• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Radiomics of rectal cancer for predicting distant metastasis and overall survival

    2020-10-29 02:04:54MouLiYuZhouZhuYongChangZhangYuFengYueHaoPengYuBinSong
    World Journal of Gastroenterology 2020年33期
    關(guān)鍵詞:閃光哲理古詩(shī)文

    Mou Li, Yu-Zhou Zhu, Yong-Chang Zhang, Yu-Feng Yue, Hao-Peng Yu, Bin Song

    Abstract

    Key words: Radiomics; Rectal cancer; Overall survival; Distant metastasis; Computed tomography

    INTRODUCTION

    More than 1.8 million cases of colorectal cancer were diagnosed in 2018 worldwide, making it the third most prevalent malignancy around the world. In terms of morbidity, colorectal cancer accounts for approximately 9.2% of cancer-related deaths worldwide, similar to that of stomach and liver cancer[1]. In up to 70% of patients with rectal cancer (RC), surgical removal of the primary tumor is successful. However, local recurrence and distant metastases are commonly detected in approximately 30% of RC patients, often within 3 years after surgery[2-4]. The overall prognosis becomes poor once distant metastases have developed, thus demonstrating the importance of prompt diagnosis and treatment of RC[5]. Some patients may be at a higher risk of developing adverse outcomes post-surgery. In these patients, alternative and adjunctive therapies, such as chemotherapy, radiotherapy, or other targeted therapies, may be needed to minimize the risk of developing distant metastases[6]. Hence, improved patient outcomes may be feasible by identifying unfavorable prognostic characteristics that could hinder the overall survival of patients. In return, personalized treatment strategies could be implemented to achieved improved outcomes in patients with RC[7]. Recently, the American Society of Clinical Oncology guidelines outlined the need for newer surveillance technologies to better characterize and detect rectal tumors during the early stages of the disease, which could aid in risk stratification and subsequent surveillance[8].

    Currently, there is no universal list of clinicoradiologic prognostic factors used for the detection of RC patients more likely to benefit from treatment. While the TNM classification system is widely utilized for staging cancer in the clinic, it has certain pitfalls that limit its clinical utility in RC[9]. While TNM considers the degree of cancer invasion, the involvement of surrounding lymph nodes, and metastatic spread of the tumor[10], it does not consider the importance of spatial heterogeneity. Spatial heterogeneity is an important characteristic of RC, as it indicates high cell density, hemorrhage, and necrosis[11].

    Radiomics can provide a comprehensive overview of intratumor heterogeneity by extracting multiple quantitative features from medical images. A wide array of parameters can be utilized to assess and quantify the degree of heterogeneity at relevant scales, such as skewness, entropy, kurtosis, and uniformity. Radiomic analysis has been successfully utilized to predict survival outcomes in other diseases[12-14]. When integrated with the clinicopathologic features, the radiomics signature is superior to that of a single biomarker, in terms of prognostic value[15-17]. Hence, in the current study, the aim was to compare the predictive abilities, in terms of distant metastases and 3-year OS, of a radiomics signature and clinicoradiologic risk model in patients with RC.

    MATERIALS AND METHODS

    Patients

    This study was approved by the medical ethics committee of our institution (No. 2019-1159; date: December 26, 2019). Patient approval or informed consent for the review of medical images was waived by the committee due to the retrospective nature of the study.

    The primary cohort of this study comprised an evaluation of the institutional database for medical records from October 2012 to December 2015 to identify patients with histologically-confirmed RC, who underwent curative surgery alone. A total of 148 patients (76 males and 72 females; mean age: 59.7 ± 11.7 years; age range: 27-94 years) were enrolled in our study according to the following inclusion criteria: (1) Patients with histologically-confirmed rectal adenocarcinoma who were treated with curative resection alone; (2) Preoperative enhanced computed tomography (CT) was performed within 1 mo before resection; (3) Follow-up was conducted for at least 3 years; and (4) T-stage, N-stage, and tumor grade were confirmed by pathology. The exclusion criteria were as follows: (1) Patients who received neoadjuvant or postoperative chemoradiotherapy; (2) Patients who presented distant metastasis at the time of diagnosis; (3) Patients who failed to undergo follow-up; (4) Lack of preoperative CT images; and (5) Poor CT image quality. The patients were allocated to a training or validation set at a ratio of 7:3, based on the scanning date. The early data before the 70thpercentile scanning date were allocated to the training set, while the other data were allocated to the validation set. The patient recruitment pathway is shown in Figure 1.

    The baseline clinical characteristics and pathological data of each patient, including age, sex, tumor size on CT, pathological TNM stage, histological grade, antigen Ki-67, circumferential resection margin, number of distant metastasis, and follow-up time were all derived from medical records, as shown in Table 1.

    這一期,我們來(lái)學(xué)習(xí)古詩(shī)文的第九個(gè)字——理。說(shuō)起古詩(shī)文,人們大多感受明顯的是兒女情長(zhǎng),其實(shí),在古詩(shī)文中也有很多哲理的閃光。只是其中需要更高的思想素養(yǎng)才能發(fā)掘,讓我們一起來(lái)發(fā)掘一下古詩(shī)文中的哲理。

    CT examinations

    Enhanced CT was performed using a 128-channel multidetector CT scanner (Simens, SOMATOM Definition AS+) with the following scanning parameters: Tube voltage of 120 kV, tube current of 200-210 mA, and slice thickness of 2.0 mm. With the mass injection (dose: 1.2 mL/kg; injection rate: 3 mL/s) of iodine contrast agent (300 mg/mL), the portal venous phase images (30 s after the trigger) were obtained.

