Multivariate modelling of prostate cancer combining magnetic resonance derived T2, diffusion, dynamic contrast-enhanced and spectroscopic parameters

Color Maps: Facilitating the Clinical Impact of Quantitative MRI

Presenting quantitative data using non-standardized color maps potentially results in unrecognized misinterpretation of data. Clinically meaningful color maps should intuitively and inclusively represent data without misleading interpretation. Uniformity of the color gradient for color maps is critically important. Maximal color and lightness contrast, readability for color vision-impaired individuals, and recognizability of the color scheme are highly desirable features. This article describes the use of color maps in five key quantitative MRI techniques: relaxometry, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE)-MRI, MR elastography (MRE), and water-fat MRI. Current display practice of color maps is reviewed and shortcomings against desirable features are highlighted. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Radiomics-based machine-learning method to diagnose prostate cancer using mp-MRI: a comparison between conventional and fused models

OBJECTIVES

Multiparametric MRI (mp-MRI) has been significantly used for detection, localization and staging of Prostate cancer (PCa). However, all the assessment suffers from poor reproducibility among the readers. The aim of this study was to evaluate radiomics models to diagnose PCa using high-resolution T2-weighted (T2-W) and dynamic contrast-enhanced (DCE) MRI.

MATERIALS AND METHODS

Thirty two patients who had high prostate specific antigen level were recruited. The prostate biopsies considered as the reference to differentiate between 66 benign and 36 malignant prostate lesions. 181 features were extracted from each modality. K-nearest neighbors, artificial neural network, decision tree, and linear discriminant analysis were used for machine-learning study. The leave-one-out cross-validation method was used to prevent overfitting and build robust models.

RESULTS

Radiomics analysis showed that T2-W images were more effective in PCa detection compare to DCE images. Local binary pattern features and speeded up robust features had the highest ability for prediction in T2-W and DCE images, respectively. The classifier fusion using decision template method showed the highest performance with accuracy, specificity, and sensitivity of 100%.

DISCUSSION

The findings of this framework provide researchers on PCa with a promising method for reliable detection of prostate lesions in MR images by fused model.

MRI Based Radiomics Compared With the PI-RADS V2.1 in the Prediction of Clinically Significant Prostate Cancer: Biparametric vs Multiparametric MRI

Purpose

To compare the performance of radiomics to that of the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 scoring system in the detection of clinically significant prostate cancer (csPCa) based on biparametric magnetic resonance imaging (bpMRI) vs. multiparametric MRI (mpMRI).

Methods

A total of 204 patients with pathological results were enrolled between January 2018 and December 2019, with 142 patients in the training cohort and 62 patients in the testing cohort. The radiomics model was compared with the PI-RADS v2.1 for the diagnosis of csPCa based on bpMRI and mpMRI by using receiver operating characteristic (ROC) curve analysis.

Results

The radiomics model based on bpMRI and mpMRI signatures showed high predictive efficiency but with no significant differences (AUC = 0.975 vs 0.981, p=0.687 in the training cohort, and 0.953 vs 0.968, p=0.287 in the testing cohort, respectively). In addition, the radiomics model outperformed the PI-RADS v2.1 in the diagnosis of csPCa regardless of whether bpMRI (AUC = 0.975 vs. 0.871, p= 0.030 for the training cohort and AUC = 0.953 vs. 0.853, P = 0.024 for the testing cohort) or mpMRI (AUC = 0.981 vs. 0.880, p= 0.030 for the training cohort and AUC = 0.968 vs. 0.863, P = 0.016 for the testing cohort) was incorporated.

Conclusions

Our study suggests the performance of bpMRI- and mpMRI-based radiomics models show no significant difference, which indicates that omitting DCE imaging in radiomics can simplify the process of analysis. Adding radiomics to PI-RADS v2.1 may improve the performance to predict csPCa.

Standard and Hypofractionated Dose Escalation to Intraprostatic Tumor Nodules in Localized Prostate Cancer: Efficacy and Toxicity in the DELINEATE Trial

PURPOSE

To report a planned analysis of the efficacy and toxicity of dose escalation to the intraprostatic dominant nodule identified on multiparametric magnetic resonance imaging using standard and hypofractionated external beam radiation therapy.

