Image Registration of In Vivo Micro-Ultrasound and Ex Vivo Pseudo-Whole Mount Histopathology Images of the Prostate: A Proof-of-Concept Study

Early diagnosis of prostate cancer significantly improves a patient's 5-year survival rate. Biopsy of small prostate cancers is improved with image-guided biopsy. MRI-ultrasound fusion-guided biopsy is sensitive to smaller tumors but is underutilized due to the high cost of MRI and fusion equipment. Micro-ultrasound (micro-US), a novel high-resolution ultrasound technology, provides a cost-effective alternative to MRI while delivering comparable diagnostic accuracy. However, the interpretation of micro-US is challenging due to subtle gray scale changes indicating cancer vs normal tissue. This challenge can be addressed by training urologists with a large dataset of micro-US images containing the ground truth cancer outlines. Such a dataset can be mapped from surgical specimens (histopathology) onto micro-US images via image registration. In this paper, we present a semi-automated pipeline for registering in vivo micro-US images with ex vivo whole-mount histopathology images. Our pipeline begins with the reconstruction of pseudo-whole-mount histopathology images and a 3-dimensional (3D) micro-US volume. Each pseudo-whole-mount histopathology image is then registered with the corresponding axial micro-US slice using a two-stage approach that estimates an affine transformation followed by a deformable transformation. We evaluated our registration pipeline using micro-US and histopathology images from 18 patients who underwent radical prostatectomy. The results showed a Dice coefficient of 0.94 and a landmark error of 2.7 mm, indicating the accuracy of our registration pipeline. This proof-of-concept study demonstrates the feasibility of accurately aligning micro-US and histopathology images. To promote transparency and collaboration in research, we will make our code and dataset publicly available

Effectiveness of pneumococcal polysaccharide vaccine for preschool-age children with chronic disease.

To estimate the effectiveness of pneumococcal polysaccharide vaccine, we serotyped isolates submitted to the Pneumococcal Sentinel Surveillance System from 1984 to 1996 from 48 vaccinated and 125 unvaccinated children 2 to 5 years of age. Effectiveness against invasive disease caused by serotypes included in the vaccine was 63%. Effectiveness against serotypes in the polysaccharide vaccine but not in a proposed seven-valent protein conjugate vaccine was 94%

Adrenal schwannoma can be FDG-Avid on PET/CT: case report and review of historic institutional pathology

Abstract Schwannomas are benign, generally indolent tumors of neural crest origin and comprise the most common histologic tumor of peripheral nerves. Schwannomas are a rare histology for retroperitoneal tumors and very rare histologic findings for tumors of the adrenal gland with fewer than 50 cases in the reported literature. Here we present a case report of a non-hormonally functional but metabolically active adrenal tumor with indeterminate imaging characteristics with final pathology showing a 6.1 cm adrenal schwannoma as well as historical institutional pathology review revealing two additional cases

Targeted Prostate Biopsy Using 68Gallium PSMA-PET/CT for Image Guidance.

Prostate specific membrane antigen (PSMA) scanning is a sensitive method of prostate cancer detection. In a 71 y.o. man with a PSA of 49 (6%F), 4 negative MRI studies and 6 negative biopsies over an 8 year interval, a 68Ga-PSMA PET/CT scan showed a PSMA-avid spot in the prostate. Using image fusion technology, the lesion was target-biopsied and Gleason 3 + 4 = 7 (cancer core length of 12 mm) was identified. This case may herald a new application for PSMA scanning and prostate cancer imaging

PathAL: An Active Learning Framework for Histopathology Image Analysis

Deep neural networks, in particular convolutional networks, have rapidly become a popular choice for analyzing histopathology images. However, training these models relies heavily on a large number of samples manually annotated by experts, which is cumbersome and expensive. In addition, it is difficult to obtain a perfect set of labels due to the variability between expert annotations. This paper presents a novel active learning (AL) framework for histopathology image analysis, named PathAL. To reduce the required number of expert annotations, PathAL selects two groups of unlabeled data in each training iteration: one "informative" sample that requires additional expert annotation, and one "confident predictive" sample that is automatically added to the training set using the model's pseudo-labels. To reduce the impact of the noisy-labeled samples in the training set, PathAL systematically identifies noisy samples and excludes them to improve the generalization of the model. Our model advances the existing AL method for medical image analysis in two ways. First, we present a selection strategy to improve classification performance with fewer manual annotations. Unlike traditional methods focusing only on finding the most uncertain samples with low prediction confidence, we discover a large number of high confidence samples from the unlabeled set and automatically add them for training with assigned pseudo-labels. Second, we design a method to distinguish between noisy samples and hard samples using a heuristic approach. We exclude the noisy samples while preserving the hard samples to improve model performance. Extensive experiments demonstrate that our proposed PathAL framework achieves promising results on a prostate cancer Gleason grading task, obtaining similar performance with 40% fewer annotations compared to the fully supervised learning scenario. An ablation study is provided to analyze the effectiveness of each component in PathAL, and a pathologist reader study is conducted to validate our proposed algorithm

