Assessment of Prognostic Factors and Molecular Subtypes of Breast Cancer With a Continuous-Time Random-Walk MR Diffusion Model: Using Whole Tumor Histogram Analysis.

BACKGROUND: The continuous-time random-walk (CTRW) diffusion model to evaluate breast cancer prognosis is rarely reported. PURPOSE: To investigate the correlations between apparent diffusion coefficient (ADC) and CTRW-specific parameters with prognostic factors and molecular subtypes of breast cancer. STUDY TYPE: Retrospective. POPULATION: One hundred fifty-seven women (median age, 50 years; range, 26-81 years) with histopathology-confirmed breast cancer. FIELD STRENGTH/SEQUENCE: Simultaneous multi-slice readout-segmented echo-planar imaging at 3.0T. ASSESSMENT: The histogram metrics of ADC, anomalous diffusion coefficient (D), temporal diffusion heterogeneity (α), and spatial diffusion heterogeneity (ß) were calculated for whole-tumor volume. Associations between histogram metrics and prognostic factors (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67 proliferation index), axillary lymph node metastasis (ALNM), and tumor grade were assessed. The performance of histogram metrics, both alone and in combination, for differentiating molecular subtypes (HER2-positive, Luminal or triple negative) was also assessed. STATISTICAL TESTS: Comparisons were made using Mann-Whitney test between different prognostic factor statuses and molecular subtypes. Receiver operating characteristic curve analysis was used to assess the performance of mean and median histogram metrics in differentiating the molecular subtypes. A P value <0.05 was considered statistically significant. RESULTS: The histogram metrics of ADC, D, and α differed significantly between ER-positive and ER-negative status, and between PR-positive and PR-negative status. The histogram metrics of ADC, D, α, and ß were also significantly different between the HER2-positive and HER2-negative subgroups, and between ALNM-positive and ALNM-negative subgroups. The histogram metrics of α and ß significantly differed between high and low Ki-67 proliferation subgroups, and between histological grade subgroups. The combination of αmean and ßmean achieved the highest performance (AUC = 0.702) to discriminate the Luminal and HER2-positive subtypes. DATA CONCLUSION: Whole-tumor histogram analysis of the CTRW model has potential to provide additional information on the prognosis and intrinsic subtyping classification of breast cancer. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Quantitative Assessment of Restriction Spectrum MR Imaging for the Diagnosis of Breast Cancer and Association With Prognostic Factors.

BACKGROUND: Restriction spectrum imaging (RSI) is an advanced quantitative diffusion-weighted magnetic resonance imaging (DWI) technique to assess breast cancer. PURPOSE: To investigate the ability of RSI to differentiate the benign and malignant breast lesions and the association with prognostic factors of breast cancer. STUDY TYPE: Retrospective. POPULATION: Seventy women (mean age, 49.6 ± 12.3 years) with 56 malignant and 19 benign breast lesions. FIELD STRENGTH/SEQUENCE: 3-T; RSI-based DWI sequence with echo-planar imaging technique. ASSESSMENT: The apparent diffusion coefficient (ADC) and RSI parameters (restricted diffusion f1 , hindered diffusion f2 , free diffusion f3 , and signal fractions f1 f2 ) were calculated by two readers for the whole lesion volume and compared between the benign and malignant groups and the subgroups with different statuses of prognostic factors in breast cancer. STATISTICAL TESTS: Mann-Whitney U test or Student's t-test was applied to compare the quantitative parameters between the different groups. Intraclass correlation coefficient (ICC) was used to assess readers' reproducibility. Binary logistic regression was used to combine parameters. Area under the curve (AUC) of receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of parameters to distinguish benign from malignant breast lesions. A P-value <0.05 was considered statistically significant. RESULTS: Malignant breast lesions showed significantly lower ADC and f3 values, and significantly higher f1 and f1 f2 values than the benign lesions, with AUC of 0.951, 0.877, 0.868, and 0.860, respectively. When RSI-derived parameters and ADC were combined, the diagnostic performance was superior to either single parameter (AUC = 0.973). The f3 value was significantly differed between estrogen receptor (ER)-positive and ER-negative tumors. The ADC, f1 , f3 , and f1 f2 values were significantly different progesterone receptor (PR)-positive and PR-negative status. DATA CONCLUSION: The RSI-derived parameters (f1 , f3 , and f1 f2 ) may facilitate the differential diagnosis between benign and malignant breast lesions. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Influence of hydroxyapatite nanoparticles on the viscosity of dimethyl sulfoxide-H2O-NaCl and glycerol-H2O-NaCl ternary systems at subzero temperatures.

