Transmembrane water-efflux rate measured by magnetic resonance imaging as a biomarker of the expression of aquaporin-4 in gliomas.

The water-selective channel protein aquaporin-4 (AQP4) contributes to the migration and proliferation of gliomas, and to their resistance to therapy. Here we show, in glioma cell cultures, in subcutaneous and orthotopic gliomas in rats, and in glioma tumours in patients, that transmembrane water-efflux rate is a sensitive biomarker of AQP4 expression and can be measured via conventional dynamic-contrast-enhanced magnetic resonance imaging. Water-efflux rates correlated with stages of glioma proliferation as well as with changes in the heterogeneity of intra-tumoural and inter-tumoural AQP4 in rodent and human gliomas following treatment with temozolomide and with the AQP4 inhibitor TGN020. Regions with low water-efflux rates contained higher fractions of stem-like slow-cycling cells and therapy-resistant cells, suggesting that maps of water-efflux rates could be used to identify gliomas that are resistant to therapies.

Multimodality MRI-based radiomics approach to predict the posttreatment response of lung cancer brain metastases to gamma knife radiosurgery.

OBJECTIVES: To develop and validate a multimodality MRI-based radiomics approach to predicting the posttreatment response of lung cancer brain metastases (LCBM) to gamma knife radiosurgery (GKRS). METHODS: We retrospectively analyzed 213 lesions from 137 patients with LCBM who received GKRS between January 2017 and November 2020. The data were divided into a primary cohort (102 patients with 173 lesions) and an independent validation cohort (35 patients with 40 lesions) according to the time of treatment. Benefit result was defined using pretreatment and 3-month follow-up MRI images based on the Response Assessment in Neuro-Oncology Brain Metastases criteria. Valuable radiomics features were extracted from pretreatment multimodality MRI images using random forests. Prediction performance among the radiomics features of tumor core (RFTC) and radiomics features of peritumoral edema (RFPE) together was evaluated separately. Then, the random forest radiomics score and nomogram were developed through the primary cohort and evaluated through an independent validation cohort. Prediction performance was evaluated by ROC curve, calibration curve, and decision curve. RESULTS: Gender (p = 0.018), histological subtype (p = 0.009), epidermal growth factor receptor mutation (p = 0.034), and targeted drug treatment (p = 0.021) were significantly associated with posttreatment response. Adding RFPE to RFTC showed improved prediction performance than RFTC alone in primary cohort (AUC = 0.848 versus AUC = 0.750; p < 0.001). Finally, the radiomics nomogram had an AUC of 0.930, a C-index of 0.930 (specificity of 83.1%, sensitivity of 87.3%) in primary cohort, and an AUC of 0.852, a C-index of 0.848 (specificity of 84.2%, sensitivity of 76.2%) in validation cohort. CONCLUSIONS: Multimodality MRI-based radiomics models can predict the posttreatment response of LCBM to GKRS. KEY POINTS: • Among the selected radiomics features, texture features basically contributed the dominant force in prediction tasks (80%), especially gray-level co-occurrence matrix features (40%). • Adding RFPE to RFTC showed improved prediction performance than RFTC alone in primary cohort (AUC = 0.848 versus AUC = 0.750; p < 0.001). • The multimodality MRI-based radiomics nomogram showed high accuracy for distinguishing the posttreatment response of LCBM to GKRS (AUC = 0.930, in primary cohort; AUC = 0.852, in validation cohort).

High-Specific Isolation and Instant Observation of Circulating Tumour Cell from HCC Patients via Glypican-3 Immunomagnetic Fluorescent Nanodevice.

PURPOSE: Specific targeting receptors for efficiently capturing and applicable nanodevice for separating and instant observing of circulating tumour cells (CTC) are critical for early diagnosis of cancer. However, the existing CTC detection system based on epithelial cell adhesion molecule (EpCAM) was seriously limited by low expression and poor specificity of targeting receptors, and not instant observation in clinical application. METHODS: Herein, an alternative glypican-3 (GPC3)-based immunomagnetic fluorescent system (C6/MMSN-GPC3) for high-specific isolation and instant observation of CTC from hepatocellular carcinoma (HCC) patients' peripheral blood was developed. The high-specific HCC targeting receptor, GPC3, was employed for improving the sensitivity and accuracy in CTC detection. GPC3 monoclonal antibody (mAb) was linked to immunomagnetic mesoporous silica for specific targeting capture and separate CTC, and fluorescent molecule coumarin-6 (C6) was loaded for instant detection of CTC. RESULTS: The cell recovery (%) of C6/MMSN-GPC3 increased in 106 HL-60 cells (from 49.7% to 83.0%) and in whole blood (from 42% to 80.3%) compared with MACS® Beads. In clinical samples, the C6/MMSN-GPC3 could capture more CTC in the 13 cases of HCC patients and the capture efficiency was improved by 83.3%-350%. Meanwhile, the capture process of C6/MMSN-GPC3 was harmless, facilitating for the subsequent culture. Significantly, the C6/MMSN-GPC3 achieved the high-specific isolation and instant observation of CTC from HCC patients' blood samples, and successfully separated CTC from one patient with early stage of HCC (Stage I) and one post-surgery patient, further indicating the potential ability of C6/MMSN-GPC3 for HCC early diagnosis and prognosis evaluation. CONCLUSION: Our study provides a feasible glypican-3 (GPC3)-based immunomagnetic fluorescent system (C6/MMSN-GPC3) for high-specific isolation and instant observation of HCC CTC.

