*Result*: Diagnostic value of transcranial ultrasonography and clinical features for Parkinson's disease based on XGBoost model and SHAP visualization analysis: a retrospective study.
Title:
Diagnostic value of transcranial ultrasonography and clinical features for Parkinson's disease based on XGBoost model and SHAP visualization analysis: a retrospective study.
Authors:
Ge Y; Department of Ultrasonography, Taizhou Second People's Hospital, Taizhou, China., Zhang X; Department of Ultrasonography, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China., Ding X; Department of Ultrasonography, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China., Zhang P; Department of Ultrasonography, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China., Su L; Department of Ultrasonography, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China. suliyang1224@126.com., Wang G; Department of Ultrasonography, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China. wanggang@enzemed.com.
Source:
European radiology [Eur Radiol] 2026 Jan; Vol. 36 (1), pp. 408-419. Date of Electronic Publication: 2025 Jul 03.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer International Country of Publication: Germany NLM ID: 9114774 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1084 (Electronic) Linking ISSN: 09387994 NLM ISO Abbreviation: Eur Radiol Subsets: MEDLINE
Imprint Name(s):
Original Publication: Berlin : Springer International, c1991-
MeSH Terms:
Comments:
Comment in: Eur Radiol. 2026 Jan;36(1):420-422. doi: 10.1007/s00330-025-12157-0.. (PMID: 41263965)
References:
Sveinbjornsdottir S (2016) The clinical symptoms of Parkinson’s disease. J Neurochem 139:318–324. (PMID: 2740194710.1111/jnc.13691)
Lew M (2007) Overview of Parkinson’s disease. Pharmacotherapy 27:155s–160s. (PMID: 1804193510.1592/phco.27.12part2.155S)
Dorsey ER, Sherer T, Okun MS, Bloem BR (2018) The emerging evidence of the Parkinson pandemic. J Parkinsons Dis 8:S3–s8. (PMID: 30584159631136710.3233/JPD-181474)
Zhang J, Fan Y, Liang H, Zhang Y (2025) Global, regional and national temporal trends in Parkinson’s disease incidence, disability-adjusted life year rates in middle-aged and older adults: a cross-national inequality analysis and Bayesian age-period-cohort analysis based on the global burden of disease 2021. Neurol Sci 46:1647–1660. (PMID: 3967304410.1007/s10072-024-07941-7)
Armstrong MJ, Okun MS (2020) Diagnosis and treatment of Parkinson disease: a review. JAMA 323:548–560. (PMID: 3204494710.1001/jama.2019.22360)
Hiseman JP, Fackrell R (2017) Caregiver burden and the nonmotor symptoms of Parkinson’s disease. Int Rev Neurobiol 133:479–497. (PMID: 2880292910.1016/bs.irn.2017.05.035)
Jankovic J (2008) Parkinson’s disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry 79:368–376. (PMID: 1834439210.1136/jnnp.2007.131045)
Rizzo G, Copetti M, Arcuti S, Martino D, Fontana A, Logroscino G (2016) Accuracy of clinical diagnosis of Parkinson disease: a systematic review and meta-analysis. Neurology 86:566–576. (PMID: 2676402810.1212/WNL.0000000000002350)
Becker G, Seufert J, Bogdahn U, Reichmann H, Reiners K (1995) Degeneration of substantia nigra in chronic Parkinson’s disease visualized by transcranial color-coded real-time sonography. Neurology 45:182–184. (PMID: 782411410.1212/WNL.45.1.182)
Walter U, Niehaus L, Probst T, Benecke R, Meyer BU, Dressler D (2003) Brain parenchyma sonography discriminates Parkinson’s disease and atypical Parkinsonian syndromes. Neurology 60:74–77. (PMID: 1252572110.1212/WNL.60.1.74)