The viscosity, at subzero temperatures, of ternary solutions commonly used in cryopreservation is tremendously important for understanding ice formation and molecular diffusion in biopreservation. However, this information is scarce in the literature. In addition, to the best of our knowledge, the effect of nanoparticles on the viscosity of these solutions has not previously been reported. The objectives of this study were thus: (i) to systematically measure the subzero viscosity of two such systems, dimethyl sulfoxide (Me2SO)-H2O-NaCl and glycerol-H2O-NaCl; (ii) to explore the effect of hydroxyapatite (HA) nanoparticles on the viscosity; and (iii) to provide models that precisely predict viscosity at multiple concentrations of cryoprotective agent (CPA) in saline solutions at subzero temperatures. Our experiments were performed in two parts. We first measured the viscosity at multiple CPA concentrations [0.3-0.75 (w/w)] in saline solution with and without nanoparticles at subzero temperatures (0 to -30°C). The data exhibited a good fit to the Williams-Landel-Ferry (WLF) equation. We then measured the viscosity of residual unfrozen ternary solutions with and without nanoparticles during equilibrium freezing. HA nanoparticles made the solution more viscous, suggesting applications for these nanoparticles in preventing cell dehydration, ice nucleation, and ice growth during freezing and thawing in cryopreservation.

Effect of hydroxyapatite nanoparticles on osmotic responses of pig iliac endothelial cells.

In order to fully explore the potential applications of nanoparticles in biopreservation, it is necessary to study the effect of nanoparticles on cell membrane permeabilities. The aim of this study is therefore to comparatively evaluate the osmotic responses of pig iliac endothelial cells in the absence and presence of commercially available hydroxyapatite nanoparticles. The results indicate that, after the introduction of 0.0 1 wt% hydroxyapatite nanoparticles, the dependence of cell membrane hydraulic conductivity (Lp) on temperature still obeys the Arrhenius relationship, while the reference value of the hydraulic conductivity of the cell membrane at 273.15K (Lpg) and the activation energy for water transport across cell membrane (ELp) change from 0.77 × 10(-14)m/Pa/s and 15.65 kJ/mol to 0.65 × 10(-14)m/Pa/s and 26.14 kJ/mol. That is to say, the reference value of the hydraulic conductivity of the cell membrane has been slightly decreased while the activation energy for water transport across cell membrane has been greatly enhanced, and thus it implies that the hydraulic conductivity of cell membrane are more sensitive to temperature in the presence of nanoparticles. These findings are of potential significance to the optimization of nanoparticles-aided cryopreservation.

Yin-Yang tongue sign: An imaging clue of lesions involving the skull base segment in the hypoglossal pathway.

OBJECTIVE: To investigate the diagnostic value of the Yin-Yang tongue sign in patients with tongue deviation. METHODS: According to the presence of the Yin-Yang tongue sign on CT/MR, 107 patients with tongue deviation were divided into a positive group and a negative group. The involvement categories of the hypoglossal canal (HC) in the positive group were evaluated and classified as HC dilation and HC erosion. The correlations between HC involvement categories and the presence of the sign were analysed. RESULTS: There were 55 cases (55/107, 51.4%) in the positive group and 52 cases (52/107, 48.6%) in the negative group. Hypoglossal nerve (HN) involvement mainly occurred in the skull base (61.8%), skull base and carotid space (10.9%), and carotid space segment (12.7%). Neurogenic (50.9%), squamous cell carcinoma (14.5%), and metastases (12.7%) were the predominant aetiologies. The sensitivity, specificity, and accuracy of this sign for suggesting skull base lesions around HC were 72.4%, 80.8%, and 76.6%, respectively. In the positive group, HC dilation was seen in 21 patients (21/55, 38.2%) and 21 cases were all benign. HC erosion were noted in 19 patients (19/55, 34.5%), of whom 12 cases were malignant. CONCLUSION: The Yin-Yang tongue sign is formed by unilateral tongue atrophy and fat infiltration caused by lesions in the HN pathway, especially compressive or invasive lesions involving the skull base segment.

Histogram analysis of multi-model high-resolution diffusion-weighted MRI in breast cancer: correlations with molecular prognostic factors and subtypes.

