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.

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.

A matched case-control study of early cervical spondylotic myelopathy based on diffusion magnetic resonance imaging.

BACKGROUND: Early cervical spondylotic myelopathy (CSM) is challenging to diagnose and easily missed. Diffusion MRI (dMRI) has the potential to identify early CSM. METHODS: Using diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI), a 1:1 matched case-control study was conducted to evaluate the potential of dMRI in identifying early CSM and assessing uncompressed segments of CSM patients. CSM patients and volunteers were matched by age and spinal location. The differences in dMRI parameters between groups were assessed by the paired t-test, the multicollinearity of the dMRI parameters was evaluated by the variance inflation factor (VIF), and the value of dMRI parameters in distinguishing controls from CSM patients was determined by logistic regression. The univariate t-test was used to analyse differences between CSM patients and volunteers in adjacent uncompressed areas. RESULTS: In total, 56 CSM patients and 56 control volunteers were included. Paired t-tests revealed significant differences in nine dMRI parameters between groups. Multicollinearity calculated through VIF and combined with logistic regression showed that the orientation division index (ODI) was significantly positively correlated (r = 2.12, p = 0.035), and the anisotropic water fraction (AWF) was significantly negatively correlated (r = -0.98, p = 0.015). The fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), isotropic volume fraction (ISOVF), ODI, and AWF were significantly different in the upper and lower uncompressed areas at all ages. CONCLUSION: dMRI can noninvasively identify early CSM patients and potentially identify the extent of CSM lesions involving the cervical spinal cord. CRITICAL RELEVANCE STATEMENT: Diffusion MRI (dMRI) can identify early cervical spondylotic myelopathy (CSM) and has the potential to help determine the extent of CSM involvement. The application of dMRI can help screen for early CSM and develop clinical surgical and rehabilitation treatment plans. KEY POINTS: • Diffusion MRI can differentiate between normal and early-stage cervical spondylotic myelopathy patients. • Diffusion MRI has the ability to identify the extent of spinal cord involvement in cervical spondylotic myelopathy. • Diffusion MRI enables the early screening of cervical spondylotic myelopathy and helps guide clinical treatment.

Discrepancies in MR- and CT-Based Femoral Version Measurements Despite Strong Correlations.

PURPOSE: To determine the correlation and classification consistency of femoral version measurements between magnetic resonance (MR) and computed tomography (CT) using 4 commonly used measurement methods. METHODS: A retrospective study was performed on patients with femoroacetabular impingement (FAI) who received preoperative CT and MR imaging assessment of the surgical hip and ipsilateral distal femur. Femoral version was measured using the Murphy method, the oblique method, the Reikerås method, and the Lee method. Intra- and inter-rater agreements were calculated. Linear regression and Bland-Altman analysis were performed for measurements using different imaging modalities and measurement methods. Femoral version measurements within the lower quartile, the middle 2 quartiles, and the upper quartile were classified into different groups based on their percentile within the sample population. Classification consistency rates between modalities and methods were calculated and compared. RESULTS: Fifty-three patients (39.4 ± 9.1 years; 32 female) were included for analysis. Intra- and inter-rater reliability were high for all modalities and methods (intrarater intraclass correlation coefficient [ICC] range, 0.963-0.993; inter-rater ICC range, 0.871-0.960). MR- and CT-based femoral version measurements showed strong correlations for all methods, with the Lee method demonstrating the strongest association (r = 0.904), while the oblique method exhibited the lowest correlation (r = 0.684) (all P < .001). MR-based measurements were smaller than CT-based measurements, with mean differences ranging from 4.5° to 10.3°. Classification consistency between MR and CT ranged from 51% to 74%, whereas the consistency between different measurement methods ranged from 68% to 85%. CONCLUSIONS: While strong correlations were observed between MR- and CT-based femoral version measurements, MR-based measurements were significantly smaller than their CT counterparts. Classification consistency between the modalities was moderate to high. Measurements between different methods showed strong correlations with high consistency rates. LEVEL OF EVIDENCE: Level III, retrospective case series.

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.

Predictive Value of the Diffusion Magnetic Resonance Imaging Technique for the Postoperative Outcome of Cervical Spondylotic Myelopathy.

