Purpose Prostate Imaging Reporting and Data System version 2 was developed to standardize the interpretation and reporting of multiparametric prostate magnetic resonance imaging and provide ...guidelines for biopsy of multiparametric magnetic resonance imaging findings. We prospectively evaluated the cancer detection rate at each overall PI-RADSv2 score. Materials and Methods This prospective study included 62 consecutive patients with 116 lesions who underwent multiparametric prostate magnetic resonance imaging at 3T with PI-RADSv2 evaluation and subsequent targeted magnetic resonance imaging/ transrectal ultrasound fusion guided biopsy and concurrent 12-core systematic prostate biopsy between May and September 2015. Median patient age and prostate specific antigen values were 65.5 years (range 50.3 to 76.6) and 7.10 ng/ml (range 0.47 to 863.0), respectively. Mean lesion size was 12.7 mm overall. Lesion based cancer detection rates for all tumors and for Gleason 3+4 or greater tumors at each PI-RADSv2 score were calculated. Univariate analysis was performed to assess differences in the cancer detection rate among PI-RADSv2 scores. Results A total of 116 lesions in 62 patients were evaluated prospectively (0 PI-RADS 1, 18 PI-RADS 2, 19 PI-RADS 3, 47 PI-RADS 4, 32 PI-RADS 5), and the patients underwent magnetic resonance/transrectal ultrasound fusion guided biopsy and systematic biopsy. Histopathology revealed 55 of 116 (47.4%) cancers (17 Gleason 3+3, 16 Gleason 3+4, 6 Gleason 4+3, 12 Gleason 4+4, 3 Gleason 4+5 and 1 Gleason 5+4). Based on targeted biopsy on a per lesion basis, the overall cancer detection rates of PI-RADS 2, 3, 4 and 5 scores for all tumors was 22.2%, 15.8%, 29.8% and 78.1%, respectively. The cancer detection rate of PI-RADS 2, 3, 4, and 5 scores for Gleason 3+4 or greater tumors was 5.6%, 0%, 21.3% and 75%, respectively. Differences in the cancer detection rate between overall PI-RADS 4 and 5 scores were significant (p <0.001 for Gleason greater than 3+3 and Gleason 3+4 or greater cancers). Conclusions A PI-RADS score of 5 had the highest prospective cancer detection rate (78%). A PI-RADS score of 4 had only a 30% cancer detection rate, which is lower than expected. Surprisingly, no or few significant cancers were detected at a PI-RADS score of 3 (16%). These early prospective data suggest that current criteria result in a high false-positive rate that lowers the cancer detection rate. Therefore, stricter criteria may be needed in the future to decrease false-positives and increase the cancer detection rate for PI-RADS scores of 3, 4 and 5.
The PI-RADS™ (Prostate Imaging Reporting and Data System), version 2 scoring system, introduced in 2015, is based on expert consensus. In the same time frame ISUP (International Society of Urological ...Pathology) introduced a new pathological scoring system for prostate cancer. Our goal was to prospectively evaluate the cancer detection rates for each PI-RADS, version 2 category and compare them to ISUP group scores in patients undergoing systematic biopsy and magnetic resonance imaging-transrectal ultrasound fusion guided biopsy.
A total of 339 treatment naïve patients prospectively underwent multiparametric magnetic resonance imaging evaluated with PI-RADS, version 2 with subsequent systematic and fusion guided biopsy from May 2015 to May 2016. ISUP scores were applied to pathological specimens. An ISUP score of 2 or greater (ie Gleason 3 + 4 or greater) was defined as clinically significant prostate cancer. Cancer detection rates were determined for each PI-RADS, version 2 category as well as for the T2 weighted PI-RADS, version 2 categories in the peripheral zone.
The cancer detection rate for PI-RADS, version 2 categories 1, 2, 3, 4 and 5 was 25%, 20.2%, 24.8%, 39.1% and 86.9% for all prostate cancer, and 0%, 9.6%, 12%, 22.1% and 72.4% for clinically significant prostate cancer, respectively. On T2-weighted magnetic resonance imaging the cancer detection rate in the peripheral zone was significantly higher for PI-RADS, version 2 category 4 than for overall PI-RADS, version 2 category 4 in the peripheral zone (all prostate cancer 36.6% vs 48.1%, p = 0.001, and clinically significant prostate cancer 22.9% vs 32.6%, p = 0.002).
The cancer detection rate increases with higher PI-RADS, version 2 categories.
