Computerized analysis of prostate lesions in the peripheral zone using dynamic contrast enhanced MRI

Prostate lesion segmentation based on a 3D end-to-end convolution neural network with deep multi-scale attention

Prostate cancer is one of the deadest cancers among human beings. To better diagnose the prostate cancer, prostate lesion segmentation becomes a very important work, but its progress is very slow due to the prostate lesions small in size, irregular in shape, and blurred in contour. Therefore, automatic prostate lesion segmentation from mp-MRI is a great significant work and a challenging task. However, the most existing multi-step segmentation methods based on voxel-level classification are time-consuming, may introduce errors in different steps and lead to error accumulation. To decrease the computation time, harness richer 3D spatial features, and fuse the multi-level contextual information of mp-MRI, we present an automatic segmentation method in which all steps are optimized conjointly as one step to form our end-to-end convolutional neural network. The proposed end-to-end network DMSA-V-Net consists of two parts: (1) a 3D V-Net is used as the backbone network, it is the first attempt in employing 3D convolutional neural network for CS prostate lesion segmentation, (2) a deep multi-scale attention mechanism is introduced into the 3D V-Net which can highly focus on the ROI while suppressing the redundant background. As a merit, the attention can adaptively re-align the context information between the feature maps at different scales and the saliency maps in high-levels. We performed experiments based on five cross-fold validation with data including 97 patients. The results show that the Dice and sensitivity are 0.7014 and 0.8652 respectively, which demonstrates that our segmentation approach is more significant and accurate compared to other methods.

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Prostate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing, weighing and coupling of multiple images not only places additional burden on the radiologist, it also complicates the reviewing process. Prostate cancer imaging has therefore been an important target for the development of computer-aided diagnostic (CAD) tools. In this survey, we discuss the advances in CAD for prostate cancer over the last decades with special attention to the deep-learning techniques that have been designed in the last few years. Moreover, we elaborate and compare the methods employed to deliver the CAD output to the operator for further medical decision making.

Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI Space

Prostate cancer is the most common noncutaneous cancer in men in the United States. The current paradigm for screening and diagnosis is imperfect, with relatively low specificity, high cost, and high morbidity. This study aims to generate new image contrasts by learning a distribution of unique image signatures associated with prostate cancer. In total, 48 patients were prospectively recruited for this institutional review board–approved study. Patients underwent multiparametric magnetic resonance imaging 2 weeks before surgery. Postsurgical tissues were annotated by a pathologist and aligned to the in vivo imaging. Radiomic profiles were generated by linearly combining 4 image contrasts (T2, apparent diffusion coefficient [ADC] 0-1000, ADC 50-2000, and dynamic contrast-enhanced) segmented using global thresholds. The distribution of radiomic profiles in high-grade cancer, low-grade cancer, and normal tissues was recorded, and the generated probability values were applied to a naive test set. The resulting Gleason probability maps were stable regardless of training cohort, functioned independent of prostate zone, and outperformed conventional clinical imaging (area under the curve [AUC] = 0.79). Extensive overlap was seen in the most common image signatures associated with high- and low-grade cancer, indicating that low- and high-grade tumors present similarly on conventional imaging.

Diagnosis of Prostate Cancer by Use of MRI-Derived Quantitative Risk Maps: A Feasibility Study

OBJECTIVE. The purpose of this study was to develop a new quantitative image analysis tool for estimating the risk of cancer of the prostate by use of quantitative multiparametric MRI (mpMRI) metrics. MATERIALS AND METHODS. Thirty patients with biopsy-confirmed prostate cancer (PCa) who underwent preoperative 3-T mpMRI were included in the study. Quantitative mpMRI metrics-apparent diffusion coefficient (ADC), T2, and dynamic contrast-enhanced (DCE) signal enhancement rate (α)-were calculated on a voxel-by-voxel basis for the whole prostate and coregistered. A normalized risk value (0-100) for each mpMRI parameter was obtained, with high risk values associated with low T2 and ADC and high signal enhancement rate. The final risk score was calculated as a weighted sum of the risk scores (ADC, 40%; T2, 40%; DCE, 20%). Data from five patients were used as training set to find the threshold for predicting PCa. In the other 25 patients, any region with a minimum of 30 con-joint voxels (≈ 4.8 mm2) with final risk score above the threshold was considered positive for cancer. Lesion-based and sector-based analyses were performed by matching prostatectomyverified malignancy and PCa predicted with the risk analysis tool. RESULTS. The risk map tool had sensitivity of 76.6%, 89.2%, and 100% for detecting all lesions, clinically significant lesions (≥ Gleason 3 + 4), and index lesions, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value for PCa detection for all lesions in the sector-based analysis were 78.9%, 88.5%, 84.4%, and 84.1%, respectively, with an ROC AUC of 0.84. CONCLUSION. The risk analysis tool is effective for detecting clinically significant PCa with reasonable sensitivity and specificity in both peripheral and transition zones.

Multiparametric MRI and radiomics in prostate cancer: a review

Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging with one or more functional MRI sequences. It has become a versatile tool for detecting and characterising prostate cancer (PCa). The traditional role of mpMRI was confined to PCa staging, but due to the advanced imaging techniques, its role has expanded to various stages in clinical practises including tumour detection, disease monitor during active surveillance and sequential imaging for patient follow-up. Meanwhile, with the growing speed of data generation and the increasing volume of imaging data, it is highly demanded to apply computerised methods to process mpMRI data and extract useful information. Hence quantitative analysis for imaging data using radiomics has become an emerging paradigm. The application of radiomics approaches in prostate cancer has not only enabled automatic localisation of the disease but also provided a non-invasive solution to assess tumour biology (e.g. aggressiveness and the presence of hypoxia). This article reviews mpMRI and its expanding role in PCa detection, staging and patient management. Following that, an overview of prostate radiomics will be provided, with a special focus on its current applications as well as its future directions.

Contouring of prostate tumors on multiparametric MRI: Evaluation of clinical delineations in a multicenter radiotherapy trial

PURPOSE

To date no guidelines are available for contouring prostate cancer inside the gland, as visible on multiparametric (mp-) MRI. We assessed inter-institutional differences in interpretation of mp-MRI in the multicenter phase III FLAME trial.

METHODS

We analyzed clinical delineations on mp-MRI and clinical characteristics from 260 patients across three institutes. We performed a logistic regression analysis to examine each institute's weighting of T2w, ADC and K^trans intensity maps in the delineation of the cancer. As reviewing of all delineations by an expert panel is not feasible, we made a selection based on discrepancies between a published tumor probability (TP) model and each institute's clinical delineations using Areas Under the ROC Curve (AUC) analysis.

RESULTS

Regression coefficients for the three institutes were -0.07, -0.27 and -0.11 for T2w, -1.96, -0.53 and -0.65 for ADC and 0.15, 0.20 and 0.62 for K^trans, with significant differences between institutes for ADC and K^trans. AUC analysis showed median AUC values of 0.92, 0.80 and 0.79. Five patients with lowest AUC values were reviewed by a uroradiologist.

CONCLUSION

Regression coefficients revealed considerably different interpretations of mp-MRI in tumor contouring between institutes and demonstrated the need for contouring guidelines. Based on AUC values outlying delineations could efficiently be identified for review.

Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network

Automated methods for detecting clinically significant (CS) prostate cancer (PCa) in multi-parameter magnetic resonance images (mp-MRI) are of high demand. Existing methods typically employ several separate steps, each of which is optimized individually without considering the error tolerance of other steps. As a result, they could either involve unnecessary computational cost or suffer from errors accumulated over steps. In this paper, we present an automated CS PCa detection system, where all steps are optimized jointly in an end-to-end trainable deep neural network. The proposed neural network consists of concatenated subnets: 1) a novel tissue deformation network (TDN) for automated prostate detection and multimodal registration and 2) a dual-path convolutional neural network (CNN) for CS PCa detection. Three types of loss functions, i.e., classification loss, inconsistency loss, and overlap loss, are employed for optimizing all parameters of the proposed TDN and CNN. In the training phase, the two nets mutually affect each other and effectively guide registration and extraction of representative CS PCa-relevant features to achieve results with sufficient accuracy. The entire network is trained in a weakly supervised manner by providing only image-level annotations (i.e., presence/absence of PCa) without exact priors of lesions' locations. Compared with most existing systems which require supervised labels, e.g., manual delineation of PCa lesions, it is much more convenient for clinical usage. Comprehensive evaluation based on fivefold cross validation using 360 patient data demonstrates that our system achieves a high accuracy for CS PCa detection, i.e., a sensitivity of 0.6374 and 0.8978 at 0.1 and 1 false positives per normal/benign patient.

Performance of Ultrafast DCE-MRI for Diagnosis of Prostate Cancer

RATIONALE AND OBJECTIVES

This study aimed to test high temporal resolution dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for different zones of the prostate and evaluate its performance in the diagnosis of prostate cancer (PCa). Determine whether the addition of ultrafast DCE-MRI improves the performance of multiparametric MRI.

MATERIALS AND METHODS

Patients (n = 20) with pathologically confirmed PCa underwent preoperative 3T MRI with T2-weighted, diffusion-weighted, and high temporal resolution (~2.2 seconds) DCE-MRI using gadoterate meglumine (Guerbet, Bloomington, IN) without an endorectal coil. DCE-MRI data were analyzed by fitting signal intensity with an empirical mathematical model to obtain parameters: percent signal enhancement, enhancement rate (α), washout rate (β), initial enhancement slope, and enhancement start time along with apparent diffusion coefficient (ADC) and T2 values. Regions of interests were placed on sites of prostatectomy verified malignancy (n = 46) and normal tissue (n = 71) from different zones.

RESULTS

Cancer (α = 6.45 ± 4.71 s^-1, β = 0.067 ± 0.042 s^-1, slope = 3.78 ± 1.90 s^-1) showed significantly (P <.05) faster signal enhancement and washout rates than normal tissue (α = 3.0 ± 2.1 s^-1, β = 0.034 ± 0.050 s^-1, slope = 1.9 ± 1.4 s^-1), but showed similar percentage signal enhancement and enhancement start time. Receiver operating characteristic analysis showed area under the curve for DCE parameters was comparable to ADC and T2 in the peripheral (DCE 0.67-0.82, ADC 0.80, T2 0.89) and transition zones (DCE 0.61-0.72, ADC 0.69, T2 0.75), but higher in the central zone (DCE 0.79-0.88, ADC 0.45, T2 0.45) and anterior fibromuscular stroma (DCE 0.86-0.89, ADC 0.35, T2 0.12). Importantly, combining DCE with ADC and T2 increased area under the curve by ~30%, further improving the diagnostic accuracy of PCa detection.

CONCLUSION

Quantitative parameters from empirical mathematical model fits to ultrafast DCE-MRI improve diagnosis of PCa. DCE-MRI with higher temporal resolution may capture clinically useful information for PCa diagnosis that would be missed by low temporal resolution DCE-MRI. This new information could improve the performance of multiparametric MRI in PCa detection.

Computer-aided diagnosis of prostate cancer with MRI

Multi-parametric magnetic resonance imaging (mp-MRI) has an increasingly important role in the diagnosis of prostate cancer. Due to the large amount of data and variations in mp-MRI, tumor detection can be affected by multiple factors, such as the observer's clinical experience, image quality, and appearance of the lesions. In order to improve the quantitative assessment of the disease and reduce the reporting time, various computer-aided diagnosis (CAD) systems have been designed to help radiologists identify lesions. This manuscript presents an overview of the literature regarding prostate CAD using mp-MRI, while focusing on the studies of the most recent five years. Current prostate CAD technologies and their utilization are discussed in this review.

Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks

Automated methods for prostate cancer (PCa) diagnosis in multi-parametric magnetic resonance imaging (MP-MRIs) are critical for alleviating requirements for interpretation of radiographs while helping to improve diagnostic accuracy (Artan et al 2010 IEEE Trans. Image Process. 19 2444-55, Litjens et al 2014 IEEE Trans. Med. Imaging 33 1083-92, Liu et al 2013 SPIE Medical Imaging (International Society for Optics and Photonics) p 86701G, Moradi et al 2012 J. Magn. Reson. Imaging 35 1403-13, Niaf et al 2014 IEEE Trans. Image Process. 23 979-91, Niaf et al 2012 Phys. Med. Biol. 57 3833, Peng et al 2013a SPIE Medical Imaging (International Society for Optics and Photonics) p 86701H, Peng et al 2013b Radiology 267 787-96, Wang et al 2014 BioMed. Res. Int. 2014). This paper presents an automated method based on multimodal convolutional neural networks (CNNs) for two PCa diagnostic tasks: (1) distinguishing between cancerous and noncancerous tissues and (2) distinguishing between clinically significant (CS) and indolent PCa. Specifically, our multimodal CNNs effectively fuse apparent diffusion coefficients (ADCs) and T2-weighted MP-MRI images (T2WIs). To effectively fuse ADCs and T2WIs we design a new similarity loss function to enforce consistent features being extracted from both ADCs and T2WIs. The similarity loss is combined with the conventional classification loss functions and integrated into the back-propagation procedure of CNN training. The similarity loss enables better fusion results than existing methods as the feature learning processes of both modalities are mutually guided, jointly facilitating CNN to 'see' the true visual patterns of PCa. The classification results of multimodal CNNs are further combined with the results based on handcrafted features using a support vector machine classifier. To achieve a satisfactory accuracy for clinical use, we comprehensively investigate three critical factors which could greatly affect the performance of our multimodal CNNs but have not been carefully studied previously. (1) Given limited training data, how can these be augmented in sufficient numbers and variety for fine-tuning deep CNN networks for PCa diagnosis? (2) How can multimodal MP-MRI information be effectively combined in CNNs? (3) What is the impact of different CNN architectures on the accuracy of PCa diagnosis? Experimental results on extensive clinical data from 364 patients with a total of 463 PCa lesions and 450 identified noncancerous image patches demonstrate that our system can achieve a sensitivity of 89.85% and a specificity of 95.83% for distinguishing cancer from noncancerous tissues and a sensitivity of 100% and a specificity of 76.92% for distinguishing indolent PCa from CS PCa. This result is significantly superior to the state-of-the-art method relying on handcrafted features.

Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications

One in six men will develop prostate cancer in his lifetime. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs. Recently, imaging of prostate cancer has greatly advanced since the introduction of multiparametric magnetic resonance imaging (mp-MRI). Mp-MRI consists of T2-weighted sequences combined with functional sequences including dynamic contrast-enhanced MRI, diffusion-weighted MRI, and magnetic resonance spectroscopy imaging. Because of the big data and variations in imaging sequences, detection can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. To improve quantitative assessment of the disease, various computer-aided detection systems have been designed to help radiologists in their clinical practice. This review paper presents an overview of literatures on computer-aided detection of prostate cancer with mp-MRI, which include the technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

Evaluation of an Automated Analysis Tool for Prostate Cancer Prediction Using Multiparametric Magnetic Resonance Imaging

OBJECTIVE

To evaluate the diagnostic performance of an automated analysis tool for the assessment of prostate cancer based on multiparametric magnetic resonance imaging (mpMRI) of the prostate.

METHODS

A fully automated analysis tool was used for a retrospective analysis of mpMRI sets (T2-weighted, T1-weighted dynamic contrast-enhanced, and diffusion-weighted sequences). The software provided a malignancy prediction value for each image pixel, defined as Malignancy Attention Index (MAI) that can be depicted as a colour map overlay on the original images. The malignancy maps were compared to histopathology derived from a combination of MRI-targeted and systematic transperineal MRI/TRUS-fusion biopsies.

RESULTS

In total, mpMRI data of 45 patients were evaluated. With a sensitivity of 85.7% (with 95% CI of 65.4-95.0), a specificity of 87.5% (with 95% CI of 69.0-95.7) and a diagnostic accuracy of 86.7% (with 95% CI of 73.8-93.8) for detection of prostate cancer, the automated analysis results corresponded well with the reported diagnostic accuracies by human readers based on the PI-RADS system in the current literature.

CONCLUSION

The study revealed comparable diagnostic accuracies for the detection of prostate cancer of a user-independent MAI-based automated analysis tool and PI-RADS-scoring-based human reader analysis of mpMRI. Thus, the analysis tool could serve as a detection support system for less experienced readers. The results of the study also suggest the potential of MAI-based analysis for advanced lesion assessments, such as cancer extent and staging prediction.

Computer-aided diagnosis: detection and localization of prostate cancer within the peripheral zone

We propose a methodology for prostate cancer detection and localization within the peripheral zone based on combining multiple segmentation techniques. We extract four image features using Gaussian and median filters. Subsequently, we use each image feature separately to generate binary segmentations. Finally, we take the intersection of all four binary segmentations, incorporating a model of the peripheral zone, and perform erosion to remove small false-positive regions. The initial evaluation of this method is based on 275 MRI images from 37 patients, and 86% of the slices were classified correctly with 87% and 86% sensitivity and specificity achieved, respectively. This paper makes two contributions: firstly, a novel computer-aided diagnosis approach, which is based on combining multiple segmentation techniques using only a small number of simple image features, and secondly, the development of the proposed method and its application in prostate cancer detection and localization using a single MRI modality with the results comparable with the state-of-the-art multimodality and advanced computer vision methods in the literature. Copyright © 2015 John Wiley & Sons, Ltd.

Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model

Computer-aided diagnosis (CAD) systems have been proposed to assist radiologists in making diagnostic decisions by providing helpful information. As one of the most important sequences in prostate magnetic resonance imaging (MRI), image features from T2-weighted images (T2WI) were extracted and evaluated for the diagnostic performances by using CAD. We extracted 12 quantitative image features from prostate T2-weighted MR images. The importance of each feature in cancer identification was compared in the peripheral zone (PZ) and central gland (CG), respectively. The performance of the computer-aided diagnosis system supported by an artificial neural network was tested. With computer-aided analysis of T2-weighted images, many characteristic features with different diagnostic capabilities can be extracted. We discovered most of the features (10/12) had significant difference (P<0.01) between PCa and non-PCa in the PZ, while only five features (sum average, minimum value, standard deviation, 10th percentile, and entropy) had significant difference in CG. CAD prediction by features from T2w images can reach high accuracy and specificity while maintaining acceptable sensitivity. The outcome is convictive and helpful in medical diagnosis.

1.5-T multiparametric MRI using PI-RADS: a region by region analysis to localize the index-tumor of prostate cancer in patients undergoing prostatectomy

BACKGROUND

The use of multiparametric magnetic resonance imaging (mpMRI) to detect and localize prostate cancer has increased in recent years. In 2010, the European Society of Urogenital Radiology (ESUR) published guidelines for mpMRI and introduced the Prostate Imaging Reporting and Data System (PI-RADS) for scoring the different parameters.

PURPOSE

To evaluate the reliability and diagnostic performance of endorectal 1.5-T mpMRI using the PI-RADS to localize the index tumor of prostate cancer in patients undergoing prostatectomy.

MATERIAL AND METHODS

This institutional review board IRB-approved, retrospective study included 63 patients (mean age, 60.7 years, median PSA, 8.0). Three observers read mpMRI parameters (T2W, DWI, and DCE) using the PI-RADS, which were compared with the results from whole-mount histopathology that analyzed 27 regions of interest. Inter-observer agreement was calculated as well as sensitivity, specificity, positive predictive value (PPV), and negative predicted value (NPV) by dichotomizing the PI-RADS criteria scores ≥3. A receiver-operating curve (ROC) analysis was performed for the different MR parameters and overall score.

RESULTS

Inter-observer agreement on the overall score was 0.41. The overall score in the peripheral zone achieved sensitivities of 0.41, 0.60, and 0.55 with an NPV of 0.80, 0.84, and 0.83, and in the transitional zone, sensitivities of 0.26, 0.15, and 0.19 with an NPV of 0.92, 0.91, and 0.92 for Observers 1, 2, and 3, respectively. The ROC analysis showed a significantly increased area under the curve (AUC) for the overall score when compared to T2W alone for two of the three observers.

CONCLUSION

1.5 T mpMRI using the PI-RADS to localize the index tumor achieved moderate reliability and diagnostic performance.

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review

Prostate cancer is the second most diagnosed cancer of men all over the world. In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systems have been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field of research for the last 10 years. This survey aims to provide a comprehensive review of the state-of-the-art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aided system. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to the research community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey.

Computer-aided analysis of prostate multiparametric MR images: an unsupervised fusion-based approach

OBJECTIVE

The aim of this study is to provide an automatic framework for computer-aided analysis of multiparametric magnetic resonance (mp-MR) images of prostate.

METHOD

We introduce a novel method for the unsupervised analysis of the images. An evidential C-means classifier was adapted for use with a segmentation scheme to address multisource data and to manage conflicts and redundancy.

RESULTS

Experiments were conducted using data from 15 patients. The evaluation protocol consisted in evaluating the method abilities to classify prostate tissues, showing the same behaviour on the mp-MR images, into homogeneous classes. As the actual diagnosis was available, thanks to the correlation with histopathological findings, the assessment focused on the ability to segment cancer foci. The method exhibited global sensitivity and specificity of 70 and 88 %, respectively.

CONCLUSION

The preliminary results obtained by these initial experiments showed that the method can be applied in clinical routine practice to help making decision especially for practitioners with limited experience in prostate MRI analysis.

