Feasibility of Weight-based Tube Voltage and Iodine Delivery Rate for Coronary Artery CT Angiography.

PURPOSE: The objective of this study was to evaluate the feasibility of weight-based tube voltage and iodine delivery rate (IDR) for coronary artery CT angiography (CCTA). METHODS: A total of 193 patients (mean age: 58 ± 12 years) with suspected coronary heart disease indicated for CCTA between May and October 2022 were prospectively enrolled. The subjects were divided into five groups according to body weight: < 60 kg, 60 - 69 kg, 70 - 79 kg, 80 - 89 kg, and ≥ 90 kg. The tube voltage and IDR settings of each group were as follows: 70 kVp/0.8 gI/s, 80 kVp/1.0 gI/s, 80 kVp/1.1 gI/s, 100 kVp/1.5 gI/s, and 100 kVp/1.5 gI/s, respectively. Objective image quality data included the CT value and standard deviation (noise) of the aortic root (AR), the proximal left anterior descending branch (LAD), and the distal right coronary artery (RCA), as well as the signal-to-noise ratio and contrast-to-noise ratio of the LAD and RCA. Subjective image quality assessment was performed based on the 18-segment model. Contrast and radiation doses, as well as effective dose (ED), were recorded. All continuous variables were compared using either the one-way ANOVA or the Kruskal-Wallis rank sum test. RESULTS: No significant differences were observed in all objective and subjective parameters of image quality between the groups (P > 0.05). However, significant differences in contrast and radiation doses were observed (P < 0.05). The contrast doses across the weight groups were 27 mL, 35 mL, 38 mL, 53 mL, and 53 mL, respectively, while the ED were 1.567 (1.30, 2.197) mSv, 1.53 (1.373, 1.78) mSv, 2.113 (1.963, 2.256) mSv, 4.22 (3.771, 4.483) mSv, and 4.786 (4.339, 5.536) mSv, respectively. CONCLUSION: Weight-based tube voltage and IDR yielded consistently high image quality, and allowed for further reduction in contrast and radiation exposure during CCTA for coronary artery diseases.

Selecting Monoclonal Cell Lineages from Somatic Reprogramming Using Robotic-Based Spatial-Restricting Structured Flow.

Somatic cell reprogramming generates induced pluripotent stem cells (iPSCs), which serve as a crucial source of seed cells for personalized disease modeling and treatment in regenerative medicine. However, the process of reprogramming often causes substantial lineage manipulations, thereby increasing cellular heterogeneity. As a consequence, the process of harvesting monoclonal iPSCs is labor-intensive and leads to decreased reproducibility. Here, we report the first in-house developed robotic platform that uses a pin-tip-based micro-structure to manipulate radial shear flow for automated monoclonal iPSC colony selection (~1 s) in a non-invasive and label-free manner, which includes tasks for somatic cell reprogramming culturing, medium changes; time-lapse-based high-content imaging; and iPSCs monoclonal colony detection, selection, and expansion. Throughput-wise, this automated robotic system can perform approximately 24 somatic cell reprogramming tasks within 50 days in parallel via a scheduling program. Moreover, thanks to a dual flow-based iPSC selection process, the purity of iPSCs was enhanced, while simultaneously eliminating the need for single-cell subcloning. These iPSCs generated via the dual processing robotic approach demonstrated a purity 3.7 times greater than that of the conventional manual methods. In addition, the automatically produced human iPSCs exhibited typical pluripotent transcriptional profiles, differentiation potential, and karyotypes. In conclusion, this robotic method could offer a promising solution for the automated isolation or purification of lineage-specific cells derived from iPSCs, thereby accelerating the development of personalized medicines.

Broken-fat pad sign: a characteristic radiographic finding to distinguish between knee rheumatoid arthritis and osteoarthritis.

