Simultaneously Enhanced Thermoelectric and Mechanical Performance in SnSe-Based Nanocomposites Produced via Sintering SnSe and KCu7S4 Nanomaterials.

Both thermoelectric and mechanical properties are important to the practical applications of thermoelectric materials. Herein, we develop a strategy for alloying KCu7S4 to improve the dimensionless figure of merit (zT), compressive strength, and Vickers hardness of polycrystalline SnSe. Through chemical synthesis and particle mixing in solutions, powders with SnSe nanoparticles and KCu7S4 nanowires are produced, and the subsequent spark plasma sintering triggers the reaction between the two chalcogenides, resulting in the formation of Cu2SnSe3 nanoparticles and substitution of Cu and S in the SnSe matrix. The composition tuning and secondary phase formation effectively enhance the power factor and diminish the lattice thermal conductivity, leading to a maximum zT of 1.13 at 823 K for the optimal sample, which is improved by 135% over that of SnSe. Simultaneously, the compressive strength and hardness are also enhanced, as exemplified by a high compressive strength of 135 MPa that is enhanced by ∼81% compared to that of SnSe. The current study demonstrates effective composite and composition design toward enhanced thermoelectric and mechanical performance in polycrystalline SnSe.

TOMM40 Correlates with Cholesterol and is Predictive of a Favorable Prognosis in Endometrial Carcinoma.

BACKGROUND: A link between cholesterol and endometrial cancer has been established, but current studies have been limited in their findings. We aimed to elucidate the causal relationship between cholesterol and endometrial cancer and to find prognostic genes for endometrial cancer. METHODS: We first explored the causal relationship between total cholesterol and endometrial cancer using two-sample Mendelian randomization and then obtained differential genes to screen for prognosis-related genes in endometrial cancer. Then, we utilized pan-cancer analysis based on RNA sequencing data to evaluate the expression pattern and immunological role of the Translocase of Outer Mitochondrial Membrane 40 (TOMM40). Through multiple transcriptome datasets and multi-omics in-depth analysis, we comprehensively explore the relationship of TOMM40 expression with clinicopathologic characteristics, clinical outcomes and mutations in endometrial cancer. Lastly, we systematically associated the TOMM40 with different cancers from immunological properties from numerous perspectives, such as immune cell infiltration, immune checkpoint inhibitors, immunotherapy, gene mutation load and microsatellite instability. RESULTS: We found a negative association between cholesterol and endometrial cancer. A total of 78 genes were enriched by relevant single nucleotide polymorphisms (SNPs), of which 12 upregulated genes and 5 downregulated genes in endometrial cancer. TOMM40 was found to be a prognostic gene associated with endometrial cancer by prognostic analysis. TOMM40 was found to be positively correlated with the infiltration of most immune cells and immunization checkpoints in a subsequent study. Meanwhile, TOMM40 also was an oncogene in many cancer types. High TOMM40 was associated with lower genome stability. CONCLUSION: The findings of our study indicate that the maintenance of normal total cholesterol metabolism is associated with a decreased risk of developing endometrial cancer. Moreover, TOMM40 may have potential as a prognostic indicator for endometrial cancer.

The Value of LI-RADS and Radiomic Features from MRI for Predicting Microvascular Invasion in Hepatocellular Carcinoma within 5 cm.