Hagenah JM, Hedrich K, Becker B, Pramstaller PP, Seidel G, Klein C (2006) Distinguishing early-onset PD from dopa-responsive dystonia with transcranial sonography. Neurology 66:1951–1952. (PMID: 1680167110.1212/01.wnl.0000219806.03849.1a)
Tsai CF, Wu RM, Huang YW, Chen LL, Yip PK, Jeng JS (2007) Transcranial color-coded sonography helps differentiation between idiopathic Parkinson’s disease and vascular Parkinsonism. J Neurol 254:501–507. (PMID: 1740151810.1007/s00415-006-0403-9)
Yilmaz R, Berg D (2018) Transcranial B-mode sonography in movement disorders. Int Rev Neurobiol 143:179–212. (PMID: 3047319510.1016/bs.irn.2018.10.008)
Walter U, Loewenbrück KF, Dodel R, Storch A, Trenkwalder C, Höglinger G (2024) Systematic review-based guideline “Parkinson’s disease” of the German Society of Neurology: diagnostic use of transcranial sonography. J Neurol 271:7387–7401. (PMID: 389634401158881210.1007/s00415-024-12502-1)
Berardelli A, Wenning GK, Antonini A et al (2013) EFNS/MDS-ES/ENS [corrected] recommendations for the diagnosis of Parkinson’s disease. Eur J Neurol 20:16–34. (PMID: 2327944010.1111/ene.12022)
Rashidi HH, Chen M (2023) Preface: artificial intelligence (AI), machine learning ML) and digital pathology integration are the next major chapter in our diagnostic pathology and laboratory medicine arena. Semin Diagn Pathol 40:69–70. (PMID: 3689002810.1053/j.semdp.2023.02.005)
Li C, Zhang XY, Wu YH et al (2024) Expert consensus on ethical requirements for artificial intelligence (AI) processing medical data. Sheng Li Xue Bao 76:937–942. (PMID: 39780570)
Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H (2018) eDoctor: machine learning and the future of medicine. J Intern Med 284:603–619. (PMID: 3010280810.1111/joim.12822)
McGonigal A, Tankisi H (2024) Artificial Intelligence (AI): Why does it matter for clinical neurophysiology?. Neurophysiol Clin 54:102993. (PMID: 3887842510.1016/j.neucli.2024.102993)
Bartova P, Skoloudik D, Bar M et al (2008) Transcranial sonography in movement disorders. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 152:251–258. (PMID: 1921921610.5507/bp.2008.039)
Huang YW, Jeng JS, Tsai CF, Chen LL, Wu RM (2007) Transcranial imaging of substantia nigra hyperechogenicity in a Taiwanese cohort of Parkinson’s disease. Mov Disord 22:550–555. (PMID: 1726034410.1002/mds.21372)
Walter U, Wittstock M, Benecke R, Dressler D (2002) Substantia nigra echogenicity is normal in non-extrapyramidal cerebral disorders but increased in Parkinson’s disease. J Neural Transm 109:191–196. (PMID: 1207585910.1007/s007020200015)
Behnke S, Berg D, Naumann M, Becker G (2005) Differentiation of Parkinson’s disease and atypical Parkinsonian syndromes by transcranial ultrasound. J Neurol Neurosurg Psychiatry 76:423–425. (PMID: 15716540173953910.1136/jnnp.2004.049221)
Gaenslen A, Unmuth B, Godau J et al (2008) The specificity and sensitivity of transcranial ultrasound in the differential diagnosis of Parkinson’s disease: a prospective blinded study. Lancet Neurol 7:417–424. (PMID: 1839496510.1016/S1474-4422(08)70067-X)
Bouwmans AE, Vlaar AM, Srulijes K, Mess WH, Weber WE (2010) Transcranial sonography for the discrimination of idiopathic Parkinson’s disease from the atypical Parkinsonian syndromes. Int Rev Neurobiol 90:121–146. (PMID: 2069249810.1016/S0074-7742(10)90009-3)
Hagenah J, König IR, Sperner J et al (2010) Life-long increase of substantia nigra hyperechogenicity in transcranial sonography. Neuroimage 51:28–32. (PMID: 2015290910.1016/j.neuroimage.2010.01.112)
Hagenah J, Seidel G (2010) Sonography of the parenchyma in Parkinson’s disease. Nervenarzt 81:1189–1195. (PMID: 2080299310.1007/s00115-010-3025-5)
