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Purpose: Magnetic resonance imaging (MRI) has great potential to improve prostate cancer diagnosis; however, subtle differences between cancer and confounding conditions render prostate MRI interpretation challenging. The tissue collected from patients who undergo radical prostatectomy provides a unique opportunity to correlate histopathology images of the prostate with preoperative MRI to accurately map the extent of cancer from histopathology images onto MRI. We seek to develop an open-source, easy-to-use platform to align presurgical MRI and histopathology images of resected prostates in patients who underwent radical prostatectomy to create accurate cancer labels on MRI. Methods: Here, we introduce RAdiology Pathology Spatial Open-Source multi-Dimensional Integration (RAPSODI), the first open-source framework for the registration of radiology and pathology images. RAPSODI relies on three steps. First, it creates a three-dimensional (3D) reconstruction of the histopathology specimen as a digital representation of the tissue before gross sectioning. Second, RAPSODI registers corresponding histopathology and MRI slices. Third, the optimized transforms are applied to the cancer regions outlined on the histopathology images to project those labels onto the preoperative MRI. Results: We tested RAPSODI in a phantom study where we simulated various conditions, for example, tissue shrinkage during fixation. Our experiments showed that RAPSODI can reliably correct multiple artifacts. We also evaluated RAPSODI in 157 patients from three institutions that underwent radical prostatectomy and have very different pathology processing and scanning. RAPSODI was evaluated in 907 corresponding histpathology-MRI slices and achieved a Dice coefficient of 0.97 ± 0.01 for the prostate, a Hausdorff distance of 1.99 ± 0.70 mm for the prostate boundary, a urethra deviation of 3.09 ± 1.45 mm, and a landmark deviation of 2.80 ± 0.59 mm between registered histopathology images and MRI. Conclusion: Our robust framework successfully mapped the extent of cancer from histopathology slices onto MRI providing labels from training machine learning methods to detect cancer on MRI.

Automated methods for detecting prostate cancer and distinguishing indolent from aggressive disease on Magnetic Resonance Imaging (MRI) could assist in early diagnosis and treatment planning. Existing automated methods of prostate cancer detection mostly rely on ground truth labels with limited accuracy, ignore disease pathology characteristics observed on resected tissue, and cannot selectively identify aggressive (Gleason Pattern≥4) and indolent (Gleason Pattern=3) cancers when they co-exist in mixed lesions. In this paper, we present a radiology-pathology fusion approach, CorrSigNIA, for the selective identification and localization of indolent and aggressive prostate cancer on MRI. CorrSigNIA uses registered MRI and whole-mount histopathology images from radical prostatectomy patients to derive accurate ground truth labels and learn correlated features between radiology and pathology images. These correlated features are then used in a convolutional neural network architecture to detect and localize normal tissue, indolent cancer, and aggressive cancer on prostate MRI. CorrSigNIA was trained and validated on a dataset of 98 men, including 74 men that underwent radical prostatectomy and 24 men with normal prostate MRI. CorrSigNIA was tested on three independent test sets including 55 men that underwent radical prostatectomy, 275 men that underwent targeted biopsies, and 15 men with normal prostate MRI. CorrSigNIA achieved an accuracy of 80% in distinguishing between men with and without cancer, a lesion-level ROC-AUC of 0.81±0.31 in detecting cancers in both radical prostatectomy and biopsy cohort patients, and lesion-levels ROC-AUCs of 0.82±0.31 and 0.86±0.26 in detecting clinically significant cancers in radical prostatectomy and biopsy cohort patients respectively. CorrSigNIA consistently outperformed other methods across different evaluation metrics and cohorts. In clinical settings, CorrSigNIA may be used in prostate cancer detection as well as in selective identification of indolent and aggressive components of prostate cancer, thereby improving prostate cancer care by helping guide targeted biopsies, reducing unnecessary biopsies, and selecting and planning treatment.

INTRODUCTION AND OBJECTIVE:PIRADS version 2.1 is a standardized method for evaluating prostate multiparametric MRI (mpMRI). While a PIRADS score of 5 is highly specific for clinically significant p...

INTRODUCTION AND OBJECTIVE:MRI is a powerful tool for prostate cancer diagnosis, yet interobserver variability remains problematic and false positive and false negative findings are common. Trainin...

