MR Imaging-Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma

Clinical-MRI radiomics enables the prediction of preoperative cerebral spinal fluid dissemination in children with medulloblastoma

BACKGROUND

Medulloblastoma (MB) is the most common pediatric embryonal tumor. Accurate identification of cerebral spinal fluid (CSF) dissemination is important in prognosis prediction. Both MRI of the central nervous system (CNS) and CSF cytology will appear false positive and negative. Our objective was to investigate the added value of preoperative-enhanced T1-weighted image-based radiomic features to clinical characteristics in predicting preoperative CSF dissemination for children with MB.

MATERIALS AND METHODS

This retrospective study included 84 children with histopathologically confirmed MB between November 2006 and November 2018 (training cohort, n=60; internal validation cohort, n=24). A set of cases between December 2018 and February 2020 were used for external validation (n=40). The children with normal head and spine magnetic resonance images (MRI) and no subsequent dissemination in 1 year were diagnosed as non-CSF dissemination. The CSF dissemination was manifested as intracranial or intraspinal nodular-enhanced lesions. Clinical features were collected, and conventional MRI features of preoperative head MRI examinations were evaluated. A total of 385 radiomic features were extracted from preoperative-enhanced T1-weighted images. Minimum redundancy, maximum correlation, and least absolute shrinkage and selection operator were performed to select the features with the best performance in predicting preoperative CSF dissemination. A combined clinical-MRI radiomic prediction model was developed using multivariable logistic regression. Receiver operating curve analysis (ROC) was used to validate the predictive performance. Nomogram and decision curve analysis (DCA) were developed to evaluate the clinical utility of the combined model.

RESULTS

One clinical and nine radiomic features were selected for predicting preoperative CSF dissemination. The combined model incorporating clinical and radiomic features had the best predictive performance in the training cohort with an AUC of 0.89. This was validated in the internal and external cohorts with AUCs of 0.87 and 0.73. The clinical utility of the model was confirmed by a clinical-MRI radiomic nomogram and DCA.

CONCLUSIONS

The combined model incorporating clinical, conventional MRI, and radiomic features could be applied to predict preoperative CSF dissemination for children with MB as a noninvasive biomarker, which could aid in risk evaluation.

Phase I study of intraventricular infusions of autologous ex vivo expanded NK cells in children with recurrent medulloblastoma and ependymoma

BACKGROUND

Recurrent pediatric medulloblastoma and ependymoma have a grim prognosis. We report a first-in-human, phase I study of intraventricular infusions of ex vivo expanded autologous natural killer (NK) cells in these tumors, with correlative studies.

METHODS

Twelve patients were enrolled, 9 received protocol therapy up to 3 infusions weekly, in escalating doses from 3 × 106 to 3 × 108 NK cells/m2/infusion, for up to 3 cycles. Cerebrospinal fluid (CSF) was obtained for cellular profile, persistence, and phenotypic analysis of NK cells. Radiomic characterization on pretreatment MRI scans was performed in 7 patients, to develop a non-invasive imaging-based signature.

RESULTS

Primary objectives of NK cell harvest, expansion, release, and safety of 112 intraventricular infusions of NK cells were achieved in all 9 patients. There were no dose-limiting toxicities. All patients showed progressive disease (PD), except 1 patient showed stable disease for one month at end of study follow-up. Another patient had transient radiographic response of the intraventricular tumor after 5 infusions of NK cell before progressing to PD. At higher dose levels, NK cells increased in the CSF during treatment with repetitive infusions (mean 11.6-fold). Frequent infusions of NK cells resulted in CSF pleocytosis. Radiomic signatures were profiled in 7 patients, evaluating ability to predict upfront radiographic changes, although they did not attain statistical significance.

CONCLUSIONS

This study demonstrated feasibility of production and safety of intraventricular infusions of autologous NK cells. These findings support further investigation of locoregional NK cell infusions in children with brain malignancies.

MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study

Background

Diffuse intrinsic pontine gliomas (DIPGs) are lethal pediatric brain tumors. Presently, MRI is the mainstay of disease diagnosis and surveillance. We identify clinically significant computational features from MRI and create a prognostic machine learning model.

Methods

We isolated tumor volumes of T1-post-contrast (T1) and T2-weighted (T2) MRIs from 177 treatment-naïve DIPG patients from an international cohort for model training and testing. The Quantitative Image Feature Pipeline and PyRadiomics was used for feature extraction. Ten-fold cross-validation of least absolute shrinkage and selection operator Cox regression selected optimal features to predict overall survival in the training dataset and tested in the independent testing dataset. We analyzed model performance using clinical variables (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variables.

