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Magnetic Resonance Materials in Physics, Biology and Medicine

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30-11-2022 | Magnetic Resonance Imaging | Research Article

A preliminary attempt to use radiomic features in the diagnosis of extra-articular long head biceps tendinitis

Authors: Lifeng Yin, Yanggang Kong, Mingkang Guo, Xingyu Zhang, Wenlong Yan, Hua Zhang

Published in: Magnetic Resonance Materials in Physics, Biology and Medicine | Issue 4/2023

Abstract

Background

This study aims to present a radiomic application in diagnosing the long head of biceps (LHB) tendinitis. Moreover, we evaluated whether machine learning-derived radiomic features recognize LHB tendinitis.

Patients and methods

A total of 170 patients were reviewed. All LHB tendinitis patients were diagnosed under arthroscopy. Radiomic features were extracted from preoperative magnetic resonance imaging (MRI), and the input dataset was divided into a training set and a test set. For feature selection, the t test and least absolute shrinkage and selection operator (LASSO) methods were used, and random forest (RF) and support vector machine (SVM) were used as machine learning classifiers. The sensitivity, specificity, accuracy, and area under the curve (AUC) of each model’s receiver operating characteristic (ROC) curves were calculated to evaluate model performance.

Results

In total, 851 radiomic features were extracted, with 109 radiomic features extracted using a t test and 20 radiomic features extracted using the LASSO method. The random forest classifier shows the highest sensitivity, specificity, accuracy, and AUC (0.52, 0.92, 0.73, and 0.72).

Conclusion

The classifier contract by 20 radiomic features demonstrated a good ability to predict extra-articular LHB tendinitis.However because of poor segmentation reliability, the value of Radiomic in LHB tendinitis still needs to be further explored.

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Mehl J (2018) Proximal biceps long head: anatomy, biomechanics, pathology. Oper Tech Sports Med 26:76–81. https://doi.org/10.1053/j.otsm.2018.02.002

Elser F, Braun S, Dewing CB, Giphart JE, Millett PJ (2011) Anatomy, function, injuries, and treatment of the long head of the biceps brachii tendon. Arthroscopy 27(4):581–592. https://doi.org/10.1016/j.arthro.2010.10.014CrossRef

Xue H, Bird S, Jiang L, Jiang J, Cui L (2022) Anchoring apparatus of long head of the biceps tendon: ultrasonographic anatomy and pathologic conditions. Diagnostics. https://doi.org/10.3390/diagnostics12030659CrossRefPubMedPubMedCentral

Szabó I, Boileau P, Walch G (2008) The proximal biceps as a pain generator and results of tenotomy. Sports Med Arthrosc Rev 16(3):180–186. https://doi.org/10.1097/JSA.0b013e3181824f1eCrossRefPubMed

Kerschbaum M, Arndt L, Bartsch M, Chen J, Gerhardt C, Scheibel M et al (2016) Using the LHB score for assessment of LHB pathologies and LHB surgery: a prospective study. Arch Orthopaedic Trauma Surg 136(4):469–475. https://doi.org/10.1007/s00402-015-2391-7CrossRef

Uschok S, Herrmann P, Perka C et al (2016) Combined arthroscopic tenodesis of the long head of biceps and rotator cuff repair in antero-superior cuff tears. Arch Orthopaedic Trauma Surg. https://doi.org/10.1007/s00402-016-2498-5CrossRef

Meraner D, Sternberg C, Vega J et al (2016) Arthroscopic tenodesis versus tenotomy of the long head of biceps tendon in simultaneous rotator cuff repair. Arch Orthopaedic Trauma Surg. https://doi.org/10.1007/s00402-015-2343-2CrossRef

Biederwolf NE (2013) A proposed evidence-based shoulder special testing examination algorithm: clinical utility based on a systematic review of the literature. J Int J Sports Phys Ther. 8(4):427–440PubMed

Arrigoni P, Ragone V, Ambrosi RD, Denard P, Randelli F, Banfi G et al (2014) Improving the accuracy of the preoperative diagnosis of long head of the biceps pathology: the biceps resisted flexion test. Joints. https://doi.org/10.11138/jts/2014.2.2.054CrossRefPubMedPubMedCentral

10.

