High-resolution Diffusion-weighted Imaging to Detect Changes in Tumor Size and ADC, and Predict Adverse Biopsy Histology during Prostate Cancer Active Surveillance.

PURPOSE: Majority of men with low-risk prostate cancer can be managed with active surveillance (AS). This study evaluates a high-resolution diffusion-weighted imaging (HR-DWI) technique to predict adverse biopsy histology (AH), defined as Gleason score ≥7 on any biopsy or ≥3 increase in number of positive biopsy cores on systematic biopsies. We test the hypothesis that high-grade disease and progressing disease undergo subtle changes during even short intervals that can be detected by HR-DWI. EXPERIMENTAL DESIGN: In a prospective clinical trial, serial multiparametric MRIs, incorporating HR-DWI and standard DWI (S-DWI) were performed approximately 12 months apart prior to prostate biopsy (n = 59). HR-DWI, which uses reduced field-of-view and motion compensation techniques, was compared with S-DWI. RESULTS: HR-DWI had a 3-fold improvement in spacial resolution compared with S-DWI as confirmed using imaging phantoms. For detecting AH, multiparametric MRI using HR-DWI had a sensitivity of 75% and specificity of 83.9%, and MRI using S-DWI had a sensitivity of 71.4% and specificity of 54.8%. The AUC for HR-DWI was significantly higher (0.794 vs. 0.631, P = 0.014). Secondary analyses of univariable predictors of AH showed tumor size increase [OR 16.8; 95% confidence interval (CI): 4.06-69.48; P < 0.001] and apparent diffusion coefficient (ADC) decrease (OR 5.06; 95% CI: 1.39-18.38; P = 0.014) on HR-DWI were significant predictors of AH. CONCLUSION: HR-DWI outperforms S-DWI in predicting AH. Patient with AH have tumors that change in size and ADC that could be detected using HR-DWI. Future studies with longer follow-up should assess HR-DWI for predicting disease progression during AS. SIGNIFICANCE: We report on a prospective clinical trial using a MRI that has three times the resolution of standard MRI. During AS for prostate cancer, two high-resolution MRIs performed approximately a year apart can detect tumor changes that predict the presence of aggressive cancers that should be considered for curative therapy such as prostatectomy or radiation.

Artificial intelligence in liver cancer research: a scientometrics analysis of trends and topics.

Background and aims: With the rapid growth of artificial intelligence (AI) applications in various fields, understanding its impact on liver cancer research is paramount. This scientometrics project aims to investigate publication trends and topics in AI-related publications in liver cancer. Materials and Methods: We employed a search strategy to identify AI-related publications in liver cancer using Scopus database. We analyzed the number of publications, author affiliations, and journals that publish AI-related publications in liver cancer. Finally, the publications were grouped based on intended application. Results: We identified 3950 eligible publications (2695 articles, 366 reviews, and 889 other document types) from 1968 to August 3, 2023. There was a 12.7-fold increase in AI-related publications from 2013 to 2022. By comparison, the number of total publications on liver cancer increased by 1.7-fold. Our analysis revealed a significant shift in trends of AI-related publications on liver cancer in 2019. We also found a statistically significant consistent increase in numbers of AI-related publications over time (tau = 0.756, p < 0.0001). Eight (53%) of the top 15 journals with the most publications were radiology journals. The largest number of publications were from China (n=1156), the US (n=719), and Germany (n=236). The three most common publication categories were "medical image analysis for diagnosis" (37%), "diagnostic or prognostic biomarkers modeling & bioinformatics" (19%), and "genomic or molecular analysis" (18%). Conclusion: Our study reveals increasing interest in AI for liver cancer research, evidenced by a 12.7-fold growth in related publications over the past decade. A common application of AI is in medical imaging analysis for various purposes. China, the US, and Germany are leading contributors.

Retrospective T2 quantification from conventional weighted MRI of the prostate based on deep learning.

