Multiparametric MRI-based radiomic model for predicting lymph node metastasis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

OBJECTIVES: To construct and validate multiparametric MR-based radiomic models based on primary tumors for predicting lymph node metastasis (LNM) following neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) patients. METHODS: A total of 150 LARC patients from two independent centers were enrolled. The training cohort comprised 100 patients from center A. Fifty patients from center B were included in the external validation cohort. Radiomic features were extracted from the manually segmented volume of interests of the primary tumor before and after nCRT. Feature selection was performed using multivariate logistic regression analysis. The clinical risk factors were selected via the least absolute shrinkage and selection operator method. The radiologist's assessment of LNM was performed. Eight models were constructed using random forest classifiers, including four single-sequence models, three combined-sequence models, and a clinical model. The models' discriminative performance was assessed via receiver operating characteristic curve analysis quantified by the area under the curve (AUC). RESULTS: The AUCs of the radiologist's assessment, the clinical model, and the single-sequence models ranged from 0.556 to 0.756 in the external validation cohort. Among the single-sequence models, modelpost_DWI exhibited superior predictive power, with an AUC of 0.756 in the external validation set. In combined-sequence models, modelpre_T2_DWI_post had the best diagnostic performance in predicting LNM after nCRT, with a significantly higher AUC (0.831) than those of the clinical model, modelpre_T2_DWI, and the single-sequence models (all p < 0.05). CONCLUSIONS: A multiparametric model that incorporates MR radiomic features before and after nCRT is optimal for predicting LNM after nCRT in LARC. CRITICAL RELEVANCE STATEMENT: This study enrolled 150 LARC patients from two independent centers and constructed multiparametric MR-based radiomic models based on primary tumors for predicting LNM following nCRT, which aims to guide therapeutic decisions and predict prognosis for LARC patients. KEY POINTS: The biological characteristics of primary tumors and metastatic LNs are similar in rectal cancer. Radiomics features and clinical data before and after nCRT provide complementary tumor information. Preoperative prediction of LN status after nCRT contributes to clinical decision-making.

Recent advances in nanomaterial-stabilized pickering foam: Mechanism, classification, properties, and applications.

Pickering foam is a type of foam stabilized by solid particles known as Pickering stabilizers. These solid stabilizers adsorb at the liquid-gas interface, providing superior stability to the foam. Because of its high stability, controllability, versatility, and minimal environmental impact, nanomaterial-stabilized Pickering foam has opened up new possibilities and development prospects for foam applications. This review provides an overview of the current state of development of Pickering foam stabilized by a wide range of nanomaterials, including cellulose nanomaterials, chitin nanomaterials, silica nanoparticles, protein nanoparticles, clay mineral, carbon nanotubes, calcium carbonate nanoparticles, MXene, and graphene oxide nanosheets. Particularly, the preparation and surface modification methods of various nanoparticles, the fundamental properties of nanomaterial-stabilized Pickering foam, and the synergistic effects between nanoparticles and surfactants, functional polymers, and other additives are systematically introduced. In addition, the latest progress in the application of nanomaterial-stabilized Pickering foam in the oil industry, food industry, porous functional material, and foam flotation field is highlighted. Finally, the future prospects of nanomaterial-stabilized Pickering foam in different fields, along with directions for further research and development directions, are outlined.

Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas.