Baptista E, Malavolta EA, Gracitelli ME, Alvarenga D, Bordalo-Rodrigues M, Ferreira Neto AA, de Barros N (2019) Diagnostic accuracy of MRI for detection of tears and instability of proximal long head of biceps tendon: an evaluation of 100 shoulders compared with arthroscopy. Skeletal radiology 48(11):1723–1733. https://doi.org/10.1007/s00256-019-03214-zCrossRefPubMed

11.

Taylor SA, Newman AM, Nguyen J, Fabricant PD, Obrien SJ, JA-tJoA, Surgery R (2016) Magnetic resonance imaging currently fails to fully evaluate the biceps-labrum complex and bicipitaltunnel. Arthroscopy 32(2):238–244CrossRefPubMed

12.

Borrero CG, Costello J, Bertolet M, Vyas D, JSR (2017) Effect of patient age on accuracy of primary MRI signs of long head of biceps tearing and instability in the shoulder: an MRI-arthroscopy correlation study. Skeletal Radiol. https://doi.org/10.1007/s00256-017-2783-6CrossRefPubMed

13.

Khil EK, Cha JG, Yi JS, Kim HJ, Min KD, Yoon YC et al (2016) Detour sign in the diagnosis of subluxation of the long head of the biceps tendon with arthroscopic correlation. Br J Radiol. https://doi.org/10.1259/bjr.20160375CrossRefPubMed

14.

Gilmer BB, DeMers AM, Guerrero D, Reid JB 3rd, Lubowitz JH, Guttmann D (2015) Arthroscopic versus open comparison of long head of biceps tendon visualization and pathology in patients requiring tenodesis. Arthroscopy 31(1):29–34. https://doi.org/10.1016/j.arthro.2014.07.025CrossRefPubMed

15.

Jordan RW, Saithna A, JKSSTA (2015) Physical examination tests and imaging studies based on arthroscopic assessment of the long head of biceps tendon are invalid. Knee Surg, Sports Traumatolo, Arthrosc. 25:1–8

16.

Taylor SA, Khair MM, Gulotta LV, Pearle AD, Baret NJ, Newman AM et al (2015) Diagnostic glenohumeral arthroscopy fails to fully evaluate the biceps-labral complex. Arthroscopy. https://doi.org/10.1016/j.arthro.2014.10.017CrossRefPubMed

17.

Marcello Z, Vito C, Di PF, Alfonso R, Raffaele N, Nicola M et al (2019) Imaging of long head biceps tendon. A multimodality pictorial essay. Acta bio-medica 90(5-S):84–94.https://doi.org/10.23750/abm.v90i5-S.8351CrossRefPubMedCentral

18.

Parnes N, Perrine J, Tomaino MM (2022) Arthroscopic evaluation of the long head of the biceps tendon: traditional versus allis clamp techniques. Orthopedics 45(1):38–42. https://doi.org/10.3928/01477447-20211124-02CrossRefPubMed

19.

Bien N, Rajpurkar P, Ball RL, Irvin J, Park A, Jones E et al (2018) Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. PLoS Med 15(11):e1002699. https://doi.org/10.1371/journal.pmed.1002699CrossRefPubMedPubMedCentral

20.

Germann C, Marbach G, Civardi F, Fucentese SF, Fritz J, Sutter R et al (2020) Deep convolutional neural network-based diagnosis of anterior cruciate ligament tears: performance comparison of homogenous versus heterogeneous knee MRI cohorts with different pulse sequence protocols and 1.5-T and 3-T magnetic field strengths. Investig Radiol 55(8): 499–506. https://doi.org/10.1097/RLI.0000000000000664CrossRef

21.

Vishwa P, Michael A, Medicine JJERoP, Development D (2016) Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev. https://doi.org/10.1080/23808993.2016.1164013CrossRef

22.

Zhang B, He X, Ouyang F, Gu D, Dong Y, Zhang L et al (2017) Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett. 403:21–27. https://doi.org/10.1016/j.canlet.2017.06.004CrossRefPubMedPubMedCentral

23.

Shi, Y., Wahle, E., Du, Q., Krajewski, L., Liang, X., Zhou, S., Zhang, C., Baine, M., & Zheng, D. (2021). Associations between Statin/Omega3 Usage and MRI-Based Radiomics Signatures in Prostate Cancer. Diagnostics (Basel, Switzerland) 11(1):85. https://doi.org/10.3390/diagnostics11010085CrossRefPubMedPubMedCentral

24.