Purpose: To develop a deep learning-based method to retrospectively quantify T2 from conventional T1- and T2-weighted images. Methods: Twenty-five subjects were imaged using a multi-echo spin-echo sequence to estimate reference prostate T2 maps. Conventional T1- and T2-weighted images were acquired as the input images. A U-Net based neural network was developed to directly estimate T2 maps from the weighted images using a four-fold cross-validation training strategy. The structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean percentage error (MPE), and Pearson correlation coefficient were calculated to evaluate the quality of network-estimated T2 maps. To explore the potential of this approach in clinical practice, a retrospective T2 quantification was performed on a high-risk prostate cancer cohort (Group 1) and a low-risk active surveillance cohort (Group 2). Tumor and non-tumor T2 values were evaluated by an experienced radiologist based on region of interest (ROI) analysis. Results: The T2 maps generated by the trained network were consistent with the corresponding reference. Prostate tissue structures and contrast were well preserved, with a PSNR of 26.41 ± 1.17 dB, an SSIM of 0.85 ± 0.02, and a Pearson correlation coefficient of 0.86. Quantitative ROI analyses performed on 38 prostate cancer patients revealed estimated T2 values of 80.4 ± 14.4 ms and 106.8 ± 16.3 ms for tumor and non-tumor regions, respectively. ROI measurements showed a significant difference between tumor and non-tumor regions of the estimated T2 maps (P < 0.001). In the two-timepoints active surveillance cohort, patients defined as progressors exhibited lower estimated T2 values of the tumor ROIs at the second time point compared to the first time point. Additionally, the T2 difference between two time points for progressors was significantly greater than that for non-progressors (P = 0.010). Conclusion: A deep learning method was developed to estimate prostate T2 maps retrospectively from clinically acquired T1- and T2-weighted images, which has the potential to improve prostate cancer diagnosis and characterization without requiring extra scans.

Predictors of disparity between targeted and in-zone systematic cores during transrectal MR/US-fusion prostate biopsy.

BACKGROUND: The combination of targeted and systematic biopsies during MR/US-fusion prostate biopsy improves cancer detection over either modality alone. OBJECTIVE: To identify factors associated with disparity in detection of prostate cancer between systematic and targeted biopsies in magnetic resonance imaging positive zones. DESIGN, SETTING, AND PARTICIPANTS: We retrospectively analyzed 171 men receiving initial MR/US fusion biopsy at our institution from 2015 to 2018. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Disparity was defined as positive targeted but negative systematic biopsy within an magnetic resonance imaging-positive zone (PIRADS 3+), or vice versa. Multivariable logistic regression was used to identify factors associated with disparity in detection of cancer on a per lesion basis. RESULTS AND LIMITATION: Three hundred and fifty-five lesions were targeted. For any cancer and clinically significant prostate cancer (csPCa), 37 (10%) and 24 (7%) lesions were target positive/systematic negative, respectively, while 30 (8%) and 23 (6%) lesions were target negative/systematic positive. In multivariable analysis, anterior location (OR 4.1, 95% CI 1.5-11.4, P = 0.007) was associated with csPCa target positive/systematic negative disparity, while higher prostate volume (OR 1.14, 95% CI 1.0-1.29, P = 0.04) was associated with csPCa target negative/systematic positive disparity. Shorter distance from apex (OR 1.02, 95% CI 1.01-1.04, P = 0.02) was associated with target positive/systematic negative disparity for any cancer. Limitations included relatively limited sample size and lack of prostatectomy specimen as a gold standard. CONCLUSIONS: Anterior or apical lesion location favors better disease capture on targeted biopsies. When doing systematic-only biopsies, surgeons may consider sampling the anterior zone separately.

MRI-based (MAST) score accurately identifies patients with NASH and significant fibrosis.

BACKGROUND & AIMS: Among the large population of patients with non-alcoholic fatty liver disease (NAFLD), identifying those with fibrotic non-alcoholic steatohepatitis (Fibro-NASH) is a clinical priority, as these patients are at the highest risk of disease progression and will benefit most from pharmacologic treatment. MRI-based proton density fat fraction (MRI-PDFF) and MR elastography (MRE) can risk-stratify patients with NAFLD by assessing steatosis and fibrosis, respectively. We developed a highly specific MRI-based score to identify patients with Fibro-NASH. METHODS: This analysis included derivation (n = 103) and validation (n = 244) cohorts of patients who underwent MRI, liver biopsy, transient elastography, and laboratory testing for NAFLD from 2016-2020 in 2 tertiary care centers. To identify Fibro-NASH, a formula was developed based on MRI-PDFF, MRE, and a third variable with highest balanced accuracy per logistic regression. The MRI-aspartate aminotransferase (MAST) score was created and compared to NAFLD fibrosis (NFS), Fibrosis-4 (FIB-4), and FibroScan-aspartate aminotransferase (FAST) scores. RESULTS: The MAST score demonstrated high performance and discrimination in the validation cohort (AUC 0.93; 95% CI 0.88-0.97). In the validation cohorts, the 90% specificity cut-off of 0.242 corresponded to a sensitivity of 75.0%, positive predictive value (PPV) of 50.0% and negative predictive value (NPV) of 96.5%, whereas the 90% sensitivity cut-off of 0.165 corresponded to a specificity of 72.2%, PPV of 29.4%, and NPV of 98.1%. Compared to NFS and FIB-4, MAST resulted in fewer patients having indeterminate scores and an overall higher AUC. Compared to FAST, MAST exhibited a higher AUC and overall better discrimination. CONCLUSION: The MAST score is an accurate, MRI-serum-based score that outperforms previous scores in non-invasively identifying patients at higher risk of Fibro-NASH. LAY SUMMARY: Identifying patients with non-alcoholic steatohepatitis and significant fibrosis - who need treatment and are at risk of clinical liver-related outcomes - is a clinical priority. We developed a more accurate score using MRI-based technologies and a laboratory blood test (aspartate aminotransferase) that outperforms previous non-invasive scores for the identification of patients at higher risk of liver disease progression.

