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.

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.

Characteristics and outcomes of patients with primary abdominopelvic aggressive angiomyxoma: a retrospective review of 12 consecutive cases from a sarcoma referral center.

BACKGROUND: Aggressive angiomyxoma (AAM) is a rare mesenchymal tumor that mostly arises from the pelvic and perineal soft tissues. Few studies reported its characteristics and outcomes previously due to its rarity and challenges of treatments. This study aimed to investigate the clinical characteristics as well as surgical and short-term survival outcomes of primary abdominopelvic AAM. METHODS: Medical records of patients who were admitted to surgery with pathological confirmation of primary abdominopelvic AAM at Peking University International Hospital from January 2016 through December 2021 were retrospectively retrieved from our retroperitoneal tumor database. Demographics, operative outcomes and pathological findings were collected. Patients received followed-up routinely after the surgery. Survival probabilities were calculated and determined through Kaplan-Meier analysis. RESULTS: A total of 12 consecutive patients (male/female 4:8) were included in this study. The median age was 45 years old. The clinical presentation varied among individuals, consisting of 2 abdominal discomforts, 4 constipations, 1 lumbago, 1 prolonged menstruation, and 1 buttock swelling. R0/R1 resection was achieved in 100% of patients. Postoperatively, 50% of patients developed various complications including 3 fistulas and 3 wound infections. No operative mortality was observed. Histopathology of all patients was suggestive of AAM. Immunohistochemistry was done with a 91.7% positive rate for estrogen and progesterone receptors. The median recurrence-free survival time was 38 months. There were no cases of deceased or presented with distal metastasis during a median of 42 months' follow-up. CONCLUSIONS: The clinical manifestations of abdominopelvic AAM are mostly atypical. Surgical resection with curative intents remains the mainstay treatment of this disease, which was strongly suggested in experienced sarcoma centers due to the high probability of severe postoperative complications. In addition, long-term follow-up is necessary due to the high rate of local recurrences.

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.

Intravoxel incoherent motion to differentiate spinal metastasis: A pilot study.

Background: To investigate the value of intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) to discriminate spinal metastasis from tuberculous spondylitis. Methods: This study included 50 patients with spinal metastasis (32 lung cancer, 7 breast cancer, 11 renal cancer), and 20 with tuberculous spondylitis. The IVIM parameters, including the single-index model (apparent diffusion coefficient (ADC)-stand), double exponential model (ADCslow, ADCfast, and f), and the stretched-exponential model parameters (distributed diffusion coefficient (DDC) and α), were acquired. Receiver operating characteristic (ROC) and the area under the ROC curve (AUC) analysis was used to evaluate the diagnostic performance. Each parameter was substituted into a logistic regression model to determine the meaningful parameters, and the combined diagnostic performance was evaluated. Results: The ADCfast and f showed significant differences between spinal metastasis and tuberculous spondylitis (all p < 0.05). The logistic regression model results showed that ADCfast and f were independent factors affecting the outcome (P < 0.05). The AUC values of ADCfast and f were 0.823 (95% confidence interval (CI): 0.719 to 0.927) and 0.876 (95%CI: 0.782 to 0.969), respectively. ADCfast combined with f showed the highest AUC value of 0.925 (95% CI: 0.858 to 0.992). Conclusions: IVIM MR imaging might be helpful to differentiate spinal metastasis from tuberculous spondylitis, and provide guidance for clinical treatment.

Case report: Castleman's disease involving the renal sinus resembling renal cell carcinoma.

