Gut microbiota composition and changes in patients with sepsis: potential markers for predicting survival.

BACKGROUND: Sepsis can cause immune dysregulation and multiple organ failure in patients and eventually lead to death. The gut microbiota has demonstrated its precise therapeutic potential in the treatment of various diseases. This study aimed to discuss the structural changes of the gut microbiota in patients with sepsis and to analyze the differences in the gut microbiota of patients with different prognoses. METHODS: We conducted a multicenter study in which rectal swab specimens were collected on the first and third days of sepsis diagnosis. A total of 70 specimens were collected, and gut microbiota information was obtained by 16S rRNA analysis. RESULTS: The relative abundance of Enterococcus decreased in rectal swab specimens during the first three days of diagnosis in patients with sepsis, while the relative abundance of inflammation-associated Bacillus species such as Escherichia coli, Enterobacteriaceae, and Bacteroidetes increased. By comparing the differences in the flora of the survival group and the death group, we found that the abundance of Veillonella and Ruminococcus in the death group showed an increasing trend (p < 0.05), while the abundance of Prevotella_6 and Prevotella_sp_S4_BM14 was increased in surviving patients (p < 0.05). CONCLUSIONS: The Firmicutes/Bacteroidetes ratio, reflecting overall gut microbial composition, was significantly lower on day three of sepsis diagnosis. Changes in the abundance of specific gut microbiota may serve as prognostic markers in patients with sepsis.

Effect of early serum phosphate disorder on in-hospital and 28-day mortality in sepsis patients: a retrospective study based on MIMIC-IV database.

BACKGROUND: This study aims to assess the influence of early serum phosphate fluctuation on the short-term prognosis of sepsis patients. METHODS: This retrospective study used the Medical Information Mart for Intensive Care IV database to analyze serum phosphate levels in sepsis patients within 3 days of ICU admission. According to the absolute value of delta serum phosphate (the maximum value minus the minimum value of serum phosphorus measured within three days), the patients were divided into four groups, 0-1.3, 1.4-2.0, 2.1-3.1, and ≥ 3.2 mg/dl. Meanwhile, the direction of delta serum phosphate was compared. With the serum phosphate change group of 0-1.3 mg/dl as the reference group, the relationship between delta serum phosphate and in-hospital mortality and 28-day mortality was analyzed by multivariate Logistics regression analysis. RESULTS: The study involved 1375 sepsis patients. Serum phosphate changes (0-1.3, 1.4-2.0, 2.1-3.1, and ≥ 3.2 mg/dl) correlated with in-hospital and 28-day mortality variations (p = 0.005, p = 0.008). Much higher serum phosphate fluctuation elevated in-hospital and 28-day mortality. Compared to the 0-1.3 mg/dl change group, adjusted odds ratios (OR) in other groups for in-hospital mortality were 1.25 (0.86-1.81), 1.28 (0.88-1.86), and 1.63 (1.10-2.43), and for 28-day mortality were 1.21 (0.86-1.72), 1.10 (0.77-1.57), and 1.49 (1.03-2.19). Under the trend of increasing serum phosphate, the ORs of in-hospital mortality and 28-day mortality in ≥ 3.2 mg/dl group were 2.52 and 2.01, respectively. CONCLUSION: In conclude, the delta serum phosphate ≥ 3.2 mg/dl was associated with in-hospital mortality and 28-day mortality in patients with sepsis.

Gut microbiota disruption during sepsis and the influence of innate metabolites on sepsis prognosis.

Sepsis causes high mortality in intensive care units. Although there have been many studies on the gut microbiota in patients with sepsis, the impact of sepsis on the gut microbiota has not been directly determined because the treatment of sepsis also affects the gut microbiota. Therefore, we designed this animal experiment to explore gut microbiota alterations during sepsis. Mice were divided into two groups, mice that survived less than 3 days and mice that survived more than 3 days. Fecal samples collected on the day of cecal ligation and puncture (CLP), as well as on the 3rd and 7th days after CLP, were subjected to microbial community analysis and nontargeted metabolomics analysis. The results showed significantly lower bacterial diversity in fecal samples after CLP. At the genus level, the fecal samples obtained on the 3rd and 7th days after CLP exhibited significantly increased relative abundances of Bacteroides, Helicobacter, etc., and significantly decreased relative abundances of Alloprevotella, Prevotella, etc. Innate metabolite levels were significantly different in mice that survived less than 3 days and mice that survived more than 3 days. In conclusion, CLP-induced sepsis in mice changes the structure of the gut microbiome, and innate metabolites affect the prognosis of septic mice.

