The applications of CT with artificial intelligence in the prognostic model of idiopathic pulmonary fibrosis.

TAKE HOME MESSAGE: The review summarizes the applications of CT and AI algorithms for prognostic models in IPF and procedures of model construction. It reveals the current limitations and prospects of AI-aid models, and helps clinicians to recognize the AI algorithms and apply them to more clinical work.

Arachidonic acid enhances hepatocyte bile acid uptake and alleviates cholestatic liver disease by upregulating OATP1 expression.

Cholestatic liver disease is caused by disorders of bile synthesis, secretion, and excretion. Over the long term, progressive liver cell damage from the disease evolves into liver fibrosis and cirrhosis, ultimately leading to liver failure and even cancer. Notably, cholestatic liver disease has a complex pathogenesis that remains relatively unclear. In this study, we generated two mouse models of cholestatic liver disease using a 0.1% 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) diet and α-naphthyl isothiocyanate (ANIT) gavage. Quantitative proteomics using liquid chromatography-tandem mass spectrometry showed that arachidonic acid metabolism was a common pathway in both models. Additionally, serum arachidonic acid concentrations were lower in both models than in the control group. Arachidonic acid supplementation in the diet of DDC model mice significantly reduced the levels of serum markers of cholestasis (alanine aminotransferase, aspartate transaminase, alkaline phosphatase, total bile acid, and total bilirubin) and decreased the degree of bile duct hyperplasia and cholestasis. To elucidate the mechanisms by which arachidonic acid improved bile stasis, we analyzed gene expression after arachidonic acid administration and found that Oatp1 was upregulated in the liver tissue of cholestatic mice. Arachidonic acid also increased Oatp1 expression in AML12 cells, which promoted bile acid uptake. Conclusively, our research showed that arachidonic acid mitigates cholestatic liver disease by upregulating Oatp1, promoting bile acid uptake by hepatocytes and participating in intestinal-hepatic circulation. Overall, these results suggest that supplementing foods with arachidonic acid in the daily diet may be an effective treatment strategy for cholestatic liver disease.

Two-stage generative adversarial networks for metal artifact reduction and visualization in ablation therapy of liver tumors.

PURPOSE: The strong metal artifacts produced by the electrode needle cause poor image quality, thus preventing physicians from observing the surgical situation during the puncture process. To address this issue, we propose a metal artifact reduction and visualization framework for CT-guided ablation therapy of liver tumors. METHODS: Our framework contains a metal artifact reduction model and an ablation therapy visualization model. A two-stage generative adversarial network is proposed to reduce the metal artifacts of intraoperative CT images and avoid image blurring. To visualize the puncture process, the axis and tip of the needle are localized, and then the needle is rebuilt in 3D space intraoperatively. RESULTS: Experiments show that our proposed metal artifact reduction method achieves higher SSIM (0.891) and PSNR (26.920) values than the state-of-the-art methods. The accuracy of ablation needle reconstruction is 2.76 mm average in needle tip localization and 1.64° average in needle axis localization. CONCLUSION: We propose a novel metal artifact reduction and an ablation therapy visualization framework for CT-guided ablation therapy of liver cancer. The experiment results indicate that our approach can reduce metal artifacts and improve image quality. Furthermore, our proposed method demonstrates the potential for displaying the relative position of the tumor and the needle intraoperatively.

Survival Analysis for Multimode Ablation Using Self-Adapted Deep Learning Network Based on Multisource Features.

Novel multimode thermal therapy by freezing before radio-frequency heating has achieved a desirable therapeutic effect in liver cancer. Compared with surgical resection, ablation treatment has a relatively high risk of tumor recurrence. To monitor tumor progression after ablation, we developed a novel survival analysis framework for survival prediction and efficacy assessment. We extracted preoperative and postoperative MRI radiomics features and vision transformer-based deep learning features. We also combined the immune features extracted from peripheral blood immune responses using flow cytometry and routine blood tests before and after treatment. We selected features using random survival forest and improved the deep Cox mixture (DCM) for survival analysis. To properly accommodate multitype input features, we proposed a self-adapted fully connected layer for locally and globally representing features. We evaluated the method using our clinical dataset. Of note, the immune features rank the highest feature importance and contribute significantly to the prediction accuracy. The results showed a promising C td-index of 0.885 ±0.040 and an integrated Brier score of 0.041 ±0.014, which outperformed state-of-the-art method combinations of survival prediction. For each patient, individual survival probability was accurately predicted over time, which provided clinicians with trustable prognosis suggestions.