METHODS AND MATERIALS

DELINEATE is a single centre prospective phase 2 multicohort study including standard (cohort A: 74 Gy in 37 fractions) and moderately hypofractionated (cohort B: 60 Gy in 20 fractions) prostate image guided intensity modulated radiation therapy in patients with National Comprehensive Cancer Network intermediate- and high-risk disease. Patients received an integrated boost of 82 Gy (cohort A) and 67 Gy (cohort B) to lesions visible on multiparametric magnetic resonance imaging. Fifty-five patients were treated in cohort A, and 158 patients were treated in cohort B; the first 50 sequentially treated patients in cohort B were included in this planned analysis. The primary endpoint was late Radiation Therapy Oncology Group rectal toxicity at 1 year. Secondary endpoints included acute and late toxicity measured with clinician- and patient-reported outcomes at other time points and biochemical relapse-free survival for cohort A. Median follow-up was 74.5 months for cohort A and 52.0 months for cohort B.

RESULTS

In cohorts A and B, 27% and 40% of patients, respectively, were classified as having National Comprehensive Cancer Network high-risk disease. The cumulative 1-year incidence of Radiation Therapy Oncology Group grade 2 or worse rectal and urinary toxicity was 3.6% and 0% in cohort A and 8% and 10% in cohort B, respectively. There was no reported late grade 3 rectal toxicity in either cohort. Within cohort A, 4 of 55 (7%) patients had biochemical relapse.

CONCLUSIONS

Delivery of a simultaneous integrated boost to intraprostatic dominant nodules is feasible in prostate radiation therapy using standard and moderately hypofractionated regimens, with rectal and genitourinary toxicity comparable to contemporary series without an intraprostatic boost.

MRI combined with PSA density in detecting clinically significant prostate cancer in patients with PSA serum levels of 4∼10ng/mL: Biparametric versus multiparametric MRI

PURPOSE

To compare the performance of biparametric magnetic resonance imaging (bpMRI) to that of multiparametric MRI (mpMRI) in combination with prostate-specific antigen density (PSAD) in detecting clinically significant prostate cancer (csPCa) in patients with PSA serum levels of 4∼10ng/mL.

MATERIALS AND METHODS

A total of 123 men (mean age, 66.3±8.9 [SD]; range: 42-83 years) with PSA serum levels of 4∼10ng/mL with suspected csPCa were included. All patients underwent mpMRI at 3 Tesla and transrectal ultrasound-guided prostate biopsy in their clinical workup and were followed-up for >1 year when no csPCa was found at initial biopsy. The mpMRI images were reinterpreted according to the Prostate Imaging Reporting and Data System (PI-RADS, v2.1) twice in two different sessions using either mpMRI sequences or bpMRI sequences. The patients were divided into 2 groups according to whether csPCa was detected. The PI-RADS (mpMRI or bpMRI) categories and PSAD were used in combination to detect csPCa. Receiver operating characteristic (ROC) curve and decision curve analyses were performed to compare the efficacy of the different models (mpMRI, bpMRI, PSAD, mpMRI+PSAD and bpMRI+PSAD).

RESULTS

Thirty-seven patients (30.1%, 37/123) had csPCa. ROC analysis showed that bpMRI (AUC=0.884 [95% confidence interval (CI): 0.814-0.935]) outperformed mpMRI (AUC=0.867 [95% CI: 0.794-0.921]) (P=0.035) and that bpMRI and mpMRI performed better than PSAD (0.682 [95% CI: 0.592-0.763]) in detecting csPCa; bpMRI+PSAD (AUC=0.907 [95% CI: 0.841-0.952]) performed similarly to mpMRI+PSAD (AUC=0.896 [95% CI: 0.828-0.944]) (P=0.151) and bpMRI (P=0.224). The sensitivity and specificity were 81.1% (95% CI: 64.8-92.0%) and 88.4% (95% CI: 79.7-94.3%), respectively for bpMRI, and 83.8% (95% CI: 68.0-93.8%) and 80.2% (95% CI: 70.2-88.0%), respectively for mpMRI (P>0.999 for sensitivity and P=0.016 for specificity). Among the 5 decision models, the decision curve analysis showed that all models (except for PSAD) achieved a high net benefit.