Results

All selected features were intensity and texture-based on the wavelet-filtered images (3 T1 gray-level co-occurrence matrix (GLCM) texture features, T2 GLCM texture feature, and T2 first-order mean). This multivariable Cox model demonstrated a concordance of 0.68 (95% CI: 0.61–0.74) in the training dataset, significantly outperforming the clinical-only model (C = 0.57 [95% CI: 0.49–0.64]). Adding clinical features to radiomics slightly improved performance (C = 0.70 [95% CI: 0.64–0.77]). The combined radiomics and clinical model was validated in the independent testing dataset (C = 0.59 [95% CI: 0.51–0.67], Noether’s test P = .02).

Conclusions

In this international study, we demonstrate the use of radiomic signatures to create a machine learning model for DIPG prognostication. Standardized, quantitative approaches that objectively measure DIPG changes, including computational MRI evaluation, could offer new approaches to assessing tumor phenotype and serve a future role for optimizing clinical trial eligibility and tumor surveillance.

MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI

Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor that could lead to death. It is considered the most common pediatric cancerous brain tumor. Precise and timely diagnosis of pediatric MB and its four subtypes (defined by the World Health Organization (WHO)) is essential to decide the appropriate follow-up plan and suitable treatments to prevent its progression and reduce mortality rates. Histopathology is the gold standard modality for the diagnosis of MB and its subtypes, but manual diagnosis via a pathologist is very complicated, needs excessive time, and is subjective to the pathologists' expertise and skills, which may lead to variability in the diagnosis or misdiagnosis. The main purpose of the paper is to propose a time-efficient and reliable computer-aided diagnosis (CADx), namely MB-AI-His, for the automatic diagnosis of pediatric MB and its subtypes from histopathological images. The main challenge in this work is the lack of datasets available for the diagnosis of pediatric MB and its four subtypes and the limited related work. Related studies are based on either textural analysis or deep learning (DL) feature extraction methods. These studies used individual features to perform the classification task. However, MB-AI-His combines the benefits of DL techniques and textural analysis feature extraction methods through a cascaded manner. First, it uses three DL convolutional neural networks (CNNs), including DenseNet-201, MobileNet, and ResNet-50 CNNs to extract spatial DL features. Next, it extracts time-frequency features from the spatial DL features based on the discrete wavelet transform (DWT), which is a textural analysis method. Finally, MB-AI-His fuses the three spatial-time-frequency features generated from the three CNNs and DWT using the discrete cosine transform (DCT) and principal component analysis (PCA) to produce a time-efficient CADx system. MB-AI-His merges the privileges of different CNN architectures. MB-AI-His has a binary classification level for classifying among normal and abnormal MB images, and a multi-classification level to classify among the four subtypes of MB. The results of MB-AI-His show that it is accurate and reliable for both the binary and multi-class classification levels. It is also a time-efficient system as both the PCA and DCT methods have efficiently reduced the training execution time. The performance of MB-AI-His is compared with related CADx systems, and the comparison verified the powerfulness of MB-AI-His and its outperforming results. Therefore, it can support pathologists in the accurate and reliable diagnosis of MB and its subtypes from histopathological images. It can also reduce the time and cost of the diagnosis procedure which will correspondingly lead to lower death rates.

Radiomics of Pediatric Low-Grade Gliomas: Toward a Pretherapeutic Differentiation of BRAF-Mutated and BRAF-Fused Tumors

BACKGROUND AND PURPOSE

B-Raf proto-oncogene, serine/threonine kinase (BRAF) status has important implications for prognosis and therapy of pediatric low-grade gliomas. Currently, BRAF status classification relies on biopsy. Our aim was to train and validate a radiomics approach to predict BRAF fusion and BRAF V600E mutation.

MATERIALS AND METHODS

In this bi-institutional retrospective study, FLAIR MR imaging datasets of 115 pediatric patients with low-grade gliomas from 2 children's hospitals acquired between January 2009 and January 2016 were included and analyzed. Radiomics features were extracted from tumor segmentations, and the predictive model was tested using independent training and testing datasets, with all available tumor types. The model was selected on the basis of a grid search on the number of trees, opting for the best split for a random forest. We used the area under the receiver operating characteristic curve to evaluate model performance.

