Objective To identify and characterize changes in the pelvic floor and pubic bone, using magnetic resonance imaging, in primiparous women with normal vaginal delivery, in comparison with nulliparous women. MethodsPelvic MR images from ten primiparous women, 6–7 weeks after normal vaginal delivery, and ten nulliparous women were obtained from January to April 2014. The selected women were scanned using a multiplanar T2FRFSE sequence and T2fsFRFSE sequence. Changes in the pelvic floor and pubic bone in primiparous women, including tears of the levator ani fibers, pubic bone edema, and fractures, were assessed on the MR images in comparison with images from normal nulliparous women. Injury to the levator ani was evaluated and scored. The incidence, location and the extent of injury to the levator ani and pubic bone were characterized further.ResultsEight out of ten primiparous women had imaging abnormalities after normal vaginal delivery. Three women had unilateral tears of the pubococcygeus, in which the defect in the muscle was located at or near its origin at the pubic bone, and one had a pubococcygeus tear accompanied by bilateral spilling of the vagina. Four women had partial tears of the iliococcygeus: one was a bilateral tear, and three were unilateral. None had a tear of the coccygeus. Eight women had pubic bone marrow edema; one was accompanied by a pubic bone fracture line. None of the nulliparous women had any abnormality of the pelvic floor and pubic bone.ConclusionAbnormalities of the pelvic floor and pubic bone were observed in primiparous women but not in nulliparous women. In primiparous women, most levator ani muscle tears are at or near the point of origin, and pubococcygeus injuries are usually accompanied by pubic bone marrow edema.

ObjectivesMucinous breast cancer (MBC), particularly pure MBC (pMBC), often tend to be confused with fibroadenoma (FA) due to their similar images and firm masses, so some MBC cases are misdiagnosed to be FA, which may cause poor prognosis. We analyzed the ultrasonic features and aimed to identify the ability of multilayer perceptron (MLP) to classify early MBC and its subtypes and FA.Materials and MethodsThe study consisted of 193 patients diagnosed with pMBC, mMBC, or FA. The area under curve (AUC) was calculated to assess the effectiveness of age and 10 ultrasound features in differentiating MBC from FA. We used the pairwise comparison to examine the differences among MBC subtypes (pure and mixed types) and FA. We utilized the MLP to differentiate MBC and its subtypes from FA.ResultsThe nine features with AUCs over 0.5 were as follows: age, echo pattern, shape, orientation, margin, echo rim, vascularity distribution, vascularity grade, and tumor size. In subtype analysis, the significant differences were obtained in 10 variables (p-value range, 0.000–0.037) among pMBC, mMBC, and FA, except posterior feature. Through MLP, the AUCs of predicting MBC and FA were both 0.919; the AUCs of predicting pMBC, mMBC, and FA were 0.875, 0.767, and 0.927, respectively.ConclusionOur study found that the MLP models based on ultrasonic characteristics and age can well distinguish MBC and its subtypes from FA. It may provide a critical insight into MBC preoperative clinical management.

BackgroundMany clinicians are facing the dilemma about whether they should apply the active surveillance (AS) strategy for managing Clinically Node-negative (cN0) PTMC patients in daily clinical practice. This research plans to construct a dynamic nomogram based on network, connected with ultrasound characteristics and clinical data, to predict the risk of central lymph node metastasis (CLNM) in cN0 PTMC patients before surgery.MethodsA retrospective analysis of 659 patients with cN0 PTMC who had underwent thyroid surgery and central compartment neck dissection. Patients were randomly (2:1) divided into the development cohort (439 patients) and validation cohort (220 patients). The group least absolute shrinkage and selection operator (Group Lasso) regression method was used to select the ultrasonic features for CLNM prediction in the development cohort. These features and clinical data were screened by the multivariable regression analysis, and the CLNM prediction model and web-based calculator were established. Receiver operating characteristic, calibration curve, Clinical impact curve and decision curve analysis (DCA) were used to weigh the performance of the prediction model in the validation set.ResultsMultivariable regression analysis showed that age, tumor size, multifocality, the number of contact surface, and real-time elastography were risk factors that could predict CLNM. The area under the curve of the prediction model in the development and validation sets were 0.78 and 0.77, respectively, with good discrimination and calibration. A web-based dynamic calculator was built. DCA proved that the prediction model had excellent net benefits and clinical practicability.ConclusionsThe web-based dynamic nomogram incorporating US and clinical features was able to forecast the risk of preoperative CLNM in cN0 PTMC patients, and has good predictive performance. As a new observational indicator, NCS can provide additional predictive information.