Early gut microbiological changes and metabolomic changes in patients with sepsis: a preliminary study.

The gut microbiota is closely related to the development of sepsis. The aim of this study was to explore changes in the gut microbiota and gut metabolism, as well as potential relationships between the gut microbiota and environmental factors in the early stages of sepsis. Fecal samples were collected from 10 septic patients on the first and third days following diagnosis in this study. The results showed that in the early stages of sepsis, the gut microbiota is dominated by microorganisms that are tightly associated with inflammation, such as Escherichia-Shigella, Enterococcus, Enterobacteriaceae, and Streptococcus. On sepsis day 3 compared to day 1, there was a significant decrease in Lactobacillus and Bacteroides and a significant increase in Enterobacteriaceae, Streptococcus, and Parabacteroides. Culturomica_massiliensis, Prevotella_7 spp., Prevotellaceae, and Pediococcus showed significant differences in abundance on sepsis day 1, but not on sepsis day 3. Additionally, 2-keto-isovaleric acid 1 and 4-hydroxy-6-methyl-2-pyrone metabolites significantly increased on sepsis day 3 compared to day 1. Prevotella_7 spp. was positively correlated with phosphate and negatively correlated with 2-keto-isovaleric acid 1 and 3-hydroxypropionic acid 1, while Prevotella_9 spp. was positively correlated with sequential organ failure assessment score, procalcitonin and intensive care unit stay time. In conclusion, the gut microbiota and metabolites are altered during sepsis, with some beneficial microorganisms decreasing and some pathogenic microorganisms increasing. Furthermore, Prevotellaceae members may play different roles in the intestinal tract, with Prevotella_7 spp. potentially possessing beneficial health properties and Prevotella_9 spp. potentially playing a promoting role in sepsis.

Investigation on the proton range uncertainty with spectral CT-based virtual monoenergetic images.

OBJECTIVE: The stopping power ratio (SPR) prediction error will contribute to the range uncertainty of proton therapy. Spectral CT is promising in reducing the uncertainty in SPR estimation. The purpose of this research is to determine the optimal energy pairs of SPR prediction for each tissue and to evaluate the dose distribution and range difference between the spectral CT with the optimal energy pairs method and the single energy CT (SECT) method. METHODS: A new method was proposed based on image segmentation to calculate the proton dose with spectral CT images for the head and body phantom. CT number of each organ region were converted to SPR with the optimal energy pairs of each organ. The CT images were segmented into different organ parts with thresholding method. Virtual monoenergetic (VM) images from 70 keV to 140 keV were investigated to determine the optimal energy pairs for each organ based on Gammex 1467 phantom. The beam data of Shanghai Advanced Proton Therapy facility (SAPT) was employed in matRad (an open-source software for radiation treatment planning) for the dose calculation. RESULTS: The optimal energy pairs were obtained for each tissue. The dose distribution of two tumor sites (brain and lung) were calculated with the aforementioned optimal energy pairs. The maximum dose deviation between spectral CT and SECT at the target region was 2.57% and 0.84% for the lung tumor and brain tumor respectively. The range difference between spectral and SECT was significant with 1.8411 mm for the lung tumor. γ passing rate was 85.95% and 95.49% for the lung tumor and brain tumor with the criterion 2%/2 mm. CONCLUSIONS: This work presents a way to determine the optimal energy pairs for each organ and to calculate the dose distribution based on the more accurate SPR prediction.

A Straightforward Bifurcation Pattern-Based Fundus Image Registration Method.

Fundus image registration is crucial in eye disease examination, as it enables the alignment of overlapping fundus images, facilitating a comprehensive assessment of conditions like diabetic retinopathy, where a single image's limited field of view might be insufficient. By combining multiple images, the field of view for retinal analysis is extended, and resolution is enhanced through super-resolution imaging. Moreover, this method facilitates patient follow-up through longitudinal studies. This paper proposes a straightforward method for fundus image registration based on bifurcations, which serve as prominent landmarks. The approach aims to establish a baseline for fundus image registration using these landmarks as feature points, addressing the current challenge of validation in this field. The proposed approach involves the use of a robust vascular tree segmentation method to detect feature points within a specified range. The method involves coarse vessel segmentation to analyze patterns in the skeleton of the segmentation foreground, followed by feature description based on the generation of a histogram of oriented gradients and determination of image relation through a transformation matrix. Image blending produces a seamless registered image. Evaluation on the FIRE dataset using registration error as the key parameter for accuracy demonstrates the method's effectiveness. The results show the superior performance of the proposed method compared to other techniques using vessel-based feature extraction or partially based on SURF, achieving an area under the curve of 0.526 for the entire FIRE dataset.

Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images.

The accurate segmentation of the optic disc (OD) in fundus images is a crucial step for the analysis of many retinal diseases. However, because of problems such as vascular occlusion, parapapillary atrophy (PPA), and low contrast, accurate OD segmentation is still a challenging task. Therefore, this paper proposes a multiple preprocessing hybrid level set model (HLSM) based on area and shape for OD segmentation. The area-based term represents the difference of average pixel values between the inside and outside of a contour, while the shape-based term measures the distance between a prior shape model and the contour. The average intersection over union (IoU) of the proposed method was 0.9275, and the average four-side evaluation (FSE) was 4.6426 on a public dataset with narrow-angle fundus images. The IoU was 0.8179 and the average FSE was 3.5946 on a wide-angle fundus image dataset compiled from a hospital. The results indicate that the proposed multiple preprocessing HLSM is effective in OD segmentation.

Identification of distinct clinical phenotypes of acute respiratory distress syndrome with differential responses to treatment.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a heterogeneous syndrome, and the identification of homogeneous subgroups and phenotypes is the first step toward precision critical care. We aimed to explore whether ARDS phenotypes can be identified using clinical data, are reproducible and are associated with clinical outcomes and treatment response. METHODS: This study is based on a retrospective analysis of data from the telehealth intensive care unit (eICU) collaborative research database and three ARDS randomized controlled trials (RCTs) (ALVEOLI, FACTT and SAILS trials). We derived phenotypes in the eICU by cluster analysis based on clinical data and compared the clinical characteristics and outcomes of each phenotype. The reproducibility of the derived phenotypes was tested using the data from three RCTs, and treatment effects were evaluated. RESULTS: Three clinical phenotypes were identified in the training cohort of 3875 ARDS patients. Of the three phenotypes identified, phenotype I (n = 1565; 40%) was associated with fewer laboratory abnormalities, less organ dysfunction and the lowest in-hospital mortality rate (8%). Phenotype II (n = 1232; 32%) was correlated with more inflammation and shock and had a higher mortality rate (18%). Phenotype III (n = 1078; 28%) was strongly correlated with renal dysfunction and acidosis and had the highest mortality rate (22%). These results were validated using the data from the validation cohort (n = 3670) and three RCTs (n = 2289) and had reproducibility. Patients with these ARDS phenotypes had different treatment responses to randomized interventions. Specifically, in the ALVEOLI cohort, the effects of ventilation strategy (high PEEP vs low PEEP) on ventilator-free days differed by phenotype (p = 0.001); in the FACTT cohort, there was a significant interaction between phenotype and fluid-management strategy for 60-day mortality (p = 0.01). The fluid-conservative strategy was associated with improved mortality in phenotype II but had the opposite effect in phenotype III. CONCLUSION: Three clinical phenotypes of ARDS were identified and had different clinical characteristics and outcomes. The analysis shows evidence of a phenotype-specific treatment benefit in the ALVEOLI and FACTT trials. These findings may improve the identification of distinct subsets of ARDS patients for exploration in future RCTs.

Lung Nodule Segmentation with a Region-Based Fast Marching Method.

When dealing with computed tomography volume data, the accurate segmentation of lung nodules is of great importance to lung cancer analysis and diagnosis, being a vital part of computer-aided diagnosis systems. However, due to the variety of lung nodules and the similarity of visual characteristics for nodules and their surroundings, robust segmentation of nodules becomes a challenging problem. A segmentation algorithm based on the fast marching method is proposed that separates the image into regions with similar features, which are then merged by combining regions growing with k-means. An evaluation was performed with two distinct methods (objective and subjective) that were applied on two different datasets, containing simulation data generated for this study and real patient data, respectively. The objective experimental results show that the proposed technique can accurately segment nodules, especially in solid cases, given the mean Dice scores of 0.933 and 0.901 for round and irregular nodules. For non-solid and cavitary nodules the performance dropped-0.799 and 0.614 mean Dice scores, respectively. The proposed method was compared to active contour models and to two modern deep learning networks. It reached better overall accuracy than active contour models, having comparable results to DBResNet but lesser accuracy than 3D-UNet. The results show promise for the proposed method in computer-aided diagnosis applications.

