Radiogenomics of diffuse intrinsic pontine gliomas (DIPGs): correlation of histological and biological characteristics with multimodal MRI features

Molecular and Pathological Features of Paediatric High-Grade Gliomas

Paediatric high-grade gliomas are among the most common malignancies found in children. Despite morphological similarities to their adult counterparts, there are profound biological and molecular differences. Furthermore, and thanks to molecular biology, the diagnostic pathology of paediatric high-grade gliomas has experimented a dramatic shift towards molecular classification, with important prognostic implications, as is appropriately reflected in both the current WHO Classification of Tumours of the Central Nervous System and the WHO Classification of Paediatric Tumours. Emphasis is placed on histone 3, IDH1, and IDH2 alterations, and on Receptor of Tyrosine Kinase fusions. In this review we present the current diagnostic categories from the diagnostic pathology perspective including molecular features.

Diffuse midline glioma invasion and metastasis rely on cell-autonomous signaling

BACKGROUND

Diffuse midline gliomas (DMG) are pediatric tumors with negligible 2-year survival after diagnosis characterized by their ability to infiltrate the central nervous system. In the hope of controlling the local growth and slowing the disease, all patients receive radiotherapy. However, distant progression occurs frequently in DMG patients. Current clues as to what causes tumor infiltration circle mainly around the tumor microenvironment, but there are currently no known determinants to predict the degree of invasiveness.

METHODS

In this study, we use patient-derived glioma stem cells (GSCs) to create patient-specific 3D avatars to model interindividual invasion and elucidate the cellular supporting mechanisms.

RESULTS

We show that GSC models in 3D mirror the invasive behavior of the parental tumors, thus proving the ability of DMG to infiltrate as an autonomous characteristic of tumor cells. Furthermore, we distinguished 2 modes of migration, mesenchymal and ameboid-like, and associated the ameboid-like modality with GSCs derived from the most invasive tumors. Using transcriptomics of both organoids and primary tumors, we further characterized the invasive ameboid-like tumors as oligodendrocyte progenitor-like, with highly contractile cytoskeleton and reduced adhesion ability driven by crucial over-expression of bone morphogenetic pathway 7 (BMP7). Finally, we deciphered MEK, ERK, and Rho/ROCK kinases activated downstream of the BMP7 stimulation as actionable targets controlling tumor cell motility.

CONCLUSIONS

Our findings identify 2 new therapeutic avenues. First, patient-derived GSCs represent a predictive tool for patient stratification in order to adapt irradiation strategies. Second, autocrine and short-range BMP7-related signaling becomes a druggable target to prevent DMG spread and metastasis.

Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging

BACKGROUND

Determination of H3 K27M mutation in diffuse midline glioma (DMG) is key for prognostic assessment and stratifying patient subgroups for clinical trials. MRI can noninvasively depict morphological and metabolic characteristics of H3 K27M mutant DMG.

PURPOSE

This study aimed to develop a deep learning (DL) approach to noninvasively predict H3 K27M mutation in DMG using T2-weighted images.

STUDY TYPE

Retrospective and prospective.

POPULATION

For diffuse midline brain gliomas, 341 patients from Center-1 (27 ± 19 years, 184 males), 42 patients from Center-2 (33 ± 19 years, 27 males) and 35 patients (37 ± 18 years, 24 males). For diffuse spinal cord gliomas, 133 patients from Center-1 (30 ± 15 years, 80 males).

FIELD STRENGTH/SEQUENCE

5T and 3T, T2-weighted turbo spin echo imaging.

ASSESSMENT

Conventional radiological features were independently reviewed by two neuroradiologists. H3 K27M status was determined by histopathological examination. The Dice coefficient was used to evaluate segmentation performance. Classification performance was evaluated using accuracy, sensitivity, specificity, and area under the curve.

STATISTICAL TESTS

Pearson's Chi-squared test, Fisher's exact test, two-sample Student's t-test and Mann-Whitney U test. A two-sided P value <0.05 was considered statistically significant.