BACKGROUND: 1p/19q co-deletion in low-grade gliomas (LGG, World Health Organization grade II and III) is of great significance in clinical decision making. We aim to use radiomics analysis to predict 1p/19q co-deletion in LGG based on amide proton transfer weighted (APTw), diffusion weighted imaging (DWI), and conventional MRI. METHODS: This retrospective study included 90 patients histopathologically diagnosed with LGG. We performed a radiomics analysis by extracting 8454 MRI-based features form APTw, DWI and conventional MR images and applied a least absolute shrinkage and selection operator (LASSO) algorithm to select radiomics signature. A radiomics score (Rad-score) was generated using a linear combination of the values of the selected features weighted for each of the patients. Three neuroradiologists, including one experienced neuroradiologist and two resident physicians, independently evaluated the MR features of LGG and provided predictions on whether the tumor had 1p/19q co-deletion or 1p/19q intact status. A clinical model was then constructed based on the significant variables identified in this analysis. A combined model incorporating both the Rad-score and clinical factors was also constructed. The predictive performance was validated by receiver operating characteristic curve analysis, DeLong analysis and decision curve analysis. P < 0.05 was statistically significant. RESULTS: The radiomics model and the combined model both exhibited excellent performance on both the training and test sets, achieving areas under the curve (AUCs) of 0.948 and 0.966, as well as 0.909 and 0.896, respectively. These results surpassed the performance of the clinical model, which achieved AUCs of 0.760 and 0.766 on the training and test sets, respectively. After performing Delong analysis, the clinical model did not significantly differ in predictive performance from three neuroradiologists. In the training set, both the radiomic and combined models performed better than all neuroradiologists. In the test set, the models exhibited higher AUCs than the neuroradiologists, with the radiomics model significantly outperforming resident physicians B and C, but not differing significantly from experienced neuroradiologist. CONCLUSIONS: Our results suggest that our algorithm can noninvasively predict the 1p/19q co-deletion status of LGG. The predictive performance of radiomics model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians, thereby offering the potential to facilitate non-invasive 1p/19q co-deletion prediction of LGG.

Development and validation of radiomics nomograms for preoperative prediction of characteristics in non-small cell lung cancer and circulating tumor cells.

To develop and validate 3 radiomics nomograms for preoperative prediction of pathological and progression diagnosis in non-small cell lung cancer (NSCLC) as well as circulating tumor cells (CTCs). A total of 224 and 134 patients diagnosed with NSCLC were respectively gathered in 2018 and 2019 in this study. There were totally 1197 radiomics features that were extracted and quantified from the images produced by computed tomography. Then we selected the radiomics features with predictive value by least absolute shrinkage and selection operator and combined them into radiomics signature. Logistic regression models were built using radiomics signature as the only predictor, which were then converted to nomograms for individualized predictions. Finally, the performance of the nomograms was assessed on both cohorts. Additionally, immunohistochemical correlation analysis was also performed. As for discrimination, the area under the curve of pathological diagnosis nomogram and progression diagnosis nomogram in NSCLC were both higher than 90% in the training cohort and higher than 80% in the validation cohort. The performance of the CTC-diagnosis nomogram was somehow unexpected where the area under the curve were range from 60% to 70% in both cohorts. As for calibration, nonsignificant statistics (P > .05) yielded by Hosmer-Lemeshow tests suggested no departure between model prediction and perfect fit. Additionally, decision curve analyses demonstrated the clinically usefulness of the nomograms. We developed radiomics-based nomograms for pathological, progression and CTC diagnosis prediction in NSCLC respectively. Nomograms for pathological and progression diagnosis were demonstrated well-performed to facilitate the individualized preoperative prediction, while the nomogram for CTC-diagnosis prediction needed improvement.

Development of a magnetic resonance imaging-derived radiomics model to predict microvascular invasion in patients with hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) with microvascular invasion (MVI) has a poor prognosis, is prone to recurrence and metastasis, and requires more complex surgical techniques. Radiomics is expected to enhance the discriminative performance for identifying HCC, but the current radiomics models are becoming increasingly complex, tedious, and difficult to integrate into clinical practice. The purpose of this study was to investigate whether a simple prediction model using noncontrast-enhanced T2-weighted magnetic resonance imaging (MRI) could preoperatively predict MVI in HCC. Methods: A total of 104 patients with pathologically confirmed HCC (training cohort, n=72; test cohort, n=32; ratio, about 7:3) who underwent liver MRI within 2 months prior to surgery were retrospectively included. A total of 851 tumor-specific radiomic features were extracted on T2-weighted imaging (T2WI) for each patient using AK software (Artificial Intelligence Kit Version; V. 3.2.0R, GE Healthcare). Univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression were used in the training cohort for feature selection. The selected features were incorporated into a multivariate logistic regression model to predict MVI, which was validated in the test cohort. The model's effectiveness was evaluated using the receiver operating characteristic and calibration curves in the test cohort. Results: Eight radiomic features were identified to establish a prediction model. In the training cohort, the area under the curve, accuracy, specificity, sensitivity, and positive and negative predictive values of the model for predicting MVI were 0.867, 72.7%, 84.2%, 64.7%, 72.7%, and 78.6%, respectively; while in the test cohort, they were 0.820, 75%, 70.6%, 73.3%, 75%, and 68.8%, respectively. The calibration curves displayed good consistency between the prediction of MVI by the model and actual pathological results in both the training and validation cohorts. Conclusions: A prediction model using radiomic features from single T2WI can predict MVI in HCC. This model has the potential to be a simple and fast method to provide objective information for decision-making during clinical treatment.