OBJECTIVES: Diagnostic imaging plays an important role in the pre-treatment workup of knee osteoarthritis (OA) and rheumatoid arthritis (RA). Herein, we identified a useful MRI sign of infrapatellar fat pad (IPFP) to improve diagnosis. METHODS: Eighty-one age- and sex-matched RA and OA patients each, with pathological diagnosis and pre-treatment MRI were retrospectively evaluated. All randomized MR images were blinded and independently reviewed by two radiologists. The assessment process included initial diagnosis, sign evaluation, and final diagnosis, with a 3-week interval between each assessment. Broken-fat pad (BFP) sign was assessed on sagittal T2-weighted-imaging in routine MRI. The area under the curve and Cohen's kappa (κ) were used to assess the classification performance. Two shape features were extracted from IPFP for quantitative interpretation. RESULTS: The median age of the study population was 57.6 years (range: 31.0-78.0 years). The BFP sign was detected more frequently in patients with RA (72.8%) than those with OA (21.0%). Both radiologists achieved better performance by referring to the BFP sign, with accuracies increasing from 58.0 to 75.9% and 72.8 to 79.6%, respectively. The inter-reader correlation coefficient showed an increase from fair (κ = 0.30) to substantial (κ = 0.75) upon the consideration of the BFP sign. For quantitative analysis, the IPFP of RA had significantly lower sphericity (0.54 ± 0.04 vs. 0.59 ± 0.03, p < 0.01). Despite larger surface-volume-ratio of RA (0.38 ± 0.05 vs. 0.37 ± 0.04, p = 0.25) than that of OA, there was no statistical difference. CONCLUSIONS: The BFP sign is a potentially important diagnostic clue for differentiating RA from OA with routine MRI and reducing misdiagnosis. CRITICAL RELEVANCE STATEMENT: With the simple and feasible broken-fat pad sign, clinicians can help more patients with early accurate diagnosis and proper treatment, which may be a valuable addition to the diagnostic workup of knee MRI assessment. KEY POINTS: • Detailed identification of infrapatellar fat pad alterations of patients may be currently ignored in routine evaluation. • Broken-fat pad sign is helpful for differentiating rheumatoid arthritis and osteoarthritis. • The quantitative shape features of the infrapatellar fat pad may provide a possible explanation of the signs. • This sign has good inter-reader agreements and is feasible for clinical application.

Automated Segmentation and Classification of Knee Synovitis Based on MRI Using Deep Learning.

OBJECTIVES: To develop a deep learning (DL) model for segmentation of the suprapatellar capsule (SC) and infrapatellar fat pad (IPFP) based on sagittal proton density-weighted images and to distinguish between three common types of knee synovitis. MATERIALS AND METHODS: This retrospective study included 376 consecutive patients with pathologically confirmed knee synovitis (rheumatoid arthritis, gouty arthritis, and pigmented villonodular synovitis) from two institutions. A semantic segmentation model was trained on manually annotated sagittal proton density-weighted images. The segmentation results of the regions of interest and patients' sex and age were used to classify knee synovitis after feature processing. Classification by the DL method was compared to the classification performed by radiologists. RESULTS: Data of the 376 patients (mean age, 42 ± 15 years; 216 men) were separated into a training set (n = 233), an internal test set (n = 93), and an external test set (n = 50). The automated segmentation model showed good performance (mean accuracy: 0.99 and 0.99 in the internal and external test sets). On the internal test set, the DL model performed better than the senior radiologist (accuracy: 0.86 vs. 0.79; area under the curve [AUC]: 0.83 vs. 0.79). On the external test set, the DL diagnostic model based on automatic segmentation performed as well or better than senior and junior radiologists (accuracy: 0.79 vs. 0.79 vs. 0.73; AUC: 0.76 vs. 0.77 vs. 0.70). CONCLUSION: DL models for segmentation of SC and IPFD can accurately classify knee synovitis and aid radiologic diagnosis.

MRI-based automated multitask deep learning system to evaluate supraspinatus tendon injuries.

OBJECTIVE: To establish an automated, multitask, MRI-based deep learning system for the detailed evaluation of supraspinatus tendon (SST) injuries. METHODS: According to arthroscopy findings, 3087 patients were divided into normal, degenerative, and tear groups (groups 0-2). Group 2 was further divided into bursal-side, articular-side, intratendinous, and full-thickness tear groups (groups 2.1-2.4), and external validation was performed with 573 patients. Visual geometry group network 16 (VGG16) was used for preliminary image screening. Then, the rotator cuff multitask learning (RC-MTL) model performed multitask classification (classifiers 1-4). A multistage decision model produced the final output. Model performance was evaluated by receiver operating characteristic (ROC) curve analysis and calculation of related parameters. McNemar's test was used to compare the differences in the diagnostic effects between radiologists and the model. The intraclass correlation coefficient (ICC) was used to assess the radiologists' reliability. p < 0.05 indicated statistical significance. RESULTS: In the in-group dataset, the area under the ROC curve (AUC) of VGG16 was 0.92, and the average AUCs of RC-MTL classifiers 1-4 were 0.99, 0.98, 0.97, and 0.97, respectively. The average AUC of the automated multitask deep learning system for groups 0-2.4 was 0.98 and 0.97 in the in-group and out-group datasets, respectively. The ICCs of the radiologists were 0.97-0.99. The automated multitask deep learning system outperformed the radiologists in classifying groups 0-2.4 in both the in-group and out-group datasets (p < 0.001). CONCLUSION: The MRI-based automated multitask deep learning system performed well in diagnosing SST injuries and is comparable to experienced radiologists. CLINICAL RELEVANCE STATEMENT: Our study established an automated multitask deep learning system to evaluate supraspinatus tendon (SST) injuries and further determine the location of SST tears. The model can potentially improve radiologists' diagnostic efficiency, reduce diagnostic variability, and accurately assess SST injuries. KEY POINTS: • A detailed classification of supraspinatus tendon tears can help clinical decision-making. • Deep learning enables the detailed classification of supraspinatus tendon injuries. • The proposed automated multitask deep learning system is comparable to radiologists.

