Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology.

PURPOSE: Accurate deformable registration of magnetic resonance imaging (MRI) scans containing pathologies is challenging due to changes in tissue appearance. In this paper, we developed a novel automated three-dimensional (3D) convolutional U-Net based deformable image registration (ConvUNet-DIR) method using unsupervised learning to establish correspondence between baseline pre-operative and follow-up MRI scans of patients with brain glioma. METHODS: This study involved multi-parametric brain MRI scans (T1, T1-contrast enhanced, T2, FLAIR) acquired at pre-operative and follow-up time for 160 patients diagnosed with glioma, representing the BraTS-Reg 2022 challenge dataset. ConvUNet-DIR, a deep learning-based deformable registration workflow using 3D U-Net style architecture as a core, was developed to establish correspondence between the MRI scans. The workflow consists of three components: (1) the U-Net learns features from pairs of MRI scans and estimates a mapping between them, (2) the grid generator computes the sampling grid based on the derived transformation parameters, and (3) the spatial transformation layer generates a warped image by applying the sampling operation using interpolation. A similarity measure was used as a loss function for the network with a regularization parameter limiting the deformation. The model was trained via unsupervised learning using pairs of MRI scans on a training data set (n = 102) and validated on a validation data set (n = 26) to assess its generalizability. Its performance was evaluated on a test set (n = 32) by computing the Dice score and structural similarity index (SSIM) quantitative metrics. The model's performance also was compared with the baseline state-of-the-art VoxelMorph (VM1 and VM2) learning-based algorithms. RESULTS: The ConvUNet-DIR model showed promising competency in performing accurate 3D deformable registration. It achieved a mean Dice score of 0.975 ± 0.003 and SSIM of 0.908 ± 0.011 on the test set (n = 32). Experimental results also demonstrated that ConvUNet-DIR outperformed the VoxelMorph algorithms concerning Dice (VM1: 0.969 ± 0.006 and VM2: 0.957 ± 0.008) and SSIM (VM1: 0.893 ± 0.012 and VM2: 0.857 ± 0.017) metrics. The time required to perform a registration for a pair of MRI scans is about 1 s on the CPU. CONCLUSIONS: The developed deep learning-based model can perform an end-to-end deformable registration of a pair of 3D MRI scans for glioma patients without human intervention. The model could provide accurate, efficient, and robust deformable registration without needing pre-alignment and labeling. It outperformed the state-of-the-art VoxelMorph learning-based deformable registration algorithms and other supervised/unsupervised deep learning-based methods reported in the literature.

Survival Predictive Nomograms for Non-Surgical Brain Metastases Patients From Non-Small Cell Lung Cancer Receiving Radiotherapy: A Population-Based Study.

OBJECTIVE: A high number of Non-Small Cell Lung Cancer (NSCLC) patients with brain metastasis who have not had surgery often have a negative outlook. Radiotherapy remains a most common and effective method. Nomograms were developed to forecast the cancer-specific survival (CSS) and overall survival (OS) in NSCLC individuals with nonoperative brain metastases who underwent radiotherapy. METHODS: Information was gathered from the Surveillance, Epidemiology, and End Results (SEER) database about patients diagnosed with NSCLC who had brain metastases not suitable for surgery. Nomograms were created and tested using multivariate Cox regression models to forecast CSS and OS at intervals of 1, 2, and 3 years. RESULTS: The research involved 3413 individuals diagnosed with NSCLC brain metastases who had undergone radiotherapy but had not experienced surgery. These participants were randomly divided into two categories. The analysis revealed that gender, age, ethnicity, marital status, tumor location, tumor laterality, tumor grade, histology, T stage, N stage, chemotherapy, tumor size, lung metastasis, bone metastasis, and liver metastasis were significant independent predictors for OS and CSS. The C-index for the training set for predicting OS was .709 (95% CI, .697-.721), and for the validation set, it was .705 (95% CI, .686-.723), respectively. The C-index for predicting CSS was .710 (95% CI, .697-.722) in the training set and .703 (95% CI, .684-.722) in the validation set, respectively. The nomograms model, as suggested by the impressive C-index, exhibits outstanding differentiation ability. Moreover, the ROC and calibration curves reveal its commendable precision and distinguishing potential. CONCLUSIONS: For the first time, highly accurate and reliable nomograms were developed to predict OS and CSS in NSCLC patients with non-surgical brain metastases, who have undergone radiotherapy treatment. The nomograms may assist in tailoring counseling strategies and choosing the most effective treatment method.

Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data.

We developed artificial intelligence models to predict the brain metastasis (BM) treatment response after stereotactic radiosurgery (SRS) using longitudinal magnetic resonance imaging (MRI) data and evaluated prediction accuracy changes according to the number of sequential MRI scans. We included four sequential MRI scans for 194 patients with BM and 369 target lesions for the Developmental dataset. The data were randomly split (8:2 ratio) for training and testing. For external validation, 172 MRI scans from 43 patients with BM and 62 target lesions were additionally enrolled. The maximum axial diameter (Dmax), radiomics, and deep learning (DL) models were generated for comparison. We evaluated the simple convolutional neural network (CNN) model and a gated recurrent unit (Conv-GRU)-based CNN model in the DL arm. The Conv-GRU model performed superior to the simple CNN models. For both datasets, the area under the curve (AUC) was significantly higher for the two-dimensional (2D) Conv-GRU model than for the 3D Conv-GRU, Dmax, and radiomics models. The accuracy of the 2D Conv-GRU model increased with the number of follow-up studies. In conclusion, using longitudinal MRI data, the 2D Conv-GRU model outperformed all other models in predicting the treatment response after SRS of BM.

Response of treatment-naive brain metastases to stereotactic radiosurgery.

With improvements in survival for patients with metastatic cancer, long-term local control of brain metastases has become an increasingly important clinical priority. While consensus guidelines recommend surgery followed by stereotactic radiosurgery (SRS) for lesions >3 cm, smaller lesions (≤3 cm) treated with SRS alone elicit variable responses. To determine factors influencing this variable response to SRS, we analyzed outcomes of brain metastases ≤3 cm diameter in patients with no prior systemic therapy treated with frame-based single-fraction SRS. Following SRS, 259 out of 1733 (15%) treated lesions demonstrated MRI findings concerning for local treatment failure (LTF), of which 202 /1733 (12%) demonstrated LTF and 54/1733 (3%) had an adverse radiation effect. Multivariate analysis demonstrated tumor size (>1.5 cm) and melanoma histology were associated with higher LTF rates. Our results demonstrate that brain metastases ≤3 cm are not uniformly responsive to SRS and suggest that prospective studies to evaluate the effect of SRS alone or in combination with surgery on brain metastases ≤3 cm matched by tumor size and histology are warranted. These studies will help establish multi-disciplinary treatment guidelines that improve local control while minimizing radiation necrosis during treatment of brain metastasis ≤3 cm.

Narrow interval dual phase 18F-FDG PET/CT: A practical approach for distinguishing tumor recurrence from radiation necrosis in brain metastasis.