Final Gleason score prediction using discriminant analysis and support vector machine based on preoperative multiparametric MR imaging of prostate cancer at 3T

Objective. This study aimed at evaluating linear discriminant analysis (LDA) and support vector machine (SVM) classifiers for estimating final Gleason score preoperatively using multiparametric magnetic resonance imaging (mp-MRI) and clinical parameters. Materials and Methods. Thirty-three patients who underwent mp-MRI on a 3T clinical MR scanner and radical prostatectomy were enrolled in this study. The input features for classifiers were age, the presence of a palpable prostate abnormality, prostate specific antigen (PSA) level, index lesion size, and Likert scales of T2 weighted MRI (T2w-MRI), diffusion weighted MRI (DW-MRI), and dynamic contrast enhanced MRI (DCE-MRI) estimated by an experienced radiologist. SVM based recursive feature elimination (SVM-RFE) was used for eliminating features. Principal component analysis (PCA) was applied for data uncorrelation. Results. Using a standard PCA before final Gleason score classification resulted in mean sensitivities of 51.19% and 64.37% and mean specificities of 72.71% and 39.90% for LDA and SVM, respectively. Using a Gaussian kernel PCA resulted in mean sensitivities of 86.51% and 87.88% and mean specificities of 63.99% and 56.83% for LDA and SVM, respectively. Conclusion. SVM classifier resulted in a slightly higher sensitivity but a lower specificity than LDA method for final Gleason score prediction for prostate cancer for this limited patient population.

Computer aided-diagnosis of prostate cancer on multiparametric MRI: a technical review of current research

Prostate cancer (PCa) is the most commonly diagnosed cancer among men in the United States. In this paper, we survey computer aided-diagnosis (CADx) systems that use multiparametric magnetic resonance imaging (MP-MRI) for detection and diagnosis of prostate cancer. We review and list mainstream techniques that are commonly utilized in image segmentation, registration, feature extraction, and classification. The performances of 15 state-of-the-art prostate CADx systems are compared through the area under their receiver operating characteristic curves (AUC). Challenges and potential directions to further the research of prostate CADx are discussed in this paper. Further improvements should be investigated to make prostate CADx systems useful in clinical practice.

Correlation between dynamic contrast-enhanced MRI and quantitative histopathologic microvascular parameters in organ-confined prostate cancer

OBJECTIVES

To correlate pharmacokinetic parameters of 3-T dynamic contrast-enhanced (DCE-)MRI with histopathologic microvascular and lymphatic parameters in organ-confined prostate cancer.

METHODS

In 18 patients with unilateral peripheral zone (pT2a) tumours who underwent DCE-MRI prior to radical prostatectomy (RP), the following pharmacokinetic parameters were assessed: permeability surface area volume transfer constant (K (trans)), extravascular extracellular volume (Ve) and rate constant (K ep). In the RP sections blood and lymph vessels were visualised immunohistochemically and automatically examined and analysed. Parameters assessed included microvessel density (MVD), area (MVA) and perimeter (MVP) as well as lymph vessel density (LVD), area (LVA) and perimeter (LVP).

RESULTS

A negative correlation was found between age and K (trans) and K ep for tumour (r = -0.60, p = 0.009; r = -0.67, p = 0.002) and normal (r = -0.54, p = 0.021; r = -0.46, p = 0.055) tissue. No correlation existed between absolute values of microvascular parameters from histopathology and DCE-MRI. In contrast, the ratio between tumour and normal tissue (correcting for individual microvascularity variations) significantly correlated between K ep and MVD (r = 0.61, p = 0.007) and MVP (r = 0.54, p = 0.022). The lymphovascular parameters showed only a correlation between LVA and K ep (r = -0.66, p = 0.003).

CONCLUSIONS

Significant correlations between DCE-MRI and histopathologic parameters were found when correcting for interpatient variations in microvascularity.

KEY POINTS

• Normal prostate tissue shows strong heterogeneity in microvascularity. • Peripheral zone prostate cancer shows increased and less heterogeneous microvascularity. • Normal and tumour tissue shows considerable variation in microvascularity between patients. • DCE-MRI should take into account the interprostatic heterogeneity of microvasculature between patients.

Multi-modality functional image guided dose escalation in the presence of uncertainties

BACKGROUND AND PURPOSE

In order to increase local tumour control by radiotherapy without increasing toxicity, it appears promising to harness functional imaging (FI) to guide dose to sub-volumes of the target with a high tumour load and perhaps de-escalate dose to low risk volumes, in order to maximise the efficiency of the deposited radiation dose.

METHODS AND MATERIALS

A number of problems have to be solved to make focal dose escalation (FDE) efficient and safe: (1) how to combine ambiguous information from multiple imaging modalities; (2) how to take into account uncertainties of FI based tissue classification; (3) how to account for geometric uncertainties in treatment delivery; (4) how to add complementary FI modalities to an existing scheme. A generic optimisation concept addresses these points and is explicitly designed for clinical efficacy and for lowering the implementation threshold to FI-guided FDE. It combines classic tumour control probability modelling with a multi-variate logistic regression model of FI accuracy and an uncomplicated robust optimisation method.

RESULTS

Its key elements are (1) that dose is deposited optimally when it achieves equivalent expected effect everywhere in the target volume and (2) that one needs to cap the certainty about the absence of tumour anywhere in the target region. For illustration, an example of a PET/MR-guided FDE in prostate cancer is given.

CONCLUSIONS

FDE can be safeguarded against FI uncertainties, at the price of a limit on the sensible dose escalation.

Atherosclerotic plaque component segmentation in combined carotid MRI and CTA data incorporating class label uncertainty

Atherosclerotic plaque composition can indicate plaque vulnerability. We segment atherosclerotic plaque components from the carotid artery on a combination of in vivo MRI and CT-angiography (CTA) data using supervised voxelwise classification. In contrast to previous studies the ground truth for training is directly obtained from 3D registration with histology for fibrous and lipid-rich necrotic tissue, and with μCT for calcification. This registration does, however, not provide accurate voxelwise correspondence. We therefore evaluate three approaches that incorporate uncertainty in the ground truth used for training: I) soft labels are created by Gaussian blurring of the original binary histology segmentations to reduce weights at the boundaries between components, and are weighted by the estimated registration accuracy of the histology and in vivo imaging data (measured by overlap), II) samples are weighted by the local contour distance of the lumen and outer wall between histology and in vivo data, and III) 10% of each class is rejected by Gaussian outlier rejection. Classification was evaluated on the relative volumes (% of tissue type in the vessel wall) for calcified, fibrous and lipid-rich necrotic tissue, using linear discriminant (LDC) and support vector machine (SVM) classification. In addition, the combination of MRI and CTA data was compared to using only one imaging modality. Best results were obtained by LDC and outlier rejection: the volume error per vessel was 0.9±1.0% for calcification, 12.7±7.6% for fibrous and 12.1±8.1% for necrotic tissue, with Spearman rank correlation coefficients of 0.91 (calcification), 0.80 (fibrous) and 0.81 (necrotic). While segmentation using only MRI features yielded low accuracy for calcification, and segmentation using only CTA features yielded low accuracy for necrotic tissue, the combination of features from MRI and CTA gave good results for all studied components.