Patient- and tumor-level risk factors for MRI-invisible prostate cancer.

BACKGROUND: Multiparametric MRI is highly sensitive for detection of clinically significant prostate cancer, but has a 10-20% false negative rate. It is unknown if there are clinical factors that predict MRI invisibility. We sought to identify predictors of MRI-invisible (MRI(-)) disease. METHODS: Men undergoing MRI/US-fusion targeted + systematic biopsy by two surgeons at our institution from 2015 to 2018 were reviewed. Patient demographics, clinical data, MRI metrics, and biopsy pathology results were obtained by chart review. An MRI(-) tumor was defined as a positive systematic biopsy in a zone without an MRI lesion. Factors associated with presence of MRI(-) tumors were identified using stepwise multivariable logistic regression. RESULTS: Of 194 men included in the analysis, 79 (41%) and 25 (13%) men had GG1+ and GG2+ MRI(-) tumors, respectively. On multivariable analysis, only Black race was associated with presence of GG1+ MRI(-) tumors (OR 2.2, 95% CI 1.02-4.96). Black race (OR 3.5, 95% CI 1.24-9.87) and higher PSA density (OR 2.0, 95% CI 1.34-3.20) were associated with presence of GG2+ MRI(-) tumors. In non-Black and Black men, detection of MRI(-) tumors on systematic biopsy upgraded patients from no disease to GG2+ disease 1% and 11% of the time, respectively, and from GG1 to GG2+ disease 42% and 60% of the time, respectively. CONCLUSIONS: Black race and PSA density were associated with presence of MRI(-) prostate cancer. Further study on racial differences is warranted based on these results. Surgeons should consider pre-biopsy risk factors before deciding to omit systematic prostate biopsy regardless of mpMRI results.

Intraoperative assessment and postsurgical treatment of prostate cancer tumors using tumor-targeted nanoprobes.

Successful visualization of prostate cancer (PCa) tumor margins during surgery remains a major challenge. The visualization of these tumors during surgery via near infrared fluorescence (NIRF) imaging would greatly enhance surgical resection, minimizing tumor recurrence and improving outcome. Furthermore, chemotherapy is typically administered to patients after surgery to treat any missed tumor tissue around the surgical area, minimizing metastasis and increasing patient survival. For these reasons, a theranostics fluorescent nanoparticle could be developed to assist in the visualization of PCa tumor margins, while also delivering chemotherapeutic drug after surgery. Methods: Ferumoxytol (FMX) conjugated to the fluorescent dye and PCa targeting agent, heptamethine carbocyanine (HMC), yielded the HMC-FMX nanoprobe that was tested in vitro with various PCa cell lines and in vivo with both subcutaneous and orthotopic PCa mouse models. Visualization of these tumors via NIRF imaging after administration of HMC-FMX was performed. In addition, delivery of chemotherapeutic drug and their effect on tumor growth was also assessed. Results: HMC-FMX internalized into PCa cells, labeling these cells and PCa tumors in mice with near infrared fluorescence, facilitating tumor margin visualization. HMC-FMX was also able to deliver drugs to these tumors, reducing cell migration and slowing down tumor growth. Conclusion: HMC-FMX specifically targeted PCa tumors in mice allowing for the visualization of tumor margins by NIRF imaging. Furthermore, delivery of anticancer drugs by HMC-FMX effectively reduced prostate tumor growth and reduced cell migration in vitro. Thus, HMC-FMX can potentially translate into the clinic as a nanotheranostics agent for the intraoperative visualization of PCa tumor margins, and post-operative treatment of tumors with HMC-FMX loaded with anticancer drugs.