Purpose of our research is to demonstrate efficacy of narrow interval dual phase [18F]-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) imaging in distinguishing tumor recurrence (TR) from radiation necrosis (RN) in patients treated for brain metastases. 35 consecutive patients (22 female, 13 male) with various cancer subtypes, lesion size > 1.0 cm3, and suspected recurrence on brain magnetic resonance imaging (MRI) underwent narrow interval dual phase FDG-PET/CT (30 and 90 min after tracer injection). Clinical outcome was determined via sequential MRIs or pathology reports. Maximum standard uptake value (SUVmax) of lesion (L), gray matter (GM), and white matter (WM) was measured on early (1) and delayed (2) imaging. Analyzed variables include % change, late phase, and early phase for L uptake, L/GM uptake, and L/WM uptake. Statistical analysis (P < .01), receiver operator characteristic (ROC) curve and area under curve (AUC) cutoff values were obtained. Change in L/GM ratio of > -2% was 95% sensitive, 91% specific, and 93% accurate (P < .001, AUC = 0.99) in distinguishing TR from RN. Change in SUVmax of lesion alone was the second-best indicator (P < .001, AUC = 0.94) with an ROC cutoff > 30.5% yielding 86% sensitivity, 83% specificity, and 84% accuracy. Other variables (L alone or L/GM ratios in early or late phase, all L/WM ratios) were significantly less accurate. Utilizing narrow interval dual phase FDG-PET/CT in patients with brain metastasis treated with radiation therapy provides a practical approach to distinguish TR from RN. Narrow time interval allows for better patient comfort, greater efficiency of PET/CT scanner, and lower disruption of workflow.

Investigation of high-dose radiotherapy's effect on brain structure aggravated cognitive impairment and deteriorated patient psychological status in brain tumor treatment.

This study aims to investigate the potential impact of high-dose radiotherapy (RT) on brain structure, cognitive impairment, and the psychological status of patients undergoing brain tumor treatment. We recruited and grouped 144 RT-treated patients with brain tumors into the Low dose group (N = 72) and the High dose group (N = 72) according to the RT dose applied. Patient data were collected by using the HADS and QLQ-BN20 system for subsequent analysis and comparison. Our analysis showed no significant correlation between the RT doses and the clinicopathological characteristics. We found that a high dose of RT could aggravate cognitive impairment and deteriorate patient role functioning, indicated by a higher MMSE and worsened role functioning in the High dose group. However, the depression status, social functioning, and global health status were comparable between the High dose group and the Low dose group at Month 0 and Month 1, while being worsened in the High dose group at Month 3, indicating the potential long-term deterioration of depression status in brain tumor patients induced by high-dose RT. By comparing patient data at Month 0, Month 1, Month 3, Month 6, and Month 9 after RT, we found that during RT treatment, RT at a high dose could aggravate cognitive impairment in the short term and lead to worsened patient role functioning, and even deteriorate the overall psychological health status of patients in the long term.

Microenvironmental reorganization in brain tumors following radiotherapy and recurrence revealed by hyperplexed immunofluorescence imaging.

The tumor microenvironment plays a crucial role in determining response to treatment. This involves a series of interconnected changes in the cellular landscape, spatial organization, and extracellular matrix composition. However, assessing these alterations simultaneously is challenging from a spatial perspective, due to the limitations of current high-dimensional imaging techniques and the extent of intratumoral heterogeneity over large lesion areas. In this study, we introduce a spatial proteomic workflow termed Hyperplexed Immunofluorescence Imaging (HIFI) that overcomes these limitations. HIFI allows for the simultaneous analysis of > 45 markers in fragile tissue sections at high magnification, using a cost-effective high-throughput workflow. We integrate HIFI with machine learning feature detection, graph-based network analysis, and cluster-based neighborhood analysis to analyze the microenvironment response to radiation therapy in a preclinical model of glioblastoma, and compare this response to a mouse model of breast-to-brain metastasis. Here we show that glioblastomas undergo extensive spatial reorganization of immune cell populations and structural architecture in response to treatment, while brain metastases show no comparable reorganization. Our integrated spatial analyses reveal highly divergent responses to radiation therapy between brain tumor models, despite equivalent radiotherapy benefit.

A Monte Carlo simulation-based decision support system for radiation oncologists in the treatment of glioblastoma multiforme.