Introduction: Castleman's disease (CD) is a rare benign lymphoproliferative disease that frequently involves the mediastinal thorax and the neck lymph nodes. It rarely affects extrathoracic presentations, with even fewer presentations in the renal sinus. Patient concerns: In this report, we present a case of a 40-year-old woman with no significant past medical history who presented Castleman's disease arising in the renal sinus. Diagnosis and interventions: The patient visited our hospital with the chief complaint of left renal sinus lesion after renal ultrasonography by regular physical examination. Subsequent abdominal computed tomography urography revealed a soft tissue mass with heterogeneous obvious enhancement in the sinus of the left kidney, which was suspected to be a renal malignant tumor. Hence, the patient underwent a left radical nephrectomy. Histological examination revealed hyperplastic lymphoid follicles in the renal sinus and was finally diagnosed as Castleman's disease of the hyaline vascular type. Outcomes: Five days after the surgery procedure, the patient was discharged. Conclusion: Due to the low incidence of Castleman's disease in renal sinus, there is a strong likelihood of missed diagnosis or misdiagnosis, and it is, therefore, important to be aware of the risk. Heightened awareness of this disease and its radiographic manifestations may prompt consideration of this diagnosis. Therefore, we explored the radiologic findings to find out some radiologic features suggesting this condition to help clinicians to schedule nephron-sparing surgery in the future.

A Multiparametric Method Based on Clinical and CT-Based Radiomics to Predict the Expression of p53 and VEGF in Patients With Spinal Giant Cell Tumor of Bone.

Purpose: This project aimed to assess the significance of vascular endothelial growth factor (VEGF) and p53 for predicting progression-free survival (PFS) in patients with spinal giant cell tumor of bone (GCTB) and to construct models for predicting these two biomarkers based on clinical and computer tomography (CT) radiomics to identify high-risk patients for improving treatment. Material and Methods: A retrospective study was performed from April 2009 to January 2019. A total of 80 patients with spinal GCTB who underwent surgery in our institution were identified. VEGF and p53 expression and clinical and general imaging information were collected. Multivariate Cox regression models were used to verify the prognostic factors. The radiomics features were extracted from the regions of interest (ROIs) in preoperative CT, and then important features were selected by the SVM to build classification models, evaluated by 10-fold crossvalidation. The clinical variables were processed using the same method to build a conventional model for comparison. Results: The immunohistochemistry of 80 patients was obtained: 49 with high-VEGF and 31 with low-VEGF, 68 with wild-type p53, and 12 with mutant p53. p53 and VEGF were independent prognostic factors affecting PFS found in multivariate Cox regression analysis. For VEGF, the Spinal Instability Neoplastic Score (SINS) was greater in the high than low groups, p < 0.001. For p53, SINS (p = 0.030) and Enneking stage (p = 0.017) were higher in mutant than wild-type groups. The VEGF radiomics model built using 3 features achieved an area under the curve (AUC) of 0.88, and the p53 radiomics model built using 4 features had an AUC of 0.79. The conventional model built using SINS, and the Enneking stage had a slightly lower AUC of 0.81 for VEGF and 0.72 for p53. Conclusion: p53 and VEGF are associated with prognosis in patients with spinal GCTB, and the radiomics analysis based on preoperative CT provides a feasible method for the evaluation of these two biomarkers, which may aid in choosing better management strategies.

Epithelioid hemangioendothelioma of the spine: an analysis of imaging findings.

BACKGROUND: Epithelioid hemangioendothelioma (EHE) is a low-grade malignant vascular neoplasm with the potential to metastasize. Primary EHE of the spine is very rare and an accurate diagnosis is crucial to treatment planning. We aim to investigate the imaging and clinical data of spinal EHE to improve the understanding of the disease. METHODS: We retrospectively analyzed the imaging manifestations and clinical data of 12 cases with pathologically confirmed spinal EHE. The imaging features analyzed included number, locations, size, border, density, signal, majority of the lesions, expansile osteolysis, residual bone trabeculae, sclerotic rim, vertebral compression, enhancement. RESULTS: Patients included 5 female and 7 male patients (mean age: 43.0 ± 19.6 years; range 15-73 years). Multiple lesions were noted in 1 case and single lesion was noted in 11 cases. The lesions were located in the thoracic, cervical, lumbar, and sacral vertebrae in 7, 3, 1, and 1 cases, respectively. They were centered in the vertebral body and posterior elements in 9 and 3 cases, respectively. Residual bone trabeculae, no sclerotic margin, and surrounding soft-tissue mass were noted in 11 cases, each, and mild expansile osteolysis and vertebral compression were noted in 10 and 6 cases, respectively. MRI was performed for 11 patients, all of whom showed isointensity on T1WI, hyperintensity or slight hyperintensity on T2WI, and hyperintensity on fat-suppressed T2WI. A marked enhancement pattern was noted in 10 cases. CONCLUSION: Spinal EHE tend to develop in the thoracic vertebrae. EHE should be considered when residual bone trabeculae can be seen in the bone destruction area, accompanied by pathological compression fracture, no sclerotic rim, and high signal intensity for a vascular tumor on T2WI.

