Fat-suppressed T2-weighted Images Research Articles

Intratumoral and Peritumoral Edema Radiomics Based on Fat-Suppressed T2- Weighted Imaging for Preoperative Prediction of Triple-Negative Breast Cancer.

Our aim was to explore the feasibility of using radiomics data derived from intratumoral and peritumoral edema on fat-suppressed T2-weighted imaging (T2 FS) to distinguish triple-negative breast cancer (TNBC) from non-triple-negative breast cancer (non-TNBC). This retrospective study enrolled 174 breast cancer patients. According to the MRI examination time, patients before 2021 were divided into training (n = 119) or internal test (n = 30) cohorts at a ratio of 8:2. Patients from 2022 were included in the external test cohort (n = 25). Four regions of interest for each lesion were defined: intratumoral regions, peritumoral edema regions, regions with a combination of intratumoral and peritumoral edema, and regions with a combination of intratumoral and 5-mm peritumoral. Four radiomic signatures were built using the least absolute shrinkage and selection operator (LASSO) method after selecting features. Furthermore, a radio mic-radiological model was constructed using a combination of intratumoral and peritumoral edema regions along with clinical-radiologic features. Area under the receiver operating characteristic curve (AUC) calculations, decision curve analysis, and calibration curve analysis were performed to assess the performance of each model. The radiomic-radiological model showed the highest AUC values of 0.906 (0.788-1.000) and 82.5 (0.622-0.947) in both the internal and external test sets, respectively. The radiology-radiomic model exhibited excellent predictive performance, as evidenced by the calibration curves and decision curve analysis. The ensemble model based on T2 FS-based radiomic features of intratumoral and peritumoral edema, along with radiological factors, performed better in distinguishing TNBC from non-TNBC than a single model. We explored the possibility of developing explainable models to support the clinical decision-making process.

Prognostic value of radiological T category using conventional MRI in patients with oral tongue cancer: comparison with pathological T category.

This study aimed to compare the radiological tumor (T)-category using multiparametric MRI with the pathological T category in patients with oral tongue squamous cell carcinoma (OTSCC) and to examine which is a better predictor of prognosis. This retrospective study included 110 consecutive patients with surgically resected primary OTSCC who underwent preoperative contrast-enhanced MRI. T categories determined by maximum diameter and depth of invasion were retrospectively assessed based on the pathological specimen and multiparametric MRI. The MRI assessment included the axial and coronal T1-weighted image (T1WI), axial T2-weighted image (T2WI), coronal fat-suppressed T2WI, and axial and coronal fat-suppressed contrast-enhanced T1WI (CET1WI). Axial and coronal CET1WI measurements were divided into two groups: measurements excluding peritumoral enhancement (MEP) and measurements including peritumoral enhancement. The prognostic values for recurrence and disease-specific survival after radiological and pathological T categorization of cases into T1/T2 and T3/T4 groups were compared. The T category of MEP on coronal CET1WI was the most relevant prognostic factor for recurrence [hazard ratio (HR) = 3.30, p = 0.001] and the HR was higher than the HR for pathological assessment (HR = 2.26, p = 0.026). The T category determined by MEP on coronal CET1WI was also the most relevant prognostic factor for disease-specific survival (HR = 3.12, p = 0.03), and the HR was higher than the HR for pathological assessment (HR = 2.02, p = 0.20). The T category determined by MEP on the coronal CET1WI was the best prognostic factor among all radiological and pathological T category measurements.

Value of radiomics-based automatic grading of muscle edema in polymyositis/dermatomyositis based on MRI fat-suppressed T2-weighted images.