INTRODUCTION AND OBJECTIVE:Focal therapy for prostate cancer relies heavily on prostate MRI, highlighting the weaknesses of MRI alone for cancer detection. Prior studies comparing MRI to pathology ...

Purpose: While multi-parametric magnetic resonance imaging (MRI) shows great promise in assisting with prostate cancer diagnosis and localization, subtle differences in appearance between cancer and normal tissue lead to many false positive and false negative interpretations by radiologists. We sought to automatically detect aggressive cancer (Gleason pattern (Formula presented.) 4) and indolent cancer (Gleason pattern 3) on a per-pixel basis on MRI to facilitate the targeting of aggressive cancer during biopsy. Methods: We created the Stanford Prostate Cancer Network (SPCNet), a convolutional neural network model, trained to distinguish between aggressive cancer, indolent cancer, and normal tissue on MRI. Ground truth cancer labels were obtained by registering MRI with whole-mount digital histopathology images from patients who underwent radical prostatectomy. Before registration, these histopathology images were automatically annotated to show Gleason patterns on a per-pixel basis. The model was trained on data from 78 patients who underwent radical prostatectomy and 24 patients without prostate cancer. The model was evaluated on a pixel and lesion level in 322 patients, including six patients with normal MRI and no cancer, 23 patients who underwent radical prostatectomy, and 293 patients who underwent biopsy. Moreover, we assessed the ability of our model to detect clinically significant cancer (lesions with an aggressive component) and compared it to the performance of radiologists. Results: Our model detected clinically significant lesions with an area under the receiver operator characteristics curve of 0.75 for radical prostatectomy patients and 0.80 for biopsy patients. Moreover, the model detected up to 18% of lesions missed by radiologists, and overall had a sensitivity and specificity that approached that of radiologists in detecting clinically significant cancer. Conclusions: Our SPCNet model accurately detected aggressive prostate cancer. Its performance approached that of radiologists, and it helped identify lesions otherwise missed by radiologists. Our model has the potential to assist physicians in specifically targeting the aggressive component of prostate cancers during biopsy or focal treatment.

Magnetic Resonance Imaging (MRI) is increasingly used to localize prostate cancer, but the subtle features of cancer vs. normal tissue renders the interpretation of MRI challenging. Computational approaches have been proposed to detect prostate cancer, yet variation in intensity distribution across different scanners, and even on the same scanner, poses significant challenges to image analysis via computational tools, such as deep learning. In this study, we developed a conditional generative adversarial network (GAN) to normalize intensity distributions on prostate MRI. We used three methods to evaluate our GAN-normalization. First, we qualitatively compared the intensity of GAN-normalized images to the intensity distributions of statistically normalized images. Second, we visually examined the GAN-normalized images to ensure the appearance of the prostate and other structures were preserved. Finally, we quantitatively evaluated the performance of deep learning holistically nested edge detection (HED) networks to identify prostate cancer on MRI when using raw, statistically normalized, and GAN-normalized images. We found the detection network trained on GAN-normalized images achieved similar accuracy and area under the curve (AUC) scores when compared to the detection networks trained on raw and statistically normalized images. Conditional GANs may hence be an effective tool for normalizing intensity distribution on MRI and can be utilized to train downstream deep learning tasks.

Prostate MRI is increasingly used to help localize and target prostate cancer. Yet, the subtle differences in MRI appearance of cancer compared to normal tissue renders MRI interpretation challenging. Deep learning methods hold promise in automating the detection of prostate cancer on MRI, however such approaches require large, well-curated datasets. Although existing methods that employed fully convolutional neural networks have shown promising results, the lack of labeled data can reduce the generalization of these models. Self-supervised learning provides a promising avenue to learn semantic features from unlabeled data. In this study, we apply the self-supervised strategy of image context restoration to detect prostate cancer on MRI and show this improves model performance for two different architectures (U-Net and Holistically Nested Edge Detector) compared to their purely supervised counterparts. We train our models on MRI exams from 381 men with biopsy confirmed cancer. Our study showed self-supervised models outperform randomly initialized models on an independent test set in a variety of training settings. We performed 3 experiments, where we trained with 5%, 25% and 100% of our labeled data, and observed that the U-Net based pre-training and downstream task outperformed other models. We observed the best improvements when training with 5% of the labeled training data, our selfsupervised U-Nets improve per-pixel Area Under the Curve (AUC, 0.71 vs 0.83) and Dice Similarity coefficient (0.19 vs 0.53). When training with 100% of the data, our U-Net-based pretraining and detection achieved an AUC of 0.85 and Dice similarity coefficient of 0.57.