The application of machine learning in early diagnosis of osteoarthritis: a narrative review.

Osteoarthritis (OA) is the commonest musculoskeletal disease worldwide, with an increasing prevalence due to aging. It causes joint pain and disability, decreased quality of life, and a huge burden on healthcare services for society. However, the current main diagnostic methods are not suitable for early diagnosing patients of OA. The use of machine learning (ML) in OA diagnosis has increased dramatically in the past few years. Hence, in this review article, we describe the research progress in the application of ML in the early diagnosis of OA, discuss the current trends and limitations of ML approaches, and propose future research priorities to apply the tools in the field of OA. Accurate ML-based predictive models with imaging techniques that are sensitive to early changes in OA ahead of the emergence of clinical features are expected to address the current dilemma. The diagnostic ability of the fusion model that combines multidimensional information makes patient-specific early diagnosis and prognosis estimation of OA possible in the future.

Applications of chemical exchange saturation transfer magnetic resonance imaging in identifying genetic markers in gliomas.

Chemical exchange saturation transfer (CEST) imaging is an important molecular magnetic resonance imaging technique that can image numerous low-concentration biomolecules with water-exchangeable protons (such as cellular proteins) and tissue pH. CEST, or more specially amide proton transfer-weighted imaging, has been widely used for the detection, diagnosis, and response assessment of brain tumors, and its feasibility in identifying molecular markers in gliomas has also been explored in recent years. In this paper, after briefing on the basic principles and quantification methods of CEST imaging, we review its early applications in identifying isocitrate dehydrogenase mutation status, MGMT methylation status, 1p/19q deletion status, and H3K27M mutation status in gliomas. Finally, we discuss the limitations or weaknesses in these studies.

Amide Proton Transfer Weighted MR Imaging for Predicting Meningioma Stiffness: A Feasibility Study.

BACKGROUND: Stiffness of meningioma is an important factor affecting the surgical resection and the prognosis of patients. PURPOSE: To examine the feasibility of APTw-magnetic resonance imaging (MRI) in evaluating meningioma stiffness. STUDY TYPE: Retrospective. POPULATION: Seventy-one patient with meningiomas, 39 were male and 32 were female; the mean age was 51 ± 10 years. FIELD STRENGTH/SEQUENCE: 3.0T; Turbo-spin-echo T1 -weighted and Gd-T1 -weighted sequence; Turbo-spin-echo T2 -weighted sequence; 2D fat-suppressed, turbo-spin-echo APTw pulse sequence. ASSESSMENT: The T1 WI signal intensity score, T2 WI signal intensity score, APTwmin , APTwmax , and APTwmean values were compared between soft, medium stiff and stiff meningiomas or non-stiff meningiomas and stiff meningiomas group. STATISTICAL TESTS: Chi-square test, one-way ANOVA analysis, independent-samples t-test, intra-class correlation coefficient, rank-sum test, receiver operating characteristic curve analysis. P < 0.05 was considered statistically significant in all tests. RESULTS: APTwmin and APTwmean in the stiff group were significantly lower than that in the non-stiff group (2.79% ± 0.42% vs. 1.90% ± 0.60% and 3.20% ± 0.31% vs. 2.55% ± 0.61%). APTwmin and APTwmean in the stiff group were significantly lower than that in the medium stiff and soft groups (1.90% ± 0.60% vs. 2.69% ± 0.40% and 3.12% ± 0.32%, 2.55% ± 0.61% vs. 3.17% ± 0.33% and 3.39% ± 0.18%), APTwmin in the medium stiff group was significantly lower than in the soft group, there was no significant difference in APTwmean between the medium stiff and soft groups (P = 0.190). APTwmin showed the best diagnostic performance for evaluating meningioma stiffness with an area under the curve of 0.913, when the APTwmin was lower than 2.4%, the meningioma was defined as a stiff tumor, the sensitivity, specificity, and accuracy were 87.1%, 87.5%, and 85.9%, respectively. DATA CONCLUSION: APTw-MRI could be used to evaluate meningioma stiffness, with APTwmin having the best evaluative efficiency. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 1.

