Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI.

Recent advances in the field of radiomics have enabled the development of a number of prognostic and predictive imaging-based tools for a variety of diseases. However, wider clinical adoption of these tools is contingent on their generalizability across multiple sites and scanners. This may be particularly relevant in the context of radiomic features derived from T1- or T2-weighted magnetic resonance images (MRIs), where signal intensity values are known to lack tissue-specific meaning and vary based on differing acquisition protocols between institutions. We present the first empirical study of benchmarking five different radiomic feature families in terms of both reproducibility and discriminability in a multisite setting, specifically, for identifying prostate tumors in the peripheral zone on MRI. Our cohort comprised 147 patient T2-weighted MRI datasets from four different sites, all of which are first preprocessed to correct for acquisition-related artifacts such as bias field, differing voxel resolutions, and intensity drift (nonstandardness). About 406 three-dimensional voxel-wise radiomic features from five different families (gray, Haralick, gradient, Laws, and Gabor) were evaluated in a cross-site setting to determine (a) how reproducible they are within a relatively homogeneous nontumor tissue region and (b) how well they could discriminate tumor regions from nontumor regions. Our results demonstrate that a majority of the popular Haralick features are reproducible in over 99% of all cross-site comparisons, as well as achieve excellent cross-site discriminability (classification accuracy of ≈ 0.8 ). By contrast, a majority of Laws features are highly variable across sites (reproducible in < 75 % of all cross-site comparisons) as well as resulting in low cross-site classifier accuracies ( < 0.6 ), likely due to a large number of noisy filter responses that can be extracted. These trends suggest that only a subset of radiomic features and associated parameters may be both reproducible and discriminable enough for use within machine learning classifier schemes.

Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer.

Importance: There has been significant recent interest in understanding the utility of quantitative imaging to delineate breast cancer intrinsic biological factors and therapeutic response. No clinically accepted biomarkers are as yet available for estimation of response to human epidermal growth factor receptor 2 (currently known as ERBB2, but referred to as HER2 in this study)-targeted therapy in breast cancer. Objective: To determine whether imaging signatures on clinical breast magnetic resonance imaging (MRI) could noninvasively characterize HER2-positive tumor biological factors and estimate response to HER2-targeted neoadjuvant therapy. Design, Setting, and Participants: In a retrospective diagnostic study encompassing 209 patients with breast cancer, textural imaging features extracted within the tumor and annular peritumoral tissue regions on MRI were examined as a means to identify increasingly granular breast cancer subgroups relevant to therapeutic approach and response. First, among a cohort of 117 patients who received an MRI prior to neoadjuvant chemotherapy (NAC) at a single institution from April 27, 2012, through September 4, 2015, imaging features that distinguished HER2+ tumors from other receptor subtypes were identified. Next, among a cohort of 42 patients with HER2+ breast cancers with available MRI and RNaseq data accumulated from a multicenter, preoperative clinical trial (BrUOG 211B), a signature of the response-associated HER2-enriched (HER2-E) molecular subtype within HER2+ tumors (n = 42) was identified. The association of this signature with pathologic complete response was explored in 2 patient cohorts from different institutions, where all patients received HER2-targeted NAC (n = 28, n = 50). Finally, the association between significant peritumoral features and lymphocyte distribution was explored in patients within the BrUOG 211B trial who had corresponding biopsy hematoxylin-eosin-stained slide images. Data analysis was conducted from January 15, 2017, to February 14, 2019. Main Outcomes and Measures: Evaluation of imaging signatures by the area under the receiver operating characteristic curve (AUC) in identifying HER2+ molecular subtypes and distinguishing pathologic complete response (ypT0/is) to NAC with HER2-targeting. Results: In the 209 patients included (mean [SD] age, 51.1 [11.7] years), features from the peritumoral regions better discriminated HER2-E tumors (maximum AUC, 0.85; 95% CI, 0.79-0.90; 9-12 mm from the tumor) compared with intratumoral features (AUC, 0.76; 95% CI, 0.69-0.84). A classifier combining peritumoral and intratumoral features identified the HER2-E subtype (AUC, 0.89; 95% CI, 0.84-0.93) and was significantly associated with response to HER2-targeted therapy in both validation cohorts (AUC, 0.80; 95% CI, 0.61-0.98 and AUC, 0.69; 95% CI, 0.53-0.84). Features from the 0- to 3-mm peritumoral region were significantly associated with the density of tumor-infiltrating lymphocytes (R2 = 0.57; 95% CI, 0.39-0.75; P = .002). Conclusions and Relevance: A combination of peritumoral and intratumoral characteristics appears to identify intrinsic molecular subtypes of HER2+ breast cancers from imaging, offering insights into immune response within the peritumoral environment and suggesting potential benefit for treatment guidance.

Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study.

BACKGROUND: For most computer-aided diagnosis (CAD) problems involving prostate cancer detection via medical imaging data, the choice of classifier has been largely ad hoc, or been motivated by classifier comparison studies that have involved large synthetic datasets. More significantly, it is currently unknown how classifier choices and trends generalize across multiple institutions, due to heterogeneous acquisition and intensity characteristics (especially when considering MR imaging data). In this work, we empirically evaluate and compare a number of different classifiers and classifier ensembles in a multi-site setting, for voxel-wise detection of prostate cancer (PCa) using radiomic texture features derived from high-resolution in vivo T2-weighted (T2w) MRI. METHODS: Twelve different supervised classifier schemes: Quadratic Discriminant Analysis (QDA), Support Vector Machines (SVMs), naïve Bayes, Decision Trees (DTs), and their ensemble variants (bagging, boosting), were compared in terms of classification accuracy as well as execution time. Our study utilized 85 prostate cancer T2w MRI datasets acquired from across 3 different institutions (1 for discovery, 2 for independent validation), from patients who later underwent radical prostatectomy. Surrogate ground truth for disease extent on MRI was established by expert annotation of pre-operative MRI through spatial correlation with corresponding ex vivo whole-mount histology sections. Classifier accuracy in detecting PCa extent on MRI on a per-voxel basis was evaluated via area under the ROC curve. RESULTS: The boosted DT classifier yielded the highest cross-validated AUC (= 0.744) for detecting PCa in the discovery cohort. However, in independent validation, the boosted QDA classifier was identified as the most accurate and robust for voxel-wise detection of PCa extent (AUCs of 0.735, 0.683, 0.768 across the 3 sites). The next most accurate and robust classifier was the single QDA classifier, which also enjoyed the advantage of significantly lower computation times compared to any of the other methods. CONCLUSIONS: Our results therefore suggest that simpler classifiers (such as QDA and its ensemble variants) may be more robust, accurate, and efficient for prostate cancer CAD problems, especially in the context of multi-site validation.

Risk factors involved in treatment delays and differences in treatment type for patients with prostate cancer by risk category in an academic safety net hospital.

OBJECTIVES: Understanding the drivers of delays from diagnosis to treatment can elucidate how to reduce the time to treatment (TTT) in patients with prostate cancer. In addition, the available treatments depending on the stage of cancer can vary widely for many reasons. This study investigated the relationship of TTT and treatment choice with sociodemographic factors in patients with prostate cancer who underwent external beam radiation therapy (RT), radical prostatectomy (RP), androgen deprivation therapy (ADT), or active surveillance (AS) at a safety-net academic medical center. METHODS AND MATERIALS: A retrospective review was performed on 1088 patients who were diagnosed with nonmetastatic prostate cancer between January 2005 and December 2013. Demographic data as well as data on TTT, initial treatment choice, American Joint Committee on Cancer stage, and National Comprehensive Cancer Network risk categories were collected. Analyses of variance and multivariable logistic regression models were performed to analyze the relationship of these factors with treatment choice and TTT. RESULTS: Age, race, and marital status were significantly related to treatment choice. Patients who were nonwhite and older than 60 years were less likely to undergo RP. Black patients were 3.8 times more likely to undergo RT compared with white patients. The median TTT was 75 days. Longer time delays were significant in patients of older age, nonwhite race/ethnicity, non-English speakers, those with noncommercial insurance, and those with non-married status. The average TTT of high-risk patients was 25 days longer than that of low-risk patients. Patients who underwent RT had an average TTT that was 34 days longer than that of RP patients. CONCLUSIONS: The treatment choice and TTT of patients with prostate cancer are affected by demographic factors such as age, race, marital status, and insurance, as well as clinical factors including stage and risk category of disease.

A 3D-Printed Patient-Specific Phantom for External Beam Radiation Therapy of Prostate Cancer.

