Identification of prognostic imaging biomarkers in H3 K27-altered diffuse midline gliomas in adults: impact of tumor oxygenation imaging biomarkers on survival.

PURPOSE: To investigate prognostic markers for H3 K27-altered diffuse midline gliomas (DMGs) in adults with clinical, qualitative and quantitative imaging phenotypes, including tumor oxygenation characteristics. METHODS: Retrospective chart and imaging reviews were conducted on 32 adults with H3 K27-altered DMGs between 2017 and 2023. Clinical and qualitative imaging characteristics were analyzed. Quantitative imaging assessment was performed from the tumor mask via automatic segmentation to calculate normalized cerebral blood volume (nCBV), capillary transit time heterogeneity (CTH), oxygen extraction fraction (OEF), relative cerebral metabolic rate of oxygen (rCMRO2), and mean ADC values. Leptomeningeal metastases (LM) was diagnosed with imaging. Cox analyses were conducted to determine predictors of overall survival (OS) in entire patients and a subgroup of patients with contrast-enhancing (CE) tumor. RESULTS: The median patient age was 40.5 years (range 19.9-75.7), with an OS of 30.3 months (interquartile range 11.3-32.3). In entire patients, the presence of LM was the only independent predictor of OS (hazard ratio [HR] = 6.01, P = 0.009). In the subgroup of 23 (71.9%) patients with CE tumors, rCMRO2 of CE tumor (HR = 1.08, P = 0.019) and the presence of LM (HR = 5.92, P = 0.043) were independent predictors of OS. CONCLUSION: The presence of LM was independently associated with poor prognosis in adult patients with H3 K27-altered DMG. In patients with CE tumors, higher rCMRO2 of CE tumor, which may reflect higher metabolic activity in the tumor oxygenation microenvironment, may be a useful imaging biomarker to predict poor prognosis.

Revisiting oligodendroglioma grading in the 2021 WHO classification: calcification and larger contrast-enhancing tumor volume may predict higher oligodendroglioma grade.

PURPOSE: To investigate whether qualitative and quantitative imaging phenotypes can predict the grade of oligodendroglioma. METHODS: Retrospective chart and imaging reviews were conducted on 180 adults with oligodendroglioma (IDH-mutant and 1p/19q codeleted) between 2005 and 2021. Qualitative imaging characteristics including tumor location, calcification, gliomatosis cerebri, cystic change, necrosis, and infiltrative pattern were analyzed. Quantitative imaging assessment was performed from the tumor mask via automatic segmentation to calculate total, contrast-enhancing (CE), non-enhancing (NE), and necrotic tumor volumes. Logistic analyses were conducted to determine predictors of oligodendroglioma grade. RESULTS: This study included 180 patients (84 [46.7%] with grade 2 and 96 [53.3%] with grade 3 oligodendrogliomas), with a median age of 42 years (range 23-76 years), comprising 91 females and 89 males. On univariable analysis, calcification (odds ratio [OR] = 6.00, P < 0.001), necrosis (OR = 21.84, P = 0.003), presence of CE tumor (OR = 7.86, P < 0.001), larger total (OR = 1.01, P < 0.001), larger CE (OR = 2.22, P = 0.010), and larger NE (OR = 1.01, P < 0.001) tumor volumes were predictors of grade 3 oligodendroglioma. On multivariable analysis, calcification (OR = 3.79, P < 0.001) and larger CE tumor volume (OR = 2.70, P = 0.043) remained as independent predictors of grade 3 oligodendroglioma. The multivariable model exhibited an AUC, accuracy, sensitivity, specificity of 0.78 (95% confidence interval 0.72-0.84), 72.8%, 79.2%, 69.1%, respectively. CONCLUSION: Presence of calcification and larger CE tumor volume may serve as useful imaging biomarkers for prediction of oligodendroglioma grade. CLINICAL RELEVANCE STATEMENT: Assessment of intratumoral calcification and CE tumor volume may facilitate accurate preoperative estimation of oligodendroglioma grade. Presence of intratumoral calcification and larger contrast-enhancing tumor volume were the significant predictors of higher grade oligodendroglioma based on the 2021 WHO classification.

Incorporating Supramaximal Resection into Survival Stratification of IDH-Wildtype Glioblastoma: A Refined Multi-institutional Recursive Partitioning Analysis.

