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Longitudinal Changes and Predictive Value of Multiparametric MRI Features for Prostate Cancer Patients Treated with MRI-Guided Lattice Extreme Ablative Dose (LEAD) Boost Radiotherapy.
Algohary, Ahmad; Alhusseini, Mohammad; Breto, Adrian L; Kwon, Deukwoo; Xu, Isaac R; Gaston, Sandra M; Castillo, Patricia; Punnen, Sanoj; Spieler, Benjamin; Abramowitz, Matthew C; Dal Pra, Alan; Kryvenko, Oleksandr N; Pollack, Alan; Stoyanova, Radka.
Afiliação
  • Algohary A; Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Alhusseini M; Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Breto AL; Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Kwon D; Biostatistics and Bioinformatics Shared Resource, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Xu IR; Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Gaston SM; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Castillo P; Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Punnen S; Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Spieler B; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Abramowitz MC; Department of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Dal Pra A; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Kryvenko ON; Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Pollack A; Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Stoyanova R; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Cancers (Basel) ; 14(18)2022 Sep 15.
Article em En | MEDLINE | ID: mdl-36139635
ABSTRACT
We investigated the longitudinal changes in multiparametric MRI (mpMRI) (T2-weighted, Apparent Diffusion Coefficient (ADC), and Dynamic Contrast Enhanced (DCE-)MRI) of prostate cancer patients receiving Lattice Extreme Ablative Dose (LEAD) radiotherapy (RT) and the capability of their imaging features to predict RT outcome based on endpoint biopsies. Ninety-five mpMRI exams from 25 patients, acquired pre-RT and at 3-, 9-, and 24-months post-RT were analyzed. MRI/Ultrasound-fused biopsies were acquired pre- and at two-years post-RT (endpoint). Five regions of interest (ROIs) were analyzed Gross tumor volume (GTV), normally-appearing tissue (NAT) and peritumoral volume in both peripheral (PZ) and transition (TZ) zones. Diffusion and perfusion radiomics features were extracted from mpMRI and compared before and after RT using two-tailed Student t-tests. Selected features at the four scan points and their differences (Δ radiomics) were used in multivariate logistic regression models to predict the endpoint biopsy positivity. Baseline ADC values were significantly different between GTV, NAT-PZ, and NAT-TZ (p-values < 0.005). Pharmaco-kinetic features changed significantly in the GTV at 3-month post-RT compared to baseline. Several radiomics features at baseline and three-months post-RT were significantly associated with endpoint biopsy positivity and were used to build models with high predictive power of this endpoint (AUC = 0.98 and 0.89, respectively). Our study characterized the RT-induced changes in perfusion and diffusion. Quantitative imaging features from mpMRI show promise as being predictive of endpoint biopsy positivity.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article