Diffusion-weighted magnetic resonance imaging in the prostate transition zone: histopathological validation using magnetic resonance-guided biopsy specimens

OBJECTIVES

The objective of this study was to evaluate the apparent diffusion coefficient (ADC) of diffusion-weighted magnetic resonance (MR) imaging for the differentiation of transition zone cancer from non-cancerous transition zone with and without prostatitis and for the differentiation of transition zone cancer Gleason grade (GG) using MR-guided biopsy specimens as a reference standard.

MATERIALS AND METHODS

From consecutive MR-guided prostate biopsies (2008-2012) in our referral center, we retrospectively included patients from whom diffusion-weighted MR imaging ADC values were acquired during MR-guided biopsy and whose biopsy cores had a (cancer) core length 10 mm or greater and originated from the transition zone. Two radiologists, who were blinded to the ADC data, annotated regions of interest on biopsy sampling locations of MR-guided biopsy confirmation scans in consensus. Median ADC (mADC) of the regions of interest was related to histopathology outcome in MR-guided biopsy core specimens. Mixed model analysis was used to evaluate mADC differences between 7 histopathology categories predefined as MR-guided biopsy core specimens with primary and secondary GG 4-5 (I), primary GG 4-5 secondary GG 2-3 (II), primary GG 2-3 secondary GG 4-5 (III) and primary and secondary GG 2-3 cancer (IV), and noncancerous tissue without (V) or with degree 1 (VI) or degree 2 prostatitis (VII). Diagnostic accuracy was evaluated using areas under the receiver operating characteristic (AUC) curve.

RESULTS

Fifty-two patients with 87 cancer-containing biopsy cores and 53 patients with 101 non-cancerous biopsy cores were included. Significant mean mADC differences were present between cancers (mean mADC, 0.77-0.86 × 10 mm/s) and noncancerous transition zone without (1.12 × 10 mm/s) and with degree 1 to 2 prostatitis (1.05-1.12 × 10 mm/s; P < 0.0001-0.05). Exceptions were mixed primary and secondary GG cancers versus a degree 2 of prostatitis (P = 0.06-0.09). No significant differences were found between subcategories of primary and secondary GG cancers (P = 0.17-0.91) and between a degree 1 and 2 prostatitis and non-cancerous transition zone without prostatitis (P = 0.48-0.94).The mADC had an AUC of 0.84 to differentiate cancer versus non-cancerous transition zone. AUCs of 0.84 and 0.56 were found for mADC to differentiate prostatitis from cancer and from non-cancerous transition zone. The mADC had an AUC of 0.62 to differentiate a primary GG 4 versus GG 3 cancer.

CONCLUSIONS

The mADC values can differentiate transition zone cancer from non-cancerous transition zone and from a degree 1, and from most cases of a degree 2 prostatitis. However, because of substantial overlap, mADC has a moderate accuracy to differentiate between different primary and secondary GG subcategories and cannot be used to differentiate non-cancerous transition zone from degrees 1 to 2 of prostatitis. Diffusion-weighted imaging ADC may therefore contribute in the detection of transition zone cancers; however, as a single functional MR imaging technique, diffusion-weighted imaging has a moderate diagnostic accuracy in separating higher from lower GG transition zone cancers and in differentiating prostatitis from non-cancerous transition zone.

Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma

Robust detection of prostatic cancer is a challenge due to the multitude of variants and their representation in MR images. We propose a pattern recognition system with an incremental learning ensemble algorithm using support vector machines (SVM) tackling this problem employing multimodal MR images and a texture-based information strategy. The proposed system integrates anatomic, texture, and functional features. The data set was preprocessed using B-Spline interpolation, bias field correction and intensity standardization. First- and second-order angular independent statistical approaches and rotation invariant local phase quantization (RI-LPQ) were utilized to quantify texture information. An incremental learning ensemble SVM was implemented to suit working conditions in medical applications and to improve effectiveness and robustness of the system. The probability estimation of cancer structures was calculated using SVM and the corresponding optimization was carried out with a heuristic method together with a 3-fold cross-validation methodology. We achieved an average sensitivity of 0.844 ± 0.068 and a specificity of 0.780 ± 0.038, which yielded superior or similar performance to current state of the art using a total database of only 41 slices from twelve patients with histological confirmed information, including cancerous, unhealthy non-cancerous and healthy prostate tissue. Our results show the feasibility of an ensemble SVM being able to learn additional information from new data while preserving previously acquired knowledge and preventing unlearning. The use of texture descriptors provides more salient discriminative patterns than the functional information used. Furthermore, the system improves selection of information, efficiency and robustness of the classification. The generated probability map enables radiologists to have a lower variability in diagnosis, decrease false negative rates and reduce the time to recognize and delineate structures in the prostate.

Assessment of prostate cancer aggressiveness using dynamic contrast-enhanced magnetic resonance imaging at 3 T

BACKGROUND

A challenge in the diagnosis of prostate cancer (PCa) is the accurate assessment of aggressiveness.

OBJECTIVE

To validate the performance of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of the prostate at 3 tesla (T) for the assessment of PCa aggressiveness, with prostatectomy specimens as the reference standard.

DESIGN, SETTINGS, AND PARTICIPANTS

A total of 45 patients with PCa scheduled for prostatectomy were included. This study was approved by the institutional review board; the need for informed consent was waived.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Subjects underwent a clinical MRI protocol including DCE-MRI. Blinded to DCE-images, PCa was indicated on T2-weighted images based on histopathology results from prostatectomy specimens with the use of anatomical landmarks for the precise localization of the tumor. PCa was classified as low-, intermediate-, or high-grade, according to Gleason score. DCE-images were used as an overlay on T2-weighted images; mean and quartile values from semi-quantitative and pharmacokinetic model parameters were extracted per tumor region. Statistical analysis included Spearman's ρ, the Kruskal-Wallis test, and a receiver operating characteristics (ROC) analysis.

RESULTS AND LIMITATIONS

Significant differences were seen for the mean and 75th percentile (p75) values of wash-in (p = 0.024 and p = 0.017, respectively), mean wash-out (p = 0.044), and p75 of transfer constant (K(trans)) (p = 0.035), all between low-grade and high-grade PCa in the peripheral zone. ROC analysis revealed the best discriminating performance between low-grade versus intermediate-grade plus high-grade PCa in the peripheral zone for p75 of wash-in, K(trans), and rate constant (Kep) (area under the curve: 0.72). Due to a limited number of tumors in the transition zone, a definitive conclusion for this region of the prostate could not be drawn.

CONCLUSIONS

Quantitative parameters (K(trans) and Kep) and semi-quantitative parameters (wash-in and wash-out) derived from DCE-MRI at 3 T have the potential to assess the aggressiveness of PCa in the peripheral zone. P75 of wash-in, K(trans), and Kep offer the best possibility to discriminate low-grade from intermediate-grade plus high-grade PCa.

Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score--a computer-aided diagnosis development study

PURPOSE

To evaluate the potential utility of a number of parameters obtained at T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced multiparametric magnetic resonance (MR) imaging for computer-aided diagnosis (CAD) of prostate cancer and assessment of cancer aggressiveness.

