Background Breast cancer tumors can be identified as different luminal molecular subtypes depending on either immunohistochemical (IHC) staining or St Gallen criteria that includes Ki-67. Purpose To characterize molecular subtypes and understand the impact of disagreement among IHC and St Gallen molecular subtype reference standards on artificial intelligence classification of luminal A and luminal B tumors with use of radiomic features extracted from dynamic contrast-enhanced (DCE) MRI scans. Materials and Methods In this retrospective study, 28 radiomic features previously extracted from DCE-MRI scans of breast tumors imaged between February 2015 and October 2017 were examined in the following groups: (a) tumors classified as luminal A by both reference standards ("agreement"), (b) tumors classified as luminal A by IHC and luminal B by St Gallen ("disagreement"), and (c) tumors classified as luminal B by both ("agreement"). Luminal A or luminal B tumor classification with use of radiomic features was conducted with use of three sets: (a) IHC molecular subtyping, (b) St Gallen molecular subtyping, and (c) agreement tumors. The Kruskal-Wallis test was followed by the Mann-Whitney U test to determine pair-wise differences of radiomic features among agreement and disagreement tumors. Fivefold cross-validation with use of stepwise feature selection and linear discriminant analysis classified tumors in each set, with performance measured with use of area under the receiver operating characteristic curve (AUC). Results A total of 877 breast cancer tumors from 872 women (mean age, 48 years [range, 19-75 years]) were analyzed. Six features (sphericity, irregularity, surface area to volume ratio, variance of radial gradient histogram, sum average, volume of most enhancing voxels) were different (P ≤ .001) among agreement and disagreement tumors. AUC (median, 0.74 [95% CI: 0.68, 0.80]) was higher than when using tumors subtyped by either reference standard (IHC, 0.66 [0.60, 0.71], P = .003; St Gallen, 0.62 [0.58, 0.67], P = .001). Conclusion Differences in reference standards can hinder artificial intelligence classification performance of luminal molecular subtypes with dynamic contrast-enhanced MRI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bae in this issue.

To develop a nomogram based on pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with triple-negative breast cancer (TNBC). A total of 108 female patients with TNBC treated with neoadjuvant chemotherapy followed by surgery between January 2017 and October 2020 were enrolled. The patients were randomly divided into the primary cohort (n = 87) and validation cohort (n = 21) at a ratio of 4:1. The pretreatment DCE-MRI and clinicopathological features were reviewed and recorded. Univariate analysis and multivariate logistic regression analyses were used to determine the independent predictors of pCR in the primary cohort. A nomogram was developed based on the predictors, and the predictive performance of the nomogram was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The validation cohort was used to test the predictive model. Tumor volume measured on DCE-MRI, time to peak (TTP), and androgen receptor (AR) status were identified as independent predictors of pCR. The AUCs of the nomogram were 0.84 (95% CI: 0.75–0.93) and 0.79 (95% CI: 0.59–0.99) in the primary cohort and validation cohort, respectively. Pretreatment DCE-MRI could predict pCR after NAC in patients with TNBC. The nomogram can be used to predict the probability of pCR and may help individualize treatment. • Pretreatment DCE-MRI findings can predict pathologic complete response (pCR) after neoadjuvant chemotherapy in patients with triple-negative breast cancer. • A nomogram based on the independent predictors of tumor volume measured on DCE-MRI, time to peak, and androgen receptor status could help personalized cancer treatment in TNBC patients.

