RPS 1511 - Insights into brain tumours: from visible to invisible and back again

Author Block: A. Toth¹, R. Edelman², J. A. Chetta¹, J. Joyce¹, M. V. Spampinato¹, R. Zi³, K. T. Block³, A. Varga-Szemes¹; ¹Charleston, SC/US, ²Evanston, IL/US, ³New York, NY/US
Purpose: The novel stack-of-stars echo unbalanced T1 relaxation-enhanced steady-state (SOS echo-uT1RESS) sequence aims to provide improved motion robustness and enhanced dark blood contrast, and to improve the visualization of small metastases and low enhancing lesions. This study compared the image quality and diagnostic utility of SOS echo-uT1RESS with the widely used magnetization-prepared rapid acquisition gradient-echo (MPRAGE) sequence in brain tumor imaging.
Methods or Background: This two-center prospective study involved 25 adults with known brain tumors (n= 5 intra-axial primary brain tumors; n= 11 intra-axial brain metastases; n= 9 extra-axial brain tumors). Each participant underwent 3T contrast enhanced MRI of the brain with both standard MPRAGE and prototype SOS echo-uT1RESS sequences. Contrast-to-noise ratio (CNR) and tumor-to-brain contrast were quantitatively analyzed. Image quality, lesion conspicuity, and image artifacts were scored on a 4-point Likert scale. Diagnostic performance and assessment of the vascular and dural involvement were compared side-by-side by 2 readers.
Results or Findings: There was no significant difference in CNR between MPRAGE and SOS echo-uT1RESS (27.0 ± 19.2 vs. 26.5 ± 14.9, respectively; p = 0.84). SOS echo-uT1RESS demonstrated a 1.6-fold improvement in tumor-to-brain contrast compared with MPRAGE (0.7 ± 0.4 vs. 0.4 ± 0.3, respectively; p < 0.001). Image quality and artifacts were similar for both sequences, while SOS echo-uT1RESS showed improved lesion conspicuity, diagnostic performance and enhanced detection of vascular and dural invasion.
Conclusion: SOS echo-uT1RESS showed promising results for post-contrast evaluation of brain tumors on 3T MRI. This technique enhanced lesions visibility, achieving approximately 1.6-fold improvement in tumor-to-brain contrast compared to MPRAGE. It offered superior diagnostic performance and improved detection of vascular and dural involvement.
Limitations: The limitations of the study are the relatively small patient cohort and that quantitative measurements were performed by a single observer.
Funding for this study: Funding was provided by NIH HHS United States (1R01CA263091 and 1R21CA273280).
Has your study been approved by an ethics committee? Yes
Ethics committee - additional information: The study was approved by the Institutional Review Board (Pro00128013)).

Author Block: G. Lin, W. Chen, J. Ji; Lishui/CN
Purpose: This study aimed to develop and validate an interpretable machine learning-based prediction model for preoperatively predicting progesterone receptor (PR) expression in meningioma patients using multiparametric magnetic resonance imaging (MRI).
Methods or Background: The study retrospectively enrolled 739 patients with pathologically confirmed meningioma from three medical centers, dividing them into four cohorts: training (n = 294), internal test (n = 126), external test 1 (n = 217), and external test 2 (n = 102). Radiomics characteristics were derived from T2-weighted and contrast-enhanced T1-weighted MRI images, followed by feature selection. A machine learning-based combined model was developed by incorporating radiomics scores (rad-scores) from the optimal radiomics model along with clinical predictors. The Shapley additive explanation (SHAP) method was employed to visually represent the process of making predictions. The prognostic value of the model was evaluated using Kaplan-Meier survival analysis.
Results or Findings: Among the 739 patients, 299 (40.5%) had negative PR expression confirmed by pathology. Twelve radiomics features derived from multiparametric MRI were selected to build the radiomics model. Tumor location and enhancement pattern were identified as key clinical predictors and were combined with rad-scores to create a combined model utilizing the extreme gradient boosting (XGBoost) algorithm. The combined model demonstrated strong accuracy and robustness, with area under the curve values of 0.907, 0.827, 0.846, and 0.807 across training, internal test, external test 1, and external test 2 cohorts, respectively. The survival analysis indicated that the combined model was able to effectively categorize patients based on recurrence outcomes.
Conclusion: The XGBoost combined model, utilizing multiparametric MRI, shows promise for predicting PR expression in meningioma patients. The SHAP visualization enhances the model’s clinical applicability.
Limitations: As a retrospective study, it is susceptible to information selection bias.
Funding for this study: This work was supported by the Key Project of Joint Construction by Provincial and Ministerial Authorities (Grant No.WKJ-ZJ-2452 to Minjiang Chen), Medical and Health General Project of Zhejiang Province (Grant No. 2023KY425 to Guihan Lin, Grant No. 2024KY562 to Shuiwei Xia), and Medical and Health Youth Innovation Project of Zhejiang Province (Grant No. 2023RC115 to Weiyue Chen).
Has your study been approved by an ethics committee? Yes
Ethics committee - additional information: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was approved by the Institutional Review Board and Human Ethics Committee of the Fifth Affiliated Hospital of Wenzhou Medical University (2024-336), the Sixth Affiliated Hospital of Wenzhou Medical University, and the Third Affiliated Hospital of Wenzhou Medical University, with the requirement for patient informed consent being waived due to its retrospective nature. All patients’ information was anonymized prior to the analysis.