The precise and objective assessment of thigh muscle edema is pivotal in diagnosing and monitoring the treatment of dermatomyositis (DM) and polymyositis (PM). Radiomic features are extracted from fat-suppressed (FS) T2-weighted (T2W) magnetic resonance imaging (MRI) of thigh muscles to enable automatic grading of muscle edema in cases of polymyositis and dermatomyositis. A total of 241 MR images were analyzed and classified into five levels using the Stramare criteria. The correlation between muscle edema grading and T2-mapping values was assessed using Spearman's correlation. The dataset was divided into a 7:3 ratio of training (168 samples) and testing (73 samples). Thigh muscle boundaries in FS T2W images were manually delineated with 3D-Slicer. Radiomics features were extracted using Python 3.7, applying Z-score normalization, Pearson correlation analysis, and recursive feature elimination for reduction. A Naive Bayes classifier was trained, and diagnostic performance was evaluated using receiver operating characteristic (ROC) curves and comparing sensitivity and specificity with senior doctors. A total of 1198 radiomics parameters were extracted and reduced to 18 features for Naive Bayes modeling. In the testing set, the model achieved an area under the ROC curve of 0.97, sensitivity of 0.85, specificity of 0.98, and accuracy of 0.91. The Naive Bayes classifier demonstrated grading performance comparable to senior doctors. A significant correlation (r = 0.82, P <0.05) was observed between Stramare edema grading and T2-mapping values. The Naive Bayes model, utilizing radiomics features extracted from thigh FS T2W images, accurately assesses the severity of muscle edema in cases of PM/DM.

Improved optic nerve visualization and treatment planning through a dedicated optic nerve MRI protocol.

This study describes an innovative optic nerve MRI protocol for better delineating optic nerve anatomy from neighboring pathology. Twenty-two patients undergoing MRI examination of the optic nerve with the dedicated protocol were identified and included for analysis of imaging, surgical strategy, and outcomes. T2-weighted and fat-suppressed T1-weighted gadolinium-enhanced images were acquired perpendicular and parallel to the long axis of the optic nerve to achieve en face and in-line views along the course of the nerve. Dedicated optic nerve MRI sequences provided enhanced visualization of the nerve, CSF within the nerve sheath, and local pathology. Optic nerve sequences leveraged the "CSF ring" within the optic nerve sheath to create contrast between pathology and normal tissue, highlighting areas of compression. Tumor was readily tracked along the longitudinal axis of the nerve by images obtained parallel to the nerve. The findings augmented treatment planning. The authors present a dedicated optic nerve MRI protocol that is simple to use and affords improved cross-sectional and longitudinal visualization of the nerve, surrounding CSF, and pathology. This improved visualization enhances radiological evaluation and treatment planning for optic nerve lesions.

High Peritumoral and Intratumoral T2 Signal Intensity in HER2-Positive Breast Cancers on Preneoadjuvant Breast MRI: Assessment of Associations With Histopathologic Characteristics.

BACKGROUND. Intratumoral necrosis and peritumoral edema are features of aggressive breast cancer that may present as high T2 signal intensity (T2 SI). Implications of high T2 SI in HER2-positive cancers are unclear. OBJECTIVE. The purpose of this study was to assess associations with histopathologic characteristics of high peritumoral T2 SI and intratumoral T2 SI of HER2-positive breast cancer on MRI performed before initiation of neoadjuvant therapy. METHODS. This retrospective study included 210 patients (age, 24-82 years) with 211 HER2 breast cancers who, from January 1, 2015, to July 30, 2022, underwent breast MRI before receiving neoadjuvant therapy. Two radiologists independently assessed cancers for high peritumoral T2 SI and high intratumoral T2 SI on fat-suppressed T2-weighted imaging and classified patterns of high peritumoral T2 SI (adjacent to tumor vs prepectoral extension). A third radiologist resolved discrepancies. Multivariable logistic regression analyses were performed to identify associations of high peritumoral and intratumoral T2 SI with histopathologic characteristics (associated ductal carcinoma in situ, hormone receptor status, histologic grade, lymphovascular invasion, and axillary lymph node metastasis). RESULTS. Of 211 HER2-positive cancers, 81 (38.4%) had high peritumoral T2 SI, and 95 (45.0%) had high intratumoral T2 SI. A histologic grade of 3 was independently associated with high peritumoral T2 SI (OR = 1.90; p = .04). Otherwise, none of the five assessed histopathologic characteristics were independently associated with high intratumoral T2 SI or high peritumoral T2 SI (p > .05). Cancers with high T2 SI adjacent to the tumor (n = 29) and cancers with high T2 SI with prepectoral extension (n = 52) showed no significant difference in frequency for any of the histopathologic characteristics (p > .05). Sensitivities and specificities for predicting the histopathologic characteristics ranged from 35.6% to 43.7% and from 59.7% to 70.7%, respectively, for high peritumoral T2 SI, and from 37.3% to 49.6% and from 49.3% to 62.7%, respectively, for high intratumoral T2 SI. Interreader agreement was almost perfect for high peritumoral T2 SI (Gwet agreement coefficient [AC] = 0.93), high intratumoral T2 SI (Gwet AC = 0.89), and a pattern of high peritumoral T2 SI (Gwet AC = 0.95). CONCLUSION. The only independent association between histopathologic characteristics and high T2 SI of HER2-positive breast cancer was observed between a histologic grade of 3 and high peritumoral T2 SI. CLINICAL IMPACT. In contrast with previously reported findings in broader breast cancer subtypes, peritumoral and intratumoral T2 SI had overall limited utility as prognostic markers of HER2-positive breast cancer.