Molecular Hallmarks of Multiparametric Magnetic Resonance Imaging Visibility in Prostate Cancer

Multiparametric magnetic resonance imaging (mpMRI) has transformed the management of localized prostate cancer by improving identification of clinically significant disease at diagnosis. Approximately 20% of primary prostate tumors are invisible to mpMRI, and we hypothesize that this invisibility reflects fundamental molecular properties of the tumor. We therefore profiled the genomes and transcriptomes of 40 International Society of Urological Pathology grade 2 tumors: 20 mpMRI-invisible (Prostate Imaging-Reporting and Data System [PI-RADS] v2 <3) and 20 mpMRI-visible (PI-RADS v2 5) tumors. mpMRI-visible tumors were enriched in hallmarks of nimbosus, an aggressive pathological, molecular, and microenvironmental phenomenon in prostate cancer. These hallmarks included genomes with increased mutation density, a higher prevalence of intraductal carcinoma/cribriform architecture pathology, and altered abundance of 102 transcripts, including overexpression of noncoding RNAs such as SCHLAP1. Multiple small nucleolar RNAs (snoRNAs) were identified, and a snoRNA signature synergized with nimbosus hallmarks to discriminate visible from invisible tumors. These data suggest a confluence of aggressive molecular and microenvironmental phenomena underlie mpMRI visibility of localized prostate cancer. PATIENT SUMMARY: We examined the correlation between tumor biology and magnetic resonance imaging (MRI) visibility in a group of patients with low- intermediate-risk prostate cancer. We observed that MRI findings are associated with biological features of aggressive prostate cancer

Dynamic contrast-enhanced (DCE) MR imaging: the role of qualitative and quantitative parameters for evaluating prostate tumors stratified by Gleason score and PI-RADS v2

PurposeTo investigate the role of qualitative and quantitative DCE-MRI parameters in prostate cancer (PCa) stratified by whole-mount histopathology (WMHP) Gleason score (GS) and PI-RADSv2.MethodsThis retrospective study included 323 PCa tumors in 254 men, who underwent 3T MRI prior to prostatectomy, 7/2009-12/2016. Qualitative DCE curve types included type 1 (progressive), type 2 (plateau) and type 3 (washout). Quantitative DCE-MRI pharmacokinetic (PK) parameters included Ktrans (influx volume transfer coefficient), Kep (efflux reflux rate constant) and iAUC (initial area under the curve). DCE-MRI features of true positive lesions were evaluated for overall, index, transition zone (TZ) and peripheral zone (PZ), based on GS grade (low = 6, high > 6) and PI-RADSv2 score using SPSSv24.ResultsThere were 57 (17.6%) low-grade and 266 (82.4%) high-grade PCa lesions. PI-RADSv2 3, 4 and 5 included 106, 120 and 97 lesions, respectively. 251 (77.7%) and 72 (22.3%) lesions were located in PZ and TZ, respectively. High-grade lesions had significantly higher proportion of Type 3 curves compared to low-grade lesions in overall (70.3% vs. 54.4%) and TZ (73.5% vs. 43.5%). As PI-RADSv2 increased, the proportion of type 3 curve significantly increased for overall (80.4-51.9%), index (80.4-54.7%) and PZ (78.7-52.1%) lesions. Among PK parameters, Ktrans (0.43 vs 0.32) and iAUC (8.99 vs 6.9) for overall PCa, Ktrans (0.43 vs 0.31) and iAUC (9 vs 6.67) for PZ PCa, and iAUC (8.94 vs 7.42) for index PCa were significantly higher for high-grade versus low-grade lesions. Also, Ktrans (0.51-0.34), Kep (1.75-1.29) and iAUC (9.79-7.6) for overall PCa, Ktrans (0.53-0.32), Kep (1.81-1.26) and iAUC (9.83-7.34) for PZ PCa; and Kep (1.79-1.17) and iAUC (11.3-8.45) for index PCa increased significantly with a higher PI-RADSv2 score.ConclusionsThe results of study show the possible utility of qualitative and quantitative DCE-MRI parameters for assessment of PCa GS and PI-RADSv2 categorization

Prostate cancer multiparametric magnetic resonance imaging visibility is a tumor-intrinsic phenomena.

Multiparametric magnetic resonance imaging (mpMRI) is an emerging standard for diagnosing and prognosing prostate cancer, but ~ 20% of clinically significant tumors are invisible to mpMRI, as defined by the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) score of one or two. To understand the biological underpinnings of tumor visibility on mpMRI, we examined the proteomes of forty clinically significant tumors (i.e., International Society of Urological Pathology (ISUP) Grade Group 2)-twenty mpMRI-visible and twenty mpMRI-invisible, with matched histologically normal prostate. Normal prostate tissue was indistinguishable between patients with visible and invisible tumors, and invisible tumors closely resembled the normal prostate. These data indicate that mpMRI-visibility arises when tumor evolution leads to large-magnitude proteomic divergences from histologically normal prostate