    Reference standard for pathology

    All 148 patients had histopathologically-verified RC, which was diagnosed with resected surgical specimens. The pathological confirmatory report was acquired from electronic medical records. Samples were processed using standard procedures, fixed in formalin, embedded in paraffin, and stained with hematoxylin and eosin (HE).

    Follow-up

    All patients were followed for at least 3 years after surgery (3-mo intervals in the first year; every 6 mo in the following 2 years). The minimum postoperative follow-up period was 1 mo for metastasis (three cases had liver, lung, and bone metastases,respectively), and 11 mo for OS.

    Table 1 Patient and tumor characteristics in the training and validation sets

    Figure 1 Flow chart of the recruitment pathway for patients included in the study. CT: Computed tomography.

    The endpoint of this study was 3-year OS, defined as the time between the surgery and the date of the patient’s death. Local recurrence was defined as recurrence in the pelvis, and distant metastasis as recurrence at sites other than the pelvis. All distant metastatic cases were diagnosed by a multidisciplinary team based on clinical examinations, serum carcinoembryonic antigen levels, chest and abdominopelvic CT, abdominopelvic magnetic resonance imaging, endoscopy, and biopsy. Follow-up information was recorded in the database.

    Texture feature extraction

    A radiologist with 7 years of experience in RC imaging used the ITK-SNAP software (open source, www.itk-snap.org) for the three-dimensional (3D) manual segmentation of the primary tumor, as shown in Figure 2. The region of interest was carefully placed in an area that avoided the intestinal lumen, calcification, blood vessels, and necrosis. In all, 396 quantitative radiomic features of three types (first-order, second-order, and higher-order) were calculated and extracted automatically using the in-house Artificial Intelligence Kit software (GE Healthcare). First-order features are calculated from the pixel intensity histogram to quantify tumor intensity characteristics. Second-order features based on the co-occurrence matrix or run-length matrix account for the location of the pixels and analyze texture in a specific direction. Higher-order features, such as contrast, compare differences and relationships between multiple pixels.

    Model building and evaluation

    Figure 2 A 58-year-old woman with rectal cancer. A-C: Representative manual segmentation of the whole lesion in the axial, sagittal, and coronal enhanced computed tomography images. Red lines represent the delineations of the regions of interest used to derive the radiomic features; D: Three-dimensional volumetric reconstruction of the segmented lesion.

    Before selecting features, the redundant features needed to be eliminated. When the Pearson correlation coefficient of any two features was higher than 0.6, one of them was selected at random, and the rest could be treated in the same manner. Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to identify the most useful prognostic features. The LASSO regression is a variable selection method for linear regression models. The LASSO does this by imposing a constraint on the model parameter (λ) that causes regression coefficients for some variables to shrink toward zero. Variables with a regression coefficient equal to zero are excluded from the model. Variables with non-zero regression coefficients are selected by the LASSO. After that, z-score transformation was used to standardize all selected features. The CT images of 30 patients were segmented twice by the same radiologist to calculate intraclass correlation coefficients for assessing the stability of the features. Radiomic features with an intraclass correlation coefficient > 0.75 were included in the analysis. Each radiomic feature was assessed by univariate logistic regression, and statistical significance was assumed at a confidence level of 0.2 to avoid missing important features. Features withP< 0.2 in univariate logistic regression analysis were entered into the multivariate analysis. A nomogram was generated for model visualization. Receiver operating characteristic curves for each model were constructed to evaluate the performance for predicting distant metastasis of RC. The association of the predicting models with 3-year OS was investigated by Kaplan-Meier survival analysis.

    Statistical analysis

    All statistical analyses were performed using R software (version 3.6.1, R Foundation for Statistical Computing, Vienna, Austria), Statistic Package for Social Science version 21 (Statistic Package for Social Science Inc., Chicago, IL, United States), Stata 15.0 (Stata Corp LLC, Lakeway Drive College Station, United States), and Medcalc 15.2.2 (Medcalc Software, Ostend, Belgium). Differences between the training and validation sets were assessed by the chi-square test, Mann-Whitney test, andt-test. Survival curves were compared by the log-rank test.P< 0.05 was considered statistically significant.

    RESULTS

    A total of 148 patients were enrolled in our study, of whom 51 had distant metastases, 26 died, and 122 survived to the date of the last follow-up visit at 3 years post-surgery (Table 2).

    Table 2 Comparison of the models between the metastasis group and the non-metastasis group, and between the death group and the survival group

    Feature selection and model building

    A total of 17 radiomic features remained after removing the redundant features. Two features, “MaxIntensity” and “RelativeDeviation”, were selected by the LASSO method from the remaining features (Figure 3). To avoid missing important features, another feature “Inertia_AllDirection_offset7_SD” was selected by the univariate logistic regression (P= 0.192). These three features were presented in this calculation formula: Rad-score = 1/{1 + EXP [-(-0.649 - 0.786 × MaxIntensity - 0.868 × Inertia_AllDirection_offset7_SD - 2.743 × RelativeDeviation)]}. The radiomics signature (Rad-score), which consisted of three selected features, was statistically different between the metastasis and non-metastasis groups (Rad-score = 0.46 and 0.32, respectively;P< 0.001), as shown in Table 2.

