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Background: Preoperative differentiation between malignant and benign tumors is important for treatment decisions., Purpose/hypothesis: To investigate/validate a radiomics nomogram for preoperative differentiation between malignant and benign masses., Study Type: Retrospective., Population: Imaging data of 91 patients., Field Strength/sequence: T 1 -weighted images (570 msec repetition time [TR]; 17.9 msec echo time [TE], 200-400 mm field of view [FOV], 208-512 × 208-512 matrix), fat-suppressed fast-spin-echo (FSE) T 2 -weighted images (T 2 WIs) (4331 msec TR; 87.9 msec TE, 200-400 mm FOV, 312 × 312 matrix), slice thickness 4 mm, and slice spacing 1 mm., Assessment: Fat-suppressed FSE T 2 WIs were selected for extraction of features. Radiomics features were extracted from fat-suppressed T 2 WIs. A radiomics signature was generated from the training dataset using least absolute shrinkage and selection operator algorithms. Independent risk factors were identified by multivariate logistic regression analysis and a radiomics nomogram was constructed. Nomogram capability was evaluated in the training dataset and validated in the validation dataset. Performance of the nomogram, radiomics signature, and clinical model were compared., Statistical Tests: 1) Independent t-test or Mann-Whitney U-test: for continuous variables. Fisher's exact test or χ 2 test: comparing categorical variables between two groups. Univariate analysis: evaluating associations between clinical/morphological characteristics and malignancy. 2) Least absolute shrinkage and selection operator (LASSO)-logistic regression model: selection of malignancy features. 3) Significant clinical/morphological characteristics and radiomics signature were input variables for multiple logistic regression analysis. Area under the curve (AUC): evaluation of ability of the nomogram to identify malignancy. Hosmer-Lemeshow test and decision curve: evaluation and validation of nomogram results., Results: The radiomics nomogram was able to differentiate malignancy from benignity in the training and validation datasets with an AUC of 0.94. The nomogram outperformed both the radiomics signature and clinical model alone., Data Conclusion: This radiomics nomogram is a noninvasive, low-cost preoperative prediction method combining the radiomics signature and clinical model., Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:155-163., (© 2019 International Society for Magnetic Resonance in Medicine.)

Purpose: To characterize and evaluate the MR imaging features of early myositis ossificans (MO) without calcification or ossification., Methods: The MRI manifestations of seven patients with pathologically proven early MO were retrospectively analyzed with regard to tumor location, size, margins, signal intensity, and enhancement appearance in MR images. Additionally, the surrounding soft-tissue edema and adjacent bone change were assessed., Results: All cases (n=7) had intramuscular tumor-like masses without calcifications. The lesions appeared as isointense in T1-weighted images (T1-WI) and inhomogeneous hyperintense in T2-weighted MR images (T2-WI). On T2-WI and postcontrast T1-WI, the heterogeneously high signal intensity in the expanded muscle interspersed with a few hypointense linear structures consistent with intact muscle fibers showed "striate pattern" in the plane parallel with muscle fibers. The relatively hypointense areas with geometrical pattern consistent with the bundles of intact muscle fibers are found within the lesion with diffuse high signal intensity, displaying the "checkerboard-like pattern" in the plane vertical to muscle fibers. A "striate pattern" (n = 7) and "checkerboard-like pattern" (n = 3) in the lesion appeared in T2-WI. In contrast-enhanced MRI images, all cases showed diffuse "striate pattern" enhancement. Among them, one case demonstrated "checkerboard-like pattern" enhancement. All cases had diffuse and prominent muscle edema that preserved the muscle fascicles. For two lesions located in the deep muscle group, the adjacent bone showed bone marrow edema., Conclusion: MR imaging has unique advantages for diagnosis of early MO without calcification or ossification: the "striate pattern" and "checkerboard-like pattern" appearance shown in T2-WI and contrast-enhanced MRI images can be helpful for differential diagnosis. MRI can delineate the extent of the tumor and provides accurate anatomical information, which is important in diagnosis, treatment, and follow-up.

