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Aberrant expression of protein kinase C (PKC) isozymes is a hallmark of cancer. The different members of the PKC family control cellular events associated with cancer development and progression. Whereas the classical/conventional PKCα isozyme has been linked to tumor suppression in most cancer types, here we demonstrate that this kinase is required for the mitogenic activity of aggressive human prostate cancer cells displaying aberrantly high PKCα expression. Immunohistochemical analysis showed abnormal up-regulation of PKCα in human primary prostate tumors. Interestingly, silencing PKCα expression from aggressive prostate cancer cells impairs cell cycle progression, proliferation and invasion, as well as their tumorigenic activity in a mouse xenograft model. Mechanistic analysis revealed that PKCα exerts a profound control of gene expression, particularly over genes and transcriptional networks associated with cell cycle progression and E2F transcription factors. PKCα RNAi depletion from PC3 prostate cancer cells led to a reduction in the expression of pro-inflammatory cytokine and epithelial-to-mesenchymal transition (EMT) genes, as well as a prominent down-regulation of the immune checkpoint ligand PD-L1. This PKCα-dependent gene expression profile was corroborated in silico using human prostate cancer databases. Our studies established PKCα as a multifunctional kinase that plays pleiotropic roles in prostate cancer, particularly by controlling genetic networks associated with tumor growth and progression. The identification of PKCα as a pro-tumorigenic kinase in human prostate cancer provides strong rationale for the development of therapeutic approaches towards targeting PKCα or its effectors., Competing Interests: Conflict of interest statement: The authors declare no potential conflicts of interest.

Background: The presence of invasive cribriform adenocarcinoma (ICC), an expanse of cells containing punched-out lumina uninterrupted by stroma, in radical prostatectomy (RP) specimens has been associated with biochemical recurrence (BCR). However, ICC identification has only moderate inter-reviewer agreement., Objective: To investigate quantitative machine-based assessment of the extent and prognostic utility of ICC, especially within individual Gleason grade groups., Design, Setting, and Participants: A machine learning approach was developed for ICC segmentation using 70 RP patients and validated in a cohort of 749 patients from four sites whose median year of surgery was 2007 and with median follow-up of 28 mo. ICC was segmented on one representative hematoxylin and eosin RP slide per patient and the fraction of tumor area composed of ICC, the cribriform area index (CAI), was measured., Outcome Measurements and Statistical Analysis: The association between CAI and BCR was measured in terms of the concordance index (c index) and hazard ratio (HR)., Results and Limitations: CAI was correlated with BCR (c index 0.62) in the validation set of 411 patients with ICC morphology, especially those with Gleason grade group 2 cancer (n = 192; c index 0.66), and was less prognostic when patients without ICC were included (c index 0.54). A doubling of CAI in the group with ICC morphology was prognostic after controlling for Gleason grade, surgical margin positivity, preoperative prostate-specific antigen level, pathological T stage, and age (HR 1.19, 95% confidence interval 1.03-1.38; p = 0.018)., Conclusions: Automated image analysis and machine learning could provide an objective, quantitative, reproducible, and high-throughput method of quantifying ICC area. The performance of CAI for grade group 2 cancer suggests that for patients with little Gleason 4 pattern, the ICC fraction has a strong prognostic role., Patient Summary: Machine-based measurement of a specific cell pattern (cribriform; sieve-like, with lots of spaces) in images of prostate specimens could improve risk stratification for patients with prostate cancer. In the future, this could help in expanding the criteria for active surveillance., (Copyright © 2021 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Background and Objective: Gleason grading system is currently the clinical gold standard for determining prostate cancer aggressiveness. Prostate cancer is typically classified into one of 5 different categories with 1 representing the most indolent disease and 5 reflecting the most aggressive disease. Grades 3 and 4 are the most common and difficult patterns to be discriminated in clinical practice. Even though the degree of gland differentiation is the strongest determinant of Gleason grade, manual grading is subjective and is hampered by substantial inter-reader disagreement, especially with regard to intermediate grade groups., Methods: To capture the topological characteristics and the degree of connectivity between nuclei around the gland, the concept of Homology Profile (HP) for prostate cancer grading is presented in this paper. HP is an algebraic tool, whereby, certain algebraic invariants are computed based on the structure of a topological space. We utilized the Statistical Representation of Homology Profile (SRHP) features to quantify the extent of glandular differentiation. The quantitative characteristics which represent the image patch are fed into a supervised classifier model for discrimination of grade patterns 3 and 4., Results: On the basis of the novel homology profile, we evaluated 43 digitized images of prostate biopsy slides annotated for regions corresponding to Grades 3 and 4. The quantitative patch-level evaluation results showed that our approach achieved an Area Under Curve (AUC) of 0.96 and an accuracy of 0.89 in terms of discriminating Grade 3 and 4 patches. Our approach was found to be superior to comparative methods including handcrafted cellular features, Stacked Sparse Autoencoder (SSAE) algorithm and end-to-end supervised learning method (DLGg). Also, slide-level quantitative and qualitative evaluation results reflect the ability of our approach in discriminating Gleason Grade 3 from 4 patterns on H&E tissue images., Conclusions: We presented a novel Statistical Representation of Homology Profile (SRHP) approach for automated Gleason grading on prostate biopsy slides. The most discriminating topological descriptions of cancerous regions for grade 3 and 4 in prostate cancer were identified. Moreover, these characteristics of homology profile are interpretable, visually meaningful and highly consistent with the rubric employed by pathologists for the task of Gleason grading., (Copyright © 2020. Published by Elsevier B.V.)