Panax notoginseng: derived exosome-like nanoparticles attenuate ischemia reperfusion injury via altering microglia polarization.

Cerebral ischemia/reperfusion (CI/R) injury is a clinical conundrum during the treatment of ischemic stroke. Cell-derived exosomes (CDE) were proved to be therapeutically effective for CI/R injury. However, production of CDE is time and effort consuming. Increasing studies reported that plants can also generate exosome-like nanoparticles (ELN) which are therapeutically effective and have higher yield compared with CDE. In this study, a commonly used Chinese herb Panax notoginseng (PN), whose active ingredients were well-documented in the treatment of CI/R injury, was chosen as a source of ELNs. It was found that Panax notoginseng derived exosome like nanoparticles (PDN) could enter the brain without modification and ameliorate cerebral infarct volume, improve behavior outcome and maintained the integrity of BBB. PDNs attenuated CI/R injury by altering the phenotype of microglia from "pro-inflammation" M1 type to "anti-inflammation" M2 type. Also, we found that lipids from PDNs were the major therapeutic effective component. As a mechanism of action, PDN was proved to exert therapeutic effect via activating pI3k/Akt pathway.

Assessment of axial spondyloarthritis activity using a magnetic resonance imaging-based multi-region-of-interest fusion model.

BACKGROUND: Identifying axial spondyloarthritis (axSpA) activity early and accurately is essential for treating physicians to adjust treatment plans and guide clinical decisions promptly. The current literature is mostly focused on axSpA diagnosis, and there has been thus far, no study that reported the use of a radiomics approach for differentiating axSpA disease activity. In this study, the aim was to develop a radiomics model for differentiating active from non-active axSpA based on fat-suppressed (FS) T2-weighted (T2w) magnetic resonance imaging (MRI) of sacroiliac joints. METHODS: This retrospective study included 109 patients diagnosed with non-active axSpA (n = 68) and active axSpA (n = 41); patients were divided into training and testing cohorts at a ratio of 8:2. Radiomics features were extracted from 3.0 T sacroiliac MRI using two different heterogeneous regions of interest (ROIs, Circle and Facet). Various methods were used to select relevant and robust features, and different classifiers were used to build Circle-based, Facet-based, and a fusion prediction model. Their performance was compared using various statistical parameters. p < 0.05 is considered statistically significant. RESULTS: For both Circle- and Facet-based models, 2284 radiomics features were extracted. The combined fusion ROI model accurately differentiated between active and non-active axSpA, with high accuracy (0.90 vs.0.81), sensitivity (0.90 vs. 0.75), and specificity (0.90 vs. 0.85) in both training and testing cohorts. CONCLUSION: The multi-ROI fusion radiomics model developed in this study differentiated between active and non-active axSpA using sacroiliac FS T2w-MRI. The results suggest MRI-based radiomics of the SIJ can distinguish axSpA activity, which can improve the therapeutic result and patient prognosis. To our knowledge, this is the only study in the literature that used a radiomics approach to determine axSpA activity.

AI-assisted accelerated MRI of the ankle: clinical practice assessment.