A comparative study of quantitative metrics in chemical exchange saturation transfer imaging for grading gliomas in adults.

PURPOSE: To evaluate the performance of different chemical exchange saturation transfer (CEST) metrics for grading gliomas with semiautomatically defined regions of interest (ROIs). METHODS: Thirty-eight adult subjects were included, including 23 high-grade gliomas and 15 low-grade gliomas confirmed by histopathology. The B0-corrected CEST z-spectra were first calculated with magnetization transfer ratio asymmetry (MTRasym) analysis at frequency offsets of 3.5, 3, 2.5, 2, 1.5, and 1 ppm to obtain the fit-free metrics and subsequently fitted with three Lorentzian functions denoting direct water saturation (DS), amide proton transfer (APT), and combined semisolid magnetization transfer and nuclear Overhauser enhancement (MT & NOE) effects to derive the fit-based metrics. Wilcoxon rank-sum test was performed to determine if a statistically significant difference was present in the CEST metrics between low- and high-grade gliomas. Receiver operating characteristic (ROC) curves were used to evaluate the differentiation of CEST metrics between low- and high-grade gliomas. Pearson correlation coefficients were employed to evaluate the correlations of CEST metrics. RESULTS: For the fit-free metrics, the highest areas under the curve (AUCs) of 0.85, 0.88, and 0.88, corresponding to MTRasym, MTRnormref (normalization by the reference scan), and MTRRex (subtraction of inverse z-spectra), respectively, were obtained at 3 ppm across various frequency offsets. In addition, the AUCs generated from the fit-based metrics (0.88-0.90) were higher than those generated from the fit-free metrics at 3 ppm. CONCLUSION: The results of this preliminary study indicate that fit-free CEST metrics at 3 ppm are superior to the other frequency offsets for grading human brain gliomas. The fit-based metrics manifested improved differentiation between low- and high-grade gliomas compared to the fit-free CEST metrics.

Alterations in whole-brain dynamic functional stability during memory tasks under dexmedetomidine sedation.

Purpose: This study aimed to explore the neurological effects of dexmedetomidine-induced sedation on memory using functional stability, a whole-brain voxel-wise dynamic functional connectivity approach. Methods: A total of 16 participants (10 men) underwent auditory memory task-related fMRI in the awake state and under dexmedetomidine sedation. Explicit and implicit memory tests were conducted 4 h after ceasing dexmedetomidine administration. One-sample Wilcoxon signed rank test was applied to determine the formation of explicit and implicit memory in the two states. Functional stability was calculated and compared voxel-wise between the awake and sedated states. The association between functional stability and memory performance was also assessed. Results: In the awake baseline tests, explicit and implicit memory scores were significantly different from zero (p < 0.05). In the tests under sedation, explicit and implicit memory scores were not significantly different from zero. Compared to that at wakeful baseline, functional stability during light sedation was reduced in the medial prefrontal cortex, left angular gyrus, and right hippocampus (all clusters, p < 0.05, GRF-corrected), whereas the left superior temporal gyrus exhibited higher functional stability (cluster p < 0.05, GRF-corrected). No significant associations were observed between functional stability and memory test scores. Conclusions: The distribution and patterns of alterations in functional stability during sedation illustrate the modulation of functional architecture by dexmedetomidine from a dynamic perspective. Our findings provide novel insight into the dynamic brain functional networks underlying consciousness and memory in humans.

Review and consensus recommendations on clinical APT-weighted imaging approaches at 3T: Application to brain tumors.

