Intra- and Intersubspecialty Variability in Lumbar Spine MRI Interpretation: A Multireader Study Comparing Musculoskeletal Radiologists and Neuroradiologists

Miskin, Nityanand; Gaviola, Glenn C.; Huang, Raymond Y.; Kim, Christine J.; Lee, Thomas C.; Small, Kirstin M.; Wieschhoff, Ged G.; Mandell, Jacob

doi:10.1067/j.cpradiol.2019.05.003

Cited by 14 publications

(10 citation statements)

References 11 publications

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“…Our method of NLP-driven analysis has several strengths, the chief one being the large sample size. First, by including a large corpus with the interpretation of 152 different attending radiologists over a 10-year period could mitigate the impact of inter- and intra-reader variation [ 29 ] by leveraging the statistical “Law of Large Numbers” to estimate the mean across our study population. It is important to note that we did not analyze reports from CT or CT myelographic imaging of the lumbar spine in our analysis, which may result in some degree of sampling bias, as patients who were unable to undergo MRI—potentially due to claustrophobia or implanted devices—were not included.…”

Section: Discussionmentioning

confidence: 99%

Effects of age and sex on the distribution and symmetry of lumbar spinal and neural foraminal stenosis: a natural language processing analysis of 43,255 lumbar MRI reports

et al. 2021

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Purpose The purpose of this study is to investigate relationship of patient age and sex to patterns of degenerative spinal stenosis on lumbar MRI (LMRI), rated as moderate or greater by a spine radiologist, using natural language processing (NLP) tools. Methods In this retrospective, IRB-approved study, LMRI reports acquired from 2007 to 2017 at a single institution were parsed with a rules-based natural language processing (NLP) algorithm for free-text descriptors of spinal canal stenosis (SCS) and neural foraminal stenosis (NFS) at each of six spinal levels (T12-S1) and categorized according to a 6-point grading scale. Demographic differences in the anatomic distribution of moderate (grade 3) or greater SCS and NFS were calculated by sex, and age and within-group differences for NFS symmetry (left vs. right) were calculated as odds ratios. Results Forty-three thousand two hundred fifty-five LMRI reports (34,947 unique patients, mean age = 54.7; sex = 54.9% women) interpreted by 152 radiologists were studied. Prevalence of significant SCS and NFS increased caudally from T12-L1 to L4-5 though less at L5-S1. NFS was asymmetrically more prevalent on the left at L2-L3 and L5-S1 (p < 0.001). SCS and NFS were more prevalent in men and SCS increased with age at all levels, but the effect size of age was largest at T12-L3. Younger patients (< 50 years) had relatively higher NFS prevalence at L5-S1. Conclusion NLP can identify patterns of lumbar spine degeneration through analysis of a large corpus of radiologist interpretations. Demographic differences in stenosis prevalence shed light on the natural history and pathogenesis of LSDD.

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Section: Discussionmentioning

confidence: 99%

Effects of age and sex on the distribution and symmetry of lumbar spinal and neural foraminal stenosis: a natural language processing analysis of 43,255 lumbar MRI reports

et al. 2021

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“…For patients experiencing simultaneously two or more of such health conditions (i.e., comorbidity of two or more symptoms), the disease may advance to a more severe or more complicated stage, causing generally a situation where differentiated health care or urgent intervention/treatment is needed (Tang et al, 2022;Hussain et al, 2023). Statistics data indicate that globally approximately more than two-fths of individuals experience lower back pain in connection with the lumbar spine within their lifetime (Miskin et al, 2020;Miskin et al, 2022;Hussain et al, 2023;Jordan et al, 2015;Balzer et al, 2022). Degeneration of lumbar spine usually is associated with alterations to the structure of LID, damage to the ligaments, and impairment in the vertebras (Wu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, patients who need differentiated or higher-priority health care may potentially miss the optimal timing of intervention/treatment and bear more strain and costs. Therefore, timely and correct identi cation of the type and the severity of the LID abnormality may play a role in addressing these concerns (Miskin et al, 2020;Chen et al, 2020;Chen et al, 2022). Designing an automatic strati cation model for LID degeneration is crucial to enhance the e ciency of physician diagnosis, optimize the operability level of LID clinical applications, and reduce the physical as well as psychiatric burden of patients.…”

Section: Introductionmentioning

confidence: 99%

VIRD: An ensemble transfer learning to improve automatic stratification of types of lumbar intervertebral disc degeneration