BACKGROUND: High-spatial resolution magnetic resonance imaging (MRI) is essential for imaging ankle joints. However, the clinical application of fast spin-echo sequences remains limited by their lengthy acquisition time. Artificial intelligence-assisted compressed sensing (ACS) technology has been recently introduced as an integrative acceleration solution. We compared ACS-accelerated 3-T ankle MRI to conventional methods of compressed sensing (CS) and parallel imaging (PI) . METHODS: We prospectively included 2 healthy volunteers and 105 patients with ankle pain. ACS acceleration factors for ankle protocol of T1-, T2-, and proton density (PD)-weighted sequences were optimized in a pilot study on healthy volunteers (acceleration factor 3.2-3.3×). Images of patients acquired using ACS and conventional acceleration methods were compared in terms of acquisition times, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), subjective image quality, and diagnostic agreement. Shapiro-Wilk test, Cohen κ, intraclass correlation coefficient, and one-way ANOVA with post hoc tests (Tukey or Dunn) were used. RESULTS: ACS acceleration reduced the acquisition times of T1-, T2-, and PD-weighted sequences by 32-43%, compared with conventional CS and PI, while maintaining image quality (mostly higher SNR with p < 0.004 and higher CNR with p < 0.047). The diagnostic agreement between ACS and conventional sequences was rated excellent (κ = 1.00). CONCLUSIONS: The optimum ACS acceleration factors for ankle MRI were found to be 3.2-3.3× protocol. The ACS allows faster imaging, yielding similar image quality and diagnostic performance. RELEVANCE STATEMENT: AI-assisted compressed sensing significantly accelerates ankle MRI times while preserving image quality and diagnostic precision, potentially expediting patient diagnoses and improving clinical workflows. KEY POINTS: • AI-assisted compressed sensing (ACS) significantly reduced scan duration for ankle MRI. • Similar image quality achieved by ACS compared to conventional acceleration methods. • A high agreement by three acceleration methods in the diagnosis of ankle lesions was observed.

MRI feature-based radiomics models to predict treatment outcome after stereotactic body radiotherapy for spinal metastases.

OBJECTIVE: This study aimed to extract radiomics features from MRI using machine learning (ML) algorithms and integrate them with clinical features to build response prediction models for patients with spinal metastases undergoing stereotactic body radiotherapy (SBRT). METHODS: Patients with spinal metastases who were treated using SBRT at our hospital between July 2018 and April 2023 were recruited. We assessed their response to treatment using the revised Response Evaluation Criteria in Solid Tumors (version 1.1). The lesions were categorized into progressive disease (PD) and non-PD groups. Radiomics features were extracted from T1-weighted image (T1WI), T2-weighted image (T2WI), and fat-suppression T2WI sequences. Feature selection involved intraclass correlation coefficients, minimal-redundancy-maximal-relevance, and least absolute shrinkage and selection operator methods. Thirteen ML algorithms were employed to construct the radiomics prediction models. Clinical, conventional imaging, and radiomics features were integrated to develop combined models. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, and the clinical value was assessed using decision curve analysis. RESULTS: We included 194 patients with 142 (73.2%) lesions in the non-PD group and 52 (26.8%) in the PD group. Each region of interest generated 2264 features. The clinical model exhibited a moderate predictive value (area under the ROC curve, AUC = 0.733), while the radiomics models demonstrated better performance (AUC = 0.745-0.825). The combined model achieved the best performance (AUC = 0.828). CONCLUSION: The MRI-based radiomics models exhibited valuable predictive capability for treatment outcomes in patients with spinal metastases undergoing SBRT. CRITICAL RELEVANCE STATEMENT: Radiomics prediction models have the potential to contribute to clinical decision-making and improve the prognosis of patients with spinal metastases undergoing SBRT. KEY POINTS: • Stereotactic body radiotherapy effectively delivers high doses of radiation to treat spinal metastases. • Accurate prediction of treatment outcomes has crucial clinical significance. • MRI-based radiomics models demonstrated good performance to predict treatment outcomes.

Comparison of miRNA transcriptome of exosomes in three categories of somatic cells with derived iPSCs.

Somatic cells can be reprogrammed into induced pluripotent stem cells (iPSCs) through epigenetic manipulation. While the essential role of miRNA in reprogramming and maintaining pluripotency is well studied, little is known about the functions of miRNA from exosomes in this context. To fill this research gap,we comprehensively obtained the 17 sets of cellular mRNA transcriptomic data with 3.93 × 1010 bp raw reads and 18 sets of exosomal miRNA transcriptomic data with 2.83 × 107 bp raw reads from three categories of human somatic cells: peripheral blood mononuclear cells (PBMCs), skin fibroblasts(SFs) and urine cells (UCs), along with their derived iPSCs. Additionally, differentially expressed molecules of each category were identified and used to perform gene set enrichment analysis. Our study provides sets of comparative transcriptomic data of cellular mRNA and exosomal miRNA from three categories of human tissue with three individual biological controls in studies of iPSCs generation, which will contribute to a better understanding of donor cell variation in functional epigenetic regulation and differentiation bias in iPSCs.

