Comparison of lung lesion assessment using free-breathing dynamic contrast-enhanced 1.5-T MRI with a golden-angle radial stack-of-stars VIBE sequence and CT.

BACKGROUND: Few studies have investigated the feasibility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a free-breathing golden-angle radial stack-of-stars volume-interpolated breath-hold examination (FB radial VIBE) sequence in the lung. PURPOSE: To investigate whether DCE-MRI using the FB radial VIBE sequence can assess morphological and kinetic parameters in patients with pulmonary lesions, with computed tomography (CT) as the reference. MATERIAL AND METHODS: In total, 43 patients (30 men; mean age = 64 years) with one lesion each were prospectively enrolled. Morphological and kinetic features on MRI were calculated. The diagnostic performance of morphological MR features was evaluated using a receiver operating characteristic (ROC) curve. Kinetic features were compared among subgroups based on histopathological subtype, lesion size, and lymph node metastasis. RESULTS: The maximum diameter was not significantly different between CT and MRI (3.66 ± 1.62 cm vs. 3.64 ± 1.72 cm; P = 0.663). Spiculation, lobulation, cavitation or bubble-like areas of low attenuation, and lymph node enlargement had an area under the ROC curve (AUC) >0.9, while pleural indentation yielded an AUC of 0.788. The lung cancer group had significantly lower Ktrans, Ve, and initial AUC values than the other cause inflammation group (0.203, 0.158, and 0.589 vs. 0.597, 0.385, and 1.626; P < 0.05) but significantly higher values than the tuberculosis group (P < 0.05). CONCLUSION: Morphology features derived from FB radial VIBE have high correlations with CT, and kinetic analyses show significant differences between benign and malignant lesions. DCE-MRI with FB radial VIBE could serve as a complementary quantification tool to CT for radiation-free assessments of lung lesions.

A Coronary CT Angiography Radiomics Model to Identify Vulnerable Plaque and Predict Cardiovascular Events.

Background A noninvasive coronary CT angiography (CCTA)-based radiomics technique may facilitate the identification of vulnerable plaques and patients at risk for future adverse events. Purpose To assess whether a CCTA-based radiomic signature (RS) of vulnerable plaques defined with intravascular US was associated with increased risk for future major adverse cardiac events (MACE). Materials and Methods In a retrospective study, an RS of vulnerable plaques was developed and validated using intravascular US as the reference standard. The RS development data set included patients first undergoing CCTA and then intravascular US within 3 months between June 2013 and December 2020 at one tertiary hospital. The development set was randomly assigned to training and validation sets at a 7:3 ratio. Diagnostic performance was assessed internally and externally from three tertiary hospitals using the area under the curve (AUC). The prognostic value of the RS for predicting MACE was evaluated in a prospective cohort with suspected coronary artery disease between April 2018 and March 2019. Multivariable Cox regression analysis was used to evaluate the RS and conventional anatomic plaque features (eg, segment involvement score) for predicting MACE. Results The RS development data set included 419 lesions from 225 patients (mean age, 64 years ± 10 [SD]; 68 men), while the prognostic cohort included 1020 lesions from 708 patients (mean age, 62 years ± 11; 498 men). Sixteen radiomic features, including two shape features and 14 textural features, were selected to build the RS. The RS yielded a moderate to good AUC in the training, validation, internal, and external test sets (AUC = 0.81, 0.75, 0.80, and 0.77, respectively). A high RS (≥1.07) was independently associated with MACE over a median 3-year follow-up (hazard ratio, 2.01; P = .005). Conclusion A coronary CT angiography-derived radiomic signature of coronary plaque enabled the detection of vulnerable plaques that were associated with increased risk for future adverse cardiac outcomes. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by De Cecco and van Assen in this issue.

Evolocumab attenuate pericoronary adipose tissue density via reduction of lipoprotein(a) in type 2 diabetes mellitus: a serial follow-up CCTA study.

