Optical manipulation of electronic dimensionality in a quantum material.

Exotic phenomena can be achieved in quantum materials by confining electronic states into two dimensions. For example, relativistic fermions are realized in a single layer of carbon atoms1, the quantized Hall effect can result from two-dimensional (2D) systems2,3, and the superconducting transition temperature can be considerably increased in a one-atomic-layer material4,5. Ordinarily, a 2D electronic system can be obtained by exfoliating the layered materials, growing monolayer materials on substrates, or establishing interfaces between different materials. Here we use femtosecond infrared laser pulses to invert the periodic lattice distortion sectionally in a three-dimensional (3D) charge density wave material (1T-TiSe2), creating macroscopic domain walls of transient 2D ordered electronic states with unusual properties. The corresponding ultrafast electronic and lattice dynamics are captured by time-resolved and angle-resolved photoemission spectroscopy6 and ultrafast electron diffraction at energies of the order of megaelectronvolts7. Moreover, in the photoinduced 2D domain wall near the surface we identify a phase with enhanced density of states and signatures of potential opening of an energy gap near the Fermi energy. Such optical modulation of atomic motion is an alternative path towards realizing 2D electronic states and will be a useful platform upon which novel phases in quantum materials may be discovered.

Prognostic implications of pre-transcatheter aortic valve replacement computed tomography-derived coronary plaque characteristics and stenosis severity.

OBJECTIVES: The study aimed to investigate the prognostic value of pre-transcatheter aortic valve replacement (TAVR) computed tomography angiography (CTA) in assessing physiological stenosis severity (CTA-derived fractional flow reserve (CT-FFR)) and high-risk plaque characteristics (HRPC). MATERIALS AND METHODS: Among TAVR patients who underwent pre-procedure CTA, the presence and number of HRPCs (minimum lumen area of < 4 mm2, plaque burden ≥ 70%, low-attenuating plaques, positive remodeling, napkin-ring sign, or spotty calcification) as well as CT-FFR were assessed. The risk of vessel-oriented composite outcome (VOCO, a composite of vessel-related ischemia-driven revascularization, vessel-related myocardial infarction, or cardiac death) was compared according to the number of HRPC and CT-FFR categories. RESULTS: Four hundred and twenty-seven patients (68.4% were male) with 1072 vessels were included. Their mean age was 70.6 ± 10.6 years. Vessels with low CT-FFR (≤ 0.80) (41.7% vs. 15.8%, adjusted hazard ratio (HRadj) 1.96; 95% confidence interval (CI): 1.28-2.96; p = 0.001) or lesions with ≥ 3 HRPC (38.7% vs. 16.0%, HRadj 1.81; 95%CI 1.20-2.71; p = 0.005) demonstrated higher VOCO risk. In the CT-FFR (> 0.80) group, lesions with ≥ 3 HRPC showed a significantly higher risk of VOCO than those with < 3 HRPC (34.7% vs. 13.0%; HRadj 2.04; 95%CI 1.18-3.52; p = 0.011). However, this relative increase in risk was not observed in vessels with positive CT-FFR (≤ 0.80). CONCLUSIONS: In TAVR candidates, both CT-FFR and the presence of ≥ 3 HRPC were associated with an increased risk of adverse clinical events. However, the value of HRPC differed with the CT-FFR category, with more incremental predictability among vessels with negative CT-FFR but not among vessels with positive CT-FFR. CLINICAL RELEVANCE STATEMENT: In transcatheter aortic valve replacement (TAVR) candidates, pre-TAVR CTA provided the opportunity to assess coronary physiological stenosis severity and high-risk plaque characteristics, both of which are associated with worse clinical outcomes. KEY POINTS: • The current study investigated the prognostic value of coronary physiology significance and plaque characteristics in transcatheter aortic valve replacement patients. • The combination of coronary plaque vulnerability and physiological significance showed improved accuracy in predicting clinical outcomes in transcatheter aortic valve replacement patients. • Pre-transcatheter aortic valve replacement CT can be a one-stop-shop tool for coronary assessments in clinical practice.

Predictive value of triple negative breast cancer based on DCE-MRI multi-phase full-volume ROI clinical radiomics model.

BACKGROUND: Since no studies compared the value of radiomics features of distinct phases of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting triple-negative breast cancer (TNBC). PURPOSE: To identify the optimal phase of DCE-MRI for diagnosing TNBC and, in combination with clinical factors, to develop a clinical-radiomics model to well predict TNBC. MATERIAL AND METHODS: This retrospective study included 158 patients with pathology-confirmed breast cancer, including 38 cases of TNBC. The patients were randomly divided into the training and validation set (7:3). Eight radiomics models were built based on eight DCE-MR phases, and their performances were evaluated using receiver operating characteristic curve (ROC) and DeLong's test. The Radscore derived from the best radiomics model was integrated with independent clinical risk factors to construct a clinical-radiomics predictive model, and evaluate its performance using ROC analysis, calibration, and decision curve analyses. RESULTS: WHO classification, margin, and T2-weighted (T2W) imaging signals were significantly correlated with TNBC and independent risk factors for TNBC (P<0.05). The clinical model yielded areas under the curve (AUCs) of 0.867 and 0.843 in the training and validation sets, respectively. The radiomics model based on DCEphase7 achieved the highest efficacy, with an AUC of 0.818 and 0.777. The AUC of the clinical-radiomics model was 0.936 and 0.886 in the training and validation sets, respectively. The decision curve showed the clinical utility of the clinical-radiomics model. CONCLUSION: The radiomics features of DCE-MRI had the potential to predict TNBC and could improve the performance of clinical risk factors for preoperative personalized prediction of TNBC.

