Evaluating limited biopsy templates for men with markedly elevated PSAs.

INTRODUCTION: To define the smallest prostate needle biopsy (PNB) template necessary for accurate tissue diagnosis in men with markedly elevated PSA while decreasing procedural morbidity. MATERIALS AND METHODS: We performed a chart review of 80 men presenting with a newly elevated PSA > 100 ng/mL who underwent biopsy (PNB or metastatic site). For patients who underwent a full 12-core biopsy, simulated templates of 2- to 10-cores were generated by randomly drawing subsets of biopsies from their full-template findings. Templates were iterated to randomize core location and generate theoretical smaller template outcomes. Simulated biopsy results were compared to full-template findings to determine accuracy to maximal Grade Group (GG) diagnosis. RESULTS: Amongst those that underwent PNB, 93% had GG 4 or 5 disease. Twenty-two (40%) underwent a full 12-core biopsy, 20 (37%) a 6-core biopsy, and only 8 (15%) had fewer than six biopsy cores sampled at our hospital. Simulated templates with 2-, 4-, 6-, and 8-cores correctly diagnosed prostate cancer in all patients, and accurately identified the maximal GG in 82%, 91%, 95%, and 97% of patients, respectively. The biopsy locations most likely to detect maximal GG were medial mid and base sites bilaterally. A 4-core template of these sites would have accurately detected the maximal GG in 95% of patients relative to a full 12-core template. CONCLUSIONS: In men presenting with PSA > 100 ng/mL, decreasing from a 12-core to a 4-core prostate biopsy template results in universal cancer detection and minimal under-grading while theoretically decreasing procedural morbidity and cost.

Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis.

CD4+ T cells are central mediators of autoimmune pathology; however, defining their key effector functions in specific autoimmune diseases remains challenging. Pathogenic CD4+ T cells within affected tissues may be identified by expression of markers of recent activation. Here we use mass cytometry to analyse activated T cells in joint tissue from patients with rheumatoid arthritis, a chronic immune-mediated arthritis that affects up to 1% of the population. This approach revealed a markedly expanded population of PD-1hiCXCR5-CD4+ T cells in synovium of patients with rheumatoid arthritis. However, these cells are not exhausted, despite high PD-1 expression. Rather, using multidimensional cytometry, transcriptomics, and functional assays, we define a population of PD-1hiCXCR5- 'peripheral helper' T (TPH) cells that express factors enabling B-cell help, including IL-21, CXCL13, ICOS, and MAF. Like PD-1hiCXCR5+ T follicular helper cells, TPH cells induce plasma cell differentiation in vitro through IL-21 secretion and SLAMF5 interaction (refs 3, 4). However, global transcriptomics highlight differences between TPH cells and T follicular helper cells, including altered expression of BCL6 and BLIMP1 and unique expression of chemokine receptors that direct migration to inflamed sites, such as CCR2, CX3CR1, and CCR5, in TPH cells. TPH cells appear to be uniquely poised to promote B-cell responses and antibody production within pathologically inflamed non-lymphoid tissues.

Left atrial ligation alters intracardiac flow patterns and the biomechanical landscape in the chick embryo.

BACKGROUND: Hypoplastic left heart syndrome (HLHS) is a major human congenital heart defect that results in single ventricle physiology and high mortality. Clinical data indicate that intracardiac blood flow patterns during cardiac morphogenesis are a significant etiology. We used the left atrial ligation (LAL) model in the chick embryo to test the hypothesis that LAL immediately alters intracardiac flow streams and the biomechanical environment, preceding morphologic and structural defects observed in HLHS. RESULTS: Using fluorescent dye injections, we found that intracardiac flow patterns from the right common cardinal vein, right vitelline vein, and left vitelline vein were altered immediately following LAL. Furthermore, we quantified a significant ventral shift of the right common cardinal and right vitelline vein flow streams. We developed an in silico model of LAL, which revealed that wall shear stress was reduced at the left atrioventricular canal and left side of the common ventricle. CONCLUSIONS: Our results demonstrate that intracardiac flow patterns change immediately following LAL, supporting the role of hemodynamics in the progression of HLHS. Sites of reduced WSS revealed by computational modeling are commonly affected in HLHS, suggesting that changes in the biomechanical environment may lead to abnormal growth and remodeling of left heart structures.

Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework.

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

ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate.

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

Bladder cancer risk stratification using a urinary mRNA biomarker panel - A path towards cystoscopy triaging.