BACKGROUND: Pericoronary adipose tissue (PCAT) density is a biomarker of vessel inflammation, which is supposed to be increased in patients with type 2 diabetes mellitus (T2DM). However, whether the coronary inflammation revealed by this novel index could be alleviated after evolocumab treatment in T2DM remains unknown. METHODS: From January 2020 to December 2022, consecutive T2DM patients with low-density lipoprotein cholesterol ≥ 70 mg/dL on maximally tolerated statin and taking evolocumab were prospectively included. In addition, patients with T2DM who were taking statin alone were recruited as control group. The eligible patients underwent baseline and follow-up coronary CT angiography with an interval of 48-week. To render patients with evolocumab as comparable to those controls, a propensity-score matching design was used to select the matched pairs with a 1:1 ratio. Obstructive lesion was defined as the extent of coronary artery stenosis ≥ 50%; the numbers inside the brackets were interquartile ranges. RESULTS: A total of 170 T2DM patients with stable chest pain were included [(mean age 64 ± 10.6 [range 40-85] years; 131 men). Among those patients, 85 were in evolocumab group and 85 were in control group. During follow-up, low-density lipoprotein cholesterol (LDL-C) level (2.02 [1.26, 2.78] vs. 3.34 [2.53, 4.14], p < 0.001), and lipoprotein(a) (12.1 [5.6, 21.8] vs. 18.9 [13.2, 27.2], p = 0.002) were reduced after evolocumab treatment. The prevalence of obstructive lesions and high-risk plaque features were significantly decreased (p < 0.05 for all). Furthermore, the calcified plaque volume were significantly increased (188.3 [115.7, 361.0] vs. 129.3 [59.5, 238.3], p = 0.015), while the noncalcified plaque volume and necrotic volume were diminished (107.5 [40.6, 180.6] vs. 125.0 [65.3, 269.7], p = 0.038; 0 [0, 4.7] vs. 0 [0, 13.4], p < 0.001, respectively). In addition, PCAT density of right coronary artery was significantly attenuated in evolocumab group (- 85.0 [- 89.0, - 82.0] vs. - 79.0 [- 83.5, - 74.0], p < 0.001). The change in the calcified plaque volume inversely correlated with achieved LDL-C level (r = - 0.31, p < 0.001) and lipoprotein(a) level (r = - 0.33, p < 0.001). Both the changes of noncalcified plaque volume and necrotic volume were positively correlated with achieved LDL-C level and Lp(a) (p < 0.001 for all). However, the change of PCATRCA density only positively correlated with achieved lipoprotein(a) level (r = 0.51, p < 0.001). Causal mediation analysis revealed Lp(a) level mediated 69.8% (p < 0.001) for the relationship between evolocumab and changes of PCATRCA. CONCLUSIONS: In patients with T2DM, evolocumab is an effective therapy to decrease noncalcified plaque volume necrotic volume, and increase calcified plaque volume. Furthermore, evolocumab could attenuate PCAT density, at least in part, via the reduction of lipoprotein(a).

Injectable and Self-Adaptive Gel Scaffold Based on Heparin Microspheres for Adipogenesis of Human Adipose-Derived Stem Cells.

An injectable and self-adaptive heparin microsphere-based cell scaffold was developed to achieve adipose regeneration. Simultaneously, the cell scaffold exhibited a dynamic architecture, self-regulated glucose levels, sustained insulin delivery, and steady viscoelastic properties for adipogenesis. The dynamic cell scaffold is cross-linked by the boronate-diol interaction among heparin-based microspheres, which have boronate and maltose groups. Because of the boronate-maltose ester bonds, the gelatinous complex would be partially dismantled and readily display glucose-sensitive performance by free glucose via competitive displacement. The dynamic cross-linking heparin microsphere scaffold can deliver the lipogenic drug insulin to enhance lipid filling, which has an impact on fat tissue enhancement. A 4-week in vitro cell culture demonstrated that the dynamic heparin microsphere-based cell scaffold, through loading with insulin, showed significantly higher efficiency in promoting ASC differentiation compared with traditional 3D culture methods. In vivo histological results further demonstrated that there was a significant increase in adipose in the proposed cell scaffold, which proved to be statistically significant compared with traditional biomaterials. Notable stain expression of the FABP4 and PPAR-γ genes was also observed in the dynamic cell scaffold containing insulin, which was more similar to natural fat.

Combination of computed tomography imaging-based radiomics and clinicopathological characteristics for predicting the clinical benefits of immune checkpoint inhibitors in lung cancer.

BACKGROUND: In this study, we tested whether a combination of radiomic features extracted from baseline pre-immunotherapy computed tomography (CT) images and clinicopathological characteristics could be used as novel noninvasive biomarkers for predicting the clinical benefits of non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs). METHODS: The data from 92 consecutive patients with lung cancer who had been treated with ICIs were retrospectively analyzed. In total, 88 radiomic features were selected from the pretreatment CT images for the construction of a random forest model. Radiomics model 1 was constructed based on the Rad-score. Using multivariate logistic regression analysis, the Rad-score and significant predictors were integrated into a single predictive model (radiomics nomogram model 1) to predict the durable clinical benefit (DCB) of ICIs. Radiomics model 2 was developed based on the same Rad-score as radiomics model 1.Using multivariate Cox proportional hazards regression analysis, the Rad-score, and independent risk factors, radiomics nomogram model 2 was constructed to predict the progression-free survival (PFS). RESULTS: The models successfully predicted the patients who would benefit from ICIs. For radiomics model 1, the area under the receiver operating characteristic curve values for the training and validation cohorts were 0.848 and 0.795, respectively, whereas for radiomics nomogram model 1, the values were 0.902 and 0.877, respectively. For the PFS prediction, the Harrell's concordance indexes for the training and validation cohorts were 0.717 and 0.760, respectively, using radiomics model 2, whereas they were 0.749 and 0.791, respectively, using radiomics nomogram model 2. CONCLUSIONS: CT-based radiomic features and clinicopathological factors can be used prior to the initiation of immunotherapy for identifying NSCLC patients who are the most likely to benefit from the therapy. This could guide the individualized treatment strategy for advanced NSCLC.

