Augmented reality in brachytherapy: A narrative review.

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.

CD137+ and regulatory T cells as independent prognostic factors of survival in advanced non-oncogene addicted NSCLC patients treated with immunotherapy as first-line.

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.

Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study.

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.

Sarcopenic Obesity in Cervical Carcinoma: A Strong and Independent Prognostic Factor beyond the Conventional Predictors (ESTHER Study-AFRAID Project).

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.

Targeting of H19/cell adhesion molecules circuitry by GSK-J4 epidrug inhibits metastatic progression in prostate cancer.

BACKGROUND: About 30% of Prostate cancer (PCa) patients progress to metastatic PCa that remains largely incurable. This evidence underlines the need for the development of innovative therapies. In this direction, the potential research focus might be on long non-coding RNAs (lncRNAs) like H19, which serve critical biological functions and show significant dysregulation in cancer. Previously, we showed a transcriptional down-regulation of H19 under combined pro-tumoral estrogen and hypoxia treatment in PCa cells that, in turn, induced both E-cadherin and ß4 integrin expression. H19, indeed, acts as transcriptional repressor of cell adhesion molecules affecting the PCa metastatic properties. Here, we investigated the role of H19/cell adhesion molecules circuitry on in vivo PCa experimental tumor growth and metastatic dissemination models. METHODS: H19 was silenced in luciferase-positive PC-3 and 22Rv1 cells and in vitro effect was evaluated by gene expression, proliferation and invasion assays before and after treatment with the histone lysine demethylase inhibitor, GSK-J4. In vivo tumor growth and metastasis dissemination, in the presence or absence of GSK-J4, were analyzed in two models of human tumor in immunodeficient mice by in vivo bioluminescent imaging and immunohistochemistry (IHC) on explanted tissues. Organotypic Slice Cultures (OSCs) from fresh PCa-explant were used as ex vivo model to test GSK-J4 effects. RESULTS: H19 silencing in both PC-3 and 22Rv1 cells increased: i) E-cadherin and ß4 integrin expression as well as proliferation and invasion, ii) in vivo tumor growth, and iii) metastasis formation at bone, lung, and liver. Of note, treatment with GSK-J4 reduced lesions. In parallel, GSK-J4 efficiently induced cell death in PCa-derived OSCs. CONCLUSIONS: Our findings underscore the potential of the H19/cell adhesion molecules circuitry as a targeted approach in PCa treatment. Modulating this interaction has proven effective in inhibiting tumor growth and metastasis, presenting a logical foundation for targeted therapy.

Real-World Outcomes of Trastuzumab Deruxtecan in Patients With HER2+ Metastatic Breast Cancer: The DE-REAL Study.

BACKGROUND: Trastuzumab deruxtecan (T-DXd) demonstrated unprecedented efficacy in patients with pretreated HER2+ metastatic breast cancer (mBC). However, few data are available about its efficacy in routine clinical practice. In this multicenter retrospective study, we examined effectiveness and safety of T-DXd in a real-world population. METHODS: Clinico-pathological information about patients with HER2+ mBC who received T-DXd were collected from 12 Italian hospitals. HER2 status was determined locally. Patients who received at least one administration of T-DXd, as any therapy line for advanced disease were included in the analysis. The primary endpoint was real-word PFS (rwPFS). RESULTS: One hundred and forty-three patients were included. Median age was 66 (range: 37-90), and 4 men were included. Hormone receptor (HR) status was positive in 108 (75%) patients and negative in 35(25%). T-DXd was administered as first, second, third, or subsequent lines in 4 (3%), 16 (11%), 42 (29%), and 81 (57%) patients, respectively. Among 123 patients with measurable disease, the ORR was 68%, and the DCR was 93% (9 CRs, 74 PRs, and 30 SD). Nine (7%) patients had a primary resistance to T-DXd. With a median follow-up of 12 months, the median rwPFS was 16 months. RwPFS was 84%, 59%, and 39% at 6, 12, and 18 months, respectively. A favorable trend in rwPFS was reported in patients receiving T-DXd as I/II line versus further lines (17 vs. 15 months; P = .098). Any-grade toxicity was registered in 84 patients (59%). Most common adverse events (AEs) reported were nausea (33%), neutropenia (21%), and asthenia (21%). Liver toxicity and diarrhea were uncommon (5% and 1%). Severe toxicities was registered in 18% of patients, and the most frequent were neutropenia, nausea/vomiting, and ILD observed in 15, 2, and 3 patients. AEs led to dose reduction in 37 patients (26%). Dose reduction and AEs do not affect patients' response and survival outcomes. CONCLUSIONS: Efficacy and safety of T-DXd were confirmed in an unselected real-world population of HER2+ mBC. These results are consistent with the results of known findings, and no new safety concerns were reported.

