Comparison of Mammography and Mammography with Supplemental Whole-Breast US Tomography for Cancer Detection in Patients with Dense Breasts.

BACKGROUND: Women with dense breasts benefit from supplemental cancer screening with US, but US has low specificity. PURPOSE: To evaluate the performance of breast US tomography (UST) combined with full-field digital mammography (FFDM) compared with FFDM alone for breast cancer screening in women with dense breasts. MATERIALS AND METHODS: This retrospective multireader multicase study included women with dense breasts who underwent FFDM and UST at 10 centers between August 2017 and October 2019 as part of a prospective case collection registry. All patients in the registry with cancer were included; patients with benign biopsy or negative follow-up imaging findings were randomly selected for inclusion. Thirty-two Mammography Quality Standards Act-qualified radiologists independently evaluated FFDM followed immediately by FFDM plus UST for suspicious findings and assigned a Breast Imaging Reporting and Data System (BI-RADS) category. The superiority of FFDM plus UST versus FFDM alone for cancer detection (assessed with area under the receiver operating characteristic curve [AUC]), BI-RADS 4 sensitivity, and BI-RADS 3 sensitivity and specificity were evaluated using the two-sided significance level of α = .05. Noninferiority of BI-RADS 4 specificity was evaluated at the one-sided significance level of α = .025 with a -10% margin. RESULTS: Among 140 women (mean age, 56 years ±10 [SD]; 36 with cancer, 104 without), FFDM plus UST achieved superior performance compared with FFDM alone (AUC, 0.60 [95% CI: 0.51, 0.69] vs 0.54 [95% CI: 0.45, 0.64]; P = .03). For FFDM plus UST versus FFDM alone, BI-RADS 4 mean sensitivity was superior (37% [428 of 1152] vs 30% [343 of 1152]; P = .03) and BI-RADS 4 mean specificity was noninferior (82% [2741 of 3328] vs 88% [2916 of 3328]; P = .004). For FFDM plus UST versus FFDM, no difference in BI-RADS 3 mean sensitivity was observed (40% [461 of 1152] vs 33% [385 of 1152]; P = .08), but BI-RADS 3 mean specificity was superior (75% [2491 of 3328] vs 69% [2299 of 3328]; P = .04). CONCLUSION: In women with dense breasts, FFDM plus UST improved cancer detection by radiologists versus FFDM alone. Clinical trial registration nos. NCT03257839 and NCT04260620 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Mann in this issue.

Recent advances in targeted protein degraders as potential therapeutic agents.

Targeted protein degradation (TPD) technology has gradually become widespread in the past 20 years, which greatly boosts the development of disease treatment. Contrary to small inhibitors that act on protein kinases, transcription factors, ion channels, and other targets they can bind to, targeted protein degraders could target "undruggable targets" and overcome drug resistance through ubiquitin-proteasome pathway (UPP) and lysosome pathway. Nowadays, some bivalent degraders such as proteolysis-targeting chimeras (PROTACs) have aroused great interest in drug discovery, and some of them have successfully advanced into clinical trials. In this review, to better understand the mechanism of degraders, we elucidate the targeted protein degraders according to their action process, relying on the ubiquitin-proteasome system or lysosome pathway. Then, we briefly summarize the study of PROTACs employing different E3 ligases. Subsequently, the effect of protein of interest (POI) ligands, linker, and E3 ligands on PROTAC degradation activity is also discussed in detail. Other novel technologies based on UPP and lysosome pathway have been discussed in this paper such as in-cell click-formed proteolysis-targeting chimeras (CLIPTACs), molecular glues, Antibody-PROTACs (Ab-PROTACs), autophagy-targeting chimeras, and lysosome-targeting chimeras. Based on the introduction of these degradation technologies, we can clearly understand the action process and degradation mechanism of these approaches. From this perspective, it will be convenient to obtain the development status of these drugs, choose appropriate degradation methods to achieve better disease treatment and provide basis for future research and simultaneously distinguish the direction of future research efforts.

Observer studies of image quality of denoising reduced-count cardiac single photon emission computed tomography myocardial perfusion imaging by three-dimensional Gaussian post-reconstruction filtering and deep learning.

