Matthew D. Greer

What Are We Missing? False-Negative Cancers at Multiparametric MR Imaging of the Prostate

Purpose To characterize clinically important prostate cancers missed at multiparametric (MP) magnetic resonance (MR) imaging. Materials and Methods The local institutional review board approved this HIPAA-compliant retrospective single-center study, which included 100 consecutive patients who had undergone MP MR imaging and subsequent radical prostatectomy. A genitourinary pathologist blinded to MP MR findings outlined prostate cancers on whole-mount pathology slices. Two readers correlated mapped lesions with reports of prospectively read MP MR images. Readers were blinded to histopathology results during prospective reading. At histopathologic examination, 80 clinically unimportant lesions (<5 mm; Gleason score, 3+3) were excluded. The same two readers, who were not blinded to histopathologic findings, retrospectively reviewed cancers missed at MP MR imaging and assigned a Prostate Imaging Reporting and Data System (PI-RADS) version 2 score to better understand false-negative lesion characteristics. Descriptive statistics were used to define patient characteristics, including age, prostate-specific antigen (PSA) level, PSA density, race, digital rectal examination results, and biopsy results before MR imaging. Student t test was used to determine any demographic differences between patients with false-negative MP MR imaging findings and those with correct prospective identification of all lesions. Results Of the 162 lesions, 136 (84%) were correctly identified with MP MR imaging. Size of eight lesions was underestimated. Among the 26 (16%) lesions missed at MP MR imaging, Gleason score was 3+4 in 17 (65%), 4+3 in one (4%), 4+4 in seven (27%), and 4+5 in one (4%). Retrospective PI-RADS version 2 scores were assigned (PI-RADS 1, n = 8; PI-RADS 2, n = 7; PI-RADS 3, n = 6; and PI-RADS 4, n = 5). On a per-patient basis, MP MR imaging depicted clinically important prostate cancer in 99 of 100 patients. At least one clinically important tumor was missed in 26 (26%) patients, and lesion size was underestimated in eight (8%). Conclusion Clinically important lesions can be missed or their size can be underestimated at MP MR imaging. Of missed lesions, 58% were not seen or were characterized as benign findings at second-look analysis. Recognition of the limitations of MP MR imaging is important, and new approaches to reduce this false-negative rate are needed.

Validation of the Dominant Sequence Paradigm and Role of Dynamic Contrast-enhanced Imaging in PI-RADS Version 2

Purpose To validate the dominant pulse sequence paradigm and limited role of dynamic contrast material-enhanced magnetic resonance (MR) imaging in the Prostate Imaging Reporting and Data System (PI-RADS) version 2 for prostate multiparametric MR imaging by using data from a multireader study. Materials and Methods This HIPAA-compliant retrospective interpretation of prospectively acquired data was approved by the local ethics committee. Patients were treatment-naïve with endorectal coil 3-T multiparametric MR imaging. A total of 163 patients were evaluated, 110 with prostatectomy after multiparametric MR imaging and 53 with negative multiparametric MR imaging and systematic biopsy findings. Nine radiologists participated in this study and interpreted images in 58 patients, on average (range, 56-60 patients). Lesions were detected with PI-RADS version 2 and were compared with whole-mount prostatectomy findings. Probability of cancer detection for overall, T2-weighted, and diffusion-weighted (DW) imaging PI-RADS scores was calculated in the peripheral zone (PZ) and transition zone (TZ) by using generalized estimating equations. To determine dominant pulse sequence and benefit of dynamic contrast-enhanced (DCE) imaging, odds ratios (ORs) were calculated as the ratio of odds of cancer of two consecutive scores by logistic regression. Results A total of 654 lesions (420 in the PZ) were detected. The probability of cancer detection for PI-RADS category 2, 3, 4, and 5 lesions was 15.7%, 33.1%, 70.5%, and 90.7%, respectively. DW imaging outperformed T2-weighted imaging in the PZ (OR, 3.49 vs 2.45; P = .008). T2-weighted imaging performed better but did not clearly outperform DW imaging in the TZ (OR, 4.79 vs 3.77; P = .494). Lesions classified as PI-RADS category 3 at DW MR imaging and as positive at DCE imaging in the PZ showed a higher probability of cancer detection than did DCE-negative PI-RADS category 3 lesions (67.8% vs 40.0%, P = .02). The addition of DCE imaging to DW imaging in the PZ was beneficial (OR, 2.0; P = .027), with an increase in the probability of cancer detection of 15.7%, 16.0%, and 9.2% for PI-RADS category 2, 3, and 4 lesions, respectively. Conclusion DW imaging outperforms T2-weighted imaging in the PZ; T2-weighted imaging did not show a significant difference when compared with DW imaging in the TZ by PI-RADS version 2 criteria. The addition of DCE imaging to DW imaging scores in the PZ yields meaningful improvements in probability of cancer detection.