Xu X, Zhang HL, Liu QP, Sun SW, Zhang J, Zhu FP et al (2019) Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol 70(6):1133–1144. https://doi.org/10.1016/j.jhep.2019.02.023CrossRefPubMed

25.

Han L, Zhu Y, Liu Z, Yu T, He C, Jiang W et al (2019) Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer. Eur Radiol 29(7): 3820–3829. https://doi.org/10.1007/s00330-018-5981-2CrossRefPubMedPubMedCentral

26.

Coroller TP, Grossmann P, Hou Y, Velazquez ER, Leijenaar RT, Hermann G, Lambin P, Haibe-Kains B, Mak RH, Aerts HJ (2015) CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 114(3):345–350. https://doi.org/10.1016/j.radonc.2015.02.015CrossRefPubMedPubMedCentral

27.

Wernick M, Yang Y, Brankov J, Yourganov G, Strother SC, JSPMI (2010) Machine learning in medical. Imaging 27(4):25–38

28.

Luo Z, Li J, Liao Y, Liu R, Shen X, Chen W (2022) Radiomics analysis of multiparametric MRI for prediction of synchronous lung metastases in osteosarcoma. Front Oncol 12:802234. https://doi.org/10.3389/fonc.2022.802234CrossRefPubMedPubMedCentral

29.

Li G, Li L, Li Y, Qian Z, Wu F, He Y et al (2022) An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas. Brain 145(3):1151–1161. https://doi.org/10.1093/brain/awab340CrossRefPubMedPubMedCentral

30.

Mouraviev A, Detsky J, Sahgal A, Ruschin M, Lee YK, Karam I et al (2020) Use of radiomics for the prediction of local control of brain metastases after stereotactic radiosurgery. Neuro Oncol 22(6):797–805. https://doi.org/10.1093/neuonc/noaa007CrossRefPubMedPubMedCentral

31.

Yang L, Gu D, Wei J, Yang C, Rao S, Wang W et al (2019) A radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Liver Cancer 8(5):373–386. https://doi.org/10.1159/000494099CrossRefPubMed

32.

Zhang R, Zhang Q, Ji A, Lv P, Zhang J, Fu C et al (2021) Identification of high-risk carotid plaque with MRI-based radiomics and machine learning. Eur Radiol 31(5):3116–3126. https://doi.org/10.1007/s00330-020-07361-zCrossRefPubMed

33.

Yu Y, He Z, Ouyang J, Tan Y, Chen Y, Gu Y et al (2021) Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: a machine learning, multicenter study. EBioMedicine 69:103460. https://doi.org/10.1016/j.ebiom.2021.103460CrossRefPubMedPubMedCentral

34.

Mayerhoefer ME, Materka A, Langs G, Haggstrom I, Szczypinski P, Gibbs P et al (2020) Introduction to radiomics. J Nucl Med 61(4):488–495. https://doi.org/10.2967/jnumed.118.222893CrossRefPubMedPubMedCentral

35.

Zhang L, Wang Y, Peng Z, Weng Y, Fang Z, Xiao F et al (2022) The progress of multimodal imaging combination and subregion based radiomics research of cancers. Int J Biol Sci 18(8):3458–3469. https://doi.org/10.7150/ijbs.71046CrossRefPubMedPubMedCentral

36.

Ejnisman B, Monteiro GC, Andreoli CV, de Castro PA (2010) Disorder of the long head of the biceps tendon. Br J Sports Med 44(5):347–354. https://doi.org/10.1136/bjsm.2009.064139CrossRefPubMed

37.

Navas-Mosqueda A, Valera-Calero JA, Varol U, Klich S, Navarro-Santana MJ, Fernandez-de-Las-Penas C et al (2022) The prevalence of shoulder disorders among professional bullfighters: a cross-sectional ultrasonography study. Tomography 8(4):1726–1734. https://doi.org/10.3390/tomography8040145CrossRefPubMedPubMedCentral

38.

Liu H, Song X, Liu P, Yu H, Zhang Q, Guo W (2021) Clinical outcomes of arthroscopic tenodesis versus tenotomy for long head of the biceps tendon lesions: a systematic review and meta-analysis of randomized clinical trials and cohort studies. Orthop J Sports Med 9(4):2325967121993805. https://doi.org/10.1177/2325967121993805CrossRefPubMedPubMedCentral

39.