Radiomics Analysis of Diffusion-Weighted Imaging and Long-Term Unfavorable Outcomes Risk for Acute Stroke.

BACKGROUND: Diffusion-weighted imaging radiomics could be used as prognostic biomarkers in acute ischemic stroke. We aimed to identify a clinical and diffusion-weighted imaging radiomics model for individual unfavorable outcomes risk assessment in acute ischemic stroke. METHODS: A total of 1716 patients with acute ischemic stroke from 2 centers were divided into a training cohort and a validation cohort. Patient outcomes were measured with the modified Rankin Scale score. An unfavorable outcome was defined as a modified Rankin Scale score greater than 2. The primary end point was all-cause mortality or outcomes 1 year after stroke. The MRI-DRAGON score was calculated based on previous publications. We extracted and selected the infarct features on diffusion-weighted imaging to construct a radiomic signature. The clinic-radiomics signature was built by measuring the Cox proportional risk regression score (CrrScore) and compared with the MRI-DRAGON score and the ClinicScore. CrrScore model performance was estimated by 1-year unfavorable outcomes prediction. RESULTS: A high radiomic signature predicted a higher probability of unfavorable outcomes than a low radiomic signature in the training (hazard ratio, 3.19 [95% CI, 2.51-4.05]; P<0.0001) and validation (hazard ratio, 3.25 [95% CI, 2.20-4.80]; P<0.0001) cohorts. The diffusion-weighted imaging Alberta Stroke Program Early CT Score, age, glucose level before therapy, National Institutes of Health Stroke Scale score on admission, glycated hemoglobin' radiomic signature, hemorrhagic infarction, and malignant cerebral edema were associated with an unfavorable outcomes risk after multivariable adjustment. A CrrScore nomogram was developed to predict outcomes and had the best performance in the training (area under the curve, 0.862) and validation cohorts (area under the curve, 0.858). The CrrScore model time-dependent areas under the curve of the probability of unfavorable outcomes at 1 year in the training and validation cohorts were 0.811 and 0.801, respectively. CONCLUSIONS: The CrrScore model allows the accurate prediction of patients with acute ischemic stroke outcomes and can potentially guide rehabilitation therapies for patients with different risks of unfavorable outcomes.

Ultrafast Switching from the Charge Density Wave Phase to a Metastable Metallic State in 1T-TiSe_{2}.

The ultrafast electronic structures of the charge density wave material 1T-TiSe_{2} were investigated by high-resolution time- and angle-resolved photoemission spectroscopy. We found that the quasiparticle populations drove ultrafast electronic phase transitions in 1T-TiSe_{2} within 100 fs after photoexcitation, and a metastable metallic state, which was significantly different from the equilibrium normal phase, was evidenced far below the charge density wave transition temperature. Detailed time- and pump-fluence-dependent experiments revealed that the photoinduced metastable metallic state was a result of the halted motion of the atoms through the coherent electron-phonon coupling process, and the lifetime of this state was prolonged to picoseconds with the highest pump fluence used in this study. Ultrafast electronic dynamics were well captured by the time-dependent Ginzburg-Landau model. Our work demonstrates a mechanism for realizing novel electronic states by photoinducing coherent motion of atoms in the lattice.

Preoperative Assessment of MRI-Invisible Early-Stage Endometrial Cancer With MRI-Based Radiomics Analysis.