Amide proton transfer-weighted (APTw) MR imaging shows promise as a biomarker of brain tumor status. Currently used APTw MRI pulse sequences and protocols vary substantially among different institutes, and there are no agreed-on standards in the imaging community. Therefore, the results acquired from different research centers are difficult to compare, which hampers uniform clinical application and interpretation. This paper reviews current clinical APTw imaging approaches and provides a rationale for optimized APTw brain tumor imaging at 3 T, including specific recommendations for pulse sequences, acquisition protocols, and data processing methods. We expect that these consensus recommendations will become the first broadly accepted guidelines for APTw imaging of brain tumors on 3 T MRI systems from different vendors. This will allow more medical centers to use the same or comparable APTw MRI techniques for the detection, characterization, and monitoring of brain tumors, enabling multi-center trials in larger patient cohorts and, ultimately, routine clinical use.

Perfusion measurement in brain gliomas using velocity-selective arterial spin labeling: comparison with pseudo-continuous arterial spin labeling and dynamic susceptibility contrast MRI.

OBJECTIVES: To evaluate the performance of velocity-selective (VS) ASL among patients with untreated gliomas by comparing with both pseudo-continuous (PC) ASL and dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI). METHODS: Forty-four consecutive patients with newly diagnosed glioma who underwent preoperative perfusion MRI including VSASL, PCASL, and DSC-PWI between 2017 and 2019 were retrospectively evaluated. Visual inspection was performed to evaluate the tumor signal intensity relative to gray matter based on 1-5 score criteria and weighted kappa was used to evaluate the pair-wise concordance between VSASL or PCASL and DSC-PWI. The relative tumor blood flow (rTBF) was measured from sampling intra-tumoral areas of hot-spot on the blood flow map and normalized against the contralateral normal gray matter blood flow. Linear regression and Bland-Altman analyses were performed to evaluate the correlation and agreement of rTBF measurements between ASL methods and DSC-PWI. The ROC analysis was constructed to determine the diagnostic performance of three perfusion methods for grading gliomas. RESULTS: TBF maps derived from VSASL were more comparable with DSC-PWI than PCASL on visual inspection (weighted kappa of 0.90 vs 0.68). In quantitative analysis, VSASL-rTBF yielded higher correlation with the values from DSC-PWI than PCASL-rTBF (R2 = 80% vs 47%, p < 0.001 for both). Both ASL and DSC-derived rTBF showed good distinction between low-grade and high-grade gliomas (p < 0.001). Compared to PCASL, VSASL yielded superior diagnostic sensitivity, specificity, and accuracy in glioma grading. CONCLUSIONS: VSASL showed great promise for accurate quantification of TBF and could potentially improve the diagnostic performance of ASL in preoperative grading of gliomas. KEY POINTS: • VSASL demonstrated a greater agreement with DSC-PWI than with PCASL on visual inspection and perfusion quantification. • VSASL showed a higher diagnostic sensitivity, negative predictive value, and accuracy than PCASL for glioma grading. • With the advantages of insensitivity to transit delay and no need of prescribing a labeling plane, VSASL could potentially improve the diagnostic performance of ASL for a more accurate, noninvasive quantification of TBF in patients with glioma.

Dynamic contrast-enhanced CEST MRI using a low molecular weight dextran.

Natural and synthetic sugars have great potential for developing highly biocompatible and translatable chemical exchange saturation transfer (CEST) MRI contrast agents. In this study, we aimed to develop the smallest clinically available form of dextran, Dex1 (molecular weight, MW ~ 1 kDa), as a new CEST agent. We first characterized the CEST properties of Dex1 in vitro at 11.7 T and showed that the Dex1 had a detectable CEST signal at ~1.2 ppm, attributed to hydroxyl protons. In vivo CEST MRI studies were then carried out on C57BL6 mice bearing orthotopic GL261 brain tumors (n = 5) using a Bruker BioSpec 11.7 T MRI scanner. Both steady-state full Z-spectral images and single offset (1.2 ppm) dynamic dextran-enhanced (DDE) images were acquired before and after the intravenous injection of Dex1 (2 g/kg). The steady-state Z-spectral analysis showed a significantly higher CEST contrast enhancement in the tumor than in contralateral brain (∆MTRasym1.2 ppm  = 0.010 ± 0.006 versus 0.002 ± 0.008, P = 0.0069) at 20 min after the injection of Dex1. Pharmacokinetic analyses of DDE were performed using the area under the curve (AUC) in the first 10 min after Dex1 injection, revealing a significantly higher uptake of Dex1 in the tumor than in brain tissue for tumor-bearing mice (AUC[0-10 min] = 21.9 ± 4.2 versus 5.3 ± 6.4%·min, P = 0.0294). In contrast, no Dex1 uptake was foundling in the brains of non-tumor-bearing mice (AUC[0-10 min] = -1.59 ± 2.43%·min). Importantly, the CEST MRI findings were consistent with the measurements obtained using DCE MRI and fluorescence microscopy, demonstrating the potential of Dex1 as a highly translatable CEST MRI contrast agent for assessing tumor hemodynamics.