Razmjou H, Fournier-Gosselin S, Christakis M, Pennings A, Elmaraghy A, Holtby R, JJoS et al (2016) Accuracy of magnetic resonance imaging in detecting biceps pathology in patients with rotator cuff disorders: comparison with arthroscopy. J Shoulder Elbow Surg 25(1):38–44CrossRefPubMed

40.

Mohtadi NG, Vellet AD, Clark ML, Hollinshead RM, Sasyniuk TM, Fick GH et al (2004) A prospective, double-blind comparison of magnetic resonance imaging and arthroscopy in the evaluation of patients presenting with shoulder pain. J Shoulder Elbow Surg 13(3):258–265. https://doi.org/10.1016/j.jse.2004.01.003CrossRefPubMed

41.

Dubrow SA, Streit JJ, Shishani Y, Robbin MR, Gobezie R (2014) Diagnostic accuracy in detecting tears in the proximal biceps tendon using standard nonenhancing shoulder MRI. Open Access J Sports Med 5:81–87. https://doi.org/10.2147/OAJSM.S58225CrossRefPubMedPubMedCentral

42.

Burke CJ, Mahanty SR, Pham H, Hoda S, Babb JS, Gyftopoulos S et al (2019) MRI, arthroscopic and histopathologic cross correlation in biceps tenodesis specimens with emphasis on the normal appearing proximal tendon. Clin Imaging 54:126–132. https://doi.org/10.1016/j.clinimag.2019.01.001CrossRefPubMed

43.

Taylor SA, Khair MM, Gulotta LV, et al(2015) Diagnostic glenohumeral arthroscopy fails to fully evaluate the biceps-labral complex. Arthroscopy 31(2):215–224. https://doi.org/10.1016/j.arthro.2014.10.017CrossRefPubMed

44.

Zhuo Z, Qu L, Zhang P, Duan Y, Cheng D, Xu X et al (2021) Prediction of H3K27M-mutant brainstem glioma by amide proton transfer-weighted imaging and its derived radiomics. Eur J Nucl Med Mol Imaging 48(13):4426–4436. https://doi.org/10.1007/s00259-021-05455-4CrossRefPubMed

45.

Tomaszewski MR, Gillies RJ (2021) The biological meaning of radiomic features. Radiology 298(3):505–516. https://doi.org/10.1148/radiol.2021202553CrossRefPubMed

46.

Bitencourt AGV, Gibbs P, Rossi Saccarelli C, Daimiel I, Lo Gullo R, Fox MJ et al (2020) MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer. EBioMedicine 61:103042. https://doi.org/10.1016/j.ebiom.2020.103042CrossRefPubMedPubMedCentral

47.

Zabrzynski J, Szukalski J, Paczesny L, Szwedowski D, Grzanka D (2019) Cigarette smoking intensifies tendinopathy of the LHBT. A microscopic study after arthroscopic treatment. Pol J Pathol 70(2):134–138. https://doi.org/10.5114/pjp.2019.87104CrossRefPubMed

48.

Blumer R, Boesmueller S, Gesslbauer B, Hirtler L, Bormann D, Pastor AM et al (2020) Structural and molecular characteristics of axons in the long head of the biceps tendon. Cell Tissue Res 380(1):43–57. https://doi.org/10.1007/s00441-019-03141-4CrossRefPubMed

Title: A preliminary attempt to use radiomic features in the diagnosis of extra-articular long head biceps tendinitis
Authors: Lifeng Yin
Yanggang Kong
Mingkang Guo
Xingyu Zhang
Wenlong Yan
Hua Zhang
Publication date: 30-11-2022
Publisher: Springer International Publishing
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Arthroscopy
Arthroscopy
Tendinitis
Published in: Magnetic Resonance Materials in Physics, Biology and Medicine / Issue 4/2023
Print ISSN: 0968-5243
Electronic ISSN: 1352-8661
DOI: https://doi.org/10.1007/s10334-022-01050-2

At a glance: The STEP trials

Springer Medicine

A preliminary attempt to use radiomic features in the diagnosis of extra-articular long head biceps tendinitis

Abstract

Background

Patients and methods

Results

Conclusion

At a glance: The STEP trials

Springer Medicine

Abstract

Background

Patients and methods

Results

Conclusion

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