Computed tomography-based radiomics prediction of CTLA4 expression and prognosis in clear cell renal cell carcinoma.

OBJECTIVES: To predict CTLA4 expression levels and prognosis of clear cell renal cell carcinoma (ccRCC) by constructing a computed tomography-based radiomics model and establishing a nomogram using clinicopathologic factors. METHODS: The clinicopathologic parameters and genomic data were extracted from 493 ccRCC cases of the Cancer Genome Atlas (TCGA)-KIRC database. Univariate and multivariate Cox regression and Kaplan-Meier analysis were performed for prognosis analysis. Cibersortx was applied to evaluate the immune cell composition. Radiomic features were extracted from the TCGA/the Cancer Imaging Archive (TCIA) (n = 102) datasets. The support vector machine (SVM) was employed to establish the radiomics signature for predicting CTLA4 expression. Receiver operating characteristic curve (ROC), decision curve analysis (DCA), and precision-recall curve were utilized to assess the predictive performance of the radiomics signature. Correlations between radiomics score (RS) and selected features were also evaluated. An RS-based nomogram was constructed to predict prognosis. RESULTS: CTLA4 was significantly overexpressed in ccRCC tissues and was related to lower overall survival. A higher CTLA4 expression was independently linked to the poor prognosis (HR = 1.458, 95% CI 1.13-1.881, p = 0.004). The radiomics model for the prediction of CTLA4 expression levels (AUC = 0.769 in the training set, AUC = 0.724 in the validation set) was established using seven radiomic features. A significant elevation in infiltrating M2 macrophages was observed in the RS high group (p < 0.001). The predictive efficiencies of the RS-based nomogram measured by AUC were 0.826 at 12 months, 0.805 at 36 months, and 0.76 at 60 months. CONCLUSIONS: CTLA4 mRNA expression status in ccRCC could be predicted noninvasively using a radiomics model based on nephrographic phase contrast-enhanced CT images. The nomogram established by combining RS and clinicopathologic factors could predict overall survival for ccRCC patients. Our findings may help stratify prognosis of ccRCC patients and identify those who may respond best to ICI-based treatments.

Separation and Structural Characterization of a Novel Exopolysaccharide from Rhizopus nigricans.

The present study aims to analyze the structural characterization and antioxidant activity of a novel exopolysaccharide from Rhizopus nigricans (EPS2-1). For this purpose, EPS2-1 was purified through DEAE-52, Sephadex G-100, and Sephadex G-75 chromatography. The structural characterization of EPS2-1 was analyzed using high-performance gel permeation chromatography (HPGPC), Fourier transform infrared spectroscopy (FT-IR), methylation analysis, nuclear magnetic resonance (NMR) spectra, transmission electron microscope (TEM), and atomic force microscope (AFM). The results revealed that EPS2-1 is composed of mannose (Man), galactose (Gal), glucose (Glc), arabinose (Ara), and Fucose (Fuc), and possesses a molecular weight of 32.803 kDa. The backbone of EPS2-1 comprised →2)-α-D-Manp-(1→ and →3)-ß-D-Galp-(1→, linked with the O-6 position of (→2,6)-α-D-Manp-(1→) of the main chain is branch α-D-Manp-(1→6)-α-D-Manp-(1→, linked with the O-6 positions of (→3)-ß-D-Galp-(1→) of the main chain are branches →4)-ß-D-Glcp-(1→ and →3)-ß-D-Galp-(1→, respectively. Finally, we demonstrated that EPS2-1 also shows free radical scavenging activity and iron ion reducing ability. At the same time, EPS2-1 could inhibit the proliferation of MFC cells and increase the cell viability of RAW264.7 cells. Our results suggested that EPS2-1 is a novel polysaccharide, and EPS2-1 has antioxidant activity. In addition, EPS2-1 may possess potential immunomodulatory and antitumor activities. This study promoted the application of EPS2-1 as the functional ingredients in the pharmaceutical and food industries.