BACKGROUND: The aim of this research was to asses perfusion-defect detection-accuracy by human observers as a function of reduced-counts for 3D Gaussian post-reconstruction filtering vs deep learning (DL) denoising to determine if there was improved performance with DL. METHODS: SPECT projection data of 156 normally interpreted patients were used for these studies. Half were altered to include hybrid perfusion defects with defect presence and location known. Ordered-subset expectation-maximization (OSEM) reconstruction was employed with the optional correction of attenuation (AC) and scatter (SC) in addition to distance-dependent resolution (RC). Count levels varied from full-counts (100%) to 6.25% of full-counts. The denoising strategies were previously optimized for defect detection using total perfusion deficit (TPD). Four medical physicist (PhD) and six physician (MD) observers rated the slices using a graphical user interface. Observer ratings were analyzed using the LABMRMC multi-reader, multi-case receiver-operating-characteristic (ROC) software to calculate and compare statistically the area-under-the-ROC-curves (AUCs). RESULTS: For the same count-level no statistically significant increase in AUCs for DL over Gaussian denoising was determined when counts were reduced to either the 25% or 12.5% of full-counts. The average AUC for full-count OSEM with solely RC and Gaussian filtering was lower than for the strategies with AC and SC, except for a reduction to 6.25% of full-counts, thus verifying the utility of employing AC and SC with RC. CONCLUSION: We did not find any indication that at the dose levels investigated and with the DL network employed, that DL denoising was superior in AUC to optimized 3D post-reconstruction Gaussian filtering.

Multimodal multi-task deep neural network framework for kinase-target prediction.

Kinase plays a significant role in various disease signaling pathways. Due to the highly conserved sequence of kinase family members, understanding the selectivity profile of kinase inhibitors remains a priority for drug discovery. Previous methods for kinase selectivity identification use biochemical assays, which are very useful but limited by the protein available. The lack of kinase selectivity can exert benefits but also can cause adverse effects. With the explosion of the dataset for kinase activities, current computational methods can achieve accuracy for large-scale selectivity predictions. Here, we present a multimodal multi-task deep neural network model for kinase selectivity prediction by calculating the fingerprint and physiochemical descriptors. With the multimodal inputs of structure and physiochemical properties information, the multi-task framework could accurately predict the kinome map for selectivity analysis. The proposed model displays better performance for kinase-target prediction based on system evaluations.

A highly sensitive and robust LC-MS platform for host cell protein characterization in biotherapeutics.

Host cell proteins (HCPs) are a major class of process-related impurities that need to be closely monitored during the production of biotherapeutics. Mass spectrometry (MS) has emerged as a promising tool for HCP analysis due to its specificity for individual HCP's identification and quantitation. However, utilization of MS as a routine characterization tool is still limited due to the time-consuming procedures, non-standardized instrumentation and methodologies, and the limited sensitivity compared to the enzyme-linked immunosorbent assays (ELISA). In this study, we introduced a sensitive (limit of detection (LOD) at 1-2 ppm) and robust HCP profiling platform method with suitable precision and accuracy that can be readily adopted to antibodies and other biotherapeutic modalities without the need for HCP enrichment. The NIST mAb and multiple in-house antibodies were analyzed, and results were benchmarked with other reported studies. In addition, a targeted analysis method with optimized sample preparation for absolute quantitation of lipases was developed and qualified with an LOD of 0.6 ppm and precision of <15%, which can be further improved to an LOD of 5 ppb by using the nano-flow LC.

KID: A Kinase-Focused Interaction Database and Its Application in the Construction of Kinase-Focused Molecule Databases.

Protein kinases are important drug targets for the treatment of several diseases. The interaction between kinases and ligands is vital in the process of small-molecule kinase inhibitor (SMKI) design. In this study, we propose a method to extract fragments and amino acid residues from crystal structures for kinase-ligand interactions. In addition, core fragments that interact with the important hinge region of kinases were extracted along with their decorations. Based on the superimposed structural data of kinases from the kinase-ligand interaction fingerprint and structure database, we obtained two libraries, namely, a hinge-unfocused fragment-amino acid pair library (FAP Lib) that contains 6672 pairs of fragments and corresponding amino-acids, and a hinge-focused hinge binder library (HB Lib) of 3560 pairs of hinge-binding scaffolds with their corresponding decorations. These two libraries constitute a kinase-focused interaction database (KID). In depth analysis was conducted on KID to explore important characteristics of fragments in the design of SMKIs. With KID, we built two kinase-focused molecule databases, one called Recomb_DB, which contains 1,72,346 molecules generated through fragment recombination based on the FAP Lib, and another called RsdHB_DB, which contains 93,030 molecules generated based on our HB Lib using molecular generation methods. Compared with five databases both commercial and non-commercial, these two databases both ranked top 3 in scaffold diversity, top 4 in molecule fingerprint diversity, and are more focused on the chemical space of kinase inhibitors. Hence, KID presents a useful addition to existing databases for the exploration of novel SMKIs.