RATIONALE AND OBJECTIVES: To explore and compare the performance of LI-RADS® and radiomics from multiparametric MRI in predicting microvascular invasion (MVI) preoperatively in patients with solitary hepatocellular carcinoma (HCC)< 5 cm. METHODS: We enrolled 143 patients with pathologically proven HCC and randomly stratified them into training (n = 100) and internal validation (n = 43) cohorts. Besides, 53 patients were enrolled to constitute an independent test cohort. Clinical factors and imaging features, including LI-RADS and three other features (non-smooth margin, incomplete capsule, and two-trait predictor of venous invasion), were reviewed and analyzed. Radiomic features from four MRI sequences were extracted. The independent clinic-imaging (clinical) and radiomics model for MVI-prediction were constructed by logistic regression and AdaBoost respectively. And the clinic-radiomics combined model was further constructed by logistic regression. We assessed the model discrimination, calibration, and clinical usefulness by using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision-curve analysis respectively. RESULTS: Incomplete tumor capsule, corona enhancement, and radiomic features were related to MVI in solitary HCC＜5 cm. The clinical model achieved AUC of 0.694/0.661 (training/internal validation). The single-sequence-based radiomic model's AUCs were 0.753-0.843/0.698-0.767 (training/internal validation). The combination model exhibited superior diagnostic performance to the clinical model (AUC: 0.895/0.848 [training/ internal validation]) and yielded an AUC of 0.858 in an independent test cohort. CONCLUSION: Incomplete tumor capsule and corona enhancement on preoperative MRI were significantly related to MVI in solitary HCC＜5 cm. Multiple-sequence radiomic features potentially improve MVI-prediction-model performance, which could potentially help determining HCC's appropriate therapy.

Preoperative prediction of disease-free survival in pancreatic ductal adenocarcinoma patients after R0 resection using contrast-enhanced CT and CA19-9.

OBJECTIVES: To investigate the efficiency of a combination of preoperative contrast-enhanced computed tomography (CECT) and carbohydrate antigen 19-9 (CA19-9) in predicting disease-free survival (DFS) after R0 resection of pancreatic ductal adenocarcinoma (PDAC). METHODS: A total of 138 PDAC patients who underwent curative R0 resection were retrospectively enrolled and allocated chronologically to training (n = 91, January 2014-July 2019) and validation cohorts (n = 47, August 2019-December 2020). Using univariable and multivariable Cox regression analyses, we constructed a preoperative clinicoradiographic model based on the combination of CECT features and serum CA19-9 concentrations, and validated it in the validation cohort. The prognostic performance was evaluated and compared with that of postoperative clinicopathological and tumor-node-metastasis (TNM) models. Kaplan-Meier analysis was conducted to verify the preoperative prognostic stratification performance of the proposed model. RESULTS: The preoperative clinicoradiographic model included five independent prognostic factors (tumor diameter on CECT > 4 cm, extrapancreatic organ infiltration, CECT-reported lymph node metastasis, peripheral enhancement, and preoperative CA19-9 levels > 180 U/mL). It better predicted DFS than did the postoperative clinicopathological (C-index, 0.802 vs. 0.787; p < 0.05) and TNM (C-index, 0.802 vs. 0.711; p < 0.001) models in the validation cohort. Low-risk patients had significantly better DFS than patients at the high-risk, defined by the model preoperatively (p < 0.001, training cohort; p < 0.01, validation cohort). CONCLUSIONS: The clinicoradiographic model, integrating preoperative CECT features and serum CA19-9 levels, helped preoperatively predict postsurgical DFS for PDAC and could facilitate clinical decision-making. CLINICAL RELEVANCE STATEMENT: We constructed a simple model integrating clinical and radiological features for the prediction of disease-free survival after curative R0 resection in patients with pancreatic ductal adenocarcinoma; this novel model may facilitate preoperative identification of patients at high risk of recurrence and metastasis that may benefit from neoadjuvant treatments. KEY POINTS: • Existing clinicopathological predictors for prognosis in pancreatic ductal adenocarcinoma (PDAC) patients who underwent R0 resection can only be ascertained postoperatively and do not allow preoperative prediction. • We constructed a clinicoradiographic model, using preoperative contrast-enhanced computed tomography (CECT) features and preoperative carbohydrate antigen 19-9 (CA19-9) levels, and presented it as a nomogram. • The presented model can predict disease-free survival (DFS) in patients with PDAC better than can postoperative clinicopathological or tumor-node-metastasis (TNM) models.