Five-dimensional quantitative low-dose Multitasking dynamic contrast- enhanced MRI: Preliminary study on breast cancer.

PURPOSE: To develop a low-dose Multitasking DCE technique (LD-MT-DCE) for breast imaging, enabling dynamic T1 mapping-based quantitative characterization of tumor blood flow and vascular properties with whole-breast coverage, a spatial resolution of 0.9 × 0.9 × 1.1 mm3 , and a temporal resolution of 1.4 seconds using a 20% gadolinium dose (0.02 mmol/kg). METHODS: Magnetic resonance Multitasking was used to reconstruct 5D images with three spatial dimensions, one T1 recovery dimension for dynamic T1 quantification, and one DCE dimension for contrast kinetics. Kinetic parameters Fp , vp , Ktrans , and ve were estimated from dynamic T1 maps using the two-compartment exchange model. The LD-MT-DCE repeatability and agreement against standard-dose MT-DCE were evaluated in 20 healthy subjects. In 7 patients with triple-negative breast cancer, LD-MT-DCE image quality and diagnostic results were compared with that of standard-dose clinical DCE in the same imaging session. One-way unbalanced analysis of variance with Tukey test was performed to evaluate the statistical significance of the kinetic parameters between control and patient groups. RESULTS: The LD-MT-DCE technique was repeatable, agreed with standard-dose MT-DCE, and showed excellent image quality. The diagnosis using LD-MT-DCE matched well with clinical results. The values of Fp , vp , and Ktrans were significantly different between malignant tumors and normal breast tissue (P < .001, < .001, and < .001, respectively), and between malignant and benign tumors (P = .020, .003, and < .001, respectively). CONCLUSION: The LD-MT-DCE technique was repeatable and showed excellent image quality and equivalent diagnosis compared with standard-dose clinical DCE. The estimated kinetic parameters were capable of differentiating between normal breast tissue and benign and malignant tumors.

Fully automated multiorgan segmentation in abdominal magnetic resonance imaging with deep neural networks.

PURPOSE: Segmentation of multiple organs-at-risk (OARs) is essential for magnetic resonance (MR)-only radiation therapy treatment planning and MR-guided adaptive radiotherapy of abdominal cancers. Current practice requires manual delineation that is labor-intensive, time-consuming, and prone to intra- and interobserver variations. We developed a deep learning (DL) technique for fully automated segmentation of multiple OARs on clinical abdominal MR images with high accuracy, reliability, and efficiency. METHODS: We developed Automated deep Learning-based abdominal multiorgan segmentation (ALAMO) technique based on two-dimensional U-net and a densely connected network structure with tailored design in data augmentation and training procedures such as deep connection, auxiliary supervision, and multiview. The model takes in multislice MR images and generates the output of segmentation results. 3.0-Tesla T1 VIBE (Volumetric Interpolated Breath-hold Examination) images of 102 subjects were used in our study and split into 66 for training, 16 for validation, and 20 for testing. Ten OARs were studied, including the liver, spleen, pancreas, left/right kidneys, stomach, duodenum, small intestine, spinal cord, and vertebral bodies. An experienced radiologist manually labeled each OAR, followed by reediting, if necessary, by a senior radiologist, to create the ground-truth. The performance was measured using volume overlapping and surface distance. RESULTS: The ALAMO technique generated segmentation labels in good agreement with the manual results. Specifically, among the ten OARs, nine achieved high dice similarity coefficients (DSCs) in the range of 0.87-0.96, except for the duodenum with a DSC of 0.80. The inference completed within 1 min for a three-dimensional volume of 320 × 288 × 180. Overall, the ALAMO model matched the state-of-the-art techniques in performance. CONCLUSION: The proposed ALAMO technique allows for fully automated abdominal MR segmentation with high accuracy and practical memory and computation time demands.

MR elastography-based liver fibrosis correlates with liver events in nonalcoholic fatty liver patients: A multicenter study.

