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The effect of estradiol add-back: a longitudinal MRI study in prostate cancer patients.

ABSTRACT: We investigated the effect of estradiol add-back therapy (EAT) on brain activation related to cognitive function and affect in addition to putative changes in gray and white matter volume in testosterone depleted participants with prostate cancer. We conducted a randomized controlled, double-blinded trial in which 40 patients received 0.9 mg of transdermal estradiol per day for 6 months or matched placebo. Anatomical MRI and three functional MRI (fMRI) scans were obtained for the emotion recognition task, verbal memory task, and visuospatial memory task. Activation in corresponding cognitive and affective brain networks was demonstrated for all tasks. Longitudinally, there was no difference in brain activation, reaction time, or accuracy in response to the fMRI tasks between the EAT group and placebo group at 6 months. In addition, there was no detectable change in whole-brain gray or white matter volume or in hippocampal volume between the two groups after 6 months. This study supports earlier findings that EAT does not improve verbal memory or affect and has no immediate effect on hippocampal volume in testosterone depleted patients with prostate cancer.

SUBMITTER: Dandash O

PROVIDER: S-EPMC10895318 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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The effect of estradiol add-back: a longitudinal MRI study in prostate cancer patients.

Dandash Orwa O Allebone James J Mirabelli Adam A Russell Nicholas N Grossmann Mathis M Gogos Andrea A Kanaan Richard A RA

Endocrine connections 20240207 3

We investigated the effect of estradiol add-back therapy (EAT) on brain activation related to cognitive function and affect in addition to putative changes in gray and white matter volume in testosterone depleted participants with prostate cancer. We conducted a randomized controlled, double-blinded trial in which 40 patients received 0.9 mg of transdermal estradiol per day for 6 months or matched placebo. Anatomical MRI and three functional MRI (fMRI) scans were obtained for the emotion recogni ...[more]

PMID: 38226639

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Project description:PurposeIntravoxel incoherent motion (IVIM) is a promising technique that can acquire perfusion information without the use of contrast agent, contrary to the more established dynamic contrast-enhanced (DCE) technique. This is of interest for treatment response monitoring, where patients can be imaged on each treatment fraction. In this study, longitudinal correlations between IVIM- and DCE parameters were assessed in prostate cancer patients receiving radiation treatment.Materials and methods20 prostate cancer patients were treated on a 1.5 T MR-linac with 20 x 3 or 3.1 Gy. Weekly IVIM and DCE scans were acquired. Tumors, the peripheral zone (PZ), and the transition zone (TZ) were delineated on a T2-weighted scan acquired on the first fraction. IVIM and DCE scans were registered to this scan and the delineations were propagated. Median values from these delineations were used for further analysis. The IVIM parameters D, f, D* and the product fD* were calculated. The Tofts model was used to calculate the DCE parameters Ktrans, kep and ve. Pearson correlations were calculated for the IVIM and DCE parameters on values from the first fraction for each region of interest (ROI). For longitudinal analysis, the repeated measures correlation coefficient was used to determine correlations between IVIM and DCE parameters in each ROI.ResultsWhen averaging over patients, an increase during treatment in all IVIM and DCE parameters was observed in all ROIs, except for D in the PZ and TZ. No significant Pearson correlations were found between any pair of IVIM and DCE parameters measured on the first fraction. Significant but low longitudinal correlations were found for some combinations of IVIM and DCE parameters in the PZ and TZ, while no significant longitudinal correlations were found in the tumor. Notably in the TZ, for both f and fD*, significant longitudinal correlations with all DCE parameters were found.ConclusionsThe increase in IVIM- and DCE parameters when averaging over patients indicates a measurable response to radiation treatment with both techniques. Although low, significant longitudinal correlations were found which suggests that IVIM could potentially be used as an alternative to DCE for treatment response monitoring.

Project description:BackgroundCurrently, prostate cancer (PCa) prebiopsy medical image diagnosis mainly relies on mpMRI and PI-RADS scores. However, PI-RADS has its limitations, such as inter- and intra-radiologist variability and the potential for imperceptible features. The primary objective of this study is to evaluate the effectiveness of a machine learning model based on radiomics analysis of MRI T2-weighted (T2w) images for predicting PCa in prebiopsy cases.MethodA retrospective analysis was conducted using 820 lesions (363 cases, 457 controls) from The Cancer Imaging Archive (TCIA) Database for model development and validation. An additional 83 lesions (30 cases, 53 controls) from Hong Kong Queen Mary Hospital were used for independent external validation. The MRI T2w images were preprocessed, and radiomic features were extracted. Feature selection was performed using Cross Validation Least Angle Regression (CV-LARS). Using three different machine learning algorithms, a total of 18 prediction models and 3 shape control models were developed. The performance of the models, including the area under the curve (AUC) and diagnostic values such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared to the PI-RADS scoring system for both internal and external validation.ResultsAll the models showed significant differences compared to the shape control model (all p < 0.001, except SVM model PI-RADS+2 Features p = 0.004, SVM model PI-RADS+3 Features p = 0.002). In internal validation, the best model, based on the LR algorithm, incorporated 3 radiomic features (AUC = 0.838, sensitivity = 76.85%, specificity = 77.36%). In external validation, the LR (3 features) model outperformed PI-RADS in predictive value with AUC 0.870 vs. 0.658, sensitivity 56.67% vs. 46.67%, specificity 92.45% vs. 84.91%, PPV 80.95% vs. 63.64%, and NPV 79.03% vs. 73.77%.ConclusionsThe machine learning model based on radiomics analysis of MRI T2w images, along with simulated biopsy, provides additional diagnostic value to the PI-RADS scoring system in predicting PCa.

Dataset Information

The effect of estradiol add-back: a longitudinal MRI study in prostate cancer patients.

Publications

The effect of estradiol add-back: a longitudinal MRI study in prostate cancer patients.

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