BACKGROUND: Radiomics-based analyses have demonstrated impact on studies of endometrial cancer (EC). However, there have been no radiomics studies investigating preoperative assessment of MRI-invisible EC to date. PURPOSE: To develop and validate radiomics models based on sagittal T2-weighted images (T2WI) and T1-weighted contrast-enhanced images (T1CE) for the preoperative assessment of MRI-invisible early-stage EC and myometrial invasion (MI). STUDY TYPE: Retrospective. POPULATION: One hundred fifty-eight consecutive patients (mean age 50.7 years) with MRI-invisible endometrial lesions were enrolled from June 2016 to March 2022 and randomly divided into the training (n = 110) and validation cohort (n = 48) using a ratio of 7:3. FIELD STRENGTH/SEQUENCE: 3-T, T2WI, and T1CE sequences, turbo spin echo. ASSESSMENT: Two radiologists performed image segmentation and extracted features. Endometrial lesions were histopathologically classified as benign, dysplasia, and EC with or without MI. In the training cohort, 28 and 20 radiomics features were selected to build Model 1 and Model 2, respectively, generating rad-score 1 (RS1) and rad-score 2 (RS2) for evaluating MRI-invisible EC and MI. STATISTICAL TESTS: The least absolute shrinkage and selection operator logistic regression method was used to select radiomics features. Mann-Whitney U tests and Chi-square test were used to analyze continuous and categorical variables. Receiver operating characteristic curve (ROC) and decision curve analysis were used for performance evaluation. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated. A P-value <0.05 was considered statistically significant. RESULTS: Model 1 had good performance for preoperative detecting of MRI-invisible early-stage EC in the training and validation cohorts (AUC: 0.873 and 0.918). In addition, Model 2 had good performance in assessment of MI of MRI-invisible endometrial lesions in the training and validation cohorts (AUC: 0.854 and 0.834). DATA CONCLUSION: MRI-based radiomics models may provide good performance for detecting MRI-invisible EC and MI. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

MRI Radiomics Signature of Pediatric Medulloblastoma Improves Risk Stratification Beyond Clinical and Conventional MR Imaging Features.

BACKGROUND: Prognostic evaluation is important for personalized treatment in children with medulloblastoma (MB). Limited data are available for risk stratification using a radiomics-based model. PURPOSE: To evaluate the incremental value of an MRI radiomics signature in stratifying the risk of pediatric MB in terms of overall survival (OS). STUDY TYPE: Retrospective. SUBJECTS: A total of 111 children (mean age 5.82 years) with pathologically confirmed MB divided into training and validation cohorts (77 and 34 children, respectively). FIELD STRENGTH/SEQUENCE: A 3 T, contrast-enhanced T1-weighted imaging with inversion recovery. ASSESSMENT: The study endpoint was OS defined as the time between the preoperative MRI study and death or last follow-up. The radiomics signature model and a clinical-MRI model were developed for personalized OS prediction. An integrative model, which combined the radiomics signature and clinical-MRI features, was also built using multivariable Cox regression model. The performance of the three models was evaluated with the C-index. The performance of integrative model was assessed by calibration curve and decision curve analysis (DCA). STATISTICAL TESTS: Independent T-test, Mann-Whitney U test, Fisher's exact tests or chi-square test, logistic regression analysis, Kaplan-Meier survival analysis, C-index, intraclass correlation coefficients (ICC). P < 0.05 was considered statistically significant. RESULTS: The media OS was 2.83 years (3.87 ± 1.85 years). Two clinical and one conventional MR imaging features (remnant, adjuvant treatment, and peritumoral edema) were selected for clinical-MRI model building. The integrative model evaluated OS (C-index 0.823) better than either the radiomics signature (C-index 0.702) or the clinical-MRI model (C-index 0.771). And it also showed good performance in the validation cohort (C-indices: 0.786, 0.756, 0.721), which was validated by the good calibration (P > 0.05) and more benefit. DATA CONCLUSIONS: This study demonstrated that the integrative model, which combined radiomics signature, clinical, and conventional MRI features, showed best performance in OS evaluation for children with MB. The radiomics signature may confer incremental value over clinical-MRI features. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Breast Cancer Growth on Serial MRI: Volume Doubling Time Based on 3-Dimensional Tumor Volume Assessment.

BACKGROUND: The volume doubling time (VDT) of breast cancer was most frequently calculated using the two-dimensional (2D) diameter, which is not reliable for irregular tumors. It was rarely investigated using three-dimensional (3D) imaging with tumor volume on serial magnetic resonance imaging (MRI). PURPOSE: To investigate the VDT of breast cancer using 3D tumor volume assessment on serial breast MRIs. STUDY TYPE: Retrospective. SUBJECTS: Sixty women (age at diagnosis: 57 ± 10 years) with breast cancer, assessed by two or more breast MRI examinations. The median interval time was 791 days (range: 70-3654 days). FIELD STRENGTH/SEQUENCE: 3-T, fast spin-echo T2-weighted imaging (T2WI), single-shot echo-planar diffusion-weighted imaging (DWI), and gradient echo dynamic contrast-enhanced imaging. ASSESSMENT: Three radiologists independently reviewed the morphological, DWI, and T2WI features of lesions. The whole tumor was segmented to measure the volume on contrast-enhanced images. The exponential growth model was fitted in the 11 patients with at least three MRI examinations. The VDT of breast cancer was calculated using the modified Schwartz equation. STATISTICAL TESTS: Mann-Whitney U test, Kruskal-Wallis test, Chi-squared test, intraclass correlation coefficients, and Fleiss kappa coefficients. A P-value <0.05 was considered statistically significant. The exponential growth model was evaluated using the adjusted R2 and root mean square error (RMSE). RESULTS: The median tumor diameter was 9.7 mm and 15.2 mm on the initial and final MRI, respectively. The median adjusted R2 and RMSE of the 11 exponential models were 0.97 and 15.8, respectively. The median VDT was 540 days (range: 68-2424 days). For invasive ductal carcinoma (N = 33), the median VDT of the non-luminal type was shorter than that of the luminal type (178 days vs. 478 days). On initial MRI, breast cancer manifesting as a focus or mass lesion showed a shorter VDT than that of a non-mass enhancement (NME) lesion (median VDT: 426 days vs. 665 days). DATA CONCLUSION: A shorter VDT was observed in breast cancer manifesting as focus or mass as compared to an NME lesion. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Attention-based Deep Learning for the Preoperative Differentiation of Axillary Lymph Node Metastasis in Breast Cancer on DCE-MRI.