RESULTS

In the testing cohort, Dice coefficients of tumor segmentation using DL were 0.87 for diffuse midline brain and 0.81 for spinal cord gliomas. In the internal prospective testing dataset, the predictive accuracies, sensitivities, and specificities of H3 K27M mutation status were 92.1%, 98.2%, 82.9% in diffuse midline brain gliomas and 85.4%, 88.9%, 82.6% in spinal cord gliomas. Furthermore, this study showed that the performance generalizes to external institutions, with predictive accuracies of 85.7%-90.5%, sensitivities of 90.9%-96.0%, and specificities of 82.4%-83.3%.

DATA CONCLUSION

In this study, an automatic DL framework was developed and validated for accurately predicting H3 K27M mutation using T2-weighted images, which could contribute to the noninvasive determination of H3 K27M status for clinical decision-making.

EVIDENCE LEVEL

2 Technical Efficacy: Stage 2.

Correlation between Multiparametric MR Imaging and Molecular Genetics in Pontine Pediatric High-Grade Glioma

BACKGROUND AND PURPOSE

Molecular profiling is a crucial feature in the "integrated diagnosis" of CNS tumors. We aimed to determine whether radiomics could distinguish molecular types of pontine pediatric high-grade gliomas that have similar/overlapping phenotypes on conventional anatomic MR images.

MATERIALS AND METHODS

Baseline MR images from children with pontine pediatric high-grade gliomas were analyzed. Retrospective imaging studies included standard precontrast and postcontrast sequences and DTI. Imaging analyses included median, mean, mode, skewness, and kurtosis of the ADC histogram of the tumor volume based on T2 FLAIR and enhancement at baseline. Histone H3 mutations were identified through immunohistochemistry and/or Sanger or next-generation DNA sequencing. The log-rank test identified imaging factors prognostic of survival from the time of diagnosis. Wilcoxon rank-sum and Fisher exact tests compared imaging predictors among groups.

RESULTS

Eighty-three patients had pretreatment MR imaging and evaluable tissue sampling. The median age was 6 years (range, 0.7-17 years); 50 tumors had a K27M mutation in H3-3A, and 11, in H3C2/3. Seven tumors had histone H3 K27 alteration, but the specific gene was unknown. Fifteen were H3 wild-type. Overall survival was significantly higher in H3C2/3- compared with H3-3A-mutant tumors (P = .003) and in wild-type tumors compared with any histone mutation (P = .001). Lower overall survival was observed in patients with enhancing tumors (P = .02) compared with those without enhancement. H3C2/3-mutant tumors showed higher mean, median, and mode ADC_total values (P < .001) and ADC_enhancement (P < .004), with lower ADC_total skewness and kurtosis (P < .003) relative to H3-3A-mutant tumors.

CONCLUSIONS

ADC histogram parameters are correlated with histone H3 mutation status in pontine pediatric high-grade glioma.

The promise of metabolic imaging in diffuse midline glioma

Recent insights into histopathological and molecular subgroups of glioma have revolutionized the field of neuro-oncology by refining diagnostic categories. An emblematic example in pediatric neuro-oncology is the newly defined diffuse midline glioma (DMG), H3 K27-altered. DMG represents a rare tumor with a dismal prognosis. The diagnosis of DMG is largely based on clinical presentation and characteristic features on conventional magnetic resonance imaging (MRI), with biopsy limited by its delicate neuroanatomic location. Standard MRI remains limited in its ability to characterize tumor biology. Advanced MRI and positron emission tomography (PET) imaging offer additional value as they enable non-invasive evaluation of molecular and metabolic features of brain tumors. These techniques have been widely used for tumor detection, metabolic characterization and treatment response monitoring of brain tumors. However, their role in the realm of pediatric DMG is nascent. By summarizing DMG metabolic pathways in conjunction with their imaging surrogates, we aim to elucidate the untapped potential of such imaging techniques in this devastating disease.