This paper presents the design evolution, fabrication, and testing of a novel patient and organ-specific, 3D printed phantom for external beam radiation therapy of prostate cancer. In contrast to those found in current practice, this phantom can be used to plan and validate treatment tailored to an individual patient. It contains a model of the prostate gland with a dominant intraprostatic lesion, seminal vesicles, urethra, ejaculatory duct, neurovascular bundles, rectal wall, and penile bulb generated from a series of combined T2-weighted/dynamic contrast-enhanced magnetic resonance images. The iterative process for designing the phantom based on user interaction and evaluation is described. Using the CyberKnife System at Boston Medical Center a treatment plan was successfully created and delivered. Dosage delivery results were validated through gamma index calculations based on radiochromic film measurements which yielded a 99.8% passing rate. This phantom is a demonstration of a methodology for incorporating high-contrast magnetic resonance imaging into computed-tomography-based radiotherapy treatment planning; moreover, it can be used to perform quality assurance.

Stereotactic core needle breast biopsy marker migration: An analysis of factors contributing to immediate marker migration.

OBJECTIVES: To evaluate breast biopsy marker migration in stereotactic core needle biopsy procedures and identify contributing factors. METHODS: This retrospective study analyzed 268 stereotactic biopsy markers placed in 263 consecutive patients undergoing stereotactic biopsies using 9G vacuum-assisted devices from August 2010-July 2013. Mammograms were reviewed and factors contributing to marker migration were evaluated. Basic descriptive statistics were calculated and comparisons were performed based on radiographically-confirmed marker migration. RESULTS: Of the 268 placed stereotactic biopsy markers, 35 (13.1%) migrated ≥1 cm from their biopsy cavity. Range: 1-6 cm; mean (± SD): 2.35 ± 1.22 cm. Of the 35 migrated biopsy markers, 9 (25.7%) migrated ≥3.5 cm. Patient age, biopsy pathology, number of cores, and left versus right breast were not associated with migration status (P> 0.10). Global fatty breast density (P= 0.025) and biopsy in the inner region of breast (P = 0.031) were associated with marker migration. Superior biopsy approach (P= 0.025), locally heterogeneous breast density, and t-shaped biopsy markers (P= 0.035) were significant for no marker migration. CONCLUSIONS: Multiple factors were found to influence marker migration. An overall migration rate of 13% supports endeavors of research groups actively developing new biopsy marker designs for improved resistance to migration. KEY POINTS: • Breast biopsy marker migration is documented in 13% of 268 procedures. • Marker migration is affected by physical, biological, and pathological factors. • Breast density, marker shape, needle approach etc. affect migration. • Study demonstrates marker migration prevalence; marker design improvements are needed.

Dosimetric impacts of endorectal balloon in CyberKnife stereotactic body radiation therapy (SBRT) for early-stage prostate cancer.

PURPOSE: In SBRT for prostate cancer, higher fractional dose to the rectum is a major toxicity concern due to using smaller PTV margin and hypofractionation. We investigate the dosimetric impact on rectum using endorectal balloon (ERB) in prostate SBRT. MATERIALS AND METHODS: Twenty prostate cancer patients were included in a retrospective study, ten with ERB and 10 without ERB. Optimized SBRT plans were generated on CyberKnife MultiPlan for 5 × 7.25 Gy to PTV under RTOG-0938 protocol for early-stage prostate cancer. For the rectum and the anterior half rectum, mean dose and percentage of volumes receiving 50%, 80%, 90%, and 100% prescription dose were compared. RESULTS: Using ERB, mean dose to the rectum was 62 cGy (P = 0.001) lower per fraction, and 50 cGy (P = 0.024) lower per fraction for the anterior half rectum. The average V50% , V80% , V90% , and V100% were lower by 9.9% (P = 0.001), 5.3% (P = 0.0002), 3.4% (P = 0.0002), and 1.2% (P = 0.005) for the rectum, and lower by 10.4% (P = 0.009), 8.3% (P = 0.0004), 5.4% (P = 0.0003), and 2.1% (P = 0.003) for the anterior half rectum. CONCLUSIONS: Significant reductions of dose to the rectum using ERB were observed. This may lead to improvement of the rectal toxicity profiles in prostate SBRT.

A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores.