The interpretation of prostate MRI suffers from low agreement across radiologists due to the subtle differences between cancer and normal tissue. Image registration addresses this issue by accurately mapping the ground-truth cancer labels from surgical histopathology images onto MRI. Cancer labels achieved by image registration can be used to improve radiologists’ interpretation of MRI by training deep learning models for early detection of prostate cancer. A major limitation of current automated registration approaches is that they require manual prostate segmentations, which is a time-consuming task, prone to errors. This paper presents a weakly supervised approach for affine and deformable registration of MRI and histopathology images without requiring prostate segmentations. We used manual prostate segmentations and mono-modal synthetic image pairs to train our registration networks to align prostate boundaries and local prostate features. Although prostate segmentations were used during the training of the network, such segmentations were not needed when registering unseen images at inference time. We trained and validated our registration network with 135 and 10 patients from an internal cohort, respectively. We tested the performance of our method using 16 patients from the internal cohort and 22 patients from an external cohort. The results show that our weakly supervised method has achieved significantly higher registration accuracy than a state-of-the-art method run without prostate segmentations. Our deep learning framework will ease the registration of MRI and histopathology images by obviating the need for prostate segmentations.

The use of magnetic resonance-ultrasound fusion targeted biopsy improves diagnosis of aggressive prostate cancer. Fusion of ultrasound & magnetic resonance images (MRI) requires accurate prostate segmentations. In this paper, we developed a 2.5 dimensional deep learning model, ProGNet, to segment the prostate on T2-weighted magnetic resonance imaging (MRI). ProGNet is an optimized U-Net model that weighs three adjacent slices in each MRI sequence to segment the prostate in a 2.5D context. We trained ProGNet on 529 cases where experts annotated the whole gland (WG) on axial T2-weighted MRI prior to targeted prostate biopsy. In 132 cases, experts also annotated the central gland (CG) on MRI. After five-fold cross-validation, we found that for WG segmentation, ProGNet had a mean Dice similarity coefficient (DSC) of 0.91±0.02, sensitivity of 0.89±0.03, specificity of 0.97±0.00, and an accuracy of 0.95±0.01. For CG segmentation, ProGNet achieved a mean DSC 0.86±0.01, sensitivity of 0.84±0.03, specificity of 0.99±0.01, and an accuracy of 0.96±0.01. We then tested the generalizability of the model on the 60-case NCI-ISBI 2013 challenge dataset and on a local, independent 61-case test set. We achieved DSCs of 0.81±0.02 and 0.72±0.02 for WG and CG segmentation on the NCI-ISBI 2013 challenge dataset, and 0.83±0.01 and 0.75±0.01 for WG and CG segmentation on the local dataset. Model performance was excellent and outperformed state-of-art U-Net and holistically-nested edge detector (HED) networks in all three datasets.

INTRODUCTION AND OBJECTIVE:Image-guided destruction of individual prostate cancer foci while sparing benign prostate tissue is emerging as a viable alternative to radical prostatectomy (RP) or radi...

Magnetic Resonance Imaging (MRI) is widely used for screening and staging prostate cancer. However, many prostate cancers have subtle features which are not easily identifiable on MRI, resulting in missed diagnoses and alarming variability in radiologist interpretation. Machine learning models have been developed in an effort to improve cancer identification, but current models localize cancer using MRI-derived features, while failing to consider the disease pathology characteristics observed on resected tissue. In this paper, we propose CorrSigNet, an automated two-step model that localizes prostate cancer on MRI by capturing the pathology features of cancer. First, the model learns MRI signatures of cancer that are correlated with corresponding histopathology features using Common Representation Learning. Second, the model uses the learned correlated MRI features to train a Convolutional Neural Network to localize prostate cancer. The histopathology images are used only in the first step to learn the correlated features. Once learned, these correlated features can be extracted from MRI of new patients (without histopathology or surgery) to localize cancer. We trained and validated our framework on a unique dataset of 75 patients with 806 slices who underwent MRI followed by prostatectomy surgery. We tested our method on an independent test set of 20 prostatectomy patients (139 slices, 24 cancerous lesions, 1.12M pixels) and achieved a per-pixel sensitivity of 0.81, specificity of 0.71, AUC of 0.86 and a per-lesion AUC of \(0.96 \pm 0.07\), outperforming the current state-of-the-art accuracy in predicting prostate cancer using MRI.