Recapitulating the Key Advances in the Diagnosis and Prognosis of High-Grade Gliomas: Second Half of 2021 Update

High-grade gliomas (World Health Organization grades III and IV) are the most frequent and fatal brain tumors, with median overall survivals of 24–72 and 14–16 months, respectively. We reviewed the progress in the diagnosis and prognosis of high-grade gliomas published in the second half of 2021. A literature search was performed in PubMed using the general terms “radio* and gliom*” and a time limit from 1 July 2021 to 31 December 2021. Important advances were provided in both imaging and non-imaging diagnoses of these hard-to-treat cancers. Our prognostic capacity also increased during the second half of 2021. This review article demonstrates slow, but steady improvements, both scientifically and technically, which express an increased chance that patients with high-grade gliomas may be correctly diagnosed without invasive procedures. The prognosis of those patients strictly depends on the final results of that complex diagnostic process, with widely varying survival rates.

Evaluating drug distribution in children and young adults with diffuse midline glioma of the pons (DIPG) treated with convection-enhanced drug delivery

AIMS

To determine an imaging protocol that can be used to assess the distribution of infusate in children with DIPG treated with CED.

METHODS

13 children diagnosed with DIPG received between 3.8 and 5.7 ml of infusate, through two pairs of catheters to encompass tumor volume on day 1 of cycle one of treatment. Volumetric T2-weighted (T2W) and diffusion-weighted MRI imaging (DWI) were performed before and after day 1 of CED. Apparent diffusion coefficient (ADC) maps were calculated. The tumor volume pre and post CED was automatically segmented on T2W and ADC on the basis of signal intensity. The ADC maps pre and post infusion were aligned and subtracted to visualize the infusate distribution.

RESULTS

There was a significant increase (p < 0.001) in mean ADC and T2W signal intensity (SI) ratio and a significant (p < 0.001) increase in mean tumor volume defined by ADC and T2W SI post infusion (mean ADC volume pre: 19.8 ml, post: 24.4 ml; mean T2W volume pre: 19.4 ml, post: 23.4 ml). A significant correlation (p < 0.001) between infusate volume and difference in ADC/T2W SI defined tumor volume was observed (ADC, r = 0.76; T2W, r = 0.70). Finally, pixel-by-pixel subtraction of the ADC maps pre and post infusion demonstrated a volume of high signal intensity, presumed infusate distribution.

CONCLUSIONS

ADC and T2W MRI are proposed as a combined parameter method for evaluation of CED infusate distribution in brainstem tumors in future clinical trials.

Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities

Purpose

Predicting H3.1, TP53, and ACVR1 mutations in DIPG could aid in the selection of therapeutic options. The contribution of clinical data and multi-modal MRI were studied for these three predictive tasks. To keep the maximum number of subjects, which is essential for a rare disease, missing data were considered. A multi-modal model was proposed, collecting all available data for each patient, without performing any imputation.

Methods

A retrospective cohort of 80 patients with confirmed DIPG and at least one of the four MR modalities (T1w, T1c, T2w, and FLAIR), acquired with two different MR scanners was built. A pipeline including standardization of MR data and extraction of radiomic features within the tumor was applied. The values of radiomic features between the two MR scanners were realigned using the ComBat method. For each prediction task, the most robust features were selected based on a recursive feature elimination with cross-validation. Five different models, one based on clinical data and one per MR modality, were developed using logistic regression classifiers. The prediction of the multi-modal model was defined as the average of all possible prediction results among five for each patient. The performances of the models were compared using a leave-one-out approach.

Results

The percentage of missing modalities ranged from 6 to 11% across modalities and tasks. The performance of each individual model was dependent on each specific task, with an AUC of the ROC curve ranging from 0.63 to 0.80. The multi-modal model outperformed the clinical model for each prediction tasks, thus demonstrating the added value of MRI. Furthermore, regardless of performance criteria, the multi-modal model came in the first place or second place (very close to first). In the leave-one-out approach, the prediction of H3.1 (resp. ACVR1 and TP53) mutations achieved a balanced accuracy of 87.8% (resp. 82.1 and 78.3%).