BACKGROUND & AIMS: Liver fibrosis assessed by liver biopsy is predictive of clinical liver events in patients with nonalcoholic fatty liver disease (NAFLD). Magnetic resonance elastography (MRE) correlates with liver biopsy in assessing liver fibrosis. However, data assessing the relationship between MRE and clinical liver events are lacking. We investigated the association between MRE and clinical liver events/death and identified the cut-off to predict clinical liver events in NAFLD patients. METHODS: We conducted a multicenter retrospective study of NAFLD patients who underwent MRE between 2016 and 2019. Clinical liver events were defined as decompensation events and death. We categorized patients into noncirrhosis, compensated cirrhosis and decompensated cirrhosis. Fisher's exact test was used to test association strength. Receiver operative curve methods were used to determine the optimal cut-off of MRE liver stiffness and to maximize the accuracy for classifying noncirrhosis, compensated cirrhosis and decompensated cirrhosis. Logistic regression modelling was used to predict decompensation. RESULTS: The study included 320 NAFLD patients who underwent MRE. The best threshold for distinguishing cirrhosis from noncirrhosis was 4.39 kPa (AUROC 0.92) and from decompensated cirrhosis was 6.48 kPa (AUROC 0.71). Odds of decompensation increased as liver stiffness increased (OR 3.28) (P < .001). Increased liver stiffness was associated with ascites, hepatic encephalopathy, oesophageal variceal bleeding and mortality (median 7.10, 10.15 and 10.15 kPa respectively). CONCLUSION: In NAFLD patients, liver stiffness measured by MRE with a cut-off of ≥6.48 kPa is associated with decompensation and mortality, and specific MRE cut-offs are predictive of individual clinical liver events.

Multicenter Safety and Practice for Off-Label Diagnostic Use of Ferumoxytol in MRI.

Background Ferumoxytol is approved for use in the treatment of iron deficiency anemia, but it can serve as an alternative to gadolinium-based contrast agents. On the basis of postmarketing surveillance data, the Food and Drug Administration issued a black box warning regarding the risks of rare but serious acute hypersensitivity reactions during fast high-dose injection (510 mg iron in 17 seconds) for therapeutic use. Whereas single-center safety data for diagnostic use have been positive, multicenter data are lacking. Purpose To report multicenter safety data for off-label diagnostic ferumoxytol use. Materials and Methods The multicenter ferumoxytol MRI registry was established as an open-label nonrandomized surveillance databank without industry involvement. Each center monitored all ferumoxytol administrations, classified adverse events (AEs) using the National Cancer Institute Common Terminology Criteria for Adverse Events (grade 1-5), and assessed the relationship of AEs to ferumoxytol administration. AEs related to or possibly related to ferumoxytol injection were considered adverse reactions. The core laboratory adjudicated the AEs and classified them with the American College of Radiology (ACR) classification. Analysis of variance was used to compare vital signs. Results Between January 2003 and October 2018, 3215 patients (median age, 58 years; range, 1 day to 96 years; 1897 male patients) received 4240 ferumoxytol injections for MRI. Ferumoxytol dose ranged from 1 to 11 mg per kilogram of body weight (≤510 mg iron; rate ≤45 mg iron/sec). There were no systematic changes in vital signs after ferumoxytol administration (P > .05). No severe, life-threatening, or fatal AEs occurred. Eighty-three (1.9%) of 4240 AEs were related or possibly related to ferumoxytol infusions (75 mild [1.8%], eight moderate [0.2%]). Thirty-one AEs were classified as allergiclike reactions using ACR criteria but were consistent with minor infusion reactions observed with parenteral iron. Conclusion Diagnostic ferumoxytol use was well tolerated, associated with no serious adverse events, and implicated in few adverse reactions. Registry results indicate a positive safety profile for ferumoxytol use in MRI. © RSNA, 2019 Online supplemental material is available for this article.

Tumor-Activatable Clinical Nanoprobe for Cancer Imaging.