RESULTS

The training cohort consisted of 94 pediatric patients with low-grade gliomas (mean age, 9.4 years; 45 boys), and the external validation cohort comprised 21 pediatric patients with low-grade gliomas (mean age, 8.37 years; 12 boys). A 4-fold cross-validation scheme predicted BRAF status with an area under the curve of 0.75 (SD, 0.12) (95% confidence interval, 0.62-0.89) on the internal validation cohort. By means of the optimal hyperparameters determined by 4-fold cross-validation, the area under the curve for the external validation was 0.85. Age and tumor location were significant predictors of BRAF status (P values = .04 and <.001, respectively). Sex was not a significant predictor (P value = .96).

CONCLUSIONS

Radiomics-based prediction of BRAF status in pediatric low-grade gliomas appears feasible in this bi-institutional exploratory study.

Molecular correlates of cerebellar mutism syndrome in medulloblastoma

BACKGROUND

Cerebellar mutism syndrome (CMS) is a common complication following resection of posterior fossa tumors, most commonly after surgery for medulloblastoma. Medulloblastoma subgroups have historically been treated as a single entity when assessing CMS risk; however, recent studies highlighting their clinical heterogeneity suggest the need for subgroup-specific analysis. Here, we examine a large international multicenter cohort of molecularly characterized medulloblastoma patients to assess predictors of CMS.

METHODS

We assembled a cohort of 370 molecularly characterized medulloblastoma subjects with available neuroimaging from 5 sites globally, including Great Ormond Street Hospital, Christian Medical College and Hospital, the Hospital for Sick Children, King Hussein Cancer Center, and Lucile Packard Children's Hospital. Age at diagnosis, sex, tumor volume, and CMS development were assessed in addition to molecular subgroup.

RESULTS

Overall, 23.8% of patients developed CMS. CMS patients were younger (mean difference -2.05 years ± 0.50, P = 0.0218) and had larger tumors (mean difference 10.25 cm3 ± 4.60, P = 0.0010) that were more often midline (odds ratio [OR] = 5.72, P < 0.0001). In a multivariable analysis adjusting for age, sex, midline location, and tumor volume, Wingless (adjusted OR = 4.91, P = 0.0063), Group 3 (adjusted OR = 5.56, P = 0.0022), and Group 4 (adjusted OR = 8.57 P = 9.1 × 10-5) tumors were found to be independently associated with higher risk of CMS compared with sonic hedgehog tumors.

CONCLUSIONS

Medulloblastoma subgroup is a very strong predictor of CMS development, independent of tumor volume and midline location. These findings have significant implications for management of both the tumor and CMS.

A pilot radiogenomic study of DIPG reveals distinct subgroups with unique clinical trajectories and therapeutic targets

An adequate understanding of the relationships between radiographic and genomic features in diffuse intrinsic pontine glioma (DIPG) is essential, especially in the absence of universal biopsy, to further characterize the molecular heterogeneity of this disease and determine which patients are most likely to respond to biologically-driven therapies. Here, a radiogenomics analytic approach was applied to a cohort of 28 patients with DIPG. Tumor size and imaging characteristics from all available serial MRIs were evaluated by a neuro-radiologist, and patients were divided into three radiographic response groups (partial response [PR], stable disease [SD], progressive disease [PD]) based on MRI within 2 months of radiotherapy (RT) completion. Whole genome and RNA sequencing were performed on autopsy tumor specimens. We report several key, therapeutically-relevant findings: (1) Certain radiologic features on first and subsequent post-RT MRIs are associated with worse overall survival, including PD following irradiation as well as present, new, and/or increasing peripheral ring enhancement, necrosis, and diffusion restriction. (2) Upregulation of EMT-related genes and distant tumor spread at autopsy are observed in a subset of DIPG patients who exhibit poorer radiographic response to irradiation and/or higher likelihood of harboring H3F3A mutations, suggesting possible benefit of upfront craniospinal irradiation. (3) Additional genetic aberrations were identified, including DYNC1LI1 mutations in a subgroup of patients with PR on post-RT MRI; further investigation into potential roles in DIPG tumorigenesis and/or treatment sensitivity is necessary. (4) Whereas most DIPG tumors have an immunologically “cold” microenvironment, there appears to be a subset which harbor a more inflammatory genomic profile and/or higher mutational burden, with a trend toward improved overall survival and more favorable radiographic response to irradiation, in whom immunotherapy should be considered. This study has begun elucidating relationships between post-RT radiographic response with DIPG molecular profiles, revealing radiogenomically distinct subgroups with unique clinical trajectories and therapeutic targets.