Aggregation caused quenching to aggregation induced emission transformation: a precise tuning based on BN-doped polycyclic aromatic hydrocarbons toward subcellular organelle specific imaging.

Polycyclic aromatic hydrocarbons (PAHs) with boron-nitrogen (BN) moieties have attracted tremendous interest due to their intriguing electronic and optoelectronic properties. However, most of the BN-fused π-systems reported to date are difficult to modify and exhibit traditional aggregation-caused quenching (ACQ) characteristics. This phenomenon greatly limits their scope of application. Thus, continuing efforts to seek novel, structurally distinct and functionally diverse structures are highly desirable. Herein, we proposed a one-stone-two-birds strategy including simultaneous exploration of reactivity and tuning of the optical and electronic properties for BN-containing π-skeletons through flexible regioselective functionalization engineering. In this way, three novel functionalized BN luminogens (DPA-BN-BFT, MeO-DPA-BN-BFT and DMA-DPA-BN-BFT) with similar structures were obtained. Intriguingly, DPA-BN-BFT, MeO-DPA-BN-BFT and DMA-DPA-BN-BFT exhibit completely different emission behaviors. Fluorogens DPA-BN-BFT and MeO-DPA-BN-BFT exhibit a typical ACQ effect; in sharp contrast, DMA-DPA-BN-BFT possesses a prominent aggregation induced emission (AIE) effect. To the best of our knowledge, this is the first report to integrate ACQ and AIE properties into one BN aromatic backbone with subtle modified structures. Comprehensive analysis of the crystal structure and theoretical calculations reveal that relatively large twisting angles, multiple intermolecular interactions and tight crystal packing modes endow DMA-DPA-BN-BFT with strong AIE behavior. More importantly, cell imaging demonstrated that luminescent materials DPA-BN-BFT and DMA-DPA-BN-BFT can highly selectively and sensitively detect lipid droplets (LDs) in living MCF-7 cells. Overall, this work provides a new viewpoint of the rational design and synthesis of advanced BN-polycyclic aromatics with AIE features and triggers the discovery of new functions and properties of azaborine chemistry.

A Novel Tumor Progression Prediction Method for Multimode Ablation Treatment.

OBJECTIVE: The multimode ablation of liver cancer, which uses radio-frequency heating after a pre-freezing process to treat the tumor, has shown significantly improved therapeutic effects and enhanced anti-tumor immune response. Unlike open surgery, the ablated lesions remain in the body after treatment, so it is critical to assess the immediate outcome and to monitor disease status over time. Here we propose a novel tumor progression prediction method for simultaneous postoperative evaluation and prognosis analysis. METHODS: We propose to leverage the intraoperative therapeutic information extracted from thermal dose distribution. For tumors with specific sensitivity reflected in medical images, different thermal doses implicitly indicate the degree of instant damage and long-term inhibition excited under specific ablation energy. We further propose a survival analysis framework for the multimode ablation treatment. It extracts carefully designed features from clinical, preoperative, intraoperative, and postoperative data, then uses random survival forest for feature selection and deep neural networks for survival prediction. RESULTS: We evaluated the proposed methods using clinical data. The results show that our method outperforms the state-of-the-art survival analysis methods with a C-index of 0.855±0.090. The thermal dose information contributes significantly to the prediction accuracy by taking up 21.7% of the overall feature importance. CONCLUSION: The proposed methods have been demonstrated to be a powerful tool in tumor progression prediction of multimode ablation therapy. SIGNIFICANCE: This kind of data-driven prognosis analysis may benefit personalized medicine and simplify the follow-up process.

