Simple Summary A high Ki-67 index usually suggests accelerated and uncontrolled cell proliferation correlated with tumor growth and is a prognostic factor that is associated with an increased recurrent risk in meningioma patients. The aim of our study is to predict the Ki-67 proliferative index in meningioma patients using machine learning technology. With 371 cases collected from two centers, we systematically analyzed the relevance between clinical/radiological features and the Ki-67 index. Moreover, with radiomic features extracted from postcontrast images, we built three radiomic models and three clinical radiological–radiomic models to predict the Ki-67 status. The models showed good performance, with an AUC of 0.837 in the internal test and 0.700 in the external test. The results provide a quantitative method to facilitate clinical decision making for meningioma patients. Abstract Background/aim This study aimed to explore the value of radiological and radiomic features retrieved from magnetic resonance imaging in the prediction of a Ki-67 proliferative index in meningioma patients using a machine learning model. Methods This multicenter, retrospective study included 371 patients collected from two centers. The Ki-67 expression was classified into low-expressed and high-expressed groups with a threshold of 5%. Clinical features and radiological features were collected and analyzed by using univariate and multivariate statistical analyses. Radiomic features were extracted from contrast-enhanced images, followed by three independent feature selections. Six predictive models were constructed with different combinations of features by using linear discriminant analysis (LDA) classifier. Results The multivariate analysis suggested that the presence of intratumoral necrosis (p = 0.032) and maximum diameter (p < 0.001) were independently correlated with a high Ki-67 status. The predictive models showed good performance with AUC of 0.837, accuracy of 0.810, sensitivity of 0.857, and specificity of 0.771 in the internal test and with AUC of 0.700, accuracy of 0.557, sensitivity of 0.314, and specificity of 0.885 in the external test. Conclusion The results of this study suggest that the predictive model can efficiently predict the Ki-67 index of meningioma patients to facilitate the therapeutic management.

/pdf/efficient-prediction-of-ki-67-proliferation-index-in-1o3gb3al.pdf

Efficient Prediction of Ki-67 Proliferation Index in Meningiomas on MRI: From Traditional Radiological Findings to a Machine Learning Approach

With the ever-increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the need for radiation dose reduction, CT denoising methods have been widely investigated and applied in low-dose CT images. Numerous noise reduction algorithms have emerged, such as iterative reconstruction and most recently, deep learning (DL)-based approaches. Given the rapid advancements in Artificial Intelligence techniques, we recognize the need for a comprehensive review that emphasizes the most recently developed methods. Hence, we have performed a thorough analysis of existing literature to provide such a review. Beyond directly comparing the performance, we focus on pivotal aspects, including model training, validation, testing, generalizability, vulnerability, and evaluation methods. This review is expected to raise awareness of the various facets involved in CT image denoising and the specific challenges in developing DL-based models.

CT image denoising methods for image quality improvement and radiation dose reduction.

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT. 

Systematic Review on Learning-based Spectral CT

Summary The process of dead cell clearance by phagocytic cells, called efferocytosis, prevents inflammatory cell necrosis and promotes resolution and repair. Defective efferocytosis contributes to the progression of numerous diseases in which cell death is prominent, including liver disease. Many gaps remain in our understanding of how hepatic macrophages carry out efferocytosis and how this process goes awry in various types of liver diseases. Thus far, studies have suggested that, upon liver injury, liver-resident Kupffer cells and infiltrating monocyte-derived macrophages clear dead cells, limit inflammation, and, through macrophage reprogramming, repair liver damage. However, in unusual settings, efferocytosis can promote liver disease. In this review, we will focus on efferocytosis in various types of acute and chronic liver diseases, including metabolic dysfunction-associated steatohepatitis. Understanding the mechanisms and consequences of efferocytosis by hepatic macrophages has the potential to shed new light on liver disease pathophysiology and to guide new treatment strategies to prevent disease progression.

Efferocytosis in liver disease

OBJECTIVE
To investigate the risk factors of reoperation after percutaneous endoscopic lumbar discectomy (PELD) due to recurrent lumbar disc herniation (rLDH) and to establish a set of individualized prediction models.


METHODS
Patients who underwent PELD successfully from January 2016 to February 2022 in a single institution were enrolled in this study. Six methods of machine learning (ML) were used to establish an individualized prediction model for reoperation in rLDH patients after PELD, and these models were compared with logistics regression model to select optimal model.