    For clinical features, pathological T-stage and N-stage were selected by the univariate logistic regression analysis (P< 0.001 andP= 0.002, respectively). The clinicoradiologic risk model was built by the multivariate logistic regression analysis, using the formula 1/{1 + EXP [-(-6.199 + 1.368 × T-stage + 0.738 × N-stage + 4.673 × Rad-score)]}. The results of the combined model were significantly different between the metastasis and non-metastasis groups by 3 years after surgery (P< 0.001), as shown in Table 2. The Hosmer-Lemeshow test yielded no statistical significance (P= 0.280 and 0.151 for the Rad-score and combined model, respectively), which suggested that there was no evidence of lack of fit.

    A nomogram to predict the distant metastasis of RC was generated for the combined model visualization (Figure 4). The nomogram could be used by locating the score for each variable on the corresponding axis, adding the points together for all of the variables, and drawing a line from the total number of points to the risk axis, which allows for the determination of distant metastasis risk. Higher total scores were associated with a greater risk of distant metastasis. The contributions of these variables to the Rad-score and the combined model were measured by the value of the standardized logistic regression coefficient (Figure 5). The contribution of “MaxIntensity” to the Rad-score, and that of “T-stage” to the combined model were greater than those of the others.

    Classification results

    Figure 3 Feature selection using the least absolute shrinkage and selection operator regression model. Dotted vertical lines were drawn at the optimal values (two features were selected) by using the minimum criteria and one standard error of the minimum criteria (i.e., the 1-SE criteria).

    Figure 4 Developed radiomics nomogram. The radiomics nomogram was developed in the training cohort, with the Rad-score, T-stage, and N-stage incorporated.

    In the case of only considering the Rad-score, the resulting area under the curve (AUC) was 0.709 (95%CI: 0.612-0.795). Improved prediction of distant metastases could be achieved by combining the radiomic features from preoperative CT images with the clinical features. The AUCs of the clinical model (T-stage combined with N-stage) and the combined model were 0.782 (95%CI: 0.689-0.857) and 0.842 (95%CI: 0.757-0.906), respectively (Table 3 and Figure 6). There was a significant difference in AUCs between the Rad-score and combined model in the training cohort (AUC = 0.709vs0.842,P= 0.005), which was confirmed using the validation cohort (AUC = 0.687vs0.802,P= 0.020). We found that the clinical variables had higher classification contributions than Rad-score to build the combined model. This finding was consistent with the higher standardized logistic regression coefficient of T-stage than that of the Rad-score (Figure 5). Moreover, the AUC value of the clinical model was also higherthan that of the Rad-score. Even so, the Rad-score greatly helped to increase the AUC value from 0.782 to 0.842 (Table 3 and Figure 6). In the subgroups of the receiver operating characteristic analysis, the AUC of the combined model decreased in the 1-year (distant metastasis occurred in the first year after surgery) and 2-year (distant metastasis occurred within 2 years after surgery) subgroups (Table 3).

    Table 3 Diagnostic performance of the three models in both training and validation cohorts

    Figure 5 Histogram showing the contribution of each variable to the models. A: the Rad-score; B: the combined model. The contributions of the variables were measured by the values of the standardized logistic regression coefficients.

    Assessment of the combined model for OS

    Using the clinicoradiologic risk model, stratified analyses were performed for the whole set, training set, and validation set to evaluate the association with OS. As shown in Figure 7, the grouping results of the combined model were significantly associated with OS in the whole, training, and validation groups (P< 0.0001,P= 0.0001, andP= 0.0137, respectively). Although there was a lack of statistical significance for stage III in the stratified subgroup analysis, according to the overall pathological stage, the low-risk group displayed a longer OS than the high-risk group (P< 0.001,P= 0.0194, andP= 0.1401 for stages I, II, and III, respectively), which was significant in terms of individualized treatment (Figure 8)[18].

    Figure 6 Receiver operating characteristic curves of the models in the training and validation cohorts. A: In the training set, the combined model [area under the curve under the curve (AUC) = 0.842] achieved a better performance than the Rad-score (AUC = 0.709) and the clinical model containing Tstage and N-stage (AUC = 0.782); B: In the validation set, the AUCs of the Rad-score, the clinical model, and the combined model were 0.687, 0.766, and 0.802, respectively.

    Figure 7 Kaplan-Meier curves for overall survival stratified by risk group, as identified by the combined model. A: The whole set. P < 0.0001; B: The training set. P = 0.0001; C: The validation set. P = 0.0137.

    Figure 8 Overall survival curves for the low- and high-risk groups classified according to the combined model in the subgroups of the overall pathological stage. A: Stage I. P < 0.001; B: Stage II. P = 0.0194; C: Stage III. P = 0.1401.

    DISCUSSION

    In this study, we developed and validated a clinicoradiologic risk model that showed potential for predicting distant metastasis of RC within 3 years after surgery, which had a better prognostic performance than Rad-score (P= 0.005 and 0.020 for the training cohort and validation cohort, respectively). The combined model was used to stratify patients into low-risk and high-risk groups for the analysis of 3-year OS. The results showed that OS rates between low- and high-risk groups were significantly different in the training cohort, which was verified in the validation cohort.