Artificial Intelligence Applied to Breast MRI for Improved Diagnosis.

Background Recognition of salient MRI morphologic and kinetic features of various malignant tumor subtypes and benign diseases, either visually or with artificial intelligence (AI), allows radiologists to improve diagnoses that may improve patient treatment. Purpose To evaluate whether the diagnostic performance of radiologists in the differentiation of cancer from noncancer at dynamic contrast material-enhanced (DCE) breast MRI is improved when using an AI system compared with conventionally available software. Materials and Methods In a retrospective clinical reader study, images from breast DCE MRI examinations were interpreted by 19 breast imaging radiologists from eight academic and 11 private practices. Readers interpreted each examination twice. In the "first read," they were provided with conventionally available computer-aided evaluation software, including kinetic maps. In the "second read," they were also provided with AI analytics through computer-aided diagnosis software. Reader diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis, with the area under the ROC curve (AUC) as a figure of merit in the task of distinguishing between malignant and benign lesions. The primary study end point was the difference in AUC between the first-read and the second-read conditions. Results One hundred eleven women (mean age, 52 years ± 13 [standard deviation]) were evaluated with a total of 111 breast DCE MRI examinations (54 malignant and 57 nonmalignant lesions). The average AUC of all readers improved from 0.71 to 0.76 (P = .04) when using the AI system. The average sensitivity improved when Breast Imaging Reporting and Data System (BI-RADS) category 3 was used as the cut point (from 90% to 94%; 95% confidence interval [CI] for the change: 0.8%, 7.4%) but not when using BI-RADS category 4a (from 80% to 85%; 95% CI: -0.9%, 11%). The average specificity showed no difference when using either BI-RADS category 4a or category 3 as the cut point (52% and 52% [95% CI: -7.3%, 6.0%], and from 29% to 28% [95% CI: -6.4%, 4.3%], respectively). Conclusion Use of an artificial intelligence system improves radiologists' performance in the task of differentiating benign and malignant MRI breast lesions. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Krupinski in this issue.

Diagnosis of Prostate Cancer by Use of MRI-Derived Quantitative Risk Maps: A Feasibility Study.

OBJECTIVE. The purpose of this study was to develop a new quantitative image analysis tool for estimating the risk of cancer of the prostate by use of quantitative multiparametric MRI (mpMRI) metrics. MATERIALS AND METHODS. Thirty patients with biopsy-confirmed prostate cancer (PCa) who underwent preoperative 3-T mpMRI were included in the study. Quantitative mpMRI metrics-apparent diffusion coefficient (ADC), T2, and dynamic contrast-enhanced (DCE) signal enhancement rate (α)-were calculated on a voxel-by-voxel basis for the whole prostate and coregistered. A normalized risk value (0-100) for each mpMRI parameter was obtained, with high risk values associated with low T2 and ADC and high signal enhancement rate. The final risk score was calculated as a weighted sum of the risk scores (ADC, 40%; T2, 40%; DCE, 20%). Data from five patients were used as training set to find the threshold for predicting PCa. In the other 25 patients, any region with a minimum of 30 con-joint voxels (≈ 4.8 mm2) with final risk score above the threshold was considered positive for cancer. Lesion-based and sector-based analyses were performed by matching prostatectomyverified malignancy and PCa predicted with the risk analysis tool. RESULTS. The risk map tool had sensitivity of 76.6%, 89.2%, and 100% for detecting all lesions, clinically significant lesions (≥ Gleason 3 + 4), and index lesions, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value for PCa detection for all lesions in the sector-based analysis were 78.9%, 88.5%, 84.4%, and 84.1%, respectively, with an ROC AUC of 0.84. CONCLUSION. The risk analysis tool is effective for detecting clinically significant PCa with reasonable sensitivity and specificity in both peripheral and transition zones.

