This study evaluates a predictive model using quantitative ultrasound features from intratumoral and peritumoral habitats to non-invasively determine HER2 status in invasive breast cancer patients. This retrospective study included 669 patients. The intra-tumoral ROI (Intra-ROI) was manually delineated, and the peri-tumoral ROI (Peri-ROI) was created by expanding 5 mm outward from the tumor boundary. Intra- and peri-tumoral subregions were established via K-Means clustering. Models were developed based on features from the intra-tumoral microenvironment, peri-tumoral microenvironment, entire intra-tumoral area, and entire peri-tumoral area. A habitat integration model was constructed by integrating two subregions, and a comprehensive model was built by combining clinical factors. The study aimed to distinguish HER2-positive and -negative cases and classify IHC 0 and 1 + subgroups among HER2-negative patients. Model effectiveness was evaluated using AUC, accuracy, sensitivity, specificity, DeLong test, Hosmer-Lemeshow test, decision curve analysis (DCA), Precision-Recall Curve and SHAP value analysis. For Task 1 (distinguishing HER2-positive from negative), the habitat + clinical combined model achieved superior performance with a test AUC of 0.757, ACC of 0.692, sensitivity of 0.692, specificity of 0.691, F1-score of 0.466, and MCC of 0.337. The intra- and peritumor habitat ensemble model also demonstrated strong performance with AUC of 0.669, ACC of 0.697, sensitivity of 0.436, specificity of 0.759, F1-score of 0.358, and MCC of 0.251. Both models significantly outperformed single-region models (Intratumor Habitat Model: AUC 0.642, F1-score 0.372; Peritumor Habitat Model: AUC 0.593, F1-score 0.335; Whole Intratumor Model: AUC 0.609, F1-score 0.344; Whole Peritumor Model: AUC 0.577, F1-score 0.326). For Task 2 (distinguishing IHC 0 from 1 + in HER2-negative patients), both the habitat + clinical combined model (test AUC 0.786, ACC 0.745, sensitivity 0.793, specificity 0.640, F1-score 0.811, MCC 0.449) and the intra- and peritumor habitat ensemble model (test AUC 0.778, ACC 0.752, sensitivity 0.838, specificity 0.560, F1-score 0.823, MCC 0.458) demonstrated robust predictive power, significantly outperforming single-region models (Intratumor Habitat Model: AUC 0.693, F1-score 0.751; Peritumor Habitat Model: AUC 0.713, F1-score 0.763; Whole Intratumor Model: AUC 0.671, F1-score 0.697; Whole Peritumor Model: AUC 0.630, F1-score 0.687). SHAP analysis highlighted progesterone receptor (PR), estrogen receptor (ER), and ensemble model probabilities as key predictive factors. Integrating intratumoral and peritumoral ultrasound features with clinical data demonstrates promising potential for non-invasive HER2 status prediction, with the ability to capture intratumoral heterogeneity. However, these findings should be interpreted with caution given the limitations of the current study, including its retrospective single-center design. Further validation through multi-center prospective studies is warranted before any clinical application. This approach may contribute to improving personalized breast cancer treatment strategies pending robust clinical validation.

The aim of this study was to construct a radiomics nomogram for prediction of breast masses (BMs) by analyzing the clinical characteristics of the patients as well as radiomics features of two-dimensional (2D) ultrasound images and strain elastography images. In this retrospective study, 219 patients diagnosed with BMs were enrolled and randomly divided into training set and testing set in a 7:3 ratio. Radiomics nomogram was constructed based on clinical features and Radscore to compare area under the receiver operating characteristic curve (AUC) with another models. The AUCs of the training set were 0.83, 0.91, 0.92, 0.96, and 0.99 for the clinical model, elastography radiomics model, 2D radiomics model, bimodal radiomics model, and nomogram, respectively, and the AUCs of the testing set were 0.86, 0.87, 0.91, 0.93, and 0.95, respectively. There were significant differences in AUC between nomogram and another models (p < 0.05). 2D ultrasound radiomics model and strain elastography radiomics model were of diagnostic value in identifying BMs. The bimodal radiomics model was superior to these two single-modal radiomics models. Nomogram can further enhance the diagnosis of BMs and contribute valuable information for clinical decision making.