Assessment of Hidden Blood Loss in Spinal Metastasis Surgery: A Comprehensive Approach with MRI-Based Radiomics Models.

BACKGROUND: Patients undergoing surgery for spinal metastasis are predisposed to hidden blood loss (HBL), which is associated with poor surgical outcomes but unpredictable. PURPOSE: To evaluate the role of MRI-based radiomics models for assess the risk of HBL in patients undergoing spinal metastasis surgery. STUDY TYPE: Retrospective. SUBJECTS: 202 patients (42.6% female) operated on for spinal metastasis with a mean age of 58 ± 11 years were divided into a training (n = 162) and a validation cohort (n = 40). FIELD STRENGTH/SEQUENCE: 1.5T or 3.0T scanners. Sagittal T1-weighted and fat-suppressed T2-weighted imaging sequences. ASSESSMENT: HBL was calculated using the Gross formula. Patients were classified as low and high HBL group, with 1000 mL as the threshold. Radiomics models were constructed with radiomics features. The radiomics score (Radscore) was obtained from the optimal radiomics model. Clinical variables were accessed using univariate and multivariate logistic regression analyses. Independent risk variables were used to build a clinical model. Clinical variables combined with Radscore were used to establish a combined model. STATISTICAL TESTS: Predictive performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score. Calibration curves and decision curves analyses were produced to evaluate the accuracy and clinical utility. RESULTS: Among the radiomics models, the fusion (T1WI + FS-T2WI) model demonstrated the highest predictive efficacy (AUC: 0.744, 95% confidence interval [CI]: 0.576-0.914). The Radscore model (AUC: 0.809, 95% CI: 0.664-0.954) performs slightly better than the clinical model (AUC: 0.721, 95% CI: 0.524-0.918; P = 0.418) and the combined model (AUC: 0.752, 95% CI: 0.593-0.911; P = 0.178). DATA CONCLUSION: A radiomics model may serve as a promising assessment tool for the risk of HBL in patients undergoing spinal metastasis surgery, and guide perioperative planning to improve surgical outcomes. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Prediction of Primary Tumor Sites in Spinal Metastases Using a ResNet-50 Convolutional Neural Network Based on MRI.

We aim to investigate the feasibility and evaluate the performance of a ResNet-50 convolutional neural network (CNN) based on magnetic resonance imaging (MRI) in predicting primary tumor sites in spinal metastases. Conventional sequences (T1-weighted, T2-weighted, and fat-suppressed T2-weighted sequences) MRIs of spinal metastases patients confirmed by pathology from August 2006 to August 2019 were retrospectively analyzed. Patients were partitioned into non-overlapping sets of 90% for training and 10% for testing. A deep learning model using ResNet-50 CNN was trained to classify primary tumor sites. Top-1 accuracy, precision, sensitivity, area under the curve for the receiver-operating characteristic (AUC-ROC), and F1 score were considered as the evaluation metrics. A total of 295 spinal metastases patients (mean age ± standard deviation, 59.9 years ± 10.9; 154 men) were evaluated. Included metastases originated from lung cancer (n = 142), kidney cancer (n = 50), mammary cancer (n = 41), thyroid cancer (n = 34), and prostate cancer (n = 28). For 5-class classification, AUC-ROC and top-1 accuracy were 0.77 and 52.97%, respectively. Additionally, AUC-ROC for different sequence subsets ranged between 0.70 (for T2-weighted) and 0.74 (for fat-suppressed T2-weighted). Our developed ResNet-50 CNN model for predicting primary tumor sites in spinal metastases at MRI has the potential to help prioritize the examinations and treatments in case of unknown primary for radiologists and oncologists.

Radiomics from Mesorectal Blood Vessels and Lymph Nodes: A Novel Prognostic Predictor for Rectal Cancer with Neoadjuvant Therapy.