A single breast cancer lesion can have different luminal molecular subtyping when using either immunohistochemical (IHC) staining alone or the St. Gallen criteria that includes Ki-67. This may impact artificial intelligence/computer aided diagnosis (AI/CADx) for determining molecular subtype from medical images. We investigated this using 28 radiomic features extracted from DCE-MR images of 877 unique lesions segmented by a fuzzy c-means method, for three groups of lesions: (1) Luminal A lesions by both reference standards (“agreement”), (2) lesions that were Luminal A by IHC and Luminal B by St. Gallen (“disagreement”), and (3) Luminal B lesions by both reference standards (“agreement”). The Kruskal-Wallis (KW) test for statistically significant differences in groups of lesions was sequentially followed by the Mann-Whitney U test to determine pair-wise statistical difference between groups for relevant features from the KW test. Classification of lesions as Luminal A or Luminal B using all available radiomic features was conducted using three sets of lesions: (1) lesions with IHC alone molecular subtyping, (2) lesions with St. Gallen molecular subtyping, and (3) agreement lesions. Five-fold cross-validation using stepwise feature selection/linear discriminant analysis classifier classified lesions in each set, with performance measured by the area under the receiver operating characteristic curve (AUC). Six features (sphericity, irregularity, surface area to volume ratio, variance of radial gradient histogram, sum average, and volume of most enhancing voxels) were significantly different among the three groups of features with mixed difference of the disagreement group of lesions to the two agreement luminal groups. When using agreement lesions, more features were selected for classification and the AUC was significantly higher (P < 0.003) than using lesions subtyped by either reference standard. The results suggest that the disagreement of reference standards may impact the development of medical imaging AI/CADx methods for determining molecular subtype.

Purpose: We demonstrate continuous learning and assess its impact on the performance of artificial intelligence of breast dynamic contrast-enhanced magnetic resonance imaging in the task of distinguishing malignant from benign lesions on an independent clinical test dataset. Approach: The study included 1979 patients with 1990 lesions who underwent breast MR imaging during 2015, 2016, and 2017, retrospectively collected under an IRB-approved protocol; there were 1494 malignant and 496 benign lesions based on histopathology. AI was conducted in the task of distinguishing malignant and benign lesions, and independent testing was performed to assess the effect of increasing the numbers of training cases. Five training sets mimicking clinical implementation of continuous AI learning included cases from (1) first quarter of 2015, (2) first half of 2015, (3) all 2015, (4) all 2015 and first half of 2016, and (5) all 2015 and 2016. All classifiers were evaluated on the 2017 independent test set. The area under the ROC curve (AUC) served as the performance metric and was calculated over all lesions in the test set, as well as only mass lesions and only non-mass enhancements. The Mann-Kendall test was used to determine if continuous learning resulted in a positive trend in classification performance. P < 0.05 was considered to be statistically significant. Results: Over the continuous training period, the selected feature subsets tended to become more similar and stable. Performance of the five training conditions on the independent test dataset yielded AUCs of 0.86 (95% CI: [0.83,0.90]), 0.87 (95% CI: [0.83,0.90]), 0.88 (95% CI: [0.84,0.91]), 0.89 (95% CI: [0.85,0.92]), and 0.89 (95% CI: [0.86,0.92]). The Mann-Kendall test indicated a statistically significant positive trend ( P = 0.0167 ) in classification performance with continuous learning. Conclusions: Improved diagnostic performance over time was observed when continuous learning of AI was implemented on an independent clinical test dataset.

PURPOSE:To develop a deep transfer learning method that incorporates four-dimensional (4D) information in dynamic contrast-enhanced (DCE) MRI to classify benign and malignant breast lesions.MATERIALS AND METHODS:The retrospective dataset is composed of 1990 distinct lesions (1494 malignant and 496 benign) from 1979 women (mean age, 47 years ± 10). Lesions were split into a training and validation set of 1455 lesions (acquired in 2015-2016) and an independent test set of 535 lesions (acquired in 2017). Features were extracted from a convolutional neural network (CNN), and lesions were classified as benign or malignant using support vector machines. Volumetric information was collapsed into two dimensions by taking the maximum intensity projection (MIP) at the image level or feature level within the CNN architecture. Performances were evaluated using the area under the receiver operating characteristic curve (AUC) as the figure of merit and were compared using the DeLong test.RESULTS:The image MIP and feature MIP methods yielded AUCs of 0.91 (95% CI: 0.87, 0.94) and 0.93 (95% CI: 0.91, 0.96), respectively, for the independent test set. The feature MIP method achieved higher performance than the image MIP method (∆AUC 95% CI: 0.003, 0.051; P = .03).CONCLUSION:Incorporating 4D information in DCE MRI by MIP of features in deep transfer learning demonstrated superior classification performance compared with using MIP images as input in the task of distinguishing between benign and malignant breast lesions.Keywords: Breast, Computer Aided Diagnosis (CAD), Convolutional Neural Network (CNN), MR-Dynamic Contrast Enhanced, Supervised learning, Support vector machines (SVM), Transfer learning, Volume Analysis © RSNA, 2021.