INTRODUCTION AND OBJECTIVES:The role of multiparametric MRI in clinical care is rapidly expanding due to its ability to improve prostate cancer detection. However, MRI interpretation suffers from a...

Prostate magnetic resonance imaging (MRI) allows the detection and treatment planning of clinically significant cancers. However, indolent cancers, e.g., those with Gleason scores 3+3, are not readily distinguishable on MRI. Thus an image-guided biopsy is still required before proceeding with a radical treatment for aggressive tumors or considering active surveillance for indolent disease. The excision of the prostate as part of radical prostatectomy treatments provides a unique opportunity to correlate whole-mount histology slices with MRI. Through a careful spatial alignment of histology slices and MRI, the extent of aggressive and indolent disease can be mapped on MRI which allows one to investigate MRI-derived features that might be able to distinguish aggressive from indolent cancers. Here, we introduce a framework for the 3D spatial integration of radiology and pathology images in the prostate. Our approach, first, uses groupwise-registration methods to reconstruct the histology specimen prior to sectioning, and incorporates the MRI as a spatial constraint, and, then, performs a multi-modal 3D affine and deformable alignment between the reconstructed histology specimen and the MRI. We tested our approach on 15 studies and found a Dice similarity coefficient of 0.94±0.02 and a urethra deviation of 1.11±0.34 mm between the histology reconstruction and the MRI. Our robust framework successfully mapped the extent of disease from histology slices on MRI and created ground truth labels for characterizing aggressive and indolent disease on MRI.

Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI suffers from high inter-observer variability across radiologists, thereby contributing to missed clinically significant cancers, overdiagnosed low-risk cancers, and frequent false positives. Interpretation of MRI could be greatly improved by providing radiologists with an answer key that clearly shows cancer locations on MRI. Registration of histopathology images from patients who had radical prostatectomy to pre-operative MRI allows such mapping of ground truth cancer labels onto MRI. However, traditional MRI-histopathology registration approaches are computationally expensive and require careful choices of the cost function and registration hyperparameters. This paper presents ProsRegNet, a deep learning-based pipeline to accelerate and simplify MRI-histopathology image registration in prostate cancer. Our pipeline consists of image preprocessing, estimation of affine and deformable transformations by deep neural networks, and mapping cancer labels from histopathology images onto MRI using estimated transformations. We trained our neural network using MR and histopathology images of 99 patients from our internal cohort (Cohort 1) and evaluated its performance using 53 patients from three different cohorts (an additional 12 from Cohort 1 and 41 from two public cohorts). Results show that our deep learning pipeline has achieved more accurate registration results and is at least 20 times faster than a state-of-the-art registration algorithm. This important advance will provide radiologists with highly accurate prostate MRI answer keys, thereby facilitating improvements in the detection of prostate cancer on MRI. Our code is freely available at https://github.com/pimed//ProsRegNet., Comment: Accepted to Medical Image Analysis (MedIA)

Background: To identify computer extracted in vivo dynamic contrast enhanced (DCE) MRI markers associated with quantitative histomorphometric (QH) characteristics of microvessels and Gleason scores (GS) in prostate cancer. Methods: This study considered retrospective data from 23 biopsy confirmed prostate cancer patients who underwent 3 Tesla multiparametric MRI before radical prostatectomy (RP). Representative slices from RP specimens were stained with vascular marker CD31. Tumor extent was mapped from RP sections onto DCE MRI using nonlinear registration methods. Seventy-seven microvessel QH features and 18 DCE MRI kinetic features were extracted and evaluated for their ability to distinguish low from intermediate and high GS. The effect of temporal sampling on kinetic features was assessed and correlations between those robust to temporal resolution and microvessel features discriminative of GS were examined. Results: A total of 12 microvessel architectural features were discriminative of low and intermediate/high grade tumors with area under the receiver operating characteristic curve (AUC) > 0.7. These features were most highly correlated with mean washout gradient (WG) (max rho 52 0.62). Independent analysis revealed WG to be moderately robust to temporal resolution (intraclass correlation coefficient [ICC] 5 0.63) and WG variance, which was poorly correlated with microvessel features, to be predictive of low grade tumors (AUC 5 0.77). Enhancement ratio was the most robust (ICC 5 0.96) and discriminative (AUC 5 0.78) kinetic feature but was moderately correlated with microvessel features (max rho 52 0.52). Conclusion: Computer extracted features of prostate DCE MRI appear to be correlated with microvessel architecture and may be discriminative of low versus intermediate and high GS. J. MAGN. RESON. IMAGING 2015;00:000–000.