Efficient quality assurance for isocentric stability in stereotactic body radiation therapy using machine learning.

This study aims to predict isocentric stability for stereotactic body radiation therapy (SBRT) treatments using machine learning (ML), covers the challenges of manual assessment and computational time for quality assurance (QA), and supports medical physicists to enhance accuracy. The isocentric parameters for collimator (C), gantry (G), and table (T) tests were conducted with the RUBY phantom during QA using TrueBeam linac for SBRT. This analysis combined statistical features from the IsoCheck EPID software. Five ML models, including logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB), and support vector machines (SVM), were used to predict the outcome of the QA procedure. 247 Winston-Lutz (WL) tests were collected from 2020 to 2022. In our study, both DT and RF achieved the highest score on test accuracy (Acc. test) ranging from 93.5% to 99.4%, and area under curve (AUC) values from 90 to 100% on three modes (C, G, and T). The precision, recall, and F1 scores indicate the DT model consistently outperforms other ML models in predicting isocenter stability deviation in QA. The QA assessment using ML models can assist error prediction early to avoid potential harm during SBRT and ensure safe and effective patient treatments.

A single centre intercomparison between commercial treatment planning systems for 90Y radioembolization using virtual and experimental phantoms.

INTRODUCTION: Dedicated Treatment Planning Systems (TPSs) were developed to personalize 90Y-transarterial radioembolization. This study evaluated the agreement among four commercial TPSs assessing volumes of interest (VOIs) volumes and dose metrics. METHODS: A homogeneous (EH) and an anthropomorphic phantom with hot and cold inserts (EA) filled with 99mTc-pertechnetate were acquired with a SPECT/CT scanner. Their virtual versions (VH and VA, respectively) and a phantom with activity inside a single voxel (VK) were generated by an in-house MATLAB script. Images and delineated VOIs were imported into the TPSs to compute voxel-based absorbed dose distributions with various dose deposition approaches: local deposition method (LDM) and dose kernel convolution (DKC) with/without local density correction (LDC). VOI volumes and mean absorbed doses were assessed against their median value across TPSs. Dose-volume histograms (DVHs) and VK-derived dose profiles were evaluated. RESULTS: Small (<2.1 %) and large (up to 42.4 %) relative volume differences were observed on large (>500 ml) and small VOIs, respectively. Mean absorbed doses relative differences were < 3 % except for small VOIs with steep dose gradients (up to 89.1 % in the VA Cold Sphere VOI). Within the same TPS, LDC negligibly affected the mean absorbed dose, while DKC and LDM showed differences up to 63 %. DHVs were mostly overlapped in experimental phantoms, with some differences in the virtual versions. Dose profiles agreed within 1 %. CONCLUSION: TPSs showed an overall good agreement except for small VOI volumes and mean absorbed doses of VOIs with steep dose gradients. These discrepancies should be considered in the dosimetry uncertainty assessment, thus requiring an appropriate harmonization.

Decoding the Complexity of Systemic Inflammation Predictors in Locally Advanced Cervical Cancer, with Hemoglobin as the Hidden Key (the ESTHER Study).

