A Diagnostic Machine Classifier Using Multi-Parametric MRI to Differentiate Benign from Malignant Myometrial Tumors


avatar Mahrooz Malek 1 , avatar Elnaz Tabibian 1 , * , avatar Milad Rahimi Dehgolan 2 , avatar Maryam Rahmani 1 , avatar Setare Akhavan 1 , avatar Shahrzad Sheikh Hasani 1 , avatar Fatemeh Nili ORCID 1 , avatar Hassan Hashemi 1

Tehran University of Medical Sciences, Tehran, Iran
Khaje Nasir Toosi University of Technology, Tehran, Iran

how to cite: Malek M, Tabibian E, Rahimi Dehgolan M, Rahmani M, Akhavan S, et al. A Diagnostic Machine Classifier Using Multi-Parametric MRI to Differentiate Benign from Malignant Myometrial Tumors. Innov J Radiol. 2019;16(Special Issue):e99154. https://doi.org/10.5812/iranjradiol.99154.



There are many conditions in medicine that decision making has crucial importance to differentiate between binary diagnoses, such as preoperative discrimination of benign from malignant tumors, e.g. uterine neoplasms. Physicians are not usually able to pool multiple parameters affecting the diagnosis, while “machine learning” techniques, especially “decision trees” with human-readable results, can process such amounts of data. Previous studies have shown that MRI could be helpful in the differentiation of uterine leiomyosarcoma from leiomyoma.


Hereby, for preoperative diagnosis of these tumors and to reduce unnecessary costs and surgeries, we applied a machine classifier using multi-parametric MRI to construct practical diagnostic algorithms.


A total of 105 myometrial lesions were included in two groups of benign and malignant, according to postoperative tissue diagnosis. Multi-parametric MRI including T1-, T2-, and diffusion-weighted images (DWI) with apparent diffusion coefficient (ADC) map, contrast-enhanced images, as well as MR spectroscopy, were performed for each lesion. Thirteen singular MRI features were extracted from the mentioned sequences. Various combination sets of selective features were fed into a machine classifier (coarse decision-tree) to predict malignant or benign tumors. The accuracy metrics of either singular or combinational models were assessed (Figures 1 and 2). Eventually, two diagnostic algorithms, including a simple decision-tree and a complex one, were proposed using the most accurate models by MATLAB 2017.


Among all singular features, the visual assessment of DWI-ADC restriction, followed by the T2 map, achieved the best accuracies as 96.2% and 92.0%, respectively. Our final simple decision-tree comprised three features including T2, central necrosis (CN), and DWI-ADC restriction with accuracy of 96.2%, sensitivity of 100%, and specificity of 95%. However, the complex tree made up of four features including T2 map, CN, DWI-ADC restriction, and Tumor to Myometrium Contrast-Enhanced Ratio yielded accuracy, sensitivity, and specificity values of 100%.


The complex diagnostic algorithm, compared to the simple model, could differentiate tumors, with equal sensitivity but higher specificity. However, it needs more advanced calculations and a high level of patient cooperation; moreover, it might be a time-consuming method. Therefore, physicians should wisely trade-off in different clinical situations and request imaging modalities in a way to reduce additional costs and most importantly, to prevent unnecessary surgeries, by helping early accurate diagnosis.

To see figures, please refer to the PDF file.

Copyright © 2019, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.