BACKGROUND: Previous studies have explored the potential on radiomics features of primary breast cancer tumor to identify axillary lymph node (ALN) metastasis. However, the value of deep learning (DL) to identify ALN metastasis remains unclear. PURPOSE: To investigate the potential of the proposed attention-based DL model for the preoperative differentiation of ALN metastasis in breast cancer on dynamic contrast-enhanced MRI (DCE-MRI). STUDY TYPE: Retrospective. POPULATION: A total of 941 breast cancer patients who underwent DCE-MRI before surgery were included in the training (742 patients), internal test (83 patients), and external test (116 patients) cohorts. FIELD STRENGTH/SEQUENCE: A 3.0 T MR scanner, DCE-MRI sequence. ASSESSMENT: A DL model containing a 3D deep residual network (ResNet) architecture and a convolutional block attention module, named RCNet, was proposed for ALN metastasis identification. Three RCNet models were established based on the tumor, ALN, and combined tumor-ALN regions on the images. The performance of these models was compared with ResNet models, radiomics models, the Memorial Sloan-Kettering Cancer Center (MSKCC) model, and three radiologists (W.L., H.S., and F. L.). STATISTICAL TESTS: Dice similarity coefficient for breast tumor and ALN segmentation. Accuracy, sensitivity, specificity, intercorrelation and intracorrelation coefficients, area under the curve (AUC), and Delong test for ALN classification. RESULTS: The optimal RCNet model, that is, RCNet-tumor+ALN , achieved an AUC of 0.907, an accuracy of 0.831, a sensitivity of 0.824, and a specificity of 0.837 in the internal test cohort, as well as an AUC of 0.852, an accuracy of 0.828, a sensitivity of 0.792, and a specificity of 0.853 in the external test cohort. Additionally, with the assistance of RCNet-tumor+ALN , the radiologists' performance was improved (external test cohort, P < 0.05). DATA CONCLUSION: DCE-MRI-based RCNet model could provide a noninvasive auxiliary tool to identify ALN metastasis preoperatively in breast cancer, which may assist radiologists in conducting more accurate evaluation of ALN status. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Pre-stenting angiography-FFR based physiological map provides virtual intervention and predicts physiological and clinical outcomes.

BACKGROUND: Angiography-derived fractional flow reserve (FFR) (angio-FFR) has been validated against FFR and could provide virtual pullback. However, whether a physiological map can be generated by angio-FFR and its clinical value remains unclear. We aimed to investigate the feasibility of physiological map created from angio-FFR pullback and its value in predicting physiological and clinical outcomes after stenting. METHODS: An angio-FFR physiological map was generated by overlaying the virtual pullback onto coronary angiogram, to calculate physiological stenosis severity, length, and intensity (Δangio-FFR/mm). This map in combination with virtual stenting was used to predict the best-case post-percutaneous coronary intervention (PCI) angio-FFR (angio-FFRpredicted ) according to the stented segments, and this was compared with the actual achieved post-PCI angio-FFR (angio-FFRachieved ). Additionally, prognostic value of predicted angio-FFR was investigated. RESULTS: Three hundred twenty-nine vessels with paired analyzable pre- and post-PCI angio-FFR were included. Physiological map was created successfully in all vessels. After successful PCI, angio-FFRpredicted and angio-FFRachieved were significantly correlated (r = 0.82, p < 0.001) with small difference (mean difference: -0.010 ± 0.035). In the virtual PCI only covering the segment with high angio-FFR intensity, the same physiological outcome can be achieved with shorter stent length (14.1 ± 8.9 vs. 34.5 ± 15.8 mm, p < 0.001). Suboptimal angio-FFRpredicted was associated with increased risk of 2-year vessel-oriented composite endpoint (adjusted hazard ratio: 3.71; 95% confidence interval: 1.50-9.17). CONCLUSIONS: Angio-FFR pullback could provide a physiological map of the interrogated coronary vessels by integrating angio-FFR pullback and angiography. Before a PCI, the physiological map can predict the physiological and clinical outcomes after stenting.

Intratumoral and peritumoral radiomics nomograms for the preoperative prediction of lymphovascular invasion and overall survival in non-small cell lung cancer.

