdominal CT examinations conducted for oncologic purposes were retrospectively selected from two time periods—prior to scanner replacement (2022) and following an upgrade (2024)—after five CT systems in our radiology department were replaced. We extracted and compared the CT dose index (CTDI) and dose length product (DLP) for each CT phase. For the objective image quality evaluation, we calculated the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) at the center of the liver and the aorta. An appropriate statistical analysis was performed and a p-value < 0.05 was considered significant. Results: We included 14,601 CT acquisitions, of which 9013 (61.7%) were performed before and 5588 (38.3%) after the replacement of the CT scanners. There were significantly lower values for the CTDI and DLP with the new CT scanners compared to the old ones. The CTDI with the new CT scanners was significantly lower in all phases (p-value = 0.002 for unenhanced phase, and p < 0.001 for arterial, portal venous, and delayed phases). The DLP using the new CT scanners was significantly lower in the arterial, portal venous, and delayed phases (p < 0.001), and it was not significantly different in the unenhanced phase (p = 0.36). There was no significant difference in the SNR at the liver level (p = 0.72) or at the aorta level (p = 0.51). There was no significant difference in the CNR at the liver level (p = 0.24), whereas the CNR was higher with the new CT scanners at the aorta level (p = 0.03). Conclusions: The transition to new-model CT scanners resulted in a significant reduction in the radiation dose delivered by chest and abdomen CT scans, without compromising the objective image quality. Full article

Mathematical modeling plays a crucial role in the advancement of cancer treatments, offering a sophisticated framework for analyzing and optimizing therapeutic strategies. This approach employs mathematical and computational techniques to simulate diverse aspects of cancer therapy, including the effectiveness of various treatment modalities such as chemotherapy, radiation therapy, targeted therapy, and immunotherapy. By incorporating factors such as drug pharmacokinetics, tumor biology, and patient-specific characteristics, these models facilitate predictions of treatment responses and outcomes. Furthermore, mathematical models elucidate the mechanisms behind cancer treatment resistance, including genetic mutations and microenvironmental changes, thereby guiding researchers in designing strategies to mitigate or overcome resistance. The application of optimization techniques allows for the development of personalized treatment regimens that maximize therapeutic efficacy while minimizing adverse effects, taking into account patient-related variables such as tumor size and genetic profiles. This study elaborates on the key applications of mathematical modeling in oncology, encompassing the simulation of various cancer treatment modalities, the elucidation of resistance mechanisms, and the optimization of personalized treatment regimens. By integrating mathematical insights with experimental data and clinical observations, mathematical modeling emerges as a powerful tool in oncology, contributing to the development of more effective and personalized cancer therapies that improve patient outcomes. Full article

Background/Objectives: Glioblastoma (GBM) is an aggressive and lethal primary brain tumor with a poor prognosis, with a 5-year survival rate of approximately 5%. Despite advances in oncologic treatments, including surgery, radiotherapy, and chemotherapy, survival outcomes have remained stagnant, largely due to the failure of conventional therapies to address the tumor’s inherent heterogeneity. Radiomics, a rapidly emerging field, provides an opportunity to extract features from MRI scans, offering new insights into tumor biology and treatment response. This study evaluates the potential of delta radiomics, the study of changes in radiomic features over time in response to treatment or disease progression, exploring the potential of delta radiomics to track temporal radiation changes in tumor morphology and microstructure. Methods: A cohort of 50 female CD1 nude mice was injected intracranially with G7 glioblastoma cells and divided into irradiated (IR) and non-irradiated (non-IR) groups. MRI scans were performed at baseline (week 11) and post-radiation (weeks 12 and 14), and radiomic features, including shape, histogram, and texture parameters, were extracted and analyzed to capture radiation-induced changes. The most robust features were those identified through intra-observer reproducibility assessment, ensuring reliability in feature selection. A machine learning model was developed to classify irradiated tumors based on delta radiomic features, and statistical analyses were conducted to evaluate feature feasibility, stability, and predictive performance. Results: Our findings demonstrate that delta radiomics effectively captured significant temporal variations in tumor characteristics. Delta radiomics features exhibited distinct patterns across different time points in the IR group, enabling machine learning models to achieve a high accuracy. Conclusions: Delta radiomics offers a robust, non-invasive method for monitoring the treatment of glioblastoma (GBM) following radiation therapy. Future research should prioritize the application of MRI delta radiomics to effectively capture short-term changes resulting from intratumoral radiation effects. This advancement has the potential to significantly enhance treatment monitoring and facilitate the development of personalized therapeutic strategies. Full article