PURPOSE: To propose a novel recursive partitioning analysis (RPA) classification model in patients with IDH-wildtype glioblastomas that incorporates the recently expanded conception of the extent of resection (EOR) in terms of both supramaximal and total resections. EXPERIMENTAL DESIGN: This multicenter cohort study included a developmental cohort of 622 patients with IDH-wildtype glioblastomas from a single institution (Severance Hospital) and validation cohorts of 536 patients from three institutions (Seoul National University Hospital, Asan Medical Center, and Heidelberg University Hospital). All patients completed standard treatment including concurrent chemoradiotherapy and underwent testing to determine their IDH mutation and MGMTp methylation status. EORs were categorized into either supramaximal, total, or non-total resections. A novel RPA model was then developed and compared to a previous RTOG RPA model. RESULTS: In the developmental cohort, the RPA model included age, MGMTp methylation status, KPS, and EOR. Younger patients with MGMTp methylation and supramaximal resections showed a more favorable prognosis (class I: median overall survival [OS] 57.3 months), while low-performing patients with non-total resections and without MGMTp methylation showed the worst prognosis (class IV: median OS 14.3 months). The prognostic significance of the RPA was subsequently confirmed in the validation cohorts, which revealed a greater separation between prognostic classes for all cohorts compared to the previous RTOG RPA model. CONCLUSIONS: The proposed RPA model highlights the impact of supramaximal versus total resections and incorporates clinical and molecular factors into survival stratification. The RPA model may improve the accuracy of assessing prognostic groups.

Leptomeningeal Metastases in IDH-wildtype Glioblastomas Revisited: Comprehensive Analysis of Incidence, Risk Factors, and Prognosis Based on Post-contrast FLAIR.

BACKGROUND: The incidence of leptomeningeal metastases (LM) has been reported diversely. This study aimed to investigate the incidence, risk factors, and prognosis of LM in patients with IDH-wildtype glioblastoma. METHODS: A total of 828 patients with IDH-wildtype glioblastoma were enrolled between 2005 and 2022. Baseline preoperative MRI including post-contrast fluid-attenuated inversion recovery (FLAIR) was used for LM diagnosis. Qualitative and quantitative features, including distance between tumor and subventricular zone (SVZ) and tumor volume by automatic segmentation of the lateral ventricles and tumor, were assessed. Logistic analysis of LM development was performed using clinical, molecular, and imaging data. Survival analysis was performed. RESULTS: The incidence of LM was 11.4%. MGMTp unmethylation (odds ratio [OR] = 1.92, P = 0.014), shorter distance between tumor and SVZ (OR = 0.94, P = 0.010), and larger contrast-enhancing tumor volume (OR = 1.02, P < 0.001) were significantly associated with LM. The overall survival (OS) was significantly shorter in patients with LM than in those without (log-rank test; P < 0.001), with median OS of 12.2 and 18.5 months, respectively. Presence of LM remained an independent prognostic factor for OS in IDH-wildtype glioblastoma (hazard ratio = 1.42, P = 0.011), along with other clinical, molecular, imaging, and surgical prognostic factors. CONCLUSION: The incidence of LM is high in patients with IDH-wildtype glioblastoma, and aggressive molecular and imaging factors are correlated with LM development. The prognostic significance of LM based on post-contrast FLAIR imaging suggests acknowledgement of post-contrast FLAIR as a reliable diagnostic tool for clinicians.

Differentiation of glioblastoma from solitary brain metastasis using deep ensembles: Empirical estimation of uncertainty for clinical reliability.

BACKGROUND AND OBJECTIVES: To develop a clinically reliable deep learning model to differentiate glioblastoma (GBM) from solitary brain metastasis (SBM) by providing predictive uncertainty estimates and interpretability. METHODS: A total of 469 patients (300 GBM, 169 SBM) were enrolled in the institutional training set. Deep ensembles based on DenseNet121 were trained on multiparametric MRI. The model performance was validated in the external test set consisting of 143 patients (101 GBM, 42 SBM). Entropy values for each input were evaluated for uncertainty measurement; based on entropy values, the datasets were split to high- and low-uncertainty groups. In addition, entropy values of out-of-distribution (OOD) data from unknown class (257 patients with meningioma) were compared to assess uncertainty estimates of the model. The model interpretability was further evaluated by localization accuracy of the model. RESULTS: On external test set, the area under the curve (AUC), accuracy, sensitivity and specificity of the deep ensembles were 0.83 (95 % confidence interval [CI] 0.76-0.90), 76.2 %, 54.8 % and 85.2 %, respectively. The performance was higher in the low-uncertainty group than in the high-uncertainty group, with AUCs of 0.91 (95 % CI 0.83-0.98) and 0.58 (95 % CI 0.44-0.71), indicating that assessment of uncertainty with entropy values ascertained reliable prediction in the low-uncertainty group. Further, deep ensembles classified a high proportion (90.7 %) of predictions on OOD data to be uncertain, showing robustness in dataset shift. Interpretability evaluated by localization accuracy provided further reliability in the "low-uncertainty and high-localization accuracy" subgroup, with an AUC of 0.98 (95 % CI 0.95-1.00). CONCLUSIONS: Empirical assessment of uncertainty and interpretability in deep ensembles provides evidence for the robustness of prediction, offering a clinically reliable model in differentiating GBM from SBM.