Author Block: Y. H. Lee, B-H. Kim, M. Kim, S-D. Kim; Ansan-Si/KR
Purpose: To determine whether amide proton transfer-weighted (APTW) histogram analysis is useful for predicting the grade of meningioma
Methods or Background: We retrospectively enrolled a total of 48 patients (M:F=16:32, mean age: 60.0 years; grade 1:grade 2/3=36:12) with pathologically proven intracranial meningioma who underwent mDIXON 3D-APT sequence of the fast spin echo method in addition to conventional 3T MR protocols prior to surgical resection. From the representative APTW images of the tumor registered with gadolinium-enhanced T1 images, the following parameters of each histogram were obtained: every 5 intervals from 5th to 95th percentile, mean, median, maximum, minimum, standard deviation, kurtosis and skewness. The diagnostic performance of each APTW histogram parameter for differentiating grade 2/3 from grade 1 intracranial meningioma were evaluated by drawing the receiver operating characteristic (ROC) curves and calculating the cut-off values
Results or Findings: Among all histogram parameters, only maximum, standard deviation and 75th,80th,85th,90th, and 95th percentiles for APTW signal in grade 2/3 were significantly higher than those of grade 1 (p<0.05). According to the ROC curve comparison analysis, the areas under the curves of 75th,80th,90th,95th percentile, maximum and standard deviation to discriminate grade 2/3 from grade 1 were 0.706,0.713,0.722,0.734, 0.738, 0.722, and 0.718, respectively.
Conclusion: Histogram analysis of APTW imaging can be used in clinical practice for grading of intracranial meningiomas
Limitations: 1.ROI-dependency, 2.small number of participants
Funding for this study: None
Has your study been approved by an ethics committee? Yes
Ethics committee - additional information: None

Author Block: J. I. Tudela Martínez, V. Vázquez Sáez; Murcia/ES
Purpose: Intratumoral susceptibility signals (ITSS) are promising radiological markers for assessing diffuse gliomas. This study evaluates the relationship between ITSS grading and key radiological and histopathological prognostic factors in adult diffuse gliomas.
Methods or Background: Between January 1st, 2022, and April 30th, 2024, we selected 99 patients diagnosed with adult diffuse glioma who met the following criteria: age over 18 years, MRI scans allowing ITSS quantification and confirmed pathological diagnosis with available molecular testing. Radiological variables included tumor volume, subventricular zone involvement and relative cerebral blood volume (rCBV) on MRI perfusion. Histopathological features examined were WHO-2021 grade, Ki-67 index, mitotic count, necrosis, microvascular proliferation, and key prognostic mutations (IDH, p53, ATRX, and CDKN2A/B). Spearman’s correlation and chi-square tests were used for quantitative and qualitative variables, respectively. Multiple logistic regression models were developed to predict WHO tumor grade, categorized as low (1-2) or high (3-4), based on ITSS grade, tumor volume, and rCBV.
Results or Findings: ITSS grades 0-1 were more common in oligodendrogliomas and astrocytomas, while grades 2-3 were linked to glioblastomas (p<0,001). ITSS grade positively correlated with rCBV, tumor volume, WHO grade, mitotic count, and Ki-67 index (p<0,001). Higher ITSS grades also showed increased necrosis and microvascular proliferation (p<0,001). IDH mutations and 1p/19q co-deletions were more prevalent in grades 0-1 (p<0,001 and p=0,001, respectively), while CDKN2A/B alterations correlated with grades 2-3 (p=0,02). Regression models showed AUCs of 0,937 and 0,960 for ITSS combined with rCBV and tumor volume, respectively (p=0,000).
Conclusion: ITSS represent valuable biomarkers for assesing diffuse gliomas, offering diagnostic and prognostic insights that can guide clinical decision-making. Additionally, combining ITSS with MRI-rCBV and tumor volume enhances predictive capacity of these radiological parameters.
Limitations: ITSS grading remains semi-quantitative; further studies should focus on fully quantifying ITSS data.
Funding for this study: None
Has your study been approved by an ethics committee? Not applicable
Ethics committee - additional information: We consulted with the ethics and research committee regarding the need for approval for the study. They confirmed that, due to its observational nature, such approval is not required.

