Fat-suppressed T2-weighted Images Research Articles

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.

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.

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

To evaluate whether deep learning reconstruction (DLR) can accelerate the acquisition of magnetic resonance imaging (MRI) sequences of the knee for clinical use. Using a 1.5-T MRI scanner, sagittal fat-suppressed T2-weighted imaging (fs-T2WI), coronal proton density-weighted imaging (PDWI), and coronal T1-weighted imaging (T1WI) were performed. DLR was applied to images with a number of signal averages (NSA) of 1 to obtain 1DLR images. Then 1NSA, 1DLR, and 4NSA images were compared subjectively, and by noise (standard deviation of intra-articular water or medial meniscus) and contrast-to-noise ratio between two anatomical structures or between an anatomical structure and intra-articular water. Twenty-seven healthy volunteers (age: 40.6±11.9 years) were enrolled. Three 1DLR image sequences were obtained within 200 s (approximately 12 minutes for 4NSA image). According to objective evaluations, PDWI 1DLR images showed the smallest noise and significantly higher contrast than 1NSA and 4NSA images. For fs-T2WI, smaller noise and higher contrast were observed in the order of 4NSA, 1DLR, and 1NSA images. According to the subjective analysis, structure visibility, image noise, and overall image quality were significantly better for PDWI 1DLR than 1NSA images; moreover, the visibility of the meniscus and bone, image noise, and overall image quality were significantly better for 1DLR than 4NSA images. Fs-T2WI and T1WI 1DLR images showed no difference between 1DLR and 4NSA images. Compared to PDWI 4NSA images, PDWI 1DLR images were of higher quality, while the quality of fs-T2WI and T1WI 1DLR images was similar to that of 4NSA images.

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.

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). 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. 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). 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.

Differentiation of Hepatocellular Carcinoma from Intrahepatic Cholangiocarcinoma through MRI Radiomics.

The purpose of this study was to investigate the efficacy of magnetic resonance imaging (MRI) radiomics in differentiating hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC). The clinical and MRI data of 129 pathologically confirmed HCC patients and 48 ICC patients treated at the Affiliated Hospital of North Sichuan Medical College between April 2016 and December 2021 were retrospectively analyzed. The patients were randomly divided at a ratio of 7:3 into a training group of 124 patients (90 with HCC and 34 with ICC) and a validation group of 53 patients (39 with HCC and 14 with ICC). Radiomic features were extracted from axial fat suppression T2-weighted imaging (FS-T2WI) and axial arterial-phase (AP) and portal-venous-phase (PVP) dynamic-contrast-enhanced MRI (DCE-MRI) sequences, and the corresponding datasets were generated. The least absolute shrinkage and selection operator (LASSO) method was used to select the best radiomic features. Logistic regression was used to establish radiomic models for each sequence (FS-T2WI, AP and PVP models), a clinical model for optimal clinical variables (C model) and a joint radiomics model (JR model) integrating the radiomics features of all the sequences as well as a radiomics-clinical model combining optimal radiomic features and clinical risk factors (RC model). The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC). The AUCs of the FS-T2WI, AP, PVP, JR, C and RC models for distinguishing HCC from ICC were 0.693, 0.863, 0.818, 0.914, 0.936 and 0.977 in the training group and 0.690, 0.784, 0.727, 0.802, 0.860 and 0.877 in the validation group, respectively. The results of this study suggest that MRI-based radiomics may help noninvasively differentiate HCC from ICC. The model integrating the radiomics features and clinical risk factors showed a further improvement in performance.