Purpose: Between 30%-40% of patients with prostate cancer experience disease recurrence following radical prostatectomy. Existing clinical models for recurrence risk prediction do not account for population-based variation in the tumor phenotype, despite recent evidence suggesting the presence of a unique, more aggressive prostate cancer phenotype in African American (AA) patients. We investigated the capacity of digitally measured, population-specific phenotypes of the intratumoral stroma to create improved models for prediction of recurrence following radical prostatectomy., Experimental Design: This study included 334 radical prostatectomy patients subdivided into training (V T , n = 127), validation 1 (V 1 , n = 62), and validation 2 (V 2 , n = 145). Hematoxylin and eosin-stained slides from resected prostates were digitized, and 242 quantitative descriptors of the intratumoral stroma were calculated using a computational algorithm. Machine learning and elastic net Cox regression models were constructed using V T to predict biochemical recurrence-free survival based on these features. Performance of these models was assessed using V 1 and V 2 , both overall and in population-specific cohorts., Results: An AA-specific, automated stromal signature, AAstro, was prognostic of recurrence risk in both independent validation datasets [V 1,AA : AUC = 0.87, HR = 4.71 (95% confidence interval (CI), 1.65-13.4), P = 0.003; V 2,AA : AUC = 0.77, HR = 5.7 (95% CI, 1.48-21.90), P = 0.01]. AAstro outperformed clinical standard Kattan and CAPRA-S nomograms, and the underlying stromal descriptors were strongly associated with IHC measurements of specific tumor biomarker expression levels., Conclusions: Our results suggest that considering population-specific information and stromal morphology has the potential to substantially improve accuracy of prognosis and risk stratification in AA patients with prostate cancer., (©2020 American Association for Cancer Research.)

We have established a microbiome signature for prostate cancer using an array-based metagenomic and capture-sequencing approach. A diverse microbiome signature (viral, bacterial, fungal and parasitic) was observed in the prostate cancer samples compared with benign prostate hyperplasia controls. Hierarchical clustering analysis identified three distinct prostate cancer-specific microbiome signatures. The three signatures correlated with different grades, stages and scores of the cancer. Thus, microbiome signature analysis potentially provides clinical diagnosis and outcome predictions. The array data were validated by PCR and targeted next-generation sequencing (NGS). Specific NGS data suggested that certain viral genomic sequences were inserted into the host somatic chromosomes of the prostate cancer samples. A randomly selected group of these was validated by direct PCR and sequencing. In addition, PCR validation of Helicobacter showed that Helicobacter cagA sequences integrated within specific chromosomes of prostate tumor cells. The viral and Helicobacter integrations are predicted to affect the expression of several cellular genes associated with oncogenic processes., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Site variation in fixation, staining, and scanning can confound automated tissue based image classifiers for disease characterization. In this study we incorporated stability into four feature selection methods for identifying the most robust and discriminating features for two prostate histopathology classification tasks. We evaluated 242 morphology features from N = 212 prostatectomy specimens from four sites for automated cancer detection and grading. We quantified instability as the rate of significant cross-site feature differences. We mapped feature stability and discriminability using 188 non-cancerous and 210 cancerous regions via 3-fold cross validation, then held one site out, creating independent training and testing sets. In training, one feature set was selected only for discriminability, another for discriminability and stability. We trained a classifier with each feature set, testing on the hold out site. Experiments were repeated with 117 Gleason grade 3 and 112 grade 4 regions. Stability was calculated across non-cancerous regions. Gland shape features yielded the best stability and area under the receiver operating curve (AUC) trade-off while co-occurrence texture features were generally unstable. Our stability-informed method produced a cancer detection AUC of 0.98 ± 0.05 and increased average Gleason grading AUC by 4.38%. Color normalization of the images tended to exacerbate feature instability.

Translation of radiomics into the clinic may require a more comprehensive understanding of the underlying morphologic tissue characteristics they reflect. In the context of prostate cancer (PCa), some studies have correlated gross histological measurements of gland lumen, epithelium, and nuclei with disease appearance on MRI. Quantitative histomorphometry (QH), like radiomics for radiologic images, is the computer based extraction of features for describing tumor morphology on digitized tissue images. In this work, we attempt to establish the histomorphometric basis for radiomic features for prostate cancer by (1) identifying the radiomic features from T2w MRI most discriminating of low vs. intermediate/high Gleason score, (2) identifying QH features correlated with the most discriminating radiomic features previously identified, and (3) evaluating the discriminative ability of QH features found to be correlated with spatially co-localized radiomic features. On a cohort of 36 patients (23 for training, 13 for validation), Gabor texture features were identified as being most predictive of Gleason grade on MRI (AUC of 0.69) and gland lumen shape features were identified as the most predictive QH features (AUC = 0.75). Our results suggest that the PCa grade discriminability of Gabor features is a consequence of variations in gland shape and morphology at the tissue level., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Dr. Madabhushi is the co-founder and stake holder in Ibris Inc., a cancer diagnostics company. Additionally he is also an equity holder in Elucid Bioimaging and in Inspirata. He is also a scientific advisory consultant for Inspirata, and holds a joint NIH R01 grant with them. Dr. Viswanath is a scientific advisory board member and equity holder in Virbio, Inc. This does not alter our adherence to PLOS ONE policies on sharingdata and materials.