    Compared with traditional image explanation, which is qualitative or subjective, radiomic analyses permit high-throughput extraction of radiomic features that can quantify differences between tissues invisible to human eyes. Recently, the use of radiomics has appeared as a potential technique for constructing decision-support models based on high-throughput quantificational characters extracted from medical images. Radiomics-based prognosis prediction models have been reported for advanced nasopharyngeal carcinoma[19], early-stage non-small cell lung cancer[20], and RC[12-14]. In their pioneering retrospective studies about the prognosis of RC[12-14], robust models and strong independent prognostic factors have been developed for the prediction of OS in patients with RC. Our results were consistent with previous studies, suggesting that radiomics could help predict the prognosis of patients with RC. The building methods between our study and those of previous studies were similar (machine learning), and all these studies lacked external validation. However, there were still some differences that need to be explained. For the follow-up time, Wanget al[13]followed the patients for 5 years, and Lovinfosseet al[12]for 4 years, which were longer than the time in the current study. These authors[12-14]focused on predicting the prognosis in locally advanced RC patients treated with neoadjuvant chemoradiation followed by surgery, which is different from our current study. Different chemoradiotherapy regimens might influence the prognostic evaluation of patients with RC. Moreover, the sample sizes of these two studies[12,14]were less than ours.

    In terms of feature selection, several relationships were uncovered between the specific features and their ability to predict distant metastasis. For the radiomic features included in this study, the first-order features show an excellent auxiliary classification effect, accounting for two of the three specified radiomic features. In the clinical features, we found that T-stage and N-stage had high standardized logistic regression coefficients, indicating the contribution to the combined model. This finding is consistent with the consensus that pathological T-stage and N-stage are very important for predicting the distant metastasis of RC. The histological grade, circumferential resection margin, Ki-67 score, and tumor diameter did not present enough predictive power for distant metastasis and prognosis. Subsequently, we integrated the Rad-score into a nomogram with clinical risk factors, and constructed a useful tool for individualized evaluation of distant metastasis and OS in patients with RC.

    In our study, only patients who did not receive chemoradiotherapy were selected for the analysis of prognosis. On the one hand, the variables caused by the different intensive chemoradiotherapy methods can be controlled. On the other hand, we could build a novel model to stratify the high-risk patients. This was in step with the current trends toward personalized medicine[21]. Considering individualized evaluation of patients with RC of different stages, the subgroup survival analysis was performed. The low-risk groups had longer OS than the high-risk groups, which was statically significant between stage I and II (P< 0.001 andP= 0.0194, respectively). However, there was no statistically significant difference for stage III (P= 0.1401). We speculate that this might be related to the small sample size of this study.

    There are several limitations to this study. The first limitation is the relatively small sample size. Second, even if we controlled therapeutic methods to surgery alone, inevitable bias may exist due to the retrospective design of this study. Therefore, prospective and external validation studies are required in future studies. Third, all of the CT images were obtained from a single institution. In the future, multicenter verification is necessary to extend the versatility of the experimental results.

    CONCLUSION

    In conclusion, this study describes a combined model that incorporates a radiomics signature and clinical risk factors. The model can aid in the individualized prediction of distant metastasis and prognosis in patients with RC.

    ARTICLE HIGHLIGHTS

    Research results

    A total of 51 (34.5%) patients had distant metastases, while 26 (17.6%) patients died, and 122 (82.4%) patients lived at least 3 years post-surgery. The values of both the Rad-score and the combined model were significantly different between the distant metastasis group and the non-metastasis group (0.46 ± 0.21vs0.32 ± 0.24 for the Radscore, 0.60 ± 0.23vs0.28 ± 0.26 for the combined model;P< 0.001 for both models). Predictors contained in the combined model included the Rad-score, pathological Nstage, and T-stage. The combined model showed good discrimination, with an area under the curve of 0.842 and 0.802 for the training set and validation set, respectively. For the survival analysis, the combined model was associated with an improved OS in the whole cohort and the respective subgroups.

    Research conclusions

    This study presents a novel model, visualized in a nomogram, that can be used to facilitate individualized prediction of distant metastasis and 3-year OS in patients with RC.

    Research perspectives

    Radiomics may change the practice of medicine, particularly for patients with RC. However, there are challenges to be overcome before its routine implementation including challenges related to sample size, model design, and the lack of robust multicenter validation set. Therefore, prospective multicenter studies of a larger size are needed to externally validate our proposed model in the future.

