Ultrasound findings in lateral elbow tendinopathy: A retrospective analysis of radiological tendon features

Droppelmann, Guillermo; Feijoo, Felipe; Greene, Cristóbal; Tello, Manuel; Rosales, Julio; Yáñez, Roberto; Jorquera, Carlos; Prieto, David Cantarero

doi:10.12688/f1000research.73441.1

Cited by 4 publications

(6 citation statements)

References 44 publications

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“…Furthermore, a wide variety of radiologic methods is used to diagnose LE, including conventional muscle sonography, CDUS, and magnetic resonance imaging. [16][17][18] SEL provides elasticity and stiffness of tissue or lesions such as breast, liver, thyroid and tendons. In recent studies, strain SEL and shear-wave SEL have been investigated in diagnosis of LE.…”

Section: Discussionmentioning

confidence: 99%

The ultrasound elastography findings in lateral epicondylitis in comparison with healthy individuals

Torun¹,

Eren²,

Balaban³

2022

Anatomy

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Objectives: To evaluate the ability of strain sonoelastography (SEL) in diagnosing lateral epicondylitis (LE) and to asses clinical and diagnostic efficacy of strain SEL in patients with clinically confirmed LE and in healthy volunteers. Methods: Strain SEL was performed on 110 patients with clinical symptoms of LE and on 56 healthy participants. The common extensor tendon (CET) was evaluated with gray-scale and color Doppler ultrasonography (CDUS). The stiffness and the elasticity of the CET was divided into 2 main types and 2 subtypes as a spectrum ranging from hard for 1a to soft for 2b. Results: The thickness of the CET in patients with LE (6.3±1.06 mm) were remarkably thicker than in healthy participants (3.8±0.62 mm) (p<0.001). There were negative significant correlations between the thickness of the CET and lower and mean strain ratios (SR) (rs= [–0.666], p<0.001 for lower SR and rs= [–0.358], p<0.001 for mean SR). Thickness of the CET varied between the groups of elastographic patterns (p<0.001). The SR of tendon in patients with LE (6.68±2.49 mm) were remarkably lower than in healthy participants (11.16±4.88 mm) (p<0.001). The SR of tendon in patients with LE also varied significantly between the groups of elastographic patterns (p<0.001). Conclusion: Strain SEL is a promising sonographic technique for musculoskeletal imaging to differentiate thickening and softening of CET in LE. SR and SEL pattern findings are compatible with gray scale ultrasound and CDUS findings.

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Section: Discussionmentioning

confidence: 99%

The ultrasound elastography findings in lateral epicondylitis in comparison with healthy individuals

Torun¹,

Eren²,

Balaban³

2022

Anatomy

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“…Tendon tear is the final phase of the degenerative tissue process, and the number of US findings could determine the severity of the tendinopathy. 20 …”

Section: Discussionmentioning

confidence: 99%

“…Intrasubstance tear was defined as a linear hypoechoic focus associated with discontinuity of tendon fibers. 20 Exclusion criteria were any previous treatment such as platelet-rich plasma (PRP), corticoid injection, or surgical intervention. Other diagnoses for lateral elbow pain, fracture around the elbow, shoulder, or neck condition were also excluded.…”

Section: Methodsmentioning

confidence: 99%

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A New Test for the Advanced Diagnosis of Lateral Elbow Tendinopathy with Concomitant Intrasubstance Tear: Failure to Resist Extension Effort (the Free Test)