The objective of our study is to investigate the predictive value of various combinations of radiomic features from intratumoral and different peritumoral regions of interest (ROIs) for achieving a good pathological response (pGR) following neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). This retrospective study was conducted using data from LARC patients who underwent nCRT between 2013 and 2021. Patients were divided into training and validation cohorts at a ratio of 4:1. Intratumoral ROIs (ROIITU) were segmented on T2-weighted imaging, while peritumoral ROIs were segmented using two methods: ROIPTU_2mm, ROIPTU_4mm, and ROIPTU_6mm, obtained by dilating the boundary of ROIITU by 2 mm, 4 mm, and 6 mm, respectively; and ROIMR_F and ROIMR_BVLN, obtained by separating the fat and blood vessels + lymph nodes in the mesorectum. After feature extraction and selection, 12 logistic regression models were established using radiomics features derived from different ROIs or ROI combinations, and five-fold cross-validation was performed. The average area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the models. The study included 209 patients, consisting of 118 pGR and 91 non-pGR patients. The model that integrated ROIITU and ROIMR_BVLN features demonstrated the highest predictive ability, with an AUC (95% confidence interval) of 0.936 (0.904-0.972) in the training cohort and 0.859 (0.745-0.974) in the validation cohort. This model outperformed models that utilized ROIITU alone (AUC = 0.779), ROIMR_BVLN alone (AUC = 0.758), and other models. The radscore derived from the optimal model can predict the treatment response and prognosis after nCRT. Our findings validated that the integration of intratumoral and peritumoral radiomic features, especially those associated with mesorectal blood vessels and lymph nodes, serves as a potent predictor of pGR to nCRT in patients with LARC. Pending further corroboration in future research, these insights could provide novel imaging markers for refining therapeutic strategies.

Feasibility of AI-assisted compressed sensing protocols in knee MR imaging: a prospective multi-reader study.

OBJECTIVES: To evaluate the image quality and diagnostic performance of AI-assisted compressed sensing (ACS) accelerated two-dimensional fast spin-echo MRI compared with standard parallel imaging (PI) in clinical 3.0T rapid knee scans. METHODS: This prospective study enrolled 130 consecutive participants between March and September 2022. The MRI scan procedure included one 8.0-min PI protocol and two ACS protocols (3.5 min and 2.0 min). Quantitative image quality assessments were performed by evaluating edge rise distance (ERD) and signal-to-noise ratio (SNR). Shapiro-Wilk tests were performed and investigated by the Friedman test and post hoc analyses. Three radiologists independently evaluated structural disorders for each participant. Fleiss κ analysis was used to compare inter-reader and inter-protocol agreements. The diagnostic performance of each protocol was investigated and compared by DeLong's test. The threshold for statistical significance was set at p  < 0.05. RESULTS: A total of 150 knee MRI examinations constituted the study cohort. For the quantitative assessment of four conventional sequences with ACS protocols, SNR improved significantly (p < 0.001), and ERD was significantly reduced or equivalent to the PI protocol. For the abnormality evaluated, the intraclass correlation coefficient ranged from moderate to substantial between readers (κ = 0.75-0.98) and between protocols (κ = 0.73-0.98). For meniscal tears, cruciate ligament tears, and cartilage defects, the diagnostic performance of ACS protocols was considered equivalent to PI protocol (Delong test, p > 0.05). CONCLUSIONS: Compared with the conventional PI acquisition, the novel ACS protocol demonstrated superior image quality and was feasible for achieving equivalent detection of structural abnormalities while reducing acquisition time by half. CLINICAL RELEVANCE STATEMENT: Artificial intelligence-assisted compressed sensing (ACS) providing excellent quality and a 75% reduction in scanning time presents significant clinical advantages in improving the efficiency and accessibility of knee MRI for more patients. KEY POINTS: • The prospective multi-reader study showed no difference in diagnostic performance between parallel imaging and AI-assisted compression sensing (ACS) was found. • Reduced scan time, sharper delineation, and less noise with ACS reconstruction. • Improved efficiency of the clinical knee MRI examination by the ACS acceleration.

Predictive model based on DCE-MRI and clinical features for the evaluation of pain response after stereotactic body radiotherapy in patients with spinal metastases.

OBJECTIVE: To investigate the correlation of conventional MRI, DCE-MRI and clinical features with pain response after stereotactic body radiotherapy (SBRT) in patients with spinal metastases and establish a pain response prediction model. METHODS: Patients with spinal metastases who received SBRT in our hospital from July 2018 to April 2022 consecutively were enrolled. All patients underwent conventional MRI and DCE-MRI before treatment. Pain was assessed before treatment and in the third month after treatment, and the patients were divided into pain-response and no-pain-response groups. A multivariate logistic regression model was constructed to obtain the odds ratio and 95% confidence interval (CI) for each variable. C-index was used to evaluate the model's discrimination performance. RESULTS: Overall, 112 independent spinal lesions in 89 patients were included. There were 73 (65.2%) and 39 (34.8%) lesions in the pain-response and no-pain-response groups, respectively. Multivariate analysis showed that the number of treated lesions, pretreatment pain score, Karnofsky performance status score, Bilsky grade, and the DCE-MRI quantitative parameter Ktrans were independent predictors of post-SBRT pain response in patients with spinal metastases. The discrimination performance of the prediction model was good; the C index was 0.806 (95% CI: 0.721-0.891), and the corrected C-index was 0.754. CONCLUSION: Some imaging and clinical features correlated with post-SBRT pain response in patients with spinal metastases. The model based on these characteristics has a good predictive value and can provide valuable information for clinical decision-making. KEY POINTS: • SBRT can accurately irradiate spinal metastases with ablative doses. • Predicting the post-SBRT pain response has important clinical implications. • The prediction models established based on clinical and MRI features have good performance.