    猜你喜歡
    閃光哲理古詩(shī)文
    閃光
    閃光的枝條
    古詩(shī)文閱讀備考指津
    哲理漫畫
    特別文摘(2016年18期)2016-09-26 18:02:20
    漫畫哲理
    雜文選刊(2016年9期)2016-09-14 19:52:43
    漫畫哲理
    雜文選刊(2016年5期)2016-05-12 20:10:00
    哲理漫畫
    特別文摘(2016年5期)2016-05-04 22:14:40
    引領(lǐng)小學(xué)生誦讀古詩(shī)文之妙招
    八月,紀(jì)念碑在閃光
    海峽姐妹(2015年8期)2015-02-27 15:12:54
    對(duì)古詩(shī)文默寫有效性的思考
    免费av观看视频| 最近视频中文字幕2019在线8| 最近最新免费中文字幕在线| 欧美国产日韩亚洲一区| 老司机深夜福利视频在线观看| 国产高潮美女av| 宅男免费午夜| 亚洲欧美清纯卡通| 国内久久婷婷六月综合欲色啪| 搡老熟女国产l中国老女人| 成人毛片a级毛片在线播放| 高清日韩中文字幕在线| 俺也久久电影网| 成人亚洲精品av一区二区| 99久久99久久久精品蜜桃| 日韩欧美 国产精品| 色吧在线观看| 青草久久国产| 国产色爽女视频免费观看| 一个人看的www免费观看视频| 色在线成人网| 欧美日韩乱码在线| 欧美3d第一页| 99久久九九国产精品国产免费| 亚洲乱码一区二区免费版| 亚洲av日韩精品久久久久久密| 久久久国产成人免费| 国产aⅴ精品一区二区三区波| 欧美激情久久久久久爽电影| 国内揄拍国产精品人妻在线| 中国美女看黄片| 国产精品久久电影中文字幕| 99视频精品全部免费 在线| 18禁黄网站禁片免费观看直播| 最近视频中文字幕2019在线8| 一进一出抽搐动态| 国产精品人妻久久久久久| 日本免费一区二区三区高清不卡| 成人特级av手机在线观看| 99精品久久久久人妻精品| 精品一区二区三区av网在线观看| 国产免费一级a男人的天堂| 亚洲,欧美精品.| 97热精品久久久久久| 别揉我奶头 嗯啊视频| 白带黄色成豆腐渣| 久久中文看片网| 成人无遮挡网站| 色尼玛亚洲综合影院| 国产白丝娇喘喷水9色精品| 久久国产乱子免费精品| 亚洲精华国产精华精| 亚洲av电影不卡..在线观看| 看免费av毛片| 日韩欧美 国产精品| 18禁黄网站禁片免费观看直播| 亚洲av成人av| 国内精品久久久久久久电影| 特级一级黄色大片| 97人妻精品一区二区三区麻豆| 舔av片在线| 99热这里只有是精品在线观看 | 亚洲美女视频黄频| 一夜夜www| 久9热在线精品视频| 宅男免费午夜| 国产视频一区二区在线看| 悠悠久久av| 亚洲国产精品999在线| www日本黄色视频网| 18禁黄网站禁片免费观看直播| 永久网站在线| 久久国产乱子免费精品| 日韩欧美一区二区三区在线观看| 亚洲欧美日韩卡通动漫| 国产人妻一区二区三区在| 亚洲成a人片在线一区二区| 久久午夜亚洲精品久久| 乱人视频在线观看| 最后的刺客免费高清国语| 无人区码免费观看不卡| 亚洲av不卡在线观看| 中文在线观看免费www的网站| 啦啦啦观看免费观看视频高清| 中文在线观看免费www的网站| 免费一级毛片在线播放高清视频| 欧美激情国产日韩精品一区| 精品久久久久久久久亚洲 | 亚洲五月婷婷丁香| 亚洲三级黄色毛片| 一进一出抽搐gif免费好疼| 少妇的逼好多水| 午夜精品久久久久久毛片777| 人妻丰满熟妇av一区二区三区| 人妻夜夜爽99麻豆av| 哪里可以看免费的av片| 成人鲁丝片一二三区免费| 在线十欧美十亚洲十日本专区| 美女免费视频网站| 欧美乱妇无乱码| 亚洲人成网站高清观看| av在线观看视频网站免费| 超碰av人人做人人爽久久| 久久草成人影院| 成人国产综合亚洲| 亚洲精品色激情综合| 国产欧美日韩一区二区精品| 色综合婷婷激情| 午夜影院日韩av| 国产高清有码在线观看视频| 亚洲不卡免费看| 九九在线视频观看精品| 亚洲无线在线观看| 亚洲男人的天堂狠狠| 小说图片视频综合网站| 欧美日韩中文字幕国产精品一区二区三区| 成人鲁丝片一二三区免费| 国产精品一区二区三区四区免费观看 | 亚洲av免费高清在线观看| 精品一区二区三区av网在线观看| 亚洲一区二区三区色噜噜| 中文在线观看免费www的网站| 中文字幕高清在线视频| 91九色精品人成在线观看| 夜夜看夜夜爽夜夜摸| 一区福利在线观看| 国产欧美日韩精品一区二区| 免费看光身美女| .