BackgroundThis study aims to explore the accuracy of Convolutional Neural Network (CNN) models in predicting malignancy in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging (DCE-BMRI).MethodsA total of 273 benign lesions (benign group) and 274 malignant lesions (malignant group) were collected and randomly divided into a training set (246 benign and 245 malignant lesions) and a testing set (28 benign and 28 malignant lesions) in a 9:1 ratio. An additional 53 lesions from 53 patients were designated as the validation set. Five models-VGG16, VGG19, DenseNet201, ResNet50, and MobileNetV2-were evaluated. Model performance was assessed using accuracy (Ac) in the training and testing sets, and precision (Pr), recall (Rc), F1 score (F1), and area under the receiver operating characteristic curve (AUC) in the validation set.ResultsThe accuracy of VGG19 on the test set (0.96) is higher than that of VGG16 (0.91), DenseNet201 (0.91), ResNet50 (0.67), and MobileNetV2 (0.88). For the validation set, VGG19 achieved higher performance metrics (Pr 0.75, Rc 0.76, F1 0.73, AUC 0.76) compared to the other models, specifically VGG16 (Pr 0.73, Rc 0.75, F1 0.70, AUC 0.73), DenseNet201 (Pr 0.71, Rc 0.74, F1 0.69, AUC 0.71), ResNet50 (Pr 0.65, Rc 0.68, F1 0.60, AUC 0.65), and MobileNetV2 (Pr 0.73, Rc 0.75, F1 0.71, AUC 0.73). S4 model achieved higher performance metrics (Pr 0.89, Rc 0.88, F1 0.87, AUC 0.89) compared to the other four fine-tuned models, specifically S1 (Pr 0.75, Rc 0.76, F1 0.74, AUC 0.75), S2 (Pr 0.77, Rc 0.79, F1 0.75, AUC 0.77), S3 (Pr 0.76, Rc 0.76, F1 0.73, AUC 0.75), and S5 (Pr 0.77, Rc 0.79, F1 0.75, AUC 0.77). Additionally, S4 model showed the lowest loss value in the testing set. Notably, the AUC of S4 for BI-RADS 3 was 0.90 and for BI-RADS 4 was 0.86, both significantly higher than the 0.65 AUC for BI-RADS 5.ConclusionsThe S4 model we propose has demonstrated superior performance in predicting the likelihood of malignancy in DCE-BMRI, making it a promising candidate for clinical application in patients with breast diseases. However, further validation is essential, highlighting the need for additional data to confirm its efficacy.

Purpose: To evaluate the capability of deep transfer learning (DTL) and fine-tuning methods in differentiating malignant from benign lesions in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods: The diagnostic efficiencies of the VGG19, ResNet50, and DenseNet201 models were tested under the same dataset. The model with the highest performance was selected and modified utilizing three fine-tuning strategies (S1-3). Fifty additional lesions were selected to form the validation set to verify the generalization abilities of these models. The accuracy (Ac) of the different models in the training and test sets, as well as the precision (Pr), recall rate (Rc), F1 score (), and area under the receiver operating characteristic curve (AUC), were primary performance indicators. Finally, the kappa test was used to compare the degree of agreement between the DTL models and pathological diagnosis in differentiating malignant from benign breast lesions. Results: The Pr, Rc, f1, and AUC of VGG19 (86.0%, 0.81, 0.81, and 0.81, respectively) were higher than those of DenseNet201 (70.0%, 0.61, 0.63, and 0.61, respectively) and ResNet50 (61.0%, 0.59, 0.59, and 0.59). After fine-tuning, the Pr, Rc, f1, and AUC of S1 (87.0%, 0.86, 0.86, and 0.86, respectively) were higher than those of VGG19. Notably, the degree of agreement between S1 and pathological diagnosis in differentiating malignant from benign breast lesions was 0.720 (κ = 0.720), which was higher than that of DenseNet201 (κ = 0.440), VGG19 (κ = 0.640), and ResNet50 (κ = 0.280). Conclusion: The VGG19 model is an effective method for identifying benign and malignant breast lesions on DCE-MRI, and its performance can be further improved via fine-tuning. Overall, our findings insinuate that this technique holds potential clinical application value.