[Advances in Radiomics of Cerebral Metastases].

Cerebral metastases are the most common intracranial tumors in adults,with an increasing incidence in recent years.Radiomics can quantitatively analyze and process medical images to guide clinical practice.In recent years,CT and MRI-based radiomics has been gradually applied to the precise diagnosis and treatment of cerebral metastases,such as the precise detection and segmentation of tumors,the differential diagnosis with other cerebral tumors,the identification of primary tumors,the evaluation of treatment efficacy,and the prediction of prognosis.This article reviews the advances in radiomics of cerebral metastases.

Clinical Significance of Preoperative CT and MR Imaging Findings in the Prediction of Postoperative Recurrence of Spinal Giant Cell Tumor of Bone.

OBJECTIVES: To explore the predictive value of preoperative imaging in patients with spinal giant cell tumor of bone (GCTB) for postoperative recurrence and risk stratification. METHODS: Clinical data for 62 cases of spinal GCTB diagnosed and treated at our hospital from 2008 to 2018 were identified. All patients were followed up for more than 2 years according to the clinical guidelines after surgery. Medical history data including baseline demographic and clinical characteristics, computed tomography (CT) and magnetic resonance imaging (MRI) findings of recurrent and non-recurrent patients were compared. Two musculoskeletal radiologists read the images and were blinded to the clinical data. The imaging features associated with postoperative recurrence were analyzed by multivariate logistic regression, and receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cutoff value of the largest lesion diameter predicting recurrence after surgery. RESULTS: According to whether the disease recurred within the follow-up period, patients were divided into the recurrence group and the non-recurrence group. Of 62 patients (29 males and 33 females), 17 had recurrence and 45 did not. The recurrence rate was 27.4%. The mean follow-up time was 73.66 (± 32.92) months. The three major treatments were total en bloc spondylectomy (n = 26), intralesional spondylectomy (n = 20), and curettage(n = 16). A total of 16 CT and MRI features were analyzed. A univariate analysis showed no significant difference in age, sex, treatment, multi-vertebral body involvement, location, boundary, expansile mass, residual bone crest, paravertebral soft tissue mass, CT value, and MRI signal on T1-weighted imaging (WI), T2-WI, and T2-WI fat suppression (FS) sequences (P > 0.05). The largest lesion diameter [(4.68 ± 1.79) vs (5.92 ± 2.17) cm, t = 2.287, P = 0.026] and the vertebral compression fracture (51% vs 82%, χ2  = 5.005, P = 0.025) were significantly different between the non-recurrence and recurrence groups. Logistic regression analysis showed that both largest lesion diameter (odds ratio [OR], 1.584; 95% confidence interval [CI], 1.108-2.264; P = 0.012) and compression fracture (OR, 8.073; 95%CI, 1.481-11.003; P = 0.016) were independent predictors of postoperative recurrence. When we set the cutoff value for the largest lesion diameter at 4.2 cm, the sensitivity and specificity for distinguishing the recurrence and non-recurrence of GCTB were 94.1% and 42.2%, respectively, and the area under the curve (AUC) was 0.671. The combined model achieved a sensitivity, specificity and accuracy of 47.1%, 97.8% and 83.9%, respectively. CONCLUSIONS: In spinal GCTB, maximum lesion diameter and the vertebral compression fracture are associated with tumor recurrence after surgery, which may provide helpful information for planning personalized treatment.

Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning.

OBJECTIVES: To evaluate the performance of deep learning using ResNet50 in differentiation of benign and malignant vertebral fracture on CT. METHODS: A dataset of 433 patients confirmed with 296 malignant and 137 benign fractures was retrospectively selected from our spinal CT image database. A senior radiologist performed visual reading to evaluate six imaging features, and three junior radiologists gave diagnostic prediction. A ROI was placed on the most abnormal vertebrae, and the smallest square bounding box was generated. The input channel into ResNet50 network was 3, including the slice with its two neighboring slices. The diagnostic performance was evaluated using 10-fold cross-validation. After obtaining the malignancy probability from all slices in a patient, the highest probability was assigned to that patient to give the final diagnosis, using the threshold of 0.5. RESULTS: Visual features such as soft tissue mass and bone destruction were highly suggestive of malignancy; the presence of a transverse fracture line was highly suggestive of a benign fracture. The reading by three radiologists with 5, 3, and 1 year of experience achieved an accuracy of 99%, 95.2%, and 92.8%, respectively. In ResNet50 analysis, the per-slice diagnostic sensitivity, specificity, and accuracy were 0.90, 0.79, and 85%. When the slices were combined to ve per-patient diagnosis, the sensitivity, specificity, and accuracy were 0.95, 0.80, and 88%. CONCLUSION: Deep learning has become an important tool for the detection of fractures on CT. In this study, ResNet50 achieved good accuracy, which can be further improved with more cases and optimized methods for future clinical implementation. KEY POINTS: • Deep learning using ResNet50 can yield a high accuracy for differential diagnosis of benign and malignant vertebral fracture on CT. • The per-slice diagnostic sensitivity, specificity, and accuracy were 0.90, 0.79, and 85% in deep learning using ResNet50 analysis. • The slices combined with per-patient diagnostic sensitivity, specificity, and accuracy were 0.95, 0.80, and 88% in deep learning using ResNet50 analysis.

Prediction of the early recurrence in spinal giant cell tumor of bone using radiomics of preoperative CT: Long-term outcome of 62 consecutive patients.

OBJECTIVES: To determine if radiomics analysis based on preoperative computed tomography (CT) can predict early postoperative recurrence of giant cell tumor of bone (GCTB) in the spine. METHODS: In a retrospective review, 62 patients with pathologically confirmed spinal GCTB from March 2008 to February 2018, with a minimum follow-up of 24 months, were identified. The mean follow-up was 73.7 months (range, 28.7-152.1 months). The clinical information including age, gender, lesion location, multi-vertebral involvement, and surgical methods, were obtained. CT images acquired before the operation were retrieved for radiomics analysis. For each case, the tumor regions of interest (ROI) was manually outlined, and a total of 107 radiomics features were extracted. The features were selected via the sequential selection process by using the support vector machine (SVM), then used to construct classification models with Gaussian kernels. The differentiation between recurrence and non-recurrence groups was evaluated by ROC analysis, using 10-fold cross-validation. RESULTS: Of the 62 patients, 17 had recurrence with a recurrence rate of 27.4%. None of the clinical information was significantly different between the two groups. Patients receiving curettage had a higher recurrence rate (6/16 = 37.5%) compared to patients receiving TES (6/26 = 23.1%) or intralesional spondylectomy (5/20 = 25%). The final radiomics model was built using 10 selected features, which achieved an accuracy of 89% with AUC of 0.78. CONCLUSIONS: The radiomics model developed based on pre-operative CT can achieve a high accuracy to predict the recurrence of spinal GCTB. Patients who have a high risk of early recurrence should be treated more aggressively to minimize recurrence.

Early response assessment after CyberKnife stereotactic radiosurgery for symptomatic vertebral hemangioma by quantitative parameters from dynamic contrast-enhanced MRI.