国产精品久久| 国产精品影院久久| 久久精品夜夜夜夜夜久久蜜豆| 99热只有精品国产| 亚洲黑人精品在线| 午夜精品久久久久久毛片777| 国产亚洲欧美98| 精品99又大又爽又粗少妇毛片 | 午夜激情福利司机影院| 国产伦在线观看视频一区| 日韩中字成人| 亚洲国产色片| 婷婷亚洲欧美| 午夜福利免费观看在线| 校园春色视频在线观看| 日韩国内少妇激情av| 免费无遮挡裸体视频| aaaaa片日本免费| 久久久国产成人精品二区| 亚洲精品456在线播放app | 亚洲精品456在线播放app | 欧美乱色亚洲激情| 永久网站在线| 精品国产三级普通话版| 国产精品精品国产色婷婷| 国产亚洲av嫩草精品影院| 如何舔出高潮| 日日摸夜夜添夜夜添av毛片 | 中文字幕熟女人妻在线| 日韩欧美国产在线观看| 午夜福利在线在线| 亚洲精品久久国产高清桃花| 99久久99久久久精品蜜桃| 色噜噜av男人的天堂激情| 亚洲欧美清纯卡通| 亚洲国产精品合色在线| 国产一区二区激情短视频| 久久草成人影院| 中文亚洲av片在线观看爽| 久久久久久久午夜电影| 欧美极品一区二区三区四区| 99久久久亚洲精品蜜臀av| 露出奶头的视频| 成人亚洲精品av一区二区| 999久久久精品免费观看国产| 亚洲国产欧美人成| 国产av一区在线观看免费| 三级国产精品欧美在线观看| 国产伦人伦偷精品视频| 日日夜夜操网爽| 婷婷色综合大香蕉| 日韩 亚洲 欧美在线| 国产探花极品一区二区| 精品午夜福利在线看| 国产视频内射| 人人妻人人看人人澡| 国产乱人伦免费视频| 蜜桃久久精品国产亚洲av| 老司机福利观看| 高清毛片免费观看视频网站| 久久久国产成人精品二区| 国产高清三级在线| 免费看光身美女| 亚洲真实伦在线观看| 久久久精品欧美日韩精品| 久久精品国产亚洲av天美| 国产高清视频在线播放一区| 欧美成人a在线观看| 日日夜夜操网爽| 少妇被粗大猛烈的视频| 国产欧美日韩一区二区精品| 波野结衣二区三区在线| 听说在线观看完整版免费高清| 亚洲精品456在线播放app | 人人妻,人人澡人人爽秒播| 夜夜爽天天搞| 中文资源天堂在线| 精品久久久久久久久av| 午夜久久久久精精品| 欧美又色又爽又黄视频| 日韩亚洲欧美综合| 又紧又爽又黄一区二区| 在线观看美女被高潮喷水网站 | 色噜噜av男人的天堂激情| 麻豆成人午夜福利视频| 黄色配什么色好看| 亚洲性夜色夜夜综合| 高清在线国产一区| 日韩成人在线观看一区二区三区| 国产人妻一区二区三区在| 成年版毛片免费区| 制服丝袜大香蕉在线| 久久国产精品人妻蜜桃| www日本黄色视频网| 成人性生交大片免费视频hd| 在线天堂最新版资源| 偷拍熟女少妇极品色| 国产综合懂色| 国产精品av视频在线免费观看| 国产麻豆成人av免费视频| 亚洲色图av天堂| 露出奶头的视频| 中文在线观看免费www的网站| 亚洲真实伦在线观看| 禁无遮挡网站| 一个人看的www免费观看视频| 久久久久久大精品| 久久久精品大字幕| 国产高清激情床上av| 亚洲人与动物交配视频| 亚洲一区二区三区色噜噜| 欧美zozozo另类| 天堂av国产一区二区熟女人妻| 少妇丰满av| 国产精品美女特级片免费视频播放器| 波野结衣二区三区在线| 久久6这里有精品| 国产av麻豆久久久久久久| 国产蜜桃级精品一区二区三区| 精品无人区乱码1区二区| 全区人妻精品视频| 嫩草影院入口| 欧美激情久久久久久爽电影| 中文字幕熟女人妻在线| 国产激情偷乱视频一区二区| 亚洲精品色激情综合| 欧美日韩亚洲国产一区二区在线观看| 国产精品影院久久| 麻豆国产av国片精品| 成年版毛片免费区| 亚洲av一区综合| 99热精品在线国产| 国产精品一区二区三区四区久久| 老司机福利观看| 日韩欧美国产一区二区入口| 99精品久久久久人妻精品| 在线观看66精品国产| 国产黄色小视频在线观看| 丁香六月欧美| www.999成人在线观看| 欧美另类亚洲清纯唯美| 国产真实伦视频高清在线观看 | 国产伦精品一区二区三区四那| 色综合亚洲欧美另类图片| 一级av片app| 国产高清视频在线播放一区| 一本一本综合久久| h日本视频在线播放| 国内毛片毛片毛片毛片毛片| 在线播放无遮挡| АⅤ资源中文在线天堂| 国产色爽女视频免费观看| 午夜a级毛片| 国产69精品久久久久777片| 12—13女人毛片做爰片一| 18美女黄网站色大片免费观看| 国产精品亚洲美女久久久| АⅤ资源中文在线天堂| 亚洲欧美日韩高清在线视频| 88av欧美| 久久久久久久亚洲中文字幕 | 脱女人内裤的视频| 十八禁网站免费在线| 