Early identification of PCa patients at risk for biochemical recurrence (BCR) post-therapy will potentially complement definitive therapy with either neo- or adjuvant therapy to improve prognosis. BCR post definitive therapy is often associated with disease progression that might cause a bulge in the prostate gland. In this work we explored if an atlas-based comparison approach reveals shape differences in the prostate capsule as observed on pre-treatment T2-weighted MRI between prostate cancer patients who do (BCR+) and do not (BCR−) have BCR following definitive therapy. A single center IRB approved study included 874 patients. Complete image datasets, clinically localized PCa, availability of Gleason score, data available for post-treatment PSA and follow-up for at least 3 years in patients without BCR were the inclusion criteria to select 77 patients out of the 874 patients. Further controlling for Gleason score, stage, age and to maintain equal number of cases for the BCR+ and BCR− categories, the total number of cases was reduced to 50. Manually segmented prostate capsules were aligned to a BCR− template for statistical comparison between the BCR+ and BCR− groups. Statistically significant shape difference between the two groups was observed towards the lateral and the posterior sides of prostate.

We seek to characterize differences in the shape of the prostate and the central gland (combined central and transitional zones) between men with biopsy confirmed prostate cancer and men who were identified as not having prostate cancer either on account of a negative biopsy or had pelvic imaging done for a non-prostate malignancy. T2w MRI from 70 men were acquired at three institutions. The cancer positive group (PCa+) comprised 35 biopsy positive (Bx+) subjects from three institutions (Gleason scores: 6–9, Stage: T1–T3). The negative group (PCa−) combined 24 biopsy negative (Bx−) from two institutions and 11 subjects diagnosed with rectal cancer but with no clinical or MRI indications of prostate cancer (Cl−). The boundaries of the prostate and central gland were delineated on T2w MRI by two expert raters and were used to construct statistical shape atlases for the PCa+, Bx− and Cl− prostates. An atlas comparison was performed via per-voxel statistical tests to localize shape differences (significance assessed at p

Aggressive cancers are known to induce field effect that affect large areas of cells at a tissue surface. This means that local deformation induced by the tumor as it grows could cause distensions in regions distant from the tumor, presumably even the surface of the organ within which the tumor is growing. In this work, we focused on evaluating whether more and less aggressive prostate cancers (i.e. tumors that subsequently resulted in disease recurrence or not) could differentially induce changes and distensions in the surface of the prostate capsule. Specifically we have developed the concept of a new imaging marker called FOrge features, that attempts to quantify the degree and nature of the deformation induced in the capsule surface on account of tumor growth and then sought to evaluate whether FOrge is predictive of the risk of biochemical recurrence in prostate cancer patients based off a pre-operative T2w MRI scan. The FOrge features were extracted from a spatially contextual surface of interest (SOI) of the prostate capsule, uniquely determined from statistically significant shape differences between prostate atlases constructed from patients who did (BCR+) and who did not (BCR−) undergo biochemical recurrence. A random forest classifier trained on the FOrge features extracted from atlas images (25 BCR+ and 25 BCR−) yielded an accuracy of 78% and an AUC of 0.72 in an independent validation set of 30 patients.