The pro-oncogenic kinase PKCε is overexpressed in human prostate cancer and cooperates with loss of the tumor suppressor Pten for the development of prostatic adenocarcinoma. However, the effectors driving PKCε-mediated phenotypes remain poorly defined. Here, using cellular and mouse models, we showed that PKCε overexpression acts synergistically with Pten loss to promote NF-κB activation and induce cyclooxygenase-2 (COX-2) expression, phenotypic traits which are also observed in human prostate tumors. Targeted disruption of PKCε from prostate cancer cells impaired COX-2 induction and PGE 2 production. Notably, COX-2 inhibitors selectively killed prostate epithelial cells overexpressing PKCε, and this ability was greatly enhanced by Pten loss. Long-term COX-2 inhibition markedly reduced adenocarcinoma formation, as well as angiogenesis in a mouse model of prostate-specific PKCε expression and Pten loss. Overall, our results provide strong evidence for the involvement of the canonical NF-κB pathway and its target gene COX2 as PKCε effectors, and highlight the potential of PKCε as a useful biomarker for the use of COX inhibition for chemopreventive and/or chemotherapeutic purposes in prostate cancer.

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.

Background: Obesity reflects a chronic inflammatory environment that may contribute to prostate cancer progression and poor treatment outcomes. However, it is not clear which mechanisms drive this association within the tumor microenvironment. The aim of this pilot study was to examine prostatic inflammation via tumor infiltrating lymphocytes and macrophages characterized by obesity and cancer severity., Methods: We studied paraffin-embedded prostatectomy tissue from 99 participants (63 non-obese and 36 obese) from the Study of Clinical Outcomes, Risk and Ethnicity (University of Pennsylvania). Pathologists analyzed the tissue for type and count of lymphocytes and macrophages, including CD3, CD8, FOXP3, and CD68. Pathology data were linked to clinical and demographic variables. Statistical analyses included frequency tables, Kruskal-Wallis tests, Spearman correlations, and multivariable models., Results: We observed positive univariate associations between the number of CD68 cells and tumor grade (p = 0.019). In multivariable analysis, CD8 counts were associated with time to biochemical failure (HR = 1.09, 95% CI = 1.004-1.192, p-value = 0.041.) There were no differences in lymphocytes or macrophages by obesity status or BMI., Conclusions: The number of lymphocytes and macrophages in the tumor microenvironment did not differ by obesity status. However, these inflammation markers were associated with poor prostate cancer outcomes. Further examination of underlying mechanisms that influence obesity-related effects on prostate cancer outcomes is warranted. Such research will guide immunotherapy protocols and weight management as they apply to diverse patient populations and phenotypes.

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 = -0.62). Independent analysis revealed WG to be moderately robust to temporal resolution (intraclass correlation coefficient [ICC] = 0.63) and WG variance, which was poorly correlated with microvessel features, to be predictive of low grade tumors (AUC = 0.77). Enhancement ratio was the most robust (ICC = 0.96) and discriminative (AUC = 0.78) kinetic feature but was moderately correlated with microvessel features (max rho = -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., (© 2015 Wiley Periodicals, Inc.)

Purpose: To determine the best features to discriminate prostate cancer from benign disease and its relationship to benign disease class and cancer grade., Materials and Methods: The institutional review board approved this study and waived the need for informed consent. A retrospective cohort of 70 patients (age range, 48-70 years; median, 62 years), all of whom were scheduled to undergo radical prostatectomy and underwent preoperative 3-T multiparametric magnetic resonance (MR) imaging, including T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced imaging, were included. The digitized prostatectomy slides were annotated for cancer and noncancerous disease and coregistered to MR imaging with an interactive deformable coregistration scheme. Computer-identified features for each of the noncancerous disease categories (eg, benign prostatic hyperplasia [BPH], prostatic intraepithelial neoplasia [PIN], inflammation, and atrophy) and prostate cancer were extracted. Feature selection was performed to identify the features with the highest discriminatory power. The performance of these five features was evaluated by using the area under the receiver operating characteristic curve (AUC)., Results: High-b-value diffusion-weighted images were more discriminative in distinguishing BPH from prostate cancer than apparent diffusion coefficient, which was most suitable for distinguishing PIN from prostate cancer. The focal appearance of lesions on dynamic contrast-enhanced images may help discriminate atrophy and inflammation from cancer. Which imaging features are discriminative for different benign lesions is influenced by cancer grade. The apparent diffusion coefficient appeared to be the most discriminative feature in identifying high-grade cancer. Classification results showed increased performance by taking into account specific benign types (AUC = 0.70) compared with grouping all noncancerous findings together (AUC = 0.62)., Conclusion: The best features with which to discriminate prostate cancer from noncancerous benign disease depend on the type of benign disease and cancer grade. Use of the best features may result in better diagnostic performance., (© RSNA, 2015)

