Breaking Bad: Uncoupling of Modularity in Centriole Biogenesis and the Generation of Excess Centrioles in Cancer.

Centrosomes are tiny yet complex cytoplasmic structures that perform a variety of roles related to their ability to act as microtubule-organizing centers. Like the genome, centrosomes are single copy structures that undergo a precise semi-conservative replication once each cell cycle. Precise replication of the centrosome is essential for genome integrity, because the duplicated centrosomes will serve as the poles of a bipolar mitotic spindle, and any number of centrosomes other than two will lead to an aberrant spindle that mis-segregates chromosomes. Indeed, excess centrosomes are observed in a variety of human tumors where they generate abnormal spindles in situ that are thought to participate in tumorigenesis by driving genomic instability. At the heart of the centrosome is a pair of centrioles, and at the heart of centrosome duplication is the replication of this centriole pair. Centriole replication proceeds through a complex macromolecular assembly process. However, while centrosomes may contain as many as 500 proteins, only a handful of proteins have been shown to be essential for centriole replication. Our observations suggest that centriole replication is a modular, bottom-up process that we envision akin to building a house; the proper site of assembly is identified, a foundation is assembled at that site, and subsequent modules are added on top of the foundation. Here, we discuss the data underlying our view of modularity in the centriole assembly process, and suggest that non-essential centriole assembly factors take on greater importance in cancer cells due to their function in coordination between centriole modules, using the Monopolar spindles 1 protein kinase and its substrate Centrin 2 to illustrate our model.

Melorheostosis with an associated para-articular enhancing soft tissue mass.

Melorheostosis is a rare non-hereditary sclerosing bone dysplasia which predominantly affects the appendicular skeleton. Although melorheostosis is typically recognized as an osseous lesion, associated soft-tissue components have been reported. Advanced imaging with MRI may allow for more complete evaluation of these soft tissue components; however, there is little information regarding their MRI characteristics which may lead to confusion with malignant processes. We present a case of melorheostosis in a 32-year-old woman with an associated paraarticular enhancing soft tissue mass and emphasize discriminating this from soft tissue sarcoma.

Utility of quantitative apparent diffusion coefficient measurements and normalized apparent diffusion coefficient ratios in the diagnosis of clinically significant peripheral zone prostate cancer.

OBJECTIVE: The aim of this study is to evaluate the utility of quantitative apparent diffusion coefficient (ADC) measurements and normalized ADC ratios in multiparametric MRI for the diagnosis of clinically significant peripheral zone (PZ) prostate cancer particularly among equivocally suspicious prostate lesions. METHODS: A retrospective analysis of 95 patients with PZ lesions by PI-RADSv2 criteria, and who underwent subsequent MRI-US fusion biopsy, was approved by an institutional review board. Two radiologists independently measured ADC values in regions of interest (ROIs) of PZ lesions and calculated normalized ADC ratio based on ROIs in the bladder lumen. Diagnostic performance was evaluated using ROC. Inter observer variability was assessed using intraclass correlation coefficient (ICC). RESULTS: Mean ADC and normalized ADC ratios for clinically significant and non-clinically significant lesions were 0.763 × 10-3 mm2 s-1, 29.8%; and 1.135 × 10-3 mm2 s-1, 47.2% (p < 0.001), respectively. Area under the ROC curve (AUC) was 0.880 [95% CI (0.816-0.944) and 0.885 (95% CI (0.814-0.955)] for ADC and ADC ratio, respectively. Optimal AUC threshold for ADC was 0.843 × 10-3 mm2 s-1 (Sn 70.5%, Sp 88.2%) and for normalized ADC was 33.1% (Sn 75.0%, Sp 95.7%). intraclass correlation coefficient was high at 0.889. CONCLUSION: Quantitative ADC measurement in PZ prostate lesions demonstrates excellent diagnostic performance in differentiating clinically significant from non-clinically significant prostate cancer with high inter observer correlation. Advances In knowledge: Quantitative ADC is presented as an additional method to evaluate lesions in mpMRI of the prostate. This technique may be incorporated in new and existing methods to improve detection and discrimination of clinically significant prostate cancer.