Greene

Droppelmann

García

et al. 2022

ORR

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Background Lateral elbow tendinopathy (LET) is one of the most common causes of musculoskeletal pain. The diagnosis is based on the clinical history and different physical maneuvers. Ultrasound (US) is a complementary diagnostic method to detect degenerative tendon changes and intrasubstance tears (IST). To date, there is no available physical maneuver to identify an IST in patients with LET. Aim To evaluate the diagnostic accuracy of an index test to detect an IST confirmed by ultrasound in patients with LET. Methods A diagnostic retrospective study was performed. Patients who presented medical records with LET were recruited. Two orthopaedic surgeons developed the physical maneuver. The index test was considered positive when the position failed to resist the wrist extension maximum effort. Clinical findings were associated with confirmation of IST by US. Data were calculated using diagnostic accuracy, sensitivity, and specificity with 95% confidence intervals. Results Thirty-nine patients (39 elbows) were analyzed, 25 (64%) women and 14 (36%) men, with an average age of 47.7 years. The index test’s sensitivity was 0.86 (95% CI, 0.67–0.96). Accuracy was 0.79 (95% CI, 0.64–0.91), and the specificity was 0.64 (95% CI, 0.31–0.89). Conclusion The index test presented very good sensitivity and good accuracy in patients with LET with US diagnostic confirmation of IST. Level of Evidence Diagnostic study, Level III.

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“…Greyscale and color Doppler US imaging are standard methods used for assessing tendon structural changes (74). Following the literature recommendations, four common prevalent degenerative findings were selected from US exams, such as hypoechogenicity, neovascularity, enthesopathy, and intrasubstance tear (75). A focal hypoechoic region was defined as being rounded and not associated with tendon disruption.…”

Section: Ultrasound Assessment Of Common Extensor Tendonmentioning

confidence: 99%

Lateral elbow tendinopathy and artificial intelligence: Binary and multilabel findings detection using machine learning algorithms

Droppelmann¹,

Tello²,

García³

et al. 2022

Front. Med.

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BackgroundUltrasound (US) is a valuable technique to detect degenerative findings and intrasubstance tears in lateral elbow tendinopathy (LET). Machine learning methods allow supporting this radiological diagnosis.AimTo assess multilabel classification models using machine learning models to detect degenerative findings and intrasubstance tears in US images with LET diagnosis.Materials and methodsA retrospective study was performed. US images and medical records from patients with LET diagnosis from January 1st, 2017, to December 30th, 2018, were selected. Datasets were built for training and testing models. For image analysis, features extraction, texture characteristics, intensity distribution, pixel-pixel co-occurrence patterns, and scales granularity were implemented. Six different supervised learning models were implemented for binary and multilabel classification. All models were trained to classify four tendon findings (hypoechogenicity, neovascularity, enthesopathy, and intrasubstance tear). Accuracy indicators and their confidence intervals (CI) were obtained for all models following a K-fold-repeated-cross-validation method. To measure multilabel prediction, multilabel accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) with 95% CI were used.ResultsA total of 30,007 US images (4,324 exams, 2,917 patients) were included in the analysis. The RF model presented the highest mean values in the area under the curve (AUC), sensitivity, and also specificity by each degenerative finding in the binary classification. The AUC and sensitivity showed the best performance in intrasubstance tear with 0.991 [95% CI, 099, 0.99], and 0.775 [95% CI, 0.77, 0.77], respectively. Instead, specificity showed upper values in hypoechogenicity with 0.821 [95% CI, 0.82, −0.82]. In the multilabel classifier, RF also presented the highest performance. The accuracy was 0.772 [95% CI, 0.771, 0.773], a great macro of 0.948 [95% CI, 0.94, 0.94], and a micro of 0.962 [95% CI, 0.96, 0.96] AUC scores were detected. Diagnostic accuracy, sensitivity, and specificity with 95% CI were calculated.ConclusionMachine learning algorithms based on US images with LET presented high diagnosis accuracy. Mainly the random forest model shows the best performance in binary and multilabel classifiers, particularly for intrasubstance tears.

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Ultrasound findings in lateral elbow tendinopathy: A retrospective analysis of radiological tendon features

Cited by 4 publications

References 44 publications

The ultrasound elastography findings in lateral epicondylitis in comparison with healthy individuals

The ultrasound elastography findings in lateral epicondylitis in comparison with healthy individuals

A New Test for the Advanced Diagnosis of Lateral Elbow Tendinopathy with Concomitant Intrasubstance Tear: Failure to Resist Extension Effort (the Free Test)

Lateral elbow tendinopathy and artificial intelligence: Binary and multilabel findings detection using machine learning algorithms

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