Locally advanced cervical cancer (LACC) is treated with concurrent chemoradiation (CRT). Predictive models could improve the outcome through treatment personalization. Several factors influence prognosis in LACC, but the role of systemic inflammation indices (IIs) is unclear. This study aims to assess the correlation between IIs and prognosis in a large patient cohort considering several clinical data. We retrospectively analyzed pretreatment IIs (NLR, PLR, MLR, SII, LLR, COP-NLR, APRI, ALRI, SIRI, and ANRI) in 173 LACC patients. Patient, tumor, and treatment characteristics were also considered. Univariate and multivariate Cox's regressions were conducted to assess associations between IIs and clinical factors with local control (LC), distant metastasis-free survival (DMFS), disease-free survival (DFS), and overall survival (OS). Univariate analysis showed significant correlations between age, HB levels, tumor stage, FIGO stage, and CRT dose with survival outcomes. Specific pretreatment IIs (NLR, PLR, APRI, ANRI, and COP-NLR) demonstrated associations only with LC. The multivariate analysis confirmed Hb levels, CRT dose, and age as significant predictors of OS, while no II was correlated with any clinical outcome. The study findings contradict some prior research on IIs in LACC, emphasizing the need for comprehensive assessments of potential confounding variables.

A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study.

BACKGROUND: Current prognostic models lack the use of pre-operative CT images to predict recurrence in endometrial cancer (EC) patients. Our study aimed to investigate the potential of radiomic features extracted from pre-surgical CT scans to accurately predict disease-free survival (DFS) among EC patients. METHODS: Contrast-Enhanced CT (CE-CT) scans from 81 EC cases were used to extract the radiomic features from semi-automatically contoured volumes of interest. We employed a 10-fold cross-validation approach with a 6:4 training to test set and utilized data augmentation and balancing techniques. Univariate analysis was applied for feature reduction leading to the development of three distinct machine learning (ML) models for the prediction of DFS: LASSO-Cox, CoxBoost and Random Forest (RFsrc). RESULTS: In the training set, the ML models demonstrated AUCs ranging from 0.92 to 0.93, sensitivities from 0.96 to 1.00 and specificities from 0.77 to 0.89. In the test set, AUCs ranged from 0.86 to 0.90, sensitivities from 0.89 to 1.00 and specificities from 0.73 to 0.90. Patients classified as having a high recurrence risk prediction by ML models exhibited significantly worse DSF (p-value < 0.001) across all models. CONCLUSIONS: Our findings demonstrate the potential of radiomics in predicting EC recurrence. While further validation studies are needed, our results underscore the promising role of radiomics in forecasting EC outcomes.

Classical Prognostic Factors Predict Prognosis Better than Inflammatory Indices in Locally Advanced Cervical Cancer: Results of a Comprehensive Observational Study including Tumor-, Patient-, and Treatment-Related Data (ESTHER Study).

Systemic inflammation indices were found to be correlated with therapeutic outcome in several cancers. This study retrospectively analyzes the predictive role of a broad range of systemic inflammatory markers in patients with locally advanced cervical cancer (LACC) including patient-, tumor-, and treatment-related potential prognostic factors. All patients underwent definitive chemoradiation and pretreatment values of several inflammatory indices (neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio, monocyte/lymphocyte ratio, systemic immune inflammation index (SII), leukocyte/lymphocyte ratio, combination of platelet count and NLR, aspartate aminotransferase/platelet ratio index, aspartate aminotransferase/lymphocyte ratio index, systemic inflammatory response index, and aspartate transaminase/neutrophil ratio index) were calculated. Their correlation with local control (LC), distant metastasis-free (DMFS), disease-free (DFS), and overall survival (OS) was analyzed. One hundred and seventy-three patients were included. At multivariable analysis significant correlations were recorded among clinical outcomes and older age, advanced FIGO stage, lower hemoglobin levels, larger tumor size, and higher body mass index values. The multivariate analysis showed only the significant correlation between higher SII values and lower DMFS rates (p < 0.01). Our analysis showed no significant correlation between indices and DSF or OS. Further studies are needed to clarify the role of inflammation indices as candidates for inclusion in predictive models in this clinical setting.

Comparative Effectiveness of Chemotherapy Alone Versus Radiotherapy-Based Regimens in Locally Advanced Pancreatic Cancer: A Real-World Multicenter Analysis (PAULA-1).