e16579 Background: Pre-treatment identification of biochemical recurrence (BCR) from MRI may enable the use of aggressive neo-adjuvant therapies for prostate cancer patients to improve prognosis. BCR is often associated with aggressive cancer growth and/or extra prostatic extension resulting in an irregular bulge and focal capsular retraction. This may induce differences in the shape of the prostate capsule between BCR positive (BCR+) and BCR negative (BCR-) patients as observed on MRI. In this work, we show that computer extracted shape features of the prostate capsule on MRI can identify patients that are at a risk of BCR post-treatment Methods: In a single centre IRB approved study, from a registry of 874 patients, availability of complete image datasets (T1w, T2w and ADC map); no treatment for PCa before MRI; presence of clinically localised PCa; availability of Gleason score; and data available for post-treatment PSA and follow-up for at least 3 years in patients without BCR were used as inclusion criteria to select 80 patients. The prostate capsule was manually segmented on T2w MRI by an experienced radiologist. Two atlases A+ and A- were created for BCR+ and BCR- patients respectively with similar Gleason score (6 to 9), similar numbers in each cohort (25 each) and similar tumor stage (T2 to T3). A t-test based analysis corrected for multiple comparison revealed statistically significant prostate shape differences between A+ and A- in surface of interest (SOI). Curvature features (magnitude and surface normal orientations) were extracted from SOI of the two cohorts. A random forest classifier was trained using the 50 training images (from A+ and A-) and validated using a hold-out validation set of 30 patients. Results: The inter-quartile range, variance, skewness and kurtosis of curvature magnitude and normal orientations were found to be predictive of BCR. The RF classifier trained using these features could predict BCR with an accuracy of 78% and an AUC of 0.71 in the validation set. Conclusions: Curvature magnitude and orientation features of the prostate capsule from the SOI may be predictive of BCR. In future a multi centre independent datasets will be used to validate the findings.

Laser interstitial thermal therapy (LITT) has recently shown great promise as a treatment strategy for localized, focal, low-grade, organ-confined prostate cancer (CaP). Additionally, LITT is compatible with multi-parametric magnetic resonance imaging (MP-MRI) which in turn enables (1) high resolution, accurate localization of ablation zones on in vivo MP-MRI prior to LITT, and (2) real-time monitoring of temperature changes in vivo via MR thermometry during LITT. In spite of rapidly increasing interest in the use of LITT for treating low grade, focal CaP, very little is known about treatment-related changes following LITT. There is thus a clear need for studying post-LITT changes via MP-MRI and consequently to attempt to (1) quantitatively identify MP-MRI markers predictive of favorable treatment response and longer term patient outcome, and (2) identify which MP-MRI markers are most sensitive to post-LITT changes in the prostate. In this work, we present the first attempt at examining focal treatment-related changes on a per-voxel basis (high resolution) via quantitative evaluation of MR parameters pre- and post-LITT. A retrospective cohort of MP-MRI data comprising both pre- and post-LITT T2-weighted (T2w) and diffusion-weighted (DWI) acquisitions was considered, where DWI MRI yielded an Apparent Diffusion Co-efficient (ADC) map. A spatially constrained affine registration scheme was implemented to first bring T2w and ADC images into alignment within each of the pre- and post-LITT acquisitions, following which the pre- and post-LITT acquisitions were aligned. Pre- and post-LITT MR parameters (T2w intensity, ADC value) were then standardized to a uniform scale (to correct for intensity drift) and then quantified via the raw intensity values as well as via texture features derived from T2w MRI. In order to quantify imaging changes as a result of LITT, absolute differences were calculated between the normalized pre- and post-LITT MRI parameters. Quantitatively combining the ADC and T2w MRI parameters enabled construction of an integrated MP-MRI difference map that was highly indicative of changes specific to the LITT ablation zone. Preliminary quantitative comparison of the changes in different MR parameters indicated that T2w texture may be highly sensitive as well as specific in identifying changes within the ablation zone pre- and post-LITT. Visual evaluation of the differences in T2w texture features pre- and post-LITT also appeared to provide an indication of LITT-related effects such as edema. Our preliminary results thus indicate great potential for non-invasive MP-MRI imaging markers for determining focal treatment related changes, and hence long- and short-term patient outcome.