Imaging features of sentinel lymph node mapped by multidetector-row computed tomography lymphography in predicting axillary lymph node metastasis.

INTRODUCTION: Accurately assessing axillary lymph node (ALN) status in breast cancer is vital for clinical decision making and prognosis. The purpose of this study was to evaluate the predictive value of sentinel lymph node (SLN) mapped by multidetector-row computed tomography lymphography (MDCT-LG) for ALN metastasis in breast cancer patients. METHODS: 112 patients with breast cancer who underwent preoperative MDCT-LG examination were included in the study. Long-axis diameter, short-axis diameter, ratio of long-/short-axis and cortical thickness were measured. Logistic regression analysis was performed to evaluate independent predictors associated with ALN metastasis. The prediction of ALN metastasis was determined with related variables of SLN using receiver operating characteristic (ROC) curve analysis. RESULTS: Among the 112 cases, 35 (30.8%) cases had ALN metastasis. The cortical thickness in metastatic ALN group was significantly thicker than that in non-metastatic ALN group (4.0 ± 1.2 mm vs. 2.4 ± 0.7 mm, P < 0.001). Multi-logistic regression analysis indicated that cortical thickness of > 3.3 mm (OR 24.53, 95% CI 6.58-91.48, P < 0.001) had higher risk for ALN metastasis. The best sensitivity, specificity, negative predictive value(NPV) and AUC of MDCT-LG for ALN metastasis prediction based on the single variable of cortical thickness were 76.2%, 88.5%, 90.2% and 0.872 (95% CI 0.773-0.939, P < 0.001), respectively. CONCLUSION: ALN status can be predicted using the imaging features of SLN which was mapped on MDCT-LG in breast cancer patients. Besides, it may be helpful to select true negative lymph nodes in patients with early breast cancer, and SLN biopsy can be avoided in clinically and radiographically negative axilla.

CT-guided core needle biopsy of the lung in patients with primary malignancy suspected of lung metastasis: 5-year experience from a single institution.

PURPOSE: We aimed to evaluate the diagnostic accuracy and safety profile of computed tomography (CT)-guided percutaneous transthoracic needle biopsy (PTNB) in patients with primary malignancy suspected of lung metastasis and assess possible factors associated with nondiagnostic results. METHODS: All PTNBs with core needle performed in our hospital from January 2014 to January 2019 were retrospectively reviewed. Overall, 108 cases were found to have a history of primary malignancy with suspected lung metastasis. Patient demographics, lesion characteristics, procedure techniques and complications were evaluated as predictors of overall diagnosis, final diagnosis of lung metastasis, and nondiagnostic results. Statistical analysis was performed using univariate analysis. RESULTS: The overall diagnostic accuracy of PTNB was 83.3%. Lung metastasis was found in 52.8% of PTNBs (57 of 108) and nondiagnostic results were present in 27.6% (18 of 108). Of the 18 cases with nondiagnostic results, 11 cases had a final diagnosis of lung metastasis (61.1%), yielding PTNB a sensitivity of 83.8% and specificity of 100% for the detection of lung metastasis. Smaller lesion size (p = 0.014), pneumothorax (p = 0.026), and hemoptysis (p = 0.014) were significantly associated with overall nondiagnostic results. Similarly, smaller lesion size (p = 0.047), pneumothorax (p = 0.019), high-grade pulmonary hemorrhage (p = 0.019), and hemoptysis (p = 0.012) were significantly correlated with unsuccessful biopsies in the diagnosis of lung metastasis. CONCLUSION: CT-guided core needle biopsy of the lung in patients with primary malignancy suspected of lung metastasis has a high diagnostic accuracy with acceptable complication rates. Small lesion size, pneumothorax, high-grade pulmonary hemorrhage, and hemoptysis are significantly associated with nondiagnostic results in the final diagnosis of lung metastasis. Repeat biopsy and clinical/radiological follow-up should be considered in cancer patients with nondiagnostic results due to the high probability of lung metastasis.