MATERIALS AND METHODS

In this institutional review board-approved HIPAA-compliant study, multiparametric MR images were acquired with an endorectal coil in 48 patients with prostate cancer (median age, 62.5 years; age range, 44-73 years) who subsequently underwent prostatectomy. A radiologist and a pathologist identified 104 regions of interest (ROIs) (61 cancer ROIs, 43 normal ROIs) based on correlation of histologic and MR findings. The 10th percentile and average apparent diffusion coefficient (ADC) values, T2-weighted signal intensity histogram skewness, and Tofts K(trans) were analyzed, both individually and combined, via linear discriminant analysis, with receiver operating characteristic curve analysis with area under the curve (AUC) as figure of merit, to distinguish cancer foci from normal foci. Spearman rank-order correlation (ρ) was calculated between cancer foci Gleason score (GS) and image features.

RESULTS

AUC (maximum likelihood estimate ± standard error) values in the differentiation of prostate cancer from normal foci of 10th percentile ADC, average ADC, T2-weighted skewness, and K(trans) were 0.92 ± 0.03, 0.89 ± 0.03, 0.86 ± 0.04, and 0.69 ± 0.04, respectively. The combination of 10th percentile ADC, average ADC, and T2-weighted skewness yielded an AUC value for the same task of 0.95 ± 0.02. GS correlated moderately with 10th percentile ADC (ρ = -0.34, P = .008), average ADC (ρ = -0.30, P = .02), and K(trans) (ρ = 0.38, P = .004).

CONCLUSION

The combination of 10th percentile ADC, average ADC, and T2-weighted skewness with CAD is promising in the differentiation of prostate cancer from normal tissue. ADC image features and K(trans) moderately correlate with GS.

Prostate cancer: computer-aided diagnosis with multiparametric 3-T MR imaging--effect on observer performance

PURPOSE

To determine the effect of computer-aided diagnosis (CAD) on less-experienced and experienced observer performance in differentiation of benign from malignant prostate lesions at 3-T multiparametric magnetic resonance (MR) imaging.

MATERIALS AND METHODS

The institutional review board waived the need for informed consent. Retrospectively, 34 patients were included who had prostate cancer and had undergone multiparametric MR imaging, including T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced MR imaging prior to radical prostatectomy. Six radiologists less experienced in prostate imaging and four radiologists experienced in prostate imaging were asked to characterize different regions suspicious for cancer as benign or malignant on multiparametric MR images first without and subsequently with CAD software. The effect of CAD was analyzed by using a multiple-reader, multicase, receiver operating characteristic analysis and a linear mixed-model analysis.

RESULTS

In 34 patients, 206 preannotated regions, including 67 malignant and 64 benign regions in the peripheral zone (PZ) and 19 malignant and 56 benign regions in the transition zone (TZ), were evaluated. Stand-alone CAD had an overall area under the receiver operating characteristic curve (AUC) of 0.90. For PZ and TZ lesions, the AUCs were 0.92 and 0.87, respectively. Without CAD, less-experienced observers had an overall AUC of 0.81, which significantly increased to 0.91 (P = .001) with CAD. For experienced observers, the AUC without CAD was 0.88, which increased to 0.91 (P = .17) with CAD. For PZ lesions, less-experienced observers increased their AUC from 0.86 to 0.95 (P < .001) with CAD. Experienced observers showed an increase from 0.91 to 0.93 (P = .13). For TZ lesions, less-experienced observers significantly increased their performance from 0.72 to 0.79 (P = .01) with CAD and experienced observers increased their performance from 0.81 to 0.82 (P = .42).

CONCLUSION

Addition of CAD significantly improved the performance of less-experienced observers in distinguishing benign from malignant lesions; when less-experienced observers used CAD, they reached similar performance as experienced observers. The stand-alone performance of CAD was similar to performance of experienced observers.

Prostate cancer aggressiveness: in vivo assessment of MR spectroscopy and diffusion-weighted imaging at 3 T

PURPOSE

To determine the individual and combined performance of magnetic resonance (MR) spectroscopic imaging and diffusion-weighted (DW) imaging at 3 T in the in vivo assessment of prostate cancer aggressiveness by using histopathologically defined regions of interest on radical prostatectomy specimens to define the prostate cancer regions to be investigated.

MATERIALS AND METHODS

The local institutional ethics review board approved this retrospective study and waived the informed consent requirement. Fifty-four patients with biopsy-proved prostate cancer underwent clinical MR spectroscopic imaging followed by prostatectomy. Guided by the histopathologic map, all spectroscopy voxels that contained tumor tissue were selected, and metabolite ratios (choline [Cho] plus creatine [Cr]-to-citrate [Cit] and Cho/Cr ratios) were derived. For each spectroscopic voxel, 25th percentile apparent diffusion coefficient (ADC) of the region corresponding to that voxel was determined, representing the most aberrant tumor part on the ADC map, which was often smaller than spectroscopic imaging voxels. Maximum metabolic ratios and minimum 25th percentile ADC of each tumor were related to tumor aggressiveness and were used to differentiate aggressiveness classes. A logistic regression model (LRM) was used to combine data from both modalities.

RESULTS

Significant correlation was found between aggressiveness classes and maximum Cho+Cr/Cit ratio (ρ=0.36), maximum Cho/Cr ratio (ρ=0.35), and minimum 25th percentile ADC (ρ=-0.63) in the peripheral zone (PZ). In the transition zone (TZ), the correlation was significant for only Cho+Cr/Cit and Cho/Cr ratios (ρ=0.58 and ρ=0.60, respectively). For differentiation between aggressiveness classes, LRM use did not result in significantly improved differentiation over any individual variables.

CONCLUSION

These findings enabled confirmation that MR spectroscopic imaging and DW imaging offer potential for in vivo noninvasive assessment of prostate cancer aggressiveness, and both modalities have comparable performance. The combination did not result in better performance. Nonetheless, the better performances of metabolite ratios in the TZ and of ADCs in the PZ suggest that they have complementary value.

Functional MRI for radiotherapy dose painting

Modern radiation therapy techniques are exceptionally flexible in the deposition of radiation dose in a target volume. Complex distributions of dose can be delivered reliably, so that the tumor is exposed to a high dose, whereas nearby healthy structures can be avoided. As a result, an increase in curative dose is no longer invariably associated with an increased level of toxicity. This modern technology can be exploited further by modulating the required dose in space so as to match the variation in radiation sensitivity in the tumor. This approach is called dose painting. For dose painting to be effective, functional imaging techniques are essential to identify regions in a tumor that require a higher dose. Several techniques are available in nuclear medicine and radiology. In recent years, there has been a considerable research effort concerning the integration of magnetic resonance imaging (MRI) into the external radiotherapy workflow motivated by the superior soft tissue contrast as compared to computed tomography. In MRI, diffusion-weighted MRI reflects the cell density of tissue and thus may indicate regions with a higher tumor load. Dynamic contrast-enhanced MRI reflects permeability of the microvasculature and blood flow, correlated to the oxygenation of the tumor. These properties have impact on its radiation sensitivity. New questions must be addressed when these techniques are applied in radiation therapy: scanning in treatment position requires alternative solutions to the standard patient setup in the choice of receive coils compared to a diagnostic department. This standard positioning also facilitates repeated imaging. The geometrical accuracy of MR images is critical for high-precision radiotherapy. In particular, when multiparametric functional data are used for dose painting, quantification of functional parameters at a high spatial resolution becomes important. In this review, we will address these issues and describe clinical developments in MRI-guided dose painting.

Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis

In this paper, a fully automatic computer-aided detection (CAD) method is proposed for the detection of prostate cancer. The CAD method consists of multiple sequential steps in order to detect locations that are suspicious for prostate cancer. In the initial stage, a voxel classification is performed using a Hessian-based blob detection algorithm at multiple scales on an apparent diffusion coefficient map. Next, a parametric multi-object segmentation method is applied and the resulting segmentation is used as a mask to restrict the candidate detection to the prostate. The remaining candidates are characterized by performing histogram analysis on multiparametric MR images. The resulting feature set is summarized into a malignancy likelihood by a supervised classifier in a two-stage classification approach. The detection performance for prostate cancer was tested on a screening population of 200 consecutive patients and evaluated using the free response operating characteristic methodology. The results show that the CAD method obtained sensitivities of 0.41, 0.65 and 0.74 at false positive (FP) levels of 1, 3 and 5 per patient, respectively. In conclusion, this study showed that it is feasible to automatically detect prostate cancer at a FP rate lower than systematic biopsy. The CAD method may assist the radiologist to detect prostate cancer locations and could potentially guide biopsy towards the most aggressive part of the tumour.

Prostate cancer detection on dynamic contrast-enhanced MRI: computer-aided diagnosis versus single perfusion parameter maps

OBJECTIVE

The purpose of this article is to assess the value of computer-aided diagnosis (CAD) for prostate cancer detection on dynamic contrast-enhanced MRI (DCE-MRI).

MATERIALS AND METHODS

DCE-MRI examinations of 42 patients with prostate cancer were used to generate perfusion parameters, including baseline and peak signal intensities, initial slope, maximum slope within the initial 50 seconds after the contrast injection (slope(50)), wash-in rate, washout rate, time to peak, percentage of relative enhancement, percentage enhancement ratio, time of arrival, efflux rate constant from the extravascular extracellular space to the blood plasma (k(ep)), first-order rate constant for eliminating gadopentetate dimeglumine from the blood plasma (k(el)), and constant depending on the properties of the tissue and represented by the size of the extravascular extracellular space (A(H)). CAD for cancer detection was established by comprehensive evaluation of parameters using a support vector machine. The diagnostic accuracy of single perfusion parameters was estimated using receiver operating characteristic analysis, which determined threshold and parametric maps for cancer detection. The diagnostic performance of CAD for cancer detection was compared with those of T2-weighted imaging (T2WI) and single perfusion parameter maps, using histologic results as the reference standard.

RESULTS

The accuracy, sensitivity, and specificity of CAD were 83%, 77%, and 77%, respectively, in the entire prostate; 77%, 91%, and 64%, respectively, in the transitional zone; and 89%, 89%, and 89%, respectively, in the peripheral zone. Values for k(ep), k(el), initial slope, slope(50), wash-in rate, washout rate, and time to peak showed greater area under the curve values (0.803-0.888) than did the other parameters (0.545-0.665) (p < 0.01) and were compared with values for CAD. In the entire prostate, accuracy was greater for CAD than for all perfusion parameters or T2WI (63-77%); sensitivity was greater for CAD than for T2WI, initial slope, wash-in rate, slope(50), and washout rate (38-77%); and specificity was greater for CAD than for T2WI, k(ep), k(el), and time to peak (59-68%) (p < 0.05).

CONCLUSION

CAD can improve the diagnostic performance of DCE-MRI in prostate cancer detection, which may vary according to zonal anatomy.

Pathologic validation of a model based on diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging for tumor delineation in the prostate peripheral zone

PURPOSE

For focal boost strategies in the prostate, the robustness of magnetic resonance imaging-based tumor delineations needs to be improved. To this end we developed a statistical model that predicts tumor presence on a voxel level (2.5×2.5×2.5 mm3) inside the peripheral zone. Furthermore, we show how this model can be used to derive a valuable input for radiotherapy treatment planning.

METHODS AND MATERIALS

The model was created on 87 radiotherapy patients. For the validation of the voxelwise performance of the model, an independent group of 12 prostatectomy patients was used. After model validation, the model was stratified to create three different risk levels for tumor presence: gross tumor volume (GTV), high-risk clinical target volume (CTV), and low-risk CTV.

RESULTS

The model gave an area under the receiver operating characteristic curve of 0.70 for the prediction of tumor presence in the prostatectomy group. When the registration error between magnetic resonance images and pathologic delineation was taken into account, the area under the curve further improved to 0.89. We propose that model outcome values with a high positive predictive value can be used to define the GTV. Model outcome values with a high negative predictive value can be used to define low-risk CTV regions. The intermediate outcome values can be used to define a high-risk CTV.

CONCLUSIONS

We developed a logistic regression with a high diagnostic performance for voxelwise prediction of tumor presence. The model output can be used to define different risk levels for tumor presence, which in turn could serve as an input for dose planning. In this way the robustness of tumor delineations for focal boost therapy can be greatly improved.

Prostate cancer: multiparametric MR imaging for detection, localization, and staging

This review presents the current state of the art regarding multiparametric magnetic resonance (MR) imaging of prostate cancer. Technical requirements and clinical indications for the use of multiparametric MR imaging in detection, localization, characterization, staging, biopsy guidance, and active surveillance of prostate cancer are discussed. Although reported accuracies of the separate and combined multiparametric MR imaging techniques vary for diverse clinical prostate cancer indications, multiparametric MR imaging of the prostate has shown promising results and may be of additional value in prostate cancer localization and local staging. Consensus on which technical approaches (field strengths, sequences, use of an endorectal coil) and combination of multiparametric MR imaging techniques should be used for specific clinical indications remains a challenge. Because guidelines are currently lacking, suggestions for a general minimal protocol for multiparametric MR imaging of the prostate based on the literature and the authors' experience are presented. Computer programs that allow evaluation of the various components of a multiparametric MR imaging examination in one view should be developed. In this way, an integrated interpretation of anatomic and functional MR imaging techniques in a multiparametric MR imaging examination is possible. Education and experience of specialist radiologists are essential for correct interpretation of multiparametric prostate MR imaging findings. Supportive techniques, such as computer-aided diagnosis are needed to obtain a fast, cost-effective, easy, and more reproducible prostate cancer diagnosis out of more and more complex multiparametric MR imaging data.

HistoStitcher(©): an interactive program for accurate and rapid reconstruction of digitized whole histological sections from tissue fragments

We present an interactive program called HistoStitcher(©) for accurate and rapid reassembly of histology fragments into a pseudo-whole digitized histological section. HistoStitcher(©) provides both an intuitive graphical interface to assist the operator in performing the stitch of adjacent histology fragments by selecting pairs of anatomical landmarks, and a set of computational routines for determining and applying an optimal linear transformation to generate the stitched image. Reconstruction of whole histological sections from images of slides containing smaller fragments is required in applications where preparation of whole sections of large tissue specimens is not feasible or efficient, and such whole mounts are required to facilitate (a) disease annotation and (b) image registration with radiological images. Unlike manual reassembly of image fragments in a general purpose image editing program (such as Photoshop), HistoStitcher(©) provides memory efficient operation on high resolution digitized histology images and a highly flexible stitching process capable of producing more accurate results in less time. Further, by parameterizing the series of transformations determined by the stitching process, the stitching parameters can be saved, loaded at a later time, refined, or reapplied to multi-resolution scans, or quickly transmitted to another site. In this paper, we describe in detail the design of HistoStitcher(©) and the mathematical routines used for calculating the optimal image transformation, and demonstrate its operation for stitching high resolution histology quadrants of a prostate specimen to form a digitally reassembled whole histology section, for 8 different patient studies. To evaluate stitching quality, a 6 point scoring scheme, which assesses the alignment and continuity of anatomical structures important for disease annotation, is employed by three independent expert pathologists. For 6 studies compared with this scheme, reconstructed sections generated via HistoStitcher(©) scored higher than reconstructions generated by an expert pathologist using Photoshop.