CONCLUSION

In patients with PSA serum levels of 4∼10ng/mL, bpMRI and bpMRI combined with PSAD achieve better performance than mpMRI in detecting csPCa; bpMRI has a higher specificity than mpMRI, which could decrease unnecessary biopsy, and may serve as a potential alternative to mpMRI to optimize clinical workup.

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

Although cancer often is referred to as "a disease of the genes," it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as "radiomics," can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1-2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of "deep learning," wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions ("habitats") within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology.

The evaluation of prostate lesions with IVIM DWI and MR perfusion parameters at 3T MRI

PURPOSE

The purpose of our study was to analyze the difference between IVIM DWI and perfusion parameters of malignant lesions and benign lesions-normal prostate tissue.

METHODS

This prospective study included 31 patients who had multiparametric prostate MRI with IVIM DWI due to elevated prostate-specific antigen level and clinical suspicion between February 2015 and September 2016.

RESULTS

For peripheral zone, the mean values of K^trans, K_ep, iAUC, χ² and f were significantly higher in malignant lesions, and the mean values of Dt were significantly lower in malignant lesions (p 0.00, p 0.02, p 0.00, p 0.02 and p 0.00, respectively). For transitional zone, the mean values of K^trans, V_e, iAUC, χ² and f were significantly higher in malignant lesions, and the mean values of Dp and Dt were significantly lower in malignant lesions (p 0.00, p 0.00, p 0.00, p 0.00, p 0.00, p 0.02 and p 0.00, respectively). For whole prostate gland, the mean values of K^trans, K_ep, V_e, iAUC, χ² and f were significantly higher in malignant lesions, and the mean values of Dp and Dt were significantly lower in malignant lesions (p 0.00, p 0.03, p 0.00, p 0.00, p 0.00, p 0.01, p 0.04 and p 0.00, respectively).

CONCLUSIONS

Restricted diffusion-pseudodiffusion and increased perfusion parameters are important to differentiate prostate cancer from benign pathologies. It is also important to keep in mind that transitional zone and peripheral zone tumors may have different perfusion and diffusion parameters. Future studies are needed to confirm our findings.

A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation

Purpose

We aimed to precisely estimate intra-tumoral heterogeneity using spatially regularized spectral clustering (SRSC) on multiparametric MRI data and compare the efficacy of SRSC with the previously reported segmentation techniques in MRI studies.

Procedures

Six NMRI nu/nu mice bearing subcutaneous human glioblastoma U87 MG tumors were scanned using a dedicated small animal 7T magnetic resonance imaging (MRI) scanner. The data consisted of T2 weighted images, apparent diffusion coefficient maps, and pre- and post-contrast T2 and T2* maps. Following each scan, the tumors were excised into 2–3-mm thin slices parallel to the axial field of view and processed for histological staining. The MRI data were segmented using SRSC, K-means, fuzzy C-means, and Gaussian mixture modeling to estimate the fractional population of necrotic, peri-necrotic, and viable regions and validated with the fractional population obtained from histology.

Results

While the aforementioned methods overestimated peri-necrotic and underestimated viable fractions, SRSC accurately predicted the fractional population of all three tumor tissue types and exhibited strong correlations (r_necrotic = 0.92, r_{peri-necrotic} = 0.82 and r_viable = 0.98) with the histology.

Conclusions

The precise identification of necrotic, peri-necrotic and viable areas using SRSC may greatly assist in cancer treatment planning and add a new dimension to MRI-guided tumor biopsy procedures.

Electronic supplementary material

The online version of this article (doi:10.1007/s11307-016-1009-y) contains supplementary material, which is available to authorized users.

Low-risk Prostate Cancer: Identification, Management, and Outcomes

CONTEXT

The incidence of low-risk prostate cancer (PCa) has increased as a consequence of prostate-specific antigen testing.

OBJECTIVE

In this collaborative review article, we examine recent literature regarding low-risk PCa and the available prognostic and therapeutic options.

EVIDENCE ACQUISITION

We performed a literature review of the Medline, Embase, and Web of Science databases. The search strategy included the terms: prostate cancer, low risk, active surveillance, focal therapy, radical prostatectomy, watchful waiting, biomarker, magnetic resonance imaging, alone or in combination.