Objectives: To explore the potential of radiomics features to predict the histologic grade of nonfunctioning pancreatic neuroendocrine tumor (NF-PNET) patients using non-contrast sequence based on MRI. Methods: Two hundred twenty-eight patients with NF-PNETs undergoing MRI at 5 centers were retrospectively analyzed. Data from center 1 (n = 115) constituted the training cohort, and data from centers 2–5 (n = 113) constituted the testing cohort. Radiomics features were extracted from T2-weighted images and the apparent diffusion coefficient. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures. The area under receiver operating characteristic curve (AUC) was performed to assess models. Results: Tumor boundary, enhancement homogeneity, and vascular invasion were used to construct the radiological model to stratify NF-PNET patients into grade 1 and 2/3 groups, which yielded AUC of 0.884 and 0.684 in the training and testing groups. A radiomics model including 4 features was constructed, with an AUC of 0.941 and 0.871 in the training and testing cohorts. The fusion model combining the radiomics signature and radiological characteristics showed good performance in the training set (AUC = 0.956) and in the testing set (AUC = 0.864), respectively. Conclusion: The developed model that integrates radiomics features with radiological characteristics could be used as a non-invasive, dependable, and accurate tool for the preoperative prediction of grade in NF-PNETs. Clinical relevance statement: Our study revealed that the fusion model based on a non-contrast MR sequence can be used to predict the histologic grade before operation. The radiomics model may be a new and effective biological marker in NF-PNETs. Key Points: The diagnostic performance of the radiomics model and fusion model was better than that of the model based on clinical information and radiological features in predicting grade 1 and 2/3 of nonfunctioning pancreatic neuroendocrine tumors (NF-PNETs). Good performance of the model in the four external testing cohorts indicated that the radiomics model and fusion model for predicting the grades of NF-PNETs were robust and reliable, indicating the two models could be used in the clinical setting and facilitate the surgeons' decision on risk stratification. The radiomics features were selected from non-contrast T2-weighted images (T2WI) and diffusion-weighted imaging (DWI) sequence, which means that the administration of contrast agent was not needed in grading the NF-PNETs. [ABSTRACT FROM AUTHOR]

To determine the accuracy, repeatability, and reproducibility of magnetic resonance imaging-based proton density fat fraction (MRI-PDFF) measurements of rotator cuff muscles between two readers and three different scanners. Twenty-seven volunteers underwent serial shoulder MRI examinations of both left and right sides on one 1.5-T MRI scanner and two 3.0-T MRI scanners. Two independent readers measured muscular PDFF of the supraspinatus, infraspinatus/teres minor muscle, and subscapularis. MR spectroscopy-based proton density fat fraction (MRS-PDFF) was regarded as the reference standard for assessing accuracy. A "coffee break" examination method was used to test the repeatability of each scanner. Bland-Altman plots, Pearson correlation, and linear regression analysis were used to assess bias and linearity. The Wilcoxon signed-rank test and Friedman test were applied to evaluate repeatability and reproducibility. MRI-PDFF measurements indicated strong linearity (R2 = 0.749) and small bias (−0.18%) in comparison with the MRS-PDFF measurements. A very strong positive Pearson correlation (r = 0.955–0.986) between the PDFF estimates of the two repeat scans indicated excellent repeatability. The PDFF measurements showed high reproducibility, with a strong positive Pearson correlation (r = 0.668–0.698) and a small mean bias (−0.04 to −0.10%) across different scanners. MRI-PDFF measurements of rotator cuff muscles were highly accurate, repeatable, and reproducible across different readers and scanners, leading us to the conclusion that PDFF can be a reliable and robust quantitative imaging biomarker for longitudinal or multi-center studies. [ABSTRACT FROM AUTHOR]

