Susceptibility-weighted imaging (SWI) is a technique useful for evaluation of the internal structures of brain tumors, including microvasculature and microbleeds. Intratumoral patterns of magnetic susceptibility can be quantified by means of fractal analysis. Here, we propose a radiomics methodological pipeline to merge advanced fractal-based computational modelling with statistical analysis to objectively characterize the fingerprint of gliomas and brain metastases. Forty-seven patients with glioma (grades II-IV, according to the WHO 2016 classification system) and fourteen with brain metastases underwent 3 Tesla MRI using a SWI protocol. All images underwent computational analysis aimed to quantify three Euclidean parameters (related to tumor and SWI volume) and five fractal-based parameters (related to the pixel distribution and geometrical complexity of the SWI patterns). Principal components analysis, linear and quadratic discriminant analysis, K-nearest neighbor and support vector machine methods were used to discriminate between tumor types. The combination of parameters offered an objective evaluation of the SWI pattern in gliomas and brain metastases. The model accurately predicted 88% of glioblastoma, according to the quantification of intratumoral SWI features, failing to discriminate the other types. SWI is not normally used to classify brain tumors, however fractal-based multi-parametric computational analysis can be used to characterize intratumoral SWI patterns to objectively quantify tumors-related features. Specific parameters still have to be identified to provide completely automatic computerized differential diagnosis.

Background: The radiological differential diagnosis between tumor recurrence and radiation-induced necrosis (ie, pseudoprogression) is of paramount importance in the management of glioma patients.

Objective: This research aims to develop a deep learning methodology for automated differentiation of tumor recurrence from radiation necrosis based on routine magnetic resonance imaging (MRI) scans.

Methods: In this retrospective study, 146 patients who underwent radiation therapy after glioma resection and presented with suspected recurrent lesions at the follow-up MRI examination were selected for analysis. Routine MRI scans were acquired from each patient, including T1, T2, and gadolinium-contrast-enhanced T1 sequences. Of those cases, 96 (65.8%) were confirmed as glioma recurrence on postsurgical pathological examination, while 50 (34.2%) were diagnosed as necrosis. A light-weighted deep neural network (DNN)(ie, efficient radionecrosis neural network [ERN-Net]) was proposed to learn radiological features of gliomas and necrosis from MRI scans. Sensitivity, specificity, accuracy, and area under the curve (AUC) were used to evaluate performance of the model in both image-wise and subject-wise classifications. Preoperative diagnostic performance of the model was also compared to that of the state-of-the-art DNN models and five experienced neurosurgeons.

Results: DNN models based on multimodal MRI outperformed single-modal models. ERN-Net achieved the highest AUC in both image-wise (0.915) and subject-wise (0.958) classification tasks. The evaluated DNN models achieved an average sensitivity of 0.947 (SD 0.033), specificity of 0.817 (SD 0.075), and accuracy of 0.903 (SD 0.026), which were significantly better than the tested neurosurgeons (P=. 02 in sensitivity and P<. 001 in specificity and accuracy).

Conclusions: Deep learning offers a useful computational tool for the differential diagnosis between recurrent gliomas and necrosis. The proposed ERN-Net model, a simple and effective DNN model, achieved excellent performance on routine MRI scans and showed a high clinical applicability.

medinform.jmir.org

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Background

Microvascular decompression (MVD) is an effective treatment for drug-resistant trigeminal neuralgia and hemifacial spasm. However, failure of symptomatic improvement can arise from difficulties in identifying and/or decompressing the offending vessel. Microscopic and endoscopic techniques have been used to improve visualisation and safety of the procedure but there are limitations to each technique.

Method

A 3D exoscopic endoscope-assisted MVD technique is described, including advice on potential pitfalls.

Conclusion

Compared with the standard microscope-assisted techniques, the 3D exoscopic endoscope-assisted MVD offers an improved visualisation without compromising the field of view within and outside the surgical field.

Purpose

Glioblastomas (GBM) and metastases are the most frequent malignant brain tumors in the adult population. Their presentation on conventional MRI is quite similar, but treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of this study was to determine whether fractal, texture, or both MR image analyses could aid in differentiating glioblastoma from solitary brain metastasis.

Method

In a retrospective study of 55 patients (30 glioblastomas and 25 solitary metastases) who underwent T2W/SWI/CET1 MRI, quantitative parameters of fractal and texture analysis were estimated, using box-counting and gray level co-occurrence matrix (GLCM) methods.

Results

All five GLCM parameters obtained from T2W images showed significant difference between glioblastomas and solitary metastases, as well as on CET1 images except correlation (S_COR), contrary to SWI images which showed different values of two parameters (angular second moment-S_ASM and contrast-S_CON). Only three fractal features (binary box dimension-D_bin, normalized box dimension-D_norm and lacunarity-λ) measured on T2W and D_norm measured on CET1 images significantly differed GBMs from solitary metastases. The highest sensitivity and specificity were obtained from inverse difference moment (S_IDM) on T2W and S_IDM on CET1 images, respectively. Combination of several GLCM parameters yielded better results. The processing of T2W images provided the most significantly different parameters between the groups, followed by CET1 and SWI images.