C18H17NO6 Inhibits Invasion and Migration of Human MNNG Osteosarcoma Cells via the PI3K/AKT Signaling Pathway.

BACKGROUND Osteosarcoma (OS) is a highly aggressive, metastatic bone tumor with a poor prognosis, and occurs more commonly in children and adolescents. Therefore, new drugs and treatments are urgently needed. In this study, we investigated the effect and potential mechanisms of C18H17NO6 on osteosarcoma cells. MATERIAL AND METHODS Human MNNG osteosarcoma cells were treated with different concentrations of C18H17NO6. The proliferation of the MNNG cells was examined via CCK-8 assay. Cell migration and invasion were tested via wound-healing assay and Transwell migration and invasion assays. ELISA was used to detect MMP-2, MMP-9, and VEGF secretion. Finally, Western blotting and qRT-PCR were used to detect protein and mRNA expressions, respectively. RESULTS C18H17NO6 inhibited MNNG proliferation in a dose- and time-dependent manner and inhibited MMP-2, MMP-9, and VEGF secretion. C18H17NO6 treatment significantly downregulated N-cadherin and Vimentin expression levels and upregulated E-cadherin expression levels in vitro and in vivo. C18H17NO6 inhibited tumor growth in a MNNG xenograft. We also found that C18H17NO6 can significantly reduce the phosphorylation of the PI3K/AKT signaling pathway in vivo and in vitro. However, 740Y-P (a PI3K agonist) had the opposite effect on proliferation, migration and invasion of MNNG cells treated with C18H17NO6. LY294002 (a PI3K inhibitor) downregulated p-PI3K and p-AKT could mimic the inhibitory effect of C18H17NO6. CONCLUSIONS Our results suggest that C18H17NO6 can inhibit human MNNG osteosarcoma cell invasion and migration via the PI3K/AKT signaling pathway both in vivo and in vitro. C18H17NO6 may be a highly effective and low-toxicity natural drug for the prevention or treatment of OS.

Interpretation Time Using a Concurrent-Read Computer-Aided Detection System for Automated Breast Ultrasound in Breast Cancer Screening of Women With Dense Breast Tissue.

OBJECTIVE: The purpose of this study was to compare diagnostic accuracy and interpretation time of screening automated breast ultrasound (ABUS) for women with dense breast tissue without and with use of a recently U.S. Food and Drug Administration-approved computer-aided detection (CAD) system for concurrent read. MATERIALS AND METHODS: In a retrospective observer performance study, 18 radiologists interpreted a cancer-enriched set (i.e., cancer prevalence higher than in the original screening cohort) of 185 screening ABUS studies (52 with and 133 without breast cancer). These studies were from a large cohort of ABUS-screened patients interpreted as BI-RADS density C or D. Each reader interpreted each case twice in a counterbalanced study, once without the CAD system and once with it, separated by 4 weeks. For each case, each reader identified abnormal findings and reported BI-RADS assessment category and level of suspicion for breast cancer. Interpretation time was recorded. Level of suspicion data were compared to evaluate diagnostic accuracy by means of the Dorfman-Berbaum-Metz method of jackknife with ANOVA ROC analysis. Interpretation times were compared by ANOVA. RESULTS: The ROC AUC was 0.848 with the CAD system, compared with 0.828 without it, for a difference of 0.020 (95% CI, -0.011 to 0.051) and was statistically noninferior to the AUC without the CAD system with respect to a margin of -0.05 (p = 0.000086). The mean interpretation time was 3 minutes 33 seconds per case without the CAD system and 2 minutes 24 seconds with it, for a difference of 1 minute 9 seconds saved (95% CI, 44-93 seconds; p = 0.000014), or a reduction in interpretation time to 67% of the time without the CAD system. CONCLUSION: Use of the concurrent-read CAD system for interpretation of screening ABUS studies of women with dense breast tissue who do not have symptoms is expected to make interpretation significantly faster and produce noninferior diagnostic accuracy compared with interpretation without the CAD system.

QSAR modeling and in silico design of small-molecule inhibitors targeting the interaction between E3 ligase VHL and HIF-1α.

