Utility of texture analysis for objective quantitative ex vivo assessment of meningioma consistency: method proposal and validation.

BACKGROUND: Tumor consistency is considered to be a critical factor for the surgical removal of meningiomas and its preoperative assessment is intensively studied. A significant drawback in the research of predictive methods is the lack of a clear shared definition of tumor consistency, with most authors resorting to subjective binary classification labeling the samples as "soft" and "hard." This classification is highly observer-dependent and its discrete nature fails to capture the fine nuances in tumor consistency. To compensate for these shortcomings, we examined the utility of texture analysis to provide an objective observer-independent continuous measure of meningioma consistency. METHODS: A total of 169 texturometric measurements were conducted using the Brookfield CT3 Texture Analyzer on meningioma samples from five patients immediately after the removal and on the first, second, and seventh postoperative day. The relationship between measured stiffness and time from sample extraction, subjectively assessed consistency grade and histopathological features (amount of collagen and reticulin fibers, presence of psammoma bodies, predominant microscopic morphology) was analyzed. RESULTS: The stiffness measurements exhibited significantly lower variance within a sample than among samples (p = 0.0225) and significant increase with a higher objectively assessed consistency grade (p = 0.0161, p = 0.0055). A significant negative correlation was found between the measured stiffness and the time from sample extraction (p < 0.01). A significant monotonic relationship was revealed between stiffness values and amount of collagen I and reticulin fibers; there were no statistically significant differences between histological phenotypes in regard to presence of psammoma bodies and predominant microscopic morphology. CONCLUSIONS: We conclude that the values yielded by texture analysis are highly representative of an intrinsic consistency-related quality of the sample despite the influence of intra-sample heterogeneity and that our proposed method can be used to conduct quantitative studies on the role of meningioma consistency.

Application of diffusion tensor imaging of the facial nerve in preoperative planning for large vestibular schwannoma: a systematic review.

The accurate identification and preservation of the facial nerve (FN) during vestibular schwannoma (VS) surgery is crucial for maintaining facial function. Investigating the application of diffusion tensor imaging (DTI) in preoperative planning for large VS surgery is provided. PubMed, Cochrane Library, Science Direct, ISI Web of Science, Embase, and additional sources were searched to identify cohort studies about the preoperative DTI usage for the FN tracking before large VS (≥ 2.5 cm) surgery published between 1990 and 2023. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed; the Newcastle-Ottawa Scale was used to assess the risk of bias and to evaluate limitations based on selection/outcome biases. A total of 8 publications yielding 149 VS (mean size 3.66 ± 0.81 cm) were included. Surgical concordance with preoperative DTI FN tracking was 91.67% (range 85-100%). Overall DTI reliability was 88.89% (range 81.81-95.83%). Larger tumor size predicted either DTI inaccurate finding or complete DTI failure (p = 0.001). VS size above > 3.5 cm was associated with a higher risk of DTI failure (p = 0.022), with a higher risk of inaccurate DTI finding preoperatively (p = 0.033), and with a higher House-Brackman score postoperatively (p = 0.007). Application of DTI in larger VS surgery is a valuable FN identification along with electrophysiological monitoring and neuronavigation, therefore also in its preservation and in lowering risk of complications. DTI represents a valuable adjunct to electrophysiological monitoring and neuronavigation in FN identification, applicable not only for smaller, but also larger VS.

Diffusion tensor imaging helps identify shunt-responsive normal pressure hydrocephalus patients among probable iNPH cohort.

The aim of this study was to investigate whether white matter changes as measured by diffusion tensor imaging (DTI) can help differentiate shunt-responsive idiopathic normal pressure hydrocephalus (iNPH) patients from patients with other causes of gait disturbances and/or cognitive decline with ventriculomegaly whose clinical symptoms do not improve significantly after cerebrospinal fluid derivation (non-iNPH). Between 2017 and 2022, 85 patients with probable iNPH underwent prospective preoperative magnetic resonance imaging (MRI) and comprehensive clinical workup. Patients with clinical symptoms of iNPH, positive result on lumbar infusion test, and gait improvement after 120-h lumbar drainage were diagnosed with iNPH and underwent shunt-placement surgery. Fractional anisotropy (FA) and mean diffusivity (MD) values for individual regions of interest were extracted from preoperative MRI, using the TBSS pipeline of FSL toolkit. These FA and MD values were then compared to results of clinical workup and established diagnosis of iNPH. An identical MRI protocol was performed on 13 age- and sex-matched healthy volunteers. Statistically significant differences in FA values of several white matter structures were found not only between iNPH patients and healthy controls but also between iNPH and non-iNPH patients. ROI that showed best diagnostic ability when differentiating iNPH among probable iNPH cohort was uncinate fasciculus, with AUC of 0.74 (p < 0.001). DTI methods of white matter analysis using standardised methods of ROI extraction can help in differentiation of iNPH patients not only from healthy patients but also from patients with other causes of gait disturbances with cognitive decline and ventriculomegaly.

Role of risk factors, scoring systems, and prognostic models in predicting the functional outcome in meningioma surgery: multicentric study of 552 skull base meningiomas.