Radiomic features extracted from medical images may demonstrate a batch effect when cases come from different sources. We investigated classification performance using training and independent test sets drawn from two sources using both pre-harmonization and post-harmonization features. In this retrospective study, a database of thirty-two radiomic features, extracted from DCE-MR images of breast lesions after fuzzy c-means segmentation, was collected. There were 944 unique lesions in Database A (208 benign lesions, 736 cancers) and 1986 unique lesions in Database B (481 benign lesions, 1505 cancers). The lesions from each database were divided by year of image acquisition into training and independent test sets, separately by database and in combination. ComBat batch harmonization was conducted on the combined training set to minimize the batch effect on eligible features by database. The empirical Bayes estimates from the feature harmonization were applied to the eligible features of the combined independent test set. The training sets (A, B, and combined) were then used in training linear discriminant analysis classifiers after stepwise feature selection. The classifiers were then run on the A, B, and combined independent test sets. Classification performance was compared using pre-harmonization features to post-harmonization features, including their corresponding feature selection, evaluated using the area under the receiver operating characteristic curve (AUC) as the figure of merit. Four out of five training and independent test scenarios demonstrated statistically equivalent classification performance when compared pre- and post-harmonization. These results demonstrate that translation of machine learning techniques with batch data harmonization can potentially yield generalizable models that maintain classification performance.

Purpose: Radiomic features extracted from medical images acquired in different countries may demonstrate a batch effect. Thus, we investigated the effect of harmonization on a database of radiomic features extracted from dynamic contrast-enhanced magnetic resonance (DCE-MR) breast imaging studies of 3150 benign lesions and cancers collected from international datasets, as well as the potential of harmonization to improve classification of malignancy. Approach: Eligible features were harmonized by category using the ComBat method. Harmonization effect on features was evaluated using the Davies-Bouldin index for degree of clustering between populations for both benign lesions and cancers. Performance in distinguishing between cancers and benign lesions was evaluated for each dataset using 10-fold cross validation with the area under the receiver operating characteristic curve (AUC) determined on the pre- and postharmonization sets of radiomic features in each dataset and a combined one. Differences in AUCs were evaluated for statistical significance. Results: The Davies-Bouldin index increased by 27% for benign lesions and by 43% for cancers, indicating that the postharmonization features were more similar. Classification performance using postharmonization features performed better than that using preharmonization features ( p < 0.001 for all three). Conclusion: Harmonization of radiomic features may enable combining databases from different populations for more comprehensive computer-aided diagnosis models of breast cancer.

Radiomic features extracted from dynamic contrast-enhanced magnetic resonance (DCE-MR) images have been previously shown to be useful for classification of breast lesions as benign or malignant. In this study, we investigated the performance of radiomics in distinguishing between lesion molecular subtypes across two populations. Clinical DCE-MR images of 847 breast lesions in the United States and 1,550 breast lesions in China were collected under HIPAA and IRB compliance. The radiomics workstation automatically segmented lesions using a fuzzy C-means method and extracted thirty-eight radiomic features describing size, shape, morphology, kinetics, and texture, using previously reported methods. Binary classification pairs included benign versus malignant, benign versus each molecular subtype, and each molecular subtype versus the other molecular subtypes grouped together. Stepwise feature selection and linear discriminant analysis with five-fold cross-validation was used for each population in each classification task to determine the posterior probability of each lesion being in the positive class. The area under the receiver operating characteristic curve (AUC) was determined using the conventional binormal model. The AUC was also determined for each feature in each classification task. Classification performance for each task was compared between populations using superiority testing relative to the difference in AUC. Three out of nine classification tasks (benign versus luminal A (p = 0.008), non-luminal B versus luminal B (p = 0.048) and non-HER2-enriched versus HER2-enriched (p = 0.001)) demonstrated significant difference in performance between the two populations. Differences in classification performance and potential for harmonization may be affected by population biology (i.e., distribution of molecular subtypes) and scanner acquisition systems.