免费在线观看亚洲国产| 欧美高清成人免费视频www| 亚洲人成伊人成综合网2020| 日韩欧美在线二视频| 国产探花极品一区二区| 久久亚洲精品不卡| 国产精品久久视频播放| 亚洲在线观看片| 日韩欧美国产一区二区入口| 日日干狠狠操夜夜爽| 亚洲第一电影网av| 永久网站在线| 亚洲性夜色夜夜综合| 蜜桃亚洲精品一区二区三区| 三级男女做爰猛烈吃奶摸视频| 国产91精品成人一区二区三区| 欧美色视频一区免费| 日韩欧美在线乱码| 淫秽高清视频在线观看| 日韩欧美在线乱码| 舔av片在线| 真人做人爱边吃奶动态| 精品久久久久久久久av| 可以在线观看毛片的网站| 高清日韩中文字幕在线| 直男gayav资源| 一a级毛片在线观看| 亚洲成av人片免费观看| 国产伦在线观看视频一区| 麻豆国产97在线/欧美| 欧美高清性xxxxhd video| 久久精品91蜜桃| 久久久久久久精品吃奶| 国产毛片a区久久久久| 99国产综合亚洲精品| 可以在线观看毛片的网站| 亚洲自偷自拍三级| 中文字幕久久专区| 女生性感内裤真人,穿戴方法视频| 日本一本二区三区精品| 亚洲av美国av| 十八禁国产超污无遮挡网站| 久久久久久久久大av| 久久精品国产亚洲av天美| 色噜噜av男人的天堂激情| 老司机午夜福利在线观看视频| 精品人妻1区二区| 51国产日韩欧美| 悠悠久久av| 亚洲欧美日韩高清专用| 国产高潮美女av| 最近在线观看免费完整版| 夜夜看夜夜爽夜夜摸| 日韩高清综合在线| 九九在线视频观看精品| 国产亚洲精品久久久com| 欧美日韩综合久久久久久 | 制服丝袜大香蕉在线| 色5月婷婷丁香| 免费在线观看成人毛片| 在线观看免费视频日本深夜| 久久午夜福利片| 亚洲av五月六月丁香网| 中亚洲国语对白在线视频| 网址你懂的国产日韩在线| 日本a在线网址| 人人妻人人看人人澡| 成人特级av手机在线观看| 国产伦精品一区二区三区四那| 免费高清视频大片| 精品久久久久久久人妻蜜臀av| 十八禁网站免费在线| 人妻丰满熟妇av一区二区三区| 国产成年人精品一区二区| 99热这里只有是精品50| 少妇的逼好多水| 天堂网av新在线| 免费高清视频大片| 中文字幕高清在线视频| 一级毛片久久久久久久久女| 欧美日韩瑟瑟在线播放| 日本 欧美在线| 麻豆成人av在线观看| 最后的刺客免费高清国语| 欧美区成人在线视频| 俺也久久电影网| 成人特级黄色片久久久久久久| 欧美成人一区二区免费高清观看| 一区二区三区激情视频| 一本一本综合久久| 久久精品人妻少妇| 欧美丝袜亚洲另类 | 极品教师在线免费播放| 天天一区二区日本电影三级| 亚洲五月天丁香| av福利片在线观看| 亚洲av免费高清在线观看| 两个人的视频大全免费| 国内精品久久久久久久电影| 91av网一区二区| 日本黄色片子视频| 不卡一级毛片| 亚洲av第一区精品v没综合| 天堂av国产一区二区熟女人妻| 色哟哟·www| 免费在线观看日本一区| a级毛片免费高清观看在线播放| 午夜免费男女啪啪视频观看 | 天堂动漫精品| 亚洲中文日韩欧美视频| 亚洲无线在线观看| 搡老熟女国产l中国老女人| 一个人免费在线观看电影| 免费看光身美女| 我要搜黄色片| 成年版毛片免费区| 国产亚洲精品久久久com| 12—13女人毛片做爰片一| 国产成人av教育| 亚洲精品在线观看二区| 国产精品伦人一区二区| 一区福利在线观看| 日本一本二区三区精品| 欧美日韩中文字幕国产精品一区二区三区| 好看av亚洲va欧美ⅴa在| 91在线精品国自产拍蜜月| 欧美激情久久久久久爽电影| 99riav亚洲国产免费| 不卡一级毛片| 在线看三级毛片| 色在线成人网| 丰满人妻熟妇乱又伦精品不卡| 亚洲激情在线av| 亚洲国产精品久久男人天堂| 国产高清视频在线观看网站| 99久久99久久久精品蜜桃| 一个人免费在线观看电影| 国产白丝娇喘喷水9色精品| 国产精品久久视频播放| 成年女人看的毛片在线观看| 无人区码免费观看不卡| www日本黄色视频网| 在线播放国产精品三级| 亚洲国产欧洲综合997久久,| 色综合婷婷激情| 一夜夜www| 亚洲专区国产一区二区| 一级作爱视频免费观看| 村上凉子中文字幕在线| 亚洲久久久久久中文字幕| 丰满人妻一区二区三区视频av| 看免费av毛片| 日韩欧美免费精品| av福利片在线观看| 成人av在线播放网站| 日本五十路高清| 亚洲一区高清亚洲精品| 黄色女人牲交| 日日摸夜夜添夜夜添小说| 黄色视频,在线免费观看| 一本综合久久免费| 99国产精品一区二区蜜桃av| 免费av观看视频| 亚洲欧美日韩卡通动漫| 永久网站在线| 