Author Block: B. Zhang, J. Zhou; Lanzhou/CN
Purpose: Molecular mechanisms and specific genes involved in the growth of Glioblastoma (GBM) are important factor in deciding the treatment strategy. In this study, we aimed to non-invasively explore the relationship between whole-tumor MRI-based fractal features and molecular features of GBM.
Methods or Background: The clinical and imaging data of 104 patients with IDH-wildtype GBM at our hospital between November 2018 and June 2024 were retrospectively analyzed. The molecular features of GBM were collected by molecular sequencing and immunohistochemical method, including MGMT promoter methylation, 1p/19q-codeleted, TERT promoter mutation, Ki67, and P53 status. The volume of interest of whole tumor was manually segmented slice-by-slice using ITK-SNAP software. Fractal features of whole-tumor in contrast-enhanced T1-weighted imaging were extracted using Image J software. As many as 24 fractal features (fractal dimensions and lacunarity) were generated within each volume of interest. Correlation analyses were performed using Spearman correlation analysis. Logistic regression was used to build prediction models.
Results or Findings: The L1 and L4 were positively correlated with 1p/19q-codeleted (correlation coefcient: 0.213 and 0.212). The L5 was positively correlated with TERT promoter mutation (correlation coefcient: 0.326). The L1, L4, and L9 were negatively correlated with TERT promoter mutation (correlation coefcient: -0.251, -0.310, -0.196, respectively). The L1, L3, and L4 were positively correlated with Ki-67 proliferation index (correlation coefcient: 0.226, 0.200, 0.241, respectively). MGMT promoter methylation and P53 had no correlation with fractal features. The AUC of fractal features predicting 1p/19q-codeleted was 0.677 and predicting TERT promoter mutation was 0.755.
Conclusion: The fractal features were correlated with 1p/19q-codeleted, TERT promoter mutation, and Ki67 status in IDH-wildtype GBM. Fractal features can be used as non-invasive quantitative parameters to predict the molecular features of GBM.
Limitations: Not
Funding for this study: This study was supported by the National Natural Science Foundation of China (grant no. 82071872 and 82371914), the Science and Technology Program of Gansu Province (grant no. 21YF5FA123 and 21JR11RA105), and the China International Medical Foundation (grant no. Z-2014-07-2101).
Has your study been approved by an ethics committee? Yes
Ethics committee - additional information: This study was approved by the Medical Ethics Committee of the Second Hospital of Lanzhou University (approval number : 2020A-070) and informed consent was waived.

Author Block: C. Monopoli, A. Romano, G. De Rosa, A. Romano, G. Moltoni, A. M. Ascolese, G. Capriotti, A. Bozzao; Rome/IT
Purpose: Radiation treatment of brain metastases creates diagnostic doubts in the differential diagnosis between disease progression or radionecrosis induced by radiosurgery. MRI with the administration of contrast medium doesn't offer the possibility of reliably distinguishing the two pathological entities. The aim of our study is to verify the presence of radionecrosis or disease recurrence through the evaluation of T1 enhancement curves.
Methods or Background: 40 brain metastases undergone to radiosurgery were evaluated (32 from lung, 4 from breast, 2 from melanoma and 2 colorectal). All patients underwent MRI examination with dynamic T1 acquisitions with contrast medium. For each lesions the T1 enhancement curve was extracted by positioning a region of interest corresponding to the solid component, excluding the necrotic areas. All patients underwent a PET-DOPA study and the result of the examination was used as the gold standard to distinguish radionecrosis from disease progression. The PET investigations identifies three stages of the disease; radionecrosis (rSUV<1.6); mixed picture (rSUV between 1.6 and 1.9); disease progression (rSUV>1.9)
Results or Findings: 4 types of T1 enhancement curves have been identified (A-D). Curve A showed constant growth over time; curve B showed faster growth in its initial portion and constant growth over time; curve C showed rapid initial growth and a final plateau; Curve D showed rapid growth and rapid final washout. Of the 40 lesions, 13 showed uptake compatible with radionecrosis, 12 with a mixed picture and 15 with disease progression. Curves A and B corresponded to radionecrosis or mixed in 90% of cases, curves C and D corresponded to a progression of the disease in 95% of cases.
Conclusion: T1 enhancement curves allows to distinguish a condition of radionecrosis from a progression of the disease.
Limitations: Small enrolled population
Funding for this study: Not funding received
Has your study been approved by an ethics committee? Not applicable
Ethics committee - additional information: Not applicable.