Conclusions

Computational-aided quantitative image analysis may potentially improve diagnostic accuracy. According to our results texture features are more significant than fractal-based features in differentiation glioblastoma from solitary metastasis.

Over the past few years, the approach to treating diseases has switched from the solo expertise of a single specialist to treatment orchestrated by a multidisciplinary team, with the intention of merging the opinions of various experts in an optimum way to treat patients. Multidisciplinary teams are the backbone of the decision making that forms the basis of modern medicine. The multi disciplinary team offers a more hierarchical, highlevel, and complex system compared with a single specialist, and aims to determine the most plausible differential diagnosis, relate this to the most probable prognosis, and select the best course of treatment. An incorrect differential diagnosis might lead to error cascades, the consequences of which are inaccurate expectations and imprecise treatments. However, multidisciplinary teams can still be limited by insufficient expertise of the single members, outofdate knowledge on the relevant evidence based medical literature, consolidated teams that are not open to new opinions or participants, and logistical or communication barriers. In recent years, the use of artificial intelligence (AI) in medicine has greatly increased, although the field is still challenged by particular concerns and the absence of validation against the opinions of experts or across different populations1 and by the absence of a standardised method of analysis. 2 AI methods can be used to help clinicians and surgeons with decision making, to reduce errors in judgment and improve differential diagnosis, and thereby improve the choice of treatment and patient outcome. The underlying fear of a dystopic challenge wherein AI is in competition with human experts can be overcome by viewing AI as a means to create a socalled augmented physician, with the exper tise of specialists enhanced by AI. This alternative view would shift the paradigm from one of humanversus machine, to humanandmachine. 3 Machines will not replace physicians, but physicians using AI will soon replace those not using it. Likewise, decisions from the multidisciplinary team meetings of the next generation are likely to be implemented by the machine, as AI will support more objective and reliable decision making, reducing the limitations of human error and subjectivity related to the single specialist or team. As stated by Enrico Coiera, 4 the way to prepare for these coming times is adapting clinical education to the digital world, to build the capacity for enhanced decision making and prognostication among physicians by means of AI. Continuous vigilance around advice validation will still be of paramount importance, 5 to avoid physicians blindly following the machinetracked pathway over the course of a patient’s diagnosis and therapy. In addressing the need to adapt in response to the fate of medicine in this time of AI, 4 the fate of multidisciplinary teams is to change as well.

Background: Following removal of an arteriovenous malformation of the brain (bAVM) the redistribution of blood can impose several clinically challenging issues including intracranial haemorrhage and arteriovenous capillary hypertensive syndrome. The underlying mechanism of such complications remains controversial, although control of blood pressure has been recognised as an integral component in haemorrhage prevention. Serial daily non-invasive monitoring for patients in this instance would be beneficial in improving management. Transcranial colour duplex (TCD) is a potential technique for providing pressure measurements and real-time, dynamic, haemodynamic spectra. Aim: To establish whether blood outflow velocity in the middle cerebral vein (MCV) can be quantified in the days following bAVM resection and whether values differ from other types of intracranial surgery. Methods: Blood pressure and TCD of 13 patients (aged 46±19 y, 7 female) having bAVMs resected and 7 patients having other intracranial surgeries (control group, aged 48±15 y, 6 female) were studied for days 1 to 3 following surgery. Ultrasound via the transtemporal window was used to obtain diameter, as well as peak and end-diastolic velocity of the MCV. Brachial blood pressure was also obtained using an automatic oscillometric blood pressure monitor. Results: Systolic (bAVM 96±2 mmHg, control 89±10 mmHg; P=0.68) and diastolic blood pressure (bAVM 55±2 mmHg, control 48±7 mmHg; P=0.92) did not differ between the groups. MCV peak systolic velocity was greater in the bAVM group (34±20 cm/s, controls 20±9 cm/s; P=0.049). The control group had peak systolic velocities ranging from 9 to 32 cm/s. Peak systolic velocity in the bAVM group varied from 9 to 98 cm/s. End diastolic velocity (bAVM 13±11 cm/s, control 13±6 cm/s; P=0.89) and diameters (bAVM 37±19 mm, control 26±7 mm; P=0.16) did not differ between the groups. Two of the bAVM patients that had sustained high MCV peak velocities had a post-operative haemorrhage. Conclusions: Unusually elevated blood flow velocities and diameters were observed in the MCV of patients following bAVM resection. Findings on this small data set provide insight into plausible vessel remodelling and elucidate a post-operative time frame when vessels may have impaired autoregulation of cerebral blood flow.

Reviews the most intriguing applications of fractal analysis in neuroscience with a focus on current and future potential, limits, advantages, and disadvantages. Will bring an understanding of fractals to clinicians and researchers also if they do not have a mathematical background, and will serve as a good tool for teaching the translational applications of computational models to students and scholars of different disciplines. This comprehensive collection is organized in four parts: (1) Basics of fractal analysis; (2) Applications of fractals to the basic neurosciences; (3) Applications of fractals to the clinical neurosciences; (4) Analysis software, modeling and methodology.