RESULTS
A total of 2603 patients were enrolled in this study. 57 patients had repeated operation due to rLDH and 114 patients were selected from the remaining 2546 nonrecurrent patients as matched controls. Multivariate logistic regression analysis showed that disc herniation type (P < .001), Modic changes (type II) (P = .003), sagittal range of motion (sROM) (P = .022), facet orientation (FO) (P = .028) and fat infiltration (FI) (P = .001) were independent risk factors for reoperation in rLDH patients after PELD. The XGBoost AUC was of 90.71%, accuracy was approximately 88.87%, sensitivity was 70.81%, specificity was 97.19%. The traditional logistic regression AUC was 77.4%, accuracy was about 77.73%, sensitivity was 47.15%, specificity was 92.12%.


CONCLUSION
This study showed that disc herniation type (extrusion, sequestration), Modic changes (type II), a large sROM, a large FO and high FI were independent risk factors for reoperation in LDH patients after PELD. The prediction efficiency of XGBoost model was higher than traditional Logistic regression analysis model.

https://journals.sagepub.com/doi/pdf/10.1177/21925682231173353

Machine Learning Prediction Model and Risk Factor Analysis of Reoperation in Recurrent Lumbar Disc Herniation Patients After Percutaneous Endoscopic Lumbar Discectomy.

To evaluate image quality and diagnostic performance of carotid dual-energy computed tomography angiography (DECTA) using deep learning image reconstruction (DLIR) compared with images using adaptive statistical iterative reconstruction-Veo (ASIR-V).Carotid DECTA datasets of 28 consecutive patients were reconstructed at 50 keV using DLIR at low, medium, and high levels (DLIR-L, DLIR-M, and DLIR-H) and 80% ASIR-V algorithms. Mean attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) at different levels of arteries were measured and calculated. Image quality for noise and texture, depiction of arteries, and diagnostic performance toward carotid plaques were assessed subjectively by two radiologists. Quantitative and qualitative parameters were compared between the ASIR-V, DLIR-L, DLIR-M, and DLIR-H groups.The image noise at aorta and common carotid artery, SNR, and CNR at all level arteries of DLIR-H images were significantly higher than those of ASIR-V images (p = 0.000-0.040). The quantitative analysis of DLIR-L and DLIR-M showed comparable denoise capability with ASIR-V. The overall image quality (p = 0.000) and image noise (p = 0.000-0.014) were significantly better in the DLIR-M and DLIR-H images. The image texture was improved by DLR at all level compared to ASIR-V images (p = 0.000-0.008). Depictions of head and neck arteries and diagnostic performance were comparable between four groups (p > 0.05).Compared with 80% ASIR-V, we recommend DLIR-H for clinical carotid DECTA reconstruction, which can significantly improve the image quality of carotid DECTA at 50 keV but maintain a desirable diagnostic performance and arterial depiction. 

/pdf/deep-learning-image-reconstruction-algorithm-for-carotid-1oc7ffas.pdf

Deep learning image reconstruction algorithm for carotid dual-energy computed tomography angiography: evaluation of image quality and diagnostic performance

Although glycogen synthase kinase β (Gsk3β) has been shown to regulate tissue inflammation, whether and how it regulates inflammation resolution versus inflammation activation is unclear. In a murine liver, partial warm ischemia/reperfusion injury (IRI) model, we found that Gsk3β inhibitory phosphorylation increased at both the early-activation and late-resolution stages of the disease. Myeloid Gsk3β deficiency not only alleviated liver injuries, it also facilitated the restoration of liver homeostasis. Depletion of Kupffer cells prior to the onset of liver ischemia diminished the differences between the WT and Gsk3β-KO mice in the activation of liver IRI. However, the resolution of liver IRI remained accelerated in Gsk3β-KO mice. In CD11b-DTR mice, Gsk3β-deficient BM-derived macrophages (BMMs) facilitated the resolution of liver IRI as compared with WT cells. Furthermore, Gsk3β deficiency promoted the reparative phenotype differentiation in vivo in liver-infiltrating macrophages and in vitro in BMMs. Gsk3 pharmacological inhibition promoted the resolution of liver IRI in WT, but not myeloid MerTK-deficient, mice. Thus, Gsk3β regulates liver IRI at both activation and resolution stages of the disease. Gsk3 inactivation enhances the proresolving function of liver-infiltrating macrophages in an MerTK-dependent manner.

