Upper cervical spine fracture patterns and blunt cerebrovascular injuries.

PURPOSE: To determine patterns of C1 and C2 vertebral fractures that are associated with blunt cerebrovascular injury (BCVI). METHODS: Retrospective chart review of clinical and imaging reports at a level 1 trauma center over 10 consecutive years was conducted in patients with C1 and C2 fractures. Student t-test and chi-squared analyses were used to determine associations between fracture levels and fracture types with the presence of BCVI on CTA and/or MRI or stroke on CT and/or MRI. RESULTS: Multilevel fractures were associated with higher incidence of BCVI compared to isolated C1 or C2 fractures (p < 0.01), but not with stroke (p = 0.16). There was no difference in incidence of BCVI or stroke between isolated C1 and isolated C2 fractures (p = 0.46, p = 0.25). Involvement of the transverse foramen (TF) alone was not associated with BCVI or stroke (p = 0.10-0.40, p = 0.34-0.43). However, TF fractures that were comminuted or contained fracture fragment(s) were associated with increased BCVI (p < 0.01, p = 0.02), though not with stroke (p = 0.11, p = 0.09). In addition, high-energy mechanism of injury was also associated with BCVI (p < 0.01) and stroke (p < 0.01). CONCLUSION: C1 and C2 fractures are associated with BCVI in the presence of high-energy mechanism of injury, concomitant fractures of other cervical vertebral body levels, comminuted TF fractures, or TF fractures with internal fragments. Attention to these fracture parameters is important in evaluating C1 and C2 fractures for BCVI.

Head CT deep learning model is highly accurate for early infarct estimation.

Non-contrast head CT (NCCT) is extremely insensitive for early (< 3-6 h) acute infarct identification. We developed a deep learning model that detects and delineates suspected early acute infarcts on NCCT, using diffusion MRI as ground truth (3566 NCCT/MRI training patient pairs). The model substantially outperformed 3 expert neuroradiologists on a test set of 150 CT scans of patients who were potential candidates for thrombectomy (60 stroke-negative, 90 stroke-positive middle cerebral artery territory only infarcts), with sensitivity 96% (specificity 72%) for the model versus 61-66% (specificity 90-92%) for the experts; model infarct volume estimates also strongly correlated with those of diffusion MRI (r2 > 0.98). When this 150 CT test set was expanded to include a total of 364 CT scans with a more heterogeneous distribution of infarct locations (94 stroke-negative, 270 stroke-positive mixed territory infarcts), model sensitivity was 97%, specificity 99%, for detection of infarcts larger than the 70 mL volume threshold used for patient selection in several major randomized controlled trials of thrombectomy treatment.

Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging.

Stroke is a leading cause of death and disability. The ability to quickly identify the presence of acute infarct and quantify the volume on magnetic resonance imaging (MRI) has important treatment implications. We developed a machine learning model that used the apparent diffusion coefficient and diffusion weighted imaging series. It was trained on 6,657 MRI studies from Massachusetts General Hospital (MGH; Boston, USA). All studies were labelled positive or negative for infarct (classification annotation) with 377 having the region of interest outlined (segmentation annotation). The different annotation types facilitated training on more studies while not requiring the extensive time to manually segment every study. We initially validated the model on studies sequestered from the training set. We then tested the model on studies from three clinical scenarios: consecutive stroke team activations for 6-months at MGH, consecutive stroke team activations for 6-months at a hospital that did not provide training data (Brigham and Women's Hospital [BWH]; Boston, USA), and an international site (Diagnósticos da América SA [DASA]; Brazil). The model results were compared to radiologist ground truth interpretations. The model performed better when trained on classification and segmentation annotations (area under the receiver operating curve [AUROC] 0.995 [95% CI 0.992-0.998] and median Dice coefficient for segmentation overlap of 0.797 [IQR 0.642-0.861]) compared to segmentation annotations alone (AUROC 0.982 [95% CI 0.972-0.990] and Dice coefficient 0.776 [IQR 0.584-0.857]). The model accurately identified infarcts for MGH stroke team activations (AUROC 0.964 [95% CI 0.943-0.982], 381 studies), BWH stroke team activations (AUROC 0.981 [95% CI 0.966-0.993], 247 studies), and at DASA (AUROC 0.998 [95% CI 0.993-1.000], 171 studies). The model accurately segmented infarcts with Pearson correlation comparing model output and ground truth volumes between 0.968 and 0.986 for the three scenarios. Acute infarct can be accurately detected and segmented on MRI in real-world clinical scenarios using a machine learning model.

Traumatic cervical spine fracture patterns on CT: a retrospective analysis at a level 1 trauma center.

PURPOSE: The purpose of our study was to determine common acute traumatic cervical spine fracture patterns on CT cervical spine (CTCS). METHODS: We retrospectively reviewed 1091 CTCS positive for traumatic fractures performed over a 10-year period at a level 1 trauma center. Fractures were classified by vertebral level, laterality, and anatomic location (anterior/posterior arch, body, odontoid, pedicle, facet, lateral mass, lamina, spinous process, transverse foramina, and transverse processes). RESULTS: C2 was the most commonly fractured vertebra (38% of all studies), followed by C7 (32.4%). 48.7% of studies had upper cervical spine (C1 and/or C2) fractures. 39.7% of positive studies involved > 1 vertebral level. Conditioned on fractures at one cervical level, the probability of fracture was greatest at adjacent levels with a 50% chance of sustaining a C7 fracture with C6 fracture. However, 31.3% (136) of studies with multi-level fractures had non-contiguous fractures. The most common isolated vertebral process fracture was of the transverse process, seen in 89 (8.2%) studies at a single level, 27 (2.5%) studies at multiple levels. Subaxial spine vertebral process fractures outnumbered body fractures with progressive dominance of vertebral process fracture down the spine. CONCLUSION: C2 was the most commonly fractured vertebral level. Multi-level traumatic cervical spine fractures constituted 40% of our cohort, most commonly at C6/C7 and C1/C2. Although the conditional probability of concurrent fracture in studies with multi-level fractures was greatest in contiguous levels, nearly one-third of multi-level fractures involved non-contiguous fractures.