EVIDENCE SYNTHESIS

Prospective randomized trials have failed to show an impact of radical treatments on cancer-specific survival in low-risk PCa patients. Several series have reported the risk of adverse pathologic outcomes at radical prostatectomy. However, it is not clear if these patients are at higher risk of death from PCa. Long-term follow-up indicates the feasibility of active surveillance in low-risk PCa patients, although approximately 30% of men starting active surveillance undergo treatment within 5 yr. Considering focal therapies, robust data investigating its impact on long-term survival outcomes are still required and therefore should be considered experimental. Magnetic resonance imaging and tissue biomarkers may help to predict clinically significant PCa in men initially diagnosed with low-risk disease.

CONCLUSIONS

The incidence of low-risk PCa has increased in recent years. Only a small proportion of men with low-risk PCa progress to clinical symptoms, metastases, or death and prospective trials have not shown a benefit for immediate radical treatments. Tissue biomarkers, magnetic resonance imaging, and ongoing surveillance may help to identify those men with low-risk PCa who harbor more clinically significant disease.

PATIENT SUMMARY

Low-risk prostate cancer is very common. Active surveillance has excellent long-term results, while randomized trials have failed to show a beneficial impact of immediate radical treatments on survival. Biomarkers and magnetic resonance imaging may help to identify which men may benefit from early treatment.

Diagnostic value and relative weight of sequence-specific magnetic resonance features in characterizing clinically significant prostate cancers

Purpose

To assess the diagnostic weight of sequence-specific magnetic resonance features in characterizing clinically significant prostate cancers (csPCa).

Materials and methods

We used a prospective database of 262 patients who underwent T2-weighted, diffusion-weighted, and dynamic contrast-enhanced (DCE) imaging before prostatectomy. For each lesion, two independent readers (R1, R2) prospectively defined nine features: shape, volume (V_Max), signal abnormality on each pulse sequence, number of pulse sequences with a marked (S_Max) and non-visible (S_Min) abnormality, likelihood of extracapsular extension (ECE) and PSA density (dPSA). Overall likelihood of malignancy was assessed using a 5-level Likert score. Features were evaluated using the area under the receiver operating characteristic curve (AUC). csPCa was defined as Gleason ≥7 cancer (csPCa-A), Gleason ≥7(4+3) cancer (csPCa-B) or Gleason ≥7 cancer with histological extraprostatic extension (csPCa-C),

Results

For csPCa-A, the Signal1 model (S_Max+S_Min) provided the best combination of signal-related variables, for both readers. The performance was improved by adding V_Max, ECE and/or dPSA, but not shape. All models performed better with DCE findings than without.

When moving from csPCa-A to csPCa-B and csPCa-C definitions, the added value of V_Max, dPSA and ECE increased as compared to signal-related variables, and the added value of DCE decreased.

For R1, the best models were Signal1+ECE+dPSA (AUC = 0,805 [95%CI:0,757–0,866]), Signal1+V_Max+dPSA (AUC = 0.823 [95%CI:0.760–0.893]) and Signal1+ECE+dPSA [AUC = 0.840 (95%CI:0.774–0.907)] for csPCa-A, csPCA-B and csPCA-C respectively. The AUCs of the corresponding Likert scores were 0.844 [95%CI:0.806–0.877, p = 0.11], 0.841 [95%CI:0.799–0.876, p = 0.52]) and 0.849 [95%CI:0.811–0.884, p = 0.49], respectively.

For R2, the best models were Signal1+V_Max+dPSA (AUC = 0,790 [95%CI:0,731–0,857]), Signal1+V_Max (AUC = 0.813 [95%CI:0.746–0.882]) and Signal1+ECE+V_Max (AUC = 0.843 [95%CI: 0.781–0.907]) for csPCa-A, csPCA-B and csPCA-C respectively. The AUCs of the corresponding Likert scores were 0. 829 [95%CI:0.791–0.868, p = 0.13], 0.790 [95%CI:0.742–0.841, p = 0.12]) and 0.808 [95%CI:0.764–0.845, p = 0.006]), respectively.

Conclusion

Combination of simple variables can match the Likert score’s results. The optimal combination depends on the definition of csPCa.