Walter U, Školoudík D (2014) Transcranial sonography (TCS) of brain parenchyma in movement disorders: quality standards, diagnostic applications and novel technologies. Ultraschall Med 35:322–331. (PMID: 2476421510.1055/s-0033-1356415)
Mei YL, Yang J, Wu ZR, Yang Y, Xu YM (2021) Transcranial sonography of the substantia nigra for the differential diagnosis of Parkinson’s disease and other movement disorders: a meta-analysis. Parkinsons Dis 2021:8891874. (PMID: 340074398110416)
Bartels AL, Leenders KL (2009) Parkinson’s disease: the syndrome, the pathogenesis and pathophysiology. Cortex 45:915–921. (PMID: 1909522610.1016/j.cortex.2008.11.010)
Berg D, Merz B, Reiners K, Naumann M, Becker G (2005) Five-year follow-up study of hyperechogenicity of the substantia nigra in Parkinson’s disease. Mov Disord 20:383–385. (PMID: 1548699910.1002/mds.20311)
van de Loo S, Walter U, Behnke S et al (2010) Reproducibility and diagnostic accuracy of substantia nigra sonography for the diagnosis of Parkinson’s disease. J Neurol Neurosurg Psychiatry 81:1087–1092. (PMID: 2054318610.1136/jnnp.2009.196352)
Tao A, Chen G, Deng Y, Xu R (2019) Accuracy of transcranial sonography of the substantia nigra for detection of Parkinson’s disease: a systematic review and meta-analysis. Ultrasound Med Biol 45:628–641. (PMID: 3061282110.1016/j.ultrasmedbio.2018.11.010)
Izawa MO, Miwa H, Kajimoto Y, Kondo T (2012) Combination of transcranial sonography, olfactory testing, and MIBG myocardial scintigraphy as a diagnostic indicator for Parkinson’s disease. Eur J Neurol 19:411–416. (PMID: 2197809110.1111/j.1468-1331.2011.03533.x)
Ambrosius W, Michalak S, Owecki M, Łukasik M, Florczak-Wyspiańska J, Kozubski W (2014) Substantia nigra hyperechogenicity in Polish patients with Parkinson’s disease. Folia Morphol (Warsz) 73:267–271. (PMID: 10.5603/FM.2014.0042)
Prati P, Bignamini A, Coppo L et al (2017) The measuring of substantia nigra hyperechogenicity in an Italian cohort of Parkinson disease patients: a case/control study (NOBIS Study). J Neural Transm 124:869–879. (PMID: 2845194210.1007/s00702-017-1724-9)
Wang LS, Yu TF, Chai B, He W (2021) Transcranial sonography in differential diagnosis of Parkinson disease and other movement disorders. Chin Med J 134:1726–1731. (PMID: 34238849831865010.1097/CM9.0000000000001503)
Driver JA, Logroscino G, Gaziano JM, Kurth T (2009) Incidence and remaining lifetime risk of Parkinson disease in advanced age. Neurology 72:432–438. (PMID: 19188574267672610.1212/01.wnl.0000341769.50075.bb)
Dorsey ER, Constantinescu R, Thompson JP et al (2007) Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030. Neurology 68:384–386. (PMID: 1708246410.1212/01.wnl.0000247740.47667.03)
Quinn NP (1993) Parkinsonism and dystonia, pseudo-Parkinsonism and pseudodystonia. Adv Neurol 60:540–543. (PMID: 8420187)
Poewe WH, Lees AJ, Stern GM (1988) Dystonia in Parkinson’s disease: clinical and pharmacological features. Ann Neurol 23:73–78. (PMID: 334506810.1002/ana.410230112)
Tolosa E, Compta Y (2006) Dystonia in Parkinson’s disease. J Neurol 253:Vii7–Vii13. (PMID: 1713123110.1007/s00415-006-7003-6)
Lew M (2007) Overview of Parkinson’s disease. Pharmacotherapy 27:155s–160s. (PMID: 1804193510.1592/phco.27.12part2.155S)
Dorsey ER, Sherer T, Okun MS, Bloem BR (2018) The emerging evidence of the Parkinson pandemic. J Parkinsons Dis 8:S3–s8. (PMID: 30584159631136710.3233/JPD-181474)
Zhang J, Fan Y, Liang H, Zhang Y (2025) Global, regional and national temporal trends in Parkinson’s disease incidence, disability-adjusted life year rates in middle-aged and older adults: a cross-national inequality analysis and Bayesian age-period-cohort analysis based on the global burden of disease 2021. Neurol Sci 46:1647–1660. (PMID: 3967304410.1007/s10072-024-07941-7)