BACKGROUND: Diffusion magnetic resonsance imaging (dMRI) can potentially predict the postoperative outcome of cervical spondylotic myelopathy (CSM). PURPOSE: To explore preoperative dMRI parameters to predict the postoperative outcome of CSM through multifactor correlation analysis. STUDY TYPE: Prospective. POPULATION: Post-surgery CSM patients; 102 total, 73 male (52.42 ± 10.60 years old) and 29 female (52.0 ± 11.45 years old). FIELD STRENGTH/SEQUENCE: 3.0 T/Turbo spin echo T1/T2-weighted, T2*-weighted multiecho gradient echo and dMRI. ASSESSMENT: Spinal cord function was evaluated using modified Japanese Orthopedic Association (mJOA) scoring at different time points: preoperative and 3, 6, and 12 months postoperative. Single-factor correlation and t test analyses were conducted based on fractional anisotropy (FA), mean diffusivity, intracellular volume fraction, isotropic volume fraction, orientation division index, increased signal intensity, compression ratio, age, sex, symptom duration and operation method, and multicollinearity was calculated. The linear quantile mixed model (LQMM) and the linear mixed-effects regression model (LMER) were used for multifactor correlation analysis using the combinations of the above variables. STATISTICAL TESTS: Distance correlation, Pearson's correlation, multiscale graph correlation and t tests were used for the single-factor correlation analyses. The variance inflation factor (VIF) was used to calculate multicollinearity. LQMM and LMER were used for multifactor correlation analyses. P < 0.05 was considered statistically significant. RESULTS: The single-factor correlation between all variables and the postoperative mJOA score was weak (all r < 0.3). The linear relationship was stronger than the nonlinear relationship, and there was no significant multicollinearity (VIF = 1.10-1.94). FA values in the LQMM and LMER models had a significant positive correlation with the mJOA score (r = 5.27-6.04), which was stronger than the other variables. DATA CONCLUSION: The FA value based on dMRI significantly positively correlated with CSM patient postoperative outcomes, helping to predict the surgical outcome and formulate a treatment plan before surgery. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

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.

Deep learning model for measuring the sagittal Cobb angle on cervical spine computed tomography.

PURPOSES: To develop a deep learning (DL) model to measure the sagittal Cobb angle of the cervical spine on computed tomography (CT). MATERIALS AND METHODS: Two VB-Net-based DL models for cervical vertebra segmentation and key-point detection were developed. Four-points and line-fitting methods were used to calculate the sagittal Cobb angle automatically. The average value of the sagittal Cobb angle was manually measured by two doctors as the reference standard. The percentage of correct key points (PCK), matched samples t test, intraclass correlation coefficient (ICC), Pearson correlation coefficient, mean absolute error (MAE), and Bland‒Altman plots were used to evaluate the performance of the DL model and the robustness and generalization of the model on the external test set. RESULTS: A total of 991 patients were included in the internal data set, and 112 patients were included in the external data set. The PCK of the DL model ranged from 78 to 100% in the test set. The four-points method, line-fitting method, and reference standard measured sagittal Cobb angles were - 1.10 ± 18.29°, 0.30 ± 13.36°, and 0.50 ± 12.83° in the internal test set and 4.55 ± 20.01°, 3.66 ± 18.55°, and 1.83 ± 12.02° in the external test set, respectively. The sagittal Cobb angle calculated by the four-points method and the line-fitting method maintained high consistency with the reference standard (internal test set: ICC = 0.75 and 0.97; r = 0.64 and 0.94; MAE = 5.42° and 3.23°, respectively; external test set: ICC = 0.74 and 0.80, r = 0.66 and 0.974, MAE = 5.25° and 4.68°, respectively). CONCLUSIONS: The DL model can accurately measure the sagittal Cobb angle of the cervical spine on CT. The line-fitting method shows a higher consistency with the doctors and a minor average absolute error.

Perioperative cerebral blood flow measured by arterial spin labeling with different postlabeling delay in patients undergoing carotid endarterectomy: a comparison study with CT perfusion.