Accurate evaluation of tumor response to preoperative chemotherapy is crucial for assigning appropriate patients with colorectal liver metastases (CRLM) to surgery or conservative therapy. However, there is no well‐recognized method for predicting pathological response before surgery. Our study constructed and validated a deep learning algorithm using prechemotherapy and postchemotherapy magnetic resonance imaging (MRI) to predict pathological response in CRLM. CRLM patients from center one who had ≤5 lesions and were scheduled to receive preoperative chemotherapy followed by liver resection between January 2013 and November 2016, were included prospectively and chronologically divided into a training cohort (80% of patients) and a testing cohort (20% of patients). Patients from center two were included January 2017 and December 2018 as an external validation cohort. MRI‐based models were constructed to discriminate according to pathology tumor regression grade (TRG) between the response (TRG1/2) and nonresponse (TRG3/4/5) groups at the lesion level. From center one, 155 patients (328 lesions) were included; chronologically, 101 (264 lesions) in the training cohort and 54 (64 lesions) in the testing cohort. The model achieved better accuracy (0.875 vs 0.578) and AUC (0.849 vs 0.615) than RECIST for discriminating response; it also distinguished the survival outcomes after hepatectomy better than the RECIST criteria. Evaluations of the external validation cohort (25 patients, 61 lesions) also showed good ability with an AUC of 0.833. In conclusion, the MRI‐based deep learning model provided accurate prediction of pathological tumor response to preoperative chemotherapy in patients with CRLM and may inform individualized treatment. What's new? Liver metastasis is a major cause of death in patients with colorectal cancer. Tumor regression grade (TRG) is a good way to assess tumor response and predict patient survival, and is typically measured by pathological evaluation. Here, the authors tested a deep learning algorithm that uses MRI data to obtain TRG in patients with colorectal liver metastasis who were undergoing preoperative chemotherapy. The deep learning model achieved diagnostic accuracy of 87.5%, a big improvement over the previous method, the RECIST criteria (57.8%). This system could help clinicians provide appropriate personalized information to patients about treatment response and prognosis. [ABSTRACT FROM AUTHOR]

Objective. To characterize and evaluate CT and MRI features of extremity soft tissue adult fibrosarcoma. Methods. CT and MRI images from 10 adult patients with pathologically proven fibrosarcomas were retrospectively analyzed with regard to tumor location, size, number, shape, margins, attenuation, signal intensity, and enhancement patterns on MR images. Additionally, the relationships between lesions, deep fascia, and change in adjacent bones were also assessed. Results. Nineteen tumor lesions in 10 patients were selected for this study. Eighteen lesions were lobulated and one was oval in shape. Most cases were located under the deep fascia, including seven cases that had a nodular lump adjacent to the deep fascia and one case that had broken lesion through the deep fascia. On CT, the adult fibrosarcomas mostly showed iso-attenuated soft tissue masses (n=6). On MRI, all the cases (n=9) displayed low signal on T1-weighted imaging (T1WI) and heterogeneous low and high intensity signals on T2-weighted imaging (T2WI), with band-like areas of low signal on both T1WI and T2WI. On contrast-enhanced MRI images, three cases showed heterogeneous peripheral enhancement and one case demonstrated a spoke-wheel-like enhancement. Eight cases showed muscle edema signals in the peritumoral muscle and one case involved adjacent bone. Conclusion. CT and MR imaging have respective advantages in diagnosing adult fibrosarcoma. Combined application of CT and MR is recommended for patients with suspected adult fibrosarcoma. [ABSTRACT FROM AUTHOR]

Aim: To evaluate whether some magnetic resonance imaging (MRI) signs suggesting idiopathic intracranial hypertension (IIH) could also be found in intracranial hypertension (IH) due to cerebral venous thrombosis (CVT) and to assess their possible contribution to diagnosing this disorder.Materials and Methods: Thirty-one patients with IH due to CVT were evaluated prospectively using MRI. A group of 33 age- and sex-matched healthy volunteers served as controls. The optic nerve and sheath, pituitary gland, and ventricles were assessed. The prevalence of each imaging feature was compared between the two groups.Results: Optic nerve sheath (ONS) dilatation and decreased pituitary gland height were the most valid signs suggesting IH in CVT patients: sensitivity 70.97% and 87.1%, respectively; specificity 96.97% and 72.73%, respectively; area under the curve 0.840 and 0.809, respectively. The MRI finding that showed the strongest association with IH in CVT patients was ONS dilatation (odds ratio 78.5).Conclusions: The combination of T1-weighted volumetric MRI and magnetic resonance venography could be helpful for diagnosing IH with CVT. Abnormalities of the ONS and the pituitary gland were reliable diagnostic signs for IH due to CVT. [ABSTRACT FROM AUTHOR]