Purpose: A successful cancer surgery requires the complete removal of cancerous tissue, while also sparing as much healthy, non-cancerous tissue as possible. To achieve this, an accurate identification of tumor boundaries during surgery is critical, but intra-operative tumor visualization remains challenging. Fluorescence imaging is a promising method to improve tumor detection and delineate tumor boundaries during surgery, but the lack of stable, long-circulating, clinically-translatable fluorescent probes that can identify tumors with high signal-to-noise ratios and low background fluorescence signals have prevented its widespread application. Methods: We screened the optical properties of several fluorescent dyes before and after nanoprobe encapsulation, and then identified nanoprobes with quenched fluorescence that were re-activated upon dye release. The physical and biological properties of these nanoprobes leading to fluorescence activation were investigated in vitro. Further, the cancer imaging properties of both free dyes and nanoprobe-encapsulated dyes were compared in vivo. Results: A novel fluorescent nanoprobe was prepared by combining two FDA-approved agents commonly used in the clinic: Feraheme (FH) and indocyanine green (ICG). The resulting FH-entrapped ICG nanoprobe [FH(ICG)] displayed quenched fluorescence compared to other nanoprobes, and this quenched fluorescence was re-activated in acidic tumor microenvironment conditions (pH 6.8) and upon uptake into cancer cells. Finally, in vivo studies in a prostate cancer mouse model demonstrated that FH(ICG) treatments enhance long-term fluorescence signals in tumors compared to ICG treatments, allowing for fluorescence-guided tumor identification using clinically relevant fluorescence cameras. Conclusions: FH(ICG) nanoprobes were identified as fluorescent nanoprobes with beneficial fluorescence activation properties compared to other FH-entrapped dyes. The activatable nature of this nanoprobe allows for a low background fluorescence signal and high signal-to-noise ratio within a long-circulating nanoagent, which allows for long-term fluorescence signals from tumors that enabled their fluorescence-guided detection. This activatable nanoprobe offers tremendous potential as a clinically translatable image-guided cancer therapy modality that can be prepared in a clinical setting.

Radiologic Reporting and Interpretation of Occult Inguinal Hernia.

BACKGROUND: Inguinal hernias are common entities. Occult inguinal hernias are difficult to diagnose on examination and can cause groin and pelvic pain. Imaging is heavily relied on to help diagnose these hernias; as such, correct interpretation of imaging studies can prevent delay in treatment for a patient with pain. We evaluated the accuracy and reliability of radiologic reports for detection of occult inguinal hernias in patients with groin and pelvic pain. STUDY DESIGN: All CT and MRI studies ordered for groin or pelvic pain during a 5-year period were analyzed. Studies were included if the original radiologic reports were available for review, and if the patient underwent operative exploration. A blinded radiologist was asked to "over-read" the images. Operative findings were considered the gold standard with which radiologic reports were compared. RESULTS: Of 322 CT and MRI studies, 125 groins met criteria. Original radiologic reports were 35% accurate, with 97% positive predictive value (PPV) and 13% negative predictive value (NPV). Over-read radiologist reports were significantly different (p < 0.0001), with 79% accuracy, 97% PPV, and 30% NPV. CONCLUSIONS: Most radiologic reports issued for CT and MRI studies were incorrect for evaluation of occult inguinal hernia. Over-read radiologist reports were more than twice as accurate when evaluating the same images. The physician who is relying on radiologic reports to determine plan of care for a patient with groin or pelvic pain should inquire further into any negative study, especially if there is strong clinical suspicion for inguinal hernia.

Genes involved in prostate cancer progression determine MRI visibility.

MRI is used to image prostate cancer and target tumors for biopsy or therapeutic ablation. The objective was to understand the biology of tumors not visible on MRI that may go undiagnosed and untreated. Methods: Prostate cancers visible or invisible on multiparametric MRI were macrodissected and examined by RNAseq. Differentially expressed genes (DEGs) based on MRI visibility status were cross-referenced with publicly available gene expression databases to identify genes associated with disease progression. Genes with potential roles in determining MRI visibility and disease progression were knocked down in murine prostate cancer xenografts, and imaged by MRI. Results: RNAseq identified 1,654 DEGs based on MRI visibility status. Comparison of DEGs based on MRI visibility and tumor characteristics revealed that Gleason score (dissimilarity test, p<0.0001) and tumor size (dissimilarity test, p<0.039) did not completely determine MRI visibility. Genes in previously reported prognostic signatures significantly correlated with MRI visibility suggesting that MRI visibility was prognostic. Cross-referencing DEGs with external datasets identified four genes (PHYHD1, CENPF, ALDH2, GDF15) that predict MRI visibility, progression free survival and metastatic deposits. Genetic modification of a human prostate cancer cell line to induce miR-101 and suppress CENPF decreased cell migration and invasion. As prostate cancer xenografts in mice, these cells had decreased visibility on diffusion weighted MRI and decreased perfusion, which correlated with immunostaining showing decreased cell density and proliferation. Conclusions: Genes involved in prostate cancer prognosis and metastasis determine MRI visibility, indicating that MRI visibility has prognostic significance. MRI visibility was associated with genetic features linked to poor prognosis.