To identify computer extracted imaging features for estrogen receptor (ER)-positive breast cancers on dynamic contrast enhanced (DCE)-MRI that are correlated with the low and high OncotypeDX risk categories. We collected 96 ER-positive breast lesions with low (< 18, N = 55) and high (> 30, N = 41) OncotypeDX recurrence scores. Each lesion was quantitatively characterize via 6 shape features, 3 pharmacokinetics, 4 enhancement kinetics, 4 intensity kinetics, 148 textural kinetics, 5 dynamic histogram of oriented gradient (DHoG), and 6 dynamic local binary pattern (DLBP) features. The extracted features were evaluated by a linear discriminant analysis (LDA) classifier in terms of their ability to distinguish low and high OncotypeDX risk categories. Classification performance was evaluated by area under the receiver operator characteristic curve (Az). The DHoG and DLBP achieved Az values of 0.84 and 0.80, respectively. The 6 top features identified via feature selection were subsequently combined with the LDA classifier to yield an Az of 0.87. The correlation analysis showed that DHoG (ρ = 0.85, P < 0.001) and DLBP (ρ = 0.83, P < 0.01) were significantly associated with the low and high risk classifications from the OncotypeDX assay. Our results indicated that computer extracted texture features of DCE-MRI were highly correlated with the high and low OncotypeDX risk categories for ER-positive cancers.

Polymer film-nanoparticle composites as new multimodality, non-migrating breast biopsy markers.

OBJECTIVES: To develop a breast biopsy marker that resists fast and slow migration and has permanent visibility under commonly used imaging modalities. METHODS: A polymer-nanoparticle composite film was prepared by embedding superparamagnetic iron oxide nanoparticles and a superelastic Nitinol wire within a flexible polyethylene matrix. MRI, mammography, and ultrasound were used to visualize the marker in agar, ex vivo chicken breast, bovine liver, brisket, and biopsy training phantoms. Fast migration caused by the "accordion effect" was quantified after simulated stereotactic, vacuum-assisted core biopsy/marker placement, and centrifugation was used to simulate accelerated long-term (i.e., slow) migration in ex vivo bovine tissue phantoms. RESULTS: Clear marker visualization under MRI, mammography, and ultrasound was observed. After deployment, the marker partially unfolds to give a geometrically constrained structure preventing fast and slow migration. The marker can be deployed through an 11G introducer without fast migration occurring, and shows substantially less slow migration than conventional markers. CONCLUSION: The polymer-nanoparticle composite biopsy marker is clearly visible on all clinical imaging modalities and does not show substantial migration, which ensures multimodal assessment of the correct spatial information of the biopsy site, allowing for more accurate diagnosis and treatment planning and improved breast cancer patient care. KEY POINTS: Polymer-nanoparticle composite biopsy markers are visualized using ultrasound, MRI, and mammography. Embedded iron oxide nanoparticles provide tuneable contrast for MRI visualization. Permanent ultrasound visibility is achieved with a non-biodegradable polymer having a distinct ultrasound signal. Flexible polymer-based biopsy markers undergo shape change upon deployment to minimize migration. Non-migrating multimodal markers will help improve accuracy of pre/post-treatment planning studies.

Improved dosimetry in prostate brachytherapy using high resolution contrast enhanced magnetic resonance imaging: a feasibility study.

PURPOSE: To assess detailed dosimetry data for prostate and clinical relevant intra- and peri-prostatic structures including neurovascular bundles (NVB), urethra, and penile bulb (PB) from postbrachytherapy computed tomography (CT) versus high resolution contrast enhanced magnetic resonance imaging (HR-CEMRI). MATERIAL AND METHODS: Eleven postbrachytherapy prostate cancer patients underwent HR-CEMRI and CT imaging. Computed tomography and HR-CEMRI images were randomized and 2 independent expert readers created contours of prostate, intra- and peri-prostatic structures on each CT and HR-CEMRI scan for all 11 patients. Dosimetry data including V100, D90, and D100 was calculated from these contours. RESULTS: Mean V100 values from CT and HR-CEMRI contours were as follows: prostate (98.5% and 96.2%, p = 0.003), urethra (81.0% and 88.7%, p = 0.027), anterior rectal wall (ARW) (8.9% and 2.8%, p < 0.001), left NVB (77.9% and 51.5%, p = 0.002), right NVB (69.2% and 43.1%, p = 0.001), and PB (0.09% and 11.4%, p = 0.005). Mean D90 (Gy) derived from CT and HR-CEMRI contours were: prostate (167.6 and 150.3, p = 0.012), urethra (81.6 and 109.4, p = 0.041), ARW (2.5 and 0.11, p = 0.003), left NVB (98.2 and 58.6, p = 0.001), right NVB (87.5 and 55.5, p = 0.001), and PB (11.2 and 12.4, p = 0.554). CONCLUSIONS: Findings of this study suggest that HR-CEMRI facilitates accurate and meaningful dosimetric assessment of prostate and clinically relevant structures, which is not possible with CT. Significant differences were seen between CT and HR-CEMRI, with volume overestimation of CT derived contours compared to HR-CEMRI.

