Simultaneous bilateral application of the Scepter mini balloon microcatheter for occlusion of ethmoidal dural arteriovenous fistulas

Ethmoidal dural arteriovenous fistulas are a rare entity accounting for 10 % of all dAVFs.3-6 Haemorrhage occurs in up to 91 % of cases, which is a particularly high risk and warrants therapeutic intervention.8-9 Endovascular treatment for these fistulas using the conventional detachable microcatheter technique is associated with certain limitations and risks; 8.3 % rate of incomplete obliteration and an 8.3 % rate of complications. Complications include reflux of liquid embolic agent, posterior ischaemic optic neuropathy, acute visual loss, and small subdural haematoma secondary to a micro-perforation.8,10-12 We present our recent experience with the Scepter Mini Balloon Microcatheter for the endovascular treatment of ethmoidal dural arteriovenous fistulas in 3 patients, involving bilateral simultaneous inflation of the balloon. It demonstrates a novel application of this technology with good outcomes. It supports the use of this microcatheter in treating ethmoidal dural arteriovenous fistulas endovascularly, either as a first-line option or as an adjunct to surgery.

Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review

Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists' proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists' capabilities and ameliorating patient outcomes in the realm of breast cancer management.

Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey

Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with social skills, repetitive activities, speech, and nonverbal communication. The Centers for Disease Control (CDC) estimates that 1 in 44 American children currently suffer from ASD. The current gold standard for ASD diagnosis is based on behavior observational tests by clinicians, which suffer from being subjective and time-consuming and afford only late detection (a child must have a mental age of at least two to apply for an observation report). Alternatively, brain imaging-more specifically, magnetic resonance imaging (MRI)-has proven its ability to assist in fast, objective, and early ASD diagnosis and detection. With the recent advances in artificial intelligence (AI) and machine learning (ML) techniques, sufficient tools have been developed for both automated ASD diagnosis and early detection. More recently, the development of deep learning (DL), a young subfield of AI based on artificial neural networks (ANNs), has successfully enabled the processing of brain MRI data with improved ASD diagnostic abilities. This survey focuses on the role of AI in autism diagnostics and detection based on two basic MRI modalities: diffusion tensor imaging (DTI) and functional MRI (fMRI). In addition, the survey outlines the basic findings of DTI and fMRI in autism. Furthermore, recent techniques for ASD detection using DTI and fMRI are summarized and discussed. Finally, emerging tendencies are described. The results of this study show how useful AI is for early, subjective ASD detection and diagnosis. More AI solutions that have the potential to be used in healthcare settings will be introduced in the future.

Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review

Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.

The Role of Structure MRI in Diagnosing Autism

This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of 97±2%. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.

Texture and shape analysis of diffusion-weighted imaging for thyroid nodules classification using machine learning

PURPOSE

To assess whether the integration between (a) functional imaging features that will be extracted from diffusion-weighted imaging (DWI); and (b) shape and texture imaging features as well as volumetric features that will be extracted from T2-weighted magnetic resonance imaging (MRI) can noninvasively improve the diagnostic accuracy of thyroid nodules classification.

PATIENTS AND METHODS

In a retrospective study of 55 patients with pathologically proven thyroid nodules, T2-weighted and diffusion-weighted MRI scans of the thyroid gland were acquired. Spatial maps of the apparent diffusion coefficient (ADC) were reconstructed in all cases. To quantify the nodules' morphology, we used spherical harmonics as a new parametric shape descriptor to describe the complexity of the thyroid nodules in addition to traditional volumetric descriptors (e.g., tumor volume and cuboidal volume). To capture the inhomogeneity of the texture of the thyroid nodules, we used the histogram-based statistics (e.g., kurtosis, entropy, skewness, etc.) of the T2-weighted signal. To achieve the main goal of this paper, a fusion system using an artificial neural network (NN) is proposed to integrate both the functional imaging features (ADC) with the structural morphology and texture features. This framework has been tested on 55 patients (20 patients with malignant nodules and 35 patients with benign nodules), using leave-one-subject-out (LOSO) for training/testing validation tests.