In the present research, we have developed a model-based crisp logic function statistical classifier decision support system supplemented with treatment planning systems for radiation oncologists in the treatment of glioblastoma multiforme (GBM). This system is based on Monte Carlo radiation transport simulation and it recreates visualization of treatment environments on mathematical anthropomorphic brain (MAB) phantoms. Energy deposition within tumour tissue and normal tissues are graded by quality audit factors which ensure planned dose delivery to tumour site thereby minimising damages to healthy tissues. The proposed novel methodology predicts tumour growth response to radiation therapy from a patient-specific medicine quality audit perspective. Validation of the study was achieved by recreating thirty-eight patient-specific mathematical anthropomorphic brain phantoms of treatment environments by taking into consideration density variation and composition of brain tissues. Dose computations accomplished through water phantom, tissue-equivalent head phantoms are neither cost-effective, nor patient-specific customized and is often less accurate. The above-highlighted drawbacks can be overcome by using open-source Electron Gamma Shower (EGSnrc) software and clinical case reports for MAB phantom synthesis which would result in accurate dosimetry with due consideration to the time factors. Considerable dose deviations occur at the tumour site for environments with intraventricular glioblastoma, haematoma, abscess, trapped air and cranial flaps leading to quality factors with a lower logic value of 0. Logic value of 1 depicts higher dose deposition within healthy tissues and also leptomeninges for majority of the environments which results in radiation-induced laceration.

SRTRP-Net: A multi-task learning network for segmentation and prediction of stereotactic radiosurgery treatment response in brain metastases.

Before the Stereotactic Radiosurgery (SRS) treatment, it is of great clinical significance to avoid secondary genetic damage and guide the personalized treatment plans for patients with brain metastases (BM) by predicting the response to SRS treatment of brain metastatic lesions. Thus, we developed a multi-task learning model termed SRTRP-Net to provide prior knowledge of BM ROI and predict the SRS treatment response of the lesion. In dual-encoder tumor segmentation Network (DTS-Net), two parallel encoders encode the original and mirrored multi-modal MRI images. The differences in the dual-encoder features between foreground and background are enhanced by the symmetrical visual difference block (SVDB). In the bottom layer of the encoder, a transformer is used to extract local contextual features in the spatial and depth dimensions of low-resolution images. Then, the decoder of DTS-Net provides the prior knowledge for predicting the response to SRS treatment by performing BM segmentation. SRS response prediction network (SRP-Net) directly utilizes shared multi-modal MRI features weighted by the signed distance map (SDM) of the masks. The bidirectional multi-dimensional feature fusion module (BMDF) fuses the shared features and the clinical text information features to obtain comprehensive tumor information for characterizing tumors and predicting SRS treatment response. Experiments based on internal and external clinical datasets have shown that SRTRP-Net achieves comparable or better results. We believe that SRTRP-Net can help clinicians accurately develop personalized first-time treatment regimens for BM patients and improve their survival.

Patient-specific temperature distribution prediction in laser interstitial thermal therapy: single-irradiation data-driven method.

Laser interstitial thermal therapy (LITT) is popular for treating brain tumours and epilepsy. The strict control of tissue thermal damage extent is crucial for LITT. Temperature prediction is useful for predicting thermal damage extent. Accurately predictingin vivobrain tissue temperature is challenging due to the temperature dependence and the individual variations in tissue properties. Considering these factors is essential for improving the temperature prediction accuracy.Objective. To present a method for predicting patient-specific tissue temperature distribution within a target lesion area in the brain during LITT.Approach. A magnetic resonance temperature imaging (MRTI) data-driven estimation model was constructed and combined with a modified Pennes bioheat transfer equation (PBHE) to predict patient-specific temperature distribution. In the PBHE for temperature prediction, the individual specificity and temperature dependence of thermal tissue properties and blood perfusion, as well as the individual specificity of optical tissue properties were considered. Only MRTI data during one laser irradiation were required in the method. This enables the prediction of patient-specific temperature distribution and the resulting thermal damage region for subsequent ablations.Main results. Patient-specific temperature prediction was evaluated based on clinical data acquired during LITT in the brain, using intraoperative MRTI data as the reference standard. Our method significantly improved the prediction performance of temperature distribution and thermal damage region. The average root mean square error was decreased by 69.54%, the average intraclass correlation coefficient was increased by 37.5%, the average Dice similarity coefficient was increased by 43.14% for thermal damage region prediction.Significance. The proposed method can predict temperature distribution and thermal damage region at an individual patient level during LITT, providing a promising approach to assist in patient-specific treatment planning for LITT in the brain.

