Your browser doesn't support javascript.
loading
A Diagnostic Algorithm using Multi-parametric MRI to Differentiate Benign from Malignant Myometrial Tumors: Machine-Learning Method.
Malek, Mahrooz; Tabibian, Elnaz; Rahimi Dehgolan, Milad; Rahmani, Maryam; Akhavan, Setareh; Sheikh Hasani, Shahrzad; Nili, Fatemeh; Hashemi, Hassan.
Afiliación
  • Malek M; Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Radiology Department, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), Tehran, No. 1419733141, Iran.
  • Tabibian E; Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Radiology Department, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), Tehran, No. 1419733141, Iran. elnaz.tabibian@gmail.com.
  • Rahimi Dehgolan M; Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, No. 1631714191, Iran.
  • Rahmani M; Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Radiology Department, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), Tehran, No. 1419733141, Iran.
  • Akhavan S; Gynecology Oncology Department, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), Tehran, No. 1419733141, Iran.
  • Sheikh Hasani S; Gynecology Oncology Department, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), Tehran, No. 1419733141, Iran.
  • Nili F; Pathology Department, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), Tehran, No. 1419733141, Iran.
  • Hashemi H; Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Radiology Department, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), Tehran, No. 1419733141, Iran.
Sci Rep ; 10(1): 7404, 2020 05 04.
Article en En | MEDLINE | ID: mdl-32366933
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.
Asunto(s)
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Uterinas / Procesamiento de Imagen Asistido por Computador / Diagnóstico por Computador / Aprendizaje Automático / Leiomioma / Miometrio Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Adult / Female / Humans / Middle aged Idioma: En Revista: Sci Rep Año: 2020 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Uterinas / Procesamiento de Imagen Asistido por Computador / Diagnóstico por Computador / Aprendizaje Automático / Leiomioma / Miometrio Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Adult / Female / Humans / Middle aged Idioma: En Revista: Sci Rep Año: 2020 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Reino Unido