Is There an Added Value of Quantitative DCE-MRI by Magnetic Resonance Dispersion Imaging for Prostate Cancer Diagnosis?

In this multicenter, retrospective study, we evaluated the added value of magnetic resonance dispersion imaging (MRDI) to standard multiparametric MRI (mpMRI) for PCa detection. The study included 76 patients, including 51 with clinically significant prostate cancer (csPCa), who underwent radical prostatectomy and had an mpMRI including dynamic contrast-enhanced MRI. Two radiologists performed three separate randomized scorings based on mpMRI, MRDI and mpMRI+MRDI. Radical prostatectomy histopathology was used as the reference standard. Imaging and histopathology were both scored according to the Prostate Imaging-Reporting and Data System V2.0 sector map. Sensitivity and specificity for PCa detection were evaluated for mpMRI, MRDI and mpMRI+MRDI. Inter- and intra-observer variability for both radiologists was evaluated using Cohen’s Kappa. On a per-patient level, sensitivity for csPCa for radiologist 1 (R1) for mpMRI, MRDI and mpMRI+MRDI was 0.94, 0.82 and 0.94, respectively. For the second radiologist (R2), these were 0.78, 0.94 and 0.96. R1 detected 4% additional csPCa cases using MRDI compared to mpMRI, and R2 detected 20% extra csPCa cases using MRDI. Inter-observer agreement was significant only for MRDI (Cohen’s Kappa = 0.4250, p = 0.004). The results of this study show the potential of MRDI to improve inter-observer variability and the detection of csPCa.</p

Clinical impact of PSMA PET/CT in primary prostate cancer compared to conventional nodal and distant staging: a retrospective single center study

BACKGROUND: To evaluate the impact of Gallium-68 [68Ga] labeled prostate specific membrane antigen (PSMA) positron emission tomography (PET)/X-ray computed tomography (CT) compared with conventional imaging on staging and clinical management of men evaluated for primary prostate cancer (PCa). METHODS: Men with newly diagnosed biopsy-proven PCa who had been staged with a conventional staging protocol including bone scintigraphy (BS) and additionally underwent [68Ga]PSMA PET/CT, were evaluated retrospectively. Imaging findings from BS, magnetic resonance imaging (MRI) and/or CT were categorized regarding locoregional nodal (N) and distant metastasis (M) status as negative, positive or equivocal before and after addition of the information of PET/CT. Also, the imaging-based level of confidence (LoC) in correct assessment of N and M status was scored. Impact of PET/CT on clinical management was evaluated by the percentage of treatment category changes after PET/CT as determined in the multidisciplinary tumour board. RESULTS: Sixty-four men with intermediate and high-risk PCa were evaluated. With additional information of PET/CT, N status was upstaged in 23%, and downstaged in 9%. M status was upstaged in 13%, and downstaged in 23%. A net increase in LoC of 20% was noted, mainly regarding M status. Treatment category changed from palliative to curative in 9%, and from curative to palliative in 3%. An undecided treatment plan changed to curative in 14%, as well as to palliative in another 9%. In total, a 36% treatment category change was noted. High negative predictive value of PET/CT for M status was indicated by 27 patients that underwent robot-assisted radical prostatectomy and reached postoperative biochemical disease-free status or had a likely other site of disease recurrence. CONCLUSIONS: PSMA PET/CT can cause considerable changes in N and M staging, as well as in management compared to conventional staging. Findings of this study support the replacement of BS and CT by PSMA PET/CT in staging primary PCa

Contouring of prostate tumors on multiparametric MRI : Evaluation of clinical delineations in a multicenter radiotherapy trial

PURPOSE: To date no guidelines are available for contouring prostate cancer inside the gland, as visible on multiparametric (mp-) MRI. We assessed inter-institutional differences in interpretation of mp-MRI in the multicenter phase III FLAME trial. METHODS: We analyzed clinical delineations on mp-MRI and clinical characteristics from 260 patients across three institutes. We performed a logistic regression analysis to examine each institute's weighting of T2w, ADC and Ktrans intensity maps in the delineation of the cancer. As reviewing of all delineations by an expert panel is not feasible, we made a selection based on discrepancies between a published tumor probability (TP) model and each institute's clinical delineations using Areas Under the ROC Curve (AUC) analysis. RESULTS: Regression coefficients for the three institutes were -0.07, -0.27 and -0.11 for T2w, -1.96, -0.53 and -0.65 for ADC and 0.15, 0.20 and 0.62 for Ktrans, with significant differences between institutes for ADC and Ktrans. AUC analysis showed median AUC values of 0.92, 0.80 and 0.79. Five patients with lowest AUC values were reviewed by a uroradiologist. CONCLUSION: Regression coefficients revealed considerably different interpretations of mp-MRI in tumor contouring between institutes and demonstrated the need for contouring guidelines. Based on AUC values outlying delineations could efficiently be identified for review

Biochemical recurrence prediction after radiotherapy for prostate cancer with T2w magnetic resonance imaging radiomic features