Conclusion

Compared with a single modality approach, the multi-modal model combining multiple MRI modalities and clinical features was the most powerful to predict H3.1, ACVR1, and TP53 mutations and provided prediction, even in the case of missing modality. It could be proposed in the absence of a conclusive biopsy.

Differences in the MRI Signature and ADC Values of Diffuse Midline Gliomas with H3 K27M Mutation Compared to Midline Glioblastomas

We conducted a two-center retrospective survey on standard MRI features including apparent diffusion coefficient mapping (ADC) of diffuse midline gliomas H3 K27M-mutant (DMG) compared to midline glioblastomas H3 K27M-wildtype (midGBM-H3wt). We identified 39 intracranial DMG and 18 midGBM-H3wt tumors. Samples were microscopically re-evaluated for microvascular proliferations and necrosis. Image analysis focused on location, peritumoral edema, degree of contrast enhancement and DWI features. Within DMG, MRI features between tumors with or without histomorphological GBM features were compared. DMG occurred in 15/39 samples from the thalamus (38%), in 23/39 samples from the brainstem (59%) and in 1/39 tumors involving primarily the cerebellum (2%). Edema was present in 3/39 DMG cases (8%) versus 78% in the control (midGBM-H3wt) group (p < 0.001). Contrast enhancement at the tumor rim was detected in 17/39 DMG (44%) versus 67% in control (p = 0.155), and necrosis in 24/39 (62%) versus 89% in control (p = 0.060). Strong contrast enhancement was observed in 15/39 DMG (38%) versus 56% in control (p = 0.262). Apparent diffusion coefficient (ADC) histogram analysis showed significantly higher skewness and kurtosis values in the DMG group compared to the controls (p = 0.0016/p = 0.002). Minimum relative ADC (rADC) values, as well as the 10th and 25th rADC-percentiles, were lower in DMGs with GBM features within the DMG group (p < 0.001/p = 0.012/p = 0.027). In conclusion, DMG cases exhibited markedly less edema than midGBM-H3wt, even if histomorphological malignancy was present. Histologically malignant DMGs and midGBM-H3wt more often displayed strong enhancement, as well as rim enhancement, than DMGs without histomorphological malignancy. DMGs showed higher skewness and kurtosis values on ADC-histogram analysis compared to midGBM-H3wt. Lower minimum rADC values in DMGs indicated malignant histomorphological features, likely representing a more complex tissue microstructure.

Magnetic Resonance Imaging Characteristics of Molecular Subgroups in Pediatric H3 K27M Mutant Diffuse Midline Glioma

Purpose

Recent research identified histone H3 K27M mutations to be associated with a dismal prognosis in pediatric diffuse midline glioma (pDMG); however, data on detailed MRI characteristics with respect to H3 K27 mutation status and molecular subgroups (H3.1 and H3.3 K27M mutations) are limited.

Methods

Standardized magnetic resonance imaging (MRI) parameters and epidemiologic data of 68 pDMG patients (age <18 years) were retrospectively reviewed and compared in a) H3 K27M mutant versus H3 K27 wildtype (WT) tumors and b) H3.1 versus H3.3 K27M mutant tumors.

Results

Intracranial gliomas (n = 58) showed heterogeneous phenotypes with isointense to hyperintense signal in T2-weighted images and frequent contrast enhancement. Hemorrhage and necrosis may be present. Comparing H3 K27M mutant to WT tumors, there were significant differences in the following parameters: i) tumor localization (p = 0.001), ii) T2 signal intensity (p = 0.021), and iii) T1 signal homogeneity (p = 0.02). No significant imaging differences were found in any parameter between H3.1 and H3.3 K27M mutant tumors; however, H3.1 mutant tumors occurred at a younger age (p = 0.004). Considering spinal gliomas (n = 10) there were no significant imaging differences between the analyzed molecular groups.