MRI-based diagnosis and treatment of pediatric brain tumors: is tissue sample always needed?

Traditional management of newly diagnosed pediatric brain tumors (PBTs) consists of cranial imaging, typically magnetic resonance imaging (MRI), and is frequently followed by tissue diagnosis, through either surgical biopsy or tumor resection. Therapy regimes are typically dependent on histological diagnosis. To date, many treatment regimens are based on molecular biology. The scope of this article is to discuss the role of diagnosis and further treatment of PBTs based solely on MRI features, in light of the latest treatment protocols. Typical MRI findings and indications for surgical biopsy of these lesions are described.

Artificial intelligence in paediatric radiology: Future opportunities

Artificial intelligence (AI) has received widespread and growing interest in healthcare, as a method to save time, cost and improve efficiencies. The high-performance statistics and diagnostic accuracies reported by using AI algorithms (with respect to predefined reference standards), particularly from image pattern recognition studies, have resulted in extensive applications proposed for clinical radiology, especially for enhanced image interpretation. Whilst certain sub-speciality areas in radiology, such as those relating to cancer screening, have received wide-spread attention in the media and scientific community, children's imaging has been hitherto neglected.In this article, we discuss a variety of possible 'use cases' in paediatric radiology from a patient pathway perspective where AI has either been implemented or shown early-stage feasibility, while also taking inspiration from the adult literature to propose potential areas for future development. We aim to demonstrate how a 'future, enhanced paediatric radiology service' could operate and to stimulate further discussion with avenues for research.

Mapping of long-term cognitive and motor deficits in pediatric cerebellar brain tumor survivors into a cerebellar white matter atlas

PURPOSE

Diaschisis of cerebrocerebellar loops contributes to cognitive and motor deficits in pediatric cerebellar brain tumor survivors. We used a cerebellar white matter atlas and hypothesized that lesion symptom mapping may reveal the critical lesions of cerebellar tracts.

METHODS

We examined 31 long-term survivors of pediatric posterior fossa tumors (13 pilocytic astrocytoma, 18 medulloblastoma). Patients underwent neuronal imaging, examination for ataxia, fine motor and cognitive function, planning abilities, and executive function. Individual consolidated cerebellar lesions were drawn manually onto patients' individual MRI and normalized into Montreal Neurologic Institute (MNI) space for further analysis with voxel-based lesion symptom mapping.

RESULTS

Lesion symptom mapping linked deficits of motor function to the superior cerebellar peduncle (SCP), deep cerebellar nuclei (interposed nucleus (IN), fastigial nucleus (FN), ventromedial dentate nucleus (DN)), and inferior vermis (VIIIa, VIIIb, IX, X). Statistical maps of deficits of intelligence and executive function mapped with minor variations to the same cerebellar structures.

CONCLUSION

We identified lesions to the SCP next to deep cerebellar nuclei as critical for limiting both motor and cognitive function in pediatric cerebellar tumor survivors. Future strategies safeguarding motor and cognitive function will have to identify patients preoperatively at risk for damage to these critical structures and adapt multimodal therapeutic options accordingly.

Imaging genomics for accurate diagnosis and treatment of tumors: A cutting edge overview

Imaging genomics refers to the establishment of the connection between invasive gene expression features and non-invasive imaging features. Tumor imaging genomics can not only understand the macroscopic phenotype of tumor, but also can deeply analyze the cellular and molecular characteristics of tumor tissue. In recent years, tumor imaging genomics has been a key in the field of medicine. The incidence of cancer in China has increased significantly, which is the main reason of disease death of urban residents. With the rapid development of imaging medicine, depending on imaging genomics, many experts have made remarkable achievements in tumor screening and diagnosis, prognosis evaluation, new treatment targets and understanding of tumor biological mechanism. This review analyzes the relationship between tumor radiology and gene expression, which provides a favorable direction for clinical staging, prognosis evaluation and accurate treatment of tumors.

Incremental prognostic value and underlying biological pathways of radiomics patterns in medulloblastoma

Background

To develop a radiomics signature for predicting overall survival (OS)/progression-free survival (PFS) in patients with medulloblastoma (MB), and to investigate the incremental prognostic value and biological pathways of the radiomics patterns.