T1rho and T2 relaxation times of the normal adult knee meniscus at 3T: analysis of zonal differences.

BACKGROUND: Prior studies describe histological and immunohistochemical differences in collagen and proteoglycan content in different meniscal zones. The aim of this study is to evaluate horizontal and vertical zonal differentiation of T1rho and T2 relaxation times of the entire meniscus from volunteers without symptom and imaging abnormality. METHODS: Twenty volunteers age between 19 and 38 who have no knee-related clinical symptoms, and no history of prior knee surgeries were enrolled in this study. Two T1rho mapping (b-FFE T1rho and SPGR T1rho) and T2 mapping images were acquired with a 3.0-T MR scanner. Each meniscus was divided manually into superficial and deep zones for horizontal zonal analysis. The anterior and posterior horns of each meniscus were divided manually into white, red-white and red zones for vertical zonal analysis. Zonal differences of average relaxation times among each zone, and both inter- and intra-observer reproducibility were statistically analyzed. RESULTS: In horizontal zonal analysis, T1rho relaxation times of the superficial zone tended to be higher than those of the deep zone, and this difference was statistically significant in the medial meniscal segments (84.3 ms vs 76.0 ms on b-FFE, p < 0.0001 and 96.5 ms vs 91.7 ms on SPGR, p = 0.004). In vertical zonal analysis, T1rho relaxation times of the white zone tended to be higher than those of the red zone, and this difference was statistically significant in the posterior horn of the medical meniscus (88.4 ms vs 77.1 ms on b-FFE, p < 0.001 and 104.9 ms vs 96.8 ms on SPGR, p =0.001). Likewise, T2 relaxation times of the superficial zone were significantly higher than those of the deep zone (80.4 ms vs 74.4 ms in the medial meniscus, p = 0.011). T2 relaxation times of the white zone were significantly higher than those of the red zone in the medial meniscus posterior horn (96.8 ms vs 84.3 ms, p < 0.001) and lateral meniscus anterior horn (104.6 ms vs 84.2 ms, p < 0.0001). Inter-class and intra-class correlation coefficients were excellent (>0.74) or good (0.60-0.74) in all meniscal segments on both horizontal and vertical zonal analysis, except for inter-class correlation coefficients of the lateral meniscus on SPGR. Compared with SPGR T1rho images, b-FFE T1rho images demonstrated more significant zonal differentiation with higher inter- and intra-observer reproducibility. CONCLUSIONS: There are zonal differences in T1rho and T2 relaxation times of the normal meniscus.

Mesenteric vasculature-guided small bowel segmentation on 3-D CT.

Due to its importance and possible applications in visualization, tumor detection and preoperative planning, automatic small bowel segmentation is essential for computer-aided diagnosis of small bowel pathology. However, segmenting the small bowel directly on computed tomography (CT) scans is very difficult because of the low image contrast on CT scans and high tortuosity of the small bowel and its close proximity to other abdominal organs. Motivated by the intensity characteristics of abdominal CT images, the anatomic relationship between the mesenteric vasculature and the small bowel, and potential usefulness of the mesenteric vasculature for establishing the path of the small bowel, we propose a novel mesenteric vasculature map-guided method for small bowel segmentation on high-resolution CT angiography scans. The major mesenteric arteries are first segmented using a vessel tracing method based on multi-linear subspace vessel model and Bayesian inference. Second, multi-view, multi-scale vesselness enhancement filters are used to segment small vessels, and vessels directly or indirectly connecting to the superior mesenteric artery are classified as mesenteric vessels. Third, a mesenteric vasculature map is built by linking vessel bifurcation points, and the small bowel is segmented by employing the mesenteric vessel map and fuzzy connectness. The method was evaluated on 11 abdominal CT scans of patients suspected of having carcinoid tumors with manually labeled reference standard. The result, 82.5% volume overlap accuracy compared with the reference standard, shows it is feasible to segment the small bowel on CT scans using the mesenteric vasculature as a roadmap.

Strategies for improved interpretation of computer-aided detections for CT colonography utilizing distributed human intelligence.