Assessment of liver metastases radiomic feature reproducibility with deep-learning-based semi-automatic segmentation software.

BACKGROUND: Good feature reproducibility enhances model reliability. The manual segmentation of gastric cancer with liver metastasis (GCLM) can be time-consuming and unstable. PURPOSE: To assess the value of a semi-automatic segmentation tool in improving the reproducibility of the radiomic features of GCLM. MATERIAL AND METHODS: Patients who underwent dual-source computed tomography were retrospectively reviewed. As an intra-observer analysis, one radiologist segmented metastatic liver lesions manually and semi-automatically twice. Another radiologist re-segmented the lesions once as an inter-observer analysis. A total of 1691 features were extracted. Spearman rank correlation was used for feature reproducibility analysis. The times for manual and semi-automatic segmentation were recorded and analyzed. RESULTS: Seventy-two patients with 168 lesions were included. Most of the GCLM radiomic features became more reliable with the tool than the manual method. For the intra-observer feature reproducibility analysis of manual and semi-automatic segmentation, the rates of features with good reliability were 45.5% and 62.3% (P < 0.02), respectively; for the inter-observer analysis, the rates were 29.3% and 46.0% (P < 0.05), respectively. For feature types, the semi-automatic method increased reliability in 6/7 types in the intra-observer analysis and 5/7 types in the inter-observer analysis. For image types, the reliability of the square and exponential types was significantly increased. The mean time of semi-automatic segmentation was significantly shorter than that of the manual method (P < 0.05). CONCLUSION: The application of semi-automated software increased feature reliability in the intra- and inter-observer analyses. The semi-automatic process took less time than the manual process.

Feasibility study of using virtual non-contrast images derived from dual-energy CT to replace true non-contrast images in patients diagnosed with papillary thyroid carcinoma.

OBJECTIVE: To assess the feasibility of using virtual non-contrast (VNC) images derived from dual-energy computed tomography (DECT) to replace true non-contrast (TNC) images of papillary thyroid carcinoma (PTC) patients. METHODS: Images of 96 PTC patients were retrospectively analyzed. TNC images were acquired under the single-energy mode of DECT after the plain scanning. The arterial and venous phase VNC (VNC-a and VNC-v) images were generated by the post-processing algorithm from the arterial phase and venous phase of contrast-enhanced CT images, respectively. Mean attenuation values, image noise, number and length of calcification were measured. Radiation dose was also calculated. Last, subjective score of image quality was evaluated by a 5-point scale. RESULTS: Signal-to-noise ratio (SNR) of each tissue in TNC images is significantly higher than that of VNC images (p<0.050). Contrast-to-noise ratio (CNR) of fat, muscle, thyroid nodules and internal carotid artery in TNC images is significantly higher than that of VNC images, while CNR in TNC images is lower for cervical vertebra (p<0.001). Calcification is detected on TNC images of 44 patients, while it is omitted on VNC images of 14 patients (31.8%). The subjective score of TNC images is higher than VNC images (p<0.001). The effective dose reduction is 47.6% by avoiding plain scanning. CONCLUSIONS: Considering the different attenuation value, SNR, CNR and especially reduced detection rate of calcification, we deem that VNC images cannot be directly used to replace TNC images in PTC patients, despite the reduced radiation dose.

Bunyamwera virus possesses a distinct nucleocapsid protein to facilitate genome encapsidation.