Lung cancer subtype classification using histopathological images based on weakly supervised multi-instance learning.

Objective.Subtype classification plays a guiding role in the clinical diagnosis and treatment of non-small-cell lung cancer (NSCLC). However, due to the gigapixel of whole slide images (WSIs) and the absence of definitive morphological features, most automatic subtype classification methods for NSCLC require manually delineating the regions of interest (ROIs) on WSIs.Approach.In this paper, a weakly supervised framework is proposed for accurate subtype classification while freeing pathologists from pixel-level annotation. With respect to the characteristics of histopathological images, we design a two-stage structure with ROI localization and subtype classification. We first develop a method called multi-resolution expectation-maximization convolutional neural network (MR-EM-CNN) to locate ROIs for subsequent subtype classification. The EM algorithm is introduced to select the discriminative image patches for training a patch-wise network, with only WSI-wise labels available. A multi-resolution mechanism is designed for fine localization, similar to the coarse-to-fine process of manual pathological analysis. In the second stage, we build a novel hierarchical attention multi-scale network (HMS) for subtype classification. HMS can capture multi-scale features flexibly driven by the attention module and implement hierarchical features interaction.Results.Experimental results on the 1002-patient Cancer Genome Atlas dataset achieved an AUC of 0.9602 in the ROI localization and an AUC of 0.9671 for subtype classification.Significance.The proposed method shows superiority compared with other algorithms in the subtype classification of NSCLC. The proposed framework can also be extended to other classification tasks with WSIs.

A Weak Supervision-based Framework for Automatic Lung Cancer Classification on Whole Slide Image.

Classification of normal lung tissue, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by pathological images is significant for clinical diagnosis and treatment. Due to the large scale of pathological images and the absence of definitive morphological features between LUAD and LUSC, it is time-consuming, laborious and challenging for pathologists to analyze the microscopic histopathology slides by visual observation. In this paper, a pixel-level annotation-free framework was proposed to classify normal tissue, LUAD and LUSC slides. This framework can be divided into two stages: tumor classification and localization, and subtype classification. In the first stage, EM-CNN was utilized to distinguish tumor slides from normal tissue slides and locate the discriminative regions for subsequent analysis with only image-level labels provided. In the second stage, a multi-scale network was proposed to improve the accuracy of subtype classification. This method achieved an AUC of 0.9978 for tumor classification and an AUC of 0.9684 for subtype classification, showing its superiority in lung pathological image classification compared with other methods.

SLIR: Synthesis, localization, inpainting, and registration for image-guided thermal ablation of liver tumors.

Thermal ablation is a minimally invasive procedure for treating small or unresectable tumors. Although CT is widely used for guiding ablation procedures, yet the contrast of tumors against normal soft tissues is often poor in CT scans, aggravating the accurate thermal ablation. In this paper, we propose a fast MR-CT image registration method to overlay pre-procedural MR (pMR) and pre-procedural CT (pCT) images onto an intra-procedural CT (iCT) image to guide the thermal ablation of liver tumors. At the pre-procedural stage, the Cycle-GAN model with mutual information constraint is employed to generate the synthesized CT (sCT) image from the input pMR. Then, pMR-pCT image registration is carried out via traditional mono-modal sCT-pCT image registration. At the intra-procedural stage, the region of the probe and its artifacts are automatically localized and inpainted in the iCT image. Then, an unsupervised registration network (UR-Net) is used to efficiently align the pCT with the inpainted iCT (inpCT) image. The final transform from pMR to iCT is obtained by concatenating the two estimated transforms, i.e., (i) from pMR image space to pCT image space (via sCT) and (ii) from pCT image space to iCT image space (via inpCT). The proposed method has been evaluated over a real clinical dataset and compared with state-of-the-art methods. Experimental results confirm that the proposed method achieves high registration accuracy with fast computation speed.

Adrenal Tumor Vessels Segmentation Using Convolutional Neural Network in Computed Tomography Angiography.

