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MRI-based radiomics analysis for differentiating phyllodes tumors of the breast from fibroadenomas

  • Breast
  • Published:
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Abstract

Objectives

To evaluate the diagnostic performance of MRI-based radiomics model for differentiating phyllodes tumors of the breast from fibroadenomas.

Methods

This retrospective study included 88 patients (32 with phyllodes tumors and 56 with fibroadenomas) who underwent MRI. Radiomic features were extracted from T2-weighted image, pre-contrast T1-weighted image, and the first-phase and late-phase dynamic contrast-enhanced MRIs. To create stable machine learning models and balanced classes, data augmentation was performed. A least absolute shrinkage and selection operator (LASSO) regression was performed to select features and build the radiomics model. A radiological model was constructed from conventional MRI features evaluated by radiologists. A combined model was constructed using both radiomics features and radiological features. Machine learning classifications were done using support vector machine, extreme gradient boosting, and random forest. The area under the receiver operating characteristic (ROC) curve (AUC) was computed to assess the performance of each model.

Results

Among 1070 features, the LASSO logistic regression selected 35 features. Among three machine learning classifiers, support vector machine had the best performance. Compared to the radiological model (AUC: 0.77 ± 0.11), the radiomics model (AUC: 0.96 ± 0.04) and combined model (0.97 ± 0.03) had significantly improved AUC values (both p < 0.01) in the validation set. The combined model had a relatively higher AUC than that of the radiomics model in the validation set, but this was not significantly different (p = 0.391).

Conclusions

Radiomics analysis based on MRI showed promise for discriminating phyllodes tumors from fibroadenomas.

Key Points

• The radiomics model and the combined model were superior to the radiological model for differentiating phyllodes tumors from fibroadenomas.

• The SVM classifier performed best in the current study.

• MRI-based radiomics model could help accurately differentiate phyllodes tumors from fibroadenomas.

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Abbreviations

ACC:

Accuracy

AUC:

Area under the curve

CI:

Confidence interval

DCE:

Dynamic contrast enhanced

FOV:

Field of view

Gd:

Gadolinium

ICC:

Interobserver correlation coefficient

LASSO:

Least absolute shrinkage and selection operator

ML:

Machine learning

MRI:

Magnetic resonance imaging

RF:

Random forest

ROC:

Receiver operating characteristic

ROI:

Region of interest

SVM:

Support vector machine

T1WI:

T1-weighted imaging

T2WI:

T2-weighted imaging

TE:

Echo time

TIC:

Time-intensity curve

TR:

Repetition time

XGB:

Extreme gradient boosting

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Acknowledgements

We thank Dr. Yoshiro Otsuki from the Department of Pathology, Seirei Hamamatsu General Hospital, for the pathological data.

Funding

The authors state that this work has not received any funding.

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Authors and Affiliations

  1. Department of Radiology, Seirei Hamamatsu General Hospital, 2-12-12, Sumiyoshi, Naka-ku, Hamamatsu city, Shizuoka, 430-8558, Japan

    Mitsuteru Tsuchiya, Takayuki Masui, Kazuma Terauchi, Takahiro Yamada & Motoyuki Katyayama

  2. Department of Radiology, Hamamatsu University School of Medicine, 1-20-1, Handayama, Higashi-ku, Hamamatsu City, Shizuoka, 431-3192, Japan

    Shintaro Ichikawa & Satoshi Goshima

  3. Department of Radiology, Gifu University, 1-1, Yanagido, Gifu City, Gifu, 501-1194, Japan

    Yoshifumi Noda

Authors
  1. Mitsuteru Tsuchiya
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Correspondence to Mitsuteru Tsuchiya.

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The scientific guarantor of this publication is Takayuki Masui, M.D., Ph.D.

Conflict of interest

MT, TM, KT, TY, MK, SI, YN, and SG declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

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No complex statistical methods were necessary for this paper.

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Study subjects or cohorts overlap

A part of our study population has been previously presented in ECR2019.

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• retrospective

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• performed at one institution

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Tsuchiya, M., Masui, T., Terauchi, K. et al. MRI-based radiomics analysis for differentiating phyllodes tumors of the breast from fibroadenomas. Eur Radiol 32, 4090–4100 (2022). https://doi.org/10.1007/s00330-021-08510-8

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