Preparation of betaine injection and its therapeutic effect in pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) is a life-threatening disease characterised by elevated pulmonary pressure, right ventricular failure (RVF) and ultimately death. Aggressive treatment of RVF is considered an important therapeutic strategy to treat PAH. Previous studies have indicated that betaine may be may a promising therapeutic approach for PAH-induced RVF. Therefore, in this study, betaine solution for injection was prepared and characterised using various techniques. The therapeutic efficacy of three different methods of administration (intragastric, nebulised inhalation and intravenous injection) were comprehensively evaluated in terms of pharmacokinetics, tissue distribution and pharmacodynamics. The pharmacokinetic results demonstrated that betaine injection administered via nebulised inhalation significantly prolonged betaine's half-life and increased its internal circulation time compared to the intragastric and intravenous routes. Biodistribution experiments verified that the betaine formulation accumulated in the lung tissue when administered via inhalation. The results of the pharmacodynamic analysis further confirmed that right ventricular systolic pressure, mean pulmonary artery pressure and right ventricular hypertrophy index increased in the model group and that inhaled betaine suppressed these pathological changes to a level comparable to those observed in the control group. Taken together, these results indicate that betaine administered by inhalation is a promising strategy for the treatment of PAH-induced RVF.

Effect of expansive optic flow and lateral motion parallax on depth estimation with normal and artificially reduced acuity.

When an observer moves in space, the retinal projection of a stationary object either expands if the motion is toward the object or shifts horizontally if the motion contains a lateral component. This study examined the impact of expansive optic flow and lateral motion parallax on the accuracy of depth perception for observers with normal or artificially reduced acuity and asked whether any benefit is due to the continuous motion or to the discrete object image displacement. Stationary participants viewed a virtual room on a computer screen. They used an on-screen slider to estimate the depth of a target object relative to a reference object after seeing 2-second videos simulating five conditions: static viewing, expansive optic flow, and lateral motion parallax in either continuous motion or image displacement. Ten participants viewed the stimuli with normal acuity in Experiment 1 and 11 with three levels of artificially reduced acuity in Experiment 2. Linear regression models represented the relationship between the depth estimates of participants and the ground truth. Lateral motion parallax produced more accurate depth estimates than expansive optic flow and static viewing. Depth perception with continuous motion was more accurate than that with displacement under mild and moderate, but not severe, acuity reduction. For observers with both normal and artificially reduced acuity, lateral motion parallax was more helpful for object depth estimation than expansive optic flow, and continuous motion parallax was more helpful than object image displacement.

A Multi-Target Localization and Vital Sign Detection Method Using Ultra-Wide Band Radar.

Life detection technology using ultra-wideband (UWB) radar is a non-contact, active detection technology, which can be used to search for survivors in disaster rescues. The existing multi-target detection method based on UWB radar echo signals has low accuracy and has difficulty extracting breathing and heartbeat information at the same time. Therefore, this paper proposes a new multi-target localization and vital sign detection method using ultra-wide band radar. A target recognition and localization method based on permutation entropy (PE) and K means++ clustering is proposed to determine the number and position of targets in the environment. An adaptive denoising method for vital sign extraction based on ensemble empirical mode decomposition (EEMD) and wavelet analysis (WA) is proposed to reconstruct the breathing and heartbeat signals of human targets. A heartbeat frequency extraction method based on particle swarm optimization (PSO) and stochastic resonance (SR) is proposed to detect the heartbeat frequency of human targets. Experimental results show that the PE-K means++ method can successfully recognize and locate multiple human targets in the environment, and its average relative error is 1.83%. Using the EEMD-WA method can effectively filter the clutter signal, and the average relative error of the reconstructed respiratory signal frequency is 4.27%. The average relative error of heartbeat frequency detected by the PSO-SR method was 6.23%. The multi-target localization and vital sign detection method proposed in this paper can effectively recognize all human targets in the multi-target scene and provide their accurate location and vital signs information. This provides a theoretical basis for the technical system of emergency rescue and technical support for post-disaster rescue.

Nomogram including tumor deposition count to noninvasively evaluate the prognosis of rectal cancer patients: A retrospective study.