Cold atmospheric plasma (CAP) is emerging as an innovative approach for cancer treatment because of its selectivity for malignant cells and absence of significant adverse effects. While modern oncological therapies face challenges such as tumor heterogeneity and treatment resistance, CAP presents itself as a low-cost and environmentally sustainable alternative. Its mechanisms of action involve reactive oxygen and nitrogen species (RONS), UV radiation, and electromagnetic fields, which induce cell death. Preclinical and clinical studies have demonstrated the efficacy of CAP, with devices such as dielectric barrier discharge (DBD) and the plasma jet developed to minimize damage to healthy cells. Some CAP devices are already approved for clinical use, showing safety and efficacy. However, the standardization of treatments remains a challenge due to the variety of devices and parameters used. Although CAP has shown promising cytotoxic effects in vitro and in animal models, especially in different cancer cell lines, further research, particularly in vivo and in veterinary medicine, is needed to optimize its clinical use and maximize its efficacy in combating cancer. Full article

Background: Approximately 50% of all oncological patients undergo radiation therapy, where personalized planning of treatment relies on gross tumor volume (GTV) delineation. Manual delineation of GTV is time-consuming, operator-dependent, and prone to variability. An increasing number of studies apply artificial intelligence (AI) techniques to automate such delineation processes. Methods: To perform a systematic review comparing the performance of AI models in tumor delineations within the body (thoracic cavity, esophagus, abdomen, and pelvis, or soft tissue and bone). A retrospective search of five electronic databases was performed between January 2017 and February 2025. Original research studies developing and/or validating algorithms delineating GTV in CT, MRI, and/or PET were included. The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis statement and checklist (TRIPOD) were used to assess the risk, bias, and reporting adherence. Results: After screening 2430 articles, 48 were included. The pooled diagnostic performance from the use of AI algorithms across different tumors and topological areas ranged 0.62–0.92 in dice similarity coefficient (DSC) and 1.33–47.10 mm in Hausdorff distance (HD). The algorithms with the highest DSC deployed an encoder–decoder architecture. Conclusions: AI algorithms demonstrate a high level of concordance with clinicians in GTV delineation. Translation to clinical settings requires the building of trust, improvement in performance and robustness of results, and testing in prospective studies and randomized controlled trials. Full article

Background: Generalizability and domain dependency are critical challenges in developing predictive models for healthcare, particularly in medical diagnostics and radiation oncology. Predictive models designed to assess tumor recurrence rely on comprehensive and high-quality datasets, encompassing treatment planning parameters, imaging protocols, and patient-specific data. However, domain dependency, arising from variations in dose calculation algorithms, computed tomography (CT) density conversion curves, imaging modalities, and institutional protocols, can significantly undermine model reliability and clinical utility. Methods: This study evaluated dose calculation differences in the head and neck cancer treatment plans of 19 patients using two treatment planning systems, Pinnacle 9.10 and RayStation 11, with similar dose calculation algorithms. Variations in the dose grid size and CT density conversion curves were assessed for their impact on domain dependency. Results: Results showed that dose grid size differences had a more significant influence within RayStation than Pinnacle, while CT curve variations introduced potential domain discrepancies. The findings underscore the critical role of precise and standardized treatment planning in enhancing the reliability of predictive modeling for tumor recurrence assessment. Conclusions: Incorporating treatment planning parameters, such as dose distribution and target volumes, as explicit features in model training can mitigate the impact of domain dependency and enhance prediction accuracy. Solutions such as multi-institutional data harmonization and domain adaptation techniques are essential to improve model generalizability and robustness. These strategies support the better integration of predictive modeling into clinical workflows, ultimately optimizing patient outcomes and personalized treatment strategies. Full article