OBJECTIVES: To evaluate the predictive value of intratumoral and peritumoral radiomics and radiomics nomogram for preoperative lymphovascular invasion (LVI) status and overall survival (OS) in patients with non-small cell lung cancer (NSCLC). METHODS: In total, 240 NSCLC patients from our institution were randomly divided into the training cohort (n = 145) and internal validation cohort (n = 95) with a ratio of 6:4, and 65 patients from the Cancer Imaging Archive were enrolled as the external validation cohort. We extracted 1217 CT-based radiomics features from the gross tumor volume (GTV) and gross tumor volume incorporating peritumoral 3, 6, and 9 mm regions (GPTV3, GPTV6, GPTV9). A radiomics nomogram based on clinical independent predictors and radiomics score (Radscore) of the best radiomics model was constructed. The correlation between factors and OS was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis. RESULTS: Compared with GTV, GPTV3, and GPTV6 radiomics models, GPTV9 radiomics model exhibited better prediction performance with the AUCs of 0.82, 0.75, and 0.67 in the training, internal validation, and external validation cohorts, respectively. In the clinical model, smoking and clinical stage were independent predictors. The nomogram incorporating independent predictors and GPTV9-Radscore was clinically useful, with the AUCs of 0.89, 0.83, and 0.66 in three cohorts. Pathological LVI, GPTV9-Radscore-predicted, and Nomoscore-predicted LVI were associated with poor OS (p < 0.05). CONCLUSIONS: CT-based radiomics nomogram can predict LVI and OS in patients with NSCLC and may help in making personalized treatment strategies before surgery. KEY POINTS: • Compared with GTV, GPTV3, and GPTV6 radiomics models, GPTV9 radiomics model showed better prediction performance for LVI status in NSCLC. • The radiomics nomogram based on GPTV9 radiomics features and clinical independent predictors could effectively predict LVI status and OS in NSCLC and outperformed the clinical model. • The radiomics nomogram had a wider scope of clinical application.

A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study.

OBJECTIVES: To develop and validate a CT-based deep learning radiomics nomogram (DLRN) for outcome prediction in clear cell renal cell carcinoma (ccRCC), and its performance was compared with the Stage, Size, Grade, and Necrosis (SSIGN) score, the University of California, Los Angeles, Integrated Staging System (UISS), the Memorial Sloan-Kettering Cancer Center (MSKCC), and the International Metastatic Renal Cell Database Consortium (IMDC). METHODS: A multicenter of 799 localized (training/ test cohort, 558/241) and 45 metastatic ccRCC patients were studied. A DLRN was developed for predicting recurrence-free survival (RFS) in localized ccRCC patients, and another DLRN was developed for predicting overall survival (OS) in metastatic ccRCC patients. The performance of the two DLRNs was compared with that of the SSIGN, UISS, MSKCC, and IMDC. Model performance was assessed with Kaplan-Meier curves, time-dependent area under the curve (time-AUC), Harrell's concordance index (C-index), and decision curve analysis (DCA). RESULTS: In the test cohort, the DLRN achieved higher time-AUCs (0.921, 0.911, and 0.900 for 1, 3, and 5 years, respectively), C-index (0.883), and net benefit than SSIGN and UISS in predicting RFS for localized ccRCC patients. The DLRN provided higher time-AUCs (0.594, 0.649, and 0.754 for 1, 3, and 5 years, respectively) than MSKCC and IMDC in predicting OS for metastatic ccRCC patients. CONCLUSIONS: The DLRN can accurately predict outcomes and outperformed the existing prognostic models in ccRCC patients. CLINICAL RELEVANCE STATEMENT: This deep learning radiomics nomogram may facilitate individualized treatment, surveillance, and adjuvant trial design for patients with clear cell renal cell carcinoma. KEY POINTS: • SSIGN, UISS, MSKCC, and IMDC may be insufficient for outcome prediction in ccRCC patients. • Radiomics and deep learning allow for the characterization of tumor heterogeneity. • The CT-based deep learning radiomics nomogram outperforms the existing prognostic models in ccRCC outcome prediction.

Incremental value of radiomics-based heterogeneity to the existing risk criteria in predicting recurrence of hepatocellular carcinoma after liver transplantation.