To build a nomogram model that includes tumor deposition (TDs) count to noninvasively evaluate the prognosis of patients with rectal cancer (RC). A total of 262 patients between January 2013 and December 2018 were recruited and divided into 2 cohorts: training (n = 171) and validation (n = 91). Axial portal venous phase computed tomography images were used to extract radiomic features, and the least absolute shrinkage and selection operator-Cox analysis was applied to develop an optimal radiomics model to derive the Rad-score. A Cox regression model combining clinicopathological factors and Rad-scores was constructed and visualized using a nomogram. And its ability to predict RC patients' survival was tested by Kaplan-Meier survival analysis. The time-dependent concordance index curve was used to demonstrate the differentiation degree of model. Calibration and decision curve analyses were used to evaluate the calibration accuracy and clinical usefulness of the nomogram model, and the prediction performance of the nomogram model was compared with the clinical and radiomics models using the likelihood test. Computed tomography-based Rad-score, pathological tumor (pT) stageT4, and TDs count were independent risk factors affecting the prognosis of RC. The whole concordance index of the nomogram model for predicting the overall survival rates of RC was higher than that of the clinical and radiomics models in the training (0.812 vs 0.59, P = .019; 0.812 vs 0.714, P = .014) and validation groups (0.725 vs 0.585, P = .002; 0.725 vs 0.751, P = .256). The nomogram model could effectively predict patients' overall survival rate (hazard ratio = 9.25, 95% CI = [1.17-72.99], P = .01). The nomogram model also showed a higher clinical net benefit than the clinical and radiomics models in the training and validation groups. The nomogram model developed in this study can be used to noninvasively evaluate the prognosis of RC patients. The TDs count is an independent risk factor for the prognosis of RC.

A Corpus Study on the Difference of Turn-Taking in Online Audio, Online Video, and Face-to-Face Conversation.

Daily conversation is usually face-to-face and characterized by rapid and fluent exchange of turns between interlocutors. With the need to communicate across long distances, advances in communication media, online audio communication, and online video communication have become convenient alternatives for an increasing number of people. However, the fluency of turn-taking may be influenced when people communicate using these different modes. In this study, we conducted a corpus analysis of face-to-face, online audio, and online video conversations collected from the internet. The fluency of turn-taking in face-to-face conversations differed from that of online audio and video conversations. Namely, the timing of turn-taking was shorter and with more overlaps in face-to-face conversations compared with online audio and video conversations. This can be explained by the limited ability of online communication modes to transmit non-verbal cues and network latency. In addition, our study could not completely exclude the effect of formality of conversation. The present findings have implications for the rules of turn-taking in human online conversations, in that the traditional rule of no-gap-no-overlap may not be fully applicable to online conversations.

Identification of triple-negative breast cancer and androgen receptor expression based on histogram and texture analysis of dynamic contrast-enhanced MRI.

BACKGROUND: Triple-negative breast cancer (TNBC) is highly malignant and has a poor prognosis due to the lack of effective therapeutic targets. Androgen receptor (AR) has been investigated as a possible therapeutic target. This study quantitatively assessed intratumor heterogeneity by histogram analysis of pharmacokinetic parameters and texture analysis on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to discriminate TNBC from non-triple-negative breast cancer (non-TNBC) and to identify AR expression in TNBC. METHODS: This retrospective study included 99 patients with histopathologically proven breast cancer (TNBC: 36, non-TNBC: 63) who underwent breast DCE-MRI before surgery. The pharmacokinetic parameters of DCE-MRI (Ktrans, Kep and Ve) and their corresponding texture parameters were calculated. The independent t-test, or Mann-Whitney U-test was used to compare quantitative parameters between TNBC and non-TNBC groups, and AR-positive (AR+) and AR-negative (AR-) TNBC groups. The parameters with significant difference between two groups were further involved in logistic regression analysis to build a prediction model for TNBC. The ROC analysis was conducted on each independent parameter and the TNBC predicting model for evaluating the discrimination performance. The area under the ROC curve (AUC), sensitivity and specificity were derived. RESULTS: The binary logistic regression analysis revealed that Kep_Range (p = 0.032) and Ve_SumVariance (p = 0.005) were significantly higher in TNBC than in non-TNBC. The AUC of the combined model for identifying TNBC was 0.735 (p < 0.001) with a cut-off value of 0.268, and its sensitivity and specificity were 88.89% and 52.38%, respectively. The value of Kep_Compactness2 (p = 0.049), Kep_SphericalDisproportion (p = 0.049), and Ve_GlcmEntropy (p = 0.008) were higher in AR + TNBC group than in AR-TNBC group. CONCLUSION: Histogram and texture analysis of breast lesions on DCE-MRI showed potential to identify TNBC, and the specific features can be possible predictors of AR expression, enhancing the ability to individualize the treatment of patients with TNBC.