Despite the importance of functional outcome, only a few scoring systems exist to predict neurologic outcome in meningioma surgery. Therefore, our study aims to identify preoperative risk factors and develop the receiver operating characteristics (ROC) models estimating the risk of a new postoperative neurologic deficit and a decrease in Karnofsky performance status (KPS). A multicentric study was conducted in a cohort of 552 consecutive patients with skull base meningiomas who underwent surgical resection from 2014 to 2019. Data were gathered from clinical, surgical, and pathology records as well as radiological diagnostics. The preoperative predictive factors of functional outcome (neurologic deficit, decrease in KPS) were analyzed in univariate and multivariate stepwise selection analyses. Permanent neurologic deficits were present in 73 (13.2%) patients and a postoperative decrease in KPS in 84 (15.2%). Surgery-related mortality was 1.3%. A ROC model was developed to estimate the probability of a new neurologic deficit (area 0.74; SE 0.0284; 95% Wald confidence limits (0.69; 0.80)) based on meningioma location and diameter. Consequently, a ROC model was developed to predict the probability of a postoperative decrease in KPS (area 0.80; SE 0.0289; 95% Wald confidence limits (0.74; 0.85)) based on the patient's age, meningioma location, diameter, presence of hyperostosis, and dural tail. To ensure an evidence-based therapeutic approach, treatment should be founded on known risk factors, scoring systems, and predictive models. We propose ROC models predicting the functional outcome of skull base meningioma resection based on the age of the patient, meningioma size, and location and the presence of hyperostosis and dural tail.

Fully automated imaging protocol independent system for pituitary adenoma segmentation: a convolutional neural network-based model on sparsely annotated MRI.

This study aims to develop a fully automated imaging protocol independent system for pituitary adenoma segmentation from magnetic resonance imaging (MRI) scans that can work without user interaction and evaluate its accuracy and utility for clinical applications. We trained two independent artificial neural networks on MRI scans of 394 patients. The scans were acquired according to various imaging protocols over the course of 11 years on 1.5T and 3T MRI systems. The segmentation model assigned a class label to each input pixel (pituitary adenoma, internal carotid artery, normal pituitary gland, background). The slice segmentation model classified slices as clinically relevant (structures of interest in slice) or irrelevant (anterior or posterior to sella turcica). We used MRI data of another 99 patients to evaluate the performance of the model during training. We validated the model on a prospective cohort of 28 patients, Dice coefficients of 0.910, 0.719, and 0.240 for tumour, internal carotid artery, and normal gland labels, respectively, were achieved. The slice selection model achieved 82.5% accuracy, 88.7% sensitivity, 76.7% specificity, and an AUC of 0.904. A human expert rated 71.4% of the segmentation results as accurate, 21.4% as slightly inaccurate, and 7.1% as coarsely inaccurate. Our model achieved good results comparable with recent works of other authors on the largest dataset to date and generalized well for various imaging protocols. We discussed future clinical applications, and their considerations. Models and frameworks for clinical use have yet to be developed and evaluated.

Methods of preoperative prediction of pituitary adenoma consistency: a systematic review.

This study aims to review the current literature on methods of preoperative prediction of pituitary adenoma consistency. Pituitary adenoma consistency may be a limiting factor for successful surgical removal of tumors. Efforts have been made to investigate the possibility of an accurate assessment of the preoperative consistency to allow for safer and more effective surgery planning. We searched major scientific databases and systematically analyzed the results. A total of 54 relevant articles were identified and selected for inclusion. These studies evaluated methods based on either MRI intensity, enhancement, radiomics, MR elastometry, or CT evaluation. The results of these studies varied widely. Most studies used the average intensity of either T2WI or ADC maps. Firm tumors appeared hyperintense on T2WI, although only 55% of the studies reported statistically significant results. There are mixed reports on ADC values in firm tumors with findings of increased values (28%), decreased values (22%), or no correlation (50%). Multiple contrast enhancement-based methods showed good results in distinguishing between soft and firm tumors. There were mixed reports on the utility of MR elastography. Attempts to develop radiomics and machine learning-based models have achieved high accuracy and AUC values; however, they are prone to overfitting and need further validation. Multiple methods of preoperative consistency assessment have been studied. None demonstrated sufficient accuracy and reliability in clinical use. Further efforts are needed to enable reliable surgical planning.

Boosting phase-contrast MRI performance in idiopathic normal pressure hydrocephalus diagnostics by means of machine learning approach.

OBJECTIVE: Phase-contrast MRI allows detailed measurements of various parameters of CSF motion. This examination is technically demanding and machine dependent. The literature on this topic is ambiguous. Machine learning (ML) approaches have already been successfully utilized in medical research, but none have yet been applied to enhance the results of CSF flowmetry. The aim of this study was to evaluate the possible contribution of ML algorithms in enhancing the utilization and results of MRI flowmetry in idiopathic normal pressure hydrocephalus (iNPH) diagnostics. METHODS: The study cohort consisted of 30 iNPH patients and 15 healthy controls examined on one MRI machine. All major phase-contrast parameters were inspected: peak positive, peak negative, and average velocities; peak amplitude; positive, negative, and average flow rates; and aqueductal area. The authors applied ML algorithms to 85 complex features calculated from a phase-contrast study. RESULTS: The most distinctive parameters with p < 0.005 were the peak negative velocity, peak amplitude, and negative flow. From the ML algorithms, the Adaptive Boosting classifier showed the highest specificity and best discrimination potential overall, with 80.4% ± 2.9% accuracy, 72.0% ± 5.6% sensitivity, 84.7% ± 3.8% specificity, and 0.812 ± 0.047 area under the receiver operating characteristic curve (AUC). The highest sensitivity was 85.7% ± 5.6%, reached by the Gaussian Naive Bayes model, and the best AUC was 0.854 ± 0.028 by the Extra Trees classifier. CONCLUSIONS: Feature extraction algorithms combined with ML approaches simplify the utilization of phase-contrast MRI. The highest-performing ML algorithm was Adaptive Boosting, which showed good calibration and discrimination on the testing data, with 80.4% accuracy, 72.0% sensitivity, 84.7% specificity, and 0.812 AUC. Phase-contrast MRI boosted by the ML approach can help to determine shunt-responsive iNPH patients.