Probe Sector Matching for Freehand 3D Ultrasound Reconstruction.

A 3D ultrasound image reconstruction technique, named probe sector matching (PSM), is proposed in this paper for a freehand linear array ultrasound probe equipped with multiple sensors, providing the position and attitude of the transducer and the pressure between the transducer and the target surface. The proposed PSM method includes three main steps. First, the imaging target and the working range of the probe are set to be the center and the radius of the imaging field of view, respectively. To reconstruct a 3D volume, the positions of all necessary probe sectors are pre-calculated inversely to form a sector database. Second, 2D cross-section probe sectors with the corresponding optical positioning, attitude and pressure information are collected when the ultrasound probe is moving around the imaging target. Last, an improved 3D Hough transform is used to match the plane of the current probe sector to the existing sector images in the sector database. After all pre-calculated probe sectors are acquired and matched into the 3D space defined by the sector database, a 3D ultrasound reconstruction is completed. The PSM is validated through two experiments: a virtual simulation using a numerical model and a lab experiment using a real physical model. The experimental results show that the PSM effectively reduces the errors caused by changes in the target position due to the uneven surface pressure or the inhomogeneity of the transmission media. We conclude that the PSM proposed in this study may help to design a lightweight, inexpensive and flexible ultrasound device with accurate 3D imaging capacity.

A super-resolution method-based pipeline for fundus fluorescein angiography imaging.

BACKGROUND: Fundus fluorescein angiography (FFA) imaging is a standard diagnostic tool for many retinal diseases such as age-related macular degeneration and diabetic retinopathy. High-resolution FFA images facilitate the detection of small lesions such as microaneurysms, and other landmark changes, in the early stages; this can help an ophthalmologist improve a patient's cure rate. However, only low-resolution images are available in most clinical cases. Super-resolution (SR), which is a method to improve the resolution of an image, has been successfully employed for natural and remote sensing images. To the best of our knowledge, no one has applied SR techniques to FFA imaging so far. METHODS: In this work, we propose a SR method-based pipeline for FFA imaging. The aim of this pipeline is to enhance the image quality of FFA by using SR techniques. Several SR frameworks including neighborhood embedding, sparsity-based, locally-linear regression and deep learning-based approaches are investigated. Based on a clinical FFA dataset collected from Second Affiliated Hospital to Xuzhou Medical University, each SR method is implemented and evaluated for the pipeline to improve the resolution of FFA images. RESULTS AND CONCLUSION: As shown in our results, most SR algorithms have a positive impact on the enhancement of FFA images. Super-resolution forests (SRF), a random forest-based SR method has displayed remarkable high effectiveness and outperformed other methods. Hence, SRF should be one potential way to benefit ophthalmologists by obtaining high-resolution FFA images in a clinical setting.

MRI evaluation of benign prostatic hyperplasia: Correlation with international prostate symptom score.

PURPOSE: To investigate the correlation between magnetic resonance imaging (MRI)-derived prostate parameters and benign prostatic hyperplasia (BPH) type with the International Prostate Symptom Score (IPSS). MATERIALS AND METHODS: In all, 61 patients (median age, 60; range, 41-81 years) who underwent preoperative MRI and prostatectomy were included in this retrospective study. The MRI-based parameters including total prostate volume (TPV), transition zone (TZ) volume (TZV), TZ index, intravesical prostatic protrusion (IPP), the anterior fibromuscular stroma (AFMS) distance, prostatic urethral angle, bladder wall thickness, urethral wall thickness, urethral compression, urethral wall changes, and BPH type were correlated with total IPSS, IPSS-storage symptom (IPSS-ss), IPSS-voiding symptom (IPSS-vs), and responses to the individual IPSS questions using Spearman (ρ) or Pearson (r) correlation coefficients, one-way analysis of variance (ANOVA), and multiple linear regression. RESULTS: TPV (r = 0.414, P = 0.001), TZV (r = 0.405, P = 0.001), IPP (r = 0.270, P = 0.04), and AFMS distance (r = 0.363, P = 0.004) correlated with total IPSS. In multiple linear regression analysis, TZV was the only predictor for total IPSS (P = 0.001), IPSS-ss (P < 0.001), IPSS-vs (P = 0.03), and the scores for the IPSS questions 1 (P = 0.03) and 4 (P = 0.001). TPV was a predictor of the scores for questions 2 (P = 0.003), 3 (P = 0.009), and 7 (P < 0.001). CONCLUSION: Several MRI-derived prostate measurements (TPV, TZV, IPP, AFMS distance) correlated with total IPSS. TZV was the only predictor for total IPSS based on multiple regression analysis. LEVEL OF EVIDENCE: 3 J. Magn. Reson. Imaging 2017;45:917-925.

miR-320a regulates high mobility group box 1 expression and inhibits invasion and metastasis in hepatocellular carcinoma.