Multiparametric Magnetic Resonance Imaging of Recurrent Prostate Cancer.

Abstract There is growing consensus that multiparametric magnetic resonance imaging (mpMRI) is an effective modality in the detection of locally recurrent prostate cancer after prostatectomy and radiation therapy. The emergence of magnetic resonance (MR)-guided focal therapies, such as cryoablation, high-intensity focused ultrasound, and laser ablation, have made the use of mpMRI even more important, as the normal anatomy is inevitably altered and the detection of recurrence is made more difficult. The aim of this article is to review the utility of mpMRI in detecting recurrent prostate cancer in patients following radical prostatectomy, radiation therapy, and focal therapy and to discuss expected post-treatment mpMRI findings, the varied appearance of recurrent tumors, and their mimics.

Does Abstinence From Ejaculation Before Prostate MRI Improve Evaluation of the Seminal Vesicles

OBJECTIVE The purpose of the present study is to determine whether abstinence from ejaculation before undergoing multiparametric prostate MRI increases seminal vesicle (SV) volume and therefore improves diagnostic interpretation of the SVs. MATERIALS AND METHODS This retrospective study included 238 patients who underwent 3-T MRI of the prostate over a 4-month period. Patients were requested to complete a questionnaire that asked how long it had been since their last ejaculation (i.e., < 3 days vs ≥ 3 days). Forty-two patients (mean patient age, 62.0 years) indicated that it had been less than 3 days since their last ejaculation and were designated as group 1, whereas the remainder indicated an interval of 3 days or more since their last ejaculation. A group of 42 age-matched subjects (mean patient age, 62.1 years) were randomly selected from the remaining 196 patients and were designated as group 2. SV volumes were measured manually. Two radiologists who were blinded to group assignment and patient characteristics scored the right and left SVs separately to determine diagnostic interpretability, which was scored on a 3-point scale as follows: a score of 1 denoted that the SVs were not dilated and the score was nondiagnostic, a score of 2 indicated that the SVs were not dilated but the score was diagnostic, and a score of 3 denoted that the SVs were dilated and the score was diagnostic. Volume differences and interpretability scores were analyzed using a t test. Interobserver agreement was analyzed using the Cohen kappa statistic. A separate analysis was performed to evaluate differences in diagnostic interpretability for patients 60 years and younger versus patients older than 60 years, by use of the chi-square test and relative risk ratio analysis. RESULTS The right, left, and total SV volumes for group 1 were 3.1 mL, 2.9 mL, and 6.0 mL, respectively, whereas those for group 2 were 4.7 mL, 4.1 mL, and 8.8 mL, respectively (p = 0.011). The mean interpretability scores for group 1 and group 2 were 2.0 and 2.5, respectively. For group 1, reader 1 and reader 2 assigned a nondiagnostic score for 10 and 13 patients, respectively, whereas for group 2, they assigned a nondiagnostic score for two and five patients, respectively (p = 0.01, for reader 1; and p = 0.03, for reader 2). For men in group 1 who were older than 60 years, reader 1 and reader 2 gave a nondiagnostic score for nine and 11 patients, respectively; whereas for men in group 2 who were older than 60 years, the readers gave a nondiagnostic score for two and five patients, respectively (p = 0.01, for reader 1; and p = 0.05, for reader 2). CONCLUSION For men older than 60 years, abstinence from ejaculation for 3 or more days before undergoing MRI examination resulted in larger SV volumes and lower rates of nondiagnostic evaluation and therefore might improve evaluation of SV invasion on multi-parametric MRI. The difference is less striking in men 60 years and younger.