Revisiting prognostic factors of gliomatosis cerebri in adult-type diffuse gliomas.

PURPOSE: There is lack of comprehensive analysis evaluating the impact of clinical, molecular, imaging, and surgical data on survival of patients with gliomatosis cerebri (GC). This study aimed to investigate prognostic factors of GC in adult-type diffuse glioma patients. METHODS: Retrospective chart and imaging review was performed in 99 GC patients from adult-type diffuse glioma (among 1,211 patients; 6 oligodendroglioma, 16 IDH-mutant astrocytoma, and 77 IDH-wildtype glioblastoma) from a single institution between 2005 and 2021. Predictors of overall survival (OS) of entire patients and IDH-wildtype glioblastoma patients were determined. RESULTS: The median OS was 16.7 months (95% confidence interval [CI] 14.2-22.2) in entire patients and 14.3 months (95% CI 12.2-61.9) in IDH-wildtype glioblastoma patients. In entire patients, KPS (hazard ratio [HR] = 0.98, P = 0.004), no 1p/19q codeletion (HR = 10.75, P = 0.019), MGMTp methylation (HR = 0.54, P = 0.028), and hemorrhage (HR = 3.45, P = 0.001) were independent prognostic factors on multivariable analysis. In IDH-wildtype glioblastoma patients, KPS (HR = 2.24, P = 0.075) was the only independent prognostic factor on multivariable analysis. In subgroup of IDH-wildtype glioblastoma with CE tumors, total resection of CE tumor did not remain as a significant prognostic factor (HR = 1.13, P = 0.685). CONCLUSIONS: The prognosis of GC patients is determined by its underlying molecular type and patient performance status. Compared with diffuse glioma without GC, aggressive surgery of CE tumor in GC patients does not improve survival.

Sex-specific Differences in IDH1-Wildtype Glioblastoma patients in the ReSPOND Consortium.

BACKGROUND: Understanding sex-based differences in glioblastoma patients is necessary for accurate personalized treatment planning to improve patient outcomes. PURPOSE: To investigate sex-specific differences in molecular, clinical and radiological tumor parameters, as well as survival outcomes in glioblastoma, isocitrate dehydrogenase-1 wildtype (IDH1-WT), grade 4 patients. METHODS: Retrospective data of 1832 glioblastoma, IDH1-WT patients with comprehensive information on tumor parameters was acquired from the Radiomics Signatures for Precision Oncology in Glioblastoma (ReSPOND) consortium. Data imputation was performed for missing values. Sex-based differences in tumor parameters, such as, age, molecular parameters, pre-operative KPS score, tumor volumes, epicenter and laterality were assessed through non-parametric tests. Spatial atlases were generated using pre-operative MRI maps to visualize tumor characteristics. Survival time analysis was performed through log-rank tests and Cox proportional hazard analyses. RESULTS: GBM was diagnosed at a median age of 64 years in females compared to 61.9 years in males (FDR = 0.003). Males had a higher Karnofsky Performance Score (above 80) as compared to females (60.4% females Vs 69.7% males, FDR = 0.044). Females had lower tumor volumes in enhancing (16.7 cm3 Vs. 20.6 cm3 in males, FDR = 0.001), necrotic core (6.18 cm3 Vs. 7.76 cm3 in males, FDR = 0.001) and edema regions (46.9 cm3 Vs. 59.2 cm3 in males, FDR = 0.0001). Right temporal region was the most common tumor epicenter in the overall population. Right as well as left temporal lobes were more frequently involved in males. There were no significant differences in survival outcomes and mortality ratios. Higher age, unmethylated O6-methylguanine-DNAmethyltransferase (MGMT) promoter and undergoing subtotal resection increased the mortality risk in both males and females. CONCLUSIONS: Our study demonstrates significant sex-based differences in clinical and radiological tumor parameters of glioblastoma, IDH1-WT, grade 4 patients. Sex is not an independent prognostic factor for survival outcomes and the tumor parameters influencing patient outcomes are identical for males and females. ABBREVIATIONS: IDH1-WT = isocitrate dehydrogenase-1 wildtype; MGMTp = O6-methylguanine-DNA-methyltransferase promoter; KPS = Karnofsky performance score; EOR = extent of resection; WHO = world health organization; FDR = false discovery rate.