Combined CT and serum CA19-9 for stratifying risk for progression in patients with locally advanced pancreatic cancer receiving intraoperative radiotherapy.

Background and purpose: The aim of this study was to evaluate the significance of baseline computed tomography (CT) imaging features and carbohydrate antigen 19-9 (CA19-9) in predicting prognosis of locally advanced pancreatic cancer (LAPC) receiving intraoperative radiotherapy (IORT) and to establish a progression risk nomogram that helps to identify the potential beneficiary of IORT. Methods: A total of 88 LAPC patients with IORT as their initial treatment were enrolled retrospectively. Clinical data and CT imaging features were analyzed. Cox regression analyses were performed to identify the independent risk factors for progression-free survival (PFS) and to establish a nomogram. A risk-score was calculated by the coefficients of the regression model to stratify the risk of progression. Results: Multivariate analyses revealed that relative enhanced value in portal-venous phase (REV-PVP), peripancreatic fat infiltration, necrosis, and CA19-9 were significantly associated with PFS (all p < 0.05). The nomogram was constructed according to the above variables and showed a good performance in predicting the risk of progression with a concordance index (C-index) of 0.779. Our nomogram stratified patients with LAPC into low- and high-risk groups with distinct differences in progression after IORT (p < 0.001). Conclusion: The integrated nomogram would help clinicians to identify appropriate patients who might benefit from IORT before treatment and to adapt an individualized treatment strategy.

Versatile Microfluidics for Biofabrication Platforms Enabled by an Agile and Inexpensive Fabrication Pipeline.

Microfluidics have transformed diagnosis and screening in regenerative medicine. Recently, they are showing much promise in biofabrication. However, their adoption is inhibited by costly and drawn-out lithographic processes thus limiting progress. Here, multi-material fibers with complex core-shell geometries with sizes matching those of human arteries and arterioles are fabricated employing versatile microfluidic devices produced using an agile and inexpensive manufacturing pipeline. The pipeline consists of material extrusion additive manufacturing with an innovative continuously varied extrusion (CONVEX) approach to produce microfluidics with complex seamless geometries including, novel variable-width zigzag (V-zigzag) mixers with channel widths ranging from 100-400 µm and hydrodynamic flow-focusing components. The microfluidic systems facilitated rapid mixing of fluids by decelerating the fluids at specific zones to allow for increased diffusion across the interfaces. Better mixing even at high flow rates (100-1000 µL min-1 ) whilst avoiding turbulence led to high cell cytocompatibility (>86%) even when 100 µm nozzles are used. The presented 3D-printed microfluidic system is versatile, simple and efficient, offering a great potential to significantly advance the microfluidic platform in regenerative medicine.

Application of transcriptomics and proteomics in pulmonary arterial hypertension.

The onset and progression of pulmonary arterial hypertension (PAH), a malignant disease, are associated with environmental and epigenetic factors. Recent advancements in transcriptomics and proteomics technology have provided new insights into PAH and identified novel gene targets involved in the development of the disease. Transcriptomic analysis has led to the discovery of possible novel pathways, such as miR-483 targeting several PAH-related genes and a mechanistic link between the increase in HERV-K mRNA and protein. Proteomic analysis has revealed crucial details, including the loss of SIRT3 activity and the significance of the CLIC4/Arf6 pathway in PAH pathogenesis. Gene profiles and protein interaction networks of PAH have been analyzed, clarifying the roles of differentially expressed genes or proteins in the occurrence and development of PAH. This article discusses these recent advances.

Performance of radiomics models derived from different CT reconstruction parameters for lung cancer risk prediction.

