Combined Use of Texture Features and Morphological Classification Based on Dynamic Contrast-enhanced MR Imaging: Differentiating Benign and Malignant Breast Masses with High Negative Predictive Value

Purpose: We evaluated the diagnostic performance of the texture features of dynamic contrast-enhanced (DCE) MRI for breast cancer diagnosis in which the discriminator was optimized, so that the specificity was maximized via the restriction of the negative predictive value (NPV) to greater than 98%.Methods: Histologically proven benign and malignant mass lesions of DCE MRI were enrolled retrospectively. Training and testing sets consist of 166 masses (49 benign, 117 malignant) and 50 masses (15 benign, 35 malignant), respectively. Lesions were classified via MRI review by a radiologist into 4 shape types: smooth (S-type, 34 masses in training set and 8 masses in testing set), irregular without rim-enhancement (I-type, 60 in training and 14 in testing), irregular with rim-enhancement (R-type, 56 in training and 22 in testing), and spicula (16 in training and 6 in testing). Spicula were immediately classified as malignant. For the remaining masses, 298 texture features were calculated using a parametric map of DCE MRI in 3D mass regions. Masses were classified into malignant or benign using two thresholds on a feature pair. On the training set, several feature pairs and their thresholds were selected and optimized for each mass shape type to maximize specificity with the restriction of NPV > 98%. NPV and specificity were computed using the testing set by comparison with histopathologic results and averaged on the selected feature pairs.Results: In the training set, 27, 12, and 15 texture feature pairs are selected for S-type, I-type, and R-type masses, respectively, and thresholds are determined. In the testing set, average NPV and specificity using the selected texture features were 99.0% and 45.2%, respectively, compared to the NPV (85.7%) and specificity (40.0%) in visually assessed MRI category-based diagnosis.Conclusion: We, therefore, suggest that the NPV of our texture-based features method described performs similarly to or greater than the NPV of the MRI category-based diagnosis.     

听神经瘤误诊分析

对经耳鼻咽喉科首诊，最终由神经外科手术全切或部分切除的经病理证实的７２例听神经瘤进行早期误诊分析，误诊时间平均５．５年，误诊病种为神经性耳聋、突发性耳聋、神经性耳鸣、颈椎病、鼻咽癌等。文中重点讨论了误诊原因。     

阴道酵母样真菌感染的3种实验室检测方法比较

目的采用3种方法平行检测阴道酵母样真菌感染，比较它们的优缺点，寻求最佳检测方法。方法对患有阴道炎病人的阴道分泌物同时用改良盐水湿片法、超高倍显微诊断仪（MCMDI）法扣酵母样真菌培养法进行检测，然后作统计分析。结果共检测420例阴道炎病人标本，改良盐水湿片法、MCMDI法扣培养法的阴道酵母样真菌检出阳性率分别为22．38％（94／420）、39．52％（166／420）争40．00％（168／420），后两法栓出总符合率达97．62％。结论改良盐水湿片法、MCMDI法都能快速检测酵母样真菌，但前者比后者阳性率低，容易漏诊；MCMDI法跟培养法的阳性率差不多，可用作早期快速诊断，而培养法虽然阳性率高，且可鉴定到菌种扣做药敏试验，给临床用药提供参考，但耗时长（平均3～5d），不利于快速诊断扣早期治疗。     

非生殖系统肿瘤卵巢转移的误诊原因及外科治疗

目的：探讨非生殖系统肿瘤卵巢转移的诊断和治疗方法.方法：回顾分析我院1990～2004年收治的41例经手术治疗及病理学确诊的非生殖系统肿瘤卵巢转移患者的临床资料.结果：患者平均年龄47.3岁,其中29（70.7%）例尚未绝经.原发灶为胃癌者17例,大肠18例,小肠2例,肺1例,肾上腺 1例,输尿管1例,膀胱1例.41例患者均行手术治疗.结论：该病90.24%（37/41）来源胃肠肿瘤,因此对于双侧卵巢肿瘤患者应注意胃肠检查.     

Machine Learning Approaches for Monitoring of Tool Wear during Grey Cast-Iron Turning

The dynamic development of new technologies enables the optimal computer technique choice to improve the required quality in today’s manufacturing industries. One of the methods of improving the determining process is machine learning. This paper compares different intelligent system methods to identify the tool wear during the turning of gray cast-iron EN-GJL-250 using carbide cutting inserts. During these studies, the experimental investigation was conducted with three various cutting speeds vc (216, 314, and 433 m/min) and the exact value of depth of cut a_p and federate f. Furthermore, based on the vibration acceleration signals, appropriate measures were developed that were correlated with the tool condition. In this work, machine learning methods were used to predict tool condition; therefore, two tool classes were proposed, namely usable and unsuitable, and tool corner wear VBc = 0.3 mm was assumed as a wear criterium. The diagnostic measures based on acceleration vibration signals were selected as input to the models. Additionally, the assessment of significant features in the division into usable and unsuitable class was caried out. Finally, this study evaluated chosen methods (classification and regression tree, induced fuzzy rules, and artificial neural network) and selected the most effective model.     

Correlation between C-reactive protein/albumin ratio and prognosis in patients with lung adenocarcinoma

ObjectiveThis study was performed to examine the relationship between the C-reactive protein/albumin ratio (CAR) and the prognosis of patients with lung adenocarcinoma and thus provide a reference for evaluating the prognosis of lung adenocarcinoma.MethodsThe clinical data of 130 patients with lung adenocarcinoma were retrospectively collected and analyzed. The patients’ overall survival (OS) time was calculated, and the factors affecting OS were statistically analyzed.ResultsThe CAR was correlated with sex, clinical stage, brain metastasis, S100 calcium-binding protein B (S100B), interleukin 17, myelin basic protein, squamous cell carcinoma antigen (SCC-Ag), and the lymphocyte count. The median OS was significantly shorter in the high- than low-CAR group (18 vs. 64 months, respectively). The CAR, clinical stage, brain metastasis, S100B, interleukin 17, SCC-Ag, C-reactive protein, albumin, and neutrophil count affected the OS of patients with lung adenocarcinoma. The CAR and clinical stage were independent risk factors for a poor prognosis in patients with lung adenocarcinoma.ConclusionsThe CAR and clinical stage are independent risk factors for OS in patients with lung adenocarcinoma.