A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation.

Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by expert manually refined segmentations of three distinct meningioma sub-compartments: enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Basic demographic data are provided including age at time of initial imaging, sex, and CNS WHO grade. The goal of releasing this dataset is to facilitate the development of automated computational methods for meningioma segmentation and expedite their incorporation into clinical practice, ultimately targeting improvement in the care of meningioma patients.

External Validation of an Artificial Intelligence Device for Intracranial Hemorrhage Detection.

BACKGROUND: Artificial intelligence applications have gained traction in the field of cerebrovascular disease by assisting in the triage, classification, and prognostication of both ischemic and hemorrhagic stroke. The Caire ICH system aims to be the first device to move into the realm of assisted diagnosis for intracranial hemorrhage (ICH) and its subtypes. METHODS: A single-center retrospective dataset of 402 head noncontrast CT scans (NCCT) with an intracranial hemorrhage were retrospectively collected from January 2012 to July 2020; an additional 108 NCCT scans with no intracranial hemorrhage findings were also included. The presence of an ICH and its subtype were determined from the International Classification of Diseases-10 code associated with the scan and validated by an expert panel. We used the Caire ICH vR1 to analyze these scans, and we evaluated its performance in terms of accuracy, sensitivity, and specificity. RESULTS: We found the Caire ICH system to have an accuracy of 98.05% (95% confidence interval [CI]: 96.44%-99.06%), a sensitivity of 97.52% (95% CI: 95.50%-98.81%), and a specificity of 100% (95% CI: 96.67%-100.00%) in the detection of ICH. Experts reviewed the 10 incorrectly classified scans. CONCLUSIONS: The Caire ICH vR1 algorithm was highly accurate, sensitive, and specific in detecting the presence or absence of an ICH and its subtypes in NCCTs. This work suggests that the Caire ICH device has potential to minimize clinical errors in ICH diagnosis that could improve patient outcomes and current workflows as both a point-of-care tool for diagnostics and as a safety net for radiologists.

The Scope, Growth, and Inequities of the Global Neurosurgery Literature: A Bibliometric Analysis.

BACKGROUND: Here, we evaluate the evolution and growth of global neurosurgery publications over time, further focusing on the contributions and impact of authors in low- and middle-income countries (LMICs). METHODS: In this systematic bibliometric analysis, we conducted a two-stage blinded screening process of global neurosurgery publications from 5 databases from inception through July 2021. Articles involving multi-national/multi-institutional research collaborations, detailing any area of global neurosurgery collaboration, or influencing global neurosurgery practice were included. Statistical hypothesis testing was conducted to analyze trends and hypotheses of LMIC authorship contributions. RESULTS: The number of global neurosurgery publications has soared in the last decade. Overall, authors from HIC countries were most commonly from the US (41.1%), Canada (4.0%), and the UK (3.9%), while authors from LMIC countries were most commonly from Uganda (4.2%), Tanzania (2.6%), Cameroon (1.8%), and India (1.8%). Over a quarter (28%) of publications had no LMIC authors, while only 11% had 3 or more LMIC authors. The proportion of LMIC authors (LMIC-R) was not correlated with the citation rate of individual articles or with the year of publication, and a positive trend emerged when the LMIC-R of top-publishing LMICs was individually examined and compared to the year of publication. CONCLUSIONS: Despite recent growth, the number of global neurosurgery publications arising from LMICs pales in comparison to those from HICs. Collaborative efforts between certain HICs and LMICs have likely contributed to the observed increase in LMIC author independence over time.

Machine Learning to Predict Successful Opioid Dose Reduction or Stabilization After Spinal Cord Stimulation.

BACKGROUND: Spinal cord stimulation (SCS) effectively reduces opioid usage in some patients, but preoperatively, there is no objective measure to predict who will most benefit. OBJECTIVE: To predict successful reduction or stabilization of opioid usage after SCS using machine learning models we developed and to assess if deep learning provides a significant benefit over logistic regression (LR). METHODS: We used the IBM MarketScan national databases to identify patients undergoing SCS from 2010 to 2015. Our models predict surgical success as defined by opioid dose stability or reduction 1 year after SCS. We incorporated 30 predictors, primarily regarding medication patterns and comorbidities. Two machine learning algorithms were applied: LR with recursive feature elimination and deep neural networks (DNNs). To compare model performances, we used nested 5-fold cross-validation to calculate area under the receiver operating characteristic curve (AUROC). RESULTS: The final cohort included 7022 patients, of whom 66.9% had successful surgery. Our 5-variable LR performed comparably with the full 30-variable version (AUROC difference <0.01). The DNN and 5-variable LR models demonstrated similar AUROCs of 0.740 (95% CI, 0.727-0.753) and 0.737 (95% CI, 0.728-0.746) ( P = .25), respectively. The simplified model can be accessed at SurgicalML.com . CONCLUSION: We present the first machine learning-based models for predicting reduction or stabilization of opioid usage after SCS. The DNN and 5-variable LR models demonstrated comparable performances, with the latter revealing significant associations with patients' pre-SCS pharmacologic patterns. This simplified, interpretable LR model may augment patient and surgeon decision making regarding SCS.

Deep Learning to Predict Traumatic Brain Injury Outcomes in the Low-Resource Setting.