OBJECTIVES: The aim of the study was to evaluate the association between the radiomics-based intratumoral heterogeneity (ITH) and the recurrence risk in hepatocellular carcinoma (HCC) patients after liver transplantation (LT), and to assess its incremental to the Milan, University of California San Francisco (UCSF), Metro-Ticket 2.0, and Hangzhou criteria. METHODS: A multicenter cohort of 196 HCC patients were investigated. The endpoint was recurrence-free survival (RFS) after LT. A CT-based radiomics signature (RS) was constructed and assessed in the whole cohort and in the subgroups stratified by the Milan, UCSF, Metro-Ticket 2.0, and Hangzhou criteria. The R-Milan, R-UCSF, R-Metro-Ticket 2.0, and R-Hangzhou nomograms which combined RS and the four existing risk criteria were developed respectively. The incremental value of RS to the four existing risk criteria in RFS prediction was evaluated. RESULTS: RS was significantly associated with RFS in the training and test cohorts as well as in the subgroups stratified by the existing risk criteria. The four combined nomograms showed better predictive capability than the existing risk criteria did with higher C-indices (R-Milan [training/test] vs. Milan, 0.745/0.765 vs. 0.677; R-USCF vs. USCF, 0.748/0.767 vs. 0.675; R-Metro-Ticket 2.0 vs. Metro-Ticket 2.0, 0.756/0.783 vs. 0.670; R-Hangzhou vs. Hangzhou, 0.751/0.760 vs. 0.691) and higher clinical net benefit. CONCLUSIONS: The radiomics-based ITH can predict outcomes and provide incremental value to the existing risk criteria in HCC patients after LT. Incorporating radiomics-based ITH in HCC risk criteria may facilitate candidate selection, surveillance, and adjuvant trial design. KEY POINTS: • Milan, USCF, Metro-Ticket 2.0, and Hangzhou criteria may be insufficient for outcome prediction in HCC after LT. • Radiomics allows for the characterization of tumor heterogeneity. • Radiomics adds incremental value to the existing criteria in outcome prediction.

Predicting peritumoral edema development after gamma knife radiosurgery of meningiomas using machine learning methods: a multicenter study.

OBJECTIVES: Edema is a complication of gamma knife radiosurgery (GKS) in meningioma patients that leads to a variety of consequences. The aim of this study is to construct radiomics-based machine learning models to predict post-GKS edema development. METHODS: In total, 445 meningioma patients who underwent GKS in our institution were enrolled and partitioned into training and internal validation datasets (8:2). A total of 150 cases from multicenter data were included as the external validation dataset. In each case, 1132 radiomics features were extracted from each pre-treatment MRI sequence (contrast-enhanced T1WI, T2WI, and ADC maps). Nine clinical features and eight semantic features were also generated. Nineteen random survival forest (RSF) and nineteen neural network (DeepSurv) models with different combinations of radiomics, clinical, and semantic features were developed with the training dataset, and evaluated with internal and external validation. A nomogram was derived from the model achieving the highest C-index in external validation. RESULTS: All the models were successfully validated on both validation datasets. The RSF model incorporating clinical, semantic, and ADC radiomics features achieved the best performance with a C-index of 0.861 (95% CI: 0.748-0.975) in internal validation, and 0.780 (95% CI: 0.673-0.887) in external validation. It stratifies high-risk and low-risk cases effectively. The nomogram based on the predicted risks provided personalized prediction with a C-index of 0.962 (95%CI: 0.951-0.973) and satisfactory calibration. CONCLUSION: This RSF model with a nomogram could represent a non-invasive and cost-effective tool to predict post-GKS edema risk, thus facilitating personalized decision-making in meningioma treatment. CLINICAL RELEVANCE STATEMENT: The RSF model with a nomogram built in this study represents a handy, non-invasive, and cost-effective tool for meningioma patients to assist in better counselling on the risks, appropriate individual treatment decisions, and customized follow-up plans. KEY POINTS: • Machine learning models were built to predict post-GKS edema in meningioma. The random survival forest model with clinical, semantic, and ADC radiomics features achieved excellent performance. • The nomogram based on the predicted risks provides personalized prediction with a C-index of 0.962 (95%CI: 0.951-0.973) and satisfactory calibration and shows the potential to assist in better counselling, appropriate treatment decisions, and customized follow-up plans. • Given the excellent performance and convenient acquisition of the conventional sequence, we envision that this non-invasive and cost-effective tool will facilitate personalized medicine in meningioma treatment.

Quantitative flow ratio derived pullback pressure gradient and CZT-SPECT measured longitudinal flow gradient for hemodynamically significant coronary artery disease.

BACKGROUND: Whether physiological coronary diffuseness assessed by quantitative flow reserve (QFR) pullback pressure gradient (PPG) correlates with longitudinal myocardial blood flow (MBF) gradient and improves diagnostic performances for myocardial ischemia remains unknown. METHODS AND RESULTS: MBF was measured in mL g-1 min-1 with 99mTc-MIBI CZT-SPECT at rest and stress, corresponding myocardial flow reserve (MFR = MBF stress/MBF rest) and relative flow reserve (RFR = MBF stenotic area/MBF reference) were calculated. Longitudinal MBF gradient was defined as apical and basal left ventricle MBF gradient. △longitudinal MBF gradient was calculated by longitudinal MBF gradient at stress and rest. QFR-PPG was acquired from virtual QFR pullback curve. QFR-PPG significantly correlated with hyperemic longitudinal MBF gradient (r = 0.45, P = 0.007) and △longitudinal MBF gradient (stress-rest) (r = 0.41, P = 0.016). Vessels with lower RFR had lower QFR-PPG (0.72 vs. 0.82, P = 0.002), hyperemic longitudinal MBF gradient (1.14 vs. 2.22, P = 0.003) and △longitudinal MBF gradient (0.50 vs. 1.02, P = 0.003). QFR-PPG, hyperemic longitudinal MBF gradient and △longitudinal MBF gradient showed comparable diagnostic performances for predicting decreased RFR (area under curve [AUC]: 0.82 vs. 0.81 vs. 0.75, P = NS) or QFR (AUC: 0.83 vs. 0.72 vs. 0.80, P = NS). In addition, QFR-PPG and QFR in combination showed incremental value compared with QFR for predicting RFR (AUC = 0.83 vs. 0.73, P = 0.046, net reclassification index = 0.508, P = 0.001). CONCLUSION: QFR-PPG significantly correlated with longitudinal MBF gradient and △longitudinal MBF gradient when used for physiological coronary diffuseness assessment. All three parameters had high accuracy in predicting RFR or QFR. Adding physiological diffuseness assessment increased accuracy for predicting myocardial ischemia.