Autodelineation of cervical cancers using multiparametric magnetic resonance imaging and machine learning

BACKGROUND

Tumour delineation is a challenging, time-consuming and complex part of radiotherapy planning. In this study, an automatic method for delineating locally advanced cervical cancers was developed using a machine learning approach.

MATERIALS AND METHODS

A method for tumour segmentation based on image voxel classification using Fisher?s Linear Discriminant Analysis (LDA) was developed. This was applied to magnetic resonance (MR) images of 78 patients with locally advanced cervical cancer. The segmentation was based on multiparametric MRI consisting of T2- weighted (T2w), T1-weighted (T1w) and dynamic contrast-enhanced (DCE) sequences, and included intensity and spatial information from the images. The model was trained and assessed using delineations made by two radiologists.

RESULTS

Segmentation based on T2w or T1w images resulted in mean sensitivity and specificity of 94% and 52%, respectively. Including DCE-MR images improved the segmentation model?s performance significantly, giving mean sensitivity and specificity of 85?93%. Comparisons with radiologists? tumour delineations gave Dice similarity coefficients of up to 0.44.

CONCLUSION

Voxel classification using a machine learning approach is a flexible and fully automatic method for tumour delineation. Combining all relevant MR image series resulted in high sensitivity and specificity. Moreover, the presented method can be extended to include additional imaging modalities.

DCE-MRI, DW-MRI, and MRS in Cancer: Challenges and Advantages of Implementing Qualitative and Quantitative Multi-parametric Imaging in the Clinic

Multi-parametric magnetic resonance imaging (mpMRI) offers a unique insight into tumor biology by combining functional MRI techniques that inform on cellularity (diffusion-weighted MRI), vascular properties (dynamic contrast-enhanced MRI), and metabolites (magnetic resonance spectroscopy) and has scope to provide valuable information for prognostication and response assessment. Challenges in the application of mpMRI in the clinic include the technical considerations in acquiring good quality functional MRI data, development of robust techniques for analysis, and clinical interpretation of the results. This article summarizes the technical challenges in acquisition and analysis of multi-parametric MRI data before reviewing the key applications of multi-parametric MRI in clinical research and practice.

Volumetry of the dominant intraprostatic tumour lesion: intersequence and interobserver differences on multiparametric MRI

OBJECTIVE

To establish the interobserver reproducibility of tumour volumetry on individual multiparametric (mp) prostate MRI sequences, validate measurements with histology and determine whether functional to morphological volume ratios reflect Gleason score.

METHODS

41 males with prostate cancer treated with prostatectomy (Cohort 1) or radical radiotherapy (Cohort 2), who had pre-treatment mpMRI [T2 weighted (T2W) MRI, diffusion-weighted (DW)-MRI and dynamic contrast-enhanced (DCE)-MRI], were studied retrospectively. Dominant intraprostatic lesions (DIPLs) were manually delineated on each sequence and volumes were compared between observers (n = 40 analyzable) and with radical prostatectomy (n = 20). Volume ratios of DW-MRI and DCE-MRI to T2W MRI were documented and compared between Gleason grade 3 + 3, 3 + 4 and 4 + 3 or greater categories.

RESULTS

Limits of agreement of DIPL volumes between observers were: T2W MRI 0.9, -1.1 cm3, DW-MRI 1.3, -1.7 cm3 and DCE-MRI 0.74, -0.89 cm3. In Cohort 1, T2W volumes overestimated fixed specimen histological volumes (+33% Observer 1, +16% Observer 2); DW- and DCE-MRI underestimated histological volume, the latter markedly so (-32% Observer 1, -79% Observer 2). Differences between T2W, DW- and DCE-MRI volumes were significant (p < 10-8). The ratio of DW-MRI volume (73.9 ± 18.1% Observer 1, 72.5 ± 21.9% Observer 2) and DCE-MRI volume (42.6 ± 24.6% Observer 1, 34.3 ± 24.9% Observer 2) to T2W volume was significantly different (p < 10-8), but these volume ratios did not differ between the Gleason grades.