Use of cylindrical coordinates to localize prostate cancers on MRI and prostatectomy pathology.

PURPOSE: To describe and test a quantitative system for designating prostate tumor location on magnetic resonance imaging (MRI) and prostatectomy. A system for describing tumor location will facilitate research correlating MRI and pathology. MATERIALS AND METHODS: The prostate cylindrical coordinate (PCC) system was developed for locating prostate tumors using 3 coordinate values. The 3 coordinate values include the angular location centered on the urethra, the radial distance to the periphery and the long axis from apex to base. To evaluate this system, 26 tumors were identified where the prostate cancer was noted by both the radiologist and the pathologist. PCC values were assigned independently to MRI lesions and corresponding tumors. Intraclass correlation coefficient (ICC) was calculated to assess agreement between PCC assigned using MRI and pathology. The coordinates were used to calculate the average distance between the centers of the same lesion measured by MRI and pathology. RESULTS: Each of the cylindrical coordinates assigned by MRI and pathology were compared and there was no significant difference. The agreement was excellent, and the ICC was 0.70 (P<0.001) for the angular coordinate, 0.81 (P<0.001) for the radial distance, and 0.94 (P<0.001) for the long axis. Compared to pathology, lesions on MRI were significantly larger (1.17 vs. 0.86cm2, P<0.001) but there was strong agreement between the measurements on MRI and pathology (ICC = 0.89, P<0.001). The distance between the centers of the lesions measured on MRI and pathology was small (10.13mm, s.d. = 8.70). CONCLUSIONS: The PCC system quantitatively characterizes lesions seen on MRI and prostatectomy pathology with good agreement.

Magnetic resonance imaging and transient elastography in the management of Nonalcoholic Fatty Liver Disease (NAFLD).

INTRODUCTION: Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease and cirrhosis worldwide and the second most common cause of liver transplantation in major medical centers. Because liver steatosis and fibrosis severity are related to disease morbidity and mortality, the extent of disease, and disease progression, they need to be assessed and monitored. In addition, innovation with new drug developments requires disease staging and monitoring in both phase 2 and 3 clinical trials. Currently, disease assessment in both clinical practice and research is mostly performed by liver biopsy, an invasive, procedure with risks. Noninvasive, highly accurate tests are needed that could be used in clinical trials as surrogate endpoints and in clinical practice for monitoring patients. Area Covered: We discuss noninvasive tests, transient elastography (TE) with controlled attenuation parameter (CAP), magnetic resonance imaging (MRI), and MR elastography (MRE), summarize the available evidence of their usefulness for assessing steatosis and fibrosis. Therefore they could be used as clinical trials outcomes and in disease monitoring in clinical practice. Expert Commentary: TE with CAP, MRI and MRE are highly accurate noninvasive diagnostic tools for quantifying hepatic steatosis and fibrosis. Therefore they could be used as clinical trials outcomes and in disease monitoring in clinical practice.

Prospective Pilot Trial to Evaluate a High Resolution Diffusion-Weighted MRI in Prostate Cancer Patients.

OBJECTIVES: High-resolution prostate imaging may allow for detection of subtle changes in tumor size, decrease the reliance on biopsies, and help define tumor boundaries during ablation. This pilot clinical trial evaluates a novel high-resolution prostate MRI for detection of small, biopsy-proven prostate tumors. METHODS: Our team developed a software that can be loaded on any modern MRI to generate high resolution diffusion-weighted imaging sequences (HR-DWI), which were compared to standard diffusion-weighted imaging sequence (S-DWI) in a prospective pilot trial in active surveillance patients. HR-DWI captures the entire volume of the prostate rather than sections, reducing streaking artifacts and geometric distortions. Multiple shots, rather than single shots, are used to differentiate signal and noise, enhancing resolution. All images were read by two radiologists. The primary outcome was the percent of biopsy-proven zones seen in 17 patients. The trial was powered to detect discordant proportions of 0.04 and 0.40 at one-sided alpha=0.05. RESULTS: The resolution was defined using standard phantoms. HR-DWI produced a 5-fold improvement in spatial resolution when compared to S-DWI. Multiparametric (MP)-MRI incorporating S-DWI was useful for predicting biopsy results (AUC 0.72, Fisher's exact p<0.001); however, using HR-DWI allowed MP-MRI to be more highly predictive of biopsy results (AUC 0.88, Fisher's exact p<0.001). AUC for MP-MRI incorporating HR-DWI was significantly larger than MP-MRI incorporating S-DWI (p=0.002). MP-MRI with HR-DWI had a sensitivity of 95.7% and identified tumor in 22 of 23 zones proven to have cancer on biopsy. In contrast, MP-MRI with S-DWI had a sensitivity of 60.9% and only identified 14 of 23 biopsy-positive zones (p=0.004). CONCLUSION: We developed a novel DWI and evaluated its improved resolution in a clinical setting. This technology has many potential applications and should be evaluated in future clinical trials as a patient management tool.