Armstrong MJ, Okun MS (2020) Diagnosis and treatment of Parkinson disease: a review. JAMA 323:548–560. (PMID: 3204494710.1001/jama.2019.22360)
Hiseman JP, Fackrell R (2017) Caregiver burden and the nonmotor symptoms of Parkinson’s disease. Int Rev Neurobiol 133:479–497. (PMID: 2880292910.1016/bs.irn.2017.05.035)
Jankovic J (2008) Parkinson’s disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry 79:368–376. (PMID: 1834439210.1136/jnnp.2007.131045)
Rizzo G, Copetti M, Arcuti S, Martino D, Fontana A, Logroscino G (2016) Accuracy of clinical diagnosis of Parkinson disease: a systematic review and meta-analysis. Neurology 86:566–576. (PMID: 2676402810.1212/WNL.0000000000002350)
Becker G, Seufert J, Bogdahn U, Reichmann H, Reiners K (1995) Degeneration of substantia nigra in chronic Parkinson’s disease visualized by transcranial color-coded real-time sonography. Neurology 45:182–184. (PMID: 782411410.1212/WNL.45.1.182)
Walter U, Niehaus L, Probst T, Benecke R, Meyer BU, Dressler D (2003) Brain parenchyma sonography discriminates Parkinson’s disease and atypical Parkinsonian syndromes. Neurology 60:74–77. (PMID: 1252572110.1212/WNL.60.1.74)
Hagenah JM, Hedrich K, Becker B, Pramstaller PP, Seidel G, Klein C (2006) Distinguishing early-onset PD from dopa-responsive dystonia with transcranial sonography. Neurology 66:1951–1952. (PMID: 1680167110.1212/01.wnl.0000219806.03849.1a)
Tsai CF, Wu RM, Huang YW, Chen LL, Yip PK, Jeng JS (2007) Transcranial color-coded sonography helps differentiation between idiopathic Parkinson’s disease and vascular Parkinsonism. J Neurol 254:501–507. (PMID: 1740151810.1007/s00415-006-0403-9)
Yilmaz R, Berg D (2018) Transcranial B-mode sonography in movement disorders. Int Rev Neurobiol 143:179–212. (PMID: 3047319510.1016/bs.irn.2018.10.008)
Walter U, Loewenbrück KF, Dodel R, Storch A, Trenkwalder C, Höglinger G (2024) Systematic review-based guideline “Parkinson’s disease” of the German Society of Neurology: diagnostic use of transcranial sonography. J Neurol 271:7387–7401. (PMID: 389634401158881210.1007/s00415-024-12502-1)
Berardelli A, Wenning GK, Antonini A et al (2013) EFNS/MDS-ES/ENS [corrected] recommendations for the diagnosis of Parkinson’s disease. Eur J Neurol 20:16–34. (PMID: 2327944010.1111/ene.12022)
Rashidi HH, Chen M (2023) Preface: artificial intelligence (AI), machine learning ML) and digital pathology integration are the next major chapter in our diagnostic pathology and laboratory medicine arena. Semin Diagn Pathol 40:69–70. (PMID: 3689002810.1053/j.semdp.2023.02.005)
Li C, Zhang XY, Wu YH et al (2024) Expert consensus on ethical requirements for artificial intelligence (AI) processing medical data. Sheng Li Xue Bao 76:937–942. (PMID: 39780570)
Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H (2018) eDoctor: machine learning and the future of medicine. J Intern Med 284:603–619. (PMID: 3010280810.1111/joim.12822)
McGonigal A, Tankisi H (2024) Artificial Intelligence (AI): Why does it matter for clinical neurophysiology?. Neurophysiol Clin 54:102993. (PMID: 3887842510.1016/j.neucli.2024.102993)
Bartova P, Skoloudik D, Bar M et al (2008) Transcranial sonography in movement disorders. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 152:251–258. (PMID: 1921921610.5507/bp.2008.039)
Huang YW, Jeng JS, Tsai CF, Chen LL, Wu RM (2007) Transcranial imaging of substantia nigra hyperechogenicity in a Taiwanese cohort of Parkinson’s disease. Mov Disord 22:550–555. (PMID: 1726034410.1002/mds.21372)