Deep learning has shown promise in the field of computer vision for image recognition. We evaluated two deep transfer learning techniques (feature extraction and fine-tuning) in the diagnosis of breast cancer compared to a lesion-based radiomics computer-aided diagnosis (CAD) method. The dataset included a total of 2006 breast lesions (1506 malignant and 500 benign) that were imaged with dynamic contrast-enhanced MRI. Pre-contrast, first post-contrast, and second post-contrast timepoint images for each lesion were combined to form an RGB image, which subsequently served as input to a VGG19 convolutional neural network (CNN) pre-trained on the ImageNet database. The first transfer learning technique was feature extraction conducted by extracting feature output from each of the five max-pooling layers in the trained CNN, average-pooling the features, performing feature reduction, and merging the CNN-features with a support vector machine in the classification of malignant and benign lesions. The second transfer learning method used a 64% training, 16% validation, and 20% testing dataset split in the fine-tuning of the final fully connected layers of the pre-trained VGG19 to classify the images as malignant or benign. The performance of each of the three CAD methods were evaluated using receiver operating characteristic (ROC) analysis with area under the ROC curve (AUC) as the performance metric in the task of distinguishing between malignant and benign lesions. The performance of the radiomics CAD (AUC = 0.90) was significantly better than that of the CNN-feature-extraction (AUC = 0.84; p<0.0001), however, we failed to show a significant difference with the fine-tuning method (AUC = 0.86; p=0.1251), and thus, we conclude that transfer learning shows potential as a comparable computer-aided diagnosis technique.

Background As artificial intelligence methods for the diagnosis of disease advance, we aimed to evaluate machine learning in the predictive task of distinguishing between malignant and benign breast lesions on an independent clinical magnetic resonance imaging (MRI) dataset within a single institution for subsequent use as a computer aid for radiologists. Methods Computer analysis was conducted on consecutive dynamic contrast-enhanced MRI (DCE-MRI) studies from 1483 breast cancer and 496 benign patients who underwent MRI examinations between February 2015 and October 2017; with the age ranges of the cancer and benign patients being 19 to 77 and 16 to 76 years old, respectively. Cases were separated into a training dataset (years 2015 & 2016; 1444 cases) and an independent testing dataset (year 2017; 535 cases) based solely on MRI examination date. After radiologist indication of the lesion, the computer automatically segmented and extracted radiomic features, which were subsequently merged with a support-vector machine (SVM) to yield a lesion signature. Area under the receiving operating characteristic (ROC) curve (AUC) with 95% confidence intervals (CI) served as the primary figure of merit in the statistical evaluation for this clinical classification task. Results In the task of distinguishing malignant and benign breast lesions DCE-MRI, the trained predictive model yielded an AUC value of 0.89 (95% CI: 0.858, 0.922) on the independent image set. AUC values of 0.88 (95% CI: 0.845, 0.926) and 0.90 (95% CI: 0.837, 0.940) were obtained for mass lesions only and non-mass lesions only, respectively. Compared with actual clinical management decisions, the predictive model achieved 99.5% sensitivity with 9.6% fewer recommended biopsies. Conclusion On an independent, consecutive clinical dataset within a single institution, a trained machine learning system yielded promising performance in distinguishing between malignant and benign breast lesions.

Rationale and Objectives: This study aimed to investigate whether quantitative radiomic features extracted from digital mammogram images are associated with molecular subtypes of breast cancer. Materials and Methods: In this institutional review board-approved retrospective study, we collected 331 Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 29 triple-negative, 45 human epidermal growth factor receptor 2 (HER2)-enriched, 36 luminal A, and 221 luminal B lesions. A set of 39 quantitative radiomic features, including morphologic, grayscale statistic, and texture features, were extracted from the segmented lesion area. Three binary classifications of the subtypes were performed: triple-negative vs non-triple-negative, HER2-enriched vs non-HER2-enriched, and luminal (A + B) vs nonluminal. The Naive Bayes machine learning scheme was employed for the classification, and the least absolute shrink age and selection operator method was used to select the most predictive features for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve and accuracy. Results: The model that used the combination of both the craniocaudal and the mediolateral oblique view images achieved the overall best performance than using either of the two views alone, yielding an area under receiver operating characteristic curve (or accuracy) of 0.865 (0.796) for triple-negative vs non-triple-negative, 0.784 (0.748) for HER2-enriched vs non-HER2-enriched, and 0.752 (0.788) for luminal vs nonluminal subtypes. Twelve most predictive features were selected by the least absolute shrink age and selection operator method and four of them (ie, roundness, concavity, gray mean, and correlation) showed a statistical significance (P < .05) in the subtype classification. Conclusions: Our study showed that quantitative radiomic imaging features of breast tumor extracted from digital mammograms are associated with breast cancer subtypes. Future larger studies are needed to further evaluate the findings.