Background Accurate differentiation of nodular fasciitis (NF) from soft tissue sarcoma (STS) before surgery is essential for the subsequent diagnosis and treatment of patients.Purpose To develop and evaluate radiomics nomograms based on clinical factors and magnetic resonance imaging (MRI) for the preoperative differentiation of NF from STS.Material and Methods This retrospective study analyzed the MRI data of 27 patients with pathologically diagnosed NF and 58 patients with STS who were randomly divided into training (n = 62) and validation (n = 23) groups. Univariate and multivariate analyses were performed to identify the clinical factors and semantic features of MRI. Radiomics analysis was applied to fat-suppressed T1-weighted (T1W-FS) images, fat-suppressed T2-weighted (T2W-FS) images, and contrast-enhanced T1-weighted (CE-T1W) images. The radiomics nomograms incorporating the radiomics signatures, clinical factors, and semantic features of MRI were developed. ROC curves and AUCs were carried out to compare the performance of the clinical factors, radiomics signatures, and clinical radiomics nomograms.Results Tumor location, size, heterogeneous signal intensity on T2W-FS imaging, heterogeneous signal intensity on CE-T1W imaging, margin definitions on CE-T1W imaging, and septa were independent predictors for differentiating NF from STS (P < 0.05). The performance of the radiomics signatures based on T2W-FS imaging (AUC = 0.961) and CE-T1W imaging (AUC = 0.938) was better than that based on T1W-FS imaging (AUC = 0.833). The radiomics nomograms had AUCs of 0.949, which demonstrated good clinical utility and calibration.Conclusion The non-invasive clinical radiomics nomograms exhibited good performance in the differentiation of NF from STS, and they have clinical application in the preoperative diagnosis of diseases.

目的 探讨以问题为导向的文献沙龙教学法在神经外科护理教学中的应用效果.方法 将2019年6月—2020年6月本院神经外科实习护生60名,根据护理部分组实习安排,采用随机分组方法,分为对照组(n=30)和试验组(n=30).对照组采用传统教学方法,试验组采用以问题为导向的文献沙龙教学方法.比较两组护生出科考试成绩、床边综合能力及教学效果、教学满意度进行对照分析.结果 以问题为导向的文献沙龙教学法护生出科考核成绩、床边综合能力成绩及教学效果评价、对带教的满意度均高于传统教学组(P<0.05).试验组教学法激发了护生学习和教师带教的积极性,提高了考核成绩、床边综合能力和评判性思维能力.结论 以问题为导向的文献沙龙教学法对护生的思维能力与学习兴趣有明显改善,启发了护生临床科研能力,临床教学效果满意.以问题为导向的文献沙龙教学法在神经外科护理教学中的应用效果优于传统教学法.

PurposeThe objective of this research was to develop and validate an ultrasound‐based radiomics nomogram for the pre‐operative assessment of Ki‐67 in breast cancer (BC).Materials and MethodsFrom December 2016 to December 2018, 515 patients with invasive ductal breast cancer who received two‐dimensional (2D) ultrasound and Ki‐67 examination were studied and analyzed retrospectively. The dataset was distributed at random into a training cohort (n = 360) and a test cohort (n = 155) in the ratio of 7:3. Each tumor region of interest was defined based on 2D ultrasound images and radiomics features were extracted. ANOVA, maximum correlation minimum redundancy (mRMR) algorithm, and minimum absolute shrinkage and selection operator (LASSO) were performed to pick features, and independent clinical predictors were integrated with radscore to construct the nomogram for predicting Ki‐67 index by univariate and multivariate logistic regression analysis. The performance and utility of the models were evaluated by plotting receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves.ResultsIn the testing cohort, the area under the receiver characteristic curve (AUC) of the nomogram was 0.770 (95% confidence interval, 0.690–0.860). In both cohorts, the nomogram outperformed both the clinical model and the radiomics model (P < .05 according to the DeLong test). The analysis of DCA proved that the model has clinical utility.ConclusionsThe nomogram based on 2D ultrasound images offered an approach for predicting Ki‐67 in BC.