RESULTS

The functionality, morphology, and texture imaging features were estimated for 55 patients. The accuracy of the computer-aided diagnosis (CAD) system steadily improved as we integrate the proposed imaging features. The fusion system combining all biomarkers achieved a sensitivity, specificity, positive predictive value, negative predictive value, F1-score, and accuracy of 92.9 % (confidence interval [CI]: 78.9 % -- 99.5 % ), 95.8 % (CI: 87.4 % -- 99.7 % ), 93 % (CI: 80.7 % -- 99.5 % ), 96 % (CI: 88.8 % -- 99.7 % ), 92.8 % (CI: 83.5 % -- 98.5 % ), and 95.5 % (CI: 88.8 % -- 99.2 % ), respectively, using the LOSO cross-validation approach.

CONCLUSION

The results demonstrated in this paper show the promise that integrating the functional features with morphology as well as texture features by using the current state-of-the-art machine learning approaches will be extremely useful for identifying thyroid nodules as well as diagnosing their malignancy.

Endoscopic endonasal surgery for Clival Chordomas - a single institution experience and short term outcomes

Purpose: Clival Chordomas are locally aggressive tumours which pose a significant treatment challenge. Endoscopic endonasal approach for clival chordomas is correlated with higher resection rates and lower morbidity rates in comparison to open approaches. We present our initial single institution experience and short-term patient outcomes following endoscopic endonasal approach for resection of clival chordomas. Materials and methods: This is a retrospective analysis of ten patients undergoing endoscopic endonasal approach for clival chordomas in our neurosurgical unit over a 6 year period between August 2010 and September 2016. The procedures were performed using two surgeons, four hands, binostril endoscopic endonasal approach with a Karl Storz^® endoscope and intraoperative BrainLab^® image guidance. Results: Overall 15 endoscopic endonasal approach resections of clival chordoma were performed in 10 patients with median follow up period of 39.5 months (range 9-76). Gross total resection was achieved in 4 cases (40%), near total resection in 4 cases (40%) and subtotal resection in 2 cases (20%). 5 cases (50%) required revision resections. Cerebrospinal fluid leak occurred in 2 patients. 1 case of meningitis occurred in a patient with revision surgery. There were no new neurological deficits post operatively with 3 patients demonstrating resolution of diplopia post operatively. No recurrence occurred following gross total resection. 1 out of 4 cases of near total resection showed evidence of progression during the follow up period. Both cases of subtotal resection demonstrated evidence of progression with one dying of unrelated cause during the follow up period. Conclusion: Endoscopic endonasal approach represents a safe technique for debulking and resection of clival chordomas. Due to the rarity of clival chordomas, it is important that patients with this pathology are managed in high volume skull base centres where a multi-disciplinary team approach is available.

Skull base surgery for tumors in children: long-term clinical and functional outcome

OBJECT

Skull base tumors in children are rare but require complex approaches with potential morbidity to the developing craniofacial skeleton, in addition to tumor-related morbidity. Reports of long-term clinical and functional outcome following skull base approaches in children are scarce. The authors report long-term outcome in children with tumors undergoing multidisciplinary skull base surgery.

METHODS

A retrospective analysis was undertaken of children undergoing surgery at a single institution between 1998 and 2008 for benign and malignant lesions of the anterior, middle, or posterior cranial base. Patients with craniopharyngioma, pituitary tumors, and optic glioma were excluded. Histology, surgical morbidity, length of hospital stay, progression-free survival, and adjuvant therapy were recorded. Functional and cognitive outcome was assessed prospectively using the Late Effects Severity Score (LESS).

RESULTS

Twenty-three children ranging in age from 13 months to 15 years underwent skull base approaches for resection of tumors during the study period. The median follow-up duration was 60 months. Tumor types included meningioma, schwannoma, rhabdomyosarcoma, neuroblastoma, angiofibroma, and chordoma. Complete resection was achieved in 12 patients (52%). Thirteen patients (57%) had benign histology. The median hospital stay was 7 days. There were 3 deaths, 1 perioperative and 2 from tumor progression. Two patients had CSF leakage (9%) and 2 developed meningitis. Two children (9%) had residual neurological deficit at last follow-up evaluation. Thirteen (59%) of 22 surviving patients received adjuvant therapy. The majority of the patients remain in mainstream education and 19 of the 20 surviving children have an LESS of 3 or lower.