Methods

A radiomics signature was constructed based on magnetic resonance imaging (MRI) from a training cohort (n = 83), and evaluated on a testing cohort (n = 83). Key pathways associated with the signature were identified by RNA-seq (GSE151519). Prognostic value of pathway genes was assessed in a public GSE85218 cohort.

Findings

The radiomics-clinicomolecular signature predicted OS (C-index 0.762) and PFS (C-index 0.697) better than either the radiomics signature (C-index: OS: 0.649; PFS: 0.593) or the clinicomolecular signature (C-index: OS: 0.725; PFS: 0.691) alone, with a better calibration and classification accuracy (net reclassification improvement: OS: 0.298, P = 0.022; PFS: 0.252, P = 0.026). Nine pathways were significantly correlated with the radiomics signature. Average expression value of pathway genes achieved significant risk stratification in GSE85218 cohort (log-rank P = 0.016).

Interpretation

This study demonstrated radiomics signature, which associated with dysregulated pathways, was an independent parameter conferring incremental value over clinicomolecular factors in survival predictions for MB patients.

Funding

A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma

The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n = 92) and evaluated on a testing cohort (n = 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.

Exclusive Hyperfractionated Radiation Therapy and Reduced Boost Volume for Standard-Risk Medulloblastoma: Pooled Analysis of the 2 French Multicentric Studies MSFOP98 and MSFOP 2007 and Correlation With Molecular Subgroups

PURPOSE

Medulloblastoma has recently been characterized as a heterogeneous disease with 4 distinct molecular subgroups: wingless (WNT), sonic hedgehog (SHH), group 3, and group 4, with a new definition of risk stratification. We report progression-free survival, overall survival, and long-term cognitive effects in children with standard-risk medulloblastoma exclusively treated with hyperfractionated radiation therapy (HFRT), reduced boost volume, and online quality control, and we explore the prognostic value of biological characteristics in this chemotherapy-naïve population.

METHODS AND MATERIALS

Patients with standard-risk medulloblastoma were enrolled in 2 successive prospective multicentric studies, MSFOP 98 and MSFOP 2007, and received exclusive HFRT (36 Gy, 1 Gy/fraction twice daily) to the craniospinal axis followed by a boost at 68 Gy restricted to the tumor bed (1.5 cm margin), with online quality assurance before treatment. Patients with MYC or MYCN amplification were not excluded at the time of the study. We report progression-free survival and overall survival in the global population, and according to molecular subgroups as per World Health Organization 2016 molecular classification, and we present cognitive evaluations based on the Wechsler scale.

RESULTS

Data from 114 patients included in the MSFOP 98 trial from December 1998 to October 2001 (n = 48) and in the MSFOP 2007 from October 2008 to July 2013 (n = 66) were analyzed. With a median follow-up of 16.2 (range, 6.4-19.6) years for the MSFOP 98 cohort and 6.5 (1.6-9.6) years for the MSFOP 2007 cohort, 5-year overall survival and progression-free survival in the global population were 84% (74%-89%) and 74% (65%-81%), respectively. Molecular classification was determined for 91 patients (WNT [n = 19], SHH [n = 12], and non-WNT/non-SHH [n = 60]-including group 3 [n = 9], group 4 [n = 29], and not specified [n = 22]). Our results showed more favorable outcome for the WNT-activated subgroup and a worse prognosis for SHH-activated patients. Three patients had isolated extra-central nervous system relapse. The slope of neurocognitive decline in the global population was shallower than that observed in patients with a normofractionated regimen combined with chemotherapy.

CONCLUSIONS

HFRT led to a 5-year survival rate similar to other treatments combined with chemotherapy, with a reduced treatment duration of only 6 weeks. We confirm the MSFOP 98 results and the prognostic value of molecular status in patients with medulloblastoma, even in the absence of chemotherapy. Intelligence quotient was more preserved in children with medulloblastoma who received exclusive HFRT and reduced local boost, and intelligence quotient decline was delayed compared with patients receiving standard regimen. HFRT may be appropriate for patients who do not consent to or are not eligible for prospective clinical trials; for patients from developing countries for whom aplasia or ileus may be difficult to manage in a context of high cost/effectiveness constraints; and for whom shortened duration of RT may be easier to implement.