Multiattribute probabilistic prostate elastic registration (MAPPER): application to fusion of ultrasound and magnetic resonance imaging.

PURPOSE: Transrectal ultrasound (TRUS)-guided needle biopsy is the current gold standard for prostate cancer diagnosis. However, up to 40% of prostate cancer lesions appears isoechoic on TRUS. Hence, TRUS-guided biopsy has a high false negative rate for prostate cancer diagnosis. Magnetic resonance imaging (MRI) is better able to distinguish prostate cancer from benign tissue. However, MRI-guided biopsy requires special equipment and training and a longer procedure time. MRI-TRUS fusion, where MRI is acquired preoperatively and then aligned to TRUS, allows for advantages of both modalities to be leveraged during biopsy. MRI-TRUS-guided biopsy increases the yield of cancer positive biopsies. In this work, the authors present multiattribute probabilistic postate elastic registration (MAPPER) to align prostate MRI and TRUS imagery. METHODS: MAPPER involves (1) segmenting the prostate on MRI, (2) calculating a multiattribute probabilistic map of prostate location on TRUS, and (3) maximizing overlap between the prostate segmentation on MRI and the multiattribute probabilistic map on TRUS, thereby driving registration of MRI onto TRUS. MAPPER represents a significant advancement over the current state-of-the-art as it requires no user interaction during the biopsy procedure by leveraging texture and spatial information to determine the prostate location on TRUS. Although MAPPER requires manual interaction to segment the prostate on MRI, this step is performed prior to biopsy and will not substantially increase biopsy procedure time. RESULTS: MAPPER was evaluated on 13 patient studies from two independent datasets­Dataset 1 has 6 studies acquired with a side-firing TRUS probe and a 1.5 T pelvic phased-array coil MRI; Dataset 2 has 7 studies acquired with a volumetric end-firing TRUS probe and a 3.0 T endorectal coil MRI. MAPPER has a root-mean-square error (RMSE) for expert selected fiducials of 3.36 ± 1.10 mm for Dataset 1 and 3.14 ± 0.75 mm for Dataset 2. State-of-the-art MRI-TRUS fusion methods report RMSE of 3.06-2.07 mm. CONCLUSIONS: MAPPER aligns MRI and TRUS imagery without manual intervention ensuring efficient, reproducible registration. MAPPER has a similar RMSE to state-of-the-art methods that require manual intervention.

Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors.

PURPOSE: To identify computer-extracted features for central gland and peripheral zone prostate cancer localization on multiparametric magnetic resonance imaging (MRI). MATERIALS AND METHODS: Preoperative T2-weighted (T2w), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI were acquired from 23 men with confirmed prostate cancer. Following radical prostatectomy, the cancer extent was delineated by a pathologist on ex vivo histology and mapped to MRI by nonlinear registration of histology and corresponding MRI slices. In all, 244 computer-extracted features were extracted from MRI, and principal component analysis (PCA) was employed to reduce the data dimensionality so that a generalizable classifier could be constructed. A novel variable importance on projection (VIP) measure for PCA (PCA-VIP) was leveraged to identify computer-extracted MRI features that discriminate between cancer and normal prostate, and these features were used to construct classifiers for cancer localization. RESULTS: Classifiers using features selected by PCA-VIP yielded an area under the curve (AUC) of 0.79 and 0.85 for peripheral zone and central gland tumors, respectively. For tumor localization in the central gland, T2w, DCE, and DWI MRI features contributed 71.6%, 18.1%, and 10.2%, respectively; for peripheral zone tumors T2w, DCE, and DWI MRI contributed 29.6%, 21.7%, and 48.7%, respectively. CONCLUSION: PCA-VIP identified relatively stable subsets of MRI features that performed well in localizing prostate cancer on MRI.

A Learning Based Fiducial-driven Registration Scheme for Evaluating Laser Ablation Changes in Neurological Disorders.