Intratumoral and peritumoral MRI-based radiomics prediction of histopathological grade in soft tissue sarcomas: a two-center study

ObjectivesThis study aims to develop a model based on intratumoral and peritumoral radiomics from fat-suppressed T2-weighted(FS-T2WI) images to predict the histopathological grade of soft tissue sarcoma (STS).MethodsThis retrospective study included 160 patients with STS from two centers, of which 82 were low-grade and 78were high-grade. Radiomics features were extracted and selected from the region of tumor mass volume (TMV) and peritumoral tumor volume (PTV) respectively. The TMV, PTV, and combined(TM-PTV) radiomics models were established in the training cohort (n = 111)for the prediction of histopathological grade. Finally, a radiomics nomogram was constructed by combining the TM-PTV radiomics signature (Rad-score) and the selected clinical-MRI predictor. The ROC and calibration curves were used to determine the performance of the TMV, PTV, and TM-PTV models in the training and validation cohort (n = 49). The decision curve analysis (DCA) and calibration curves were used to investigate the clinical usefulness and calibration of the nomogram, respectively.ResultsThe TMV model, PTV model, and TM-PTV model had AUCs of 0.835, 0.879, and 0.917 in the training cohort and 0.811, 0.756, 0.896 in the validation cohort. The nomogram, including the TM-PTV signatures and peritumoral hyperintensity, achieved good calibration and discrimination with a C-index of 0.948 (95% CI, 0.906 to 0.990) in the training cohort and 0.921 (95% CI, 0.840 to 0.995) in the validation cohort. Decision curve analysis demonstrated the clinical usefulness of the nomogram.ConclusionThe proposed model based on intratumoral and peritumoral radiomics showed good performance in distinguishing low-grade from high-grade STSs.

Assessment of Lymphovascular Invasion in Breast Cancer Using a Combined MRI Morphological Features, Radiomics, and Deep Learning Approach Based on Dynamic Contrast-Enhanced MRI.

Assessment of lymphovascular invasion (LVI) in breast cancer (BC) primarily relies on preoperative needle biopsy. There is an urgent need to develop a non-invasive assessment method. To develop an effective model to assess the LVI status in patients with BC using magnetic resonance imaging morphological features (MRI-MF), Radiomics, and deep learning (DL) approaches based on dynamic contrast-enhanced MRI (DCE-MRI). Cross-sectional retrospective cohort study. The study included 206 BC patients, with 136 in the training set [97 LVI(-) and 39 LVI(+) cases; median age: 51.5 years] and 70 in the test set [52 LVI(-) and 18 LVI(+) cases; median age: 48 years]. 1.5 T/T1-weighted images, fat-suppressed T2-weighted images, diffusion-weighted imaging (DWI), and DCE-MRI. The MRI-MF model was developed with conventional MR features using logistic analyses. The Radiomic feature extraction process involved collecting data from categorized DCE-MRI datasets, specifically the first and second post-contrast images (A1 and A2). Next, a DL model was implemented to determine LVI. Finally, we established a joint diagnosis model by combining the MRI-MF, Radiomics, and DL approaches. Diagnostic performance was compared using receiver operating characteristic curve analysis, confusion matrix, and decision curve analysis. Rim sign and peritumoral edema features were used to develop the MRI-MF model, while six Radiomics signature from the A1 and A2 images were used for the Radiomics model. The joint model (MRI-MF + Radiomics + DL models) achieved the highest accuracy (area under the curve [AUC] = 0.857), being significantly superior to the MRI-MF (AUC = 0.724), Radiomics (AUC = 0.736), or DL (AUC = 0.740) model. Furthermore, it also outperformed the pairwise combination models: Radiomics + MRI-MF (AUC = 0.796), DL + MRI-MF (AUC = 0.796), or DL + Radiomics (AUC = 0.826). The joint model incorporating MRI-MF, Radiomics, and DL approaches can effectively determine the LVI status in patients with BC before surgery. 4 TECHNICAL EFFICACY: Stage 2.

A Nomogram of Magnetic Resonance Imaging for Preoperative Assessment of Microvascular Invasion and Prognosis of Hepatocellular Carcinoma.