BACKGROUND: This study analysed the performance of radiomics features extracted from computed tomography (CT) images with different reconstruction parameters in differentiating malignant and benign pulmonary nodules. METHODS: We evaluated routine chest CT images acquired from 148 participants with pulmonary nodules, which were pathologically diagnosed during surgery in West China Hospital, including a 5 mm unenhanced lung window, a 5 mm unenhanced mediastinal window, a 5 mm contrast-enhanced mediastinal window and a 1 mm unenhanced lung window. The pulmonary nodules were segmented, and 1409 radiomics features were extracted for each window. Then, we created 15 cohorts consisting of single windows or multiple windows. Univariate correlation analysis and principal component analysis were performed to select the features, and logistic regression analysis was performed to establish models for each cohort. The area under the curve (AUC) was applied to compare model performance. RESULTS: There were 75 benign and 73 malignant pulmonary nodules, with mean diameters of 18.63 and 19.86 mm, respectively. For the single-window setting, the AUCs of the radiomics model from the 5 mm unenhanced lung window, 5 mm unenhanced mediastinal window, 5 mm contrast-enhanced mediastinal window and 1 mm unenhanced lung window were 0.771, 0.808, 0.750, and 0.771 in the training set and 0.711, 0.709, 0.684, and 0.674 in the test set, respectively. Regarding the multiple-window setting, the radiomics model based on all four windows showed an AUC of 0.825 in the training set and 0.743 in the test set. Statistically, the 15 models demonstrated comparable performances (P > 0.05). CONCLUSION: A single chest CT window was acceptable in predicting the malignancy of pulmonary nodules, and additional windows did not statistically improve the performance of the radiomics models. In addition, slice thickness and contrast enhancement did not affect the diagnostic performance.

A radiomics model based on aortic computed tomography angiography: the impact on predicting the prognosis of patients with aortic intramural hematoma (IMH).

Background: The prognosis of aortic intramural hematoma (IMH) is unpredictable, but computed tomography angiography (CTA) plays an important role of high diagnostic performance in the initial diagnosis and during follow-up of patients. In this study, we investigated the value of a radiomics model based on aortic CTA for predicting the prognosis of patients with medically treated IMH. Method: A total of 120 patients with IMH were enrolled in this study. The follow-up duration ranged from 32 to 1,346 days (median 232 days). Progression of these patients was classified as follows: destabilization, which refers to deterioration in the aortic condition, including significant increases in the thickness of the IMH, the progression of IMH to a penetrating aortic ulcer (PAU), aortic dissection (AD), or rupture; or stabilization, which refers to an unchanged appearance or a decrease in the size or disappearance of the IMH. The patients were divided into a training cohort (n=84) and a validation cohort (n=36). Six different machine learning classifiers were applied: random forest (RF), K-nearest neighbor (KNN), Gaussian Naive Bayes, decision tree, logistic regression, and support vector machine (SVM). The clinical-radiomics combined nomogram model was established by multivariate logistic regression. The area under the receiver operating characteristic (ROC) curve (AUC) was implemented to evaluate the discrimination performance of the models. The calibration curves and Hosmer-Lemeshow test were used for evaluating model calibration. DeLong's test was performed to compare the AUC performance of models. Results: Among all of the patients, 60 patients showed destabilization and 60 patients remained stable. A total of 12 radiomic features were retained after application of the least absolute shrinkage and selection operator (LASSO). These features were used for the machine learning model construction. The SVM-radial basis function (SVM-RBF) model obtained the best performance with an AUC of 0.765 (95% CI, 0.593-0.906). In the validation cohort, the combined clinical-radiomics model [AUC =0.787; 95% confidence interval (CI), 0.619-0.923] showed a significantly higher performance than did the clinical model (AUC =0.596; 95% CI, 0.413-0.796; P=0.021) and had a similar performance to the radiomics model (AUC =0.765; 95% CI, 0.589-0.906; P=0.672). Conclusions: A quantitative nomogram based on radiomic features of CTA images can be used to predict disease progression in patients with IMH and may help improve clinical decision-making.

Follow-Up Infarct Volume Prediction by CTP-Based Hypoperfusion Index, and the Discrepancy between Small Follow-Up Infarct Volume and Poor Functional Outcome-A Multicenter Study.