The value of MRI-based radiomics for evaluating early parotid gland injury in primary Sjögren's syndrome.

This study aimed to evaluate the value of machine learning models (ML) based on MRI radiomics in diagnosing early parotid gland injury in primary Sjögren's syndrome (pSS). A total of 164 patients (114 in the training cohort and 50 in the testing cohort) with pSS (n=82) or healthy controls (HC) (n=82) were enrolled. Itksnap software was used to perform two-dimensional segmentation of the bilateral parotid glands on T1-weighted (T1WI) and fat-suppressed T2-weighted imaging (fs-T2WI) images. A total of 1548 texture features of the parotid glands were extracted using radiomics software. A radiomics score (Radscore) was constructed and calculated. A t-test was used to compare the Radscore between the two groups. Finally, five machine learning models were trained and tested to identify early pSS parotid injury, and the performance of the machine learning models was evaluated by calculating the acceptance operating curve (ROC) and other parameters. The Radscores between the pSS and HC groups showed significant statistical differences (p<0.001). Among the five machine learning models, the Extra Trees Classifier (ETC) model performed high predictive efficacy in identifying early pSS parotid injury, with an AUC of 0.87 in the testing set. MRI radiomics-based machine learning models can effectively diagnose early parotid gland injury in primary Sjögren's syndrome.

Peritumoral MRI Radiomics Features Increase the Evaluation Efficiency for Response to Chemotherapy in Patients With Epithelial Ovarian Cancer.

It remains unclear whether extracting peritumoral volume (PTV) radiomics features are useful tools for evaluating response to chemotherapy of epithelial ovarian cancer (EOC). To evaluate MRI radiomics signatures (RS) capturing subtle changes of PTV and their added evaluation performance to whole tumor volume (WTV) for response to chemotherapy in patients with EOC. Retrospective. 219 patients aged from 15 to 79 years were enrolled. 3.0 or 1.5T, axial fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), and contrast enhanced T1-weighted imaging (CE-T1WI). MRI features were extracted from the four axial sequences and six different volumes of interest (VOIs) (WTV and WTV + PTV (WPTV)) with different peritumor sizes (PS) ranging from 1 to 5 mm. Those features underwent preprocessing, and the most informative features were selected using minimum redundancy maximum relevance and least absolute shrinkage and selection operator to construct the RS. The optimal RS, with the highest area under the curve (AUC) of receiver operating characteristic was then integrated with independent clinical characteristics through multivariable logistic regression to construct the radiomics-clinical model (RCM). Mann-Whitney U test, chi-squared test, DeLong test, log-rank test. P < 0.05 indicated a significant difference. All the RSs constructed on WPTV exhibited higher AUCs (0.720-0.756) than WTV (0.671). Of which, RS with PS = 2 mm displayed a significantly better performance (AUC = 0.756). International Federation of Gynecology and Obstetrics (FIGO) stage was identified as the exclusive independent clinical evaluation characteristic, and the RCM demonstrated higher AUC (0.790) than the RS, but without statistical significance (P = 0.261). The radiomics features extracted from PTV could increase the efficiency of WTV radiomics for evaluating the chemotherapy response of EOC. The cut-off of 2 mm PTV was a reasonable value to obtain effective evaluation efficiency. 4 TECHNICAL EFFICACY: Stage 2.

