Detection of Prostate Cancer: Quantitative Multiparametric MR Imaging Models Developed Using Registered Correlative Histopathology

Abstract

Purpose To develop multiparametric magnetic resonance imaging (mpMRI) models for generating a quantitative, user-independent, voxel-wise composite biomarker score (CBS) for prostate cancer (PCa) detection utilizing co-registered correlative histopathology and comparing CBS-based detection performance against single quantitative MRI (qMR) parameters. Materials and Methods After providing informed consent to participate in an IRB approved protocol, patients with an initial diagnosis of PCa electing surgery as definitive therapy were imaged preoperatively with an mpMRI protocol from which quantitative MR parameters (qMR) were calculated. Per patient, all voxels in the prostate from an MRI imaging slice were classified as cancer or non-cancer based on deformably mapped histologically determined regions of disease to assess individual qMR parameters. Predictive models were developed using more than one qMR to generate CBS maps for cancer detection. Model development and evaluations of individual qMR and CBS were performed separately for the peripheral zone (PZ) alone and the whole gland (WG). Model accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) and confidence intervals were calculated using the bootstrap procedure. The improvement in classification accuracy was evaluated by comparing the AUC for our multiparametric model and the single best performing qMR both at the individual level and in aggregate. Results T2TSE, ADC, Ktrans, kep and AUGC90 were significantly different between cancer and non-cancer voxels (p < 0.001 in all cases), with ADC exhibiting the best classification accuracy (AUC 0.82 for PZ and 0.74 for WG). A 4 parameter PZ-Model (AUC =0.85; p = 0.010 vs. ADC alone) and a 4 parameter WG-Model (AUC = 0.77; p = 0.043 vs. ADC alone) had the best performance of the multiparametric models considered. Based on individual-level analysis, we observed a statistically significant improvement in the AUC in 82% (23/28) and 71% (24/34) of patients when using the PZ and WG models, respectively, compared to ADC alone. Model-based CBS maps for cancer detection demonstrate improved visualization of cancer location and extent. Conclusions Co-registered correlative histopathology data was used as the ground truth for development of quantitative mpMRI models yielding voxel-wise CBS which outperform single qMR parameters for PCa detection especially when assessed at the individual level.

First in‐vivo human imaging at 10.5T: Imaging the body at 447 MHz

“…62 ADC values, which should be independent of field strength, also appear to be larger than expected compared to quantitative studies reported at 3T. 63 However, for all parameters, the acquisition-, subject-, and field-dependent variability cannot be adequately assessed until more extensive targeted evaluations are performed.…”

Characterization of Prostate Cancer with Gleason Score of at Least 7 by Using Quantitative Multiparametric MR Imaging: Validation of a Computer-aided Diagnosis System in Patients Referred for Prostate Biopsy

“…However, its interpretation remains difficult and multiparametric MR imaging is characterized by substantial interreader variability (3-6), which limits its broad acceptance. Computer-aided diagnosis (CAD) systems combining various image features have shown promising results in characterizing prostate tissues (7)(8)(9)(10)(11)(12)(13)(14)(15)(16). However, the intensity and texture of MR images depend on the manufacturer and settings of the MR imager.…”

Prostate Cancer Classification on VERDICT DW-MRI Using Convolutional Neural Networks

“…DW-MRI has been demonstrated to be the most important component of mp-MRI compared to T2W-MRI and DCE-MRI due to its high sensitivity to microstructural changes related to cancer [5], [6]. However, mp-MRI studies use DW-MRI in its simplest form by deriving the ADC map.…”