In this work, we present a novel learning based fiducial driven registration (LeFiR) scheme which utilizes a point matching technique to identify the optimal configuration of landmarks to better recover deformation between a target and a moving image. Moreover, we employ the LeFiR scheme to model the localized nature of deformation introduced by a new treatment modality - laser induced interstitial thermal therapy (LITT) for treating neurological disorders. Magnetic resonance (MR) guided LITT has recently emerged as a minimally invasive alternative to craniotomy for local treatment of brain diseases (such as glioblastoma multiforme (GBM), epilepsy). However, LITT is currently only practised as an investigational procedure world-wide due to lack of data on longer term patient outcome following LITT. There is thus a need to quantitatively evaluate treatment related changes between post- and pre-LITT in terms of MR imaging markers. In order to validate LeFiR, we tested the scheme on a synthetic brain dataset (SBD) and in two real clinical scenarios for treating GBM and epilepsy with LITT. Four experiments under different deformation profiles simulating localized ablation effects of LITT on MRI were conducted on 286 pairs of SBD images. The training landmark configurations were obtained through 2000 iterations of registration where the points with consistently best registration performance were selected. The estimated landmarks greatly improved the quality metrics compared to a uniform grid (UniG) placement scheme, a speeded-up robust features (SURF) based method, and a scale-invariant feature transform (SIFT) based method as well as a generic free-form deformation (FFD) approach. The LeFiR method achieved average 90% improvement in recovering the local deformation compared to 82% for the uniform grid placement, 62% for the SURF based approach, and 16% for the generic FFD approach. On the real GBM and epilepsy data, the quantitative results showed that LeFiR outperformed UniG by 28% improvement in average.

Prostatome: a combined anatomical and disease based MRI atlas of the prostate.

PURPOSE: In this work, the authors introduce a novel framework, the anatomically constrained registration (AnCoR) scheme and apply it to create a fused anatomic-disease atlas of the prostate which the authors refer to as the prostatome. The prostatome combines a MRI based anatomic and a histology based disease atlas. Statistical imaging atlases allow for the integration of information across multiple scales and imaging modalities into a single canonical representation, in turn enabling a fused anatomical-disease representation which may facilitate the characterization of disease appearance relative to anatomic structures. While statistical atlases have been extensively developed and studied for the brain, approaches that have attempted to combine pathology and imaging data for study of prostate pathology are not extant. This works seeks to address this gap. METHODS: The AnCoR framework optimizes a scoring function composed of two surface (prostate and central gland) misalignment measures and one intensity-based similarity term. This ensures the correct mapping of anatomic regions into the atlas, even when regional MRI intensities are inconsistent or highly variable between subjects. The framework allows for creation of an anatomic imaging and a disease atlas, while enabling their fusion into the anatomic imaging-disease atlas. The atlas presented here was constructed using 83 subjects with biopsy confirmed cancer who had pre-operative MRI (collected at two institutions) followed by radical prostatectomy. The imaging atlas results from mapping thein vivo MRI into the canonical space, while the anatomic regions serve as domain constraints. Elastic co-registration MRI and corresponding ex vivo histology provides "ground truth" mapping of cancer extent on in vivo imaging for 23 subjects. RESULTS: AnCoR was evaluated relative to alternative construction strategies that use either MRI intensities or the prostate surface alone for registration. The AnCoR framework yielded a central gland Dice similarity coefficient (DSC) of 90%, and prostate DSC of 88%, while the misalignment of the urethra and verumontanum was found to be 3.45 mm, and 4.73 mm, respectively, which were measured to be significantly smaller compared to the alternative strategies. As might have been anticipated from our limited cohort of biopsy confirmed cancers, the disease atlas showed that most of the tumor extent was limited to the peripheral zone. Moreover, central gland tumors were typically larger in size, possibly because they are only discernible at a much later stage. CONCLUSIONS: The authors presented the AnCoR framework to explicitly model anatomic constraints for the construction of a fused anatomic imaging-disease atlas. The framework was applied to constructing a preliminary version of an anatomic-disease atlas of the prostate, the prostatome. The prostatome could facilitate the quantitative characterization of gland morphology and imaging features of prostate cancer. These techniques, may be applied on a large sample size data set to create a fully developed prostatome that could serve as a spatial prior for targeted biopsies by urologists. Additionally, the AnCoR framework could allow for incorporation of complementary imaging and molecular data, thereby enabling their careful correlation for population based radio-omics studies.