Multiparametric MRI-based radiomics nomogram for identifying cervix-corpus junction cervical adenocarcinoma from endometrioid adenocarcinoma.

To developed a magnetic resonance imaging (MRI) radiomics nomogram to identify adenocarcinoma at the cervix-corpus junction originating from the endometrium or cervix in order to better guide clinical treatment. Between February 2011 and September 2021, the clinicopathological data and MRI in 143 patients with histopathologically confirmed cervical adenocarcinoma (CAC, n = 86) and endometrioid adenocarcinoma (EAC, n = 57) were retrospectively analyzed at the cervix-corpus junction. Radiomics features were extracted from fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, and delayed phase contrast-enhanced T1-weighted imaging (CE-T1WI) sequences. A radiomics nomogram was developed integrating radscore with independent clinical risk factors. The area under the curve (AUC) was used to evaluate the diagnostic efficacy of the radscore, nomogram and two different experienced radiologists in differentiating CAC from EAC at the cervix-corpus junction, and Delong test was applied to compare the differences of their diagnostic performance. In the training cohort, the AUC was 0.93 for radscore; 0.97 for radiomics nomograms; 0.85 and 0.86 for radiologists 1 and 2, respectively. Delong test showed that the differential efficacy of nomogram was significant better than those of radiologists in the training cohort (both P < 0.05). The nomogram based on radscore and clinical risk factors could better differentiate CAC from EAC at the cervix-corpus junction than radiologists, and preoperatively and non-invasively identify the origin of adenocarcinoma at the cervix-corpus junction, which facilitates clinicians to make individualized treatment decision.

Evaluation of high temporal resolution magnetic resonance imaging of the liver with gadoxetate disodium in combination with compressed sensing and parallel imaging under single breath-holding using a 1.5-T magnetic resonance system

BackgroundThis study aimed to determine the optimal scan time for high temporal resolution magnetic resonance (MR) imaging of the liver with gadoxetate disodium injection in combination with compressed sensing (CS) and parallel imaging (PI) techniques under single breath-holding using a 1.5-T MR system.MethodsSixty-two participants underwent multiple arterial phases of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the liver with gadoxetate disodium using fat-suppressed GRE T1-weighted imaging—liver acquisition with volume acceleration (LAVA)—in combination with CS and PI using a 1.5-T MR system. Forty-six and 22 participants underwent 6-s and 10-s scans, respectively. Pre-contrast, multiple arterial, portal venous, and hepatobiliary phase images were acquired. Two radiologists evaluated the visual scores for the outline of the liver, inferior right hepatic vein (IRHV), right portal vein, right hepatic artery, appropriateness of the arterial phase, and overall image quality using a 4- or 5-point scale.ResultsThe overall image quality and the image quality of the outline of the liver in the pre-contrast and arterial phases and IRHV in the pre-contrast phase were significantly better (P < 0.05) in the 10-s scan group than those in the 6-s scan group. No significant difference was observed between the two groups in terms of the appropriateness of the arterial phase (obtaining the optimal arterial phase) (P = 0.731).ConclusionsA 10-s scan protocol is recommended for high temporal resolution DCE-MRI of the liver with gadoxetate disodium injection in combination with CS and PI under single breath-holding using a 1.5-T MR system.

Intradiscal administration of autologous platelet-rich plasma in patients with Modic type 1 associated low back pain: A prospective pilot study.