(1) Background: Follow-up infarct volume (FIV) may have implications for prognostication in acute ischemic stroke patients. Factors predicting the discrepancy between FIV and 90-day outcomes are poorly understood. We aimed to develop a comprehensive predictive model of FIV and explore factors associated with the discrepancy. (2) Methods: Patients with acute anterior circulation large vessel occlusion were included. Baseline clinical and CT features were extracted and analyzed, including the CTP-based hypoperfusion index (HI) and the NCCT-based e-ASPECT, measured by automated software. FIV was assessed on follow-up NCCT at 3−7 days. Multiple linear regression was used to construct the predictive model. Subgroup analysis was performed to explore factors associated with poor outcomes (90-mRS scores 3−6) in small FIV (<70 mL). (3) Results: There were 170 patients included. Baseline e-ASPECT, infarct core volume, hypoperfusion volume, HI, baseline international normalized ratio, and successful recanalization were associated with FIV and included in constructing the predictive model. Baseline NIHSS, baseline hypertension, stroke history, and current tobacco use were associated with poor outcomes in small FIV. (4) Conclusions: A comprehensive predictive model (including HI) of FIV was constructed. We also emphasized the importance of hypertension and smoking status at baseline for the functional outcomes in patients with a small FIV.

Preoperative Computed Tomography Radiomics Analysis for Predicting Receptors Status and Ki-67 Levels in Breast Cancer.

BACKGROUND: To assess the prediction performance of preoperative chest computed tomography (CT) based radiomics features for estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2+), and Ki-67 status of breast cancer. MATERIALS AND METHODS: This study enrolled 108 breast cancer patients who received preoperative chest CT examinations in our institution from July 2018 to January 2020. Radiomics features were separately extracted from nonenhanced, arterial, and portal-venous phases CT images. The least absolute shrinkage and selection operator logistic regression was used for feature selection. Then the radiomics signatures for each phase and a combined model of 3 phases were built. Finally, the receiver operating characteristic curves and calibration curves were used to confirm the performance of the radiomics signatures and combined model. In addition, the decision curves were performed to estimate the clinical usefulness of the combined model. RESULTS: The 20 most predictive features were finally selected to build radiomics signatures for each phase. The combined model achieved the overall best performance than using either of the nonenhanced, arterial and portal-venous phases alone, achieving an area under the receiver operating characteristic curve of 0.870 for ER+ versus ER-, 0.797 for PR+ versus PR-, 0.881 for HER2+ versus HER2-, and 0.726 for Ki-67. The decision curve demonstrated that the CT-based radiomics features were clinically useful. CONCLUSION: This study indicated preopreative chest CT radiomics analysis might be able to assess ER, PR, HER2+, and Ki-67 status of breast cancer. The findings need further to be verified in future larger studies.

Radiomics analysis of T2 -weighted images for differentiating invasive placentas in women at high risks.

PURPOSE: To develop and validate an MRI-based radiomics model for differentiating invasive placentas in patients with high risks. METHODS: A total of 181 pregnant women suspected of placenta accreta spectrum (PAS) disorders and who underwent MRI for placenta evaluation were retrospectively enrolled. The data set was randomly divided into the training (n = 125; invasive = 63, noninvasive = 62) and test (n = 56; invasive = 28, noninvasive = 28) groups. Radiomics features were extracted from half-Fourier acquisition single-shot turbo spin echo (HASTE) and sagittal true fast imaging in steady-state precession (TRUFISP) sequences independently and mainly selected based on their correlations with invasive placentas to construct two radiomics signatures including HASTE-Radscore and TRUFISP-Radscore. Then, the predictive performance of radiomic signatures, clinical features, radiographic features, and their combination were evaluated. The model with the best predictive performance was validated with its discrimination ability, calibration, and clinical usefulness. RESULTS: Five radiomics features from HASTE and three radiomics features from TRUFISP were retained, respectively, for predicting invasive placentas. The combination of radiomics signatures and clinical features including prior cesarean delivery, placenta previa, and radiographic feature, the placental thickness resulted in the best discrimination ability, with area under the curve of 0.898 (95% confidence interval [CI] 0.844-0.9522) and 0.858 (95% confidence interval 0.7514-0.9655) in the training and test cohort, respectively. The combined model showed a significantly better area under the curve performance and clinical usefulness than independent clinical or radiographic model according to DeLong test (p < .05), net reclassification improvement and integrated discrimination improvement analysis (positive improvement) and decision curve analysis (higher net benefit). CONCLUSIONS: The T2 -weighted imaging MRI radiomics model could serve as a potential prenatal diagnosis tool for identifying invasive placentas in patients with high risks.