Assessing the sensitivity and suitability of a range of detectors for SIMT PSQA.

PURPOSE: Single-isocenter multi-target intracranial stereotactic radiotherapy (SIMT) is an effective treatment for brain metastases with complex treatment plans and delivery optimization necessitating rigorous quality assurance. This work aims to assess five methods for quality assurance of SIMT treatment plans in terms of their suitability and sensitivity to delivery errors. METHODS: Sun Nuclear ArcCHECK and SRS MapCHECK, GafChromic EBT Radiochromic Film, machine log files, and Varian Portal Dosimetry were all used to measure 15 variations of a single SIMT plan. Variations of the original plan were created with Python. They comprised various degrees of systematic MLC offsets per leaf up to 2 mm, random per-leaf variations with differing minimum and maximum magnitudes, simulated collimator, and dose miscalibrations (MU scaling). The erroneous plans were re-imported into Eclipse and plan-quality degradation was assessed by comparing each plan variation to the original clinical plan in terms of the percentage of clinical goals passing relative to the original plan. Each erroneous plan could be then ranked by the plan-quality degradation percentage following recalculation in the TPS so that the effects of each variation could be correlated with γ pass rates and detector suitability. RESULTS & CONCLUSIONS: It was found that 2%/1 mm is a good starting point for the ArcCHECK, Portal Dosimetry, and the SRS MapCHECK methods, respectively, and provides clinically relevant error detection sensitivity. Looser dose criteria of 5%/1 mm or 5%/1.5 mm are suitable for film dosimetry and log-file-based methods. The statistical methods explored can be expanded to other areas of patient-specific QA and detector assessment.

Nuclear autoantigenic sperm protein facilitates glioblastoma progression and radioresistance by regulating the ANXA2/STAT3 axis.

AIMS: Although radiotherapy is a core treatment modality for various human cancers, including glioblastoma multiforme (GBM), its clinical effects are often limited by radioresistance. The specific molecular mechanisms underlying radioresistance are largely unknown, and the reduction of radioresistance is an unresolved challenge in GBM research. METHODS: We analyzed and verified the expression of nuclear autoantigenic sperm protein (NASP) in gliomas and its relationship with patient prognosis. We also explored the function of NASP in GBM cell lines. We performed further mechanistic experiments to investigate the mechanisms by which NASP facilitates GBM progression and radioresistance. An intracranial mouse model was used to verify the effectiveness of combination therapy. RESULTS: NASP was highly expressed in gliomas, and its expression was negatively correlated with the prognosis of glioma. Functionally, NASP facilitated GBM cell proliferation, migration, invasion, and radioresistance. Mechanistically, NASP interacted directly with annexin A2 (ANXA2) and promoted its nuclear localization, which may have been mediated by phospho-annexin A2 (Tyr23). The NASP/ANXA2 axis was involved in DNA damage repair after radiotherapy, which explains the radioresistance of GBM cells that highly express NASP. NASP overexpression significantly activated the signal transducer and activator of transcription 3 (STAT3) signaling pathway. The combination of WP1066 (a STAT3 pathway inhibitor) and radiotherapy significantly inhibited GBM growth in vitro and in vivo. CONCLUSION: Our findings indicate that NASP may serve as a potential biomarker of GBM radioresistance and has important implications for improving clinical radiotherapy.

Dosimetric impact of contour editing on CT and MRI deep-learning autosegmentation for brain OARs.