In this study we explore the ability of a novel machine learning approach, in conjunction with computer-extracted features describing prostate cancer morphology on pre-treatment MRI, to predict whether a patient will develop biochemical recurrence within ten years of radiation therapy. Biochemical recurrence, which is characterized by a rise in serum prostate-specific antigen (PSA) of at least 2 ng/mL above the nadir PSA, is associated with increased risk of metastasis and prostate cancer-related mortality. Currently, risk of biochemical recurrence is predicted by the Kattan nomogram, which incorporates several clinical factors to predict the probability of recurrence-free survival following radiation therapy (but has limited prediction accuracy). Semantic attributes on T2w MRI, such as the presence of extracapsular extension and seminal vesicle invasion and surrogate measure- ments of tumor size, have also been shown to be predictive of biochemical recurrence risk. While the correlation between biochemical recurrence and factors like tumor stage, Gleason grade, and extracapsular spread are well- documented, it is less clear how to predict biochemical recurrence in the absence of extracapsular spread and for small tumors fully contained in the capsule. Computer{extracted texture features, which quantitatively de- scribe tumor micro-architecture and morphology on MRI, have been shown to provide clues about a tumor's aggressiveness. However, while computer{extracted features have been employed for predicting cancer presence and grade, they have not been evaluated in the context of predicting risk of biochemical recurrence. This work seeks to evaluate the role of computer-extracted texture features in predicting risk of biochemical recurrence on a cohort of sixteen patients who underwent pre{treatment 1.5 Tesla (T) T2w MRI. We extract a combination of first-order statistical, gradient, co-occurrence, and Gabor wavelet features from T2w MRI. To identify which of these T2w MRI texture features are potential independent prognostic markers of PSA failure, we implement a partial least squares (PLS) method to embed the data in a low{dimensional space and then use the variable importance in projections (VIP) method to quantify the contributions of individual features to classification on the PLS embedding. In spite of the poor resolution of the 1.5 T MRI data, we are able to identify three Gabor wavelet features that, in conjunction with a logistic regression classifier, yield an area under the receiver operating characteristic curve of 0.83 for predicting the probability of biochemical recurrence following radiation therapy. In comparison to both the Kattan nomogram and semantic MRI attributes, the ability of these three computer-extracted features to predict biochemical recurrence risk is demonstrated.

Multi-modal image registration is needed to align medical images collected from different protocols or imaging sources, thereby allowing the mapping of complementary information between images. One challenge of multimodal image registration is that typical similarity measures rely on statistical correlations between image intensities to determine anatomical alignment. The use of alternate image representations could allow for mapping of intensities into a space or representation such that the multimodal images appear more similar, thus facilitating their co-registration. In this work, we present a spectral embedding based registration (SERg) method that uses non-linearly embedded representations obtained from independent components of statistical texture maps of the original images to facilitate multimodal image registration. Our methodology comprises the following main steps: 1) image-derived textural representation of the original images, 2) dimensionality reduction using independent component analysis (ICA), 3) spectral embedding to generate the alternate representations, and 4) image registration. The rationale behind our approach is that SERg yields embedded representations that can allow for very different looking images to appear more similar, thereby facilitating improved co-registration. Statistical texture features are derived from the image intensities and then reduced to a smaller set by using independent component analysis to remove redundant information. Spectral embedding generates a new representation by eigendecomposition from which only the most important eigenvectors are selected. This helps to accentuate areas of salience based on modality-invariant structural information and therefore better identifies corresponding regions in both the template and target images. The spirit behind SERg is that image registration driven by these areas of salience and correspondence should improve alignment accuracy. In this work, SERg is implemented using Demons to allow the algorithm to more effectively register multimodal images. SERg is also tested within the free-form deformation framework driven by mutual information. Nine pairs of synthetic T1-weighted to T2-weighted brain MRI were registered under the following conditions: five levels of noise (0%, 1%, 3%, 5%, and 7%) and two levels of bias field (20% and 40%) each with and without noise. We demonstrate that across all of these conditions, SERg yields a mean squared error that is 81.51% lower than that of Demons driven by MRI intensity alone. We also spatially align twenty-six ex vivo histology sections and in vivo prostate MRI in order to map the spatial extent of prostate cancer onto corresponding radiologic imaging. SERg performs better than intensity registration by decreasing the root mean squared distance of annotated landmarks in the prostate gland via both Demons algorithm and mutual information-driven free-form deformation. In both synthetic and clinical experiments, the observed improvement in alignment of the template and target images suggest the utility of parametric eigenvector representations and hence SERg for multimodal image registration.

