Dataset Information

Automatic Detection and Quantitative DCE-MRI Scoring of Prostate Cancer Aggressiveness.

ABSTRACT: Purpose:To develop a robust and clinically applicable automated method for analyzing Dynamic Contrast Enhanced (DCE-) MRI of the prostate as a guide for targeted biopsies and treatments. Materials and methods:An unsupervised pattern recognition (PR) method was used to analyze prostate DCE-MRI from 71 sequential radiotherapy patients. Identified regions of interest (ROIs) with increased perfusion were assigned either to the peripheral (PZ) or transition zone (TZ). Six quantitative features, associated with the washin and washout part of the weighted average DCE curve from the ROI, were calculated. The associations between the assigned DCE-scores and Gleason Score (GS) were investigated. A heatmap of tumor aggressiveness covering the entire prostate was generated and validated with histopathology from MRI-ultrasound fused (MRI-US) targeted biopsies. Results:The volumes of the PR-identified ROI's were significantly correlated with the highest GS from the biopsy session for each patient. Following normalization (and only after normalization) with gluteus maximus muscle's DCE signal, the quantitative features in PZ were significantly correlated with GS. These correlations straightened in subset of patients with available MRI-US biopsies when GS from the individual biopsies were used. Area under the receiver operating characteristics curve for discrimination between indolent vs aggressive cancer for the significant quantitative features reached 0.88-0.95. When DCE-scores were calculated in normal appearing tissues, the features were highly discriminative for cancer vs no cancer both in PZ and TZ. The generated heatmap of tumor aggressiveness coincided with the location and GS of the MRI-US biopsies. Conclusion:A quantitative approach for DCE-MRI analysis was developed. The resultant map of aggressiveness correlated well with tumor location and GS and is applicable for integration in radiotherapy/radiology imaging software for clinical translation.

SUBMITTER: Parra NA

PROVIDER: S-EPMC5686056 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Automatic Detection and Quantitative DCE-MRI Scoring of Prostate Cancer Aggressiveness.

Parra Nestor Andres NA Pollack Alan A Chinea Felix M FM Abramowitz Matthew C MC Marples Brian B Munera Felipe F Castillo Rosa R Kryvenko Oleksandr N ON Punnen Sanoj S Stoyanova Radka R

Frontiers in oncology 20171110

<h4>Purpose</h4>To develop a robust and clinically applicable automated method for analyzing Dynamic Contrast Enhanced (DCE-) MRI of the prostate as a guide for targeted biopsies and treatments.<h4>Materials and methods</h4>An unsupervised pattern recognition (PR) method was used to analyze prostate DCE-MRI from 71 sequential radiotherapy patients. Identified regions of interest (ROIs) with increased perfusion were assigned either to the peripheral (PZ) or transition zone (TZ). Six quantitative ...[more]

PMID: 29177134

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Project description:PURPOSE:The aim of this study was to develop a portable perfusion phantom and validate its utility in quantitative dynamic contrast-enhanced magnetic resonance imaging of the abdomen. METHODS:A portable perfusion phantom yielding a reproducible contrast enhancement curve (CEC) was developed. A phantom package including perfusion and static phantoms were imaged simultaneously with each of three healthy human volunteers in two different 3T MR scanners. Look-up tables correlating reference (known) contrast concentrations with measured ones were created using either the static or perfusion phantom. Contrast maps of image slices showing four organs (liver, spleen, pancreas, and paravertebral muscle) were generated before and after data correction using the look-up tables. The contrast concentrations at 4.5 min after dosing in each of the four organs were averaged for each volunteer. The mean contrast concentrations (4 organs × 3 volunteers = 12) were compared for the two scanners, and the intra-class correlation coefficient (ICC) was calculated. Also, the ICC of the mean Ktrans values between the two scanners was calculated before and after data correction. RESULTS:The repeatability coefficient of CECs of perfusion phantom was higher than 0.997 in all measurements. The ICC of the tissue contrast concentrations between the two scanners was 0.693 before correction, but increased to 0.974 after correction using the look-up tables (LUTs) of perfusion phantom. However, the ICC was not increased after correction using static phantom (ICC: 0.617). Similarly, the ICC of the Ktrans values was 0.899 before correction, but increased to 0.996 after correction using perfusion phantom LUTs. The ICC of the Ktrans values, however, was not increased when static phantom LUTs were used (ICC: 0.866). CONCLUSIONS:The perfusion phantom reduced variability in quantitating contrast concentration and Ktrans values of human abdominal tissues across different MR units, but static phantom did not. The perfusion phantom has the potential to facilitate multi-institutional clinical trials employing quantitative DCE-MRI to evaluate various abdominal malignancies.

Project description:Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the radiologist with an objective and noninvasive tool for supporting the decision-making in clinical practice and decreasing intra- and inter-reader variability. In this view, high dimensional radiomics features and Machine Learning (ML) techniques, along with Deep Learning (DL) methods working on raw images directly, could assist the radiologist in the clinical workflow. The aim of this study was to develop and validate ML/DL frameworks on mpMRI data to characterize PCas according to their aggressiveness. We optimized several ML/DL frameworks on T2w, ADC and T2w+ADC data, using a patient-based nested validation scheme. The dataset was composed of 112 patients (132 peripheral lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 3) acquired following both PI-RADS 2.0 and 2.1 guidelines. Firstly, ML/DL frameworks trained and validated on PI-RADS 2.0 data were tested on both PI-RADS 2.0 and 2.1 data. Then, we trained, validated and tested ML/DL frameworks on a multi PI-RADS dataset. We reported the performances in terms of Area Under the Receiver Operating curve (AUROC), specificity and sensitivity. The ML/DL frameworks trained on T2w data achieved the overall best performance. Notably, ML and DL frameworks trained and validated on PI-RADS 2.0 data obtained median AUROC values equal to 0.750 and 0.875, respectively, on unseen PI-RADS 2.0 test set. Similarly, ML/DL frameworks trained and validated on multi PI-RADS T2w data showed median AUROC values equal to 0.795 and 0.750, respectively, on unseen multi PI-RADS test set. Conversely, all the ML/DL frameworks trained and validated on PI-RADS 2.0 data, achieved AUROC values no better than the chance level when tested on PI-RADS 2.1 data. Both ML/DL techniques applied on mpMRI seem to be a valid aid in predicting PCa aggressiveness. In particular, ML/DL frameworks fed with T2w images data (objective, fast and non-invasive) show good performances and might support decision-making in patient diagnostic and therapeutic management, reducing intra- and inter-reader variability.

Dataset Information

Automatic Detection and Quantitative DCE-MRI Scoring of Prostate Cancer Aggressiveness.

Publications

Automatic Detection and Quantitative DCE-MRI Scoring of Prostate Cancer Aggressiveness.

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