PURPOSE: To establish the clinical applicability of deep-learning organ-at-risk autocontouring models (DL-AC) for brain radiotherapy. The dosimetric impact of contour editing, prior to model training, on performance was evaluated for both CT and MRI-based models. The correlation between geometric and dosimetric measures was also investigated to establish whether dosimetric assessment is required for clinical validation. METHOD: CT and MRI-based deep learning autosegmentation models were trained using edited and unedited clinical contours. Autosegmentations were dosimetrically compared to gold standard contours for a test cohort. D1%, D5%, D50%, and maximum dose were used as clinically relevant dosimetric measures. The statistical significance of dosimetric differences between the gold standard and autocontours was established using paired Student's t-tests. Clinically significant cases were identified via dosimetric headroom to the OAR tolerance. Pearson's Correlations were used to investigate the relationship between geometric measures and absolute percentage dose changes for each autosegmentation model. RESULTS: Except for the right orbit, when delineated using MRI models, the dosimetric statistical analysis revealed no superior model in terms of the dosimetric accuracy between the CT DL-AC models or between the MRI DL-AC for any investigated brain OARs. The number of patients where the clinical significance threshold was exceeded was higher for the optic chiasm D1% than other OARs, for all autosegmentation models. A weak correlation was consistently observed between the outcomes of dosimetric and geometric evaluations. CONCLUSIONS: Editing contours before training the DL-AC model had no significant impact on dosimetry. The geometric test metrics were inadequate to estimate the impact of contour inaccuracies on dose. Accordingly, dosimetric analysis is needed to evaluate the clinical applicability of DL-AC models in the brain.

Standard therapy or additionally radioactive iodine (131I) therapy; which will stop the recurrence of glioblastoma multiforme (GBM)?

Glioblastoma multiforme (GBM) is the most aggressive malignant brain tumour. The average survival time for a patient diagnosed with GBM, using standard treatment methods, is several months. Authors of the article pose a direct question: Is it possible to treat GBM solely with radioactive iodine (¹³¹I) therapy without employing the sodium iodide symporter (NIS) gene? After all, NIS has been detected not only in the thyroid but also in various tumours. The main author of this article (A.C.), with the assistance of her colleagues (physicians and pharmacologists), underwent ¹³¹I therapy after prior iodine inhibition, resulting in approximately 30% reduction in tumour size as revealed by magnetic resonance imaging (MRI). Classical therapy for GBM encompasses neurosurgery, conventional radiotherapy, and chemotherapy (e.g. temozolomide). Currently, tyrosine kinase inhibitors (imatinib, sunitinib, and sorafenib) are being used. Additionally, novel drugs such as crizotinib, entrectinib, or larotrectinib are being applied. Recently, personalised multimodal immunotherapy (IMI) based on anti-tumour vaccines derived from oncolytic viruses has been developed, concomitant with the advancement of cellular and molecular immunology. Thus, ¹³¹I therapy has been successfully employed for the first time in the case of GBM recurrence.

Profile of miRNAs in small extracellular vesicles released from glioblastoma cells treated by boron neutron capture therapy.