Purpose: We studied the ethnicity-specific expression of prostate cancer (PC) -associated biomarkers to evaluate whether genetic/biologic factors affect ethnic disparities in PC pathogenesis and disease progression., Patients and Methods: A total of 154 African American (AA) and 243 European American (EA) patients from four medical centers were matched according to the Cancer of the Prostate Risk Assessment postsurgical score within each institution. The distribution of mRNA expression levels of 20 validated biomarkers reported to be associated with PC initiation and progression was compared with ethnicity using false discovery rate, adjusted Wilcoxon-Mann-Whitney, and logistic regression models. A conditional logistic regression model was used to evaluate the interaction between ethnicity and biomarkers for predicting clinicopathologic outcomes., Results: Of the 20 biomarkers examined, six showed statistically significant differential expression in AA compared with EA men in one or more statistical models. These include ERG (P < .001), AMACR (P < .001), SPINK1 (P = .001), NKX3-1 (P = .03), GOLM1 (P = .03), and androgen receptor (P = .04). Dysregulation of AMACR (P = .036), ERG (P = .036), FOXP1 (P = .041), and GSTP1 (P = .049) as well as loss-of-function mutations for tumor suppressors NKX3-1 (P = .025) and RB1 (P = .037) predicted risk of pathologic T3 disease in an ethnicity-dependent manner. Dysregulation of GOLM1 (P = .037), SRD5A2 (P = .023), and MKi67 (P = .023) predicted clinical outcomes, including 3-year biochemical recurrence and metastasis at 5 years. A greater proportion of AA men than EA men had triple-negative (ERG-negative/ETS-negative/SPINK1-negative) disease (51% v 35%; P = .002)., Conclusion: We have identified a subset of PC biomarkers that predict the risk of clinicopathologic outcomes in an ethnicity-dependent manner. These biomarkers may explain in part the biologic contribution to ethnic disparity in PC outcomes between EA and AA men., (© 2015 by American Society of Clinical Oncology.)

In this work, we present a new methodology to facilitate prediction of recurrent prostate cancer (CaP) following radical prostatectomy (RP) via the integration of quantitative image features and protein expression in the excised prostate. Creating a fused predictor from high-dimensional data streams is challenging because the classifier must 1) account for the "curse of dimensionality" problem, which hinders classifier performance when the number of features exceeds the number of patient studies and 2) balance potential mismatches in the number of features across different channels to avoid classifier bias towards channels with more features. Our new data integration methodology, supervised Multi-view Canonical Correlation Analysis (sMVCCA), aims to integrate infinite views of highdimensional data to provide more amenable data representations for disease classification. Additionally, we demonstrate sMVCCA using Spearman's rank correlation which, unlike Pearson's correlation, can account for nonlinear correlations and outliers. Forty CaP patients with pathological Gleason scores 6-8 were considered for this study. 21 of these men revealed biochemical recurrence (BCR) following RP, while 19 did not. For each patient, 189 quantitative histomorphometric attributes and 650 protein expression levels were extracted from the primary tumor nodule. The fused histomorphometric/proteomic representation via sMVCCA combined with a random forest classifier predicted BCR with a mean AUC of 0.74 and a maximum AUC of 0.9286. We found sMVCCA to perform statistically significantly (p < 0.05) better than comparative state-of-the-art data fusion strategies for predicting BCR. Furthermore, Kaplan-Meier analysis demonstrated improved BCR-free survival prediction for the sMVCCA-fused classifier as compared to histology or proteomic features alone.

Quantitative histomorphometry (QH) refers to the application of advanced computational image analysis to reproducibly describe disease appearance on digitized histopathology images. QH thus could serve as an important complementary tool for pathologists in interrogating and interpreting cancer morphology and malignancy. In the US, annually, over 60,000 prostate cancer patients undergo radical prostatectomy treatment. Around 10,000 of these men experience biochemical recurrence within 5 years of surgery, a marker for local or distant disease recurrence. The ability to predict the risk of biochemical recurrence soon after surgery could allow for adjuvant therapies to be prescribed as necessary to improve long term treatment outcomes. The underlying hypothesis with our approach, co-occurring gland angularity (CGA), is that in benign or less aggressive prostate cancer, gland orientations within local neighborhoods are similar to each other but are more chaotically arranged in aggressive disease. By modeling the extent of the disorder, we can differentiate surgically removed prostate tissue sections from (a) benign and malignant regions and (b) more and less aggressive prostate cancer. For a cohort of 40 intermediate-risk (mostly Gleason sum 7) surgically cured prostate cancer patients where half suffered biochemical recurrence, the CGA features were able to predict biochemical recurrence with 73% accuracy. Additionally, for 80 regions of interest chosen from the 40 studies, corresponding to both normal and cancerous cases, the CGA features yielded a 99% accuracy. CGAs were shown to be statistically signicantly ([Formula: see text]) better at predicting BCR compared to state-of-the-art QH methods and postoperative prostate cancer nomograms.

Background: Automated classification of histopathology involves identification of multiple classes, including benign, cancerous, and confounder categories. The confounder tissue classes can often mimic and share attributes with both the diseased and normal tissue classes, and can be particularly difficult to identify, both manually and by automated classifiers. In the case of prostate cancer, they may be several confounding tissue types present in a biopsy sample, posing as major sources of diagnostic error for pathologists. Two common multi-class approaches are one-shot classification (OSC), where all classes are identified simultaneously, and one-versus-all (OVA), where a "target" class is distinguished from all "non-target" classes. OSC is typically unable to handle discrimination of classes of varying similarity (e.g. with images of prostate atrophy and high grade cancer), while OVA forces several heterogeneous classes into a single "non-target" class. In this work, we present a cascaded (CAS) approach to classifying prostate biopsy tissue samples, where images from different classes are grouped to maximize intra-group homogeneity while maximizing inter-group heterogeneity., Results: We apply the CAS approach to categorize 2000 tissue samples taken from 214 patient studies into seven classes: epithelium, stroma, atrophy, prostatic intraepithelial neoplasia (PIN), and prostate cancer Gleason grades 3, 4, and 5. A series of increasingly granular binary classifiers are used to split the different tissue classes until the images have been categorized into a single unique class. Our automatically-extracted image feature set includes architectural features based on location of the nuclei within the tissue sample as well as texture features extracted on a per-pixel level. The CAS strategy yields a positive predictive value (PPV) of 0.86 in classifying the 2000 tissue images into one of 7 classes, compared with the OVA (0.77 PPV) and OSC approaches (0.76 PPV)., Conclusions: Use of the CAS strategy increases the PPV for a multi-category classification system over two common alternative strategies. In classification problems such as histopathology, where multiple class groups exist with varying degrees of heterogeneity, the CAS system can intelligently assign class labels to objects by performing multiple binary classifications according to domain knowledge.