Background and purpose: High-risk prostate cancer patients are frequently treated with external-beam radiotherapy (EBRT). Of all patients receiving EBRT, 15–35% will experience biochemical recurrence (BCR) within five years. Magnetic resonance imaging (MRI) is commonly acquired as part of the diagnostic procedure and imaging-derived features have shown promise in tumour characterisation and biochemical recurrence prediction. We investigated the value of imaging features extracted from pre-treatment T2w anatomical MRI to predict five year biochemical recurrence in high-risk patients treated with EBRT. Materials and methods: In a cohort of 120 high-risk patients, imaging features were extracted from the whole-prostate and a margin surrounding it. Intensity, shape and textural features were extracted from the original and filtered T2w-MRI scans. The minimum-redundancy maximum-relevance algorithm was used for feature selection. Random forest and logistic regression classifiers were used in our experiments. The performance of a logistic regression model using the patient’s clinical features was also investigated. To assess the prediction accuracy we used stratified 10-fold cross validation and receiver operating characteristic analysis, quantified by the area under the curve (AUC). Results: A logistic regression model built using whole-prostate imaging features obtained an AUC of 0.63 in the prediction of BCR, outperforming a model solely based on clinical variables (AUC = 0.51). Combining imaging and clinical features did not outperform the accuracy of imaging alone. Conclusions: These results illustrate the potential of imaging features alone to distinguish patients with an increased risk of recurrence, even in a clinically homogeneous cohort. Keywords: Prostate cancer, T2-weighted MRI, Radiomics, External beam radiotherap

Improved repeatability of dynamic contrastenhanced MRI using the complex MRI signal to derive arterial input functions: a test-retest study in prostate cancer patients (vol 81, pg 3358, 2019): Improved repeatability of dynamic contrast-enhanced MRI using the complex MRI signal to derive arterial input functions: a test-retest study in prostate cancer patients (Magn Reson Med., (2019), 81, (3358–3369), 10.1002/mrm.27646)

The antilog for the within-subject coefficient of variation (wCV) of log-transformed data was performed incorrectly. This leads to an increase by a factor of about 2 in the numbers in abstract, results section, Figure 4, Tables 2 and 3. Although this affected all wCV values reported in the manuscript, the conclusion remains the same. The authors regret this mistake and apologize for any inconvenience this may have caused. 4 FIGURE (Figure presented.) Bar plot of wCV values for Ktrans and kep for the three AIF methods including 95% CI bars 2 TABLE Median, range and wCV with 95% confidence interval of the AIF curve characteristics between the two consecutive exams (Table presented.) 3 TABLE The wCV between left and right AIFs, per method (magnitude, phase and complex signal), with 95% confidence interval for all curve characteristics (Table presented.) Abstract (changes in wCV values): Results: The wCV for peak height and full-width at half maximum for AIF COMPLEX (15% and 17%) indicated an improved repeatability compared to AIF MAGN (28% and 26%) and AIF PHASE (27% and 16%). This translated in lower wCV values for K trans (24%) with AIF COMPLEX in comparison to AIF MAGN (52%) and AIF PHASE (32%). For k ep the wCV was 35% with AIF MAGN, 29% with AIF PHASE, and 29% with AIF COMPLEX. Results section, “3.2 AIF curve characteristics per method”, P6 (changes in numbers): Without a B 1 correction, the peak height ratio for AIF MAGN increased to 1.5, whereas the wCV increased from 33 to 43%. Results section, “3. Tracer kinetic analysis”, P6 (changes in p-values): The wCV for K trans obtained with AIF MAGN was significantly larger than for the other two methods (p = 0.0026 and < 0.001 for AIF PHASE and AIF COMPLEX respectively), however, for k ep the wCV were not significantly larger (p = 0.59 and 0.63 for AIF PHASE and AIF COMPLEX respectively). Discussion, P9 (the word “higher” becomes “similar”): However, in general the reported wCV of K trans in ROIs is similar than what we observe for K trans obtained with AIF COMPLEX: range between 12.5% to 57%. 46-50

Prostate MRI for Improving Personalized Risk Prediction of Incontinence and Surgical Planning: The Role of Membranous Urethral Length Measurements and the Use of 3D Models

Prostate MRI has an important role in prostate cancer diagnosis and treatment, including detection, the targeting of prostate biopsies, staging and guiding radiotherapy and active surveillance. However, there are other ‘’less well-known’’ applications which are being studied and frequently used in our highly specialized medical center. In this review, we focus on two research topics that lie within the expertise of this study group: (1) anatomical parameters predicting the risk of urinary incontinence after radical prostatectomy, allowing more personalized shared decision-making, with special emphasis on the membranous urethral length (MUL); (2) the use of three-dimensional models to help the surgical planning. These models may be used for training, patient counselling, personalized estimation of nerve sparing and extracapsular extension and may help to achieve negative surgical margins and undetectable postoperative PSA values

A standardized method to measure the membranous urethral length (MUL) on MRI of the prostate with high inter- and intra-observer agreement