Different options for locally advanced pancreatic cancer (LAPC) are available based on international guidelines: chemotherapy (CHT), chemoradiation (CRT), and stereotactic body radiotherapy (SBRT). However, the role of radiotherapy is debated in LAPC. We retrospectively compared CHT, CRT, and SBRT ± CHT in a real-world setting in terms of overall survival (OS), local control (LC), and distant metastasis-free survival (DMFS). LAPC patients from a multicentric retrospective database were included (2005-2018). Survival curves were calculated using the Kaplan-Meier method. Multivariable Cox analysis was performed to identify predictors of LC, OS, and DMFS. Of the 419 patients included, 71.1% were treated with CRT, 15.5% with CHT, and 13.4% with SBRT. Multivariable analysis showed higher LC rates for CRT (HR: 0.56, 95%CI 0.34-0.92, p = 0.022) or SBRT (HR: 0.27, 95%CI 0.13-0.54, p < 0.001), compared to CHT. CRT (HR: 0.44, 95%CI 0.28-0.70, p < 0.001) and SBRT (HR: 0.40, 95%CI 0.22-0.74, p = 0.003) were predictors of prolonged OS with respect to CHT. No significant differences were recorded in terms of DMFS. In selected patients, the addition of radiotherapy to CHT is still an option to be considered. In patients referred for radiotherapy, CRT can be replaced by SBRT considering its duration, higher LC rate, and OS rate, which are at least comparable to that of CRT.

Circulating CD137+ T Cell Levels Are Correlated with Response to Pembrolizumab Treatment in Advanced Head and Neck Cancer Patients.

Pembrolizumab, an anti-PD-1 antibody, has been approved as first-line treatment for recurrent or metastatic head and neck squamous cell carcinoma ((R/M) HNSCC). However, only a minority of patients benefit from immunotherapy, which highlights the need to identify novel biomarkers to optimize treatment strategies. CD137+ T cells have been identified as tumour-specific T cells correlated with immunotherapy responses in several solid tumours. In this study, we investigated the role of circulating CD137+ T cells in (R/M) HNSCC patients undergoing pembrolizumab treatment. PBMCs obtained from 40 (R/M) HNSCC patients with a PD-L1 combined positive score (CPS) ≥1 were analysed at baseline via cytofluorimetry for the expression of CD137, and it was found that the percentage of CD3+CD137+ cells is correlated with the clinical benefit rate (CBR), PFS, and OS. The results show that levels of circulating CD137+ T cells are significantly higher in responder patients than in non-responders (p = 0.03). Moreover, patients with CD3+CD137+ percentage ≥1.65% had prolonged OS (p = 0.02) and PFS (p = 0.02). Multivariate analysis, on a combination of biological and clinical parameters, showed that high levels of CD3+CD137+ cells (≥1.65%) and performance status (PS) = 0 are independent prognostic factors of PFS (CD137+ T cells, p = 0.007; PS, p = 0.002) and OS (CD137+ T cells, p = 0.006; PS, p = 0.001). Our results suggest that levels of circulating CD137+ T cells could serve as biomarkers for predicting the response of (R/M) HNSCC patients to pembrolizumab treatment, thus contributing to the success of anti-cancer treatment.

Stereotactic Arrhythmia Radioablation (STAR): A Multidisciplinary Narrative Minireview of Preclinical Studies.

The aim of this narrative review of the literature was to collect and analyze the results of the published preclinical studies on stereotactic arrhythmia radioablation (STAR) in the treatment of refractory cardiac arrhythmias. A literature search was conducted on PubMed using the following terms: ("stereotactic" OR "SBRT" OR "SABR" OR "radioablation" OR "radiosurgery") AND ("arrhythmia" OR "tachycardia"). Preclinical and pathological reports published in English without time limit, comprising studies of STAR in animal models and histological analyzes of explanted animal and human hearts were included. The analyzed studies confirm that doses lower than 25 Gy seem to produce sub-optimal therapeutic results whereas doses >35 Gy are less safe in terms of radiation-induced toxicity. However, long-term results (>1 year) are still missing and reporting outcomes based on low dose irradiation (≤15 Gy). Finally, STAR proved to be an effective therapy in the analyzed studies despite the irradiation of rather different cardiac targets. Therefore, additional studies are needed to: 1) compare the outcomes of STAR at doses of 25 Gy versus 30 Gy; 2) evaluate the long-term results (>1 year) in animal models irradiated at doses similar to those used in the clinic; 3) define the optimal target.