Diagnosis of prostate cancer (CaP) currently involves examining tissue samples for CaP presence and extent via a microscope, a time-consuming and subjective process. With the advent of digital pathology, computer-aided algorithms can now be applied to disease detection on digitized glass slides. The size of these digitized histology images (hundreds of millions of pixels) presents a formidable challenge for any computerized image analysis program. In this paper, we present a boosted Bayesian multiresolution (BBMR) system to identify regions of CaP on digital biopsy slides. Such a system would serve as an important preceding step to a Gleason grading algorithm, where the objective would be to score the invasiveness and severity of the disease. In the first step, our algorithm decomposes the whole-slide image into an image pyramid comprising multiple resolution levels. Regions identified as cancer via a Bayesian classifier at lower resolution levels are subsequently examined in greater detail at higher resolution levels, thereby allowing for rapid and efficient analysis of large images. At each resolution level, ten image features are chosen from a pool of over 900 first-order statistical, second-order co-occurrence, and Gabor filter features using an AdaBoost ensemble method. The BBMR scheme, operating on 100 images obtained from 58 patients, yielded: 1) areas under the receiver operating characteristic curve (AUC) of 0.84, 0.83, and 0.76, respectively, at the lowest, intermediate, and highest resolution levels and 2) an eightfold savings in terms of computational time compared to running the algorithm directly at full (highest) resolution. The BBMR model outperformed (in terms of AUC): 1) individual features (no ensemble) and 2) a random forest classifier ensemble obtained by bagging multiple decision tree classifiers. The apparent drop-off in AUC at higher image resolutions is due to lack of fine detail in the expert annotation of CaP and is not an artifact of the classifier. The implicit feature selection done via the AdaBoost component of the BBMR classifier reveals that different classes and types of image features become more relevant for discriminating between CaP and benign areas at different image resolutions.

Introduction: The advent of digital slides offers new opportunities within the practice of pathology such as the use of image analysis techniques to facilitate computer aided diagnosis (CAD) solutions. Use of CAD holds promise to enable new levels of decision support and allow for additional layers of quality assurance and consistency in rendered diagnoses. However, the development and testing of prostate cancer CAD solutions requires a ground truth map of the cancer to enable the generation of receiver operator characteristic (ROC) curves. This requires a pathologist to annotate, or paint, each of the malignant glands in prostate cancer with an image editor software - a time consuming and exhaustive process. Recently, two CAD algorithms have been described: probabilistic pairwise Markov models (PPMM) and spatially-invariant vector quantization (SIVQ). Briefly, SIVQ operates as a highly sensitive and specific pattern matching algorithm, making it optimal for the identification of any epithelial morphology, whereas PPMM operates as a highly sensitive detector of malignant perturbations in glandular lumenal architecture., Methods: By recapitulating algorithmically how a pathologist reviews prostate tissue sections, we created an algorithmic cascade of PPMM and SIVQ algorithms as previously described by Doyle el al. [1] where PPMM identifies the glands with abnormal lumenal architecture, and this area is then screened by SIVQ to identify the epithelium., Results: The performance of this algorithm cascade was assessed qualitatively (with the use of heatmaps) and quantitatively (with the use of ROC curves) and demonstrates greater performance in the identification of malignant prostatic epithelium., Conclusion: This ability to semi-autonomously paint nearly all the malignant epithelium of prostate cancer has immediate applications to future prostate cancer CAD development as a validated ground truth generator. In addition, such an approach has potential applications as a pre-screening/quality assurance tool.

Background: Multimodal data, especially imaging and non-imaging data, is being routinely acquired in the context of disease diagnostics; however, computational challenges have limited the ability to quantitatively integrate imaging and non-imaging data channels with different dimensionalities and scales. To the best of our knowledge relatively few attempts have been made to quantitatively fuse such data to construct classifiers and none have attempted to quantitatively combine histology (imaging) and proteomic (non-imaging) measurements for making diagnostic and prognostic predictions. The objective of this work is to create a common subspace to simultaneously accommodate both the imaging and non-imaging data (and hence data corresponding to different scales and dimensionalities), called a metaspace. This metaspace can be used to build a meta-classifier that produces better classification results than a classifier that is based on a single modality alone. Canonical Correlation Analysis (CCA) and Regularized CCA (RCCA) are statistical techniques that extract correlations between two modes of data to construct a homogeneous, uniform representation of heterogeneous data channels. In this paper, we present a novel modification to CCA and RCCA, Supervised Regularized Canonical Correlation Analysis (SRCCA), that (1) enables the quantitative integration of data from multiple modalities using a feature selection scheme, (2) is regularized, and (3) is computationally cheap. We leverage this SRCCA framework towards the fusion of proteomic and histologic image signatures for identifying prostate cancer patients at the risk of 5 year biochemical recurrence following radical prostatectomy., Results: A cohort of 19 grade, stage matched prostate cancer patients, all of whom had radical prostatectomy, including 10 of whom had biochemical recurrence within 5 years of surgery and 9 of whom did not, were considered in this study. The aim was to construct a lower fused dimensional metaspace comprising both the histological and proteomic measurements obtained from the site of the dominant nodule on the surgical specimen. In conjunction with SRCCA, a random forest classifier was able to identify prostate cancer patients, who developed biochemical recurrence within 5 years, with a maximum classification accuracy of 93%., Conclusions: The classifier performance in the SRCCA space was found to be statistically significantly higher compared to the fused data representations obtained, not only from CCA and RCCA, but also two other statistical techniques called Principal Component Analysis and Partial Least Squares Regression. These results suggest that SRCCA is a computationally efficient and a highly accurate scheme for representing multimodal (histologic and proteomic) data in a metaspace and that it could be used to construct fused biomarkers for predicting disease recurrence and prognosis.