Diffusion- and Perfusion-Weighted MRI Radiomics for Survival Prediction in Patients with Lower-Grade Gliomas.

PURPOSE: Lower-grade gliomas of histologic grades 2 and 3 follow heterogenous clinical outcomes, which necessitates risk stratification. This study aimed to evaluate whether diffusion-weighted and perfusion-weighted MRI radiomics allow overall survival (OS) prediction in patients with lower-grade gliomas and investigate its prognostic value. MATERIALS AND METHODS: In this retrospective study, radiomic features were extracted from apparent diffusion coefficient, relative cerebral blood volume map, and Ktrans map in patients with pathologically confirmed lower-grade gliomas (January 2012-February 2019). The radiomics risk score (RRS) calculated from selected features constituted a radiomics model. Multivariable Cox regression analysis, including clinical features and RRS, was performed. The models' integrated area under the receiver operating characteristic curves (iAUCs) were compared. The radiomics model combined with clinical features was presented as a nomogram. RESULTS: The study included 129 patients (median age, 44 years; interquartile range, 37-57 years; 63 female): 90 patients for training set and 39 patients for test set. The RRS was an independent risk factor for OS with a hazard ratio of 6.01. The combined clinical and radiomics model achieved superior performance for OS prediction compared to the clinical model in both training (iAUC, 0.82 vs. 0.72, p=0.002) and test sets (0.88 vs. 0.76, p=0.04). The radiomics nomogram combined with clinical features exhibited good agreement between the actual and predicted OS with C-index of 0.83 and 0.87 in the training and test sets, respectively. CONCLUSION: Adding diffusion- and perfusion-weighted MRI radiomics to clinical features improved survival prediction in lower-grade glioma.

Deep learning-based metastasis detection in patients with lung cancer to enhance reproducibility and reduce workload in brain metastasis screening with MRI: a multi-center study.

OBJECTIVES: To assess whether a deep learning-based system (DLS) with black-blood imaging for brain metastasis (BM) improves the diagnostic workflow in a multi-center setting. MATERIALS AND METHODS: In this retrospective study, a DLS was developed in 101 patients and validated on 264 consecutive patients (with lung cancer) having newly developed BM from two tertiary university hospitals, which performed black-blood imaging between January 2020 and April 2021. Four neuroradiologists independently evaluated BM either with segmented masks and BM counts provided (with DLS) or not provided (without DLS) on a clinical trial imaging management system (CTIMS). To assess reading reproducibility, BM count agreement between the readers and the reference standard were calculated using limits of agreement (LoA). Readers' workload was assessed with reading time, which was automatically measured on CTIMS, and were compared between with and without DLS using linear mixed models considering the imaging center. RESULTS: In the validation cohort, the detection sensitivity and positive predictive value of the DLS were 90.2% (95% confidence interval [CI]: 88.1-92.2) and 88.2% (95% CI: 85.7-90.4), respectively. The difference between the readers and the reference counts was larger without DLS (LoA: -0.281, 95% CI: -2.888, 2.325) than with DLS (LoA: -0.163, 95% CI: -2.692, 2.367). The reading time was reduced from mean 66.9 s (interquartile range: 43.2-90.6) to 57.3 s (interquartile range: 33.6-81.0) (P <.001) in the with DLS group, regardless of the imaging center. CONCLUSION: Deep learning-based BM detection and counting with black-blood imaging improved reproducibility and reduced reading time, on multi-center validation.

Clinical, qualitative imaging biomarkers, and tumor oxygenation imaging biomarkers for differentiation of midline-located IDH wild-type glioblastomas and H3 K27-altered diffuse midline gliomas in adults.