Various treatments for chronic low back pain (LBP) have been reported; among them, platelet-rich plasma (PRP) as a regenerative medicine has attracted much attention. Although Modic type 1 change (MC1) is associated with LBP, no treatment has been established so far. In addition, no studies have administered PRP to intervertebral discs (IVDs) in patients with LBP, targeting MC1 only. Thus, the purpose of this study was to determine the safety and efficacy of PRP administration to the IVDs in patients with MC1 experiencing LBP. PRP was injected intradiscally to 10 patients with MC1 experiencing LBP. Patients were followed prospectively for up to 24 weeks after primary administration. Physical condition, laboratory data, and lumbar x-ray images were evaluated for safety assessment. Furthermore, to evaluate the effectiveness of PRP, patient-reported outcomes were considered. In addition, changes in MC1 were assessed using magnetic resonance imaging (MRI). There were no adverse events in the laboratory data or lumbar X-ray images after administration. The mean visual analog scale, which was 70.0 ± 13.3 before the treatment, significantly decreased 1 week after PRP administration and was 39.0 ± 28.8 at the last observation. Oswestry disability index and Roland Morris disability questionnaire scores promptly improved after treatment, and both improved significantly 24 weeks after PRP administration. Follow-up MRI 24 weeks after treatment showed a significant decrease in the mean high-signal intensity of fat-suppressed T2-weighted imaging from 10.1 to 7.90 mm2 compared with that before PRP administration. The safety and efficacy of PRP administration to the IVDs of patients with MC1 experiencing LBP were identified. Post-treatment MRI suggested improvement in inflammation, speculating that PRP suppressed inflammation and consequently relieved the patient's symptoms. Despite the small number of patients, this treatment is promising for patients with MC1 experiencing LBP. The study protocol has been reviewed and approved by the Certified Committee for Regenerative Medicine and the Japanese Ministry of Health, Labor and Welfare (Japan Registry of Clinical Trials [jRCT] No. jRCTb042210159).

Faster acquisition of magnetic resonance imaging sequences of the knee via deep learning reconstruction: a volunteer study

Faster acquisition of magnetic resonance imaging sequences of the knee via deep learning reconstruction: a volunteer study

The Signal Intensity Ratio of Pancreas to Spleen in Magnetic Resonance Imaging Can Predict Nonalcoholic Fatty Liver Disease After Pancreaticoduodenectomy.

Signal intensity ratio of pancreas to spleen (SI ratio p/s ) on fat-suppressed T1-weighted images of magnetic resonance imaging has been associated with pancreatic exocrine function. We here investigated the predictive value of the SI ratio p/s for the development of nonalcoholic fatty liver disease (NAFLD) after pancreaticoduodenectomy (PD). This study comprised 208 patients who underwent PD. NAFLD was defined as a liver-to-spleen attenuation ratio of <0.9 calculated by a computed tomography 1 year after surgery. SI ratio p/s was calculated by dividing the average pancreas SI by the spleen SI. We retrospectively investigated the association of clinical variables including the SI ratio p/s and NAFLD by univariate and multivariate analyses. NAFLD after 1 year was developed in 27 patients (13%). In multivariate analysis, the SI ratio p/s < 1 ( P < 0.001) was an independent predictor of incidence of NAFLD. The SI ratio p/s < 1 was associated with low amylase level of the pancreatic juice ( P < 0.001) and progressed pancreatic fibrosis ( P = 0.017). According to the receiver operating characteristics curve, the SI ratio p/s had better prognostic ability of NAFLD than the remnant pancreas volume. The SI ratio p/s is useful to predict NAFLD development after PD. Moreover, the SI ratio p/s can be a surrogate marker, which represents exocrine function of the pancreas.

Primary hepatic undiferentiated pleomorphic sarcoma with recurrence and widespread metastasis.