Tobacco smoking is responsible for over 90% of lung cancer cases, and yet the precise molecular alterations induced by smoking in lung that develop into cancer and impact survival have remained ...obscure.
We performed gene expression analysis using HG-U133A Affymetrix chips on 135 fresh frozen tissue samples of adenocarcinoma and paired noninvolved lung tissue from current, former and never smokers, with biochemically validated smoking information. ANOVA analysis adjusted for potential confounders, multiple testing procedure, Gene Set Enrichment Analysis, and GO-functional classification were conducted for gene selection. Results were confirmed in independent adenocarcinoma and non-tumor tissues from two studies. We identified a gene expression signature characteristic of smoking that includes cell cycle genes, particularly those involved in the mitotic spindle formation (e.g., NEK2, TTK, PRC1). Expression of these genes strongly differentiated both smokers from non-smokers in lung tumors and early stage tumor tissue from non-tumor tissue (p<0.001 and fold-change >1.5, for each comparison), consistent with an important role for this pathway in lung carcinogenesis induced by smoking. These changes persisted many years after smoking cessation. NEK2 (p<0.001) and TTK (p = 0.002) expression in the noninvolved lung tissue was also associated with a 3-fold increased risk of mortality from lung adenocarcinoma in smokers.
Our work provides insight into the smoking-related mechanisms of lung neoplasia, and shows that the very mitotic genes known to be involved in cancer development are induced by smoking and affect survival. These genes are candidate targets for chemoprevention and treatment of lung cancer in smokers.
In this study involving 2103 men with elevated PSA levels, the use of both MRI-targeted and 12-core systematic biopsies was more effective at detecting clinically significant prostate cancers than ...either biopsy method alone.
Purpose
Multiparametric MRI (mpMRI) improves the detection of clinically significant prostate cancer, but is limited by interobserver variation. The second version of theProstate Imaging Reporting ...and Data System (PIRADSv2) was recently proposed as a standard for interpreting mpMRI. To assess the performance and interobserver agreement of PIRADSv2 we performed a multi‐reader study with five radiologists of varying experience.
Materials and Methods
Five radiologists (n = 2 prostate dedicated, n = 3 general body) blinded to clinicopathologic results detected and scored lesions on prostate mpMRI using PIRADSv2. The endorectal coil 3 Tesla MRI included T2W, diffusion‐weighted imaging (apparent diffusion coefficient, b2000), and dynamic contrast enhancement. Thirty‐four consecutive patients were included. Results were correlated with radical prostatectomy whole‐mount histopathology produced with patient‐specific three‐dimensional molds. An index lesion was defined on pathology as the lesion with highest Gleason score or largest volume if equivalent grades. Average sensitivity and positive predictive values (PPVs) for all lesions and index lesions were determined using generalized estimating equations. Interobserver agreement was evaluated using index of specific agreement.
Results
Average sensitivity was 91% for detecting index lesions and 63% for all lesions across all readers. PPV was 85% for PIRADS ≥ 3 and 90% for PIRADS ≥ 4. Specialists performed better only for PIRADS ≥ 4 with sensitivity 90% versus 79% (P = 0.01) for index lesions. Index of specific agreement among readers was 93% for the detection of index lesions, 74% for the detection of all lesions, and 85% for scoring index lesions, and 58% for scoring all lesions.
Conclusion
By using PIRADSv2, general body radiologists and prostate specialists can detect high‐grade index prostate cancer lesions with high sensitivity and agreement.
Level of Evidence: 1
J. Magn. Reson. Imaging 2017;45:579–585.
Background
The Prostate Imaging Reporting and Data System version 2 (PI‐RADSv2) has been in use since 2015; while interreader reproducibility has been studied, there has been a paucity of studies ...investigating the intrareader reproducibility of PI‐RADSv2.
Purpose
To evaluate both intra‐ and interreader reproducibility of PI‐RADSv2 in the assessment of intraprostatic lesions using multiparametric magnetic resonance imaging (mpMRI).
Study Type
Retrospective.
Population/Subjects
In all, 102 consecutive biopsy‐naïve patients who underwent prostate MRI and subsequent MR/transrectal ultrasonography (MR/TRUS)‐guided biopsy.
Field Strength/Sequences
Prostate mpMRI at 3T using endorectal with phased array surface coils (TW MRI, DW MRI with ADC maps and b2000 DW MRI, DCE MRI).
Assessment
Previously detected and biopsied lesions were scored by four readers from four different institutions using PI‐RADSv2. Readers scored lesions during two readout rounds with a 4‐week washout period.