Bunyamwera virus (BUNV), which belongs to the genus Orthobunyavirus, is the prototypical virus of the Bunyaviridae family. Similar to other negative-sense single-stranded RNA viruses, bunyaviruses possess a nucleocapsid protein (NP) to facilitate genomic RNA encapsidation and virus replication. The structures of two NPs of members of different genera within the Bunyaviridae family have been reported. However, their structures, RNA-binding features, and functions beyond RNA binding significantly differ from one another. Here, we report the crystal structure of the BUNV NP-RNA complex. The polypeptide of the BUNV NP was found to possess a distinct fold among viral NPs. An N-terminal arm and a C-terminal tail were found to interact with neighboring NP protomers to form a tetrameric ring-shaped organization. Each protomer bound a 10-nt RNA molecule, which was acquired from the expression host, in the positively charged crevice between the N and C lobes. Inhomogeneous oligomerization was observed for the recombinant BUNV NP-RNA complex, which was similar to the Rift Valley fever virus NP-RNA complex. This result suggested that the flexibility of one NP protomer with adjacent protomers underlies the BUNV ribonucleoprotein complex (RNP) formation. Electron microscopy revealed that the monomer-sized NP-RNA complex was the building block of the natural BUNV RNP. Combined with previous results indicating that mutagenesis of the interprotomer or protein-RNA interface affects BUNV replication, our structure provides a great potential for understanding the mechanism underlying negative-sense single-stranded RNA RNP formation and enables the development of antiviral therapies targeting BUNV RNP formation.

Ion adsorption promotes Frank-van der Merwe growth of 2D transition metal tellurides.

Reliable synthesis methods for high-quality, large-sized, and uniform two-dimensional (2D) transition-metal dichalcogenides (TMDs) are crucial for their device applications. However, versatile approaches to growing high-quality, large-sized, and uniform 2D transition-metal tellurides are rare. Here, we demonstrate an ion adsorption strategy that facilitates the Frank-van der Merwe growth of 2D transition-metal tellurides. By employing this method, we grow MoTe2 and WTe2 with enhanced lateral size, reduced thickness, and improved uniformity. Comprehensive characterizations confirm the high quality of as-grown MoTe2. Moreover, various characterizations verify the adsorption of K+ and Cl- ions on the top surface of MoTe2. X-ray photoelectron spectroscopy (XPS) analysis reveals that the MoTe2 is stoichiometric without K+ and Cl- ions and exhibits no discernable oxidation after washing. This top surface control strategy provides a new controlling knob to optimize the growth of 2D transition-metal tellurides and holds the potential for generalized to other 2D materials.

Cardiac magnetic resonance imaging detection of intramyocardial hemorrhage in patients with ST-elevated myocardial infarction: comparison between susceptibility-weighted imaging and T1/T2 mapping techniques.

Background: Susceptibility-weighted imaging (SWI) and T1/T2 mapping can be used to detect reperfusion intramyocardial hemorrhage (IMH) in ST-segment elevation myocardial infarction (STEMI) patients. However, the sensitivity and accuracy of the SWI and T1/T2 mapping sequences were not systematically compared. The study aimed to evaluate image quality and diagnostic performance of SWI in patients with IMH, compared with T1/T2 mapping. Methods: A prospective study was conducted on consecutive acute STEMI patients who were recruited from January to July 2022. Within 2-6 days after reperfusion treatment, all patients underwent a 3T cardiac magnetic resonance (CMR) examination, including T2-weighted short-tau inversion recovery (T2W-STIR), T1/T2 mapping, and SWI. A total of 36 patients [age, 56.50±17.25 years; males, 83.33% (30/36)] were enrolled. The relative infarct-remote myocardium signal intensity ratio (SIinfarct-remote) and contrast-to-noise ratio (CNR) were calculated for each patient on T1/T2 mapping and SWI, and the difference between relative signal intensity-to-noise ratio (rSNR) in the IMH (rSNRIMH) was measured for IMH patients on T1/T2 mapping and SWI. SIinfarct-remote, CNR, and rSNRIMH were compared among the three sequences. Receiver operating characteristic (ROC) analyses were used to evaluate the diagnostic performance of three sequences by SIinfarct-remote and visual assessment. Results: A total of 26 (72.22%) patients had IMH. Quantitatively, the SIinfarct-remote of three sequences had excellent diagnostic performance for detecting IMH [SWI area under the curve (AUC) =1.000, 95% confidence interval (CI): 1.000-1.000 vs. T1 mapping AUC =0.954, 95% CI: 0.885-1.000 vs. T2 mapping AUC =0.985, 95% CI: 0.955-1.000; SWI vs. T1 mapping, P=0.300; SWI vs. T2 mapping, P=0.188; T1 mapping vs. T2 mapping, P=0.302). Qualitatively, three sequences had similar performance on detecting IMH (SWI AUC =0.895, 95% CI: 0.784-1.000; T1 mapping AUC =0.835, 95% CI: 0.711-0.958; and T2 mapping AUC =0.855, 95% CI: 0.735-0.974; SWI vs. T1 mapping, P=0.172; SWI vs. T2 mapping, P=0.317; T1 mapping vs. T2 mapping, P=0.710). The rSNRIMH was highest in T1 mapping, followed by T2 mapping and SWI, but SWI had the highest CNR. Conclusions: SWI, as well as T1/T2 mapping, is a feasible and accurate approach for clinical diagnosis of IMH with excellent performance.