To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are associated with Ki67 expression of breast cancer. In this institutional review board approved retrospective study, we collected 377 cases Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 53 low-Ki67 expression (Ki67 proliferation index less than 14%) and 324 cases with high-Ki67 expression (Ki67 proliferation index more than 14%). A binary-classification of low- vs. high- Ki67 expression was performed. A set of 52 quantitative radiomics features, including morphological, gray scale statistic, and texture features, were extracted from the segmented lesion area. Three most common machine learning classification methods, including Naive Bayes, k-Nearest Neighbor and support vector machine with Gaussian kernel, were employed for the classification and the least absolute shrink age and selection operator (LASSO) method was used to select most predictive features set for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. The model that used Naive Bayes classification method achieved the best performance than the other two methods, yielding 0.773 AUC value, 0.757 accuracy, 0.777 sensitivity and 0.769 specificity. Our study showed that quantitative radiomics imaging features of breast tumor extracted from DCE-MRI are associated with breast cancer Ki67 expression. Future larger studies are needed in order to further evaluate the findings.

The aim of the present study was to investigate the association between Ki-67 expression and radiomics features of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with invasive breast cancer. A total of 53 cases with low-Ki-67 expression (Ki-67 proliferation index <14%) and 106 cases with high-Ki-67 expression (Ki-67 proliferation index >14%) were investigated. A systematic approach was applied that focused on the automated segmentation of lesions and extraction of radiomics features. For each lesion 5 morphology, 4 gray-scale histogram and 6 texture features were obtained, and statistical analyzes were performed to assess the differences in these features between the low- and high-Ki-67 expressions. One morphology metric (area), 3 gray-scale histogram indexes (standard deviation, skewness and kurtosis) and 3 texture features (contrast, homogeneity and inverse differential moment) demonstrated a significant difference (P<0.05), with low-Ki-67 expression lesions tending to be smaller, clearer and heterogeneous when compared with the high-Ki-67 expressed cases. These results may provide a noninvasive means to better understand the proliferation of breast cancer.

This study aimed to analyze the correlation between mammographic density obtained by density analysis software (DAS)/radiologists visual (RV) classification with molecular subtype, and the expression levels of estrogen receptor (ER), progesterone receptor (PR), Ki67 antigen (Ki-67), p53 gene (p53), and human epidermal growth factor receptor-2 (HER2). A total of 688 breast cancer patients with digital mammography and complete molecular pathological results in Tianjin Medical University Cancer Institute and Hospital between February 2015 and February 2016 were collected. The DAS-density grade (DASD) and the radiologists visually classified density grade (RVD) were evaluated by 3 radiologists. The correlation between density grade and the expression levels of ER, PR, Ki-67, p53, HER2 and breast cancer molecular subtype (PMS) were analyzed. The agreement between DASD and RVD was explored. ER, PR and HER-2 positive rate were significantly different among patients with different RVD grades (P< 0.05). HER2 positive rates showed an increasing trend following RVD upgrading (P𝑡𝑟𝑒𝑛𝑑< 0.05). HER-2 positive rate in RVD D1 + D2 was 7.69%, which was higher than that in D3 + D4 (P< 0.05). The ER and Ki-67 expressions in patients were markedly different among DASD (P= 0.009 and 0.002) and RVD (P= 0.012 and 0.036) with different grades. The kappa value of each DASD to RVD was 0.31 (P< 0.01). The RVD 3 proportion was 14.58% (63/432) in HER2 Over-expressing subtype, which was apparently higher than RVD1 (2.43%, 1/41) (P< 0.05). Breast density may be partial correlated with molecular pathology in breast cancer.

