Automatic quantification of the microvascular density on whole slide images, applied to paediatric brain tumours

Angiogenesis is a key phenomenon for tumour progression, diagnosis and treatment in brain tumours and more generally in oncology. Presently, its precise, direct quantitative assessment can only be done on whole tissue sections immunostained to reveal vascular endothelial cells. But this is a tremendous task for the pathologist and a challenge for the computer since digitised whole tissue sections, whole slide images (WSI), contain typically around ten gigapixels. We define and implement an algorithm that determines automatically, on a WSI at objective magnification $40\times$, the regions of tissue, the regions without blur and the regions of large puddles of red blood cells, and constructs the mask of blur-free, significant tissue on the WSI. Then it calibrates automatically the optical density ratios of the immunostaining of the vessel walls and of the counterstaining, performs a colour deconvolution inside the regions of blur-free tissue, and finds the vessel walls inside these regions by selecting, on the image resulting from the colour deconvolution, zones which satisfy a double-threshold criterion. A mask of vessel wall regions on the WSI is produced. The density of microvessels is finally computed as the fraction of the area of significant tissue which is occupied by vessel walls. We apply this algorithm to a set of 186 WSI of paediatric brain tumours from World Health Organisation grades I to IV. The segmentations are of very good quality although the set of slides is very heterogeneous. The computation time is of the order of a fraction of an hour for each WSI on a modest computer. The computed microvascular density is found to be robust and strongly correlates with the tumour grade. This method requires no training and can easily be applied to other tumour types and other stainings

Laser interstitial thermal therapy is effective and safe for the treatment of brain tumors in NF1 patients after cerebral revascularization for moyamoya angiopathy: a report on two cases

BackgroundThe co-occurrence of moyamoya vasculopathy and extra-optic pathway tumors is rare in neurofibromatosis type 1 (NF1), with only four cases described in the literature. Brain surgery in these patients may be challenging because of the risk of brain infarction after skin and dural incision. Given its percutaneous and minimally invasive nature, laser interstitial thermal therapy (LITT) is an ideal option for the treatment of brain tumors in these patients. Here, we report on two patients with NF1 and moyamoya syndrome (MMS) treated for a brain glioma with LITT, after cerebral revascularization.CasesThe first patient, with familial NF1, underwent bilateral indirect revascularization with multiple burr holes (MBH) for symptomatic MMS. Two years later, she was diagnosed with a left temporal tumor, with evidence of radiologic progression over 10 months. The second patient, also with familial NF1, developed unilateral MMS when he was 6 years old and was treated with MBH. At the age of 15 years, MRI showed a right cingular lesion, growing on serial MRIs. Both patients underwent LITT with no perioperative complications; they are progression free at 10 and 12 months, respectively, and the tumors have decreased in volume.DiscussionWhile the association of extra-optic neoplasm and moyamoya angiopathy is seldom reported in NF1, tumor treatment is challenging in terms of both avoiding stroke and achieving oncological control. Here, we show in 2 cases, that LITT could be a safe and effective option in these rare conditions

European recommendations on practices in pediatric neuroradiology: consensus document from the European Society of Neuroradiology (ESNR), European Society of Paediatric Radiology (ESPR) and European Union of Medical Specialists Division of Neuroradiology (UEMS)

Pediatric neuroradiology is a subspecialty within radiology, with possible pathways to train within the discipline from neuroradiology or pediatric radiology. Formalized pediatric neuroradiology training programs are not available in most European countries. We aimed to construct a European consensus document providing recommendations for the safe practice of pediatric neuroradiology. We particularly emphasize imaging techniques that should be available, optimal site conditions and facilities, recommended team requirements and specific indications and protocol modifications for each imaging modality employed for pediatric neuroradiology studies. The present document serves as guidance to the optimal setup and organization for carrying out pediatric neuroradiology diagnostic and interventional procedures. Clinical activities should always be carried out in full agreement with national provisions and regulations. Continued education of all parties involved is a requisite for preserving pediatric neuroradiology practice at a high level

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

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

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

International audiencePurpose: 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 dierent 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 dierent 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 biops

Phenotypic diversity of brain MRI patterns in mitochondrial aminoacyl-tRNA synthetase mutations

International audienceBackground and purpose: Mitochondrial aminoacyl-tRNA synthetases-encoded by ARS2 genes-are evolutionarily conserved enzymes that catalyse the attachment of amino acids to their cognate tRNAs, ensuring the accuracy of the mitochondrial translation process. ARS2 gene mutations are associated with a wide range of clinical presentations affecting the CNS.Methods: Two senior neuroradiologists analysed brain MRI of 25 patients (age range: 3 d-25 yrs.; 11 males; 14 females) with biallelic pathogenic variants of 11 ARS2 genes in a retrospective study conducted between 2002 and 2019.Results: Though several combinations of brain MRI anomalies were highly suggestive of specific aetiologies (DARS2, EARS2, AARS2 and RARS2 mutations), our study detected no MRI pattern common to all patients. Stroke-like lesions were associated with pathogenic SARS2 and FARS2 variants. We also report early onset cerebellar atrophy and calcifications in AARS2 mutations, early white matter involvement in RARS2 mutations, and absent involvement of thalami in EARS2 mutations. Finally, our findings show that normal brain MRI results do not exclude the presence of ARS2 mutations: 5 patients with normal MRI images were carriers of pathogenic IARS2, YARS2, and FARS2 variants.Conclusion: Our study extends the spectrum of brain MRI anomalies associated with pathogenic ARS2 variants and suggests ARS2 mutations are largely underdiagnosed

A novel LARGE1-AFF2 fusion expanding the molecular alterations associated with the methylation class of neuroepithelial tumors with PATZ1 fusions

International audienceA novel DNA methylation class of tumor within the central nervous system, the "neuroepithelial tumor (NET), PATZ1 fusion-positive" has recently been identified in the literature, characterized by EWSR1-and MN1-PATZ1 fusions. The cellular origin of this tumor type remains unknown, wavering between glioneuronal or mesenchymal (as round cell sarcomas with EWSR1-PATZ1 of the soft tissue). Because of the low number of reported cases, this tumor type will not be added to the 2021 World Health Organization Classification of Tumors of the Central Nervous System (CNS). Herein, we report one case of a CNS tumor classified by DNA methylation analysis as NET-PATZ1 but harboring a novel LARGE1-AFF2 fusion which has until now never been described in soft tissue or the CNS. We compare its clinical, histopathological, immunophenotypical, and genetic features with those previously described in NET-PATZ1. Interestingly, the current case presented histopathological (astroblastoma-like features, glioneuronal phenotype), clinical (with a favorable course), genetic (1p loss), and epigenetic (DNA-methylation profiling) similarities to previously reported cases of NET-PATZ1. Our results added data suggesting that different histomolecular tumor subtypes seem to be included within the methylation class "NET, PATZ1 fusion-positive", including non PATZ1 fusions, and that further cases are needed to better characterize them