Dose-Lowering in Contrast-Enhanced MRI of the Central Nervous System: A Retrospective, Parallel-Group Comparison Using Gadobenate Dimeglumine.

BACKGROUND: Concerns over gadolinium (Gd) retention encourage the use of lower Gd doses. However, lower Gd doses may compromise imaging performance. Higher relaxivity gadobenate may be suited to reduced dose protocols. PURPOSE: To compare 0.05 mmol/kg and 0.1 mmol/kg gadobenate in patients undergoing enhanced MRI of the central nervous system (CNS). STUDY TYPE: Retrospective, multicenter. POPULATION: Three hundred and fifty-two patients receiving 0.05 (n = 181) or 0.1 (n = 171) mmol/kg gadobenate. FIELD STRENGTH/SEQUENCES: 1.5 T and 3.0 T/precontrast and postcontrast T1-weighted spin echo/fast spin echo (SE/FSE) and/or gradient echo/fast field echo (GRE/FFE); precontrast T2-weighted FSE and T2-FLAIR. ASSESSMENT: Images of patients with extra-axial lesions at 1.5 T or any CNS lesion at 3.0 T were reviewed by three blinded, independent neuroradiologists for qualitative (lesion border delineation, internal morphology visualization, contrast enhancement; scores from 1 = poor to 4 = excellent) and quantitative (lesion-to-brain ratio [LBR], contrast-to-noise ratio [CNR]; SI measurements at regions-of-interest on lesion and normal parenchyma) enhancement measures. Noninferiority of 0.05 mmol/kg gadobenate was determined for each qualitative endpoint if the lower limit of the 95% confidence interval (CI) for the difference in precontrast + postcontrast means was above a noninferiority margin of -0.4. STATISTICAL TESTS: Student's t-test for comparison of mean qualitative endpoint scores, Wilcoxon signed rank test for comparison of LBR and CNR values; Wilcoxon rank sum test for comparison of SI changes. Tests were significant for P < 0.05. RESULTS: The mean change from precontrast to precontrast + postcontrast was significant for all endpoints. Readers 1, 2, and 3 evaluated 304, 225, and 249 lesions for 0.05 mmol/kg gadobenate, and 382, 309, and 298 lesions for 0.1 mmol/kg gadobenate. The lower limit of the 95% CI was above -0.4 for all comparisons. Significantly, higher LBR and CNR was observed with the higher dose. DATA CONCLUSION: 0.05 mmol/kg gadobenate was noninferior to 0.1 mmol/kg gadobenate for lesion visualization. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.

Association between coexisting intracranial artery and extracranial carotid artery atherosclerotic diseases and ipsilateral cerebral infarction: a Chinese Atherosclerosis Risk Evaluation (CARE-II) study.