BACKGROUND & AIMS: Several studies have shown that miR-320a induces apoptosis, inhibits cell proliferation, and affects cell cycle progression as a tumour suppressor in many cancers. However, the involvement of miR-320a in the invasion and metastasis of hepatocellular carcinoma (HCC) is still unknown. METHODS: Endogenous miR-320a and high mobility group box 1 (HMGB1) expressions were assayed by real-time PCR. Luciferase activities were measured using a dual-luciferase reporter assay system. Western blots were used to determine the protein expressions of HMGB1, MMP2, and MMP9. Invasion and metastasis of tumour cells were, respectively, evaluated by the transwell invasion assay and the wound healing assay. RESULTS: The expression of miR-320a was significantly decreased in 24 of 32 (75%) HCC tissues and associated with the invasion and metastasis of HCC. Furthermore, we demonstrated that HMGB1 was a direct target of miR-320a and there was a significant negative correlation between miR-320a and HMGB1 expression in HCC. Ectopic expression or inhibition of miR-320a potently regulated the invasion and metastasis of HCC cells in HMGB1-dependent manner. CONCLUSIONS: Our results showed that miR-320a was involved in the invasion and metastasis by targeting HMGB1 and had an anti-metastasis effect in HCC.

Pilot Study of the Use of Hybrid Multidimensional T2-Weighted Imaging-DWI for the Diagnosis of Prostate Cancer and Evaluation of Gleason Score.

OBJECTIVE: The objective of our study was to evaluate the role of a hybrid T2-weighted imaging-DWI sequence for prostate cancer diagnosis and differentiation of aggressive prostate cancer from nonaggressive prostate cancer. MATERIALS AND METHODS: Twenty-one patients with prostate cancer who underwent preoperative 3-T MRI and prostatectomy were included in this study. Patients underwent a hybrid T2-weighted imaging-DWI examination consisting of DW images acquired with TEs of 47, 75, and 100 ms and b values of 0 and 750 s/mm(2). The apparent diffusion coefficient (ADC) and T2 were calculated for cancer and normal prostate ROIs at each TE and b value. Changes in ADC and T2 as a function of increasing the TE and b value, respectively, were analyzed. A new metric termed "PQ4" was defined as the percentage of voxels within an ROI that has increasing T2 with increasing b value and has decreasing ADC with increasing TE. RESULTS: ADC values were significantly higher in normal ROIs than in cancer ROIs at all TEs (p < 0.0001). With increasing TE, the mean ADC increased 3% in cancer ROIs and increased 12% in normal ROIs. T2 was significantly higher in normal ROIs than in cancer ROIs at both b values (p ≤ 0.0002). The mean T2 decreased with increasing b value in cancer ROIs (ΔT2 = -17 ms) and normal ROIs (ΔT2 = -52 ms). PQ4 clearly differentiated normal ROIs from prostate cancer ROIs (p = 0.0004) and showed significant correlation with Gleason score (ρ = 0.508, p < 0.0001). CONCLUSION: Hybrid MRI measures the response of ADC and T2 to changing TEs and b values, respectively. This approach shows promise for detecting prostate cancer and determining its aggressiveness noninvasively.

A hyperbranched polyethylenimine functionalized stationary phase for hydrophilic interaction liquid chromatography.

A hyperbranched stationary phase for hydrophilic interaction liquid chromatography (HILIC) has been prepared by grafting polyethylenimine (PEI) onto silica gel (termed as PEI-Sil). Rich primary, secondary, and tertiary amino groups associated with PEI render its good hydrophility. More importantly, the hyperbranched structure of PEI molecule is greatly helpful in improving interaction with polar analytes. For several kinds of model polar compounds, including organic acids, nucleosides, nucleic acid bases, amino acids, cephalosporins, and non-reducing sugars, PEI-Sil demonstrated excellent separation performance in terms of running stability, reproducibility, and separation efficiency (e.g., plate count ~74,000/m). In addition, PEI-Sil also exhibited much better separation selectivity toward inorganic anions when operated in the mode of ion chromatography relative to a commercial amino propyl-bonded column.