Author Block: E. Masiello, M. Barbera, F. Fallanca, S. Paola, A. Castellano, A. Falini, N. E. Anzalone; Milan/IT
Purpose: Perfusion-weighted MRI (PWI) and 11C-methionine PET (MET-PET) provide valuable hemodynamic and metabolic insights for assessing brain tumors. This study aimed to investigate the diagnostic role of PWI and MET-PET, using radiomic analysis, in distinguishing progression (PD), pseudoprogression (PsP), and radionecrosis (RN) in patients with brain tumors treated with radiotherapy (RT) or radiochemotherapy.
Methods or Background: Patients with primary and secondary brain neoplasms who developed post-treatment lesions of at least 1 cm within the radiation field were retrospectively enrolled. All patients underwent simultaneous PET/MRI examinations according to a standardized protocol. Radiomics features were extracted from the 3D-segmentation of parametric maps, including relative cerebral blood volume (rCBV) from DSC, plasma volume (Vp) and vascular permeability (Ktrans) from DCE, relative cerebral blood flow (CBF) based on pseudo-Continuous Arterial Spin Labeling (pCASL), and Standardized Uptake Value (SUV) from MET-PET. For each lesion, imaging data were compared with outcomes based on RANO criteria or histological examination.
Results or Findings: A semi-automatic 3D segmentation of 52 lesions (23 PD and 29 RN) was performed using PMod software (v. 3.7) to extract 263 radiomic features. After feature selection, CBF Maximum gray level and SUV Maximum gray level were the most correlated (p < 0.001). In terms of accuracy, the highest area under the curve (AUC) for PET features was SUV Entropy, while Vp Mean showed the highest AUC for PWI features. SUV Entropy achieved the highest sensitivity for detecting PD (95.65%), while rCBV Entropy demonstrated the highest specificity (79.31%). PET and PWI parameters exhibited similar overall accuracy, ranging from 71.15% to 76.92%.
Conclusion: Radiomics features from MET-PET and PWI demonstrate strong potential in accurately distinguishing PD and PsP from RN. Combining both modalities using radiomics enhances overall diagnostic accuracy.
Limitations: Small and heterogeneous population
Funding for this study: No funding was received for this study.
Has your study been approved by an ethics committee? Not applicable
Ethics committee - additional information: The study is retrospective.

Author Block: J. Zhao¹, X. Ke¹, T. Gan¹, W. Hu¹, C. Xue², S. Li³, Q. Zhou¹, J. Zhou¹; ¹Lanzhou/CN, ²Qingdao/CN, ³Chengdu/CN
Purpose: To test the hypothesis that combining features from multiparametric MRI with clinically relevant prognostic risk factors provides a more accurate prediction of TERTp genotype and overall survival(OS)in patients with oligodendrogliomas(OGS).
Methods or Background: Preoperative multiparametric MRI sequences (T1WI, T2WI, and CE-T1-3D) from 135 patients with OGS (grades 2 and 3) were collected and randomly divided into training (n = 95) and validation (n = 40) sets. Radiomics features were extracted, and the least absolute shrinkage and selection operator regression was used to select the most relevant features. Clinical relevant features identified through univariate and multivariate logistic regression analyses were incorporated to establish a clinical radiomics model. This model was used to develop a predictive TERTp genotype nomogram. Kaplan–Meier curves were used to assess OS differences between TERTp groups; the log-rank test determined significance.
Results or Findings: The T2WI-based clinical radiomics model demonstrated superior performance in predicting the TERTp genotype, with mean area under the receiver operating characteristic curve (AUC) values of 0.90 (95% CI: 0.88, 0.92; P = 0.0002) in the training set and 0.83 (95% CI: 0.82, 0.85; P < 0.0001) in the validation set. The one year, two year and three year survival probability prediction models achieved AUC values of 0.85, 0.80, and 0.79, respectively.
Conclusion: The multiparametric MRI-based clinical radiomics model provides the most accurate prediction of the TERTp genotype. Combined with clinical relevant prognostic risk factors, the prognostic model offers precise prediction of OS in patients with OGS.
Limitations: The limitation of this study was three-dimensional tumor segmentation was manually performed, future research should explore automated and efficient segmentation methods to reduce workload and simplify clinical application.
Funding for this study: Funding were provided by National Natural Science Foundation of China (82071872, 82371914), Science and Technology Program Funding Project of Gansu Province ( 21JR7RA404).
Has your study been approved by an ethics committee? Yes
Ethics committee - additional information: The ethics committee notification can be found under the number 2021A-348.

