BackgroundPreoperative differentiation between malignant and benign soft‐tissue masses is important for treatment decisions.Purpose/HypothesisTo construct/validate a radiomics‐based machine method for differentiation between malignant and benign soft‐tissue masses.Study TypeRetrospective.PopulationIn all, 206 cases.Field Strength/SequenceThe T1 sequence was acquired with the following range of parameters: relaxation time / echo time (TR/TE), 352–550/2.75–19 msec. The T2 sequence was acquired with the following parameters: TR/TE, 700–6370/40–120 msec. The data were divided into a 3.0T training cohort, a 1.5T MR validation cohort, and a 3.0T external validationcohort.AssessmentTwelve machine‐learning methods were trained to establish classification models to predict the likelihood of malignancy of each lesion. The data of 206 cases were separated into a training set (n = 69) and two validation sets (n = 64, 73, respectively).Statistical Tests1) Demographic characteristics: a one‐way analysis of variance (ANOVA) test was performed for continuous variables as appropriate. The χ2 test or Fisher's exact test was performed for comparing categorical variables as appropriate. 2) The performance of four feature selection methods (least absolute shrinkage and selection operator [LASSO], Boruta, Recursive feature elimination [RFE, and minimum redundancy maximum relevance [mRMR]) and three classifiers (support vector machine [SVM], generalized linear models [GLM], and random forest [RF]) were compared for selecting the likelihood of malignancy of each lesion. The performance of the radiomics model was assessed using area under the receiver‐operating characteristic curve (AUC) and accuracy (ACC) values.ResultsThe LASSO feature method + RF classifier achieved the highest AUC of 0.86 and 0.82 in the two validation cohorts. The nomogram achieved AUCs of 0.96 and 0.88, respectively, in the two validation sets, which was higher than that of the radiomic algorithm in the two validation sets and clinical model of the validation 1 set (0.92, 0.88 respectively). The accuracy, sensitivity, and specificity of the radiomics nomogram were 90.5%, 100%, and 80.6%, respectively, for validation set 1; and 80.8%, 75.8%, and 85.0% for validation set 2.Data ConclusionA machine‐learning nomogram based on radiomics was accurate for distinguishing between malignant and benign soft‐tissue masses.Evidence Level3Technical EfficacyStage 2 J. Magn. Reson. Imaging 2020;52:873–882.
IntroductionThe long non-coding RNAs (lncRNAs) urothelial cancer associated 1 (UCA1) and metastasis associated lung adenocarcinoma transcript 1 (MALAT1) are known to impact cancer cell regulation. The aim of the present study was to determine the relationship between the expression of these lncRNAs in esophageal squamous cell carcinoma (ESCC) tissues and disease prognosis.Material and methodsThe expression of UCA1 and MALAT1 lncRNAs was assessed in ESCC and adjacent carcinoma tissues (5 cm away from the tumor) and evaluated in relation to overall survival (OS) and disease-free survival (DFS) of patients. This prospective study included 100 ESCC patients who were admitted to the First Hospital of Yulin City between January 2007 and January 2014.ResultsThe expression levels of UCA1 and MALAT1 lncRNAs in ESCC tissues were significantly higher than those in adjacent carcinoma tissues, and there were statistically significant differences in TNM staging between the patients with high lncRNA expression and low lncRNA expression. The OS and DFS of patients with high UCA1 and MALAT1 lncRNA expression levels were significantly shorter than those with low expression levels. Furthermore, the OS and DFS of ESCC patients appeared to be correlated with TNM staging.ConclusionsThese results imply that the up-regulation of UCA1 and MALAT1 lncRNAs in ESCC tissues can impact the degree of tumor progression and is predictive of postoperative survival. Therefore, the expression levels of these lncRNAs can be used as measurement indexes to determine the prognosis of ESCC patients.
This paper focuses on a mechanical regulator free and front‐ wheel drive bicycle robot. We present a scheme to achieve the robotʹs track‐stand motion and circular motion under zero forward speed. In a situation where the robotʹs front‐bar is locked at 90 degrees, a kinetic constraint about the rotating rate of the front‐wheel and the yawing rate of the frame is derived. Using the constraint as a basis, we developed a simplified model of two independent velocities for the robot. The model suggests there is an under‐ actuated rolling angle in the system. Our control strategy originates from the under‐ actuated characteristics of the robot system. Concretely, we linearize the rolling angle of the frame and set the bicycle robot to regulate its tilting by rotating the front‐wheel. In the track‐stand motion, we control the position and the rotational rate of the front‐wheel; but in the circular motion, only the rotational rate of the front‐wheel is strictly regulated. Both simulations and physical experiments results show that our strategy is effective for achieving these two motions
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.
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.
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