Protein-protein interactions (PPIs) have attracted much attention recently because of their preponderant role in most biological processes. The prevention of the interaction between E3 ligase VHL and HIF-1[Formula: see text] may improve tolerance to hypoxia and ameliorate the prognosis of many diseases. To obtain novel potent inhibitors of VHL/HIF-1[Formula: see text] interaction, a series of hydroxyproline-based inhibitors were investigated for structural optimization using a combination of QSAR modeling and molecular docking. Here, 2D- and 3D-QSAR models were developed by genetic function approximation (GFA) and comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods, respectively. The top-ranked models with strict validation revealed satisfactory statistical parameters (CoMFA with [Formula: see text], 0.637; [Formula: see text], 0.955; [Formula: see text], 0.944; CoMSIA with [Formula: see text], 0.649; [Formula: see text], 0.954; [Formula: see text], 0.911; GFA with [Formula: see text], 0.721; [Formula: see text], 0.801; [Formula: see text], 0.861). The selected five 2D-QSAR descriptors were in good accordance with the 3D-QSAR results, and contour maps gave the visualization of feature requirements for inhibitory activity. A new diverse molecular database was created by molecular fragment replacement and BREED techniques for subsequent virtual screening. Eventually, 31 novel hydroxyproline derivatives stood out as potential VHL/HIF-1[Formula: see text] inhibitors with favorable predictions by the CoMFA, CoMSIA and GFA models. The reliability of this protocol suggests that it could also be applied to the exploration of lead optimization of other PPI targets.

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.

Quantitative Multiparametric MRI Features and PTEN Expression of Peripheral Zone Prostate Cancer: A Pilot Study.

OBJECTIVE: The objective of our study was to investigate associations between quantitative image features of multiparametric MRI of the prostate and PTEN expression of peripheral zone prostate cancer. MATERIALS AND METHODS: A total of 45 peripheral zone cancer foci from 30 patients who had undergone multiparametric prostate MRI before prostatectomy were identified by a genitourinary pathologist and a radiologist who reviewed histologic findings and MR images. Histologic sections of cancer foci underwent immunohistochemical analysis and were scored according to the percentage of tumor-positive cells expressing PTEN as negative (0-20%), mixed (20-80%), or positive (80-100%). Average and 10th percentile apparent diffusion coefficient (ADC) values, skewness of T2-weighted signal intensity histogram, and quantitative perfusion parameters (i.e., forward volume transfer constant [K(trans)], extravascular extracellular volume fraction [ve], and reverse reflux rate constant between the extracellular space and plasma [k(ep)]) from the Tofts model were calculated for each cancer focus. Associations between the quantitative image features and PTEN expression were analyzed with the Spearman rank correlation coefficient (r). RESULTS: Analysis of the 45 cancer foci revealed that 21 (47%) were PTEN-positive, 12 (27%) were PTEN-negative, and 12 (27%) were mixed. There was a weak but significant negative correlation between Gleason score and PTEN expression (r = -0.30, p = 0.04) and between k(ep) and PTEN expression (r = -0.35, p = 0.02). There was no significant correlation between other multiparametric MRI features and PTEN expression. CONCLUSION: This preliminary study of radiogenomics of peripheral zone prostate cancer revealed weak-but significant-associations between the quantitative dynamic contrast-enhanced MRI feature k(ep) and Gleason score with PTEN expression. These findings warrant further investigation and validation with the aim of using multiparametric MRI to improve risk assessment of patients with prostate cancer.

Kinetic Analysis of Benign and Malignant Breast Lesions With Ultrafast Dynamic Contrast-Enhanced MRI: Comparison With Standard Kinetic Assessment.

OBJECTIVE: The purposes of this study were to evaluate diagnostic parameters measured with ultrafast MRI acquisition and with standard acquisition and to compare diagnostic utility for differentiating benign from malignant lesions. MATERIALS AND METHODS: Ultrafast acquisition is a high-temporal-resolution (7 seconds) imaging technique for obtaining 3D whole-breast images. The dynamic contrast-enhanced 3-T MRI protocol consists of an unenhanced standard and an ultrafast acquisition that includes eight contrast-enhanced ultrafast images and four standard images. Retrospective assessment was performed for 60 patients with 33 malignant and 29 benign lesions. A computer-aided detection system was used to obtain initial enhancement rate and signal enhancement ratio (SER) by means of identification of a voxel showing the highest signal intensity in the first phase of standard imaging. From the same voxel, the enhancement rate at each time point of the ultrafast acquisition and the AUC of the kinetic curve from zero to each time point of ultrafast imaging were obtained. RESULTS: There was a statistically significant difference between benign and malignant lesions in enhancement rate and kinetic AUC for ultrafast imaging and also in initial enhancement rate and SER for standard imaging. ROC analysis showed no significant differences between enhancement rate in ultrafast imaging and SER or initial enhancement rate in standard imaging. CONCLUSION: Ultrafast imaging is useful for discriminating benign from malignant lesions. The differential utility of ultrafast imaging is comparable to that of standard kinetic assessment in a shorter study time.

