A Multireader Multicase (MRMC) Receiver Operating Characteristic (ROC) Study Evaluating Noninferiority of Quantitative Transmission (QT) Ultrasound to Digital Breast Tomosynthesis (DBT) on Detection and Recall of Breast Lesions.

RATIONALE AND OBJECTIVES: Quantitative transmission (QT) imaging is an emerging volumetric ultrasound modality for women too young for mammography. QT images tissue without overlap seen in mammography, thereby can potentially improve breast mass detection and characterization and noncancer recall. We compared radiologists' interpretation of QT vs digital breast tomosynthesis (DBT) with a multireader multicase observer performance study. MATERIALS AND METHODS: Study subjects received screening DBT and QT scans in HIPAA-compliant, institutional review board-approved prospective case-collection studies at four clinical sites. Twenty-four Mammography Quality Standards Act-qualified radiologists interpreted 177 cases (66 with cancer, atypia, or solid mass and 111 normal or with nonsolid benign abnormality), first QT, then 2 weeks later DBT synthesized 2D-views. Readers reported up to three findings per case and for each finding a recall or no recall decision and confidence of that decision. The study hypothesis was area under receiver operating characteristic curve (AUC) of QT was noninferior to DBT. Sensitivity and specificity were also compared. RESULTS: AUC of QT (0.746 ± 0.028, mean ± SD) was noninferior to DBT (0.700 ± 0.028) for AUC difference margin of -0.05 (P < .05). AUC difference was 0.046 ± 0.028 (95% CI: [-0.008, 0.101]). Sensitivity was 70.6 ± 7.2% for QT and 85.2 ± 6.4% for DBT, specificity was 60.1 ± 12.3% vs 37.2 ± 11.0%, and both differences were statistically significant. Of a total of 21 cases of cysts, readers recommended recall, on average, in 1.1 ± 1.4 cases with QT, but not with DBT, and 10.6 ± 2.2 cases with DBT, but not with QT. CONCLUSION: QT can be a potential alternative to mammography for breast cancer screening of women too young to undergo mammography.

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.

Epigenetic target identification strategy based on multi-feature learning.

The identification of potential epigenetic targets for a known bioactive compound is essential and promising as more and more epigenetic drugs are used in cancer clinical treatment and the availability of chemogenomic data related to epigenetics increases. In this study, we introduce a novel epigenetic target identification strategy (ETI-Strategy) that integrates a multi-task graph convolutional neural network prior model and a protein-ligand interaction classification discriminating model using large-scale bioactivity data for a panel of 55 epigenetic targets. Our approach utilizes machine learning techniques to achieve an AUC value of 0.934 for the prior model and 0.830 for the discriminating model, outperforming inverse docking in predicting protein-ligand interactions. When comparing with other open-source target identification tools, it was found that only our tool was able to accurately predict all the targets corresponding to each compound. This further demonstrates the ability of our strategy to take full advantage of molecular-level information as well as protein-level information in molecular activity prediction. Our work highlights the contribution of machine learning in the identification of potential epigenetic targets and offers a novel approach for epigenetic drug discovery and development.Communicated by Ramaswamy H. Sarma.

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.

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.

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.

Discovery of orally active 1,4,5,6,8-pentaazaacenaphthylens as novel, selective, and potent covalent BTK inhibitors for the treatment of rheumatoid arthritis.

Bruton's tyrosine kinase (BTK) plays a crucial role in adaptive and immune responses by modulating B-cell, Fc, toll-like, and chemokine receptor signaling pathways. BTK inhibition is a promising therapeutic approach for the treatment of inflammatory and autoimmune diseases. The development of novel, highly selective, and less toxic BTK inhibitors may be beneficial for the treatment of autoimmune diseases with unmet medical needs. In this study, structure-based drug design was used to discover a series of novel, potent, and selective covalent BTK inhibitors with a 1,4,5,6,8-pentaazaacenaphthylen scaffold. Among them, compound 36R exhibited high kinase selectivity, long target occupancy time, appropriate pharmacokinetic properties, and dose-dependent efficacy in a rat model of collagen-induced arthritis. Therefore, 36R is a novel BTK inhibitor requiring further development for the treatment of autoimmune diseases.

Discovery of 2-Amino-7-sulfonyl-7H-pyrrolo[2,3-d]pyrimidine Derivatives as Potent Reversible FGFR Inhibitors with Gatekeeper Mutation Tolerance: Design, Synthesis, and Biological Evaluation.

Fibroblast growth factor receptors (FGFRs) play key roles in promoting cancer cell proliferation, differentiation, and migration. However, acquired resistance to FGFR inhibitors has become an emerging challenge in long-term cancer therapies, especially for hepatocellular carcinoma (HCC). Gatekeeper (GK) mutations are the main mechanism of resistance. Herein, we describe the discovery of a series of reversible FGFR inhibitors, particularly for GK mutations with the 2-amino-7-sulfonyl-7H-pyrrolo[2,3-d]pyrimidine scaffold. Rational design, optimization, and pharmacokinetic screening provided representative compound 19 with potent FGFR inhibition in vitro, high bioavailability, and an acceptable half-life. GK mutation tolerance was supported by assays against FGFR4V550L and Ba/F3-TEL-FGFR4V550L cells. Moreover, compound 19 exhibited potent antitumor potency in HUH7 xenograft mouse models with no obvious toxicity observed. Compound 19 was identified as a potential candidate for overcoming GK mutations for HCC treatment.

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.

Factors associated with postpartum women's knowledge, attitude and practice regarding human milk banks and milk donation: A cross-sectional survey.