Background: Arterial spin labeling (ASL) is a non-invasive technique for measuring cerebral perfusion. Its accuracy is affected by the arterial transit time. This study aimed to (1) evaluate the accuracy of ASL in measuring the cerebral perfusion of patients who underwent carotid endarterectomy (CEA) and (2) determine a better postlabeling delay (PLD) for pre- and postoperative perfusion imaging between 1.5 and 2.0 s. Methods: A total of 24 patients scheduled for CEA due to severe carotid stenosis were included in this study. All patients underwent ASL with two PLDs (1.5 and 2.0 s) and computed tomography perfusion (CTP) before and after surgery. Cerebral blood flow (CBF) values were measured on the registered CBF images of ASL and CTP. The correlation in measuring perioperative relative CBF (rCBF) and difference ratio of CBF (DRCBF) between ASL with PLD of 1.5 s (ASL1.5) or 2.0 s (ASL2.0) and CTP were also determined. Results: There were no significant statistical differences in preoperative rCBF measurements between ASL1.5 and CTP (p = 0.17) and between ASL2.0 and CTP (p = 0.42). Similarly, no significant differences were found in rCBF between ASL1.5 and CTP (p = 0.59) and between ASL2.0 and CTP (p = 0.93) after CEA. The DRCBF measured by CTP was found to be marginally lower than that measured by ASL2.0_1.5 (p = 0.06) and significantly lower than that measured by ASL1.5_1.5 (p = 0.01), ASL2.0_2.0 (p = 0.03), and ASL1.5_2.0 (p = 0.007). There was a strong correlation in measuring perioperative rCBF and DRCBF between ASL and CTP (r = 0.67-0.85, p < 0.001). Using CTP as the reference standard, smaller bias can be achieved in measuring rCBF by ASL2.0 (-0.02) than ASL1.5 (-0.07) before CEA. In addition, the same bias (0.03) was obtained by ASL2.0 and ASL1.5 after CEA. The bias of ASL2.0_2.0 (0.31) and ASL2.0_1.5 (0.32) on DRCBF measurement was similar, and both were smaller than that of ASL1.5_1.5 (0.60) and ASL1.5_2.0 (0.60). Conclusion: Strong correlation can be found in assessing perioperative cerebral perfusion between ASL and CTP. During perioperative ASL imaging, the PLD of 2.0 s is better than 1.5 s for preoperative scan, and both 1.5 and 2.0 s are suitable for postoperative scan.

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.

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.

Study on the Evolution of Fault Permeability and the Retention of Coal (Rock) Pillar under the Mining Conditions of Thick Coal Seam in the Footwall of Large Normal Fault.

As a typical geological structure, the fault often threatens the safe mining of coal mines. In order to investigate the permeability evolution of the significant normal fault under the mining disturbance of the thick coal seam of the fault footwall and to propose a scientific and reasonable coal (rock) pillar retention plan, this paper took the YinJiaWa Fault (YJW Fa), a large normal fault, in Fucun Coal Mine, Shandong Province, China, as a research object, conducted a coupled fluid and solid simulation study on permeability evolution of the fault using COMSOL Multiphysics, based on the revealed geological data and rock mechanical parameters, and combined the theoretical calculation results to determine the width of the waterproof coal (rock) pillar. The results show that the width of the waterproof coal (rock) pillar of YJW Fa is negatively correlated with the porosity, permeability, and flow velocity of each monitoring point. With the width of 60 m as the dividing point, as the width left less than 60 m and gradually reduced to 30 m, its water-blocking capacity is destroyed, increasing the seepage velocity in the water-flowing fractured zone, forming a water channel, causing water inrush accidents. The formula and numerical simulation results are used to determine the width of the waterproof coal (rock) pillar of the YJW Fa to be 74.44-84.08 m, to ensure the safe mining of the fault footwall. This paper provides a theoretical basis for further understanding of the fault permeability development rules and safety guidance for coal seam mining of the fault footwall.

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.

BgNet: Classification of benign and malignant tumors with MRI multi-plane attention learning.

Objectives: To propose a deep learning-based classification framework, which can carry out patient-level benign and malignant tumors classification according to the patient's multi-plane images and clinical information. Methods: A total of 430 cases of spinal tumor, including axial and sagittal plane images by MRI, of which 297 cases for training (14072 images), and 133 cases for testing (6161 images) were included. Based on the bipartite graph and attention learning, this study proposed a multi-plane attention learning framework, BgNet, for benign and malignant tumor diagnosis. In a bipartite graph structure, the tumor area in each plane is used as the vertex of the graph, and the matching between different planes is used as the edge of the graph. The tumor areas from different plane images are spliced at the input layer. And based on the convolutional neural network ResNet and visual attention learning model Swin-Transformer, this study proposed a feature fusion model named ResNetST for combining both global and local information to extract the correlation features of multiple planes. The proposed BgNet consists of five modules including a multi-plane fusion module based on the bipartite graph, input layer fusion module, feature layer fusion module, decision layer fusion module, and output module. These modules are respectively used for multi-level fusion of patient multi-plane image data to realize the comprehensive diagnosis of benign and malignant tumors at the patient level. Results: The accuracy (ACC: 79.7%) of the proposed BgNet with multi-plane was higher than that with a single plane, and higher than or equal to the four doctors' ACC (D1: 70.7%, p=0.219; D2: 54.1%, p<0.005; D3: 79.7%, p=0.006; D4: 72.9%, p=0.178). Moreover, the diagnostic accuracy and speed of doctors can be further improved with the aid of BgNet, the ACC of D1, D2, D3, and D4 improved by 4.5%, 21.8%, 0.8%, and 3.8%, respectively. Conclusions: The proposed deep learning framework BgNet can classify benign and malignant tumors effectively, and can help doctors improve their diagnostic efficiency and accuracy. The code is available at https://github.com/research-med/BgNet.