Correlation of placental thickness and placenta percreta in patients with placenta previa: findings from MRI.

INTRODUCTION: This study aimed to identify if placental thickness measured from MRI images correlated with placenta percreta in patients with placenta previa. METHODS: Placental thickness was retrospectively measured in 161 patients from July 2018 to August 2020. The measurements were performed at the thickest part of the placenta in the lower uterine segment on the mid-sagittal plane MR images by two independent radiologists. Intraoperative and pathologic findings were the standard of reference. Univariate and multivariate analyses were performed to identify the relationship between clinical features, placental thickness, and placenta percreta. The predictive ability of placental thickness was demonstrated using receiver operating characteristic curve analysis. RESULTS: Placental thickness in patients with placenta percreta was significantly higher than in patients with placenta increta, placenta accreta, and normal placentas (p < 0.05). Multivariate analysis revealed that placental thickness was the only independent risk factor for placenta percreta. The cutoff value of placental thickness was 4.35 cm for differentiating placenta percreta in patients with placenta previa. DISCUSSION: Patients with placenta percreta had the highest placental thickness. Placental thickness was correlated with placenta percreta.

Application of computed tomography-based radiomics in differential diagnosis of adenocarcinoma and squamous cell carcinoma at the esophagogastric junction.

BACKGROUND: The biological behavior of carcinoma of the esophagogastric junction (CEGJ) is different from that of gastric or esophageal cancer. Differentiating squamous cell carcinoma of the esophagogastric junction (SCCEG) from adenocarcinoma of the esophagogastric junction (AEG) can indicate Siewert stage and whether the surgical route for patients with CEGJ is transthoracic or transabdominal, as well as aid in determining the extent of lymph node dissection. With the development of neoadjuvant therapy, preoperative determination of pathological type can help in the selection of neoadjuvant radiotherapy and chemotherapy regimens. AIM: To establish and evaluate computed tomography (CT)-based multiscale and multiphase radiomics models to distinguish SCCEG and AEG preoperatively. METHODS: We retrospectively analyzed the preoperative contrasted-enhanced CT imaging data of single-center patients with pathologically confirmed SCCEG (n = 130) and AEG (n = 130). The data were divided into either a training (n = 182) or a test group (n = 78) at a ratio of 7:3. A total of 1409 radiomics features were separately extracted from two dimensional (2D) or three dimensional (3D) regions of interest in arterial and venous phases. Intra-/inter-observer consistency analysis, correlation analysis, univariate analysis, least absolute shrinkage and selection operator regression, and backward stepwise logical regression were applied for feature selection. Totally, six logistic regression models were established based on 2D and 3D multi-phase features. The receiver operating characteristic curve analysis, the continuous net reclassification improvement (NRI), and the integrated discrimination improvement (IDI) were used for assessing model discrimination performance. Calibration and decision curves were used to assess the calibration and clinical usefulness of the model, respectively. RESULTS: The 2D-venous model (5 features, AUC: 0.849) performed better than 2D-arterial (5 features, AUC: 0.808). The 2D-arterial-venous combined model could further enhance the performance (AUC: 0.869). The 3D-venous model (7 features, AUC: 0.877) performed better than 3D-arterial (10 features, AUC: 0.876). And the 3D-arterial-venous combined model (AUC: 0.904) outperformed other single-phase-based models. The venous model showed a positive improvement compared with the arterial model (NRI > 0, IDI > 0), and the 3D-venous and combined models showed a significant positive improvement compared with the 2D-venous and combined models (P < 0.05). Decision curve analysis showed that combined 3D-arterial-venous model and 3D-venous model had a higher net clinical benefit within the same threshold probability range in the test group. CONCLUSION: The combined arterial-venous CT radiomics model based on 3D segmentation can improve the performance in differentiating EGJ squamous cell carcinoma from adenocarcinoma.