Fully Automated Prostate Magnetic Resonance Imaging and Transrectal Ultrasound Fusion via a Probabilistic Registration Metric.

In this work, we present a novel, automated, registration method to fuse magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) images of the prostate. Our methodology consists of: (1) delineating the prostate on MRI, (2) building a probabilistic model of prostate location on TRUS, and (3) aligning the MRI prostate segmentation to the TRUS probabilistic model. TRUS-guided needle biopsy is the current gold standard for prostate cancer (CaP) diagnosis. Up to 40% of CaP lesions appear isoechoic on TRUS, hence TRUS-guided biopsy cannot reliably target CaP lesions and is associated with a high false negative rate. MRI is better able to distinguish CaP from benign prostatic tissue, but requires special equipment and training. MRI-TRUS fusion, whereby MRI is acquired pre-operatively and aligned to TRUS during the biopsy procedure, allows for information from both modalities to be used to help guide the biopsy. The use of MRI and TRUS in combination to guide biopsy at least doubles the yield of positive biopsies. Previous work on MRI-TRUS fusion has involved aligning manually determined fiducials or prostate surfaces to achieve image registration. The accuracy of these methods is dependent on the reader's ability to determine fiducials or prostate surfaces with minimal error, which is a difficult and time-consuming task. Our novel, fully automated MRI-TRUS fusion method represents a significant advance over the current state-of-the-art because it does not require manual intervention after TRUS acquisition. All necessary preprocessing steps (i.e. delineation of the prostate on MRI) can be performed offline prior to the biopsy procedure. We evaluated our method on seven patient studies, with B-mode TRUS and a 1.5 T surface coil MRI. Our method has a root mean square error (RMSE) for expertly selected fiducials (consisting of the urethra, calcifications, and the centroids of CaP nodules) of 3.39 ± 0.85 mm.

Diagnosis of relevant prostate cancer using supplementary cores from magnetic resonance imaging-prompted areas following multiple failed biopsies.

OBJECTIVES: To establish the value of MRI in targeting re-biopsy for undiagnosed prostate cancer despite multiple negative biopsies and determine clinical relevance of detected tumors. MATERIALS AND METHODS: Thirty-eight patients who underwent MRI after 2 or more negative biopsies due to continued clinical suspicion and later underwent TRUS-guided biopsy supplemented by biopsy of suspicious areas depicted by MRI were identified. Diagnostic performance of endorectal 3T MRI in diagnosing missed cancer foci was assessed using biopsy results as the standard of reference. Ratio of positive biopsies using systematic versus MRI-prompted approaches was compared. Gleason scores of detected cancers were used as surrogate for clinical relevance. RESULTS: Thirty-four percent of patients who underwent MRI before re-biopsy had prostate cancer on subsequent biopsy. The positive biopsy yield with systematic sampling was 23% versus 92% with MRI-prompted biopsies(p<0.0001). Seventy-seven percent of tumors were detected exclusively in the MRI-prompted zones. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy of MRI to provide a positive biopsy were 92%, 60%, 55%, 94% and 71%, respectively. The anterior gland and apical regions contained most tumors; 75% of cancers detected by MRI-prompted biopsy had Gleason score≥7. CONCLUSIONS: Clinically relevant tumors missed by multiple TRUS-guided biopsies can be detected by a MRI-prompted approach.

Statistical 3D Prostate Imaging Atlas Construction via Anatomically Constrained Registration.