Scalable Synthesis of High-Quality Ultrathin Ferroelectric Magnesium Molybdenum Oxide.

The development of ultrathin, stable ferroelectric materials is crucial for advancing high-density, low-power electronic devices. Nonetheless, ultrathin ferroelectric materials are rare due to the critical size effect. Here, a novel ferroelectric material, magnesium molybdenum oxide (Mg2Mo3O8) is presented. High-quality ultrathin Mg2Mo3O8 crystals are synthesized using chemical vapor deposition (CVD). Ultrathin Mg2Mo3O8 has a wide bandgap (≈4.4 eV) and nonlinear optical response. Mg2Mo3O8 crystals of varying thicknesses exhibit out-of-plane ferroelectric properties at room temperature, with ferroelectricity retained even at a 2 nm thickness. The Mg2Mo3O8 exhibits a relatively large remanent polarization ranging from 33 to 52 µC cm- 2, which is tunable by changing its thickness. Notably, Mg2Mo3O8 possesses a high Curie temperature (>980 °C) across various thicknesses. Moreover, the as-grown Mg2Mo3O8 crystals display remarkable stability under harsh environments. This work introduces nolanites-type crystal into ultrathin ferroelectrics. The scalable synthesis of stable ultrathin ferroelectric Mg2Mo3O8 expands the scope of ferroelectric materials and may prosper applications of ferroelectrics.

Association of Lipoprotein(a) Levels With Myocardial Infarction in Patients With Low-Attenuation Plaque.

BACKGROUND: Lipoprotein(a) (Lp[a]) is associated with an increased risk of myocardial infarction (MI). However, the mechanism underlying this association has yet to be fully elucidated. OBJECTIVES: This multicenter study aimed to investigate whether association between Lp(a) and MI risk is reinforced by the presence of low-attenuation plaque (LAP) identified by coronary computed tomography angiography (CCTA). METHODS: In a derivation cohort, a total of 5,607 patients with stable chest pain suspected of coronary artery disease who underwent CCTA and Lp(a) measurement were prospectively enrolled. In validation cohort, 1,122 patients were retrospectively collected during the same period. High Lp(a) was defined as Lp(a) ≥50 mg/dL. The primary endpoint was a composite of time to fatal or nonfatal MI. Associations were estimated using multivariable Cox proportional hazard models. RESULTS: During a median follow-up of 8.2 years (Q1-Q3: 7.2-9.3 years), the elevated Lp(a) levels were associated with MI risk (adjusted HR [aHR]: 1.91; 95% CI: 1.46-2.49; P < 0.001). There was a significant interaction between Lp(a) and LAP (Pinteraction <0.001) in relation to MI risk. When stratified by the presence or absence of LAP, Lp(a) was associated with MI in patients with LAP (aHR: 3.03; 95% CI: 1.92-4.76; P < 0.001). Mediation analysis revealed that LAP mediated 73.3% (P < 0.001) for the relationship between Lp(a) and MI. The principal findings remained unchanged in the validation cohort. CONCLUSIONS: Elevated Lp(a) augmented the risk of MI during 8 years of follow-up, especially in patients with LAP identified by CCTA. The presence of LAP could reinforce the relationship between Lp(a) and future MI occurrence.

Identification of heart failure with preserved ejection fraction in patients with hypertension: a left atrial myocardial strain cardiac magnetic resonance study.

Background: The noninvasive diagnosis of heart failure with preserved ejection fraction (HFpEF) remains challenging. The role of left atrial (LA) functional changes in patients with HFpEF has attracted increased attention. This study aimed to evaluate LA deformation in patients with hypertension (HTN) using cardiac magnetic resonance tissue tracking and to investigate the diagnostic value of LA strain for HFpEF. Methods: This retrospective study consecutively enrolled 24 HTN patients with HFpEF (HTN-HFpEF) and 30 patients with pure HTN based on clinical indications. Thirty age-matched healthy participants were also enrolled. All participants underwent a laboratory examination and 3.0 T cardiovascular magnetic resonance (CMR). The LA strain and strain rate, including total strain (εs), passive strain (εe), active strain (εa), peak positive strain rate (SRs), peak early negative strain rate (SRe), and peak late negative strain rate (SRa), were evaluated using CMR tissue tracking and compared among the 3 groups. Receiver operating characteristic (ROC) analysis was used to identify HFpEF. Spearman correlation was used to analyze the correlation between LA strain and brain natriuretic peptide (BNP) level. Results: Patients with HTN-HFpEF had significantly lower εs (17.70%, IQR 14.65% to 19.70%, εe 7.83%±2.86%), εa (9.08%±3.19%), SRs (0.88±0.24 s-1), SRe (-0.60 s-1, IQR -0.90 to -0.50 s-1), and SRa (-1.10±0.47 s-1) than did patients with HTN and control participants (all P values <0.05). Compared to the control group, patients with HTN had lower εs (25.35%, IQR 21.80% to 27.25%), εe (11.49%±2.64%), SRs (1.10 s-1, IQR 1.00 to 1.48 s-1), and SRe (-1.11±0.37 s-1) (all P values <0.05). The values of εa and SRa were not significantly different between the HTN and control groups. LA total strain εs was independently associated with HFpEF (odds ratio 0.009; P<0.05) with a cutoff value of 19.55% (95% CI: 0.882-0.996), and the sensitivity and specificity were 75% and 97%, respectively. There was a good correlation between the LA strain parameters and BNP level (all P values <0.05). Conclusions: LA function impairment exists in patients with HFpEF. The LA strain parameter has potential value in diagnosing HFpEF.