OBJECTIVES: The risk of bladder cancer (BCa) diagnosis and recurrence necessitates cystoscopy. Improved risk stratification may inform personalized triage and surveillance strategies. We aim to develop a urinary mRNA biomarker panel for risk stratification in patients undergoing BCa screening and surveillance. METHODS AND MATERIALS: Urine samples were collected from patients undergoing cystoscopy for BCa screening or surveillance. In patients who underwent transurethral resection of bladder tumor, urine samples were categorized based on tumor histopathology, size, and focality. Subjects with intermediate and high-risk BCa based on American Urological Association (AUA) guideline for non-muscle invasive bladder cancer were classified as "increased-risk"; those with no cancer and AUA low-risk BCa were classified as "low-risk". Urine was evaluated for ROBO1, WNT5A, CDC42BPB, ABL1, CRH, IGF2, ANXA10, and UPK1B expression. A diagnostic model to detect "increased-risk" BCa was created using forward logistic regression analysis of cycle threshold values. Model validation was performed with ten-fold cross-validation. Sensitivity and specificity for detection of "increased-risk" BCa was determined and net benefit analysis performed. RESULTS: Urine samples (n = 257) were collected from 177 patients (95 screening, 76 surveillance, 6 both). There were 65 diagnoses of BCa (12 low, 22 intermediate, 31 high risk). ROBO1, CRH, and IGF2 expression correlated with "increased-risk" disease yielding sensitivity of 92.5% (95% CI, 84.9%-98.1%) and specificity of 73.5% (95% CI, 67.7-79.9%). The overall calculated standardized net benefit of the model was 0.81 (95%CI, 0.71-0.90). CONCLUSIONS: A 3-marker urinary mRNA panel allows for non-invasive identification of "increased-risk" BCa and with further validation may prove to be a tool to reduce the need for cystoscopies in low-risk patients.

3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstruction.

The use of MRI for prostate cancer diagnosis and treatment is increasing rapidly. However, identifying the presence and extent of cancer on MRI remains challenging, leading to high variability in detection even among expert radiologists. Improvement in cancer detection on MRI is essential to reducing this variability and maximizing the clinical utility of MRI. To date, such improvement has been limited by the lack of accurately labeled MRI datasets. Data from patients who underwent radical prostatectomy enables the spatial alignment of digitized histopathology images of the resected prostate with corresponding pre-surgical MRI. This alignment facilitates the delineation of detailed cancer labels on MRI via the projection of cancer from histopathology images onto MRI. We introduce a framework that performs 3D registration of whole-mount histopathology images to pre-surgical MRI in three steps. First, we developed a novel multi-image super-resolution generative adversarial network (miSRGAN), which learns information useful for 3D registration by producing a reconstructed 3D MRI. Second, we trained the network to learn information between histopathology slices to facilitate the application of 3D registration methods. Third, we registered the reconstructed 3D histopathology volumes to the reconstructed 3D MRI, mapping the extent of cancer from histopathology images onto MRI without the need for slice-to-slice correspondence. When compared to interpolation methods, our super-resolution reconstruction resulted in the highest PSNR relative to clinical 3D MRI (32.15 dB vs 30.16 dB for BSpline interpolation). Moreover, the registration of 3D volumes reconstructed via super-resolution for both MRI and histopathology images showed the best alignment of cancer regions when compared to (1) the state-of-the-art RAPSODI approach, (2) volumes that were not reconstructed, or (3) volumes that were reconstructed using nearest neighbor, linear, or BSpline interpolations. The improved 3D alignment of histopathology images and MRI facilitates the projection of accurate cancer labels on MRI, allowing for the development of improved MRI interpretation schemes and machine learning models to automatically detect cancer on MRI.

Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging.

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

Registration of presurgical MRI and histopathology images from radical prostatectomy via RAPSODI.

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

Applying the PRECISION approach in biopsy naïve and previously negative prostate biopsy patients.

OBJECTIVES: The PRECISION trial provides level 1 evidence supporting prebiopsy multiparametric magnetic resonance imaging (mpMRI) followed by targeted biopsy only when mpMRI is abnormal [1]. This approach reduced over-detection of low-grade cancer while increasing detection of clinically significant cancer (CSC). Still, important questions remain regarding the reproducibility of these findings outside of a clinical trial and quantifying missed CSC diagnoses using this approach. To address these issues, we retrospectively applied the PRECISION strategy in men who each underwent prebiopsy mpMRI followed by systematic and targeted biopsy. METHODS AND MATERIALS: Clinical, imaging, and pathology data were prospectively collected from 358 biopsy naïve men and 202 men with previous negative biopsies. To apply the PRECISION approach, a retrospective analysis was done comparing the cancer yield from 2 diagnostic strategies: (1) mpMRI followed by targeted biopsy alone for men with Prostate Imaging Reporting and Data System ≥ 3 lesions and (2) systematic biopsy alone for all men. Primary outcomes were biopsies avoided and the proportion of CSC cancer (Grade Group 2-5) and non-CSC (Grade Group 1). RESULTS: In biopsy naïve patients, the mpMRI diagnostic strategy would have avoided 19% of biopsies while detecting 2.5% more CSC (P= 0.480) and 12% less non-CSC (P< 0.001). Thirteen percent (n= 9) of men with normal mpMRI had CSC on systematic biopsy. For previous negative biopsy patients, the mpMRI diagnostic strategy avoided 21% of biopsies, while detecting 1.5% more CSC (P= 0.737) and 13% less non-CSC (P< 0.001). Seven percent (n= 3) of men with normal mpMRI had CSC on systematic biopsy. CONCLUSIONS: Our results provide external validation of the PRECISION finding that mpMRI followed by targeted biopsy of suspicious lesions reduces biopsies and over-diagnosis of low-grade cancer. Unlike PRECISION, we did not find increased diagnosis of CSC. This was true in both biopsy naïve and previously negative biopsy cohorts. We have incorporated this information into shared decision making, which has led some men to choose to avoid biopsy. However, we continue to recommend targeted and systematic biopsy in men with abnormal MRI.