Microvascular invasion (MVI) is a predictor of recurrence and overall survival in hepatocellular carcinoma (HCC), the preoperative diagnosis of MVI through noninvasive methods play an important role in clinical treatment. To investigate the effectiveness of radiomics features in evaluating MVI in HCC before surgery. We included 190 patients who had undergone contrast-enhanced MRI and curative resection for HCC between September 2015 and November 2021 from two independent institutions. In the training cohort of 117 patients, MVI-related radiomics models based on multiple sequences and multiple regions from MRI were constructed. An independent cohort of 73 patients was used to validate the proposed models. A final Clinical-Imaging-Radiomics nomogram for preoperatively predicting MVI in HCC patients was generated. Recurrence-free survival was analyzed using the log-rank test. For tumor-extracted features, the performance of signatures in fat-suppressed T1-weighted images and hepatobiliary phase was superior to that of other sequences in a single-sequence model. The radiomics signatures demonstrated better discriminatory ability than that of the Clinical-Imaging model for MVI. The nomogram incorporating clinical, imaging and radiomics signature showed excellent predictive ability and achieved well-fitted calibration curves, outperforming both the Radiomics and Clinical-Radiomics models in the training and validation cohorts. The Clinical-Imaging-Radiomics nomogram model of multiple regions and multiple sequences based on serum alpha-fetoprotein, three MRI characteristics, and 12 radiomics signatures achieved good performance for predicting MVI in HCC patients, which may help clinicians select optimal treatment strategies to improve subsequent clinical outcomes.

The dynamic contrast enhanced-magnetic resonance imaging and diffusion-weighted imaging features of alveolar soft part sarcoma.

Alveolar soft part sarcoma (ASPS) is a rare type of soft tissue sarcoma that predominantly affects adolescents and young adults. Early diagnosis of ASPS is crucial for optimal therapeutic planning and improving prognosis, but its diagnostic features are not well delineated. This study aimed to retrospectively analyze the imaging features of ASPS with an emphasis on the dynamic contrast-enhanced-magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) findings to identify imaging findings that might suggest the diagnosis to radiologists. The imaging features of 34 patients with pathologically proven limb ASPS were retrospectively analyzed. A total of 23 underwent DCE-MRI, and 12 underwent DWI. Among the 34 cases of ASPS, 31 tumors were in the lower extremities, and 3 were in the upper extremities. The maximum tumor diameters ranged from 3.0 to 19.4 cm (mean, 8.7±3.96 cm). A total of 28 cases had well-defined borders. The masses demonstrated heterogeneous high signal intensity on T2-weighted imaging (T2WI) and the fat-suppressed (FS) T2WI sequence and slight hyperintensity on T1-weighted imaging (T1WI). A total of 25 lesions had thin hypointense bands on T1WI and T2WI. Intra- and peri-tumoral tubular areas of flow void were exhibited on both T1WI and T2WI in all cases. A total of 12 cases showed a high signal on DWI, and the mean apparent diffusion coefficient (ADC) value was (0.86±0.07)×10-3 mm2/s [range, (0.6-1.4)×10-3 mm2/s]. Persistent remarkable enhancement of the lesion was displayed on contrast-enhanced scans. The time-intensity curves (TICs) of 23 masses showed early arterial enhancement and slow washout of contrast. ASPS most commonly presents in the lower extremities of adolescents or young adults. Hyperintense T1WI, T2WI, and DWI signals, low ADC, flow voids, early arterial enhancement are frequent MRI features.

Spontaneous Intracranial Hypotension in Pregnancy.

Spontaneous intracranial hypotension (SIH) is a debilitating disease typically featuring orthostatic headache and caused by a spinal CSF leak. This review will describe the characteristics of SIH in pregnant patients and the associated unique management and treatment considerations. Herein, a novel case is reported of a 41-year-old woman who presented with SIH pre-conception but saw marked improvement of symptoms after 5weeks antepartum and symptom recurrence 2months post-partum. A literature review of SIH in pregnancy revealed 14 reported patients across 10 studies since 2000. All the reported cases resulted in delivery of healthy infants and symptomatic improvement with conservative management or a variety of treatment modalities including non-targeted epidural blood patch (EBP). Clinical and imaging features of SIH in pregnancy are reviewed. We hypothesize an antenatal protective mechanism against SIH symptoms through cephalad redistribution of CSF volume from the spinal to intracranial compartments related to uterine growth and decreased CSF volume within the lumbar cistern. Treatment recommendations are discussed including duration of bed rest and decision for non-targeted multi-site EBPs. When required, non-invasive diagnostic spine MRI using fat-suppressed axial T2-weighted imaging may be helpful.