Decellularised spinal cord matrix manipulates glial niche into repairing phase via serglycin-mediated signalling pathway.

Astrocytes are the most abundant and widespread glial cells in the central nervous system. The heterogeneity of astrocytes plays an essential role in spinal cord injury (SCI) repair. Decellularised spinal cord matrix (DSCM) is advantageous for repairing SCI, but little is known regarding the exact mechanisms and niche alterations. Here, we investigated the DSCM regulatory mechanism of glial niche in the neuro-glial-vascular unit using single-cell RNA sequencing. Our single cell sequencing, molecular and biochemical experiments validated that DSCM facilitated the differentiation of neural progenitor cells through increasing the number of immature astrocytes. Upregulation of mesenchyme-related genes, which maintained astrocyte immaturity, causing insensitivity to inflammatory stimuli. Subsequently, we identified serglycin (SRGN) as a functional component of DSCM, which involves inducing CD44-AKT signalling to trigger human spinal cord-derived primary astrocytes (hspASCs) proliferation and upregulation of genes related to epithelial-mesenchymal transition, thus impeding astrocyte maturation. Finally, we verified that SRGN-COLI and DSCM had similar functions in the human primary cell co-culture system to mimic the glia niche. In conclusion, our work revealed that DSCM reverted astrocyte maturation and altered the glia niche into the repairing phase through the SRGN-mediated signalling pathway.

Deep learning-assisted classification of calcaneofibular ligament injuries in the ankle joint.

Background: The classification of calcaneofibular ligament (CFL) injuries on magnetic resonance imaging (MRI) is time-consuming and subject to substantial interreader variability. This study explores the feasibility of classifying CFL injuries using deep learning methods by comparing them with the classifications of musculoskeletal (MSK) radiologists and further examines image cropping screening and calibration methods. Methods: The imaging data of 1,074 patients who underwent ankle arthroscopy and MRI examinations in our hospital were retrospectively analyzed. According to the arthroscopic findings, patients were divided into normal (class 0, n=475); degeneration, strain, and partial tear (class 1, n=217); and complete tear (class 2, n=382) groups. All patients were divided into training, validation, and test sets at a ratio of 8:1:1. After preprocessing, the images were cropped using Mask region-based convolutional neural network (R-CNN), followed by the application of an attention algorithm for image screening and calibration and the implementation of LeNet-5 for CFL injury classification. The diagnostic effects of the axial, coronal, and combined models were compared, and the best method was selected for outgroup validation. The diagnostic results of the models in the intragroup and outgroup test sets were compared with those results of 4 MSK radiologists of different seniorities. Results: The mean average precision (mAP) of the Mask R-CNN using the attention algorithm for the left and right image cropping of axial and coronal sequences was 0.90-0.96. The accuracy of LeNet-5 for classifying classes 0-2 was 0.92, 0.93, and 0.92, respectively, for the axial sequences and 0.89, 0.92, and 0.90, respectively, for the coronal sequences. After sequence combination, the classification accuracy for classes 0-2 was 0.95, 0.97, and 0.96, respectively. The mean accuracies of the 4 MSK radiologists in classifying the intragroup test set as classes 0-2 were 0.94, 0.91, 0.86, and 0.85, all of which were significantly different from the model. The mean accuracies of the MSK radiologists in classifying the outgroup test set as classes 0-2 were 0.92, 0.91, 0.87, and 0.85, with the 2 senior MSK radiologists demonstrating similar diagnostic performance to the model and the junior MSK radiologists demonstrating worse accuracy. Conclusions: Deep learning can be used to classify CFL injuries at similar levels to those of MSK radiologists. Adding an attention algorithm after cropping is helpful for accurately cropping CFL images.

Primary leiomyosarcoma of the spine: an analysis of imaging manifestations and clinicopathological findings.