Conclusion

With this study, we are the first to provide detailed MR imaging data on H3 K27M mutant pDMG with respect to molecular subgroup status in a large patient cohort. Our findings may support diagnosis and future targeted therapeutic trials of pDMG within the framework of the radiogenomics concept.

Supplementary Information

The online version of this article (10.1007/s00062-021-01120-3) contains supplementary material, which is available to authorized users.

Object Detection Improves Tumour Segmentation in MR Images of Rare Brain Tumours

Simple Summary

This study evaluates the impact of adding an object detection framework into brain tumour segmentation models, especially when the models are applied to different domains. In recent years, multiple models have been successfully applied to brain tumour segmentation tasks. However, the performance and stability of these models have never been evaluated when the training and target domain differ. In this study, we identify object detection as a simpler problem that can be injected into a segmentation model as an a priori, and which can increase the performance of our models. We propose an automatic segmentation model that, without model retraining or adaptation, showed good results when applied to a rare brain tumour.

Abstract

Tumour lesion segmentation is a key step to study and characterise cancer from MR neuroradiological images. Presently, numerous deep learning segmentation architectures have been shown to perform well on the specific tumour type they are trained on (e.g., glioblastoma in brain hemispheres). However, a high performing network heavily trained on a given tumour type may perform poorly on a rare tumour type for which no labelled cases allows training or transfer learning. Yet, because some visual similarities exist nevertheless between common and rare tumours, in the lesion and around it, one may split the problem into two steps: object detection and segmentation. For each step, trained networks on common lesions could be used on rare ones following a domain adaptation scheme without extra fine-tuning. This work proposes a resilient tumour lesion delineation strategy, based on the combination of established elementary networks that achieve detection and segmentation. Our strategy allowed us to achieve robust segmentation inference on a rare tumour located in an unseen tumour context region during training. As an example of a rare tumour, Diffuse Intrinsic Pontine Glioma (DIPG), we achieve an average dice score of 0.62 without further training or network architecture adaptation.

A Novel Radiogenomics Biomarker Based on Hypoxic-Gene Subset: Accurate Survival and Prognostic Prediction of Renal Clear Cell Carcinoma

Purpose

To construct a novel radiogenomics biomarker based on hypoxic-gene subset for the accurate prognostic prediction of clear cell renal cell carcinoma (ccRCC).

Materials and Methods

Initially, we screened for the desired hypoxic-gene subset by analysis using the GSEA database. Through univariate and multivariate cox regression hazard ratio analysis, survival-related hypoxia genes were identified, and a genomics signature was constructed in the TCGA database. Building on this, a hypoxia-gene related radiogenomics biomarker (prediction of hypoxia-genes signature by contrast-enhanced CT radiomics) was constructed in the TCIA-KIRC database by extracting features in the venous phase of contrast-enhanced CT images, selecting features using the mRMR and LASSO algorithms, and building logistic regression models. Finally, we validated the prognostic capability of the new biomarker for patients with ccRCC in an independent validation cohort at Huashan Hospital of Fudan University, Shanghai, China.

Results

The hypoxia-related genomics signature consisting of five genes (IFT57, PABPN1, RNF10, RNF19B and UBE2T) was shown to be significantly associated with survival for patients with ccRCC in the TCGA database, delineated by grouping of the signature expression as either low- or high-risk. In the TCIA database, we constructed a radiogenomics biomarker consisting of 13 radiomics features that were optimal predictors of hypoxia-gene signature expression levels (low- or high-risk) in patients at each institution, that demonstrated AUC values of 0.91 and 0.91 in the training and validation groups, respectively. In the independent validation cohort at Huashan Hospital, our radiogenomics biomarker was significantly associated with prognosis in patients with ccRCC (p=0.0059).

Conclusions

The novel prognostic radiogenomics biomarker that was constructed achieved excellent correlation with prognosis in both the cohort of TCGA/TCIA-KIRC database and the independent validation cohort of Huashan hospital patients with ccRCC. It is anticipated that this work may assist in clinical preferential treatment decisions and promote the process of precision theranostics in the future.