3D high-resolution diffusion-weighted MRI at 3T: Preliminary application in prostate cancer patients undergoing active surveillance protocol for low-risk prostate cancer.

PURPOSE: To improve spatial resolution and image quality of diffusion-weighted (DW) MRI in detecting low-risk prostate cancer (lrPC) in patients undergoing active surveillance protocol (AS-PC), we propose the application of a diffusion-prepared balanced steady-state free precession (bSSFP) technique capable of multishot acquisition. METHODS: Diffusion-prepared bSSFP was compared with single-shot DW echo planar imaging (SS-DW-EPI) at two prescribed resolutions (2.1 × 2.1 × 3.5mm(3) , 0.9 × 0.9 × 3.5 mm(3) ) in nine healthy subjects and nine AS-PC patients. Geometric distortion and susceptibility artifacts were quantitatively assessed in all subjects. In AS-PC patients, lesion detection via blinded multiparametric MRI including T1-weighted, T2-weighted, dynamic contrast-enhanced imaging, and along with either of two DW methods were evaluated against 12-point biopsy. RESULTS: Geometric distortion and susceptibility artifacts were significantly less for diffusion-prepared bSSFP at both prescribed spatial resolutions than SS-DW-EPI. Apparent diffusion coefficients of healthy prostate tissue were concordant between the two DW methods at both spatial resolutions. In AS-PC patients, multiparametric MRI with diffusion-prepared bSSFP had greater sensitivity (94%, 63%), accuracy (76%, 67%), positive-predictive value (54%, 48%), negative-predictive value (97%, 82%), and area under the curve (0.80, 0.67) than with SS-DW-EPI. CONCLUSIONS: The proposed diffusion-prepared technique with higher spatial resolution and improved image quality over SS-DW-EPI resulted in better multiparametric MRI detection of lrPC in AS-PC patients.

Multiparametric MRI Improves Accuracy of Clinical Nomograms for Predicting Extracapsular Extension of Prostate Cancer.

OBJECTIVE: To compare the accuracy of multiparametric magnetic resonance imaging (MP-MRI) with the Partin tables and Memorial Sloan-Kettering (MSK) nomogram for predicting extracapsular extension (ECE) in prostate cancer and to create a tool for clinicians to estimate pathologic ECE risk. METHODS: A retrospective review of 112 patients who underwent 3T MP-MRI of the prostate and radical prostatectomy was performed. Regression analyses were carried out to identify predictors of ECE. Predictive accuracy of models based on nomogram and MP-MRI were compared. RESULTS: A total of 33 of patients (29%) had ECE on MP-MRI whereas 26 patients (23%) had ECE on final pathology. Mean age was 62.8 years and mean prostate-specific antigen was 8.2 ng/dL. MRI was a significant predictor of ECE that was independent of age, prostate-specific antigen, Gleason score, clinical stage, and percent positive cores on biopsy. Sensitivity, specificity, positive predictive value, and negative predictive value of MP-MRI for ECE were 84.6%, 87.2%, 66.7%, and 94.9%, respectively. Areas under the curve for Partin and MSK nomograms for predicting ECE were 0.85 and 0.86, respectively. Area under the curve increased to 0.92 and 0.94, respectively, when MP-MRI was added to each nomogram. We provide an online tool that integrates Partin or MSK nomogram results with ECE status determined from MRI to predict pathologic ECE. Within the typical range of risks for ECE provided by the clinical nomograms (ie, 15%-40%), MRI was useful for predicting pathologic ECE. CONCLUSION: MP-MRI may be a useful adjunct for clinically staging prostate cancer. MP-MRI improved accuracy of existing clinical nomograms for prediction of pathologic ECE.