The predictive value of coronary microvascular dysfunction for left ventricular reverse remodelling in dilated cardiomyopathy.

Aims: To evaluate the degree of coronary microvascular dysfunction (CMD) in dilated cardiomyopathy (DCM) patients by cardiac magnetic resonance (CMR) first-pass perfusion parameters and to examine the correlation between myocardial perfusion and left ventricle reverse remodelling (LVRR). Methods: In this study, 94 DCM patients and 35 healthy controls matched for age and sex were included. Myocardial perfusion parameters, including upslope, time to maximum signal intensity (Timemax), maximum signal intensity (SImax), baseline signal intensity (SIbaseline), and the difference between maximum and baseline signal intensity (SImax-baseline) were measured. Additionally, left ventricular (LV) structure, function parameters, and late gadolinium enhancement (LGE) were also recorded. The parameters were compared between healthy controls and DCM patients. Univariable and multivariable logistic regression analyses were used to determine the predictors of LVRR. Results: With a median follow-up period of 12 months [interquartile range (IQR), 8-13], 41 DCM patients (44%) achieved LVRR. Compared with healthy controls, DCM patients presented CMD with reduced upslope, SIbaseline, and increased Timemax (all p < 0.01). Timemax, SImax, and SImax-baseline were further decreased in LVRR than non-LVRR group (Timemax: 60.35 [IQR, 51.46-74.71] vs. 72.41 [IQR, 59.68-97.70], p = 0.017; SImax: 723.52 [IQR, 209.76-909.27] vs. 810.92 [IQR, 581.30-996.89], p = 0.049; SImax-baseline: 462.99 [IQR, 152.25-580.43] vs. 551.13 [IQR, 402.57-675.36], p = 0.038). In the analysis of multivariate logistic regression, Timemax [odds ratio (OR) 0.98; 95% confidence interval (CI) 0.95-1.00; p = 0.032)], heart rate (OR 1.04; 95% CI 1.01-1.08; p = 0.029), LV remodelling index (OR 1.73; 95% CI 1.06-3.00; p = 0.038) and LGE extent (OR 0.85; 95% CI 0.73-0.96; p = 0.021) were independent predictors of LVRR. Conclusions: CMD could be found in DCM patients and was more impaired in patients with non-LVRR than LVRR patients. Timemax at baseline was an independent predictor of LVRR in DCM.

The potential predictive value of cardiac mechanics for left ventricular reverse remodelling in dilated cardiomyopathy.

AIMS: Left ventricular reverse remodelling (LVRR) is an important objective of optimal medical management for dilated cardiomyopathy (DCM) patients, as it is associated with favourable long-term outcomes. Cardiac magnetic resonance (CMR) can comprehensively assess cardiac structure and function. We aimed to assess the CMR parameters at baseline and investigate independent variables to predict LVRR in DCM patients. METHODS AND RESULTS: Nighty-eight initially diagnosed DCM patients who underwent CMR and echocardiography examinations at baseline were included. CMR parameters and feature tracking (FT) based left ventricular (LV) global strain (nStrain) and nStrain indexed to LV cardiac mass index (rStrain) were measured. The predictors of LVRR were determined by multivariate logistic regression analyses. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of CMR parameters and were compared by the DeLong test. At a median follow-up time of 9 [interquartile range, 7-12] months, 35 DCM patients (36%) achieved LVRR. The patients with LVRR had lower LV volume, mass, LGE extent and stroke volume index (LVSVi) and higher left ventricular remodelling index (LVRI), nStrains, rStrains, and peak systolic strain rate (PSSR) in the longitudinal direction and rStrains in the circumferential direction at baseline (all P < 0.05). In the multivariate logistic regression analyses, LVRI [per SD, odds ratio (OR) 1.79; 95% confidence interval (CI) 1.08-2.98; P = 0.024] and the ratio of global longitudinal peak strain (rGLPS) (per SD, OR 1.88; 95% CI 1.18-3.01; P = 0.008) were independent predictors of LVRR. The combination of LVSVi, LVRI, and rGLPS had a greater area under the curve (AUC) than the combination of LVSVi and LVRI (0.75 vs. 0.68), but not significantly (P = 0.09). CONCLUSIONS: Patients with LVRR had a lower LV volume index, lower LVSV index, lower LGE extent, higher LVRI, and preserved myocardial deformation in the longitudinal direction at baseline. LVRI and rGLPS at baseline were independent determinants of LVRR.