Mixed-effects association of single cells identifies an expanded effector CD4+ T cell subset in rheumatoid arthritis.

High-dimensional single-cell analyses have improved the ability to resolve complex mixtures of cells from human disease samples; however, identifying disease-associated cell types or cell states in patient samples remains challenging because of technical and interindividual variation. Here, we present mixed-effects modeling of associations of single cells (MASC), a reverse single-cell association strategy for testing whether case-control status influences the membership of single cells in any of multiple cellular subsets while accounting for technical confounders and biological variation. Applying MASC to mass cytometry analyses of CD4+ T cells from the blood of rheumatoid arthritis (RA) patients and controls revealed a significantly expanded population of CD4+ T cells, identified as CD27- HLA-DR+ effector memory cells, in RA patients (odds ratio, 1.7; P = 1.1 × 10-3). The frequency of CD27- HLA-DR+ cells was similarly elevated in blood samples from a second RA patient cohort, and CD27- HLA-DR+ cell frequency decreased in RA patients who responded to immunosuppressive therapy. Mass cytometry and flow cytometry analyses indicated that CD27- HLA-DR+ cells were associated with RA (meta-analysis P = 2.3 × 10-4). Compared to peripheral blood, synovial fluid and synovial tissue samples from RA patients contained about fivefold higher frequencies of CD27- HLA-DR+ cells, which comprised ~10% of synovial CD4+ T cells. CD27- HLA-DR+ cells expressed a distinctive effector memory transcriptomic program with T helper 1 (TH1)- and cytotoxicity-associated features and produced abundant interferon-γ (IFN-γ) and granzyme A protein upon stimulation. We propose that MASC is a broadly applicable method to identify disease-associated cell populations in high-dimensional single-cell data.

Immune cell profiling to guide therapeutic decisions in rheumatic diseases.

Biomarkers are needed to guide treatment decisions for patients with rheumatic diseases. Although the phenotypic and functional analysis of immune cells is an appealing strategy for understanding immune-mediated disease processes, immune cell profiling currently has no role in clinical rheumatology. New technologies, including mass cytometry, gene expression profiling by RNA sequencing (RNA-seq) and multiplexed functional assays, enable the analysis of immune cell function with unprecedented detail and promise not only a deeper understanding of pathogenesis, but also the discovery of novel biomarkers. The large and complex data sets generated by these technologies--big data--require specialized approaches for analysis and visualization of results. Standardization of assays and definition of the range of normal values are additional challenges when translating these novel approaches into clinical practice. In this Review, we discuss technological advances in the high-dimensional analysis of immune cells and consider how these developments might support the discovery of predictive biomarkers to benefit the practice of rheumatology and improve patient care.

Computational hemodynamic optimization predicts dominant aortic arch selection is driven by embryonic outflow tract orientation in the chick embryo.

In the early embryo, a series of symmetric, paired vessels, the aortic arches, surround the foregut and distribute cardiac output to the growing embryo and fetus. During embryonic development, the arch vessels undergo large-scale asymmetric morphogenesis to form species-specific adult great vessel patterns. These transformations occur within a dynamic biomechanical environment, which can play an important role in the development of normal arch configurations or the aberrant arch morphologies associated with congenital cardiac defects. Arrested migration and rotation of the embryonic outflow tract during late stages of cardiac looping has been shown to produce both outflow tract and several arch abnormalities. Here, we investigate how changes in flow distribution due to a perturbation in the angular orientation of the embryonic outflow tract impact the morphogenesis and growth of the aortic arches. Using a combination of in vivo arch morphometry with fluorescent dye injection and hemodynamics-driven bioengineering optimization-based vascular growth modeling, we demonstrate that outflow tract orientation significantly changes during development and that the associated changes in hemodynamic load can dramatically influence downstream aortic arch patterning. Optimization reveals that balancing energy expenditure with diffusive capacity leads to multiple arch vessel patterns as seen in the embryo, while minimizing energy alone led to the single arch configuration seen in the mature arch of aorta. Our model further shows the critical importance of the orientation of the outflow tract in dictating morphogenesis to the adult single arch and accurately predicts arch IV as the dominant mature arch of aorta. These results support the hypothesis that abnormal positioning of the outflow tract during early cardiac morphogenesis may lead to congenital defects of the great vessels due to altered hemodynamic loading.