Statistical imaging atlases allow for integration of information from multiple patient studies collected across different image scales and modalities, such as multi-parametric (MP) MRI and histology, providing population statistics regarding a specific pathology within a single canonical representation. Such atlases are particularly valuable in the identification and validation of meaningful imaging signatures for disease characterization in vivo within a population. Despite the high incidence of prostate cancer, an imaging atlas focused on different anatomic structures of the prostate, i.e. an anatomic atlas, has yet to be constructed. In this work we introduce a novel framework for MRI atlas construction that uses an iterative, anatomically constrained registration (AnCoR) scheme to enable the proper alignment of the prostate (Pr) and central gland (CG) boundaries. Our current implementation uses endorectal, 1.5T or 3T, T2-weighted MRI from 51 patients with biopsy confirmed cancer; however, the prostate atlas is seamlessly extensible to include additional MRI parameters. In our cohort, radical prostatectomy is performed following MP-MR image acquisition; thus ground truth annotations for prostate cancer are available from the histological specimens. Once mapped onto MP-MRI through elastic registration of histological slices to corresponding T2-w MRI slices, the annotations are utilized by the AnCoR framework to characterize the 3D statistical distribution of cancer per anatomic structure. Such distributions are useful for guiding biopsies toward regions of higher cancer likelihood and understanding imaging profiles for disease extent in vivo. We evaluate our approach via the Dice similarity coefficient (DSC) for different anatomic structures (delineated by expert radiologists): Pr, CG and peripheral zone (PZ). The AnCoR-based atlas had a CG DSC of 90.36%, and Pr DSC of 89.37%. Moreover, we evaluated the deviation of anatomic landmarks, the urethra and veromontanum, and found 3.64 mm and respectively 4.31 mm. Alternative strategies that use only the T2-w MRI or the prostate surface to drive the registration were implemented as comparative approaches. The AnCoR framework outperformed the alternative strategies by providing the lowest landmark deviations.

Prediction of prostate cancer extracapsular extension with high spatial resolution dynamic contrast-enhanced 3-T MRI.

OBJECTIVES: To assess the value of dynamic contrast-enhanced (DCE) combined with T2-weighted (T2W) endorectal coil (ERC) magnetic resonance imaging (MRI) at 3 T for determining extracapsular extension (ECE) of prostate cancer. METHODS: In this IRB-approved study, ERC 3-T MRI of the prostate was performed in 108 patients before radical prostatectomy. T2W fast spin-echo and DCE 3D gradient echo images were acquired. The interpretations of readers with varied experience were analysed. MRI-based staging results were compared with radical prostatectomy histology. Descriptive statistics were generated for prediction of ECE and staging accuracies were determined by the area under the receiver-operating characteristic curve. RESULTS: The overall sensitivity, specificity, positive predictive value and negative predictive value for ECE were 75 %, 92 %, 79 % and 91 %, respectively. Diagnostic accuracy for staging was 86 %, 80 % and 91 % for all readers, experienced and less experienced readers, respectively. CONCLUSIONS: ERC 3-T MRI of the prostate combining DCE and T2W imaging is an accurate pretherapeutic staging tool for assessment of ECE in clinical practice across varying levels of reader experience. KEY POINTS : • Endorectal coil (ERC) magnetic resonance imaging is widely used for imaging prostatic disease. • ERC 3-T MRI is reasonably accurate for local prostate cancer staging. • High diagnostic accuracy is achievable across different levels of reader experience. • MRI facilitates therapeutic decisions in patients with prostate cancer.

Intravenous, contrast-enhanced MR colonography using air as endoluminal contrast agent: impact on colorectal polyp detection.

PURPOSE: To compare diagnostic accuracy and patient tolerance of MR colonography with intravenous contrast and luminal air (MRC) to conventional colonoscopy (CC). MATERIALS AND METHODS: IRB approval and written informed consent were obtained. Forty-six patients, both screening and symptomatic, underwent MRC followed by CC. The MRC technique employed 3D T1W spoiled gradient echo sequences performed after the administration of gadopenetate dimeglumine, with parallel imaging. The diagnostic accuracy and tolerance of patients for MRC was compared to CC. RESULTS: Twenty-four polyps were detected in eighteen patients with CC (5 polyps ≥ 10 mm, 4 polyps 6-9 mm, 15 polyps ≤ 5 mm). MRC was 66.7% (12/18) sensitive and 96.4% (27/28) specific for polyp detection on a per-patient basis. When analyzed by polyp size, sensitivity and specificity of MRC was 100% (5/5) and 100% (19/19), respectively, for lesions greater than 10mm, 100% (4/4) and 100% (20/20) for lesions 6-9 mm, and sensitivity of 20% (3/15) lesions less than 5mm. The sensitivity and specificity of MRC for detecting significant lesions (>6mm) was 100% (9/9) and 100% (15/15), respectively. Regarding tolerance of the exams, there were no significant differences between MRC and CC. Thirty-five percent (n=16) of patients preferred MRC as a future screening test compared to 33% (n=15) for CC. CONCLUSION: MRC using air as an intraluminal contrast agent is a feasible and well-tolerated technique for detecting colonic polyps ≥ 6 mm in size. Further studies are warranted.