Using the data from 2010 China Family Panel Studies,this study examined the determinants of married women's housework time in China.Their time spent on paid work and their absolute earnings were found to be negatively associated with their time spent on domestic chores.This study also specifically examined the impact of women's relative income on their time for housework.The literature in this regard indicated that,when women outearned their husbands,they tended not to reduce their housework time as their relative earnings increased,aphenomenon known asgender display.In other words,the wife's bargaining power for housework with her relative income was constrained by the gender ideology.This study found that there were urban-rural and regional differences in the effect of the wife's relative income on her housework time.The results indicated that increased relative income could help urban married women continuously reduce their housework time.However,for rural married women,the effect of relative income on reducing housework time is limited by their transitional gender ideology,and thegender displayphenomenon existed.Linking the survey data to the prefecture-level indicator of modernization,this study found that,in the rural areas,the effect of relative income on housework time varied with the level of modernization.Specially,the bargaining power of wife's relative income in housework time was stronger when the rural areas were more modernized.In contrast,the bargaining power was more limited in rural areas with lower modernization level,andgender displaywas more likely to exist.

Wula Mountain National Forest Park is an important ecological barrier in the western region of Inner Mongolia.Its tourism industry is in initial stage.The assessment of the development potential of tourism resources is an important step before tourism development.The analytical hierarchy process(AHP)was applied to do quantitative evaluation of the development potential.The evaluation system was built from tourism resources,infrastructure conditions and regional characteristics,including 21specific indicators.The score of evaluation was 7.832 5;the tourism potential development was at level I.The qualitative analysis was made through analyzing the strengths,weakness,opportunities and threats of the park's development potential by SWOT analysis.Finally,four proposals were put for the park's tourism development:1)to give priority to conservation in the development of tourism;2)to strengthen the construction of park's infrastructure;3)to take advantage of tourism resources,and to highlight the characteristics of forest eco-tourism;4)to develop tourism orderly.

In the history process of human civilization,the formation and development of culture is essential for the development of human beings. Different geographical location and natural environment created human cultural diversity. Cultural transmission makes people of different nationalities and countries share the common achievements of human splendid culture.There exist big differences of sports,leisure and entertainment between the different peoples and countries. Cultural identity is premise and foundation for intercultural communication and development of sports entertainment.

Background: A dense breast not only reduces the sensitivity of mammography but also is a moderate independent risk factor for breast cancer. The percentage of Western women with fat breast tissue is higher aged 40 years or older. To a certain extent, mammography as a first choice of screening imaging method for Western women of this group is reasonable. Hitherto, the frequency and age distribution of mammographic breast density patterns among Chinese women had not been characterized. The purpose of this study was to investigate the frequency and age distribution of mammographic breast density patterns among a group of Chinese screening women and breast cancer patients in order to provide useful information for age-specific guidelines for breast cancer screening in Chinese women. Methods: A retrospective review of a total of 3,394 screening women between August and December 2009 and 2,527 breast cancer patients between July 2011 and June 2012 was conducted. Descriptive analyses were used to examine the association between age and breast density. The significance of differences of breast density between the screening women and the breast cancer patients was examined using nonparametric tests. Results: There was a significant inverse relationship between age and breast density overall (r=-0.37, p< 0.01). Breast density of the breast cancer patients in the subgroups of 40-49 years old was greater compared with that of the screening women, the same in those aged 50-54 years and in those 55 years old or older, less than in the screening group. Conclusions: With regard to the Chinese women younger than 55 years old, the diagnostic efficiency of breast cancer screening imaging examinations may be potentially improved by combining screening mammography with ultrasound.

Through the explanation of low-carbon concept and the practical analysis of low-carbon city construction of Yinchuan and drawing lessons from the experience at home and broad,this article proposes scientific urban planning strategy to promote low-carbon development of Yinchuan.

Beauty then of woman protagonist whom American writer Margaret Mitchel moulded in its master-piece Gone with the Wind is a distinct , vivid , abundant , complicated personage image, the glamour of this im-age is prolonged, which the writer succeeded in moulding the personage's personality. The beauty's personality be-haves in two aspects mainly then, namely water general tenderness and fire are general and warm. Two personali-ties in beauty blend each other on one's body then, interdepend, make image distinct, vivid, full personage ,whichthe contradiction of announcing personage's personality is unified successfully, also demonstrates author's ex-tremely high attainments in moulding personages.