BACKGROUND: Primary leiomyosarcoma of the spine is extremely rare and lacks specific clinical symptoms. This study investigated the imaging manifestations and clinicopathological findings of primary leiomyosarcoma of the spine, aiming to improve the radiologists' understanding of the disease and reduce misdiagnoses. METHODS: The clinical, imaging, and pathological manifestations in eleven patients with pathologically confirmed primary leiomyosarcoma of the spine were retrospectively analyzed. The imaging features analyzed included lesion location, shape, border, size, and density/intensity, and adjacent bone destruction status, residual bone trabeculae, vertebral compression, and contrast enhancement. RESULTS: The patients' primary clinical symptom was usually focal pain. Primary leiomyosarcoma of the spine was mostly a solitary lesion and tended to occur in the posterior elements. The tumors had a lobulated shape with osteolytic bone destruction, ill-defined borders, and could involve multiple segments. Computed tomography (CT) examination showed isodense masses. Six patients showed residual bone trabeculae. Two patients had miscellany T2-weighted imaging (T2WI) signals, while the tumor and spinal cord of the remaining patients were isointense on T1-weighted imaging (T1WI) and T2WI. Among the seven patients who underwent contrast-enhanced scanning, six displayed homogeneous enhancement. Eight patients underwent gross-total tumor resection with no recurrence. CONCLUSIONS: Primary leiomyosarcoma of the spine tends to be a solitary lesion in the posterior elements and appears as a lobulated mass with osteolytic bone destruction and an ill-defined border. The tumor and spinal cord can be isointense on T1WI and T2WI. Contrast-enhanced scanning displays homogeneous enhancement. The lesion tends not to recur after surgical gross-total tumor resection.

Differentiation of predominantly osteolytic from osteoblastic spinal metastases based on standard magnetic resonance imaging sequences: a comparison of radiomics model versus semantic features logistic regression model findings.

Background: The aim of this study was to compare the ability of a standard magnetic resonance imaging (MRI)-based radiomics model and a semantic features logistic regression model in differentiating between predominantly osteolytic and osteoblastic spinal metastases. Methods: We retrospectively analyzed standard MRIs and computed tomography (CT) images of 78 lesions of spinal metastases, of which 52 and 26 were predominantly osteolytic and osteoblastic, respectively. CT images were used as references for determining the sensitivity and specificity of standard MRI. Five standard MRI semantic features of each lesion were evaluated and used for constructing a logistic regression model to differentiate between predominantly osteolytic and osteoblastic metastases. For each lesion, 107 radiomics features were extracted. Six features were selected using a support vector machine (SVM) and were used for constructing classification models. Model performance was measured by means of the area under the curve (AUC) approach and compared using receiver operating characteristics (ROC) curve analysis. Results: The signal intensity on T1-weighted (T1W), T2-weighted (T2W), and fat-suppressed T2-weighted (FS-T2W) MRI sequences were significantly different between predominantly osteolytic and osteoblastic spinal metastases (P<0.001), as is the case with the existence of soft-tissue masses. The overall prediction accuracy of the models based on radiomics and semantic features was 78.2% and 75.6%, respectively, with corresponding AUCs of 0.82 and 0.79, respectively. Conclusions: The standard MRI-based radiomics model outperformed the semantic features logistic regression model with regard to differentiating predominantly osteolytic and osteoblastic spinal metastases.

Prognostic Value and Quantitative CT Analysis in RANKL Expression of Spinal GCTB in the Denosumab Era: A Machine Learning Approach.

The receptor activator of the nuclear factor kappa B ligand (RANKL) is the therapeutic target of denosumab. In this study, we evaluated whether radiomics signature and machine learning analysis can predict RANKL status in spinal giant cell tumors of bone (GCTB). This retrospective study consisted of 107 patients, including a training set (n = 82) and a validation set (n = 25). Kaplan-Meier survival analysis was used to validate the prognostic value of RANKL status. Radiomic feature extraction of three heterogeneous regions (VOIentire, VOIedge, and VOIcore) from pretreatment CT were performed. Followed by feature selection using Selected K Best and least absolute shrinkage and selection operator (LASSO) analysis, three classifiers (random forest (RF), support vector machine, and logistic regression) were used to build models. The area under the curve (AUC), accuracy, F1 score, recall, precision, sensitivity, and specificity were used to evaluate the models' performance. Classification of 75 patients with eligible follow-up based on RANKL status resulted in a significant difference in progression-free survival (p = 0.035). VOIcore-based RF classifier performs best. Using this model, the AUCs for the training and validation cohorts were 0.880 and 0.766, respectively. In conclusion, a machine learning approach based on CT radiomic features could discriminate prognostically significant RANKL status in spinal GCTB, which may ultimately aid clinical decision-making.