Objectives: The membranous urethral length (MUL), defined as the length between the apex and penile base as measured on preoperative prostate magnetic resonance imaging (MRI), is an important predictor for urinary incontinence after radical prostatectomy. Literature on inter- and intra - observer agreement of MUL measurement is limited. We studied the inter- and intra-observer agreement between radiologists using a well-defined method to measure the MUL on the prostate MRI. Methods: Prostate cancer patients underwent a preoperative MRI and robot-assisted radical prostatectomy (RARP) at one high-volume RARP center. MUL measurement was based on well-defined landmarks on sagittal T2-weighted (anatomical) images. Three radiologists independently performed MUL measurements retrospectively in 106 patients blinded to themselves, to each other, and to clinical outcomes. The inter- and intra-observer agreement of MUL measurement between the radiologists were calculated, expressed as intra-class correlation coefficient (ICC). Results: The initial inter-observer agreement was ICC 0.63; 95% confidence interval (CI) 0.28–0.81. Radiologist 3 measured the MUL mean 3.9 mm (SD 3.3) longer than the other readers, interpreting the caudal point of the MUL (penile base) differently. After discussion on the correct anatomical definition, radiologist 3 re-assessed all scans, which resulted in a high inter-observer agreement (ICC 0.84; 95% CI 0.66–0.91). After a subsequent reading by radiologists 1 and 2, the intra-observer agreements were ICC 0.93; 95% CI 0.89–0.96, and ICC 0.98; 95% CI 0.97–0.98, respectively. Limitation is the monocenter design. Conclusions: The MUL can be measured reliably with high agreement among radiologists. Key Points: • After discussion on the correct anatomical definition, the inter- and intra - observer agreements of membranous urethral length (MUL) measurement on magnetic resonance imaging (MRI) were high. • A reproducible method to measure the MUL can improve the clinical usefulness of prediction models for urinary continence after RARP which may benefit patient counselling

Prostate MRI for Improving Personalized Risk Prediction of Incontinence and Surgical Planning: The Role of Membranous Urethral Length Measurements and the Use of 3D Models

Prostate MRI has an important role in prostate cancer diagnosis and treatment, including detection, the targeting of prostate biopsies, staging and guiding radiotherapy and active surveillance. However, there are other ''less well-known'' applications which are being studied and frequently used in our highly specialized medical center. In this review, we focus on two research topics that lie within the expertise of this study group: (1) anatomical parameters predicting the risk of urinary incontinence after radical prostatectomy, allowing more personalized shared decision-making, with special emphasis on the membranous urethral length (MUL); (2) the use of three-dimensional models to help the surgical planning. These models may be used for training, patient counselling, personalized estimation of nerve sparing and extracapsular extension and may help to achieve negative surgical margins and undetectable postoperative PSA values

Multiparametric MRI Tumor Probability Model for the Detection of Locally Recurrent Prostate Cancer After Radiation Therapy: Pathologic Validation and Comparison With Manual Tumor Delineations

Purpose: Focal salvage treatments of recurrent prostate cancer (PCa) after radiation therapy require accurate delineation of the target volume. Magnetic resonance imaging (MRI) is used for this purpose; however, radiation therapy–induced changes complicate image interpretation, and guidelines are lacking on the assessment and delineation of recurrent PCa. A tumor probability (TP) model was trained and independently tested using multiparametric magnetic resonance imaging (mp-MRI) of patients with radio-recurrent PCa. The resulting probability maps were used to derive target regions for radiation therapy treatment planning. Methods and Materials: Two cohorts of patients with radio-recurrent PCa were used in this study. All patients underwent mp-MRI (T2 weighted, diffusion-weighted imaging, and dynamic contrast enhanced). A logistic regression model was trained using imaging features from 21 patients with biopsy-proven recurrence who qualified for salvage treatment. The test cohort consisted of 17 patients treated with salvage prostatectomy. The model was tested against histopathology-derived tumor delineations. The voxel-wise TP maps were clustered using k-means to generate a gross tumor volume (GTV) contour for voxel-level comparisons with manual tumor delineations performed by 2 radiologists and with histopathology-validated contours. Later, k-means was used with 3 clusters to define a clinical target volume (CTV), high-risk CTV, and GTV, with increasing tumor risk. Results: In the test cohort, the model obtained a median (range) area under the curve of 0.77 (0.41-0.99) for the whole prostate. The GTV delineation resulted in a median sensitivity of 0.31 (0-0.87) and specificity of 0.97 (0.84-1.0) with no significant differences between model and manual delineations. The 3-level clustering GTV and high-risk CTV delineations had median sensitivities of 0.17 (0-0.59) and 0.49 (0-0.97) and specificities of 0.98 (0.84-1.00) and 0.94 (0.84-0.99), respectively. Conclusions: The TP model had a good performance in predicting voxel-wise presence of recurrent tumor. Model-derived tumor risk levels achieved sensitivity and specificity similar to manual delineations in localizing recurrent tumor. Voxel-wise TP derived from mp-MRI can in this way be incorporated for target definition in focal salvage of radio-recurrent PCa