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Magnetic resonance radiomics for prediction of extraprostatic extension in non-favorable intermediate- and high-risk prostate cancer patients.
Losnegård, Are; Reisæter, Lars A R; Halvorsen, Ole J; Jurek, Jakub; Assmus, Jörg; Arnes, Jarle B; Honoré, Alfred; Monssen, Jan A; Andersen, Erling; Haldorsen, Ingfrid S; Lundervold, Arvid; Beisland, Christian.
Afiliación
  • Losnegård A; Department of Radiology, Haukeland University Hospital, Bergen, Norway.
  • Reisæter LAR; Department of Clinical Medicine, University of Bergen, Norway.
  • Halvorsen OJ; Department of Radiology, Haukeland University Hospital, Bergen, Norway.
  • Jurek J; Department of Clinical Medicine, University of Bergen, Norway.
  • Assmus J; Department of Pathology, Haukeland University Hospital, Bergen, Norway.
  • Arnes JB; Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Norway.
  • Honoré A; Institute of Electronics, Technical University of Lodz, Poland.
  • Monssen JA; Centre for Clinical Research, Haukeland University Hospital, Norway.
  • Andersen E; Department of Pathology, Haukeland University Hospital, Bergen, Norway.
  • Haldorsen IS; Department of Urology, Haukeland University Hospital, Bergen, Norway.
  • Lundervold A; Department of Radiology, Haukeland University Hospital, Bergen, Norway.
  • Beisland C; Department of Clinical Engineering, Haukeland University Hospital, Norway.
Acta Radiol ; 61(11): 1570-1579, 2020 Nov.
Article en En | MEDLINE | ID: mdl-32108505
BACKGROUND: To investigate whether magnetic resonance (MR) radiomic features combined with machine learning may aid in predicting extraprostatic extension (EPE) in high- and non-favorable intermediate-risk patients with prostate cancer. PURPOSE: To investigate the diagnostic performance of radiomics to detect EPE. MATERIAL AND METHODS: MR radiomic features were extracted from 228 patients, of whom 86 were diagnosed with EPE, using prostate and lesion segmentations. Prediction models were built using Random Forest. Further, EPE was also predicted using a clinical nomogram and routine radiological interpretation and diagnostic performance was assessed for individual and combined models. RESULTS: The MR radiomic model with features extracted from the manually delineated lesions performed best among the radiomic models with an area under the curve (AUC) of 0.74. Radiology interpretation yielded an AUC of 0.75 and the clinical nomogram (MSKCC) an AUC of 0.67. A combination of the three prediction models gave the highest AUC of 0.79. CONCLUSION: Radiomic analysis combined with radiology interpretation aid the MSKCC nomogram in predicting EPE in high- and non-favorable intermediate-risk patients.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Imagen por Resonancia Magnética Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Humans / Male / Middle aged Idioma: En Revista: Acta Radiol Año: 2020 Tipo del documento: Article País de afiliación: Noruega Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Imagen por Resonancia Magnética Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Humans / Male / Middle aged Idioma: En Revista: Acta Radiol Año: 2020 Tipo del documento: Article País de afiliación: Noruega Pais de publicación: Reino Unido