CONCLUSIONS

Children tolerate complex skull base procedures well, with minimal surgical-related morbidity as well as good long-term tumor control rates and functional outcomes from maximal safe resection combined with adjuvant treatment when required.

Markers of cell division cycle in glioblastoma: significance in prediction of treatment response and patient prognosis

OBJECTIVE

To investigate whether expression of regulatory components of the cell division cycle can be used independently to predict survival and response to adjuvant therapy in glioblastomas.

METHOD

A tissue micro-array, constructed using glioblastomas (n = 66), was stained using antibodies against minichromosome maintenance protein-2 (Mcm-2), expressed throughout the cell-division cycle; geminin, a protein that prevents re-initiation of DNA replication; and cyclin A, an S-phase cyclin. A semi-quantitative labelling index (LI) was calculated using an average of 18 high-power fields (hpf) in three replicate cores. The patients were divided into two groups: Group 1 (n = 50) underwent surgery and radiotherapy with 24 patients receiving temozolomide, and Group 2 (n = 16) received surgical treatment only.

RESULTS

The LIs (median +/- IQR) for Group 1 were as follows: Mcm-2, 36.7% (22.9%-51.8%); geminin, 7.8% (5.8%-10.5%); and cyclin A, 4.2% (2.4%-6.9%). Elevated LIs, higher than the median, for geminin and cyclin A correlated with prolonged survival when the tumours received adjuvant therapy (Kaplan-Meier curves, p = 0.0046 and p = 0.0063 for geminin and cyclin A, respectively). Linear regression analysis revealed positive correlations with survival for Mcm-2 (p = 0.0376), geminin (p = 0.0006) and cyclin A (p = 0.004). In Group 2, there was no relationship between the patient survival and the LI for any marker.

CONCLUSIONS

Geminin and cyclin A, each show potential as independent prognostic markers in glioblastomas receiving adjuvant therapy. This may reflect the fact that both geminin and cyclin A estimate proliferating tumour cell subpopulations sensitive to radio/chemotherapy. These markers could provide valuable prognostic information, even in small biopsies, especially if combined with O(6)MGMT expression and 1p;19q deletion status.

Importance of intraoperative magnetic resonance imaging for pediatric brain tumor surgery

Background:

High-field intraoperative MRI (IoMRI) is gaining increasing recognition as an invaluable tool in pediatric brain tumor surgery where the extent of tumor resection is a major prognostic factor. We report the initial experience of a dedicated pediatric 3-T intraoperative MRI (IoMRI) unit with integrated neuronavigation in the management of pediatric brain tumors.

Methods:

Seventy-three children (mean age 9.5 years; range 0.2–19 years) underwent IoMRI between October 2009 and January 2012, during 79 brain tumor resections using a 3-T MR scanner located adjacent to the neurosurgical operating theater that is equipped with neuronavigation facility. IoMRI was performed either to assess the extent of tumor resection after surgical impression of complete/intended tumor resection or to update neuronavigation. The surgical aims, IoMRI findings, extent of tumor resection, and follow-up data were reviewed.

Results:

Complete resection was intended in 47/79 (59%) operations. IoMRI confirmed complete resection in 27/47 (57%). IoMRI findings led to further resection in 12/47 (26%). In 7/47 (15%), IoMRI was equivocal for residual tumor and no evidence of residual tumor was found on re-inspection. In 32/79 (41%) operations, the surgical aim was partial tumor resection. In this subset, surgical resection was extended following IoMRI in 13/32 (41%) operations. None of the patients required early second look procedure for residual disease.

Conclusions:

At our institution, IoMRI has led to increased rate of tumor resection and a change in surgical strategy with further tumor resection in 32% of patients. While interpreting IoMRI, it is important to be aware of the known pitfalls.