/pdf/gsk3b-regulates-the-resolution-of-liver-ischemia-reperfusion-1drs3xn9.pdf

Gsk3β regulates the resolution of liver ischemia/reperfusion injury via MerTK

Purpose This study aimed to evaluate the difference in vertebral mechanical properties estimated by finite element analysis (FEA) with different computed tomography (CT) reconstruction kernels and evaluate their accuracy in the screening and classification of osteoporosis. Methods There were 31 patients enrolled retrospectively from the quantitative CT database of our hospital, uniformly covering the range from osteoporosis to normal. All subjects’ CT raw data were reconstructed both with a smooth standard convolution kernel (B40f) and a sharpening bone convolution kernel (B70f), and FEA was performed on L1 of each subject based on two reconstructed images to obtain vertebral estimated strength and stiffness. The trabecular volumetric bone mineral density (vBMD) of the same vertebral body was also measured. FEA measurements between two kernels and their accuracy for osteoporosis screening were compared. Results The vertebral stiffness and strength measured in FEA-B40f were significantly lower compared with those of FEA-B70f (12.0%, p = 0.000 and 10.7%, p = 0.000, respectively). The correlation coefficient between FEA-B70F and vBMD was slightly higher than that of FEA-B40F in both vertebral strength and stiffness (strength: r 2-B40f = 0.21, p = 0.009 vs. r 2-B70f = 0.27, p = 0.003; stiffness: r 2-B40f = 0.37, p = 0.002 vs. r 2-B70f = 0.45, p=0.000). The receiver operator characteristic curve showed little difference in the classification of osteoporosis between FEA-B40f and FEA-B70f. Conclusion Two kernels both seemed to be applicable to the opportunistic screening of osteoporosis by CT-FEA despite variance in FE-estimated bone strength and bone stiffness. A protocol for CT acquisition and FEA is still required to guarantee the reproducibility of clinical use.

/pdf/influence-of-image-reconstruction-kernel-on-computed-1zx9rgml.pdf

Influence of image reconstruction kernel on computed tomography-based finite element analysis in the clinical opportunistic screening of osteoporosis—A preliminary result

Thus, the aim of this study is to evaluate the performance of deep learning imaging reconstruction (DLIR) algorithm in different image sets derived from carotid dual-energy computed tomography angiography (DECTA) for evaluating cervical intervertebral discs (IVDs) and compare them with those reconstructed using adaptive statistical iterative reconstruction-Veo (ASiR-V). Forty-two patients who underwent carotid DECTA were included in this retrospective analysis. Three types of image sets (70 keV, water-iodine, and water-calcium) were reconstructed using 50% ASiR-V and DLIR at medium and high levels (DLIR-M and DLIR-H). The diagnostic acceptability and conspicuity of IVDs were assessed using a 5-point scale. Hounsfield Units (HU) and water concentration (WC) values of the IVDs; standard deviation (SD); and coefficient of variation (CV) were calculated. Measurement parameters of the 50% ASIR-V, DLIR-M, and DLIR-H groups were compared. The DLIR-H group showed higher scores for diagnostic acceptability and conspicuity, as well as lower SD values for HU and WC than the ASiR-V and DLIR-M groups for the 70 keV and water-iodine image sets (all p < .001). However, there was no significant difference in scores and SD among the three groups for the water-calcium image set (all p > .005). The water-calcium image set showed better diagnostic accuracy for evaluating IVDs compared to the other image sets. The inter-rater agreement using ASiR-V, DLIR-M, and DLIR-H was good for the 70 keV image set, excellent for the water-iodine and water-calcium image sets. DLIR improved the visualization of IVDs in the 70 keV and water-iodine image sets. However, its improvement on color-coded water-calcium image set was limited. 

Deep Learning Imaging Reconstruction Algorithm for Carotid Dual Energy CT Angiography: Opportunistic Evaluation of Cervical Intervertebral Discs-A Preliminary Study.

Feasibility of 100 kV Low-voltage Bone Mineral Density Measurement with Quantitative CT: Phantom and Clinical Research.

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Papers

Deep learning image reconstruction algorithm for carotid dual-energy computed tomography angiography: evaluation of image quality and diagnostic performance

Gsk3β regulates the resolution of liver ischemia/reperfusion injury via MerTK

Influence of image reconstruction kernel on computed tomography-based finite element analysis in the clinical opportunistic screening of osteoporosis—A preliminary result

Deep Learning Imaging Reconstruction Algorithm for Carotid Dual Energy CT Angiography: Opportunistic Evaluation of Cervical Intervertebral Discs-A Preliminary Study.

Feasibility of 100 kV Low-voltage Bone Mineral Density Measurement with Quantitative CT: Phantom and Clinical Research.