Objective: To investigate the correlations between quantitative diffusion parameters and prognostic factors and molecular subtypes of breast cancer, based on a single fast high-resolution diffusion-weighted imaging (DWI) sequence with mono-exponential (Mono), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) models. Materials and Methods: A total of 143 patients with histopathologically verified breast cancer were included in this retrospective study. The multi-model DWI-derived parameters were quantitatively measured, including Mono-ADC, IVIM-D, IVIM-D*, IVIM-f, DKI-Dapp, and DKI-Kapp. In addition, the morphologic characteristics of the lesions (shape, margin, and internal signal characteristics) were visually assessed on DWI images. Next, Kolmogorov-Smirnov test, Mann-Whitney U test, Spearman's rank correlation, logistic regression, receiver operating characteristic (ROC) curve, and Chi-squared test were utilized for statistical evaluations. Results: The histogram metrics of Mono-ADC, IVIM-D, DKI-Dapp, and DKI-Kapp were significantly different between estrogen receptor (ER)-positive vs. ER-negative groups, progesterone receptor (PR)-positive vs. PR-negative groups, Luminal vs. non-Luminal subtypes, and human epidermal receptor factor-2 (HER2)-positive vs. non-HER2-positive subtypes. The histogram metrics of Mono-ADC, DKI-Dapp, and DKI-Kapp were also significantly different between triple-negative (TN) vs. non-TN subtypes. The ROC analysis revealed that the area under the curve considerably improved when the three diffusion models were combined compared with every single model, except for distinguishing lymph node metastasis (LNM) status. For the morphologic characteristics of the tumor, the margin showed substantial differences between ER-positive and ER-negative groups. Conclusions: Quantitative multi-model analysis of DWI showed improved diagnostic performance for determining the prognostic factors and molecular subtypes of breast lesions. The morphologic characteristics obtained from high-resolution DWI can be identifying ER statuses of breast cancer.

Functional magnetic resonance imaging evaluation of masticatory muscle dysfunction in unilateral exodontia rabbits.

OBJECTIVE: Occlusal alteration due to tooth loss may cause overload of masticatory muscle and promote muscle dysfunction. This study explored the feasibility of using functional magnetic resonance imaging (fMRI) to evaluate muscle dysfunction in an established unilateral exodontia animal model. METHODS: 6 rabbits were extracted right maxillary molars. T2 mapping, T2* mapping and Iterative Decomposition of water and fat with Echo Asymmetry and Least Square Estimation (IDEAL-IQ) were performed one day before extraction and every 2 weeks (2th~12th week) after extraction. The T2 and T2* values and fat fraction (FF) of bilateral temporal muscle (TM), masseter muscle (MM) and medial pterygoid muscle (MPM) were measured and compared between the extraction side and the contralateral side. Parameters of three monitoring time points (0th, 6th, 12th week) were also analyzed. RESULTS: T2 values of MM on extraction side were significantly higher than those of contralateral side-from fourth week to 12th week after extraction (p < 0.05). T2 values of MM and MPM on extraction side and TM on contralateral side were significantly higher in 12th week than those in 0th week (p < 0.05). And FF of bilateral MM was significantly higher in 12th week than those in 0th week (p < 0.05). T2* value showed no significant difference between extraction side and contralateral side and also at above three time points. CONCLUSION: T2 and T2* value and FF can be used as indicators of masticatory muscle dysfunction. fMRI is expected to be a non-invasive method for in vivo and real-time evaluation of masticatory muscle functional abnormality.

Dynamic MRI reconstruction with end-to-end motion-guided network.

Temporal correlation in dynamic magnetic resonance imaging (MRI), such as cardiac MRI, is informative and important to understand motion mechanisms of body regions. Modeling such information into the MRI reconstruction process produces temporally coherent image sequence and reduces imaging artifacts and blurring. However, existing deep learning based approaches neglect motion information during the reconstruction procedure, while traditional motion-guided methods are hindered by heuristic parameter tuning and long inference time. We propose a novel dynamic MRI reconstruction approach called MODRN and an end-to-end improved version called MODRN(e2e), both of which enhance the reconstruction quality by infusing motion information into the modeling process with deep neural networks. The central idea is to decompose the motion-guided optimization problem of dynamic MRI reconstruction into three components: Dynamic Reconstruction Network, Motion Estimation and Motion Compensation. Extensive experiments have demonstrated the effectiveness of our proposed approach compared to other state-of-the-art approaches.

Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images.

Instance segmentation is of great importance for many biological applications, such as study of neural cell interactions, plant phenotyping, and quantitatively measuring how cells react to drug treatment. In this paper, we propose a novel box-based instance segmentation method. Box-based instance segmentation methods capture objects via bounding boxes and then perform individual segmentation within each bounding box region. However, existing methods can hardly differentiate the target from its neighboring objects within the same bounding box region due to their similar textures and low-contrast boundaries. To deal with this problem, in this paper, we propose an object-guided instance segmentation method. Our method first detects the center points of the objects, from which the bounding box parameters are then predicted. To perform segmentation, an object-guided coarse-to-fine segmentation branch is built along with the detection branch. The segmentation branch reuses the object features as guidance to separate target object from the neighboring ones within the same bounding box region. To further improve the segmentation quality, we design an auxiliary feature refinement module that densely samples and refines point-wise features in the boundary regions. Experimental results on three biological image datasets demonstrate the advantages of our method. The code will be available at https://github.com/yijingru/ObjGuided-Instance-Segmentation.

Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images.

Nuclei segmentation is a fundamental task in histopathology image analysis. Typically, such segmentation tasks require significant effort to manually generate accurate pixel-wise annotations for fully supervised training. To alleviate such tedious and manual effort, in this paper we propose a novel weakly supervised segmentation framework based on partial points annotation, i.e., only a small portion of nuclei locations in each image are labeled. The framework consists of two learning stages. In the first stage, we design a semi-supervised strategy to learn a detection model from partially labeled nuclei locations. Specifically, an extended Gaussian mask is designed to train an initial model with partially labeled data. Then, self-training with background propagation is proposed to make use of the unlabeled regions to boost nuclei detection and suppress false positives. In the second stage, a segmentation model is trained from the detected nuclei locations in a weakly-supervised fashion. Two types of coarse labels with complementary information are derived from the detected points and are then utilized to train a deep neural network. The fully-connected conditional random field loss is utilized in training to further refine the model without introducing extra computational complexity during inference. The proposed method is extensively evaluated on two nuclei segmentation datasets. The experimental results demonstrate that our method can achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods while requiring significantly less annotation effort.

Attentive neural cell instance segmentation.

Neural cell instance segmentation, which aims at joint detection and segmentation of every neural cell in a microscopic image, is essential to many neuroscience applications. The challenge of this task involves cell adhesion, cell distortion, unclear cell contours, low-contrast cell protrusion structures, and background impurities. Consequently, current instance segmentation methods generally fall short of precision. In this paper, we propose an attentive instance segmentation method that accurately predicts the bounding box of each cell as well as its segmentation mask simultaneously. In particular, our method builds on a joint network that combines a single shot multi-box detector (SSD) and a U-net. Furthermore, we employ the attention mechanism in both detection and segmentation modules to focus the model on the useful features. The proposed method is validated on a dataset of neural cell microscopic images. Experimental results demonstrate that our approach can accurately detect and segment neural cell instances at a fast speed, comparing favorably with the state-of-the-art methods. Our code is released on GitHub. The link is https://github.com/yijingru/ANCIS-Pytorch.

Comparison of the Fitting Validity Between the 2P Model and the Nondilute Solution Model Using Statistical Methods in Modeling Cell Membrane Permeabilities.

In cryopreservation, the two-parameter (2P) model and the nondilute solution model have been developed to study the membrane transport properties of cells. However, to our knowledge, comparison of the fitting validity has never been made between the two models. In this study, to make this comparison, the permeability parameters of porcine adipose-derived stem cells (pADSCs) were first determined with the two models, and then the errors between the predictions and the experimental data were tested using the Lilliefors test. The results indicate that the 2P model is slightly better than the nondilute solution model in predicting the mass transport across cell membrane. The causes for this phenomenon are discussed and suggestions on using these two models are given.

Active contour-based cell segmentation during freezing and its application in cryopreservation.

Water permeability of the plasma membrane plays an important role in making optimal cryopreservation protocols for different types of cells. To quantify water permeability effectively, automated cell volume segmentation during freezing is necessary. Unfortunately, there exists so far no efficient and accurate segmentation method to handle this kind of image processing task gracefully. The existence of extracellular ice and variable background present significant challenges for most traditional segmentation algorithms. In this paper, we propose a novel approach to reliably extract cells from the extracellular ice, which attaches to or surrounds cells. Our method operates on temporal image sequences and is composed of two steps. First, for each image from the sequence, a greedy search strategy is employed to track approximate locations of cells in motion. Second, we utilize a localized competitive active contour model to obtain the contour of each cell. Based on the first step's result, the initial contour for level set evolution can be determined appropriately, thus considerably easing the pain of initialization for an active contour model. Experimental results demonstrate that the proposed method is efficient and effective in segmenting cells during freezing.

An improved model for nucleation-limited ice formation in living cells during freezing.

Ice formation in living cells is a lethal event during freezing and its characterization is important to the development of optimal protocols for not only cryopreservation but also cryotherapy applications. Although the model for probability of ice formation (PIF) in cells developed by Toner et al. has been widely used to predict nucleation-limited intracellular ice formation (IIF), our data of freezing Hela cells suggest that this model could give misleading prediction of PIF when the maximum PIF in cells during freezing is less than 1 (PIF ranges from 0 to 1). We introduce a new model to overcome this problem by incorporating a critical cell volume to modify the Toner's original model. We further reveal that this critical cell volume is dependent on the mechanisms of ice nucleation in cells during freezing, i.e., surface-catalyzed nucleation (SCN) and volume-catalyzed nucleation (VCN). Taken together, the improved PIF model may be valuable for better understanding of the mechanisms of ice nucleation in cells during freezing and more accurate prediction of PIF for cryopreservation and cryotherapy applications.