Development and Validation of CCTA-based Radiomics Signature for Predicting Coronary Plaques With Rapid Progression.

BACKGROUND: Rapid plaque progression (RPP) is associated with a higher risk of acute coronary syndromes compared with gradual plaque progression. We aimed to develop and validate a coronary computed tomography angiography (CCTA)-based radiomics signature (RS) of plaques for predicting RPP. METHODS: A total of 214 patients who underwent serial CCTA examinations from 2 tertiary hospitals (development group, 137 patients with 164 lesions; validation group, 77 patients with 101 lesions) were retrospectively enrolled. Conventional CCTA-defined morphological parameters (eg, high-risk plaque characteristics and plaque burden) and radiomics features of plaques were analyzed. RPP was defined as an annual progression of plaque burden ≥1.0% on lesion-level at follow-up CCTA. RS was built to predict RPP using XGBoost method. RESULTS: RS significantly outperformed morphological parameters for predicting RPP in both the development group (area under the receiver operating characteristic curve, 0.82 versus 0.74; P=0.04) and validation group (area under the receiver operating characteristic curve, 0.81 versus 0.69; P=0.04). Multivariable analysis identified RS (odds ratio, 2.35 [95% CI, 1.32-4.46]; P=0.005) as an independent predictor of subsequent RPP in the validation group after adjustment of morphological confounders. Unlike unchanged RS in the non-RPP group, RS increased significantly in the RPP group at follow-up in the whole dataset (P<0.001). CONCLUSIONS: The proposed CCTA-based RS had a better discriminative value to identify plaques at risk of rapid progression compared with conventional morphological plaque parameters. These data suggest the promising utility of radiomics for predicting RPP in a low-risk group on CCTA.

Effects of slice thickness on CT radiomics features and models for staging liver fibrosis caused by chronic liver disease.

OBJECTIVES: To investigate the effects of slice thickness on CT radiomics features and models for staging liver fibrosis. METHODS: A total of 108 pathologically confirmed liver fibrosis patients from a single center were retrospectively collected and divided into different groups. Both thick (5- or 7-mm) and thin slices (1.3- or 2-mm) were analyzed. A fivefold cross-validation with 100 repeats was conducted. The minimum redundancy-maximum relevance algorithm was used to reduce the radiomics features, and the top 10 ranking features were included for further analysis for each loop. The random forest was used for model establishment. The models with median AUC were selected for the assessment of the discriminative performance for both datasets. Mutual features selected by the models with AUC > 0.8 were searched and considered as the most predictive ones. RESULTS: A total of 162 and 643 radiomics features with excellent reliability were selected from thick- and thin-slice datasets, respectively. The overall discriminative performance of the 500 AUCs from the thin-slice dataset was better than the thick slice. The median AUC values of the thick-sliced datasets were significantly lower than those of the thin-sliced datasets (0.78 and 0.90 for differentiating F1 vs. F2-4, 0.72 and 0.85 for differentiating F1-2 vs. F3-4, both P = 0.03). For differentiating F1-3 vs. F4, no significant difference was found (0.85 vs 0.94, P = 0.15). Six mutual predictive features across all the datasets were found. CONCLUSIONS: The radiomics features extracted from thin-slice images and their corresponding models were better and more stable for staging liver fibrosis.

Development and Validation of a DeepSurv Nomogram to Predict Survival Outcomes and Guide Personalized Adjuvant Chemotherapy in Non-Small Cell Lung Cancer.