PURPOSE: To compare the clinical, qualitative and quantitative imaging phenotypes, including tumor oxygenation characteristics of midline-located IDH-wildtype glioblastomas (GBMs) and H3 K27-altered diffuse midline gliomas (DMGs) in adults. METHODS: Preoperative MRI data of 55 adult patients with midline-located IDH-wildtype GBM or H3 K27-altered DMG (32 IDH-wildtype GBM and 23 H3 K27-altered DMG patients) were included. Qualitative imaging assessment was performed. Quantitative imaging assessment including the tumor volume, normalized cerebral blood volume, capillary transit time heterogeneity (CTH), oxygen extraction fraction (OEF), relative cerebral metabolic rate of oxygen values, and mean ADC value were performed from the tumor mask via automatic segmentation. Univariable and multivariable logistic analyses were performed. RESULTS: On multivariable analysis, age (odds ratio [OR] = 0.92, P = 0.015), thalamus or medulla location (OR = 10.48, P = 0.013), presence of necrosis (OR = 0.15, P = 0.038), and OEF (OR = 0.01, P = 0.042) were independent predictors to differentiate H3 K27-altered DMG from midline-located IDH-wildtype GBM. The area under the curve, accuracy, sensitivity, and specificity of the multivariable model were 0.88 (95 % confidence interval: 0.77-0.95), 81.8 %, 82.6 %, and 81.3 %, respectively. CONCLUSIONS: Along with younger age, tumor location, less frequent necrosis, and lower OEF may be useful imaging biomarkers to differentiate H3 K27-altered DMG from midline-located IDH-wildtype GBM. Tumor oxygenation imaging biomarkers may reflect the less hypoxic nature of H3 K27-altered DMG than IDH-wildtype GBM and may contribute to differentiation.

Rethinking extent of resection of contrast-enhancing and non-enhancing tumor: different survival impacts on adult-type diffuse gliomas in 2021 World Health Organization classification.

OBJECTIVES: Extent of resection (EOR) of contrast-enhancing (CE) and non-enhancing (NE) tumors may have different impacts on survival according to types of adult-type diffuse gliomas in the molecular era. This study aimed to evaluate the impact of EOR of CE and NE tumors in glioma according to the 2021 World Health Organization classification. METHODS: This retrospective study included 1193 adult-type diffuse glioma patients diagnosed between 2001 and 2021 (183 oligodendroglioma, 211 isocitrate dehydrogenase [IDH]-mutant astrocytoma, and 799 IDH-wildtype glioblastoma patients) from a single institution. Patients had complete information on IDH mutation, 1p/19q codeletion, and O6-methylguanine-methyltransferase (MGMT) status. Cox survival analyses were performed within each glioma type to assess predictors of overall survival, including clinical, imaging data, histological grade, MGMT status, adjuvant treatment, and EOR of CE and NE tumors. Subgroup analyses were performed in patients with CE tumor. RESULTS: Among 1193 patients, 935 (78.4%) patients had CE tumors. In entire oligodendrogliomas, gross total resection (GTR) of NE tumor was not associated with survival (HR = 0.56, p = 0.223). In 86 (47.0%) oligodendroglioma patients with CE tumor, GTR of CE tumor was the only independent predictor of survival (HR = 0.16, p = 0.004) in multivariable analysis. GTR of CE and NE tumors was independently associated with better survival in IDH-mutant astrocytoma and IDH-wildtype glioblastoma (all ps < 0.05). CONCLUSIONS: GTR of both CE and NE tumors may significantly improve survival within IDH-mutant astrocytomas and IDH-wildtype glioblastomas. In oligodendrogliomas, the EOR of CE tumor may be crucial in survival; aggressive GTR of NE tumor may be unnecessary, whereas GTR of the CE tumor is recommended. CLINICAL RELEVANCE STATEMENT: Surgical strategies on contrast-enhancing (CE) and non-enhancing (NE) tumors should be reassessed considering the different survival outcomes after gross total resection depending on CE and NE tumors in the 2021 World Health Organization classification of adult-type diffuse gliomas. KEY POINTS: The survival impact of extent of resection of contrast-enhancing (CE) and non-enhancing (NE) tumors was evaluated in adult-type diffuse gliomas. Gross total resection of both CE and NE tumors may improve survival in isocitrate dehydrogenase (IDH)-mutant astrocytomas and IDH-wildtype glioblastomas, while only gross total resection of the CE tumor improves survival in oligodendrogliomas. Surgical strategies should be reconsidered according to types in adult-type diffuse gliomas.