Author Block: M. R. López De La Torre Carretero, C. Mbongo, P. Corral Alonso, D. A. Zambrano, Á. R. Cabrera Abud, C. D. Solano, J. M. Rodríguez Ortega, M. Macías de la Corte Hidalgo, M. Calvo Imirizaldu; Pamplona/ES
Purpose: Intraoperative magnetic resonance imaging (iMRI) is an increasingly valuable tool in neurosurgical oncology, particularly for guiding tumour resection by real-time detection of residual tumour and margins, but also acute ischemic complications using diffusion weighted imaging (DWI). However, some studies suggest iMRI may underestimate ischemia compared to early postoperative MRI (epMRI), highlighting the importance of thorough postoperative evaluation. Our study aimed to compare iMRI´s ability to detect ischemia against epMRI and late postoperative MRI (lpMRI), and to identify predictive factors for postoperative ischemia in patients undergoing brain tumour resection.
Methods or Background: This retrospective study included 106 patients undergoing brain tumor resection at our centre. iMRI, epMRI (5-7 days post-surgery), and lpMRI (30 days post-surgery) were performed. Two radiologists analysed imaging to detect intraoperative ischemia (IOI), early postoperative ischemia (EPI), and late postoperative ischemia (LPI) using (DWI) and quantifying ischaemic volume on apparent diffusion coefficient (ADC) maps. Variables such as age, sex and tumour histology were recorded. Statistical analysis was conducted (StataNow 18.5) using McNemar tests, Pearson’s chi-squared test, and multivariate logistic regression.
Results or Findings: • McNemar test revealed that iMRI tends to underestimate ischaemia compared to epMRI (p= 0.0039).
• Only tumour type was a significant predictor of EPI (p=0.041), with glioblastoma patients having lower probabilities of EPI compared to other tumour types (p=0.041).
• EPI and LPI were significantly associated (p <0.001), indicating ischaemia detected in epMRI didn’t progress in lpMRI.
Conclusion: Our results suggest iMRI may underestimate the ischemia compared to epMRI. Glioblastoma was associated with a lower risk of ischemia, highlighting the importance of personalized surgical approach. The strong relationship between EPI and LPI emphasizes the need for close follow-up to monitor late complications.
Limitations: Retrospective study.
Clinical outcomes could be useful (further studies)
Funding for this study: No funding was provided for this study
Has your study been approved by an ethics committee? Not applicable
Ethics committee - additional information: Not applicable

Author Block: A. Çolakoğlu, B. Genç, K. Aslan, L. Incesu; Samsun/TR
Purpose: Neuroplasticity that develops in intracranial tumors may guide the management of post-surgical or radiotherapy treatment. To our knowledge, there is no study investigating morphometric changes in the brain of patients with cerebellopontine angle tumors. Our aim in this study is to investigate supratentorial morphometric changes in patients with cerebellopontine angle tumors.
Methods or Background: The study included 29 patients with cerebellopontine angle tumors who had not yet received any treatment, and 53 age- and sex-matched healthy controls. Voxel-based morphometry and surface-based morphometry analyses were performed using CAT12, running under SPM12, to examine gray matter volume changes and cortical thickness changes in these patients. A general linear model was used for statistical analysis, and a p-value<0.05 with family-wise error (FWE) correction was considered statistically significant.
Results or Findings: Our VBM results showed an increase in gray matter volume in the thalamus, ventral diencephalon, cingulate gyrus, precuneus, cuneus, superior parietal lobe, and parahippocampal gyrus in these patients (p<0.05 FWE). Our SBM results revealed an increase in cortical thickness in the right superior parietal and paracentral gyri in patients with cerebellopontine angle tumors (p<0.05 FWE).
Conclusion: Our study is the first to demonstrate an increase in gray matter volume and cortical thickness in the supratentorial region of patients with cerebellopontine angle tumors. These findings may be associated with neuroplastic changes in these patients.
Limitations: The limitations of the study are its single-center design, its retrospective nature, and the absence of neurocognitive tests.
Funding for this study: No funding was received for this study.
Has your study been approved by an ethics committee? Yes
Ethics committee - additional information: The study is approved by clinical studies ethics committee of Ondokuz Mayıs University. The reference number is 2024090641.