Dynamic Contrast-enhanced MR Imaging Curve-type Analysis: Is It Helpful in the Differentiation of Prostate Cancer from Healthy Peripheral Zone?

PURPOSE: To evaluate the performance and interobserver agreement of qualitative dynamic contrast material enhanced magnetic resonance (MR) imaging curve analysis as described in the Prostate Imaging Reporting and Data System (PI-RADS) for the differentiation of prostate cancer (PCa) from healthy prostatic tissue in the peripheral zone (PZ). MATERIALS AND METHODS: This Health Insurance Portability and Accountability Act-compliant institutional review board-approved retrospective analysis included 120 consecutive pretreatment dynamic contrast-enhanced (DCE) MR imaging PCa examinations. Regions of interest (ROIs) were placed in 251 spots, including 95 (37.8%) in healthy PZ tissue and 156 (62.2%) in PCa, by using detailed histologic-multiparametric MR correlation review. Three radiologists reviewed the DCE time curves and assessed qualitative curve types as described in PI-RADS: type 1 (progressive), type 2 (plateau), or type 3 (washout). Receiver operating characteristic curve analysis was used to assess accuracy in differentiating PCa from healthy tissue on the basis of curve type, and κ was calculated to assess interobserver agreement. RESULTS: Receiver operating characteristic curves were similar for all observers, but mean areas under the receiver operating characteristic curve were poor (0.58 ± 0.04 [standard deviation] to 0.63 ± 0.04). No differences in accuracy were seen for varying DCE time resolution and imaging length. Observer agreement in assessment of type 3 versus types 1 or 2 curves was substantial (0.66 < κ < 0.79), better for PCa ROIs than for healthy-tissue ROIs. The agreement between type 1 and type 2 curves was moderate to substantial (0.49 < κ < 0.78). CONCLUSION: Qualitative DCE MR imaging time-curve-type analysis performs poorly for differentiation of PCa from healthy prostatic tissue. Interobserver agreement is excellent in assessment of type 3 curves but only moderate for type 1 and 2 curves.

MRI-based prostate volume-adjusted prostate-specific antigen in the diagnosis of prostate cancer.

PURPOSE: To determine whether prostate-specific antigen (PSA) levels adjusted by prostate and zonal volumes estimated from magnetic resonance imaging (MRI) improve the diagnosis of prostate cancer (PCa) and differentiation between patients who harbor high-Gleason-sum PCa and those without PCa. MATERIALS AND METHODS: This retrospective study was Health Insurance Portability and Accountability Act (HIPAA)-compliant and approved by the Institutional Review Board of participating medical institutions. T2 -weighted MR images were acquired for 61 PCa patients and 100 patients with elevated PSA but without PCa. Computer methods were used to segment prostate and zonal structures and to estimate the total prostate and central-gland (CG) volumes, which were then used to calculate CG volume fraction, PSA density, and PSA density adjusted by CG volume. These quantities were used to differentiate patients with and without PCa. Area under the receiver operating characteristic curve (AUC) was used as the figure of merit. RESULTS: The total prostate and CG volumes, CG volume fraction, and PSA density adjusted by the total prostate and CG volumes were statistically significantly different between patients with PCa and patients without PCa (P ≤ 0.007). AUC values for the total prostate and CG volumes, and PSA density adjusted by CG volume, were 0.68 ± 0.04, 0.68 ± 0.04, and 0.66 ± 0.04, respectively, and were significantly better than that of PSA (P < 0.02), for differentiation of PCa patients from patients without PCa. CONCLUSION: The total prostate and CG volumes estimated from T2 -weighted MR images and PSA density adjusted by these volumes can improve the effectiveness of PSA for the diagnosis of PCa and differentiation of high-Gleason-sum PCa patients from patients without PCa.

Short-term reproducibility of apparent diffusion coefficient estimated from diffusion-weighted MRI of the prostate.