CONCLUSION

The low variability of the DIPL volume on T2W MRI between Observers and agreement with histology indicates its suitability for delineation of gross tumour volume for radiotherapy planning. The volume of cellular tumour represented by DW-MRI is greater than the vascular (DCE) abnormality; ratios of both to T2W volume are independent of Gleason score. Advances in knowledge: (1) Manual volume measurement of tumour is reproducible within 1 cm3 between observers on all sequences, confirming suitability across observers for radiotherapy planning. (2) Volumes derived on T2W MRI most accurately represent in vivo lesion volumes. (3) The proportion of cellular (DW-MRI) or vascular (DCE-MRI) volume to morphological (T2W MRI) volume is not affected by Gleason score.

Choline-based imaging of prostate cancer with combined [18F] fluorocholine PET and 1H MR spectroscopy by means of integrated PET/MRI

PURPOSE

To evaluate integrated PET/MRI/¹H MR spectroscopy in patients with prostate cancer.

SUBJECTS AND METHODS

Data analysis comprised calculations of correlations of standardized uptake values (SUVs) and ratios of (choline+creatine)/citrate as well as of single metabolite values and a logistic regression analysis of PET data and MR spectroscopy data in 22 patients.

RESULTS

SUVmean and integral values of choline correlated significantly in tumors. Logistic regression analysis demonstrated diagnostic superiority of PET over spectroscopy.

CONCLUSION

Simultaneous acquisition of PET and MR spectroscopy with integrated PET/MRI is feasible. Choline compounds and choline metabolism show a positive significant correlation.

The role of imaging in the diagnosis of primary prostate cancer

Ultrasound and magnetic resonance imaging (MRI) are key imaging modalities in prostate cancer diagnosis. MRI offers a range of intrinsic contrast mechanisms (T2, diffusion-weighted imaging (DWI), MR spectroscopy (MRS)) and extrinsic contrast-generating options based on tumour vascular state following injection of weakly paramagnetic agents such as gadolinium. Together these parameters are referred to as multiparametric (mp)MRI and are used for detecting and guiding biopsy and staging prostate cancer. Although sensitivity of mpMRI is <75% for disease detection, specificity is >90% and a standardised reporting system together with MR-guided targeted biopsy is the optimal diagnostic pathway. Shear wave ultrasound elastography is a new technique which also holds promise for future studies. This article describes the developments in imaging the primary site of prostate cancer and reviews their current and future utility for screening, diagnosis and T-staging the disease.

Evaluation of extracapsular extension in prostate cancer using qualitative and quantitative multiparametric MRI

PURPOSE

To investigate the value of multiparametric magnetic resonance imaging (mpMRI) for extracapsular extension (ECE) in prostate cancer (PCa).

MATERIALS AND METHODS

In all, 292 patients who received radical prostatectomy and underwent preoperative mpMRI at 3T were enrolled retrospectively. For determining the associations with ECE, the likelihood of ECE was assessed qualitatively on T₂ -weighted imaging (T₂ WI) and combined T₂ WI and diffusion-weighted imaging (DWI) or dynamic contrast-enhanced imaging (DCEI). Quantitative MRI parameters were measured in PCa based on histopathological findings. Two models for detecting ECE including imaging and clinical parameters were developed using multivariate analysis: Model 1 excluding combined T₂ WI and DWI and DCEI and Model 2 excluding combined T₂ WI and DWI, and combined T₂ WI and DCEI. Diagnostic performance of imaging parameters and models was evaluated using the area under the receiver operating characteristics curve (Az).

RESULTS

For detecting ECE, the specificity, accuracy, and Az of combined T₂ WI and DWI or DCEI were statistically better than those of T₂ WI (P < 0.05), and all quantitative MRI parameters showed a statistical difference between the patients with and without ECE (P < 0.05). On multivariate analysis, significant independent markers in Model 1 were combined T₂ WI and DWI, combined T₂ WI and DCEI, and K^trans (P < 0.05). In Model 2, significant markers were combined T₂ WI and DWI and DCEI, K^trans , K_ep , and Ve (P < 0.05). The Az values of models 1 and 2 were 0.944 and 0.957, respectively.

CONCLUSION

mpMRI may be useful to improve diagnostic accuracy of the models for determining the associations with ECE in PCa.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;45:1760-1770.