BACKGROUND: To evaluate the association between coexisting intracranial and extracranial carotid artery atherosclerotic diseases and ipsilateral acute cerebral infarct (ACI) in symptomatic patients by using magnetic resonance (MR) vessel wall imaging. METHODS: Symptomatic patients were recruited from a cross-sectional, multicentre study of Chinese Atherosclerosis Risk Evaluation (CARE-II). All patients underwent MR imaging for extracranial carotid arterial wall, intracranial artery and brain. Coexisting intracranial stenosis ≥50% and extracranial carotid artery mean wall thickness (MWT) ≥1 mm and plaque compositions at the same side were evaluated and the ipsilateral ACI was identified. The association between coexisting atherosclerotic diseases and ACI was evaluated using logistic regression. RESULTS: 351 patients were recruited. Patients with ipsilateral ACI had significantly greater prevalence of coexisting intracranial stenosis ≥50% and carotid MWT ≥1 mm (20.5% vs 4.9%, p<0.001), calcification (15.1% vs 4.4%, p=0.001) and lipid-rich necrotic core (LRNC) (19.2% vs 7.8%, p=0.002) compared with those without. Coexisting intracranial artery stenosis ≥50% and carotid MWT ≥1 mm (OR 5.043, 95% CI 2.378 to 10.694; p<0.001), calcification (OR 3.864, 95% CI 1.723 to 8.664; p=0.001) and LRNC (OR 2.803, 95% CI 1.455 to 5.401; p=0.002) were significantly associated with ipsilateral ACI. After adjusting for confounding factors, the aforementioned associations remained statistically significant (intracranial stenosis ≥50% coexisting with carotid MWT ≥1 mm: OR 4.313, 95% CI 1.937 to 9.601, p<0.001; calcification: OR 3.606, 95% CI 1.513 to 8.593, p=0.004; LRNC: OR 2.358, 95% CI 1.166 to 4.769, p=0.017). CONCLUSIONS: Coexistence of intracranial artery severe stenosis and extracranial carotid artery large burden and intraplaque components of calcification and LRNC are independently associated with ipsilateral ACI. TRIAL REGISTRATION NUMBER: https://www.clinicaltrials.gov/. Unique identifier: NCT02017756.

Quantitative evaluation of passive muscle stiffness by shear wave elastography in healthy individuals of different ages.

OBJECTIVES: To investigate age-related changes on passive muscle stiffness in healthy individuals and measure the shear modulus in different age groups. METHODS: Shear wave elastography (SWE) movies of gastrocnemius medialis (GM) were collected during passive stretching induced by ankle rotation from plantarflexion (PF) to dorsiflexion (DF). A series of SWE images at ankle angles of PF 40°, PF 30°, PF 20°, PF 10°, 0°, DF 10°, DF 20°, and DF 30° were collected and shear moduli measured accordingly for analyses. RESULTS: Eighty-six healthy volunteers (27 children, 31 middle-aged adults, and 28 older people) were recruited. No significant difference was observed in the shear modulus between the three groups at ankle angles of PF 40°, PF 30°, PF 20°, PF 10°, and 0° (p > 0.05). The difference in the shear modulus among the three groups became significant as DF increased. At ankle angles of DF 10°, DF 20°, and DF 30°, the shear modulus was the greatest in the older group, followed by the middle-aged group and then the children group (p = 0.007, 0.000, and 0.000, respectively). CONCLUSIONS: Passive muscle stiffness increases with age, and the difference between age groups was pronounced only after reaching a certain degree of stretching. KEY POINTS: • The influence of age on passive muscle stiffness becomes pronounced only after reaching a certain degree of stretching. • Age should be considered when evaluating passive muscle stiffness in muscular disorders.

Various Diseases and Clinical Heterogeneity Are Associated With "Hot Cross Bun".

Objective: To characterize the clinical phenotypes associated with the "hot cross bun" sign (HCBs) on MRI and identify correlations between neuroimaging and clinical characteristics. Methods: Firstly, we screened a cohort of patients with HCBs from our radiologic information system (RIS) in our center. Secondly, we systematically reviewed published cases on HCBs and classified all these cases according to their etiologies. Finally, we characterized all HCBs cases in detail and classified the disease spectra and their clinical heterogeneity. Results: Out of a total of 3,546 patients who were screened, we identified 40 patients with HCBs imaging sign in our cohort; systemic literature review identified 39 cases, which were associated with 14 diseases. In our cohort, inflammation [neuromyelitis optica spectrum disorders (NMOSD), multiple sclerosis (MS), and acute disseminated encephalomyelitis (ADEM)] and toxicants [toxic encephalopathy caused by phenytoin sodium (TEPS)] were some of the underlying etiologies. Published cases by systemic literature review were linked to metabolic abnormality, degeneration, neoplasm, infection, and stroke. We demonstrated that the clinical phenotype, neuroimaging characteristics, and HCBs response to therapy varied greatly depending on underlying etiologies. Conclusion: This is the first to report HCBs spectra in inflammatory and toxication diseases. Our study and systemic literature review demonstrated that the underpinning disease spectrum may be broader than previously recognized.