PURPOSE: The purpose of the study is to determine short-term reproducibility of apparent diffusion coefficient (ADC) estimated from diffusion-weighted magnetic resonance (DW-MR) imaging of the prostate. METHODS: Fourteen patients with biopsy-proven prostate cancer were studied under an Institutional Review Board-approved protocol. Each patient underwent two, consecutive and identical DW-MR scans on a 3T system. ADC values were calculated from each scan and a deformable registration was performed to align corresponding images. The prostate and cancerous regions of interest (ROIs) were independently analyzed by two radiologists. The prostate volume was analyzed by sextant. Per-voxel absolute and relative percentage variations in ADC were compared between sextants. Per-voxel and per-ROI variations in ADC were calculated for cancerous ROIs. RESULTS: Per-voxel absolute difference in ADC in the prostate ranged from 0 to 1.60 × 10(-3) mm(2)/s (per-voxel relative difference 0% to 200%, mean 10.5%). Variation in ADC was largest in the posterior apex (0% to 200%, mean 11.6%). Difference in ADC variation between sextants was not statistically significant. Cancer ROIs' per-voxel variation in ADC ranged from 0.001 × 10(-3) to 0.841 × 10(-3) mm(2)/s (0% to 67.4%, mean 11.2%) and per-ROI variation ranged from 0 to 0.463 × 10(-3) mm(2)/s (mean 0.122 × 10(-3) mm(2)/s). CONCLUSIONS: Variation in ADC within the human prostate is reasonably small, and is on the order of 10%.

Validation of quantitative analysis of multiparametric prostate MR images for prostate cancer detection and aggressiveness assessment: a cross-imager study.

PURPOSE: To validate three previously identified quantitative image features across multiparametric magnetic resonance (MR) images acquired with imagers made by two different manufacturers to differentiate prostate cancer (PC) from normal prostatic tissue and to assess cancer aggressiveness. MATERIALS AND METHODS: This study was HIPAA-compliant and approved by the institutional review board. Preoperative 1.5-T multiparametric endorectal MR images of 119 PC patients (dataset A, 71 patients; dataset B, 48 patients) were analyzed, and 265 PC and normal peripheral zone regions of interests (ROIs) were identified through histologic and MR consensus review. The 10th percentile average apparent diffusion coefficient (ADC) value, average ADC value, and skewness of T2-weighted signal-intensity histogram were evaluated with area under the receiver operating characteristic curve (AUC). The image features were combined with a linear discriminant analysis classifier and evaluated both on the image dataset of each type of imager alone (leave-one-patient-out evaluation) and across the datasets (training on one dataset, testing on the other). Spearman correlation coefficient was calculated between the image features and ROI-specific Gleason scores. RESULTS: AUC values of the image features combined were 0.95 ± 0.02 (standard error) and 0.88 ± 0.03 on dataset B and dataset A alone, respectively, and 0.96 ± 0.02 and 0.89 ± 0.03 when training on dataset A and testing on dataset B and vice versa, respectively. Spearman correlation coefficients between Gleason scores and the ADC features were between -0.27 and -0.34. CONCLUSION: Consistently across images from datasets A and B, the 10th percentile ADC value, average ADC value, and T2-weighted skewness can distinguish PC from normal-tissue ROIs, and ADC features correlate moderately with ROI-specific Gleason scores.

Revisiting the central gland anatomy via MRI: does the central gland extend below the level of verumontanum?

PURPOSE: To determine whether the central gland of the prostate, composed of both central and transition zones, extends below the verumontanum in patients over age 43 based on prostate magnetic resonance imaging (MRI). MATERIALS AND METHODS: In all, 82 patients who underwent preoperative, multiparametric endorectal MRI (1.5T and 3T) at our institution were included. The central gland was defined as a combination of the central zone and transition zone. Two radiologists measured central gland extension below verumontanum using axial, coronal, and sagittal T2-weighted images and coregistration picture archiving and communication system software. RESULTS: The final cohort consisted of 63 patients with a mean age of 60.2 years (standard deviation: 7.1, range: 43-76). The central gland extended below the verumontanum in 60/63 (95%) patients. Mean central gland extension below the verumontanum was 6.5 mm (standard deviation = 3.7, range: 0, 18). Weak (r = 0.35), but significant (P = 0.005) positive correlation was found between age and the amount of central gland extension below the verumontanum. CONCLUSION: Contrary to McNeal's classical prostatic anatomy teaching, the central gland extends below the verumontanum in 95% of men over 43, likely due to deformation of the gland by benign prostatic hyperplasia. It is important for pathologists to be aware of this observation for accurate characterization of the zonal origin of prostate cancer below the level of the verumontanum.