A 59-year-old man was referred to us for evaluation of asymptomatic mass in the liver that had been detected on ultrasonography performed during a physical screening. He had no history of hepatitis, and was otherwise well. Tumor markers were normal, including alpha fetoprotein, carcinoembryonic antigen, neuron-specific enolase and cancer antigen 199. Abdominal CT showed a 5.0 cm diameter low density mass in the S6 segment of his liver, with mild peripheral enhancement. Abdominal MR demonstrated the mass with hypointensity on T1WI, inhomogeneous hyperintensity on fat-suppressed T2WI, and mild peripheral enhancement. Partial hepatectomy was performed and pathological examination indicated undiferentiated pleomorphic sarcoma, with immunohistochemical results as follows: Vimentin (+), CK (-), CD68 (+), INI-1 (+), Hepa (-), Gly-3 (-), Arg-1 (-), HMB45 (-), CAM5,2 (-), CD31 (-), CD34 (-), CK7 (-), CK19 (-),Desmin (-), SMA (+), EMA (-), S-100 (-), Ki67 (50%+). The patient underwent postoperative chemotherapy with karelizumab and gemcitabine. However, repeat MR 3 months later showed multiple nodules with rim-like enhancement around the surgical margin. Subsequent repeat CT 6 months later reveled marked progression of the lesions.

The application of radiomics machine learning models based on multimodal MRI with different sequence combinations in predicting cervical lymph node metastasis in oral tongue squamous cell carcinoma patients.

The purpose of this study was to explore preliminary the performance of radiomics machine learning models based on multimodal MRI to predict the risk of cervical lymph node metastasis (CLNM) for oral tongue squamous cell carcinoma (OTSCC) patients. A total of 400 patients were enrolled in this study and divided into six groups according to the different combinations of MRI sequences. Group I consisted of patients with T1-weighted images (T1WI) and FS-T2WI (fat-suppressed T2-weighted images), group II consisted of patients with T1WI, FS-T2WI, and contrast enhanced MRI (CE-MRI), group III consisted of patients with T1WI, FS-T2WI, and T2-weighted images (T2WI), group IV consisted of patients with T1WI, FS-T2WI, CE-MRI, and T2WI, group V consisted of patients with T1WI, FS-T2WI, T2WI, and apparent diffusion coefficient map (ADC), and group VI consisted of patients with T1WI, FS-T2WI, CE-MRI, T2WI, and ADC. Machine learning models were constructed. The performance of the models was compared in each group. The machine learning model in group IV including T1WI, FS-T2WI, T2WI, and CE-MRI presented best prediction performance, with AUCs of 0.881 and 0.868 in the two sets. The models with CE-MRI performed better than the models without CE-MRI(I vs. II, III vs. IV, V vs. VI). The radiomics machine learning models based on CE-MRI showed great accuracy and stability in predicting the risk of CLNM for OTSCC patients.

Differentiation of parotid pleomorphic adenoma from Warthin tumor using signal intensity ratios on fat-suppressed T2-weighted magnetic resonance imaging

ObjectiveTo investigate the value of magnetic resonance imaging (MRI) signal intensity ratios (SIRs) based on fat-suppressed T2-weighted imaging (FS-T2WI), together with demographic features, MRI anatomical characteristics, and SIRs of histopathological patterns of the tumors, in the differentiation of parotid pleomorphic adenoma (PA) from Warthin tumor (WT). Study DesignIn total, 90 patients with PA and 56 patients with WT were enrolled in the study. SIRs of tumor to normal parotid gland (SIR-T/P), spinal cord (SIR-T/S), and muscle (SIR-T/M) were calculated. Demographic and MRI anatomical features of the two patient groups were compared with univariate analysis and multivariate logistic regression analysis. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were analyzed to evaluate the utility of SIRs in distinguishing between PA and WT. ResultsSIR-T/P exhibited outstanding discriminating ability (AUC=0.934), SIR-T/S had excellent discrimination (AUC=0.839), and SIR-T/M showed acceptable discrimination (AUC=0.728). When SIR-T/P of 1.96 was selected as the cutoff value, sensitivity and specificity were 0.756 and 0.982, respectively. SIR-T/P, age, sex, and number of lesions were identified as independent predictors by multivariate logistic regression analysis. Differences in SIRs between histopathological patterns were significant. ConclusionSIR-T/P based on FS-T2WI is an effective discriminator in the differential diagnosis between PA and WT. Age, sex, and number of lesions provided additional value in the differentiation.

Differentiation of bone metastases from benign red marrow depositions: utilizing qualitative and quantitative analysis of conventional T1-weighted imaging and fat-suppressed T2-weighted imaging.