The adrenal glands are important endocrine glands in humans. They are in complex environments with thin vessels around them. It's meaningful to get the accurate dissection before surgery. However, images used in hospitals are now unable to help doctors with many surgeries, which are produced by digital subtraction angiography. In this study, we used a 3D U-Net model to segment the adrenal tumor vessels in 3D computed tomography angiography slices. The model was evaluated by dice similarity coefficient (DSC) and mean intersection over union (MIoU) with the manually labeled ground truth. The DSC in this model is 94.69% and the MIoU is 90.22%.

A 3D Convolutional Neural Network Framework for Polyp Candidates Detection on the Limited Dataset of CT Colonography.

Proper training of convolutional neural networks (CNNs) requires annotated training datasets oflarge size, which are not currently available in CT colonography (CTC). In this paper, we propose a well-designed framework to address the challenging problem of data shortage in the training of 3D CNN for the detection of polyp candidates, which is the first and crucial part of the computer-aided diagnosis (CAD) of CTC. Our scheme relies on the following two aspects to reduce overfitting: 1) mass data augmentation, and 2) a flat 3D residual fully convolutional network (FCN). In the first aspect, we utilize extensive rotation, translation, and scaling with continuous value to provide numerous data samples. In the second aspect, we adapt the well-known V-Net to a flat residual FCN to resolve the problem of detection other than segmentation. Our proposed framework does not rely on accurate colon segmentation nor any electrical cleansing of tagged fluid, and experimental results show that it can still achieve high sensitivity with much fewer false positives. Code has been made available at: http://github.com/chenyzstju/ctc_screening_cnn.

Quantitative study of the influence of swimming therapy on osteoporosis rat models based on synchrotron radiation computed tomogaphy.

Osteoporosis is a bone disease with a variety of causes, leading to bone pain and fragility to fracture. Major treatment methods include nutrition therapy, exercise therapy, drug therapy and surgical treatment, among which exercise therapy, such as swimming, is the most effective. To investigate the optimal swimming therapy regime for postmenopausal women, the effects of eight weeks of different intensity swimming exercises were studied in rat models. After the swimming program, lumbar vertebrae were dissected from all the rats and scanned by synchrotron radiation computed tomography (SRCT). Histomorphometry analysis and finite-element analysis were carried out on the trabecular structure of the L4 lumbar based on the acquired SRCT slices. Histomorphometry analysis showed that swimming can alleviate the decrease in bone strength induced by estrogen deficiency, and moderate-intensity swimming was found to have the most significant effect.

Limited-Range Few-View CT: Using Historical Images for ROI Reconstruction in Solitary Lung Nodules Follow-up Examination.

Repeated CT scans are known to increase the risk of cancer; thus, it is paradoxical to use multiple follow-up CT scans to monitor the development of a lung nodule and conduct early treatment of the nodule. In the case of a solitary lung nodule, regional scanning and region of interest (ROI) reconstruction are likely to restore the internal area at the nodule. A limited-range few-view CT is proposed in this paper for lung nodule follow-ups with extremely reduced X-radiation. For a planned scanning of an ROI, where a solitary lung nodule is positioned, a limited-range few-view CT can be employed, and thus, less tissue is exposed to X-radiation per view. An ROI reconstruction method is also proposed that makes full use of the former standard lung scan. The experimental results show that the nodule size and shape are preserved. In the case of a 40-mm ROI, the number of exposed X-rays can be reduced by 99.6% for a circular scan and 99.9% for a 3-D scan.

A squaraine-based colorimetric and F(-) dependent chemosensor for recyclable CO2 gas detection: highly sensitive off-on-off response.

An unsymmetrical squaraine-based chemosensor SH2 has been synthesized, and its sensing behavior towards CO2 gas was described in detail by UV-vis and (1)H NMR spectroscopies in DMSO. The results indicated that the extremely sensitive "naked-eye" CO2 gas detection can be operated in the presence of excess [Bu4N]F (TBAF) and the sensor is easy to recycle. These properties enable SH2 to act as a CO2 and F(-) controlled "OFF-ON-OFF" switch. Combining theoretical analyses, a plausible sensing mechanism was proposed to illustrate how the receptor SH2 works as a CO2 sensitive and selective colorimetric probe in the present system.