PURPOSE: Boron neutron capture therapy (BNCT) is a tumor cell-selective particle-radiation therapy. In BNCT, administered p-boronophenylalanine (BPA) is selectively taken up by tumor cells, and the tumor is irradiated with thermal neutrons. High-LET α-particles and recoil 7Li, which have a path length of 5-9 µm, are generated by the capture reaction between 10B and thermal neutrons and selectively kill tumor cells that have uptaken 10B. Although BNCT has prolonged the survival time of malignant glioma patients, recurrences are still to be resolved. miRNAs, that are encapsulated in small extracellular vesicles (sEVs) in body fluids and exist stably may serve critical role in recurrence. In this study, we comprehensively investigated microRNAs (miRNAs) in sEVs released from post-BNCT glioblastoma cells. METHOD: Glioblastoma U87 MG cells were treated with 25 ppm of BPA in the culture media and irradiated with thermal neutrons. After irradiation, they were plated into dishes and cultured for 3 days in the 5% CO2 incubator. Then, sEVs released into the medium were collected by column chromatography, and miRNAs in sEVs were comprehensively investigated using microarrays. RESULT: An increase in 20 individual miRNAs (ratio > 2) and a decrease in 2 individual miRNAs (ratio < 0.5) were detected in BNCT cells compared with non-irradiated cells. Among detected miRNAs, 20 miRNAs were associated with worse prognosis of glioma in Kaplan Meier Survival Analysis of overall survival in TCGA. CONCLUSION: These miRNA after BNCT may proceed tumors, modulate radiation resistance, or inhibit invasion and affect the prognosis of glioma.

Simultaneous boost radiotherapy versus conventional dose radiotherapy for patients with newly diagnosed glioblastoma: a multi-institutional analysis.

We compared survival outcomes of high-dose concomitant boost radiotherapy (HDCBRT) and conventional dose radiotherapy (CRT) for newly diagnosed glioblastoma (GB). Patients treated with intensity-modulated radiation therapy for newly diagnosed GB were included. In HDCBRT, specific targets received 69, 60, and 51 Gy in 30 fractions, while 60 Gy in 30 fractions was administered with a standard radiotherapy method in CRT. Overall survival (OS) and progression-free survival (PFS) were compared using the Log-rank test, followed by multivariate Cox analysis. The inverse probability of treatment weighting (IPTW) method was also applied to each analysis. Among 102 eligible patients, 45 received HDCBRT and 57 received CRT. With a median follow-up of 16 months, the median survival times of OS and PFS were 21 and 9 months, respectively. No significant differences were observed in OS or PFS in the Kaplan-Meier analyses. In the multivariate analysis, HDCBRT correlated with improved OS (hazard ratio, 0.49; 95% confidence interval, 0.27-0.90; P = 0.021), and this result remained consistent after IPTW adjustments (P = 0.028). Conversely, dose suppression due to the proximity of normal tissues and IMRT field correlated with worse OS and PFS (P = 0.008 and 0.049, respectively). A prospective study with a stricter protocol is warranted to validate the efficacy of HDCBRT for GB.

A novel immune cell signature for predicting glioblastoma after radiotherapy prognosis and guiding therapy.

Background: Glioblastoma, a highly aggressive brain tumor, poses a significant clinical challenge, particularly in the context of radiotherapy. In this study, we aimed to explore infiltrating immune cells and identify immune-related genes associated with glioblastoma radiotherapy prognosis. Subsequently, we constructed a signature based on these genes to discern differences in molecular and tumor microenvironment immune characteristics, ultimately informing potential therapeutic strategies for patients with varying risk profiles. Methods: We leveraged UCSC Xena and CGGA gene expression profiles from post-radiotherapy glioblastoma as verification cohorts. Infiltration ratios were stratified into high and low groups based on the median value. Differential gene expression was determined through Limma differential analysis. A signature comprising four genes was constructed, guided by Gene Ontology (GO) functional enrichment results and Kaplan-Meier survival analysis. We evaluated differences in cell infiltration levels, Immune Score, Stromal Score, and ESTIMATE Score and their Pearson correlations with the signature. Spearman's correlation was computed between the signature and patient drug sensitivity (IC50), predicted using Genomics of Drug Sensitivity in Cancer (GDSC) and CCLE databases. Results: Notably, the infiltration of central memory CD8+T cells exhibited a significant correlation with glioblastoma radiotherapy prognosis. Samples were dichotomized into high- and low-risk groups based on the optimal signature threshold (2.466642). Kaplan-Meier (K-M) survival analysis revealed that the high-risk group experienced a significantly poorer prognosis (p = .0068), with AUC values exceeding 0.82 at 1, 3, and 5 years, underscoring the robust predictive potential of the signature scoring system. Independent validation sets substantiated the validity of the signature. Statistically significant differences in tumor microenvironments (p < .05) were observed between high- and low-risk groups, and these differences were significantly correlated with the signature (p < .05). Furthermore, there were significant correlations between high and low-risk groups regarding immune checkpoint expressions, Immune Prognostic Score (IPS), and Tumor Immune Dysfunction and Exclusion (TIDE) scores. Conclusion: The immune cell signature, comprising SDC-1, PLAUR, FN1, and CXCL13, holds promise as a predictive tool for assessing glioblastoma prognosis following radiotherapy. This signature also offers valuable guidance for tailoring treatment strategies, emphasizing its potential clinical relevance in improving patient outcomes.