How I Treat Medulloblastoma in Children

Medulloblastoma (MB) is the most common malignant tumor of the central nervous system in children with up to a third of these tumors presenting in children under 3 years of age. Its exquisite radio and chemosensitivity renders high cure rates in children in whom optimal resection has been achieved. Optimal surgery followed by radiation alone can cure about half of these children. The addition of chemotherapy has improved the outcomes dramatically and over 70% of children over 3 years of age with optimal resection and no metastasis can expect to be cured. Increasingly, the focus is on limiting the long-term sequelae of treatment. Precise molecular characterization can enable us to identify patients who can achieve optimal outcomes even in the absence of radiation. Insights into disease biology and molecular characterization have led to dramatic changes in our understanding, risk stratification, prognostication, and treatment approach in these children. In India, there is limited access to molecular profiling, making it challenging to apply biology driven approach to treatment in each child with MB. The Indian Society of Neuro-Oncology guidelines and the SIOP PODC adapted treatment recommendations for standard-risk MB based on the current evidence and logistic realities of low-middle income countries are a useful adjunct to guide clinical practice on a day-to-day basis in our setting.

Molecular subgrouping of medulloblastoma based on few-shot learning of multitasking using conventional MR images: a retrospective multicenter study

Background

The determination of molecular subgroups—wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4—of medulloblastomas is very important for prognostication and risk-adaptive treatment strategies. Due to the rare disease characteristics of medulloblastoma, we designed a unique multitask framework for the few-shot scenario to achieve noninvasive molecular subgrouping with high accuracy.

Methods

We introduced a multitask technique based on mask regional convolutional neural network (Mask-RCNN). By effectively utilizing the comprehensive information including genotyping, tumor mask, and prognosis, multitask technique, on the one hand, realized multi-purpose modeling and simultaneously, on the other hand, promoted the accuracy of the molecular subgrouping. One hundred and thirteen medulloblastoma cases were collected from 4 hospitals during the 8-year period in the retrospective study, which were divided into 3-fold cross-validation cohorts (N = 74) from 2 hospitals and independent testing cohort (N = 39) from the other 2 hospitals. Comparative experiments of different auxiliary tasks were designed to illustrate the effect of multitasking in molecular subgrouping.

Results

Compared to the single-task framework, the multitask framework that combined 3 tasks increased the average accuracy of molecular subgrouping from 0.84 to 0.93 in cross-validation and from 0.79 to 0.85 in independent testing. The average area under the receiver operating characteristic curves (AUCs) of molecular subgrouping were 0.97 in cross-validation and 0.92 in independent testing. The average AUCs of prognostication also reached to 0.88 in cross-validation and 0.79 in independent testing. The tumor segmentation results achieved the Dice coefficient of 0.90 in both cohorts.

Conclusions

The multitask Mask-RCNN is an effective method for the molecular subgrouping and prognostication of medulloblastomas with high accuracy in few-shot learning.

Artificial intelligence and radiomics in pediatric molecular imaging

In the past decade, a new approach for quantitative analysis of medical images and prognostic modelling has emerged. Defined as the extraction and analysis of a large number of quantitative parameters from medical images, radiomics is an evolving field in precision medicine with the ultimate goal of the discovery of new imaging biomarkers for disease. Radiomics has already shown promising results in extracting diagnostic, prognostic, and molecular information latent in medical images. After acquisition of the medical images as part of the standard of care, a region of interest is defined often via a manual or semi-automatic approach. An algorithm then extracts and computes quantitative radiomics parameters from the region of interest. Whereas radiomics captures quantitative values of shape and texture based on predefined mathematical terms, neural networks have recently been used to directly learn and identify predictive features from medical images. Thereby, neural networks largely forego the need for so called "hand-engineered" features, which appears to result in significantly improved performance and reliability. Opportunities for radiomics and neural networks in pediatric nuclear medicine/radiology/molecular imaging are broad and can be thought of in three categories: automating well-defined administrative or clinical tasks, augmenting broader administrative or clinical tasks, and unlocking new methods of generating value. Specific applications include intelligent order sets, automated protocoling, improved image acquisition, computer aided triage and detection of abnormalities, next generation voice dictation systems, biomarker development, and therapy planning.

Applications of artificial intelligence in neuro-oncology

PURPOSE OF REVIEW

To discuss recent applications of artificial intelligence within the field of neuro-oncology and highlight emerging challenges in integrating artificial intelligence within clinical practice.

RECENT FINDINGS

In the field of image analysis, artificial intelligence has shown promise in aiding clinicians with incorporating an increasing amount of data in genomics, detection, diagnosis, classification, risk stratification, prognosis, and treatment response. Artificial intelligence has also been applied in epigenetics, pathology, and natural language processing.

