Comparative Analysis of Body Radiologist to Neuroradiologist Evaluation of the Spine in Trauma Settings

A deep learning-based method for the diagnosis of vertebral fractures on spine MRI: retrospective training and validation of ResNet

PURPOSE

To improve the performance of less experienced clinicians in the diagnosis of benign and malignant spinal fracture on MRI, we applied the ResNet50 algorithm to develop a decision support system.

METHODS

A total of 190 patients, 50 with malignant and 140 with benign fractures, were studied. The visual diagnosis was made by one senior MSK radiologist, one fourth-year resident, and one first-year resident. The MSK radiologist also gave the binary score for 15 qualitative imaging features. Deep learning was implemented using ResNet50, using one abnormal spinal segment selected from each patient as input. The T1W and T2W images of the lesion slice and its two neighboring slices were considered. The diagnostic performance was evaluated using tenfold cross-validation.

RESULTS

The overall reading accuracy was 98, 96, and 66% for the senior MSK radiologist, fourth-year resident, and first-year resident, respectively. Of the 15 imaging features, 10 showed a significant difference between benign and malignant groups with p <  = 0.001. The accuracy achieved by using the ResNet50 deep learning model for the identified abnormal vertebral segment was 92%. Compared to the first-year resident's reading, the model improved the sensitivity from 78 to 94% (p < 0.001) and the specificity from 61 to 91% (p < 0.001).

CONCLUSION

Our deep learning-based model may provide information to assist less experienced clinicians in the diagnosis of spinal fractures on MRI. Other findings away from the vertebral body need to be considered to improve the model, and further investigation is required to generalize our findings to real-world settings.

Traumatic lumbar spine fractures: Transverse process fractures dominate

PURPOSE

With motor vehicle collisions (MVC) predominating as a source of trauma now, we sought to 1) reassess the types of traumatic lumbar spine fractures, 2) highlight the coincidence of transverse process fractures (TPF) with visceral injuries and 3) emphasize the difference in management between compression fracture (CF) and TPF.

METHODS

We retrospectively reviewed the reports of lumbar spine and abdominopelvic CT scans from 2017 and 2018 to classify the types of spine fractures, their mechanism of injury, treatment and coexistence of abdominopelvic injuries.

RESULTS

2.2% of patients had posttraumatic lumbar spine fractures (113/5229), including 58 patients (51.3%) with isolated TPF and 42 (37.2%) with isolated CF; 13 patients had mixed types. TPF accounted for 70% of all fractures (195/277) as opposed to 24% for CF (67/277). MVC was responsible for 60.3% (35/58) of TPF but falls accounted for 73.8% (31/42) of CF. The odds ratio of having isolated TPF from MVC was 4.1[1.8-9.0] versus CF after a fall from standing was 4.5[2.0-10.5]. Of patients with both visceral injuries and lumbar spine fractures, 75% (27/36) had isolated TPF (odds ratio of visceral injury with TPF was 4.4[1.8-10.7]). No TPF were treated with an intervention, however 77% (40/52) of CF were addressed surgically or with braces.

CONCLUSION

TPF are the most common lumbar spine fractures and are often associated with MVC. There is a high association between TPF and abdominopelvic visceral injury requiring radiologists' attentiveness even though the TPF is not directly addressed.

Survey of peer review programs among pediatric radiologists: report from the SPR Quality and Safety Committee

During the last 15 years, peer review has been widely incorporated into radiology quality improvement programs. However, current implementations are variable and carry concerns, including subjectivity of numerical scores and a sense of merely satisfying regulatory requirements. The Society for Pediatric Radiology (SPR) Quality and Safety Committee sought to evaluate the state of peer review programs in pediatric radiology practices, including implementation methods, perceived functions, strengths and weaknesses, and opportunities for improvement. We distributed an online 16-question survey to SPR members. Questions pertained to the type of peer review system, the use of numerical scores and comments, how feedback on discordances is given and received, and the use of peer learning conferences. We collected 219 responses (15% of survey invitations), 80% of which were from children's hospitals. Fifty percent of respondents said they use a picture archiving and communication system (PACS)-integrated peer review system. Comment-enhanced feedback for interpretive discordances was either very important or somewhat important to performance improvement in 86% of responses, compared to 48% with a similar perception of numerical scores. Sixty-eight percent of respondents said they either rarely or never check their numerical scores, and 82% either strongly or somewhat agreed that comments are more effective feedback than numerical scores. Ninety-three percent either strongly or somewhat agreed that peer learning conferences would be beneficial to their practice. Forty-eight percent thought that their current peer review system should be modified. Survey results demonstrate that peer review systems in pediatric radiology practices are implemented variably, and nearly half of respondents believe their systems should be modified. Most respondents prefer feedback in the form of comments and peer learning conferences, which are thought to be more beneficial for performance improvement than numerical scores.