OBJECTIVE: Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these settings, accurate patient prognostication is both difficult and essential for high-quality patient care. With the ultimate goal of enhancing TBI triage in LMICs, we aim to develop the first deep learning model to predict outcomes after TBI and compare its performance with that of less complex algorithms. METHODS: TBI patients' data were prospectively collected in Kampala, Uganda, from 2016 to 2020. To predict good versus poor outcome at hospital discharge, we created deep neural network, shallow neural network, and elastic-net regularized logistic regression models. Predictors included 13 easily acquirable clinical variables. We assessed model performance with 5-fold cross-validation to calculate areas under both the receiver operating characteristic curve and precision-recall curve (AUPRC), in addition to standardized partial AUPRC to focus on comparisons at clinically relevant operating points. RESULTS: We included 2164 patients for model training, of which 12% had poor outcomes. The deep neural network performed best as measured by the area under the receiver operating characteristic curve (0.941) and standardized partial AUPRC in region maximizing recall (0.291), whereas the shallow neural network was best by the area under the precision-recall curve (0.770). In several other comparisons, the elastic-net regularized logistic regression was noninferior to the neural networks. CONCLUSIONS: We present the first use of deep learning for TBI prognostication, with an emphasis on LMICs, where there is great need for decision support to allocate limited resources. Optimal algorithm selection depends on the specific clinical setting; deep learning is not a panacea, though it may have a role in these efforts.

Machine Learning for Predicting In-Hospital Mortality After Traumatic Brain Injury in Both High-Income and Low- and Middle-Income Countries.

BACKGROUND: Machine learning (ML) holds promise as a tool to guide clinical decision making by predicting in-hospital mortality for patients with traumatic brain injury (TBI). Previous models such as the international mission for prognosis and clinical trials in TBI (IMPACT) and the corticosteroid randomization after significant head injury (CRASH) prognosis calculators can potentially be improved with expanded clinical features and newer ML approaches. OBJECTIVE: To develop ML models to predict in-hospital mortality for both the high-income country (HIC) and the low- and middle-income country (LMIC) settings. METHODS: We used the Duke University Medical Center National Trauma Data Bank and Mulago National Referral Hospital (MNRH) registry to predict in-hospital mortality for the HIC and LMIC settings, respectively. Six ML models were built on each data set, and the best model was chosen through nested cross-validation. The CRASH and IMPACT models were externally validated on the MNRH database. RESULTS: ML models built on National Trauma Data Bank (n = 5393, 84 predictors) demonstrated an area under the receiver operating curve (AUROC) of 0.91 (95% CI: 0.85-0.97) while models constructed on MNRH (n = 877, 31 predictors) demonstrated an AUROC of 0.89 (95% CI: 0.81-0.97). Direct comparison with CRASH and IMPACT models showed significant improvement of the proposed LMIC models regarding AUROC (P = .038). CONCLUSION: We developed high-performing well-calibrated ML models for predicting in-hospital mortality for both the HIC and LMIC settings that have the potential to influence clinical management and traumatic brain injury patient trajectories.

Machine Learning for Predicting Discharge Disposition After Traumatic Brain Injury.

BACKGROUND: Current traumatic brain injury (TBI) prognostic calculators are commonly used to predict the mortality and Glasgow Outcome Scale, but these outcomes are most relevant for severe TBI. Because mild and moderate TBI rarely reaches severe outcomes, there is a need for novel prognostic endpoints. OBJECTIVE: To generate machine learning (ML) models with a strong predictive capacity for trichotomized discharge disposition, an outcome not previously used in TBI prognostic models. The outcome can serve as a proxy for patients' functional status, even in mild and moderate patients with TBI. METHODS: Using a large data set (n = 5292) of patients with TBI from a quaternary care center and 84 predictors, including vitals, demographics, mechanism of injury, initial Glasgow Coma Scale, and comorbidities, we trained 6 different ML algorithms using a nested-stratified-cross-validation protocol. After optimizing hyperparameters and performing model selection, isotonic regression was applied to calibrate models. RESULTS: When maximizing the microaveraged area under the receiver operating characteristic curve during hyperparameter optimization, a random forest model exhibited top performance. A random forest model was also selected when maximizing the microaveraged area under the precision-recall curve. For both models, the weighted average area under the receiver operating characteristic curves was 0.84 (95% CI 0.81-0.87) and the weighted average area under the precision-recall curves was 0.85 (95% CI 0.82-0.88). CONCLUSION: Our group presents high-performing ML models to predict trichotomized discharge disposition. These models can assist in optimization of patient triage and treatment, especially in cases of mild and moderate TBI.

Fractionated follow-up chemotherapy delays the onset of resistance in bone metastatic prostate cancer.

Prostate cancer to bone metastases are almost always lethal. This results from the ability of metastatic prostate cancer cells to co-opt bone remodeling leading to what is known as the vicious cycle. Understanding how tumor cells can disrupt bone homeostasis through their interactions with the stroma and how metastatic tumors respond to treatment is key to the development of new treatments for what remains an incurable disease. Here we describe an evolutionary game theoretical model of both the homeostatic bone remodeling and its co-option by prostate cancer metastases. This model extends past the evolutionary aspects typically considered in game theoretical models by also including ecological factors such as the physical microenvironment of the bone. Our model recapitulates the current paradigm of the "vicious cycle" driving tumor growth and sheds light on the interactions of heterogeneous tumor cells with the bone microenvironment and treatment response. Our results show that resistant populations naturally become dominant in the metastases under conventional cytotoxic treatment and that novel schedules could be used to better control the tumor and the associated bone disease compared to the current standard of care. Specifically, we introduce fractionated follow up therapy - chemotherapy where dosage is administered initially in one solid block followed by alternating smaller doeses and holidays - and argue that it is better than either a continuous application or a periodic one. Furthermore, we also show that different regimens of chemotherapy can lead to different amounts of pathological bone that are known to correlate with poor quality of life for bone metastatic prostate cancer patients.