患者女,37岁,3个月前无意间发现左大腿后侧肿物,约鸡蛋黄大小,未在意,未经系统治疗;近期发现肿物逐渐增大伴有轻压痛,左大腿后侧皮肤完好无破溃,无皮下青紫及瘀斑,可触及大小约11 cm×9 cm肿物,边界清晰,质韧,无明显活动性,局部压痛感明显,左下肢皮温血运良好,活动略受限,左足各趾感觉运动良好.外院CT提示:左侧半膜肌、半腱肌区域占位性病变,股二头肌长头、半膜肌、半腱肌、臀大肌及部分筋膜水肿.

Objective To investigate the diagnostic value of conventional ultrasound (US) and strain elastography (SE) in malignant soft tissue tumors. Method A total of 83 soft tissue masses were included prospectively. US and SE imaging were performed at the same time. Two observers assessed the B mode, color Doppler, elastic scores (ES), strain ratio (SR), and SE size to B mode size (EI/B) ratio and compared the consistency of the data between the observers. According to the pathological diagnosis of resection, the cases were divided into malignant and nonmalignant groups. The diagnostic value of conventional US and SE in the prediction of malignant soft tissue tumors was assessed. Results The pathology results divided cases into 36 malignant lesions and 47 nonmalignant lesions. There was no statistically significant difference in gender, location, maximum diameter, echo, tail sign, cystic component, Doppler scores, or SR between the two groups (p > 0.05). However, significant differences between the two groups were found in age, depth, heterogeneity, edge, ES, and EI/B (p < 0.05). The biggest area under the receiver operating characteristics curve (0.934) was the combination model of age, heterogeneity, edge, ES, and EI/B, and the sensitivity and specificity were 0.861 and 0.957, respectively. Conclusions Conventional US and SE are significant for the diagnosis of malignant soft tissue tumors, and SE can be used as a complementary technique to the characterization of STTs using conventional US.

患者男,51岁,发现左大腿内侧包块2个月,无红肿、发热、疼痛,局部无皮疹.超声:左侧大收肌内约45 mm×38 mm×34 mm类圆形混合回声团,形态尚规则,以实性为主,边界欠清晰,无明显包膜,实性部分回声细腻均匀(图1A);CDFI见其内多条短棒状血流信号(图1B);峰值流速(peak velocity,Vp)=17 cm/s,阻力指数(resistance index,RI) =0.79,弹性评分3分(图1C);提示左侧大收肌内混合性肿物(恶性可能性大).行左大腿肿物切除术,术中见肿物外覆肌纤维组织,切面灰黄、灰红,质地略韧、细腻,与周围组织分界尚清.病理检查:光镜下见组织内增生的成纤维细胞及毛细血管,间质黏液变性,较多胶原纤维呈条索状分布,淋巴细胞较大,浆细胞浸润,小灶性出血.免疫组织化学:SMA(+),S-100(-),STAT6(-),CD34(-) ,P53(-),Ki-67(10％ +) .

目的:探讨高频超声在类固醇激素注射治疗拇长屈肌腱腱鞘炎后随访中的应用价值.方法:纳入2018年1月～2018年10月于本院骨科门诊诊断又经超声证实为拇长屈肌腱腱鞘炎患者及成人健康体检者为观察对象,拇长屈肌腱腱鞘炎患者为观察组(n=50例),行超声引导下等剂量类固醇激素注射治疗,超声观察治疗前、治疗后2周、1个月、3个月时A1滑车及拇长屈肌腱变化;成人健康者为对照组(n=50例),左右拇指共100例,比较对照组与观察组之间A1滑车及拇长屈肌腱有无差异.结果:观察组A1滑车厚度、滑车内拇长屈肌腱最小面积/同侧大鱼际中点处面积值:治疗前>治疗后2周>1个月>3个月>对照组,差异具有统计学意义(P<0.05).结论:高频超声可为类固醇治疗拇长屈肌腱腱鞘炎后期随访提供可靠的客观依据.