Automated Breast Ultrasound in Breast Cancer Screening of Women With Dense Breasts: Reader Study of Mammography-Negative and Mammography-Positive Cancers.

OBJECTIVE: The objective of our study was to assess and compare, in a reader study, radiologists' performance in the detection of breast cancer using full-field digital mammography (FFDM) alone and using FFDM with 3D automated breast ultrasound (ABUS). MATERIALS AND METHODS: In this multireader, multicase, sequential-design reader study, 17 Mammography Quality Standards Act-qualified radiologists interpreted a cancer-enriched set of FFDM and ABUS examinations. All imaging studies were of asymptomatic women with BI-RADS C or D breast density. Readers first interpreted FFDM alone and subsequently interpreted FFDM combined with ABUS. The analysis included 185 cases: 133 noncancers and 52 biopsy-proven cancers. Of the 52 cancer cases, the screening FFDM images were interpreted as showing BI-RADS 1 or 2 findings in 31 cases and BI-RADS 0 findings in 21 cases. For the cases interpreted as BI-RADS 0, a forced BI-RADS score was also given. Reader performance was compared in terms of AUC under the ROC curve, sensitivity, and specificity. RESULTS: The AUC was 0.72 for FFDM alone and 0.82 for FFDM combined with ABUS, yielding a statistically significant 14% relative improvement in AUC (i.e., change in AUC = 0.10 [95% CI, 0.07-0.14]; p < 0.001). When a cutpoint of BI-RADS 3 was used, the sensitivity across all readers was 57.5% for FFDM alone and 74.1% for FFDM with ABUS, yielding a statistically significant increase in sensitivity (p < 0.001) (relative increase = 29%). Overall specificity was 78.1% for FFDM alone and 76.1% for FFDM with ABUS (p = 0.496). For only the mammography-negative cancers, the average AUC was 0.60 for FFDM alone and 0.75 for FFDM with ABUS, yielding a statistically significant 25% relative improvement in AUC with the addition of ABUS (p < 0.001). CONCLUSION: Combining mammography with ABUS, compared with mammography alone, significantly improved readers' detection of breast cancers in women with dense breast tissue without substantially affecting specificity.

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.

A selectivity study of sodium-dependent glucose cotransporter 2/sodium-dependent glucose cotransporter 1 inhibitors by molecular modeling.

Sodium-dependent glucose cotransporters (SGLTs) play an important role in glucose reabsorption in the kidney and have been identified as promising targets to treat diabetes. Because of the side effects like glucose and galactose malabsorption by targeting SGLT1, highly selective SGLT2 inhibitors are more promising in the treatment of diabetes. To understand the mechanism of selectivity, we conducted selectivity-based three-dimensional quantitative structure-activity relationship studies to highlight the structure requirements for highly selective SGLT2 inhibitors. The best comparative molecular field analysis and comparative molecular similarity indices analysis models showed the noncross-validated coefficient (r(2) ) of 0.967 and 0.943, respectively. The predicted correlation coefficients (r(2) pred ) of 0.974 and 0.938 validated the reliability and predictability of these models. Besides, homology models of SGLT2 and SGLT1 were also constructed to investigate the selective mechanism from structure-based perspective. Molecular dynamics simulation and binding free energy calculation were performed on the systems of a potent and selective compound interacting with SGLT2 and SGLT1 to compare the different binding modes. The simulation results showed that the stretch of the methylthio group on Met241 had an essential effect on the different binding modes between SGLT1 and SGLT2, which was consistent with the three-dimensional quantitative structure-activity relationship analysis. Hydrogen bond analysis and binding free energy calculation revealed that SGLT2 binding complex was more stable and favorable than SGLT1 complex, which was highly correlated with the experimental results. Our obtained results give useful information for the investigation of the inhibitors' selectivity between SGLT2 and SGLT1 and will help for further development of highly selective SGLT2 inhibitors.

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.