PURPOSE: The present study aimed to explore the value of DCE-MRI to evaluate the early efficacy of CyberKnife stereotactic radiosurgery in patients with symptomatic vertebral hemangioma (SVH). METHODS: A retrospective analysis of patients with spinal SVH who underwent CyberKnife stereotactic radiosurgery from January 2017 to August 2019 was performed. All patients underwent DCE-MRI before treatment and three months after treatment. The parameters included volume transfer constant (Ktrans), transfer rate constant (Kep), and extravascular extracellular space volume fraction (Ve). RESULTS: A total of 11 patients (11 lesions) were included. After treatment, six patients (54.5%) had a partial response, five patients (45.4%) had stable disease, and three patients (27.3%) presented with reossification. Ktrans and Kep decreased significantly in the third month after treatment (p = 0.003 and p = 0.026, respectively). ΔKtrans was -46.23% (range, -87.37 to -23.78%), and ΔKep was -36.18% (range, -85.62 to 94.40%). The change in Ve was not statistically significant (p = 0.213), and ΔVe was -28.01% (range, -58.24 to 54.76%). CONCLUSION: DCE-MRI parameters Ktrans and Kep change significantly after CyberKnife stereotactic radiosurgery for SVH. Thus, DCE-MRI may be of value in determining the early efficacy of CyberKnife stereotactic radiosurgery.

Cerebral carbon dioxide embolism after kidney cancer laparoscopic surgery with full neurological recovery: A case report.

RATIONALE: Cerebral carbon dioxide embolism (CCDE) is a rare cause of stroke and is a recognized life-threatening complication.CCDE may result from direct intravascular CO2 insufflation during surgery. Due to the lack of typical clinical manifestations, the disease is often missed or mistaken for another condition. The clinical signs and symptoms depend on the speed and volume of embolized gas entering the blood and the patient's condition. In particular, patent foramen ovale has been found to be associated, in rare cases, with the intraoperative entry of gas into the arterial system. PATIENT CONCERNS: In this report, we present the case of a 35-year-old woman with kidney cancer who underwent laparoscopic right partial nephrectomy. DIAGNOSIS: After the laparoscopic surgery, the patient was initially diagnosed with acute cerebral infarction. INTERVENTIONS: The patient was treated according to the standard method for treatment of acute cerebrovascular disease. OUTCOMES: Three days after the laparoscopic procedure, the patient gained consciousness, and she was discharged without any neurologic sequelae on postoperative day 12. LESSONS SUBSECTIONS AS PER STYLE: Due to the low incidence and sudden occurrence of CCDE, there is a strong likelihood of missed diagnosis or misdiagnosis, and it is; therefore, important to be aware of the risk. The findings from this report would be highly useful as a reference to clinicians in the future.

[Applications of Artificial Intelligence in Musculoskeletal System Imaging].

Artificial intelligence (AI) represents the latest wave of computer revolution and is considered revolutionary technology in many industries including healthcare. AI has been applied in medical imaging mainly due to the improvement of computational learning,big data mining,and innovations of neural network architecture. AI can improve the efficiency and accuracy of imaging diagnosis and reduce medical cost;also,it can be used to predict the disease risk. In this article we summarize and analyze the application of AI in musculoskeletal imaging.

Use of monoexponential diffusion-weighted imaging and diffusion kurtosis imaging and dynamic contrast-enhanced-MRI for the differentiation of spinal tumors.