Detection of prostate cancer in multiparametric MRI using random forest with instance weighting

Abstract. A prostate computer-aided diagnosis (CAD) based on random forest to detect prostate cancer using a combination of spatial, intensity, and texture features extracted from three sequences, T2W, ADC, and B2000 images, is proposed. The random forest training considers instance-level weighting for equal treatment of small and large cancerous lesions as well as small and large prostate backgrounds. Two other approaches, based on an AutoContext pipeline intended to make better use of sequence-specific patterns, were considered. One pipeline uses random forest on individual sequences while the other uses an image filter described to produce probability map-like images. These were compared to a previously published CAD approach based on support vector machine (SVM) evaluated on the same data. The random forest, features, sampling strategy, and instance-level weighting improve prostate cancer detection performance [area under the curve (AUC) 0.93] in comparison to SVM (AUC 0.86) on the same test data. Using a simple image filtering technique as a first-stage detector to highlight likely regions of prostate cancer helps with learning stability over using a learning-based approach owing to visibility and ambiguity of annotations in each sequence.

All over the map: An interobserver agreement study of tumor location based on the PI-RADSv2 sector map: Agreement on Prostate mpMRI PI-RADS Map

Prostate imaging reporting and data system version 2 (PI‐RADSv2) recommends a sector map for reporting findings of prostate cancer mulitparametric MRI (mpMRI). Anecdotally, radiologists may demonstrate inconsistent reproducibility with this map.

Computer-aided diagnosis prior to conventional interpretation of prostate mpMRI: an international multi-reader study

ObjectivesTo evaluate if computer-aided diagnosis (CAD) prior to prostate multi-parametric MRI (mpMRI) can improve sensitivity and agreement between radiologists.MethodsNine radiologists (three each high, intermediate, low experience) from eight institutions participated. A total of 163 patients with 3-T mpMRI from 4/2012 to 6/2015 were included: 110 cancer patients with prostatectomy after mpMRI, 53 patients with no lesions on mpMRI and negative TRUS-guided biopsy. Readers were blinded to all outcomes and detected lesions per PI-RADSv2 on mpMRI. After 5 weeks, readers re-evaluated patients using CAD to detect lesions. Prostatectomy specimens registered to MRI were ground truth with index lesions defined on pathology. Sensitivity, specificity and agreement were calculated per patient, lesion level and zone—peripheral (PZ) and transition (TZ).ResultsIndex lesion sensitivity was 78.2% for mpMRI alone and 86.3% for CAD-assisted mpMRI (p = 0.013). Sensitivity was comparable for TZ lesions (78.7% vs 78.1%; p = 0.929); CAD improved PZ lesion sensitivity (84% vs 94%; p = 0.003). Improved sensitivity came from lesions scored PI-RADS < 3 as index lesion sensitivity was comparable at PI-RADS ≥ 3 (77.6% vs 78.1%; p = 0.859). Per patient specificity was 57.1% for CAD and 70.4% for mpMRI (p = 0.003). CAD improved agreement between all readers (56.9% vs 71.8%; p < 0.001).ConclusionsCAD-assisted mpMRI improved sensitivity and agreement, but decreased specificity, between radiologists of varying experience.Key Points• Computer-aided diagnosis (CAD) assists clinicians in detecting prostate cancer on MRI.• CAD assistance improves agreement between radiologists in detecting prostate cancer lesions.• However, this CAD system induces more false positives, particularly for less-experienced clinicians and in the transition zone.• CAD assists radiologists in detecting cancer missed on MRI, suggesting a path for improved diagnostic confidence.

Multiparametric MRI for the detection of local recurrence of prostate cancer in the setting of biochemical recurrence after low dose rate brachytherapy

PURPOSE Prostate multiparametric magnetic resonance imaging (mpMRI) has utility in detecting post-radiotherapy local recurrence. We conducted a multireader study to evaluate the diagnostic performance of mpMRI for local recurrence after low dose rate (LDR) brachytherapy. METHODS A total of 19 patients with biochemical recurrence after LDR brachytherapy underwent 3T endorectal coil mpMRI with T2-weighted imaging, dynamic contrast-enhanced imaging (DCE) and diffusion-weighted imaging (DWI) with pathologic confirmation. Prospective reads by an experienced prostate radiologist were compared with reads from 4 radiologists of varying experience. Readers identified suspicious lesions and rated each MRI detection parameter. MRI-detected lesions were considered true-positive with ipsilateral pathologic confirmation. Inferences for sensitivity, specificity, positive predictive value (PPV), kappa, and index of specific agreement were made with the use of bootstrap resampling. RESULTS Pathologically confirmed recurrence was found in 15 of 19 patients. True positive recurrences identified by mpMRI were frequently located in the transition zone (46.7%) and seminal vesicles (30%). On patient-based analysis, average sensitivity of mpMRI was 88% (standard error [SE], 3.5%). For highly suspicious lesions, specificity of mpMRI was 75% (SE, 16.5%). On lesion-based analysis, the average PPV was 62% (SE, 6.7%) for all lesions and 78.7% (SE, 10.3%) for highly suspicious lesions. The average PPV for lesions invading the seminal vesicles was 88.8% (n=13). The average PPV was 66.6% (SE, 5.8%) for lesions identified with T2-weighted imaging, 64.9% (SE, 7.3%) for DCE, and 70% (SE, 7.3%) for DWI. CONCLUSION This series provides evidence that mpMRI after LDR brachytherapy is feasible with a high patient-based cancer detection rate. Radiologists of varying experience demonstrated moderate agreement in detecting recurrence.