High Radiation Dose to the Fornix Causes Symptomatic Radiation Necrosis in Patients with Anaplastic Oligodendroglioma.

PURPOSE: Surgery, radiotherapy (RT), and chemotherapy have prolonged the survival of patients with anaplastic oligodendroglioma. However, whether RT induces long-term toxicity remains unknown. We analyzed the relationship between the RT dose to the fornix and symptomatic radiation necrosis (SRN). MATERIALS AND METHODS: A total of 67 patients treated between 2009 and 2019 were analyzed. SRN was defined according to the following three criteria: 1) radiographic findings, 2) symptoms attributable to the lesion, and 3) treatment resulting in symptom improvement. Various contours, including the fornix, were delineated. Univariate and multivariate analyses of the relationship between RT dose and SRN, as well as receiver operating characteristic curve analysis for cut-off values, were performed. RESULTS: The most common location was the frontal lobe (n=40, 60%). Gross total resection was performed in 38 patients (57%), and 42 patients (63%) received procarbazine, lomustine, and vincristine chemotherapy. With a median follow-up of 42 months, the median overall and progression-free survival was 74 months. Sixteen patients (24%) developed SRN. In multivariate analysis, age and maximum dose to the fornix were associated with the development of SRN. The cut-off values for the maximum dose to the fornix and age were 59 Gy (equivalent dose delivered in 2 Gy fractions) and 46 years, respectively. The rate of SRN was higher in patients whose maximum dose to the fornix was >59 Gy (13% vs. 43%, p=0.005). CONCLUSION: The maximum dose to the fornix was a significant factor for SRN development. While fornix sparing may help maintain neurocognitive function, additional studies are needed.

Dynamic contrast-enhanced MRI radiomics model predicts epidermal growth factor receptor amplification in glioblastoma, IDH-wildtype.

PURPOSE: To develop and validate a dynamic contrast-enhanced (DCE) MRI-based radiomics model to predict epidermal growth factor receptor (EGFR) amplification in patients with glioblastoma, isocitrate dehydrogenase (IDH) wildtype. METHODS: Patients with pathologically confirmed glioblastoma, IDH wildtype, from January 2015 to December 2020, with an EGFR amplification status, were included. Patients who did not undergo DCE or conventional brain MRI were excluded. Patients were categorized into training and test sets by a ratio of 7:3. DCE MRI data were used to generate volume transfer constant (Ktrans) and extracellular volume fraction (Ve) maps. Ktrans, Ve, and conventional MRI were then used to extract the radiomics features, from which the prediction models for EGFR amplification status were developed and validated. RESULTS: A total of 190 patients (mean age, 59.9; male, 55.3%), divided into training (n = 133) and test (n = 57) sets, were enrolled. In the test set, the radiomics model using the Ktrans map exhibited the highest area under the receiver operating characteristic curve (AUROC), 0.80 (95% confidence interval [CI], 0.65-0.95). The AUROC for the Ve map-based and conventional MRI-based models were 0.74 (95% CI, 0.58-0.90) and 0.76 (95% CI, 0.61-0.91). CONCLUSION: The DCE MRI-based radiomics model that predicts EGFR amplification in glioblastoma, IDH wildtype, was developed and validated. The MRI-based radiomics model using the Ktrans map has higher AUROC than conventional MRI.

The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 3-Summary of Imaging Findings on Glioneuronal and Neuronal Tumors.

The fifth edition of the World Health Organization classification of central nervous system tumors published in 2021 reflects the current transitional state between traditional classification system based on histopathology and the state-of-the-art molecular diagnostics. This Part 3 Review focuses on the molecular diagnostics and imaging findings of glioneuronal and neuronal tumors. Histological and molecular features in glioneuronal and neuronal tumors often overlap with pediatric-type diffuse low-grade gliomas and circumscribed astrocytic gliomas (discussed in the Part 2 Review). Due to this overlap, in several tumor types of glioneuronal and neuronal tumors the diagnosis may be inconclusive with histopathology and genetic alterations, and imaging features may be helpful to distinguish difficult cases. Thus, it is crucial for radiologists to understand the underlying molecular diagnostics as well as imaging findings for application on clinical practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

A Radiomics-Based Model for Potentially More Accurate Identification of Subtypes of Breast Cancer Brain Metastases.