SUMMARY

Although nascent, applications of artificial intelligence within neuro-oncology show significant promise. Artificial intelligence algorithms will likely improve our understanding of brain tumors and help drive future innovations in neuro-oncology.

Molecular Heterogeneity and Cellular Diversity: Implications for Precision Treatment in Medulloblastoma

Medulloblastoma, the most common pediatric malignant brain tumor, continues to have a high rate of morbidity and mortality in childhood. Recent advances in cancer genomics, single-cell sequencing, and sophisticated tumor models have revolutionized the characterization and stratification of medulloblastoma. In this review, we discuss heterogeneity associated with four major subgroups of medulloblastoma (WNT, SHH, Group 3, and Group 4) on the molecular and cellular levels, including histological features, genetic and epigenetic alterations, proteomic landscape, cell-of-origin, tumor microenvironment, and therapeutic approaches. The intratumoral molecular heterogeneity and intertumoral cellular diversity clearly underlie the divergent biology and clinical behavior of these lesions and highlight the future role of precision treatment in this devastating brain tumor in children.

PET imaging of medulloblastoma with an 18F-labeled tryptophan analogue in a transgenic mouse model

In vivo positron emission tomography (PET) imaging is a key modality to evaluate disease status of brain tumors. In recent years, tremendous efforts have been made in developing PET imaging methods for pediatric brain tumors. Carbon-11 labelled tryptophan derivatives are feasible as PET imaging probes in brain tumor patients with activation of the kynurenine pathway, but the short half-life of carbon-11 limits its application. Using a transgenic mouse model for the sonic hedgehog (Shh) subgroup of medulloblastoma, here we evaluated the potential of the newly developed 1-(2-[¹⁸F]fluoroethyl)-L-tryptophan (1-L-[¹⁸F]FETrp) as a PET imaging probe for this common malignant pediatric brain tumor. 1-L-[¹⁸F]FETrp was synthesized on a PETCHEM automatic synthesizer with good chemical and radiochemical purities and enantiomeric excess values. Imaging was performed in tumor-bearing Smo/Smo medulloblastoma mice with constitutive actvation of the Smoothened (Smo) receptor using a PerkinElmer G4 PET-X-Ray scanner. Medulloblastoma showed significant and specific accumulation of 1-L-[¹⁸F]FETrp. 1-L-[¹⁸F]FETrp also showed significantly higher tumor uptake than its D-enantiomer, 1-D-[¹⁸F]FETrp. The uptake of 1-L-[¹⁸F]FETrp in the normal brain tissue was low, suggesting that 1-L-[¹⁸F]FETrp may prove a valuable PET imaging probe for the Shh subgroup of medulloblastoma and possibly other pediatric and adult brain tumors.

Pediatric Patients With SHH Medulloblastoma Fail Differently as Compared With Adults: Possible Implications for Treatment Modifications

PURPOSE

The purpose of this work was to study the diversity of sonic hedgehog (SHH) medulloblastoma across different age groups with an emphasis on patterns of relapse.

METHODS

All data for the study were obtained through review of medical records, imaging, radiation charts, treatment planning, and chemotherapy details.

RESULTS

Sixty-three patients with SHH medulloblastoma were identified from a prospectively maintained database and classified into 3 groups-infantile: ≤3 years (i-SHH, n=11); pediatric: >3 to <18 years (p-SHH, n=21); and adult: ≥18 years (a-SHH; n=31). Lateralized tumors were common with increasing age (81% a-SHH, 67% p-SHH, 27% i-SHH; P=0.01). Large cell anaplastic histology was relatively common for p-SHH (33%), while the nodular/desmoplastic variant was more frequent in i-SHH (64%) and adults (51%). Median follow-up was 38 months (range, 5 to 91 mo). Five-year event-free survival was 80%, 31%, and 52% for i-SHH, p-SHH, and a-SHH, respectively (P=0.001). Median time to failure for p-SHH and a-SHH were 12 and 36 months, respectively. For p-SHH, 83% were metastatic relapses compared with localized failure in 75% for a-SHH. Five-year overall survival for i-SHH, p-SHH, and a-SHH were 91%, 31%, and 70%, respectively (P=0.001). On univariate analysis, event-free survival was significantly worse for superiorly located tumors (P=0.01), nondesmoplastic histology (P=0.02), and histology alone for overall survival (P=0.04) (none on multivariate analysis).

