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Novel radiomic analysis on bi-parametric MRI for characterizing differences between MR non-visible and visible clinically significant prostate cancer.
Li, Lin; Shiradkar, Rakesh; Tirumani, Sree Harsha; Bittencourt, Leonardo Kayat; Fu, Pingfu; Mahran, Amr; Buzzy, Christina; Stricker, Phillip D; Rastinehad, Ardeshir R; Magi-Galluzzi, Cristina; Ponsky, Lee; Klein, Eric; Purysko, Andrei S; Madabhushi, Anant.
Afiliação
  • Li L; Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA.
  • Shiradkar R; Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA.
  • Tirumani SH; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology.
  • Bittencourt LK; Department of Radiology, University Hospitals, Cleveland, OH, USA.
  • Fu P; Department of Radiology, University Hospitals, Cleveland, OH, USA.
  • Mahran A; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.
  • Buzzy C; Urology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
  • Stricker PD; Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA.
  • Rastinehad AR; Department of Urology, St. Vincent's Clinic, Sydney, NSW 2010, Australia.
  • Magi-Galluzzi C; Department of Urology, Lenox Hill Hospital, Northwell Health, New York, NY, USA.
  • Ponsky L; Department of Pathology, University of Alabama at Birmingham, AL, USA.
  • Klein E; Urology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
  • Purysko AS; Case Western Reserve University School of Medicine, Cleveland, OH, USA.
  • Madabhushi A; Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA.
Eur J Radiol Open ; 10: 100496, 2023.
Article em En | MEDLINE | ID: mdl-37396490
ABSTRACT

Background:

around one third of clinically significant prostate cancer (CsPCa) foci are reported to be MRI non-visible (MRI─).

Objective:

To quantify the differences between MR visible (MRI+) and MRI─ CsPCa using intra- and peri-lesional radiomic features on bi-parametric MRI (bpMRI).

Methods:

This retrospective and multi-institutional study comprised 164 patients with pre-biopsy 3T prostate multi-parametric MRI from 2014 to 2017. The MRI─ CsPCa referred to lesions with PI-RADS v2 score < 3 but ISUP grade group > 1. Three experienced radiologists were involved in annotating lesions and PI-RADS assignment. The validation set (Dv) comprised 52 patients from a single institution, the remaining 112 patients were used for training (Dt). 200 radiomic features were extracted from intra-lesional and peri-lesional regions on bpMRI.Logistic regression with least absolute shrinkage and selection operator (LASSO) and 10-fold cross-validation was applied on Dt to identify radiomic features associated with MRI─ and MRI+ CsPCa to generate corresponding risk scores RMRI─ and RMRI+. RbpMRI was further generated by integrating RMRI─ and RMRI+. Statistical significance was determined using the Wilcoxon signed-rank test.

Results:

Both intra-lesional and peri-lesional bpMRI Haralick and CoLlAGe radiomic features were significantly associated with MRI─ CsPCa (p < 0.05). Intra-lesional ADC Haralick and CoLlAGe radiomic features were significantly different among MRI─ and MRI+ CsPCa (p < 0.05). RbpMRI yielded the highest AUC of 0.82 (95 % CI 0.72-0.91) compared to AUCs of RMRI+ 0.76 (95 % CI 0.63-0.89), and PI-RADS 0.58 (95 % CI 0.50-0.72) on Dv. RbpMRI correctly reclassified 10 out of 14 MRI─ CsPCa on Dv.

Conclusion:

Our preliminary results demonstrated that both intra-lesional and peri-lesional bpMRI radiomic features were significantly associated with MRI─ CsPCa. These features could assist in CsPCa identification on bpMRI.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article