Background: Spinal metastases affect approximately 40% of patients with systemic cancers; metastatic epidural spinal cord compression (MESCC) occurs in up to 20% of cases and leads to potential significant morbidity. Recent advancements in high-dose conformal radiation techniques, such as Stereotactic Body Radiation Therapy (SBRT) and Stereotactic Radiosurgery (SRS), enable histology-independent ablative treatments, yet optimal dose fractionation remains undetermined. Methods and Results: This case of vertebral metastases with high-grade ESCC exemplifies the model of a comprehensive treatment workflow that emphasizes interdisciplinary collaboration, within the framework of a personalized medicine. The “Hybrid Therapy” combines Separation Surgery, aimed at achieving circumferential spinal cord decompression, with SBRT/SRS. The oncologic resection has been performed in a navigation-assisted technique that is tailored to the SBRT target, pre-operatively defined on the neuronavigation station. Conclusions: This seamless integration during initial planning of surgery with the ideal radio-oncological target is aimed at avoiding delays in referral and limitations in subsequent treatment options. This integrative holistic strategy not only prioritizes functional preservation, minimizing surgical invasiveness, but also promotes tumor control, thus offering potential promising new avenues for patient-centered oncologic care. Future high-quality studies are warranted to validate the widespread potential utility and safety of this approach. Full article

Artificial Intelligence (AI) applications are increasingly prevalent in radiotherapy, including commercial software solutions for automatic segmentation of anatomical structures for 3D Computed Tomography (CT). However, their use in intraoperative electron radiotherapy (IOERT) remains limited. In particular, no AI solution is available for contouring cone beam CT (CBCT) images acquired with a mobile CBCT device. The U-Net convolutional neural network architecture has gained huge success for medical image segmentation but still has difficulties capturing the global context. To increase the accuracy in CBCT segmentation for IOERT, three different AI architectures were trained and evaluated. The features of the natural language processing models Transformer and xLSTM were added to the popular U-Net architecture and compared with the standard U-Net and manual segmentation performance. These networks were trained and tested using 55 CBCT scans obtained from breast cancer patients undergoing IOERT in the department of radiotherapy and radiation oncology in Salzburg, and each architecture’s segmentation performance was assessed using the dice coefficient (DSC) as a similarity measure. The average DSC values were 0.83 for the standard U-Net, 0.88 for the U-Net with transformer features, and 0.66 for the U-Net with xLSTM. The hybrid U-Net architecture, including Transformer features, achieved the best segmentation accuracy, demonstrating an improvement of 5% on average over the standard U-Net, while the U-Net with xLSTM showed inferior performance compared to the standard U-Net. With the help of automatic contouring, synthetic CT images can be generated, and IOERT challenges related to the time-consuming nature of 3D image-based treatment planning can be addressed. Full article

Background. Survival of patients with brain metastases (BMs) is poor. It has become clear that targeted therapy has an effect on BMs and patient’ prognosis. The question remains which patients benefit from additional systemic therapy. This assumption was evaluated in a large single-center cohort. Methods. Patients consecutively planned to undergo local radiotherapy for their BMs in 2006–2017 were selected (n = 200). Prognosis, using CERENAL, disease-specific graded prognostic assessment (DS-GPA), and Radiation Therapy Oncology Group recursive partitioning analysis (RTOG RPA), was evaluated. Results. Ninety-three (46.5%) patients received at least one additional line of systemic therapy subsequent to the diagnosis of their BMs. The median overall survival (OS) was 6.3 months. Having received subsequent systemic therapy resulted in a more favorable OS (10.4 versus 3.9 months). Interestingly, using dichotomized scores, CERENAL showed prognostic properties in all patients for disease-specific survival on multivariate analysis, whereas RTOG RPA and DS-GPA were not withheld in the model. Lastly, only having a favorable DS-GPA resulted in prolonged progression-free survival for first systemic therapy following BM diagnosis. Conclusions. Receiving subsequent systemic therapy has a profound influence on outcome in patients with BMs, indicating the effect of systemic therapy on BMs. Use of the CERENAL brain prognostic score shows potential for further prognostication of patients with more favorable outcomes. Full article