Background: Supervised classifiers for digital pathology can improve the ability of physicians to detect and diagnose diseases such as cancer. Generating training data for classifiers is problematic, since only domain experts (e.g. pathologists) can correctly label ground truth data. Additionally, digital pathology datasets suffer from the "minority class problem", an issue where the number of exemplars from the non-target class outnumber target class exemplars which can bias the classifier and reduce accuracy. In this paper, we develop a training strategy combining active learning (AL) with class-balancing. AL identifies unlabeled samples that are "informative" (i.e. likely to increase classifier performance) for annotation, avoiding non-informative samples. This yields high accuracy with a smaller training set size compared with random learning (RL). Previous AL methods have not explicitly accounted for the minority class problem in biomedical images. Pre-specifying a target class ratio mitigates the problem of training bias. Finally, we develop a mathematical model to predict the number of annotations (cost) required to achieve balanced training classes. In addition to predicting training cost, the model reveals the theoretical properties of AL in the context of the minority class problem., Results: Using this class-balanced AL training strategy (CBAL), we build a classifier to distinguish cancer from non-cancer regions on digitized prostate histopathology. Our dataset consists of 12,000 image regions sampled from 100 biopsies (58 prostate cancer patients). We compare CBAL against: (1) unbalanced AL (UBAL), which uses AL but ignores class ratio; (2) class-balanced RL (CBRL), which uses RL with a specific class ratio; and (3) unbalanced RL (UBRL). The CBAL-trained classifier yields 2% greater accuracy and 3% higher area under the receiver operating characteristic curve (AUC) than alternatively-trained classifiers. Our cost model accurately predicts the number of annotations necessary to obtain balanced classes. The accuracy of our prediction is verified by empirically-observed costs. Finally, we find that over-sampling the minority class yields a marginal improvement in classifier accuracy but the improved performance comes at the expense of greater annotation cost., Conclusions: We have combined AL with class balancing to yield a general training strategy applicable to most supervised classification problems where the dataset is expensive to obtain and which suffers from the minority class problem. An intelligent training strategy is a critical component of supervised classification, but the integration of AL and intelligent choice of class ratios, as well as the application of a general cost model, will help researchers to plan the training process more quickly and effectively.

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., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

Multimodal data, especially imaging and non-imaging data, is being routinely acquired in the context of disease diagnostics; however computational challenges have limited the ability to quantitatively integrate imaging and non-imaging data channels with different dimensionalities for making diagnostic and prognostic predictions. The objective of this work is to create a common subspace to simultaneously accommodate both the imaging and non-imaging data, called a metaspace. This metaspace can be used to build a meta-classifier that produces better classification results than a classifier that is based on a single modality alone. In this paper, we present a novel Supervised Regularized Canonical Correlation Analysis (SRCCA) algorithm that (1) enables the quantitative integration of data from multiple modalities using a feature selection scheme, (2) is regularized, and (3) is computationally cheap. We leverage this SRCCA framework towards the fusion of proteomic and histologic image signatures for identifying prostate cancer patients at risk for biochemical recurrence following radical prostatectomy. For a cohort of 19 prostate cancer patients, SRCCA was able to yield a lower fused dimensional metaspace comprising both the histological and proteomic attributes. In conjunction with SRCCA, a random forest classifier was able to identify patients at risk for biochemical failure with a maximum accuracy of 93%. The classifier performance in the SRCCA space was statistically significantly higher compared to the fused data representations obtained either with Canonical Correlation Analysis (CCA) or Regularized CCA.

In this work, we present a scheme for the registration of digitally reconstructed whole mount histology (WMH) to pre-operative in vivo multiprotocol prostate MR imagery (T2w and DCE) using spatially weighted mutual information (SWMI). Spatial alignment of ex vivo histological sections to pre-operative in vivo MRI for prostate cancer (CaP) patients undergoing radical prostatectomy is a necessary first step in the discovery of quantitative multiprotocol MRI signatures for CaP. This may be done by spatially mapping delineated extent of disease on ex vivo histopathology onto pre-operative in vivo MRI via image registration. Apart from the challenges in spatially registering multi-modal data (histology and MRI) on account of (a) modality specific differences, (b) deformation due to the endorectal coil and tissue loss on histology, another complication is that the ex vivo histological sections, in the lab, are usually obtained as quadrants. This means they need to be reconstituted as a pseudo-whole mount histologic section (WMHS) prior to registration with MRI. An additional challenge is that most registration techniques rely on availability of the pre-segmented prostate capsule on T2w MRI. The novel contribution of this paper is that it leverages a spatially weighted mutual information (SWMI) scheme to automatically register and map CaP extent from WMHS onto pre-operative, multiprotocol MRI. The SWMI scheme obviates the need for pre-segmentation of the prostate capsule on MRI. Additionally, we leverage a program developed by our group, Histostitcher©, for interactive stitching of individual histology quadrants to digitally reconstruct the pseudo WMHS. Our registration methodology comprises the following main steps, (1) affine registration of T2w and DCE MRI, (2) affine registration of stitched WMHS to multiprotocol T2w and DCE MRI, and (3) multimodal image registration of WMHS to multiprotocol T2w and DCE MRI using SWMI. We quantitatively and qualitatively evaluated all aspects of our methodology in the multimodal registration of a total of 7 corresponding histology and MRI sections from 2 different patients. For the 7 studies, we obtained an average Hausdorff distance of 1.85 mm, mean absolute distance of 0.99 mm, RMS of 1.65 mm, and DICE of 0.83, when comparing the capsular alignment on MRI to histology.