PURPOSE: To explore the diagnostic value of monoexponential diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and dynamic contrast-enhanced (DCE)-MRI for differentiating between spinal malignant and non-malignant tumors lacking typical imaging signs and correlation between the parameters of the three models. METHODS: DWI, DKI, and DCE-MRI examinations were performed in 39 and 27 cases of spinal malignant and non-malignant tumors, respectively. Two radiologists independently evaluated apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) of the DWI and DKI models, and volume transfer constant (Ktrans), rate constant (kep), and extracellular extravascular volume ratio (ve) of the DCE-MRI model for post-processing analyses. Statistical differences of parameters were compared using an independent sample t test. The sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve were determined. Pearson correlation analysis was used to evaluate the correlation between these parameters. RESULTS: ADC, MD, and ve were significantly lower, while MK and kep were significantly higher for spinal malignant tumors than for non-malignant tumors. The MK had the highest area under the ROC curve of 0.940 and sensitivity (96.3%). Ve was weakly positively correlated with ADC (r = 0.468) and MD (r = 0.363) and weakly negatively correlated with MK (r = -0.469). kep was weakly positively correlated with MK (r = 0.375). Ktrans was weakly positively correlated with ADC (r = 0.325). CONCLUSIONS: Monoexponential DWI, DKI, and DCE-MRI have potential value in the differentiation of spinal malignant from non-malignant tumors lacking typical imaging signs, and there is a certain correlation between the parameters of the three models. These slides can be retrieved under Electronic Supplementary Material.

Diagnosis of spinal lesions using perfusion parameters measured by DCE-MRI and metabolism parameters measured by PET/CT.

PURPOSE: To investigate the correlation of parameters measured by dynamic-contrast-enhanced MRI (DCE-MRI) and 18F-FDG PET/CT in spinal tumors, and their role in differential diagnosis. METHODS: A total of 49 patients with pathologically confirmed spinal tumors, including 38 malignant, six benign and five borderline tumors, were analyzed. The MRI and PET/CT were done within 3 days, before biopsy. On MRI, the ROI was manually placed on area showing the strongest enhancement to measure pharmacokinetic parameters Ktrans and kep. On PET, the maximum standardized uptake value SUVmax was measured. The parameters in different histological groups were compared. ROC was performed to differentiate between the two largest subtypes, metastases and plasmacytomas. Spearman rank correlation was performed to compare DCE-MRI and PET/CT parameters. RESULTS: The Ktrans, kep and SUVmax were not statistically different among malignant, benign and borderline groups (P = 0.95, 0.50, 0.11). There was no significant correlation between Ktrans and SUVmax (r = - 0.20, P = 0.18), or between kep and SUVmax (r = - 0.16, P = 0.28). The kep was significantly higher in plasmacytoma than in metastasis (0.78 ± 0.17 vs. 0.61 ± 0.18, P = 0.02); in contrast, the SUVmax was significantly lower in plasmacytoma than in metastasis (5.58 ± 2.16 vs. 9.37 ± 4.26, P = 0.03). In differential diagnosis, the AUC of kep and SUVmax was 0.79 and 0.78, respectively. CONCLUSIONS: The vascular parameters measured by DCE-MRI and glucose metabolism measured by PET/CT from the most aggressive tumor area did not show a significant correlation. The results suggest they provide complementary information reflecting different aspects of the tumor, which may aid in diagnosis of spinal lesions. These slides can be retrieved under Electronic Supplementary Material.

Enhancing Prostate-Cancer-Specific MRI by Genetic Amplified Nanoparticle Tumor Homing.

Precise localization and visualization of early-stage prostate cancer (PCa) is critical to improve the success of focal ablation and reduce cancer mortality. However, it remains challenging under the current imaging techniques due to the heterogeneous nature of PCa and the suboptimal sensitivity of the techniques themselves. Herein, a novel genetic amplified nanoparticle tumor-homing strategy to enhance the MRI accuracy of ultrasmall PCa lesions is reported. This strategy could specifically drive TfR expressions in PCa under PCa-specific DD3 promoter, and thus remarkably increase Tf-USPIONs concentrations in a highly accurate manner while minimizing their non-specific off-target effects on normal tissues. Consequently, this strategy can pinpoint an ultrasmall PCa lesion, which is otherwise blurred in the current MRI, and thereby addresses the unmet key need in MRI imaging for focal therapy. With this proof-of-concept experiment, the synergistic gene-nano strategy holds great promise to boost the MRI effects of a wide variety of commonly used nanoscale and molecular probes that are otherwise limited. In addition, such a strategy may also be translated and applied to MR-specific imaging of other types of cancers by using their respective tumor-specific promoters.