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

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

Topographic variability of the normal circle of Willis anatomy on a paediatric population

International audienceAbstract Long-term sequelae are major limitations of radiation therapy use, especially for childhood brain tumour. Circle of Willis irradiation strongly increases the long-term risk of stroke, but to establish dose-response relationship, anticipating long-term effects of new techniques, requires to perform accurate and reproducible dosimetric estimations in large cohorts of patients having received radiotherapy decades ago. For the accuracy of retrospective dose reconstruction, the topographic variability of the Circle of Willis arteries is crucial. In order to improve retrospective dosimetric studies and dose-volume estimates to the typical Circle of Willis arteries, we aim to study the inter-individual topographic variability of these structures. Thirty-eight time of flight MRI sequences of children aged 2–17 years in both genders were investigated. A region growth algorithm was used for the segmentation of the cerebral arteries. A rigid registration in a common skull was performed following the anatomy of skull base foramina. The Posterior clinoid processes of the sella turcica were used as reference landmark (R0), and 5 key landmarks were chosen in each segmented Circle of Willis, then distances between the 5 landmarks and R0 were calculated for each of the 38 subjects. The distance between R0 and each landmark of the Circle of Willis followed a normal distribution, the average values ranging from 13.6 to 17.0 mm, and the standard deviations ranged from 2.6 to 3.0 mm, i.e. less than a fifth of the average value. The perimeter of the Circle of Willis was longer in older subjects, this increase being isotropic. Our study shows a remarkably low topographic variability of the typical Circle of Willis. An important result, allowing reliable anthropomorphic phantoms-based retrospective estimations of the radiation doses delivered to these arterial structures during radiotherapy treatment

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

NF2 and ZFTA evaluation in the diagnostic algorithm of pediatric posterior fossa ependymoma with H3K27ME3 retained expression

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