Automated Machine-Learning Framework Integrating Histopathological and Radiological Information for Predicting IDH1 Mutation Status in Glioma.

Diffuse gliomas are the most common malignant primary brain tumors. Identification of isocitrate dehydrogenase 1 (IDH1) mutations aids the diagnostic classification of these tumors and the prediction of their clinical outcomes. While histology continues to play a key role in frozen section diagnosis, as a diagnostic reference and as a method for monitoring disease progression, recent research has demonstrated the ability of multi-parametric magnetic resonance imaging (MRI) sequences for predicting IDH genotypes. In this paper, we aim to improve the prediction accuracy of IDH1 genotypes by integrating multi-modal imaging information from digitized histopathological data derived from routine histological slide scans and the MRI sequences including T1-contrast (T1) and Fluid-attenuated inversion recovery imaging (T2-FLAIR). In this research, we have established an automated framework to process, analyze and integrate the histopathological and radiological information from high-resolution pathology slides and multi-sequence MRI scans. Our machine-learning framework comprehensively computed multi-level information including molecular level, cellular level, and texture level information to reflect predictive IDH genotypes. Firstly, an automated pre-processing was developed to select the regions of interest (ROIs) from pathology slides. Secondly, to interactively fuse the multimodal complementary information, comprehensive feature information was extracted from the pathology ROIs and segmented tumor regions (enhanced tumor, edema and non-enhanced tumor) from MRI sequences. Thirdly, a Random Forest (RF)-based algorithm was employed to identify and quantitatively characterize histopathological and radiological imaging origins, respectively. Finally, we integrated multi-modal imaging features with a machine-learning algorithm and tested the performance of the framework for IDH1 genotyping, we also provided visual and statistical explanation to support the understanding on prediction outcomes. The training and testing experiments on 217 pathologically verified IDH1 genotyped glioma cases from multi-resource validated that our fully automated machine-learning model predicted IDH1 genotypes with greater accuracy and reliability than models that were based on radiological imaging data only. The accuracy of IDH1 genotype prediction was 0.90 compared to 0.82 for radiomic result. Thus, the integration of multi-parametric imaging features for automated analysis of cross-modal biomedical data improved the prediction accuracy of glioma IDH1 genotypes.

Convolutional neural network for accelerating the computation of the extended Tofts model in dynamic contrast-enhanced magnetic resonance imaging.

Quantitative physiological parameters can be obtained from nonlinear pharmacokinetic models, such as the extended Tofts (eTofts) model, applied to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). However, the computation of such nonlinear models is time consuming. The aim of this study was to develop a convolutional neural network (CNN) for accelerating the computation of fitting eTofts model without sacrificing agreement with conventional nonlinear-least-square (NLLS) fitting. This was a retrospective study, which included 13 patients with brain glioma for training (75%) and validation (25%), and 11 patients (three glioma, four brain metastases, and four lymphoma) for testing. CAIPIRINHA-Dixon-TWIST DCE-MRI and double flip angle T1 map acquired at 3 T were used. A CNN with both local pathway and global pathway modules was designed to estimate the eTofts model parameters, the volume transfer constant (Ktrans ), blood volume fraction (vp ), and volume fraction of extracellular extravascular space (ve ), from DCE-MRI data of tumor and normal-appearing voxels. The CNN was trained on mixed dataset consisting of synthetic and patient data. The CNN result and computation speed were compared with NLLS fitting. The robustness to noise variations and generalization to brain metastases and lymphoma data were also evaluated. Statistical tests used were Student's t test on mean absolute error, concordance correlation coefficient (CCC), and normalized root mean squared error. Including global pathway modules in the CNN and training the network with mixed data significantly (p < 0.05) improved the CNN performance. Compared with NLLS fitting, CNN yields an average CCC greater than 0.986 for Ktrans , greater than 0.965 for vp , and greater than 0.948 for ve . The CNN accelerated computation speed approximately 2000 times compared to NLLS, showed robustness to noise (signal-to-noise ratio >34.42 dB), and had no significant (p > 0.21) difference applied to brain metastases and lymphoma data. In conclusion, the proposed CNN to estimate eTofts parameters showed comparable result as NLLS fitting while significantly reducing the computation time. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 1.