Background In clinical practice, reducing unnecessary biopsies for mammographic BI-RADS 4 lesions is crucial. The objective of this study was to explore the potential value of deep transfer learning (DTL) based on the different fine-tuning strategies for Inception V3 to reduce the number of unnecessary biopsies that residents need to perform for mammographic BI-RADS 4 lesions. Methods A total of 1980 patients with breast lesions were included, including 1473 benign lesions (185 women with bilateral breast lesions), and 692 malignant lesions collected and confirmed by clinical pathology or biopsy. The breast mammography images were randomly divided into three subsets, a training set, testing set, and validation set 1, at a ratio of 8:1:1. We constructed a DTL model for the classification of breast lesions based on Inception V3 and attempted to improve its performance with 11 fine-tuning strategies. The mammography images from 362 patients with pathologically confirmed BI-RADS 4 breast lesions were employed as validation set 2. Two images from each lesion were tested, and trials were categorized as correct if the judgement (≥ 1 image) was correct. We used precision (Pr), recall rate (Rc), F1 score (F1), and the area under the receiver operating characteristic curve (AUROC) as the performance metrics of the DTL model with validation set 2. Results The S5 model achieved the best fit for the data. The Pr, Rc, F1 and AUROC of S5 were 0.90, 0.90, 0.90, and 0.86, respectively, for Category 4. The proportions of lesions downgraded by S5 were 90.73%, 84.76%, and 80.19% for categories 4 A, 4B, and 4 C, respectively. The overall proportion of BI-RADS 4 lesions downgraded by S5 was 85.91%. There was no significant difference between the classification results of the S5 model and pathological diagnosis ( P = 0.110). Conclusion The S5 model we proposed here can be used as an effective approach for reducing the number of unnecessary biopsies that residents need to conduct for mammographic BI-RADS 4 lesions and may have other important clinical uses.

目的 探讨基于DenseNet 201深度迁移学习(DTL)模型在改善乳腺MRI乳腺影像报告和数据系统(BI-RADS)4类病变的细分类潜力.方法 以DenseNet 201神经网络为基础,对11 256幅良性组和5 448幅恶性组乳腺动态对比增强磁共振成像(DCE-MRI)图像建立DTL模型,将良性组、恶性组图像分别按照9:1随机分为训练集(良性组10 146幅,恶性组4 908幅)和测试集(良性组1 110幅,恶性组540幅).选取BI-RADS 4类患者81例作为验证集,所有患者乳腺病变均经病理证实,其中60例恶性,21例良性.每例患者选择10幅DCE-MRI增强图像进行验证,如果8幅图像归类正确则认定为此例患者归类正确.以验证集准确率、召回率、F1评分及受试者工作特征(ROC)曲线的曲线下面积(AUC)作为性能指标.结果 DTL模型在训练集和测试集的准确率均为100.00％.验证集准确率、召回率、F1评分及AUC分别为98.00％、0.98、0.98和0.97.21例良性病变中,DTL模型预测正确20例(占95.24％),预测恶性概率为3.50％～27.60％.60例恶性病变中,DTL模型预测正确58例(占96.67％),预测恶性概率为51.50％～93.60％.基于DenseNet 201的DTL模型与病理组织学在对乳腺MRI良恶性病变的分类诊断结果差异无统计学意义(P=0.859).结论 基于DenseNet 201的DTL模型可作为乳腺MRI BI-RADS 4类病变细分的有效方法.

It is crucial to diagnose breast cancer early and accurately to optimize treatment. Presently, most deep learning models used for breast cancer detection cannot be used on mobile phones or low-power devices. This study intended to evaluate the capabilities of MobileNetV1 and MobileNetV2 and their fine-tuned models to differentiate malignant lesions from benign lesions in breast dynamic contrast-enhanced magnetic resonance images (DCE-MRI).

目的 探讨下腔静脉(IVC)基于圆周长的直径(CD)与前后位投照横径(PD)的差异.方法 前瞻性收集2021年1月至2月在常州第二人民医院接受腹部CT检查患者的临床和影像资料.根据IVC影像分为5型(Ⅰ型:椭圆形,长轴与水平线成一定角度;Ⅱ型:正圆形;Ⅲ型:垂直长轴直径大于水平长轴直径的椭圆形;Ⅳ型:IVC水平长轴与水平线平行的椭圆形;Ⅴ型:不规则形状).采用圆周长公式计算CD,前后位投照获得PD(在CT横断位模拟).分析CD与患者性别、年龄、身高、体质量及体质量指数(BMI)的相关性.结果 共纳入516例患者,其中男286例(55.4％),年龄(58.4±14.1)岁.PD、CD分别为(20.93±3.21)mm、(19.36±2.58)mm(P<0.01).IVC影像分型Ⅰ型371例(71.9％),PD>CD[(21.04±3.02)mm比(19.43±2.42)mm,P<0.01];Ⅱ型18例(3.5％),PD≈CD[(20.26±2.19)mm比(20.11±1.90)mm,P=0.224];Ⅲ型11例(2.1％),PDCD[(22.68±3.12)mm比(19.96±2.58)mm,P<0.01];Ⅴ型91例(17.6％),PD>CD[(20.69±3.60)mm比(18.85±3.15)mm,P<0.01].男性CD值大于女性[(19.79±2.63)mm比(18.83±2.41)mm,P<0.01].CD影响因素分析显示,CD与年龄呈负相关,与身高、体质量呈正相关,与BMI无关.结论 正圆形IVC(Ⅱ型)少见,96.5％患者CD比PD能更好地反映IVC真实直径.大多数临床情况下,依据PD选择滤器可能加重滤器与IVC不匹配程度,CD值对于选择滤器具有一定的参考价值.

