Machine Learning-Based Ultrasound Texture Analysis in Differentiation of Benign Phyllodes Tumors from Borderline-Malignant Phyllodes Tumors

Abstract

Purpose Phyllodes tumors (PTs) are uncommon fibroepithelial breast lesions that are classified as three different forms as benign phyllodes tumor (BPT), borderline phyllodes tumor (BoPT), and malignant phyllodes tumor (MPT). Conventional radiologic methods make only a limited contribution to exact diagnosis, and texture analysis data increase the diagnostic performance. In this study, we aimed to evaluate the contribution of texture analysis of US images (TAUI) of PTs in order to discriminate between BPTs and BoPTs-MPTs.

Methods The number of patients was 63 (41 BPTs, 12 BoPTs, and 10 MPTs). Patients were divided into two groups (Group 1-BPT, Group 2-BoPT/MPT). TAUI with LIFEx software was performed retrospectively. An independent machine learning approach, MATLAB R2020a (Math- Works, Natick, Massachusetts) was used with the dataset with p < 0.004. Two machine learning approaches were used to build prediction models for differentiating between Group 1 and Group 2. Receiver operating characteristics (ROC) curve analyses were performed to evaluate the diagnostic performance of statistically significant texture data between phyllodes subgroups.

Results In TAUI, 10 statistically significant second order texture values were identified as significant factors capable of differentiating among the two groups (p < 0.05). Both of the models of our dataset make a diagnostic contribution to the discrimination between BopTs-MPTs and BPTs.

Conclusion In PTs, US is the main diagnostic method. Adding machine learning-based TAUI to conventional US findings can provide optimal diagnosis, thereby helping to choose the correct surgical method. Consequently, decreased local recurrence rates can be achieved.

Zusammenfassung

Hintergrund Phylloides-Tumore (PTs) sind seltene fibroepitheliale Brustläsionen, die in drei verschiedene Typen eingeteilt werden: Benigner (BPT), borderline (BoPT) und maligner (MPT) Phylloides-Tumor. Herkömmliche radiologische Verfahren leisten nur einen begrenzten Beitrag zur exakten Diagnose, und Texturanalysedaten erhöhen die diagnostische Leistung. In dieser Studie wollten wir den Beitrag der Texturanalyse von US-Bildern (TAUI) von PTs bewerten, um zwischen BPTs und BoPTs-MPTs zu differenzieren.

Methoden Es gab 63 Patienten (41 BPTs, 12 BoPTs und 10 MPTs). Die Patienten wurden in zwei Gruppen eingeteilt (Gruppe 1-BPT, Gruppe 2-BoPT/MPT). TAUI mit LIFEx-Software wurde retrospektiv durchgeführt. Ein unabhängiger maschineller Lernansatz, MATLAB R2020a (Math Works, Natick, Massachusetts), wurde mit dem Datensatz mit p < 0,004 verwendet. Zwei maschinelle Lernansätze wurden verwendet, um Vorhersagemodelle für die Differenzierung zwischen Gruppe 1 und Gruppe 2 zu erstellen. Es wurden Receiver-Operating-Characteristics (ROC)-Kurvenanalysen durchgeführt, um die diagnostische Leistung von statistisch signifikanten Texturdaten zwischen den Phylloides-Untergruppen zu bewerten.

Ergebnisse Mittels TAUI wurden 10 statistisch signifikante Texturwerte zweiter Ordnung als signifikante Faktoren identifiziert, die zwischen den beiden Gruppen differenzieren können (p < 0,05). Beide Modelle unseres Datensatzes leisten einen diagnostischen Beitrag zur Differenzierung zwischen BopTs-MPTs und BPTs.

Schlussfolgerung Bei PTs ist die US die wichtigste diagnostische Methode. Die Ergänzung der konventionellen US-Befunde durch eine auf maschinellem Lernen basierende TAUI kann eine optimale Diagnose liefern und damit bei der Auswahl der optimalen chirurgischen Methode helfen. Infolgedessen können die Lokalrezidivraten gesenkt werden.

