Dynamic contrast-enhanced MRI for automatic detection of foci of residual or recurrent disease after prostatectomy

The role of MRI in prostate cancer: current and future directions

There has been an increasing role of magnetic resonance imaging (MRI) in the management of prostate cancer. MRI already plays an essential role in the detection and staging, with the introduction of functional MRI sequences. Recent advancements in radiomics and artificial intelligence are being tested to potentially improve detection, assessment of aggressiveness, and provide usefulness as a prognostic marker. MRI can improve pretreatment risk stratification and therefore selection of and follow-up of patients for active surveillance. MRI can also assist in guiding targeted biopsy, treatment planning and follow-up after treatment to assess local recurrence. MRI has gained importance in the evaluation of metastatic disease with emerging technology including whole-body MRI and integrated positron emission tomography/MRI, allowing for not only better detection but also quantification. The main goal of this article is to review the most recent advances on MRI in prostate cancer and provide insights into its potential clinical roles from the radiologist's perspective. In each of the sections, specific roles of MRI tailored to each clinical setting are discussed along with its strengths and weakness including already established material related to MRI and the introduction of recent advancements on MRI.

Worth a local treatment? - Analysis of modern radiotherapy concepts for oligometastatic prostate cancer

Background

Prostate cancer (PCA) is the most-prevalent non-skin cancer in men worldwide. Nevertheless, the treatment of oligometastatic, especially lymph-node (ln) recurrent, PCA remains elusive. The aim of our study was to provide insights in radiotherapy (RT)-treatment of recurrent PCA exhibiting ln- or osseous (oss)-oligometastases.

Methods

Between April 2012 and April 2017, 27 oligometastatic PCA patients (19 ln and 8 single oss) were treated with RT at our institution.

Results

The metastasis-free survival (MFS) was 24.8 m (22.0–36.0 m) and 25.4 m (23.9–28.1 m) for the ln- and oss-subgroup resulting in 1-year MFS of 75.4 and 100% and 2-year MFS of 58.7 and 83.3% for ln- and oss-metastatic patients, respectively. Of notice, none of the recurrences for ln-patients was in the RT-field, constituting a local control of 100%.

Within the ln-group, pre-RT median-PSA was 2.6 ng/ml, median post-RT PSA was 0.3 ng/ml, which was significant (p = 0.003). Median biochemical-free survival (bfS) was 12.2 m. PCA that was initially confined to the prostate had a better bfS (p < 0.001) and MFS (p = 0.013). The oss-group had a median PSA of 4.9 ng/ml pre-treatment which dropped to a median value of 0.14 ng/ml (p = 0.004).

Toxicities were moderate, with only 1 case of III° toxicity. There were no deaths in the ln-group, thus overall survial was 100% here.

Conclusion

Our study points out the feasibility of RT as a treatment option in recurrent PCA and demonstrates an excellent local control with a low-toxicity profile.

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

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.