How smart is artificial intelligence in organs delineation? Testing a CE and FDA-approved Deep-Learning tool using multiple expert contours delineated on planning CT images.

Background: A CE- and FDA-approved cloud-based Deep learning (DL)-tool for automatic organs at risk (OARs) and clinical target volumes segmentation on computer tomography images is available. Before its implementation in the clinical practice, an independent external validation was conducted. Methods: At least a senior and two in training Radiation Oncologists (ROs) manually contoured the volumes of interest (VOIs) for 6 tumoral sites. The auto-segmented contours were retrieved from the DL-tool and, if needed, manually corrected by ROs. The level of ROs satisfaction and the duration of contouring were registered. Relative volume differences, similarity indices, satisfactory grades, and time saved were analyzed using a semi-automatic tool. Results: Seven thousand seven hundred sixty-five VOIs were delineated on the CT images of 111 representative patients. The median (range) time for manual VOIs delineation, DL-based segmentation, and subsequent manual corrections were 25.0 (8.0-115.0), 2.3 (1.2-8) and 10.0 minutes (0.3-46.3), respectively. The overall time for VOIs retrieving and modification was statistically significantly lower than for manual contouring (p<0.001). The DL-tool was generally appreciated by ROs, with 44% of vote 4 (well done) and 43% of vote 5 (very well done), correlated with the saved time (p<0.001). The relative volume differences and similarity indexes suggested a better inter-agreement of manually adjusted DL-based VOIs than manually segmented ones. Conclusions: The application of the DL-tool resulted satisfactory, especially in complex delineation cases, improving the ROs inter-agreement of delineated VOIs and saving time.

Evolution of Portable Sensors for In-Vivo Dose and Time-Activity Curve Monitoring as Tools for Personalized Dosimetry in Molecular Radiotherapy.

Treatment personalization in Molecular Radiotherapy (MRT) relies on pre- and post-treatment SPECT/ PET-based images and measurements to obtain a patient-specific absorbed dose-rate distribution map and its evolution over time. Unfortunately, the number of time points that are available per patient to investigate individual pharmacokinetics is often reduced by limited patient compliance or SPECT or PET/CT scanner availability for dosimetry in busy departments. The adoption of portable sensors for in-vivo dose monitoring during the entire treatment could improve the assessment of individual biokinetics in MRT and, thus, the treatment personalization. The evolution of portable devices, non-SPECT/PET-based options, already used for monitoring radionuclide activity transit and accumulation during therapy with radionuclides (i.e., MRT or brachytherapy), is presented to identify valuable ones, which combined with conventional nuclear medicine imaging systems could be effective in MRT. External probes, integration dosimeters and active detecting systems were included in the study. The devices and their technology, the range of applications, the features and limitations are discussed. Our overview of the available technologies encourages research and development of portable devices and dedicated algorithms for MRT patient-specific biokinetics study. This would represent a crucial advancement towards personalized treatment in MRT.

A novel tool for predicting the dose distribution of non-sealed 188 Re (Rhenium) resin in non-melanoma skin cancers (NMSC) patients.