Comparison of 2D-QCA, 3D-QCA and coronary angiography derived FFR in predicting myocardial ischemia assessed by CZT-SPECT MPI.

BACKGROUND: Angiography derived fractional flow reserve (angio-FFR) has been proposed. This study aimed to assess its diagnostic performance with cadmium-zinc-telluride single emission computed tomography (CZT-SPECT) as reference. METHODS AND RESULTS: Patients underwent CZT-SPECT within 3 months of coronary angiography were included. Angio-FFR computation was performed using computational fluid dynamics. Percent diameter (%DS) and area stenosis (%AS) were measured by quantitative coronary angiography. Myocardial ischemia was defined as a summed difference score ≥ 2 in a vascular territory. Angio-FFR ≤ 0.80 was considered abnormal. 282 coronary arteries in 131 patients were analyzed. Overall accuracy of angio-FFR to detect ischemia on CZT-SPECT was 90.43%, with a sensitivity of 62.50% and a specificity of 98.62%. The diagnostic performance (= area under ROC = AUC) of angio-FFR [AUC = 0.91, 95% confidence intervals (CI) 0.86-0.95] was similar as those of %DS (AUC = 0.88, 95% CI 0.84-0.93, p = 0.326) and %AS (AUC = 0.88, 95% CI 0.84-0.93 p = 0.241) by 3D-QCA, but significantly higher than those of %DS (AUC = 0.59, 95% CI 0.51-0.67, p < 0.001) and %AS (AUC = 0.59, 95% CI 0.51-0.67, p < 0.001) by 2D-QCA. However, in vessels with 50-70% stenoses, AUC of angio-FFR was significantly higher than those of %DS (0.80 vs. 0.47, p < 0.001) and %AS (0.80 vs. 0.46, p < 0.001) by 3D-QCA and %DS (0.80 vs. 0.66, p = 0.036) and %AS (0.80 vs. 0.66, p = 0.034) by 2D-QCA. CONCLUSION: Angio-FFR had a high accuracy in predicting myocardial ischemia assessed by CZT-SPECT, which is similar as 3D-QCA but significantly higher than 2D-QCA. While in intermediate lesions, angio-FFR is better than 3D-QCA and 2D-QCA in assessing myocardial ischemia.

Association of Clinical Factors and Degree of Early Background Parenchymal Enhancement on Contrast-Enhanced Mammography.

BACKGROUND. Background parenchymal enhancement (BPE) may impact contrast-enhanced mammography (CEM) interpretation, although factors influencing the degree of BPE on CEM are poorly understood. OBJECTIVE. The purpose of our study was to evaluate relationships between clinical factors and the degree of early BPE on CEM. METHODS. This retrospective study included 207 patients (median age, 46 years) who underwent CEM between April 2020 and September 2021. Two radiologists independently assessed the degree of BPE on CEM as minimal, mild, moderate, or marked on the basis of two criteria (criterion 1, using the first of four obtained views; criterion 2, using the first two of four obtained views). The radiologists reached consensus for breast density on CEM. The EMR was reviewed for clinical factors. Radiologists' agreement for degree of BPE was assessed using weighted kappa coefficients. Univariable and multivariable analyses were performed to assess relationships between clinical factors and degree of BPE, treating readers' independent assessments as repeated measurements. RESULTS. Interreader agreement for degree of BPE, expressed as kappa, was 0.80 for both criteria. For both criteria, univariable analyses found degree of BPE to be negatively associated with age (both OR = 0.94), personal history of breast cancer (OR = 0.22-0.30), history of chemotherapy (OR = 0.18-0.21), history of radiation therapy (OR = 0.20-0.21), perimenopausal status (OR = 0.22-0.34), and postmenopausal status (OR = 0.10-0.11) and to be positively associated with dense breasts (OR = 4.13-4.26) and premenopausal status with irregular menstrual cycles (OR = 7.94-14.02). Among premenopausal patients with regular menstrual cycles, degree of BPE was lowest (using postmenopausal patients as reference) for patients in menstrual cycle days 8-14 (OR = 2.56-3.30). In multivariable analysis for both criteria, the only independent predictors of degree of BPE related to menstrual status and time of menstrual cycle (e.g., using premenopausal patients in days 1-7 as reference: OR = 0.21 for both criteria for premenopausal patients in days 8-14 and OR = 0.03-0.04 for postmenopausal patients). CONCLUSION. Clinical factors, including history of breast cancer or breast cancer treatment, breast density, menstrual status, and time of menstrual cycle, are associated with degree of early BPE on CEM. In premenopausal patients, the degree of BPE is lowest on days 8-14 of the menstrual cycle. CLINICAL IMPACT. Given the potential impact of BPE on diagnostic performance, the findings have implications for CEM scheduling and interpretation.