Quantitative Analysis of Prostate Multiparametric MR Images for Detection of Aggressive Prostate Cancer in the Peripheral Zone: A Multiple Imager Study

Purpose To assess the intermanufacturer variability of quantitative models in discriminating cancers with a Gleason score of at least 7 among peripheral zone (PZ) lesions seen at 3-T multiparametric magnetic resonance (MR) imaging. Materials and Methods An institutional review board-approved prospective database of 257 patients who gave written consent and underwent T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced imaging before prostatectomy was retrospectively reviewed. It contained outlined lesions found to be suspicious for malignancy by two independent radiologists and classified as malignant or benign after correlation with prostatectomy whole-mount specimens. One hundred six patients who underwent imaging with 3-T MR systems from two manufacturers were selected (data set A, n = 72; data set B, n = 34). Eleven parameters were calculated in PZ lesions: normalized T2-weighted signal intensity, skewness and kurtosis of T2-weighted signal intensity, T2 value, wash-in rate, washout rate, time to peak (TTP), mean apparent diffusion coefficient (ADC), 10th percentile of the ADC, and skewness and kurtosis of the histogram of the ADC values. Parameters were selected on the basis of their specificity for a sensitivity of 0.95 in diagnosing cancers with a Gleason score of at least 7, and the area under the receiver operating characteristic curve (AUC) for the models was calculated. Results The model of the 10th percentile of the ADC with TTP yielded the highest AUC in both data sets. In data set A, the AUC was 0.90 (95% confidence interval [CI]: 0.85, 0.95) or 0.89 (95% CI: 0.82, 0.94) when it was trained in data set A or B, respectively. In data set B, the AUC was 0.84 (95% CI: 0.74, 0.94) or 0.86 (95% CI: 0.76, 0.95) when it was trained in data set A or B, respectively. No third variable added significantly independent information in any data set. Conclusion The model of the 10th percentile of the ADC with TTP yielded accurate results in discriminating cancers with a Gleason score of at least 7 among PZ lesions at 3 T in data from two manufacturers. (©) RSNA, 2016 Online supplemental material is available for this article.

Metabolomic analysis of prostate cancer risk in a prospective cohort: The alpha-tocolpherol, beta-carotene cancer prevention (ATBC) study

Despite decades of concerted epidemiological research, relatively little is known about the etiology of prostate cancer. As genome-wide association studies have identified numerous genetic variants, so metabolomic profiling of blood and other tissues represents an agnostic, "broad-spectrum" approach for examining potential metabolic biomarkers of prostate cancer risk. To this end, we conducted a prospective analysis of prostate cancer within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study cohort based on 200 cases (100 aggressive) and 200 controls (age- and blood collection date-matched) with fasting serum collected up to 20 years prior to case diagnoses. Ultrahigh performance liquid chromatography/mass spectroscopy and gas chromatography/mass spectroscopy identified 626 compounds detected in >95% of the men and the odds ratio per 1-standard deviation increase in log-metabolite levels and risk were estimated using conditional logistic regression. We observed strong inverse associations between energy and lipid metabolites and aggressive cancer (p = 0.018 and p = 0.041, respectively, for chemical class over-representation). Inositol-1-phosphate showed the strongest association (OR = 0.56, 95% CI = 0.39-0.81, p = 0.002) and glycerophospholipids and fatty acids were heavily represented; e.g., oleoyl-linoleoyl-glycerophosphoinositol (OR = 0.64, p = 0.004), 1-stearoylglycerophosphoglycerol (OR=0.65, p = 0.025), stearate (OR=0.65, p = 0.010) and docosadienoate (OR = 0.66, p = 0.014). Both alpha-ketoglutarate and citrate were associated with aggressive disease risk (OR = 0.69, 95% CI = 0.51-0.94, p = 0.02; OR = 0.69, 95% CI = 0.50-0.95, p = 0.02), as were elevated thyroxine and trimethylamine oxide (OR = 1.65, 95% CI = 1.08-2.54, p = 0.021; and OR = 1.36, 95% CI = 1.02-1.81, p = 0.039). Serum PSA adjustment did not alter the findings. Our data reveal several metabolomic leads that may have pathophysiological relevance to prostate carcinogenesis and should be examined through additional research.