Nakamoto,

Chen,

Wang

et al. 2023

Preprint

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The degeneration of the intervertebral discs in the lumbar spine is the common cause of neurological and physical dysfunctions and chronic disability of patients, which can be stratified into single- (e.g., disc herniation, disc prolapse, or disc bulge) and comorbidity-type degeneration (i.e., simultaneous presence of two or more conditions of disc herniation, prolapse, and bulge in a patient) respectively. The degenerative changes of the lumbar spine differentiate in the level of severity and type. A sample of lumbar magnetic resonance images from multiple clinical hospitals in China was collected and used in the proposal assessment. Theoretically, we devised a novel transfer learning framework VIRD by ensembling four pre-trained models including Densenet169, ResNet101, InceptionResNetV2, and VGG16. Thereafter, the proposed approach was applied to the clinical data and achieved 99% accuracy versus 94%, 96%, 96%, 96%, and 98% for compositional benchmark models of VGG16, InceptionResNetV2, DenseNet169, ResNet101, and other ensemble deep learning respectively. Furthermore, improved performance was observed as well for the metric of the area under the curve, producing a 1% increase relative to other ensemble learning, a 2% increase relative to most-studied models, and a 3% increase relative to the baseline models. The novel methodology can be used as a guide in the initial and efficient screening of complex degeneration of lumbar intervertebral discs and assist in the early-stage selection of clinically differentiated treatment options.

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“…Compared with other imaging reports, LMRI reporting is typically structured by anatomic level and each level is assigned a grade of relative severity of stenosis [8]. Prior work has shown that inter-radiologist agreement for spinal canal stenosis (SCS) and neural foraminal stenosis (NFS) is moderate to strong [9][10][11][12][13]. Moreover, the descriptive terminology for LSDD is standardized by an interdisciplinary consortium of national societies allowing comparison between radiologists with different training and levels of experience [14].…”

Section: Introductionmentioning

confidence: 99%

The Composite Severity Score for Lumbar Spine MRI: a Metric of Cumulative Degenerative Disease Predicts Time Spent on Interpretation and Reporting

et al. 2021

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Conventional measures of radiologist efficiency, such as the relative value unit, fail to account for variations in the complexity and difficulty of a given study. For lumbar spine MRI (LMRI), an ideal performance metric should account for the global severity of lumbar degenerative disease (LSDD) which may influence reporting time (RT), thereby affecting clinical productivity. This study aims to derive a global LSDD metric and estimate its effect on RT. A 10-year archive of LMRI reports comprising 13,388 exams was reviewed. Objective reporting timestamps were used to calculate RT. A natural language processing (NLP) tool was used to extract radiologist-assigned stenosis severity using a 6-point scale (0 = “normal” to 5 = “severe”) at each lumbar level. The composite severity score (CSS) was calculated as the sum of each of 18 stenosis grades. The predictive values of CSS, sex, age, radiologist identity, and referring service on RT were examined with multiple regression models. The NLP tool accurately classified LSDD in 94.8% of cases in a validation set. The CSS increased with patient age and differed between men and women. In a univariable model, CSS was a significant predictor of mean RT (R2 = 0.38, p < 0.001) and independent predictor of mean RT (p < 0.001) controlling for patient sex, patient age, service location, and interpreting radiologist. The predictive strength of CSS was stronger for the low CSS range (CSS = 0–25, R2 = 0.83, p < 0.001) compared to higher CSS values (CSS > 25, R2 = 0.15, p = 0.05). Individual radiologist study volume was negatively correlated with mean RT (Pearson’s R = − 0.35, p < 0.001). The composite severity score predicts radiologist reporting efficiency in LMRI, providing a quantitative measure of case complexity which may be useful for workflow planning and performance evaluation.

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Intra- and Intersubspecialty Variability in Lumbar Spine MRI Interpretation: A Multireader Study Comparing Musculoskeletal Radiologists and Neuroradiologists

Cited by 14 publications

References 11 publications

Effects of age and sex on the distribution and symmetry of lumbar spinal and neural foraminal stenosis: a natural language processing analysis of 43,255 lumbar MRI reports

Effects of age and sex on the distribution and symmetry of lumbar spinal and neural foraminal stenosis: a natural language processing analysis of 43,255 lumbar MRI reports

VIRD: An ensemble transfer learning to improve automatic stratification of types of lumbar intervertebral disc degeneration

The Composite Severity Score for Lumbar Spine MRI: a Metric of Cumulative Degenerative Disease Predicts Time Spent on Interpretation and Reporting

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