Can Apparent Diffusion Coefficient Values Assist PI-RADS Version 2 DWI Scoring? A Correlation Study Using the PI-RADSv2 and International Society of Urological Pathology Systems

OBJECTIVE The purposes of this study were to assess correlation of apparent diffusion coefficient (ADC) and normalized ADC (ratio of tumor to nontumor tissue) with the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) and updated International Society of Urological Pathology (ISUP) categories and to determine how to optimally use ADC metrics for objective assistance in categorizing lesions within PI-RADSv2 guidelines. MATERIALS AND METHODS In this retrospective study, 100 patients (median age, 62 years; range, 44-75 years; prostate-specific antigen level, 7.18 ng/mL; range, 1.70-84.56 ng/mL) underwent 3-T multiparametric MRI of the prostate with an endorectal coil. Mean ADC was extracted from ROIs based on subsequent prostatectomy specimens. Histopathologic analysis revealed 172 lesions (113 peripheral, 59 transition zone). Two radiologists blinded to histopathologic outcome assigned PI-RADSv2 categories. Kendall tau was used to correlate ADC metrics with PI-RADSv2 and ISUP categories. ROC curves were used to assess the utility of ADC metrics in differentiating each readers PI-RADSv2 DWI category 4 or 5 assessment in the whole prostate and by zone. RESULTS ADC metrics negatively correlated with ISUP category in the whole prostate (ADC, τ = -0.21, p = 0.0002; normalized ADC, τ = -0.21, p = 0.0001). Moderate negative correlation was found in expert PI-RADSv2 DWI categories (ADC, τ = -0.34; normalized ADC, τ = -0.31; each p < 0.0001) maintained across zones. In the whole prostate, AUCs of ADC and normalized ADC were 87% and 82% for predicting expert PI-RADSv2 DWI category 4 or 5. A derived optimal cutoff ADC less than 1061 and normalized ADC less than 0.65 achieved positive predictive values of 83% and 84% for correct classification of PI-RADSv2 DWI category 4 or 5 by an expert reader. Consistent relations and predictive values were found by an independent novice reader. CONCLUSION ADC and normalized ADC inversely correlate with PI-RADSv2 and ISUP categories and can serve as quantitative metrics to assist with assigning PI-RADSv2 DWI category 4 or 5.

The Problems with the Kappa Statistic as a Metric of Interobserver Agreement on Lesion Detection Using a Third-reader Approach When Locations Are Not Prespecified

RATIONALE AND OBJECTIVES To point out the problems with Cohen kappa statistic and to explore alternative metrics to determine interobserver agreement on lesion detection when locations are not prespecified. MATERIALS AND METHODS Use of kappa and two alternative methods, namely index of specific agreement (ISA) and modified kappa, for measuring interobserver agreement on the location of detected lesions are presented. These indices of agreement are illustrated by application to a retrospective multireader study in which nine readers detected and scored prostate cancer lesions in 163 consecutive patients (n = 110 cases, n = 53 controls) using the guideline of Prostate Imaging Reporting and Data System version 2 on multiparametric magnetic resonance imaging. RESULTS The proposed modified kappa, which properly corrects for the amount of agreement by chance, is shown to be approximately equivalent to the ISA. In the prostate cancer data, average kappa, modified kappa, and ISA equaled 30%, 55%, and 57%, respectively, for all lesions and 20%, 87%, and 87%, respectively, for index lesions. CONCLUSIONS The application of kappa could result in a substantial downward bias in reader agreement on lesion detection when locations are not prespecified. ISA is recommended for assessment of reader agreement on lesion detection.