In this paper we present a system for detecting regions of carcinoma of the prostate (CaP) in H&E stained radical prostatectomy specimens using the color fractal dimension. Color textural information is known to be a valuable characteristic to distinguish CaP from benign tissue. In addition to color information, we know that cancer tends to form contiguous regions. Our system leverages the color staining information of histology as well as spatial dependencies. The color and textural information is first captured using color fractal dimension. To incorporate spatial dependencies, we combine the probability map constructed via color fractal dimension with a novel Markov prior called the Probabilistic Pairwise Markov Model (PPMM). To demonstrate the capability of this CaP detection system, we applied the algorithm to 27 radical prostatectomy specimens from 10 patients. A per pixel evaluation was conducted with ground truth provided by an expert pathologist using only the color fractal feature first, yielding an area under the receiver operator characteristic curve (AUC) curve of 0.790. In conjunction with a Markov prior, the resultant color fractal dimension + Markov random field (MRF) classifier yielded an AUC of 0.831.

In this paper we present a high-throughput system for detecting regions of carcinoma of the prostate (CaP) in HSs from radical prostatectomies (RPs) using probabilistic pairwise Markov models (PPMMs), a novel type of Markov random field (MRF). At diagnostic resolution a digitized HS can contain 80Kx70K pixels - far too many for current automated Gleason grading algorithms to process. However, grading can be separated into two distinct steps: (1) detecting cancerous regions and (2) then grading these regions. The detection step does not require diagnostic resolution and can be performed much more quickly. Thus, we introduce a CaP detection system capable of analyzing an entire digitized whole-mount HS (2x1.75cm(2)) in under three minutes (on a desktop computer) while achieving a CaP detection sensitivity and specificity of 0.87 and 0.90, respectively. We obtain this high-throughput by tailoring the system to analyze the HSs at low resolution (8microm per pixel). This motivates the following algorithm: (Step 1) glands are segmented, (Step 2) the segmented glands are classified as malignant or benign, and (Step 3) the malignant glands are consolidated into continuous regions. The classification of individual glands leverages two features: gland size and the tendency for proximate glands to share the same class. The latter feature describes a spatial dependency which we model using a Markov prior. Typically, Markov priors are expressed as the product of potential functions. Unfortunately, potential functions are mathematical abstractions, and constructing priors through their selection becomes an ad hoc procedure, resulting in simplistic models such as the Potts. Addressing this problem, we introduce PPMMs which formulate priors in terms of probability density functions, allowing the creation of more sophisticated models. To demonstrate the efficacy of our CaP detection system and assess the advantages of using a PPMM prior instead of the Potts, we alternately incorporate both priors into our algorithm and rigorously evaluate system performance, extracting statistics from over 6000 simulations run across 40 RP specimens. Perhaps the most indicative result is as follows: at a CaP sensitivity of 0.87 the accompanying false positive rates of the system when alternately employing the PPMM and Potts priors are 0.10 and 0.20, respectively., (Copyright 2010 Elsevier B.V. All rights reserved.)

Rationale and Objectives: Needle biopsy is currently the only way to confirm prostate cancer. To increase prostate cancer diagnostic rate, needles are expected to be deployed at suspicious cancer locations. High-contrast magnetic resonance (MR) imaging provides a powerful tool for detecting suspicious cancerous tissues. To do this, MR appearances of cancerous tissue should be characterized and learned from a sufficient number of prostate MR images with known cancer information. However, ground-truth cancer information is only available in histologic images. Therefore it is necessary to warp ground-truth cancerous regions in histological images to MR images by a registration procedure. The objective of this article is to develop a registration technique for aligning histological and MR images of the same prostate., Material and Methods: Five pairs of histological and T2-weighted MR images of radical prostatectomy specimens are collected. For each pair, registration is guided by two sets of correspondences that can be reliably established on prostate boundaries and internal salient bloblike structures of histologic and MR images., Results: Our developed registration method can accurately register histologic and MR images. It yields results comparable to manual registration, in terms of landmark distance and volume overlap. It also outperforms both affine registration and boundary-guided registration methods., Conclusions: We have developed a novel method for deformable registration of histologic and MR images of the same prostate. Besides the collection of ground-truth cancer information in MR images, the method has other potential applications. An automatic, accurate registration of histologic and MR images actually builds a bridge between in vivo anatomical information and ex vivo pathologic information, which is valuable for various clinical studies.