CONCLUSIONS

SHH medulloblastoma demonstrates varied outcomes depending on age, with p-SHH associated with early and metastatic relapses, while for a-SHH it tends to be delayed and localized.

Texture Analysis of T1-Weighted Contrast-Enhanced Magnetic Resonance Imaging Potentially Predicts Outcomes of Patients with Non-Wingless-Type/Non-Sonic Hedgehog Medulloblastoma

OBJECTIVE

To investigate whether tumor texture features derived from preoperative T1-weighted magnetic resonance imaging (MRI) are associated with overall survival (OS) of patients with non-wingless-type (WNT)/non-sonic hedgehog (SHH) medulloblastoma.

METHODS

We retrospectively reviewed 38 patients with non-WNT/non-SHH (encompassing group 3 and group 4) medulloblastoma treated with surgery in our institution from 2013 to 2016. All patients were followed-up for at least 2 years or until death. Primary tumor traditional parameters were evaluated, and texture features were extracted from preoperative T1-weighted MRI, including 4 features from the histogram matrix and 6 textures from the gray-level co-occurrence matrix (GLCM). Texture features were dichotomized into 2 subgroups based on their optimal cutoff values obtained from receiver operating characteristics curve analysis. Two-year OS was compared between the dichotomized subgroups using the Kaplan-Meier analysis and log-rank test. Multivariate Cox regression analysis was performed to determine independent prognostic factors.

RESULTS

The therapy regimen was the only basic characteristic significantly related to 2-year OS (P = 0.015). Two features of the GLCM were shown to be significantly associated with 24-month OS. Multivariate Cox regression analysis revealed that GLCM homogeneity (adjusted hazard ratio, 0.145; P = 0.013) was an independent prognostic predictor for patients.

CONCLUSIONS

Texture analysis on T1-weighted contrast-enhanced MRI potentially serves as a prognostic predictor of survival for patients with non-WNT/non-SHH medulloblastoma.

Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes

BACKGROUND

Radiomics-based non-invasive biomarkers are promising to facilitate the translation of therapeutically related molecular subtypes for treatment allocation of patients with head and neck squamous cell carcinoma (HNSCC).

METHODS

We included 113 HNSCC patients from The Cancer Genome Atlas (TCGA-HNSCC) project. Molecular phenotypes analyzed were RNA-defined HPV status, five DNA methylation subtypes, four gene expression subtypes and five somatic gene mutations. A total of 540 quantitative image features were extracted from pre-treatment CT scans. Features were selected and used in a regularized logistic regression model to build binary classifiers for each molecular subtype. Models were evaluated using the average area under the Receiver Operator Characteristic curve (AUC) of a stratified 10-fold cross-validation procedure repeated 10 times. Next, an HPV model was trained with the TCGA-HNSCC, and tested on a Stanford cohort (N = 53).

FINDINGS

Our results show that quantitative image features are capable of distinguishing several molecular phenotypes. We obtained significant predictive performance for RNA-defined HPV+ (AUC = 0.73), DNA methylation subtypes MethylMix HPV+ (AUC = 0.79), non-CIMP-atypical (AUC = 0.77) and Stem-like-Smoking (AUC = 0.71), and mutation of NSD1 (AUC = 0.73). We externally validated the HPV prediction model (AUC = 0.76) on the Stanford cohort. When compared to clinical models, radiomic models were superior to subtypes such as NOTCH1 mutation and DNA methylation subtype non-CIMP-atypical while were inferior for DNA methylation subtype CIMP-atypical and NSD1 mutation.

INTERPRETATION

Our study demonstrates that radiomics can potentially serve as a non-invasive tool to identify treatment-relevant subtypes of HNSCC, opening up the possibility for patient stratification, treatment allocation and inclusion in clinical trials. FUND: Dr. Gevaert reports grants from National Institute of Dental & Craniofacial Research (NIDCR) U01 DE025188, grants from National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health (NIBIB), R01 EB020527, grants from National Cancer Institute (NCI), U01 CA217851, during the conduct of the study; Dr. Huang and Dr. Zhu report grants from China Scholarship Council (Grant NO:201606320087), grants from China Medical Board Collaborating Program (Grant NO:15-216), the Cyrus Tang Foundation, and the Zhejiang University Education Foundation during the conduct of the study; Dr. Cintra reports grants from São Paulo State Foundation for Teaching and Research (FAPESP), during the conduct of the study.