Walter U, Wittstock M, Benecke R, Dressler D (2002) Substantia nigra echogenicity is normal in non-extrapyramidal cerebral disorders but increased in Parkinson’s disease. J Neural Transm 109:191–196. (PMID: 1207585910.1007/s007020200015)
Behnke S, Berg D, Naumann M, Becker G (2005) Differentiation of Parkinson’s disease and atypical Parkinsonian syndromes by transcranial ultrasound. J Neurol Neurosurg Psychiatry 76:423–425. (PMID: 15716540173953910.1136/jnnp.2004.049221)
Gaenslen A, Unmuth B, Godau J et al (2008) The specificity and sensitivity of transcranial ultrasound in the differential diagnosis of Parkinson’s disease: a prospective blinded study. Lancet Neurol 7:417–424. (PMID: 1839496510.1016/S1474-4422(08)70067-X)
Bouwmans AE, Vlaar AM, Srulijes K, Mess WH, Weber WE (2010) Transcranial sonography for the discrimination of idiopathic Parkinson’s disease from the atypical Parkinsonian syndromes. Int Rev Neurobiol 90:121–146. (PMID: 2069249810.1016/S0074-7742(10)90009-3)
Hagenah J, König IR, Sperner J et al (2010) Life-long increase of substantia nigra hyperechogenicity in transcranial sonography. Neuroimage 51:28–32. (PMID: 2015290910.1016/j.neuroimage.2010.01.112)
Hagenah J, Seidel G (2010) Sonography of the parenchyma in Parkinson’s disease. Nervenarzt 81:1189–1195. (PMID: 2080299310.1007/s00115-010-3025-5)
Walter U, Školoudík D (2014) Transcranial sonography (TCS) of brain parenchyma in movement disorders: quality standards, diagnostic applications and novel technologies. Ultraschall Med 35:322–331. (PMID: 2476421510.1055/s-0033-1356415)
Mei YL, Yang J, Wu ZR, Yang Y, Xu YM (2021) Transcranial sonography of the substantia nigra for the differential diagnosis of Parkinson’s disease and other movement disorders: a meta-analysis. Parkinsons Dis 2021:8891874. (PMID: 340074398110416)
Bartels AL, Leenders KL (2009) Parkinson’s disease: the syndrome, the pathogenesis and pathophysiology. Cortex 45:915–921. (PMID: 1909522610.1016/j.cortex.2008.11.010)
Berg D, Merz B, Reiners K, Naumann M, Becker G (2005) Five-year follow-up study of hyperechogenicity of the substantia nigra in Parkinson’s disease. Mov Disord 20:383–385. (PMID: 1548699910.1002/mds.20311)
van de Loo S, Walter U, Behnke S et al (2010) Reproducibility and diagnostic accuracy of substantia nigra sonography for the diagnosis of Parkinson’s disease. J Neurol Neurosurg Psychiatry 81:1087–1092. (PMID: 2054318610.1136/jnnp.2009.196352)
Tao A, Chen G, Deng Y, Xu R (2019) Accuracy of transcranial sonography of the substantia nigra for detection of Parkinson’s disease: a systematic review and meta-analysis. Ultrasound Med Biol 45:628–641. (PMID: 3061282110.1016/j.ultrasmedbio.2018.11.010)
Izawa MO, Miwa H, Kajimoto Y, Kondo T (2012) Combination of transcranial sonography, olfactory testing, and MIBG myocardial scintigraphy as a diagnostic indicator for Parkinson’s disease. Eur J Neurol 19:411–416. (PMID: 2197809110.1111/j.1468-1331.2011.03533.x)
Ambrosius W, Michalak S, Owecki M, Łukasik M, Florczak-Wyspiańska J, Kozubski W (2014) Substantia nigra hyperechogenicity in Polish patients with Parkinson’s disease. Folia Morphol (Warsz) 73:267–271. (PMID: 10.5603/FM.2014.0042)
Prati P, Bignamini A, Coppo L et al (2017) The measuring of substantia nigra hyperechogenicity in an Italian cohort of Parkinson disease patients: a case/control study (NOBIS Study). J Neural Transm 124:869–879. (PMID: 2845194210.1007/s00702-017-1724-9)
