ArticleScientific reports2020
A Diagnostic Algorithm using Multi-parametric MRI to Differentiate Benign from Malignant Myometrial Tumors: Machine-Learning Method.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- Artificial Intelligence in Obstetric and Gynecological MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- MRI machine learning model predicts nerve root sedimentation in lumbar stenosis: a prospective study.Frontiers in neurology · 2025Article
- Advancement in Multi-omics approaches for Uterine Sarcoma.Biomarker research · 2024Review
- The automatic diagnosis artificial intelligence system for preoperative magnetic resonance imaging of uterine sarcoma.Journal of gynecologic oncology · 2024Article
- Development and validation of a radiomics model based on T2-weighted imaging for predicting the efficacy of high intensity focused ultrasound ablation in uterine fibroids.Quantitative imaging in medicine and surgery · 2024Article
- Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases.Scientific reports · 2022Article
- Review
- Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review.Frontiers in oncology · 2022Article
- Radiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review.Journal of personalized medicine · 2021Review
- Differentiating uterine sarcoma from leiomyoma: BETThe British journal of radiology · 2021Review
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to develop a diagnostic algorithm for preoperative differentiating uterine sarcoma from leiomyoma through a supervised machine-learning method using multi-parametric MRI. A total of 65 participants with 105 myometrial tumors were included: 84 benign and 21 malignant lesions (belonged to 51 and 14 patients, respectively; based on their postoperative tissue diagnosis). Multi-parametric MRI including T1-, T2-, and diffusion-weighted (DW) sequences with ADC-map, contrast-enhanced images, as well as MR spectroscopy (MRS), was 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. Eventually, two diagnostic algorithms, a simple decision-tree and a complex one were proposed using the most accurate models. Our final simple decision-tree obtained accuracy = 96.2%, sensitivity = 100% and specificity = 95%; while the complex tree yielded accuracy, sensitivity and specificity of 100%. To summarise, the complex diagnostic algorithm, compared to the simple one, can differentiate tumors with equal sensitivity, but a higher specificity and accuracy. However, it needs some further time-consuming modalities and difficult imaging calculations. Trading-off costs and benefits in appropriate situations must be determinative.
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