In this paper we present a computer-aided diagnosis (CAD) system to automatically detect prostatic adenocarcinoma from high resolution digital histopathological slides. This is especially desirable considering the large number of tissue slides that are currently analyzed manually - a laborious and time-consuming task. Our methodology is novel in that texture-based classification is performed using a hierarchical classifier within a multi-scale framework. Pyramidal decomposition is used to reduce an image into its constituent scales. The cascaded image analysis across multiple scales is similar to the manner in which pathologists analyze histopathology. Nearly 600 different image texture features at multiple orientations are extracted at every pixel at each image scale. At each image scale the classifier only analyzes those image pixels that have been determined to be tumor at the preceding lower scale. Results of quantitative evaluation on 20 patient studies indicate (1) an overall accuracy of over 90% and (2) an approximate 8-fold savings in terms of computational time. Both the AdaBoost and Decision Tree classifiers were considered and in both cases tumor detection sensitivity was found to be relatively constant across different scales. Detection specificity was however found to increase at higher scales reflecting the availability of additional discriminatory information.

This paper presents a deformable registration method to co-register histological images with MR images of the same prostate. By considering various distortion and cutting artifacts in histological images and also fundamentally different nature of histological and MR images, our registration method is thus guided by two types of landmark points that can be reliably detected in both histological and MR images, i.e., prostate boundary points, and internal salient points that can be identified by a scale-space analysis method. The similarity between these automatically detected landmarks in histological and MR images are defined by geometric features and normalized mutual information, respectively. By optimizing a function, which integrates the similarities between landmarks with spatial constraints, the correspondences between the landmarks as well as the deformable transformation between histological and MR images can be simultaneously obtained. The performance of our proposed registration algorithm has been evaluated by various designed experiments. This work is part of a larger effort to develop statistical atlases of prostate cancer using both imaging and histological information, and to use these atlases for optimal biopsy and therapy planning.

Current diagnosis of prostatic adenocarcinoma is done by manual analysis of biopsy tissue samples for tumor presence. However, the recent advent of whole slide digital scanners has made histopathological tissue specimens amenable to computer-aided diagnosis (CAD). In this paper, we present a CAD system to assist pathologists by automatically detecting prostate cancer from digitized images of prostate histological specimens. Automated diagnosis on very large high resolution images is done via a multi-resolution scheme similar to the manner in which a pathologist isolates regions of interest on a glass slide. Nearly 600 image texture features are extracted and used to perform pixel-wise Bayesian classification at each image scale to obtain corresponding likelihood scenes. Starting at the lowest scale, we apply the AdaBoost algorithm to combine the most discriminating features, and we analyze only pixels with a high combined probability of malignancy at subsequent higher scales. The system was evaluated on 22 studies by comparing the CAD result to a pathologist's manual segmentation of cancer (which served as ground truth) and found to have an overall accuracy of 88%. Our results show that (1) CAD detection sensitivity remains consistently high across image scales while CAD specificity increases with higher scales, (2) the method is robust to choice of training samples, and (3) the multi-scale cascaded approach results in significant savings in computational time.

Prostatic adenocarcinoma is the most commonly occurring cancer among men in the United States, second only to skin cancer. Currently, the only definitive method to ascertain the presence of prostatic cancer is by trans-rectal ultrasound (TRUS) directed biopsy. Owing to the poor image quality of ultrasound, the accuracy of TRUS is only 20%-25%. High-resolution magnetic resonance imaging (MRI) has been shown to have a higher accuracy of prostate cancer detection compared to ultrasound. Consequently, several researchers have been exploring the use of high resolution MRI in performing prostate biopsies. Visual detection of prostate cancer, however, continues to be difficult owing to its apparent lack of shape, and the fact that several malignant and benign structures have overlapping intensity and texture characteristics. In this paper, we present a fully automated computer-aided detection (CAD) system for detecting prostatic adenocarcinoma from 4 Tesla ex vivo magnetic resonance (MR) imagery of the prostate. After the acquired MR images have been corrected for background inhomogeneity and nonstandardness, novel three-dimensional (3-D) texture features are extracted from the 3-D MRI scene. A Bayesian classifier then assigns each image voxel a "likelihood" of malignancy for each feature independently. The "likelihood" images generated in this fashion are then combined using an optimally weighted feature combination scheme. Quantitative evaluation was performed by comparing the CAD results with the manually ascertained ground truth for the tumor on the MRI. The tumor labels on the MR slices were determined manually by an expert by visually registering the MR slices with the corresponding regions on the histology slices. We evaluated our CAD system on a total of 33 two-dimensional (2-D) MR slices from five different 3-D MR prostate studies. Five slices from two different glands were used for training. Our feature combination scheme was found to outperform the individual texture features, and also other popularly used feature combination methods, including AdaBoost, ensemble averaging, and majority voting. Further, in several instances our CAD system performed better than the experts in terms of accuracy, the expert segmentations being determined solely from visual inspection of the MRI data. In addition, the intrasystem variability (changes in CAD accuracy with changes in values of system parameters) was significantly lower than the corresponding intraobserver and interobserver variability. CAD performance was found to be very similar for different training sets. Future work will focus on extending the methodology to guide high-resolution MRI-assisted in vivo prostate biopsies.

Recently there has been a great deal of interest in algorithms for constructing low-dimensional feature-space embeddings of high dimensional data sets in order to visualize inter- and intra-class relationships. In this paper we present a novel application of graph embedding in improving the accuracy of supervised classification schemes, especially in cases where object class labels cannot be reliably ascertained. By refining the initial training set of class labels we seek to improve the prior class distributions and thus classification accuracy. We also present a novel way of visualizing the class embeddings which makes it easy to appreciate inter-class relationships and to infer the presence of new classes which were not part of the original classification. We demonstrate the utility of the method in detecting prostatic adenocarcinoma from high-resolution MRI.