Survival prediction in patients with gynecological cancer irradiated for brain metastases.

BACKGROUND AND PURPOSE: This large population-based, retrospective, single-center study aimed to identify prognostic factors in patients with brain metastases (BM) from gynecological cancers. MATERIAL AND METHODS: One hundred and forty four patients with BM from gynecological cancer treated with radiotherapy (RT) were identified. Primary cancer diagnosis, age, performance status, number of BM, presence of extracranial disease, and type of BM treatment were assessed. Overall survival (OS) was calculated using the Kaplan-Meier method and the Cox proportional hazards regression model was used for multivariable analysis. A prognostic index (PI) was developed based on scores from independent predictors of OS. RESULTS: Median OS for the entire study population was 6.2 months. Forty per cent of patients died within 3 months after start of RT. Primary cancer with the origin in cervix or vulva (p = 0.001),  Eastern Cooperative Oncology Group (ECOG) 3-4 (p < 0.001), and the presence of extracranial disease (p = 0.001) were associated with significantly shorter OS. The developed PI based on these factors, categorized patients into three risk groups with a median OS of 13.5, 4.0, and 2.4 months for the good, intermediate, and poor prognosis group, respectively. INTERPRETATION: Patients with BM from gynecological cancers carry a poor prognosis. We identified prognostic factors and developed a scoring tool to select patients with better or worse prognosis. Patients in the high-risk group have a particular poor prognosis, and omission of RT could be considered.

Short-term predictors of stereotactic radiosurgery outcome for untreated single non-small cell lung cancer brain metastases: a restrospective cohort study.

Stereotactic radiosurgery (SRS) is an option for brain metastases (BM) not eligible for surgical resection, however, predictors of SRS outcomes are poorly known. The aim of this study is to investigate predictors of SRS outcome in patients with BM secondary to non-small cell lung cancer (NSCLC). The secondary objective is to analyze the value of volumetric criteria in identifying BM progression. This retrospective cohort study included patients >18 years of age with a single untreated BM secondary to NSCLC. Demographic, clinical, and radiological data were assessed. The primary outcome was treatment failure, defined as a BM volumetric increase 12 months after SRS. The unidimensional measurement of the BM at follow-up was also assessed. One hundred thirty-five patients were included, with a median BM volume at baseline of 1.1 cm3 (IQR 0.4-2.3). Fifty-two (38.5%) patients had SRS failure at follow-up. Only right BM laterality was associated with SRS failure (p=0.039). Using the volumetric definition of SRS failure, the unidimensional criteria demonstrated a sensibility of 60.78% (46.11%-74.16%), specificity of 89.02% (80.18%-94.86%), positive LR of 5.54 (2.88-10.66) and negative LR of 0.44 (0.31-0.63). SRS demonstrated a 61.5% local control rate 12 months after treatment. Among the potential predictors of treatment outcome analyzed, only the right BM laterality had a significant association with SRS failure. The volumetric criteria were able to identify more subtle signs of BM increase than the unidimensional criteria, which may allow earlier diagnosis of disease progression and use of appropriate therapies.