PURPOSE: Breast cancer brain metastases (BCBM) may involve subtypes that differ from the primary breast cancer lesion. This study aimed to develop a radiomics-based model that utilizes preoperative brain MRI for multiclass classification of BCBM subtypes and to investigate whether the model offers better prediction accuracy than the assumption that primary lesions and their BCBMs would be of the same subtype (non-conversion model) in an external validation set. MATERIALS AND METHODS: The training and external validation sets each comprised 51 cases (102 cases total). Four machine learning classifiers combined with three feature selection methods were trained on radiomic features and primary lesion subtypes for prediction of the following four subtypes: 1) hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)-, 2) HR+/HER2+, 3) HR-/HER2+, and 4) triple-negative. After training, the performance of the radiomics-based model was compared to that of the non-conversion model in an external validation set using accuracy and F1-macro scores. RESULTS: The rate of discrepant subtypes between primary lesions and their respective BCBMs were 25.5% (n=13 of 51) in the training set and 23.5% (n=12 of 51) in the external validation set. In the external validation set, the accuracy and F1-macro score of the radiomics-based model were significantly higher than those of the non-conversion model (0.902 vs. 0.765, p=0.004; 0.861 vs. 0.699, p=0.002). CONCLUSION: Our radiomics-based model represents an incremental advance in the classification of BCBM subtypes, thereby facilitating a more appropriate personalized therapy.

Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning.

OBJECTIVES: To establish a robust interpretable multiparametric deep learning (DL) model for automatic noninvasive grading of meningiomas along with segmentation. METHODS: In total, 257 patients with pathologically confirmed meningiomas (162 low-grade, 95 high-grade) who underwent a preoperative brain MRI, including T2-weighted (T2) and contrast-enhanced T1-weighted images (T1C), were included in the institutional training set. A two-stage DL grading model was constructed for segmentation and classification based on multiparametric three-dimensional U-net and ResNet. The models were validated in the external validation set consisting of 61 patients with meningiomas (46 low-grade, 15 high-grade). Relevance-weighted Class Activation Mapping (RCAM) method was used to interpret the DL features contributing to the prediction of the DL grading model. RESULTS: On external validation, the combined T1C and T2 model showed a Dice coefficient of 0.910 in segmentation and the highest performance for meningioma grading compared to the T2 or T1C only models, with an area under the curve (AUC) of 0.770 (95% confidence interval: 0.644-0.895) and accuracy, sensitivity, and specificity of 72.1%, 73.3%, and 71.7%, respectively. The AUC and accuracy of the combined DL grading model were higher than those of the human readers (AUCs of 0.675-0.690 and accuracies of 65.6-68.9%, respectively). The RCAM of the DL grading model showed activated maps at the surface regions of meningiomas indicating that the model recognized the features at the tumor margin for grading. CONCLUSIONS: An interpretable multiparametric DL model combining T1C and T2 can enable fully automatic grading of meningiomas along with segmentation. KEY POINTS: • The multiparametric DL model showed robustness in grading and segmentation on external validation. • The diagnostic performance of the combined DL grading model was higher than that of the human readers. • The RCAM interpreted that DL grading model recognized the meaningful features at the tumor margin for grading.

The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 2-Summary of Imaging Findings on Pediatric-Type Diffuse High-Grade Gliomas, Pediatric-Type Diffuse Low-Grade Gliomas, and Circumscribed Astrocytic Gliomas.

The fifth edition of the World Health Organization (WHO) classification of central nervous system tumors published in 2021 advances the role of molecular diagnostics in the classification of gliomas by emphasizing integrated diagnoses based on histopathology and molecular information and grouping tumors based on genetic alterations. This Part 2 review focuses on the molecular diagnostics and imaging findings of pediatric-type diffuse high-grade gliomas, pediatric-type diffuse low-grade gliomas, and circumscribed astrocytic gliomas. Each tumor type in pediatric-type diffuse high-grade glioma mostly harbors a distinct molecular marker. On the other hand, in pediatric-type diffuse low-grade gliomas and circumscribed astrocytic gliomas, molecular diagnostics may be extremely complicated at a glance in the 2021 WHO classification. It is crucial for radiologists to understand the molecular diagnostics and imaging findings and leverage the knowledge in clinical practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.