To distinguish bone metastases (BMs) from benign red marrow depositions (BRMs) by qualitative and quantitative analyses of T1-weighted imaging and fat-suppressed T2-weighted imaging (T2 FS). For 75 lesions including 38 BMs and 37 BRMs, tworadiologists independently evaluated magnetic resonance images by qualitative (signal intensity [SI] of lesions compared to that of normal muscle [NM] or normal bone marrow [NBM]) and quantitative (parameters of the region of interests in the lesions, including T1 ratio [T1 SI ratio of lesion and NM], T2FMu ratio [T2 FS SI ratio of lesion and NM], and T2FMa ratio [T2 FS SI ratio of lesion and NBM]) analyses. Hyperintensity relative to NM or NBM on T2 FS was more frequent in BMs than in BRMs (100% vs 59.5%-78.4%, respectively; P ≤ 0.001) but also was present in more than half of BRMs. All quantitative parameters showed a significant difference between BMs and BRMs (T1 ratio, 1.075 vs 1.227 [P = 0.002]; T2FMu ratio, 2.094 vs 1.282 [P < 0.001]; T2FMa ratio, 3.232 vs 1.810 [P < 0.001]). The receiver operating characteristics areas under the curves of T2FMu and T2FMa ratios were clinically useful (0.781 and 0.841, respectively) and did not demonstrate statistically significant differences. The quantitative analysis of T2 FS facilitates distinguishing between BMs and BRMs, regardless of whether the reference was NM or NBM. Quantitative parameters derived from T2 FS facilitate differentiation of BMs BRMs without additional scans. The role of NBM as an internal standard for T2 FS to differentiate between BMs and BRMs is similar to that of NM.

MRI-based interpretable radiomics nomogram for discrimination between Brucella spondylitis and Pyogenic spondylitis

BackgroundPyogenic spondylitis (PS) and Brucella spondylitis (BS) are commonly seen spinal infectious diseases. Both types can lead to vertebral destruction, kyphosis, and long-term neurological deficits if not promptly diagnosed and treated. Therefore, accurately diagnosis is crucial for personalized therapy. Distinguishing between PS and BS in everyday clinical settings is challenging due to the similarity of their clinical symptoms and imaging features. Hence, this study aims to evaluate the effectiveness of a radiomics nomogram using magnetic resonance imaging (MRI) to accurately differentiate between the two types of spondylitis. MethodsClinical and MRI data from 133 patients (2017–2022) with pathologically confirmed PS and BS (68 and 65 patients, respectively) were collected. We have divided patients into training and testing cohorts. In order to develop a clinical diagnostic model, logistic regression was utilized to fit a conventional clinical model (M1). Radiomics features were extracted from sagittal fat-suppressed T2-weighted imaging (FS-T2WI) sequence. The radiomics features were preprocessed, including scaling using Z-score and undergoing univariate analysis to eliminate redundant features. Furthermore, the Least Absolute Shrinkage and Selection Operator (LASSO) was employed to develop a radiomics score (M2). A composite model (M3) was created by combining M1 and M2. Subsequently, calibration and decision curves were generated to evaluate the nomogram's performance in both training and testing groups. The diagnostic performance of each model and the indication was assessed using the receiver operating curve (ROC) with its area under the curve (AUC). Finally, we used the SHapley Additive exPlanations (SHAP) model explanations technique to interpret the model result. ResultsWe have finally selected 9 significant features from sagittal FS-T2WI sequences. In the differential diagnosis of PS and BS, the AUC values of M1, M2, and M3 in the testing set were 0.795, 0.859, and 0.868. The composite model exhibited a high degree of concurrence with the ideal outcomes, as evidenced by the calibration curves. The nomogram's possible clinical application values were indicated by the decision curve analysis. By using SHAP values to represent prediction outcomes, our model’s prediction results are more understandable. ConclusionsThe implementation of a nomogram that integrates MRI and clinical data has the potential to significantly enhance the accuracy of discriminating between PS and BS within clinical settings.