Key words

machine learning - texture analysis - ultrasonography - phyllodes tumor

Publikationsverlauf

Eingereicht: 24. November 2020

Angenommen: 18. August 2021

Artikel online veröffentlicht:
21. Oktober 2021

• References

• 1 Cui WJ, Wang C, Jia L. et al. Differentiation between G1 and G2/G3 phyllodes tumors of breast using mammography and mammographic texture analysis. Front Oncol 2019; 29: 433
• 2 Zhang Y, Kleer CG. Phyllodes tumor of the breast: histopathologic features, differential diagnosis, and molecular/ genetic updates. Arch Pathol Lab Med 2016; 140: 665-671
• 3 Lim RS, Corderio E, Lau J. et al. Phyllodes tumors—the predictors and detection of recurrence. Can Assoc Radiol J 2020; DOI: 10.1177/0846537119899553.
• 4 Duman L, Gezer NS, Balci P. et al. Differentiation between phyllodes tumors and fibroadenomas based on mammographic sonographic and MRI features. Breast Care 2016; 11: 123-127
• 5 Chu B, Crystal P. Imaging of fibroepithelial lesions: a pictorial essay. Can Assoc Radiol J 2012; 63: 135-145
• 6 Mai H, Mao Y, Dong T. et al. The utility of texture analysis based on breast magnetic resonance imaging in differentiating phyllodes tumors from fibroadenomas. Front Oncol 2019; 9: 1021
• 7 Shubham S, Ahuja A, Bhardwaj M. Immunohistochemical expression of Ki‐67, p53, and CD10 in phyllodes tumor and their correlation with its histological grade. J Lab Physicians 2019; 11: 330-334
• 8 Tan BY, Acs G, Apple SK. et al. Phyllodes tumours of the breast: a consensus review. Histopathology 2016; 68: 5-21
• 9 Mishra SP, Tiwary SK, Mishra M. et al. Phyllodes tumor of breast: a review article. ISRN Surg 2013; 2013: 361469
• 10 Xiao M, Zhu Q, Jiang Y. et al. Local recurrent phyllodes tumors of the breast: clinical and sonographic features. J Ultrasound Med 2015; 34: 1631-1638
• 11 Neron M, Sajous C, Thezenas S. et al. Surgical Margins and Adjuvant Therapies in Malignant Phyllodes Tumors of the Breast: A Multicenter Retrospective Study. Ann Surg Oncol 2020; 27: 1818-1827
• 12 Pezner RD, Schultheiss TE, Paz IB. Malignant phyllodes tumor of the breast: local control rates with surgery alone. Int J Radiat Oncol Biol Phys 2008; 71: 710-713
• 13 Basara AkinI, Ozgul H, Simsek K. et al. Texture Analysis of Ultrasound Images to Differentiate Simple Fibroadenomas From Complex Fibroadenomas and Benign Phyllodes Tumors. J Ultrasound Med 2020; 39: 1993-2003
• 14 Yang G, Gong A, Nie P. et al. Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma. Mol Imaging 2019; DOI: 10.1177/1536012119883161.
• 15 Ma W, Guo X, Liu L. et al. Magnetic resonance imaging semantic and quantitative features analyses: an additional diagnostic tool for breast phyllodes tumors. Am J Transl Res 2020; 12: 2083-2092
• 16 Lee SE, Han K, Kwak JY. et al. Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenoma. Sci Rep 2018; DOI: 10.1038/s41598-018-31906-4.
• 17 Collewet G, Strzelecki M, Mariette F. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging 2004; 22: 81-91
• 18 Azzopardi JG, Chepick OF, Hartmann WH. et al. The World Health Organization histological typing of breast tumors – second edition. Am J Clin Pathol 1982; 78: 806-816
• 19 Koçak B, Durmaz EŞ, Ateş E. et al. Radiomics with artificial intelligence: a practical guide for beginners. Diagn Interv Radiol 2019; 25: 485-495
• 20 Liberman L, Bonaccio E, Hamela-Bena D. et al. Benign and malignant phyllodes tumors: mammographic and sonographic findings. Radiology 1996; 198: 121-124
• 21 Chao TC, Lo YF, Chen SC. et al. Phyllodes tumors of the breast. Eur Radiol 2003; 13: 88-93
• 22 Hasdemir S, Tolunay Ş, Özşen M. et al. Phyllodes Tumor of the Breast: A Clinicopathological Evaluation of 55 Cases. Eur J Breast Health 2019; 16: 32-38
• 23 Adamietz BR, Kahmann L, Fasching PA. et al. Differentiation between phyllodes tumor and fibroadenoma using real-time elastography.  Ultraschall in Med  2011; 32: 75-79
• 24 Wiratkapun C, Piyapan P, Lertsithichai P. et al. Fibroadenoma versus phyllodes tumor: distinguishing factors in patients diagnosed with fibroepithelial lesions after a core needle biopsy. Diagn Interv Radiol 2014; 20: 27-33
• 25 Kato H, Kanematsu M, Zhang X. et al. Computer-aided diagnosis of hepatic fibrosis: preliminary evaluation of MRI texture analysis using the finite difference method and an artificial neural network. Am J Roentgenol 2007; 189: 117-122
• 26 Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016; 278: 563-577
• 27 Sevinç Aİ, Aksoy SÖ, Güray Durak M. et al. Is the extent of surgical resection important in patient outcome in benign and borderline phyllodes tumors of the breast?. Turk J Med Sci 2018; 48: 28-33
• 28 Lee SE, Han K, Kwak JY. et al. Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenoma. Sci Rep 2018; 8: 13546