Wang LS, Yu TF, Chai B, He W (2021) Transcranial sonography in differential diagnosis of Parkinson disease and other movement disorders. Chin Med J 134:1726–1731. (PMID: 34238849831865010.1097/CM9.0000000000001503)
Driver JA, Logroscino G, Gaziano JM, Kurth T (2009) Incidence and remaining lifetime risk of Parkinson disease in advanced age. Neurology 72:432–438. (PMID: 19188574267672610.1212/01.wnl.0000341769.50075.bb)
Dorsey ER, Constantinescu R, Thompson JP et al (2007) Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030. Neurology 68:384–386. (PMID: 1708246410.1212/01.wnl.0000247740.47667.03)
Quinn NP (1993) Parkinsonism and dystonia, pseudo-Parkinsonism and pseudodystonia. Adv Neurol 60:540–543. (PMID: 8420187)
Poewe WH, Lees AJ, Stern GM (1988) Dystonia in Parkinson’s disease: clinical and pharmacological features. Ann Neurol 23:73–78. (PMID: 334506810.1002/ana.410230112)
Tolosa E, Compta Y (2006) Dystonia in Parkinson’s disease. J Neurol 253:Vii7–Vii13. (PMID: 1713123110.1007/s00415-006-7003-6)
Contributed Indexing:
Keywords: Machine learning; Parkinson's disease; Substantia Nigra; Transcranial; Transcranial ultrasonography
Entry Date(s):
Date Created: 20250703 Date Completed: 20251217 Latest Revision: 20251217
Update Code:
20260130
DOI:
10.1007/s00330-025-11789-6
PMID:
40610840
Database:
MEDLINE
*Further Information*
*Objectives: Parkinson's disease (PD) requires early diagnosis for optimal management. This study aims to evaluate whether combining transcranial ultrasonography (TCS) and clinical data using an interpretable machine learning model improves diagnostic accuracy.
Materials and Methods: In this retrospective study, data from patients who underwent TCS between May 2023 and December 31, 2024, were retrospectively collected. Key clinical and TCS features were identified using the Boruta algorithm. An XGBoost model (an advanced gradient boosting algorithm) was developed based on these features, and Shapley Additive Explanations (SHAP, a method for interpreting machine learning predictions) was applied to visualize their contributions to PD diagnosis.
Results: The study included 599 patients (480 training, 119 validation) and achieved area under the curve (AUC) values of 0.863 and 0.811 in training and validation datasets, respectively. SHAP analysis revealed that bilateral substantia nigra hyperechoic (SNHA) and the substantia nigra/midbrain ratio (S/M) were the most influential predictors.
Conclusion: Integrating TCS with clinical data via XGBoost and SHAP provides high diagnostic performance and clear interpretability, supporting early PD diagnosis.
Key Points: Question Can TCS features combined with machine learning provide reliable diagnostic support for PD? Findings XGBoost model integrating TCS and clinical features achieved a high diagnostic performance (AUC = 0.811) and interpretable outputs via SHAP visualization analysis. Clinical relevance This interpretable AI model supports early PD diagnosis and individualized decision making using non-invasive imaging and routine clinical parameters.
(© 2025. The Author(s), under exclusive licence to European Society of Radiology.)*
*Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is Yanan Ge. Conflict of interest: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Statistics and biometry: No complex statistical methods were necessary for this paper. Informed consent: Written informed consent was waived by the Institutional Review Board. Ethical approval: Institutional Review Board approval was obtained. Study subjects or cohorts overlap: No study subject or cohort has been previously reported. Methodology: Retrospective Observational Performed at one institution*