BACKGROUND: Breast milk is the optimal method of human nutrition, and donor human milk is often needed to reduce the incidence of necrotizing enterocolitis and septicemia in preterm infants and improve their survival rate. Donor human milk is recommended as the first alternative when mothers' milk is not available. The establishment of human milk banks is of great significance to promote the breastfeeding of preterm infants. However, there are insufficient studies on human milk banks and milk donation in China. OBJECTIVES: (1) To investigate postpartum women's knowledge, attitude and practice regarding human milk banks and milk donation and to analyze the influencing factors. (2) To explore reasons why postpartum women reject milk donation and donor milk. DESIGN AND SETTINGS: A cross-sectional survey was conducted from February 2019 to July 2019 at two hospitals in Wuhan, a large city in central China. PARTICIPANTS: Mothers who returned to hospital for postpartum follow-up within six months participated in this survey (N = 1078). METHODS: Questionnaires were used to obtain sociodemographic data and to determine participants' knowledge, attitude and practice regarding human milk banks and milk donation. FINDINGS: Of the respondents, 216 (20%) had prior knowledge of human milk banks and milk donation. For the sub-domain of knowledge, the item with the highest correct response rate was the benefit of breast milk, and the item with the lowest correct rate was the acceptance of donor human milk. For the sub-domain of attitude, 811(75.3%) of participants held a supportive attitude for the establishment of human milk banks, and 877(81.3%) were supportive of donating breast milk while 412 (38.3%) were supportive of accepting donor human milk. For the sub-domain of practice, the practice of milk donation was not optimistic as participants lacked interest in donating breast milk and spreading knowledge of breast milk banks, and only 28.3% of participants indicated that they would donate breast milk continuously. Participants' age, educational background, weight of the newborn and having prior knowledge of human milk banks were factors that could positively predict their knowledge, attitude and practice associated with human milk banks and milk donation; medication usage during pregnancy or lactation was a factor negatively predicting their knowledge about human milk banks and milk donation. CONCLUSION: This study reveals that a majority of postpartum women are supportive of human milk banks and more willing to donate breast milk than receive donor milk. Lack of knowledge about human milk banks and safety concerns are the main factors hindering postpartum women from donating or accepting donor milk. Findings suggest that it is important to enhance public awareness regarding human milk banks as potential resources for life-saving therapy for preterm infants.This information should be disseminated during the early stage of the establishment of human milk banks. Moreover, health education of pregnant women should include the importance of human milk as well as the alternative and safety of donor milk from milk banks, especially for promoting the health of preterm infants and infants who are unable to receive mothers' breastmilk.

Receiver Operating Characteristic (ROC) Analysis of Image Search-and-Localize Tasks.

RATIONALE AND OBJECTIVES: Receiver operating characteristic (ROC) analysis for the common image search-and-localize task, in which readers search an image for lesion or lesions not knowing a priori any exists, has been studied for over four decades. However, a satisfactory solution seems elusive. MATERIALS AND METHODS: We show that the ROC curve predictive of clinical outcomes where readers are penalized appropriately for not correctly localizing known lesions cannot be obtained because it is a missing data problem. Further, this ROC curve is between the case-based ROC curve where readers are not penalized and the lesion-based ROC curve where penalty applies. Moreover, the lesion-based ROC curve is the LROC curve proposed by Starr et al. We show maximum-likelihood (ML) estimation of the LROC curve, validation of this procedure with Monte Carlo simulations, and its application to reader ROC datasets. RESULTS: Monte Carlo simulations validated ML estimation of area under the LROC curve (AUC) and its variance. Example applications showed that ML estimate of LROC curve fits experimental datasets. CONCLUSION: The ROC curve predictive of clinical performance cannot be estimated from reader ROC data alone because it is a missing data problem, and is between the case-based ROC curve where readers are not penalized for not correctly identifying known lesions and the lesion-based ROC curve where penalty applies. The lesion-based ROC curve is the LROC curve proposed by Starr et al. and can be estimated via ML estimation.

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.

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.

Accuracy of the Vancouver Lung Cancer Risk Prediction Model Compared With That of Radiologists.

BACKGROUND: Risk models have been developed that include the subject's pretest risk profile and imaging findings to predict the risk of cancer in an objective way. We assessed the accuracy of the Vancouver Lung Cancer Risk Prediction Model compared with that of trainee and experienced radiologists using a subset of size-matched nodules from the National Lung Screening Trial (NLST). METHODS: One hundred cases from the NLST database were selected (size range, 4-20 mm), including 20 proven cancers and 80 size-matched benign nodules. Three experienced thoracic radiologists and three trainee radiologists were asked to estimate the likelihood of cancer in each case, first independently, and then with knowledge of the model's risk prediction. The results generated by the model alone also were estimated using receiver operating characteristic (ROC) analysis. The area under the ROC curve (AUC) for each viewing condition was calculated, and statistical significance in their differences was tested by using the Dorfman-Berbaum-Metz method. RESULTS: Human observers were more accurate (AUC value of 0.85 ± 0.05 [SD]) than was the model (0.77 ± 0.06) in estimating the risk of malignancy (P = .0010), and use of the model did not improve their accuracy (0.84 ± 0.06). Experienced radiologists performed better than did trainees. Human observers could distinguish benign from malignant nodule morphology more accurately than could the model, which relies mainly on nodule size for risk estimation. CONCLUSIONS: Experienced and trainee radiologists had superior ability to predict the risk of cancer in size-matched nodules from a screening trial compared with that of the Vancouver model, and use of the model did not improve their accuracy.

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.

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.

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.