目的 探讨基于MobileNetV2深度迁移学习(DTL)对乳腺X线摄影乳腺影像报告和数据系统(BI-RADS)4类病变降级分类的价值.方法 将良性组、恶性组图像分别按照9:1随机分为训练集(良性组9346幅,恶性组4421幅)和测试集(良性组1038幅,恶性组491幅).通过模型微调构建基于MobileNetV2的DTL模型,并对9346幅良性组和4421幅恶性组乳腺X线图像进行学习,另外搜集由5位影像科医师报告的乳腺X线BI-RADS 4类病变患者共382例作验证集.每个病变均选择头尾位(CC位)和内外斜位(MLO位)两幅图像进行验证,如有1幅图像归类正确,则判断为该例归类正确.以验证集准确率、召回率、F1评分及受试者工作特征曲线(ROC)曲线下面积(AUC)作为DTL模型的性能指标.结果 模型在训练集和测试集准确率分别为100％、98％.在验证集准确率、召回率、F1评分及AUC分别为0.91、0.91、0.91和0.91.模型对BI-RADS 4A、4B、4C类病变降级比例分别为87.7％、80.2％和75.2％.对BI-RADS 4类病变总体降级比例为81.9％,且DTL模型与病理组织学在对乳腺X线摄影良恶性病变的分类诊断结果差异无统计学意义(P=0.206).结论 基于MobileNetV2的DTL模型是乳腺X线摄影BI-RADS 4类病变降级的有效方法.

目的:探讨基于DenseNet201深度迁移学习(DTL)在改善乳腺MRI BI-RADS 3类病变分类诊断的潜力.方法:采用基于DenseNet201的DTL模型对11 256幅良性组和5 448幅恶性组乳腺DCE-MRI图像进行学习.将良性组、恶性组图像按照9:1随机分为训练集(良性组:10 146幅;恶性组4908幅)和测试集(良性组:1110幅;恶性组:540幅).收集乳腺MRI报告BI-RADS 3类患者201例作验证集,其中197例良性,4例恶性.以验证集准确度、召回率、F1评分及ROC曲线下面积作为性能指标.结果:训练集和测试集最高准确度分别为100.00％和99.52％.验证集平均准确度、平均召回率、平均F1评分及ROC曲线下面积分别为98.00％、0.98、0.98和0.98.DTL模型对201例病变归类正确199例,归类准确度99.00％.结论:基于DenseNet201的DTL模型是提高乳腺MRIBI-RADS 3类病变良恶性诊断准确性的有效方法.

In order to achieve better performance, artificial intelligence is used in breast cancer diagnosis. In this study, we evaluated the efficacy of different fine-tuning strategies of deep transfer learning (DTL) based on the DenseNet201 model to differentiate malignant from benign lesions on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We collected 4260 images of benign lesions and 4140 images of malignant lesions of the breast pertaining to pathologically confirmed cases. The benign and malignant groups was randomly divided into a training set and a testing set at a ratio of 9:1. A DTL model based on the DenseNet201 model was established, and the effectiveness of 4 fine-tuning strategies (S0: strategy 0, S1: strategy; S2: strategy; and S3: strategy) was compared. Additionally, DCE-MRI images of 48 breast lesions were selected to verify the robustness of the model. Ten images were obtained for each lesion. The classification was considered correct if more than 5 images were correctly classified. The metrics for model performance evaluation included accuracy (Ac) in the training and testing sets, precision (Pr), recall rate (Rc), f1 score (f1), and area under the receiver operating characteristic curve (AUROC) in the validation set. The Ac of the 4 fine-tuning strategies reached 100.00% in the training set. The S2 strategy exhibited good convergence in the testing set. The Ac of S2 was 98.01% in the testing set, which was higher than those of S0 (93.10%), S1 (90.45%), and S3 (93.90%). The average classification Pr, Rc, f1, and AUROC of S2 in the validation set were (89.00%, 80.00%, 0.81, and 0.79, respectively) higher than those of S0 (76.00%, 67.00%, 0.69, and 0.65, respectively), S1 (60.00%, 60.00%, 0.60, 0.66, and respectively), and S3 (77.00%, 73.00%, 0.74, 0.72, respectively). The degree of coincidence between S2 and the histopathological method for differentiating between benign and malignant breast lesions was high (κ = 0.749). The S2 strategy can improve the robustness of the DenseNet201 model in relatively small breast DCE-MRI datasets, and this is a reliable method to increase the Ac of discriminating benign from malignant breast lesions on DCE-MRI.