To determine if dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters reflect histological grade of soft tissue sarcoma (STS) The medical records of 50 patients diagnosed with pathologically confirmed STS were retrospectively reviewed. Each STS was assessed with conventional contrast-enhanced MRI and DCE-MRI using a 3.0-T MRI system. The conventional MRI characteristics of low-grade (grade 1) and high-grade (grade 2 and grade 3) tumors were analyzed. Semi-quantitative parameters, including iAUC and TTP, and quantitative parameters, including Ktrans, Kep, and Ve, were derived from DCE-MRI. The diagnostic performances and optimal thresholds of various combinations of DCE-MRI parameters for predicting histological grades of STS were investigated using receiver operator characteristic (ROC) curves. On conventional MRI, high-grade STSs were significantly larger (≥ 5 cm) and more likely to show a heterogeneous signal intensity on T2WI (> 75%), peritumoral hyperintensity on T2WI, or tumor necrosis (> 50%) compared with low-grade STS. On DCE-MRI, iAUC, TTP, Ktrans, and Kep were significant predictors of STS histological grade. Ktrans had a high diagnostic value for differentiating between high-grade and low-grade STSs. The combination of iAUC, TTP, and Ktrans yielded a higher AUC value (0.841) than the other models. High-grade STSs were usually larger than low-grade STSs, had unclear boundaries, a heterogeneous signal intensity on T2-weighted image (T2WI), and extensive necrosis. On DCE-MRI, iAUC, TTP, Ktrans, and Kep could differentiate between high-grade and low-grade STSs. The combination of iAUC, TTP, and Ktrans had a high diagnostic performance for differentiating between STS histological grades.

目的 探讨超声引导下射频消融(RFA)治疗腹股沟疝无张力修补术后慢性疼痛的疗效.方法 回顾性分析8例行超声引导下腹股沟疝无张力修补术后慢性疼痛患者RFA治疗前后的临床资料,测量手术前后补片周围瘢痕的厚度,使用VAS评分评估手术前后疼痛程度,比较手术前后瘢痕厚度的变化同时观察手术前后疼痛程度的变化.结果 补片周围瘢痕的厚度:术前及术后1d,1、3、6个月的瘢痕厚度分别为(7.1±0.8)、(7.1±0.8)、(4.8±0.7)、(3.8±0.7)、(3.4±0.7) mm,手术后1d、1、3、6个月的瘢痕厚度相互比较及手术后1、3、6个月的瘢痕厚度分别与手术前比较差异均有统计学意义,P＜0.05;疼痛的VAS评分:术前及术后1d,1、3、6个月的VAS评分分别为(7.6±1.0)、(4.6±0.8)、(2.4±0.8)、(1.6±1.0)、(1.1±0.7)分,手术后1d、1、3、6个月的VAS评分相互比较及分别与手术前比较差异均有统计学意义,P＜0.05.术后1例因精索静脉出血形成阴囊血肿,经手术治愈.结论 超声引导下的RFA治疗能有效缓解腹股沟疝无张力修补术后慢性疼痛,临床效果显著,并发症较少,是一种有效、安全的治疗方法.

临床创面处理多采用清创后在挫、擦伤处使用湿润烧伤膏,但临床实践中发现湿润烧伤膏油性较强,易污染衣服和被服,在不易暴露的部位应用不便,影响患者的活动,且需要每3～4 h更换1次药膏,每次换药前需要将原有药膏和液化的坏死组织擦净,容易引起患者疼痛和创面出血,还有个别患者应用后出现局部过敏反应[1].