The promise of dynamic contrast-enhanced imaging in radiation therapy

Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and computed tomography (CT) scanning are emerging as valuable tools to quantitatively map the spatial distribution of vascular parameters, such as perfusion, vascular permeability, blood volume, and mean transit time in tumors and normal organs. DCE MRI/CT have shown prognostic and predictive value for response of certain cancers to chemotherapy and radiation therapy. DCE MRI/CT offer the promise of early assessment of tumor response to radiation therapy, opening a window for adaptively optimizing radiation therapy based upon functional alterations that occur earlier than morphologic changes. DCE MRI/CT has also shown the potential of mapping dose responses in normal organs and tissue for evaluation of individual sensitivity to radiation, providing additional opportunities to minimize risks of radiation injury. The evidence for potentially applying DCE MRI and CT for selection and delineation of radiation boost targets is growing. The clinical use of DCE MRI and CT scanning as a biomarker or even a surrogate endpoint for radiation therapy assessment of tumor and normal organs must consider technical validation issues, including standardization, reproducibility, accuracy and robustness, and clinical validation of the sensitivity and specificity for each specific problem of interest. Although holding great promise, to date, DCE MRI and CT scanning have not been qualified as a surrogate endpoint for radiation therapy assessment or for treatment modification in any prospective phase III clinical trial for any tumor site.

Elastic registration of multimodal prostate MRI and histology via multiattribute combined mutual information

PURPOSE

By performing registration of preoperative multiprotocol in vivo magnetic resonance (MR) images of the prostate with corresponding whole-mount histology (WMH) sections from postoperative radical prostatectomy specimens, an accurate estimate of the spatial extent of prostate cancer (CaP) on in vivo MR imaging (MRI) can be retrospectively established. This could allow for definition of quantitative image-based disease signatures and lead to development of classifiers for disease detection on multiprotocol in vivo MRI. Automated registration of MR and WMH images of the prostate is complicated by dissimilar image intensities, acquisition artifacts, and nonlinear shape differences.

METHODS

The authors present a method for automated elastic registration of multiprotocol in vivo MRI and WMH sections of the prostate. The method, multiattribute combined mutual information (MACMI), leverages all available multiprotocol image data to drive image registration using a multivariate formulation of mutual information.

RESULTS

Elastic registration using the multivariate MI formulation is demonstrated for 150 corresponding sets of prostate images from 25 patient studies with T2-weighted and dynamic-contrast enhanced MRI and 85 image sets from 15 studies with an additional functional apparent diffusion coefficient MRI series. Qualitative results of MACMI evaluation via visual inspection suggest that an accurate delineation of CaP extent on MRI is obtained. Results of quantitative evaluation on 150 clinical and 20 synthetic image sets indicate improved registration accuracy using MACMI compared to conventional pairwise mutual information-based approaches.

CONCLUSIONS

The authors' approach to the registration of in vivo multiprotocol MRI and ex vivo WMH of the prostate using MACMI is unique, in that (1) information from all available image protocols is utilized to drive the registration with histology, (2) no additional, intermediate ex vivo radiology or gross histology images need be obtained in addition to the routinely acquired in vivo MRI series, and (3) no corresponding anatomical landmarks are required to be identified manually or automatically on the images.

Determining histology-MRI slice correspondences for defining MRI-based disease signatures of prostate cancer

Mapping the spatial disease extent in a certain anatomical organ/tissue from histology images to radiological images is important in defining the disease signature in the radiological images. One such scenario is in the context of men with prostate cancer who have had pre-operative magnetic resonance imaging (MRI) before radical prostatectomy. For these cases, the prostate cancer extent from ex vivo whole-mount histology is to be mapped to in vivo MRI. The need for determining radiology-image-based disease signatures is important for (a) training radiologist residents and (b) for constructing an MRI-based computer aided diagnosis (CAD) system for disease detection in vivo. However, a prerequisite for this data mapping is the determination of slice correspondences (i.e. indices of each pair of corresponding image slices) between histological and magnetic resonance images. The explicit determination of such slice correspondences is especially indispensable when an accurate 3D reconstruction of the histological volume cannot be achieved because of (a) the limited tissue slices with unknown inter-slice spacing, and (b) obvious histological image artifacts (tissue loss or distortion). In the clinic practice, the histology-MRI slice correspondences are often determined visually by experienced radiologists and pathologists working in unison, but this procedure is laborious and time-consuming. We present an iterative method to automatically determine slice correspondence between images from histology and MRI via a group-wise comparison scheme, followed by 2D and 3D registration. The image slice correspondences obtained using our method were compared with the ground truth correspondences determined via consensus of multiple experts over a total of 23 patient studies. In most instances, the results of our method were very close to the results obtained via visual inspection by these experts.

Focal therapy for prostate cancer: possibilities and limitations

CONTEXT

A significant proportion of patients diagnosed with prostate cancer have well-differentiated, low-volume tumors at minimal risk of impacting their quality of life or longevity. The selection of a treatment strategy, among the multitude of options, has enormous implications for individuals and health care systems.

OBJECTIVE

Our aim was to review the rationale, patient selection criteria, diagnostic imaging, biopsy schemes, and treatment modalities available for the focal therapy of localized prostate cancer. We gave particular emphasis to the conceptual possibilities and limitations.

EVIDENCE ACQUISITION

A National Center for Biotechnology Information PubMed search (www.pubmed.gov) was performed from 1995 to 2009 using medical subject headings "focal therapy" or "ablative" and "prostate cancer." Additional articles were extracted based on recommendations from an expert panel of authors.

EVIDENCE SYNTHESIS

Focal therapy of the prostate in patients with low-risk cancer characteristics is a proposed treatment approach in development that aims to eradicate all known foci of cancer while minimizing damage to adjacent structures necessary for the preservation of urinary, sexual, and bowel function. Conceptually, focal therapy has the potential to minimize treatment-related toxicity without compromising cancer-specific outcome. Limitations include the inability to stage or grade the cancer(s) accurately, suboptimal imaging capabilities, uncertainty regarding the natural history of untreated cancer foci, challenges with posttreatment monitoring, and the lack of quality-of-life data compared with alternative treatment strategies. Early clinical experiences with modest follow-up evaluating a variety of modalities are encouraging but hampered by study design limitations and small sample sizes.

CONCLUSIONS

Prostate focal therapy is a promising and emerging treatment strategy for men with a low risk of cancer progression or metastasis. Evaluation in formal prospective clinical trials is essential before this new strategy is accepted in clinical practice. Adequate trials must include appropriate end points, whether absence of cancer on biopsy or reduction in progression of cancer, along with assessments of safety and longitudinal alterations in quality of life.