HA-ADT suppresses esophageal squamous cell carcinoma progression via apoptosis promotion and autophagy inhibition.

Esophageal squamous cell carcinoma (ESCC) is a major cause of cancer-related deaths. We have previously connected a non-sulfated glycosaminoglycan, hyaluronic acid (HA), with a common hydrogen sulfide (H2S) donor, 5-(4-hydroxyphenyl)-3H-1,2-dithiol-3-thione (ADT-OH), to reconstruct a novel conjugate, HA-ADT. In this study, we determined the effect of HA-ADT on the growth of ESCC. Our data suggested that HA-ADT exerted more potent effects than sodium hydrosulfide (NaHS, a fast H2S-releasing donor) and morpholin-4-ium (4-methoxyphenyl)-morpholin-4-ylsulfanylidenesulfido-λ5-phosphane (GYY4137, a slow H2S-releasing donor) on inhibiting the viability, proliferation, migration, and invasion of human ESCC cells. HA-ADT increased apoptosis by suppressing the protein expressions of phospho (p)-Ser473-protein kinase B (PKB/AKT), p-Tyr199/Tyr458-phosphatidylinositol 3-kinase (PI3K), and p-Ser2448-mammalian target of rapamycin (mTOR), but suppressed autophagy through the inhibition of the protein levels of p-Ser552-ß-catenin, p-Ser9-glycogen synthase kinase-3ß (GSK-3ß), and Wnt3a in human ESCC cells. In addition, HA-ADT was more effective in terms of the growth inhibition of human ESCC xenograft tumor than NaHS and GYY4137. In conclusion, HA-ADT can suppress ESCC progression via apoptosis promotion and autophagy inhibition. HA-ADT might be efficacious for the treatment of cancer.

CT-based radiomics nomogram analysis for assessing BRCA mutation status in patients with high-grade serous ovarian cancer.

BACKGROUND: Radiomics nomogram analysis is widely preoperatively used to assess gene mutations in various tumors. PURPOSE: To explore the value of computed tomography (CT)-based radiomics nomogram analysis for assessing BRCA gene mutation status of patients with high-grade serous ovarian cancer (HGSOC). MATERIAL AND METHODS: In total, 96 patients with HGSOC were retrospectively screened and randomly divided into primary (n = 68) and validation cohorts (n = 28). The clinical model was constructed based on clinical features and CT morphological features using univariate and multivariate logistic analyses. Maximum-relevance and minimum-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were performed for feature dimensionality reduction and radiomics score was calculated. The nomogram model combining the clinical model and the radiomics score was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curves were generated to assess models' performance. The calibration analysis and decision curve analysis (DCA) were also performed. RESULTS: The clinical model consisted of CA125 level and supradiaphragmatic lymphadenopathy and yielded an area under the curve (AUC) of 0.69 (primary cohort) and 0.81 (validation cohort). The radiomics model was built with seven selected features and showed an AUC of 0.87 (primary cohort) and 0.81 (validation cohort). The nomogram finally showed the highest AUC of 0.89 (primary cohort) and 0.87 (validation cohort). The nomogram presented favorable calibrations in both the primary and validation cohorts. DCA further confirmed the clinical benefits of the constructed nomogram. CONCLUSION: CT-based radiomics nomogram provides a non-invasive method to discriminate BRCA gene mutation status of HGSOC and potentially helps develop precise medical strategies.

Anomalous Contribution to the Nematic Electronic States from the Structural Transition in FeSe Revealed by Time- and Angle-Resolved Photoemission Spectroscopy.

High-resolution time- and angle-resolved photoemission measurements were made on FeSe superconductors. With ultrafast photoexcitation, two critical excitation fluences that correspond to two ultrafast electronic phase transitions were found only in the d_{yz}-orbit-derived band near the Brillouin-zone center within our time and energy resolution. Upon comparison to the detailed temperature dependent measurements, we conclude that there are two equilibrium electronic phase transitions (at approximately 90 and 120 K) above the superconducting transition temperature, and an anomalous contribution on the scale of 10 meV to the nematic states from the structural transition is experimentally determined. Our observations strongly suggest that the electronic phase transition at 120 K must be taken into account in the energy band development of FeSe, and, furthermore, the contribution of the structural transition plays an important role in the nematic phase of iron-based high-temperature superconductors.