Ultraschall Med 2023; 44(03): 318-326
DOI: 10.1055/a-1640-9508
Original Article

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

Auf maschinellem Lernen basierende Ultraschall-Texturanalyse zur Differenzierung zwischen benignen und borderline-malignen Phylloides-Tumoren
1   Radiology, Dokuz Eylul University Faculty of Medicine, Izmir, Turkey
Hakan Abdullah Ozgul
1   Radiology, Dokuz Eylul University Faculty of Medicine, Izmir, Turkey
1   Radiology, Dokuz Eylul University Faculty of Medicine, Izmir, Turkey
2   Pathology, Dokuz Eylul University Faculty of Medicine, Izmir, Turkey
3   General Surgery, Dokuz Eylul University Faculty of Medicine, Izmir, Turkey
Ali Ibrahim Sevinc
3   General Surgery, Dokuz Eylul University Faculty of Medicine, Izmir, Turkey
1   Radiology, Dokuz Eylul University Faculty of Medicine, Izmir, Turkey
4   Family Medicine, İzmir Democracy University, Izmir, Turkey
1   Radiology, Dokuz Eylul University Faculty of Medicine, Izmir, Turkey
› Author Affiliations


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.


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.

Publication History

Received: 24 November 2020

Accepted: 18 August 2021

Article published online:
21 October 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

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