Objective: To develop and validate a DeepSurv nomogram based on radiomic features extracted from computed tomography images and clinicopathological factors, to predict the overall survival and guide individualized adjuvant chemotherapy in patients with non-small cell lung cancer (NSCLC). Patients and Methods: This retrospective study involved 976 consecutive patients with NSCLC (training cohort, n=683; validation cohort, n=293). DeepSurv was constructed based on 1,227 radiomic features, and the risk score was calculated for each patient as the output. A clinical multivariate Cox regression model was built with clinicopathological factors to determine the independent risk factors. Finally, a DeepSurv nomogram was constructed by integrating the risk score and independent clinicopathological factors. The discrimination capability, calibration, and clinical usefulness of the nomogram performance were assessed using concordance index evaluation, the Greenwood-Nam-D'Agostino test, and decision curve analysis, respectively. The treatment strategy was analyzed using a Kaplan-Meier curve and log-rank test for the high- and low-risk groups. Results: The DeepSurv nomogram yielded a significantly better concordance index (training cohort, 0.821; validation cohort 0.768) with goodness-of-fit (P<0.05). The risk score, age, thyroid transcription factor-1, Ki-67, and disease stage were the independent risk factors for NSCLC.The Greenwood-Nam-D'Agostino test showed good calibration performance (P=0.39). Both high- and low-risk patients did not benefit from adjuvant chemotherapy, and chemotherapy in low-risk groups may lead to a poorer prognosis. Conclusions: The DeepSurv nomogram, which is based on the risk score and independent risk factors, had good predictive performance for survival outcome. Further, it could be used to guide personalized adjuvant chemotherapy in patients with NSCLC.

Radiomics from Primary Tumor on Dual-Energy CT Derived Iodine Maps can Predict Cervical Lymph Node Metastasis in Papillary Thyroid Cancer.

RATIONALE AND OBJECTIVES: To develop and validate 2 iodine maps based radiomics nomograms for preoperatively predicting cervical lymph node metastasis (LNM) and central lymph node metastasis (CLNM) in papillary thyroid cancer (PTC). MATERIALS AND METHODS: A total of 346 patients with PTC were enrolled and allocated to training (242) and validation (104) sets. Radiomics features were extracted from arterial and venous phase iodine maps, respectively. Aggregated machine-learning strategy was applied for features selection and construction of 2 radiomics scores (LN rad-score; CLN rad-score). Logistic regression model was employed to establish two radiomics nomograms (nomogram 1: predicting LNM; nomogram 2: predicting CLNM) after incorporating LN or CLN rad-score with clinical predictors. Nomograms performance was determined by discrimination, calibration and clinical usefulness. RESULTS: Nomogram 1 incorporated LN rad-score, age (categorized by 55) and CT reported LN status; Nomogram 2 incorporated CLN rad-score, capsule contact >25% and CT reported CLN status. 2 nomograms both showed good discrimination and calibration in the training (AUC = 0.847; AUC = 0.837) and validation cohorts (AUC = 0.807; AUC = 0.795). Significant improved AUC, net reclassification index (NRI) and integrated discriminatory improvement (IDI) confirmed additional great predictive value of 2 rad-scores, compared with clinical models without radiomics. Decision curve analysis indicated clinical utility of nomograms. 2 nomograms both demonstrated favorable predictive efficacy in CT reported LN or CLN negative subgroup (AUC = 0.766; AUC = 0.744). CONCLUSION: The presented 2 radiomics nomograms are useful tools for preoperative prediction of LNM and CLNM in PTC.

Radiomic features of plaques derived from coronary CT angiography to identify hemodynamically significant coronary stenosis, using invasive FFR as the reference standard.

OBJECTIVE: This study aimed to investigate the diagnostic performance of radiomics features derived from coronary computed tomography angiography (CCTA) in the identification of ischemic coronary stenosis plaque using invasive fractional flow reserve (FFR) as the reference standard. MATERIALS AND METHODS: 174 plaques of 149 patients (age: 62.21 ±â€¯8.47 years, 96 males) with at least one lesion stenosis degree between 30 % and 90 % were retrospectively included. Stenosis degree and plaque characteristics were recorded, and a conventional multivariate logistic model was established. Over 1000 radiomics features of the plaque were derived from CCTA images. The plaques were randomly divided into training set (n = 139) and validation set (n = 35). A random forest model was built. The area under the curve (AUC) of the models was compared. RESULTS: Fifty-eight radiomics features were correlated with functionally significant stenosis (p <  0.05), wherein 56 features had an AUC of >0.6. NCP volume, NRS, remodeling index, and spotty calcification were included in the conventional model. Ultimately, 14 features were integrated to build the radiomics model. The AUC showed an improvement: 0.71 vs 0.82 for the training set and 0.70 vs 0.77 for the validation set (conventional model and radiomics model, respectively); however, it was not statistically significant (p =  0.58). CONCLUSION: The radiomics analysis of plaques showed improvement compared with conventional plaques assessment in identifying hemodynamically significant coronary stenosis. The statistical advancement of machine learning for plaques to predict hemodynamic stenosis with a noninvasive approach still needs further studies on a large-scale dataset.