Statistical Tests
Kappa (κ) statistics and specific agreement (Po) were calculated to quantify intra‐ and interreader reproducibility of PI‐RADSv2 scoring. Lesion measurement agreement was calculated using the intraclass correlation coefficient (ICC).
Results
Overall intrareader reproducibility was moderate to substantial (κ = 0.43–0.67, Po = 0.60–0.77), while overall interreader reproducibility was poor to moderate (κ = 0.24, Po = 46). Readers with more experience showed greater interreader reproducibility than readers with intermediate experience in the whole prostate (P = 0.026) and peripheral zone (P = 0.002). Sequence‐specific interreader agreement for all readers was similar to the overall PI‐RADSv2 score, with κ = 0.24, 0.24, and 0.23 and Po = 0.47, 0.44, and 0.54 in T2‐weighted, diffusion‐weighted imaging (DWI), and dynamic contrast‐enhanced (DCE), respectively. Overall intrareader and interreader ICC for lesion measurement was 0.82 and 0.71, respectively.
Data Conclusion
PI‐RADSv2 provides moderate intrareader reproducibility, poor interreader reproducibility, and moderate interreader lesion measurement reproducibility. These findings suggest a need for more standardized reader training in prostate MRI.
Level of Evidence: 2
Technical Efficacy: Stage 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.
RSNA, 2017 An earlier incorrect version of this article appeared online. This article was corrected on July 27, 2017. Online supplemental material is available for this article.
To evaluate accuracy and interobserver variability with the use of the Prostate Imaging Reporting and Data System (PI-RADS) version 2.0 for detection of prostate cancer at multiparametric magnetic ...resonance (MR) imaging in a biopsy-naïve patient population.
This retrospective HIPAA-compliant study was approved by the local ethics committee, and written informed consent was obtained from all patients for use of their imaging and histopathologic data in future research studies. In 101 biopsy-naïve patients with elevated prostate-specific antigen levels who underwent multiparametric MR imaging of the prostate and subsequent transrectal ultrasonography (US)-MR imaging fusion-guided biopsy, suspicious lesions detected at multiparametric MR imaging were scored by five readers who were blinded to pathologic results by using to the newly revised PI-RADS and the scoring system developed in-house. Interobserver agreement was evaluated by using κ statistics, and the correlation of pathologic results with each of the two scoring systems was evaluated by using the Kendall τ correlation coefficient.
Specimens of 162 lesions in 94 patients were sampled by means of transrectal US-MR imaging fusion biopsy. Results for 87 (54%) lesions were positive for prostate cancer. Kendall τ values with the PI-RADS and the in-house-developed scoring system, respectively, at T2-weighted MR imaging in the peripheral zone were 0.51 and 0.17 and in the transitional zone, 0.45 and -0.11; at diffusion-weighted MR imaging, 0.42 and 0.28; at dynamic contrast material-enhanced MR imaging, 0.23 and 0.24, and overall suspicion scores were 0.42 and 0.49. Median κ scores among all possible pairs of readers for PI-RADS and the in-house-developed scoring system, respectively, for T2-weighted MR images in the peripheral zone were 0.47 and 0.15; transitional zone, 0.37 and 0.07; diffusion-weighted MR imaging, 0.41 and 0.57; dynamic contrast-enhanced MR imaging, 0.48 and 0.41; and overall suspicion scores, 0.46 and 0.55.
Use of the revised PI-RADS provides moderately reproducible MR imaging scores for detection of clinically relevant disease.
Incomplete coverage by cancer registries can lead to an underreporting of cancers and a resulting bias in risk estimates. When registries are defined by geographic region, gaps in observation can ...arise for individuals who reside outside of or migrate from the total registry catchment area. Moreover, the exact periods of non‐observation for an individual may be unknown due to intermittent reporting of residential histories. The motivating example for this work is the U.S. Radiologic Technologist (USRT) study which ascertained cancer outcomes for a national cohort through 43 state/regional registries; similar gaps in outcome ascertainment can appear in other registry or electronic health record‐ based cohort studies. We propose a two‐step procedure for estimating relative and absolute risk in these settings. First, using a mover stayer model fitted to individuals' known residential history, we obtain individual posterior probabilities of residing outside the registry catchment area each year. Second, we incorporate these probabilities in the survival data likelihood for competing risks to account for unobserved events. We assess the performance of the proposed method in extensive simulation studies. Compared to several simple alternative approaches, the proposed method reduces bias and improves efficiency. Finally, we apply the proposed method to a study of first primary lung cancers in the USRT cohort.
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