Computer-aided detection (CAD) systems have been shown to improve the diagnostic performance of CT colonography (CTC) in the detection of premalignant colorectal polyps. Despite the improvement, the overall system is not optimal. CAD annotations on true lesions are incorrectly dismissed, and false positives are misinterpreted as true polyps. Here, we conduct an observer performance study utilizing distributed human intelligence in the form of anonymous knowledge workers (KWs) to investigate human performance in classifying polyp candidates under different presentation strategies. We evaluated 600 polyp candidates from 50 patients, each case having at least one polyp ≥6 mm, from a large database of CTC studies. Each polyp candidate was labeled independently as a true or false polyp by 20 KWs and an expert radiologist. We asked each labeler to determine whether the candidate was a true polyp after looking at a single 3D-rendered image of the candidate and after watching a video fly-around of the candidate. We found that distributed human intelligence improved significantly when presented with the additional information in the video fly-around. We noted that performance degraded with increasing interpretation time and increasing difficulty, but distributed human intelligence performed better than our CAD classifier for "easy" and "moderate" polyp candidates. Further, we observed numerous parallels between the expert radiologist and the KWs. Both showed similar improvement in classification moving from single-image to video interpretation. Additionally, difficulty estimates obtained from the KWs using an expectation maximization algorithm correlated well with the difficulty rating assigned by the expert radiologist. Our results suggest that distributed human intelligence is a powerful tool that will aid in the development of CAD for CTC.

Seeing is believing: video classification for computed tomographic colonography using multiple-instance learning.

In this paper, we present development and testing results for a novel colonic polyp classification method for use as part of a computed tomographic colonography (CTC) computer-aided detection (CAD) system. Inspired by the interpretative methodology of radiologists using 3-D fly-through mode in CTC reading, we have developed an algorithm which utilizes sequences of images (referred to here as videos) for classification of CAD marks. For each CAD mark, we created a video composed of a series of intraluminal, volume-rendered images visualizing the detection from multiple viewpoints. We then framed the video classification question as a multiple-instance learning (MIL) problem. Since a positive (negative) bag may contain negative (positive) instances, which in our case depends on the viewing angles and camera distance to the target, we developed a novel MIL paradigm to accommodate this class of problems. We solved the new MIL problem by maximizing a L2-norm soft margin using semidefinite programming, which can optimize relevant parameters automatically. We tested our method by analyzing a CTC data set obtained from 50 patients from three medical centers. Our proposed method showed significantly better performance compared with several traditional MIL methods.

Distributed human intelligence for colonic polyp classification in computer-aided detection for CT colonography.

PURPOSE: To assess the diagnostic performance of distributed human intelligence for the classification of polyp candidates identified with computer-aided detection (CAD) for computed tomographic (CT) colonography. MATERIALS AND METHODS: This study was approved by the institutional Office of Human Subjects Research. The requirement for informed consent was waived for this HIPAA-compliant study. CT images from 24 patients, each with at least one polyp of 6 mm or larger, were analyzed by using CAD software to identify 268 polyp candidates. Twenty knowledge workers (KWs) from a crowdsourcing platform labeled each polyp candidate as a true or false polyp. Two trials involving 228 KWs were conducted to assess reproducibility. Performance was assessed by comparing the area under the receiver operating characteristic curve (AUC) of KWs with the AUC of CAD for polyp classification. RESULTS: The detection-level AUC for KWs was 0.845 ± 0.045 (standard error) in trial 1 and 0.855 ± 0.044 in trial 2. These were not significantly different from the AUC for CAD, which was 0.859 ± 0.043. When polyp candidates were stratified by difficulty, KWs performed better than CAD on easy detections; AUCs were 0.951 ± 0.032 in trial 1, 0.966 ± 0.027 in trial 2, and 0.877 ± 0.048 for CAD (P = .039 for trial 2). KWs who participated in both trials showed a significant improvement in performance going from trial 1 to trial 2; AUCs were 0.759 ± 0.052 in trial 1 and 0.839 ± 0.046 in trial 2 (P = .041). CONCLUSION: The performance of distributed human intelligence is not significantly different from that of CAD for colonic polyp classification.