Radical prostatectomy (RP) and radiation therapy (RT) are the most common treatment options for prostate cancer (PCa). Despite advancements in radiation delivery and surgical procedures, RP and RT can result in failure rates as high as 40% and >25%, respectively. Treatment failure is characterized by biochemical recurrence (BcR), which is defined in terms of prostate specific antigen (PSA) concentrations and its variation following treatment. PSA is expected to decrease following treatment, thereby its presence in even small concentrations (e.g 0.2 ng/ml for surgery or 2 ng/ml over the nadir PSA for radiation therapy) is indicative of treatment failure. Early identification of treatment failure may enable the use of more aggressive or neo-adjuvant therapies. Moreover, predicting failure prior to treatment may spare the patient from a procedure that is unlikely to be successful. Our goal is to identify differences on pre-treatment MRI between patients who have BcR and those who remain disease-free at 5 years post-treatment. Specifically, we focus on (1) identifying statistically significant differences in MRI intensities, (2) establishing morphological differences in prostatic anatomic structures, and (3) comparing these differences with the natural variability of prostatic structures. In order to attain these objectives, we use an anatomically constrained registration framework to construct BcR and non-BcR statistical atlases based on the pre-treatment magnetic resonance images (MRI) of the prostate. The patients included in the atlas either underwent RP or RT and were followed up for at least 5 years. The BcR atlas was constructed from a combined population of 12 pre-RT 1.5 Tesla (T) MRI and 33 pre-RP 3T MRI from patients with BcR within 5 years of treatment. Similarly, the non-BcR atlas was built based on a combined cohort of 20 pre-RT 1.5T MRI and 41 pre-RP 3T MRI from patients who remain disease-free 5 years post treatment. We chose the atlas framework as it enables the mapping of MR images from all subjects into the same canonical space, while constructing both an imaging and a morphological statistical atlas. Such co-registration allowed us to perform voxel-by-voxel comparisons of MRI intensities and capsule and central gland morphology to identify statistically significant differences between the BcR and non-BcR patient populations. To assess whether the morphological differences are valid, we performed an additional experiment where we constructed sub-population atlases by randomly sampling RT patients to construct the BcR and non-BcR atlases. Following these experiments we observed that: (1) statistically significant MRI intensity differences exist between BcR and non-BcR patients, specifically on the border of the central gland; (2) statistically significant morphological differences are visible in the prostate and central gland, specifically in the proximity of the apex, and (3) the differences between the BcR and non-BcR cohorts in terms of shape appeared to be consistent across these sub-population atlases as observed in our RT atlases.

Item does not contain fulltext Laser interstitial thermal therapy (LITT) is a new therapeutic strategy being explored in prostate cancer (CaP), which involves focal ablation of organlocalized tumor via an interstitial laser fiber. While little is known about treatment-related changes following LITT, studying post-LITT changes via imaging is extremely significant for enabling early image-guided intervention and follow-up. In this work, we present the first attempt at examining focal treatment-related changes on a per-voxel basis via quantitative comparison of MRI features pre- and post-LITT, and hence identifying computerized MRI features that are highly sensitive as well as specific to post-LITT changes within the ablation zone in the prostate. A retrospective cohort of 5 patient datasets comprising both pre- and post-LITT T2-weighted (T2w) and diffusion-weighted (DWI) acquisitions was considered, where DWI MRI yielded an Apparent Diffusion Co-efficient (ADC) map. Our scheme involved (1) inter-protocol registration of T2w and ADC MRI, as well as inter-acquisition registration of pre- and post-LITT MRI, (2) quantitation of MRI parameters by correcting for intensity drift in order to examine tissuespecific response, and (3) quantification of the information captured by T2w MRI and ADC maps via texture and intensity features. Correction of parameter drift resulted in visually discernible improvements in highlighting tissue-specific response in different MRI features. Quantitative, voxel-wise comparison of the changes in different MRI features indicated that steerable and non-steerable gradient texture features, rather than the original T2w intensity and ADC values, were highly sensitive as well as specific in identifying changes within the ablation zone pre- and post-LITT. The highest ranked texture feature yielded a normalized percentage change of 186\% within the ablation zone and 43\% in a spatially distinct normal region, relative to its pre-LITT value. By comparison, both the original T2w intensity and ADC value demonstrated a markedly less sensitive and specific response to changes within the ablation zone. Qualitative as well as quantitative evaluation of co-occurrence texture features indicated the presence of LITT-related effects such as edema adjacent to the ablation zone, which were indiscernible on the original T2w and ADC images. Our preliminary results thus indicate great potential for non-invasive computerized MRI imaging features for determining focal treatment related changes, informing image-guided interventions, as well as predicting long- and short-term patient outcome.

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.

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.