Shutter-Speed DCE-MRI Analyses of Human Glioblastoma Multiforme (GBM) Data.

BACKGROUND: The shutter-speed model dynamic contrast-enhanced (SSM-DCE) MRI pharmacokinetic analysis adds a metabolic dimension to DCE-MRI. This is of particular interest in cancers, since abnormal metabolic activity might happen. PURPOSE: To develop a DCE-MRI SSM analysis framework for glioblastoma multiforme (GBM) cases considering the heterogeneous tissue found in GBM. STUDY TYPE: Prospective. SUBJECTS: Ten GBM patients. FIELD STRENGTH/SEQUENCE: 3T MRI with DCE-MRI. ASSESSMENTS: The corrected Akaike information criterion (AICc ) was used to automatically separate DCE-MRI data into proper SSM versions based on the contrast agent (CA) extravasation in each pixel. The supra-intensive parameters, including the vascular water efflux rate constant (kbo ), the cellular efflux rate constant (kio ), and the CA vascular efflux rate constant (kpe ), together with intravascular and extravascular-extracellular water mole fractions (pb and po , respectively) were determined. Further error analyses were also performed to eliminate unreliable estimations on kio and kbo . STATISTICAL TESTS: Student's t-test. RESULTS: For tumor pixels of all subjects, 88% show lower AICc with SSM than with the Tofts model. Compared to normal-appearing white matter (NAWM), tumor tissue showed significantly larger pb (0.045 vs. 0.011, P < 0.001) and higher kpe (3.0 × 10-2 s-1 vs. 6.1 × 10-4 s-1 , P < 0.001). In the contrast, significant kbo reduction was observed from NAWM to GBM tumor tissue (2.8 s-1 vs. 1.0 s-1 , P < 0.001). In addition, kbo is four orders and two orders of magnitude greater than kpe in the NAWM and GBM tumor, respectively. These results indicate that CA and water molecule have different transmembrane pathways. The mean tumor kio of all subjects was 0.57 s-1 . DATA CONCLUSION: We demonstrate the feasibility of applying SSM models in GBM cases. Within the proposed SSM analysis framework, kio and kbo could be estimated, which might be useful biomarkers for GBM diagnosis and survival prediction in future. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 1 J. Magn. Reson. Imaging 2020;52:850-863.

Enhanced γ-Glutamyltranspeptidase Imaging That Unravels the Glioma Recurrence in Post-radio/Chemotherapy Mixtures for Precise Pathology via Enzyme-Triggered Fluorescent Probe.

Accurate pathological diagnosis of gliomas recurrence is crucial for the optimal management and prognosis prediction. The study here unravels that our newly developed γ-glutamyltranspeptidase (GGT) fluorescence probe (Figure 1A) imaging in twenty recurrent glioma tissues selectively recognizes the most malignant portion from treatment responsive tissues induced by radio/chemo-therapy (Figure 1B). The overexpression of GGT in recurrent gliomas and low level in radiation necrosis were validated by western blot analysis and immunohistochemistry. Furthermore, the ki-67 index evaluation demonstrated the significant increase of malignancy, aided by the GGT-responsive fluorescent probe to screen out the right specimen through fast enhanced imaging of enzyme activity. Importantly, our GGT-targeting probe can be used for accurate determination of pathologic evaluation of tumor malignancy, and eventually for guiding the following management in patients with recurrent gliomas.

Quantitative dynamic susceptibility contrast perfusion-weighted imaging-guided customized gamma knife re-irradiation of recurrent high-grade gliomas.

INTRODUCTION: Treatment of recurrent high-grade gliomas (rHGG) has always been challenging. This study aimed to explore the treatment effect of quantitative dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI)-guided gamma knife radiosurgery (GKRS) on rHGG. METHODS: Between April 2014 and July 2016, 26 consecutive patients were treated by quantitative DSC-PWI-guided GKRS as salvage treatment for rHGG. The gross tumor volume (GTV) was defined as the high perfusion area on absolute cerebral blood volume maps, with a cutoff value of 22 ml/100 g. The clinical target volume (CTV) encompassed the GTV by 3 mm. Overall survival (OS) and progression-free survival (PFS) were calculated by the Kaplan-Meier method. Prognostic factors were tested by the log-rank (Mantel-Cox) test. RESULTS: With a median follow-up of 32 months, the median PFS after GKRS was 8 months (95% CI [6, 12]); the 1- and 2-year survival rates were 30.8 and 11.5%, respectively. The median OS was 25.5 months (95% CI [18, 40]); the 1- and 2-year survival rates were 96.2 and 57.7%, respectively. Pathology grade and CTV were identified as prognostic factors for PFS. However, none of the parameters tested were independent prognostic factors for OS among these selected patients. No severe radiotoxicity was observed among all patients. CONCLUSIONS: Quantitative DSC-PWI-guided GKRS is feasible for the treatment of rHGG and that these outcomes remain to be validated. Despite this, we think that carefully selected patients can benefit from this treatment method.

Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations.

Gliomas are the most common primary malignant brain tumors in adults. Accurate grading is crucial as therapeutic strategies are often disparate for different grades and may influence patient prognosis. This study aims to provide an automated glioma grading platform on the basis of machine learning models. In this paper, we investigate contributions of multi-parameters from multimodal data including imaging parameters or features from the Whole Slide images (WSI) and the proliferation marker Ki-67 for automated brain tumor grading. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. On the basis of machine learning models, our platform classifies gliomas into grades II, III, and IV. Furthermore, we quantitatively interpret and reveal the important parameters contributing to grading with the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. The performance of our grading model was evaluated with cross-validation, which randomly divided the patients into non-overlapping training and testing sets and repeatedly validated the model on the different testing sets. The primary results indicated that this modular platform approach achieved the highest grading accuracy of 0.90 ± 0.04 with support vector machine (SVM) algorithm, with grading accuracies of 0.91 ± 0.08, 0.90 ± 0.08, and 0.90 ± 0.07 for grade II, III, and IV gliomas, respectively.

Coalescence of sessile microdroplets subject to a wettability gradient on a solid surface.

While there are intensive studies on the coalescence of sessile macroscale droplets, there is little study on the coalescence of sessile microdroplets. In this paper, the coalescence process of two sessile microdroplets is studied by using a many-body dissipative particle dynamics numerical method. A comprehensive parametric study is conducted to investigate the effects on the coalescence process from the wettability gradient, hydrophilicity of the solid surface, and symmetric or asymmetric configurations. A water bridge is formed after two microdroplets contact. The temporal evolution of the coalescence process is characterized by the water bridge's radii parallel to the solid surface (W_{m}) and perpendicular to the solid surface (H_{m}). It is found that the changes of both H_{m} and W_{m} with time follow a power law; i.e., H_{m}=ß_{1}τ^{ß} and W_{m}=α_{1}τ^{α}. The growth of H_{m} and W_{m} depends on the hydrophilicity of the substrate. W_{m} grows faster than H_{m} on a hydrophilic surface, and H_{m} grows faster than W_{m} on a hydrophobic surface. This is due to the strong competition between capillary forces induced by the water-bridge curvature and the solid substrate hydrophobicity. Also, flow structure analysis shows that regardless of the coalescence type once the liquid bridge is formed the liquid flow direction inside the capillary bridge is to expand the bridge radius. Finally, we do not observe oscillation of the merged droplet during the coalescence process, possibly due to the significant effects of the viscous forces.

Numerical Simulations of the Digital Microfluidic Manipulation of Single Microparticles.

Single-cell analysis techniques have been developed as a valuable bioanalytical tool for elucidating cellular heterogeneity at genomic, proteomic, and cellular levels. Cell manipulation is an indispensable process for single-cell analysis. Digital microfluidics (DMF) is an important platform for conducting cell manipulation and single-cell analysis in a high-throughput fashion. However, the manipulation of single cells in DMF has not been quantitatively studied so far. In this article, we investigate the interaction of a single microparticle with a liquid droplet on a flat substrate using numerical simulations. The droplet is driven by capillary force generated from the wettability gradient of the substrate. Considering the Brownian motion of microparticles, we utilize many-body dissipative particle dynamics (MDPD), an off-lattice mesoscopic simulation technique, in this numerical study. The manipulation processes (including pickup, transport, and drop-off) of a single microparticle with a liquid droplet are simulated. Parametric studies are conducted to investigate the effects on the manipulation processes from the droplet size, wettability gradient, wetting properties of the microparticle, and particle-substrate friction coefficients. The numerical results show that the pickup, transport, and drop-off processes can be precisely controlled by these parameters. On the basis of the numerical results, a trap-free delivery of a hydrophobic microparticle to a destination on the substrate is demonstrated in the numerical simulations. The numerical results not only provide a fundamental understanding of interactions among the microparticle, the droplet, and the substrate but also demonstrate a new technique for the trap-free immobilization of single hydrophobic microparticles in the DMF design. Finally, our numerical method also provides a powerful design and optimization tool for the manipulation of microparticles in DMF systems.