夜夜夜夜夜久久久久| eeuss影院久久| 国产精品久久电影中文字幕| 久久精品久久久久久噜噜老黄 | 级片在线观看| 国产精品一区二区三区四区久久| 亚洲午夜理论影院| 亚洲欧美日韩卡通动漫| 欧美日韩黄片免| 少妇人妻精品综合一区二区 | 亚洲五月婷婷丁香| 欧美日韩综合久久久久久 | 国产91精品成人一区二区三区| 蜜桃亚洲精品一区二区三区| 国产三级在线视频| 非洲黑人性xxxx精品又粗又长| 全区人妻精品视频| 一个人观看的视频www高清免费观看| 在线播放无遮挡| 中文字幕熟女人妻在线| 亚洲中文日韩欧美视频| 亚洲av日韩精品久久久久久密| 欧美最黄视频在线播放免费| 国产私拍福利视频在线观看| 亚洲不卡免费看| 婷婷六月久久综合丁香| 国产精品久久电影中文字幕| 少妇丰满av| 午夜福利成人在线免费观看| 自拍偷自拍亚洲精品老妇| 桃色一区二区三区在线观看| 97碰自拍视频| 国产成人av教育| 男人舔奶头视频| 三级毛片av免费| 日韩人妻高清精品专区| 国产麻豆成人av免费视频| 国产乱人视频| 精品人妻熟女av久视频| 日本免费一区二区三区高清不卡| 国产真实乱freesex| 大型黄色视频在线免费观看| 日本撒尿小便嘘嘘汇集6| 亚洲美女视频黄频| 看片在线看免费视频| 天美传媒精品一区二区| 亚洲电影在线观看av| 51国产日韩欧美| av欧美777| 久久久精品大字幕| 搡老妇女老女人老熟妇| 日本黄大片高清| 国产成人欧美在线观看| 亚州av有码| 色综合婷婷激情| 中亚洲国语对白在线视频| 国产乱人视频| 日本在线视频免费播放| 欧美日韩中文字幕国产精品一区二区三区| 黄色一级大片看看| 亚洲人成电影免费在线| 中文字幕人成人乱码亚洲影| 啦啦啦韩国在线观看视频| 日本a在线网址| 不卡一级毛片| 亚洲欧美精品综合久久99| www.999成人在线观看| 一个人看视频在线观看www免费| 97热精品久久久久久| 亚洲av成人不卡在线观看播放网| 日韩欧美在线二视频| 国产亚洲欧美在线一区二区| h日本视频在线播放| 国产麻豆成人av免费视频| 国产高清激情床上av| 精品久久久久久久人妻蜜臀av| 亚洲国产精品成人综合色| 三级国产精品欧美在线观看| 91麻豆av在线| 国产一区二区在线av高清观看| 欧美+日韩+精品| 俺也久久电影网| 午夜福利18| 中文在线观看免费www的网站| 我要看日韩黄色一级片| 国产老妇女一区| 99精品久久久久人妻精品| 在线观看一区二区三区| 成人精品一区二区免费| 一边摸一边抽搐一进一小说| 国产白丝娇喘喷水9色精品| 亚洲人成电影免费在线| 搡女人真爽免费视频火全软件 | 一级黄片播放器| 国产伦精品一区二区三区视频9| 欧美一级a爱片免费观看看| 91在线精品国自产拍蜜月| 成人av一区二区三区在线看| 免费黄网站久久成人精品 | 久久久久久久久中文| 男女视频在线观看网站免费| 91字幕亚洲| 又爽又黄a免费视频| 18禁在线播放成人免费| 欧美日韩黄片免| 午夜福利成人在线免费观看| 欧美日韩中文字幕国产精品一区二区三区| 亚洲精品一卡2卡三卡4卡5卡| 能在线免费观看的黄片| 国产主播在线观看一区二区| 国产精品人妻久久久久久| 久久亚洲真实| 日本五十路高清| 国产成人a区在线观看| 成人午夜高清在线视频| 嫩草影视91久久| 别揉我奶头 嗯啊视频| 最近视频中文字幕2019在线8| 夜夜看夜夜爽夜夜摸| 中出人妻视频一区二区| 亚洲精品色激情综合| 黄色一级大片看看| 在线免费观看不下载黄p国产 | 禁无遮挡网站| 中文字幕人成人乱码亚洲影| 成人精品一区二区免费| 嫁个100分男人电影在线观看| 亚洲欧美日韩无卡精品| 日日夜夜操网爽| 中国美女看黄片| 国产真实伦视频高清在线观看 | 日韩免费av在线播放| 欧美国产日韩亚洲一区| 我要搜黄色片| 久久久久久九九精品二区国产| 国产精品永久免费网站| 亚洲av一区综合| 久久久久久九九精品二区国产| 国产精品永久免费网站| 少妇裸体淫交视频免费看高清| 成人av一区二区三区在线看| 在线播放国产精品三级| 国产伦人伦偷精品视频| 大型黄色视频在线免费观看| 久久国产精品人妻蜜桃| 亚洲aⅴ乱码一区二区在线播放| 91午夜精品亚洲一区二区三区 | 国产极品精品免费视频能看的| 老司机福利观看| 嫩草影院精品99| 国产一级毛片七仙女欲春2| 欧洲精品卡2卡3卡4卡5卡区| 美女xxoo啪啪120秒动态图 | 一级av片app| 亚洲av日韩精品久久久久久密| 熟女电影av网| 亚洲欧美精品综合久久99| 亚洲av电影在线进入| 亚洲精品在线美女|