A machine learning approach for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted MRI parameters

•None of PWI parameters differed significantly between uterine sarcoma and leiomyomas.•Using a machine-learning method, PWI parameters were aggregated.•Computer was 92% accurate in uterine sarcoma/ leiomyomas classification. To propose a computer-assisted method for distinguishing uterine sarcoma fr...

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Veröffentlicht in:	European journal of radiology 2019-01, Vol.110, p.203-211
Hauptverfasser:	Malek, Mahrooz, Gity, Masoumeh, Alidoosti, Azadeh, Oghabian, Zeinab, Rahimifar, Pariya, Seyed Ebrahimi, Seyede Mahdieh, Tabibian, Elnaz, Oghabian, Mohammad Ali
Format:	Artikel
Sprache:	eng
Schlagworte:	Adult Computer-aided diagnosis Diagnosis, Differential Female Humans Image Processing, Computer-Assisted - methods Leiomyoma - diagnostic imaging Leiomyoma - pathology Leiomyomas Machine Learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Middle Aged Perfusion weighted imaging Reproducibility of Results Sarcoma - diagnostic imaging Sarcoma - pathology Sensitivity and Specificity Uterine Neoplasms - diagnostic imaging Uterine Neoplasms - pathology Uterine sarcoma
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