Magnetic Resonance Imaging Features for Differentiating Low-Grade and High-Grade Malignant Peripheral Nerve Sheath Tumors

Objective This study aimed to assess the usefulness of magnetic resonance imaging (MRI) findings for differentiating low-grade and high-grade malignant peripheral nerve sheath tumors (MPNSTs). Methods This study included 31 patients (onset age range, 19–83 years; mean onset age, 57 years; 9 men and 22 women) with 36 histopathologically proven MPNSTs (7 low-grade MPNSTs and 29 high-grade MPNSTs) who underwent preoperative MRI between December 2007 and October 2022. Quantitative and qualitative MRI findings were retrospectively evaluated and compared between the 2 subtypes. Results The maximum tumor diameter (106.1 ± 64.0 vs 54.9 ± 19.8 mm, P = 0.032) and tumor-to-muscle signal intensity ratio (SIR) of fat-suppressed gadolinium-enhanced T1-weighted images (2.69 ± 1.40 vs 1.62 ± 0.40, P = 0.005) were significantly higher in high-grade MPNSTs than in low-grade MPNSTs. The receiver operating characteristic analysis revealed that the tumor-to-muscle SIR of fat-suppressed gadolinium-enhanced T1-weighted images exhibited the highest area under the curve value (0.88), followed by the maximum tumor diameter (0.76). The sensitivity and specificity of the tumor-to-muscle SIR of fat-suppressed gadolinium-enhanced T1-weighted images for diagnosing high-grade MPNST at an optimal SIR threshold of greater than 1.73 were 90% and 83%, respectively. However, other MRI findings showed no significant differences between the 2 subtypes (P = 0.16–1.00). Conclusions Although the MRI findings of low-grade and high-grade MPNST overlapped considerably, the maximum tumor diameter and degree of contrast enhancement can be used to differentiate low-grade MPNST from high-grade MPNST.

Predicting the microvascular invasion and tumor grading of intrahepatic mass-forming cholangiocarcinoma based on magnetic resonance imaging radiomics and morphological features.

Preoperative diagnosis of microvascular invasion (MVI) and tumor grading of intrahepatic mass-forming cholangiocarcinoma (IMCC) using imaging findings can facilitate patient treatment decision-making. This study was conducted to establish and validate nomograms based on magnetic resonance imaging (MRI) radiomics and morphological features for predicting the MVI and tumor grading of IMCC before radical hepatectomy. A total of 235 patients with resected IMCC at the Chinese Academy of Medical Sciences and Peking Union Medical College were divided into a training set (n=167) and a validation set (n=68), retrospectively. Clinical data and MRI morphological features were recorded. Univariate and multivariate analyses were conducted to identify the significant features for the prediction of MVI and tumor grading. Radiomics features were extracted from T2-weighted imaging fat-suppressed and diffusion-weighted imaging (DWI). Radiomics signatures (rad_scores) were built based on the least absolute shrinkage and selection operator (LASSO) method. Then, the nomograms were constructed by combining the rad_scores and the significant clinical or MRI morphologic features. The predictive performances for MVI and tumor grading were evaluated by the area under the receiver operating characteristic curve (AUC), calibration, and clinical utility. Totals of 16 and 9 radiomics features were selected to build the rad_scores for the prediction of MVI and tumor grading for the training and validation set, respectively. The nomogram for the prediction of MVI comprised the morphologic features including number of tumors, tumor margin, and rad_score. For the prediction of tumor grading, the nomogram comprised the number of tumors, tumor necrosis, and rad_score. The best discriminations were observed in the training and validation sets for the MVI nomogram [AUCs of 0.874, 95% confidence interval (CI): (0.822-0.926) and 0.869 (0.783-0955)] and tumor grading nomogram [AUCs of 0.827 (0.763-0.891) and 0.848 (0.759-0.937)]. Decision curve analysis (DCA) further confirmed the clinical utilities of the nomograms. Nomograms based on MRI radiomics and morphological features can effectively predict the individualized risks of MVI and tumor grading for IMCC.

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

BackgroundIdentifying 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.MethodsThis 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.ResultsFor 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.ConclusionThe 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.