BACKGROUND: High-dose rate brachytherapy using a non-sealed 188 Rhenium resin (188 Re) is a recently approved treatment option for non-melanoma skin cancer (NMSC). The treatment goal is to deliver a personalized absorbed dose to the deepest point of neoplastic infiltration corresponding to the minimal target dose. The treatment consists of the application of a 188 Re-based resin over a plastic foil placed on the target skin surface. However, there is no treatment planning tool to assess the 188 Re activity needed for a personalized treatment. PURPOSE: The paper aims to present a novel Monte Carlo (MC)-based tool for 188 Re-based resin activity and dose calculation, experimentally validated using Gafchromic EBT3 films. METHODS: MC simulations were carried out using FLUKA modeling density and composition of 188 Re resin. The MC-based look up table (LUT) was incorporated in an ad hoc developed tool. The proposed tool allows the personalized calculation of treatment parameters (i.e., activity to be dispensed, the treatment duration, and dose volume histograms), according to the target dimension. The proposed tool was compared using Bland-Altman analysis to the previous calculation approaches conducted using VARSKIN in a retrospective cohort of 76 patients. The tool was validated in ad hoc experimental set ups using a stack of calibrated Gafchromic EBT3 films covered by a plastic film and exposed using a homogenous activity distribution of 188 Re eluate and a heterogeneous activity distribution of 188 Re resin mimic the patient treatment. RESULTS: The agreement between the proposed tool and VARSKIN was evaluated on the investigated cohort with median range of target area, target depth, and treatment time equal to 4.8 [1.0-60.1] cm2 , 1.1 [0.2-3.0] mm, and 70 [21-285] min, with a median range of target dose (Gy) of 23.5 [10-54.9]. The calculated minimal target doses, ranged from 1% to 10% for intermediate target depths (1.2 ± 0.7 mm), while showing significant differences in the estimation of superficial (maximal) target doses. The agreement between MC calculation and measurements at different plans in a stack of Gafchromic EBT3 films was within 10% for both the homogenous and heterogeneous activity distribution of 188 Re. Worst agreements were observed for absorbed doses lower than 0.3 Gy. CONCLUSIONS: Our results support the implementation of our MC-based tool in the practical routine for calculating the 188 Re resin activity and treatment parameters necessary for obtaining the prescribed minimal target dose.

Effectiveness of Flattening-Filter-Free versus Flattened Beams in V79 and Glioblastoma Patient-Derived Stem-like Cells.

Literature data on the administration of conventional high-dose beams with (FF) or without flattening filters (FFF) show conflicting results on biological effects at the cellular level. To contribute to this field, we irradiated V79 Chinese hamster lung fibroblasts and two patient-derived glioblastoma stem-like cell lines (GSCs-named #1 and #83) using a clinical 10 MV accelerator with FF (at 4 Gy/min) and FFF (at two dose rates 4 and 24 Gy/min). Cell killing and DNA damage induction, determined using the γ-H2AX assay, and gene expression were studied. No significant differences in the early survival of V79 cells were observed as a function of dose rates and FF or FFF beams, while a trend of reduction in late survival was observed at the highest dose rate with the FFF beam. GSCs showed similar survival levels as a function of dose rates, both delivered in the FFF regimen. The amount of DNA damage measured for both dose rates after 2 h was much higher in line #1 than in line #83, with statistically significant differences between the two dose rates only in line #83. The gene expression analysis of the two GSC lines indicates gene signatures mimicking the prognosis of glioblastoma (GBM) patients derived from a public database. Overall, the results support the current use of FFF and highlight the possibility of identifying patients with candidate gene signatures that could benefit from irradiation with FFF beams at a high dose rate.

PVA-Microbubbles as a Radioembolization Platform: Formulation and the In Vitro Proof of Concept.

This proof-of-concept study lays the foundations for the development of a delivery strategy for radioactive lanthanides, such as Yttrium-90, against recurrent glioblastoma. Our appealing hypothesis is that by taking advantage of the combination of biocompatible polyvinyl alcohol (PVA) microbubbles (MBs) and endovascular radiopharmaceutical infusion, a minimally invasive selective radioembolization can be achieved, which can lead to personalized treatments limiting off-target toxicities for the normal brain. The results show the successful formulation strategy that turns the ultrasound contrast PVA-shelled microbubbles into a microdevice, exhibiting good loading efficiency of Yttrium cargo by complexation with a bifunctional chelator. The selective targeting of Yttrium-loaded MBs on the glioblastoma-associated tumor endothelial cells can be unlocked by the biorecognition between the overexpressed αVß3 integrin and the ligand Cyclo(Arg-Gly-Asp-D-Phe-Lys) at the PVA microbubble surface. Hence, we show the suitability of PVA MBs as selective Y-microdevices for in situ injection via the smallest (i.e., 1.2F) neurointerventional microcatheter available on the market and the accumulation of PVA MBs on the HUVEC cell line model of integrin overexpression, thereby providing ~6 × 10-15 moles of Y90 per HUVEC cell. We further discuss the potential impact of using such versatile PVA MBs as a new therapeutic chance for treating glioblastoma multiforme recurrence.