In earlier studies on an animal model we observed protective properties of outer membrane proteins (OMPs) of Shigella, Hafnia, and Escherichia coli strains. In order to investigate human sera for reactivity with OMPs we subjected these proteins to immunoblotting with umbilical cord plasma and sera from children and adults. The IgG and IgA antibodies interacted primarily with a 38-kDa protein, in similar way for several enterobacterial strains, but different for Pseudomonas aeruginosa. This observation prompted us to determine the reactivity with the purified 38-kDa OMP in the sera of several groups of children. The reactivity of the protein from Shigella flexneri serotype 3a with sera in ELISA was age dependent, increasing from low reactivity in infants to the adult antibody level. The IgG and IgA antibody specific response thus revealed the normal pattern of immunity. The level of IgA and IgG antibody was significantly low in child patients with IgA and/or IgG immunoglobulin deficiencies, but was at the healthy control level in children with recurrent respiratory tract inflammation. These data correlated with total IgA and IgG levels in immunoglobulin-deficient children. The results indicate that this protein may serve as an immunodiagnostic marker, but also as an antigen carrier in vaccines.

Abstract Background Early and rapid diagnosis of breast cancer is very important. Traditional method for detecting and diagnosing breast cancer may lead to a false positive or negative result. Presently, most of the deep learning models used in breast cancer detection prevents their use on mobile phones or low-configuration devices. This study intends to evaluate the capability of MobileNetV1 and MobileNetV2 and their fine-tuned models to differentiate malignant from benign lesions in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods The diagnostic efficiencies of MobileNetV1_False(V1_False), MobileNetV1_True (V1_True), MobileNetV2_False(V2_False) and MobileNetV2_True(V2_True) were tested and evaluated under the same program architecture and dataset (2124 images in benign group and 2226 images in malignant group). A relatively optimal model was selected (with the highest accuracy in the training set and test set), and two fine-tuning strategies (S0 and S1) were used to modify it. The accuracy (Ac) of different models in the training and test sets, as well as the precision (Pr), recall rate (Rc), f1 score(f1) and area under the receiver operating characteristic curve (AUC), were taken as the performance indicators. Results The Ac of V1_True (0.9815) was higher than those of V1_False (0.9749), V2_False (0.9672) and V2_True (0.9699). Overfitting were observed in all models. Pr, Rc, f1 and AUC of V1_True were 0.79,0.73,0.74 and 0.74, respectively, they were higher than those of V1_False (0.77,0.73,0.73 and 0.73), V2_False (0.74,0.65,0.66 and 0.65) and V2_True (0.76,0.67,0.68 and 0.67). Conclusion The MobileNetV1_True model can differentiate between benign and malignant breast lesions on breast DCE-MRI, and has the potential to be deployed on an embedded mobile platform. Future work is necessary to improve the generalization capability of the proposed method.

目的:评价基于CT双期增强图像的不同深度迁移学习(DTL)模型对甲状腺良恶性结节的分类效能.方法:采用相同程序架构和相同数据集对3种DTL模型(VGG19、ResNet50和DenseNet201)的分类诊断效能进行测试和评估.以不同模型在训练集和测试集中的最高预测符合率和在验证集中的符合率、召回率、F1评分和受试者工作特性曲线(ROC)下面积作为评估模型效能的指标.结果:DenseNet201模型获得了最好的训练和测试结果,在训练集和测试集中的最高预测符合率分别为1.00和0.98;VGG19模型用时最长,其在训练集和测试集中的预测符合率分别为0.99和0.98,较DenseNet201略差;ResNet50模型用时最短,但测试结果最差,在训练集和测试集中的最高符合率分别为0.93和0.92.VGG19、ResNet50和DenseNet201模型在验证集中的平均符合率为0.96、0.92和0.98),召回率分别为0.96、0.91和0.98,F1评分分别为0.96、0.91和0.98.DenseNet201模型的ROC曲线下面积为0.98,高于VGG19模型(0.95)和ResNet50模型(0.91).结论:基于DenseNet201的DTL模型对甲状腺CT良恶性结节具有较高的分类效能,有助于提高影像诊断准确性.