In-line phase-contrast and grating-based phase-contrast synchrotron imaging study of brain micrometastasis of breast cancer.

Current bio-medical imaging researches aim to detect brain micrometastasis in early stage for its increasing incidence and high mortality rates. Synchrotron phase-contrast imaging techniques, such as in-line phase-contrast (IPC) and grating-based phase-contrast (GPC) imaging, could provide a high spatial and density imaging study of biological specimens' 3D structures. In this study, we demonstrated the detection efficiencies of these two imaging tools on breast cancer micrometastasis in an ex vivo mouse brain. We found that both IPC and GPC can differentiate abnormal brain structures induced by micrometastasis from the surrounding normal tissues. We also found that GPC was more sensitive in detecting the small metastasis as compared to IPC.

Grating-based phase-contrast imaging of tumor angiogenesis in lung metastases.

PURPOSE: To assess the feasibility of the grating-based phase-contrast imaging (GPI) technique for studying tumor angiogenesis in nude BALB/c mice, without contrast agents. METHODS: We established lung metastatic models of human gastric cancer by injecting the moderately differentiated SGC-7901 gastric cancer cell line into the tail vein of nude mice. Samples were embedded in a 10% formalin suspension and dried before imaging. Grating-based X-ray phase-contrast images were obtained at the BL13W beamline of the Shanghai Synchrotron Radiation Facility (SSRF) and compared with histological sections. RESULTS: Without contrast agents, grating-based X-ray phase-contrast imaging still differentiated angiogenesis within metastatic tumors with high spatial resolution. Vessels, down to tens of microns, showed gray values that were distinctive from those of the surrounding tumors, which made them easily identifiable. The vessels depicted in the imaging study were similar to those identified on histopathology, both in size and shape. CONCLUSIONS: Our preliminary study demonstrates that grating-based X-ray phase-contrast imaging has the potential to depict angiogenesis in lung metastases.

Quantitative study of osteoporosis model based on synchrotron radiation.

To investigate the changes of different periods of primary osteoporosis, we made quantitative analysis of osteoporosis using synchrotron radiation computed tomography (SRCT), together with histomorphometry analysis and finite element analysis (FEA). Tibias, femurs and lumbar vertebras were dissected from sham-ovariectomy rats and ovariectomized rats suffering from osteoporosis at certain time points. The samples were scanned by SRCT and then FEA was applied based on reconstructed slices. Histomorphometry analysis showed that the structure of some trabecular in osteoporosis degraded as the bone volume decreased, for femurs, the bone volume fraction (BV/TV) decreased from 69% to 43%. That led to the increase of the thickness of trabecular separation (from 45.05µm to 97.09µm) and the reduction of the number of trabecular (from 7.99 mm(-1) to 5.97mm(-1)). Simulation of various mechanical tests indicated that, with the exacerbation of osteoporosis, the bones' ability of resistance to compression, bending and torsion gradually became weaker. The compression stiffness decreased from 1770.96 Fµm(-1) to 697.41 Fµm(-1), and it matched the histomorphometry analysis. This study suggested that the combination of both analysis could quantitatively analyze the bone strength in good accuracy.

X-ray grating interferometer for biomedical imaging applications at Shanghai Synchrotron Radiation Facility.

An X-ray grating interferometer was installed at the BL13W beamline of Shanghai Synchrotron Radiation Facility (SSRF) for biomedical imaging applications. Compared with imaging results from conventional absorption-based micro-computed tomography, this set-up has shown much better soft tissue imaging capability. In particular, using the set-up, the carotid artery and the carotid vein in a formalin-fixed mouse can be visualized in situ without contrast agents, paving the way for future applications in cancer angiography studies. The overall results have demonstrated the broad prospects of the existing set-up for biomedical imaging applications at SSRF.