• <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 ?

    Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning

    2021-01-13 09:34:14HyunJongJangAhwonLeeKangInHyeSongSungHakLee
    World Journal of Gastroenterology 2020年40期

    Hyun-Jong Jang, Ahwon Lee, J Kang, In Hye Song, Sung Hak Lee

    Abstract

    Key Words: Colorectal cancer; Mutation; Deep learning; Computational pathology; Computer-aided diagnosis; Digital pathology

    INTRODUCTION

    Identifying genetic mutations in cancer patients has been increasingly important because mutational status can be very informative to determine the optimal therapeutic strategy[1]. However, molecular analysis is not performed routinely in every cancer patient, since it is not time and cost effective[2]. Thus, cost-effective alternatives for current molecular tests can be helpful in making appropriate treatment decisions. It has long been recognized that the histologic phenotypes reflect the genetic alterations in cancer tissues[3]. Since hematoxylin and eosin (H&E)-stained tissue slides are produced for almost every cancer patient, mutation prediction from the tissue slides can be a time- and cost-effective alternative method for individualized treatment. Thus, researchers attempted to examine the genotype–phenotype relationship in the H&E-stained tissue slides, and some gross tissue patterns related to specific molecular aberrations have been reported[4-9]. However, it remains largely unknown how specific molecular abnormalities are related to the specific histomorphologic findings, as it is not easy to capture the subtle features underlying the specific molecular alterations with the naked eye. To overcome the limitation of visual inspection of tissue structures by pathologists, various image analysis techniques have been applied for many decades to detect the subvisual characteristics of tissue patterns, not discernible to the unaided eyes[1]. Particularly, deep learning has been successfully applied to perform tasks considered too challenging for conventional image analysis techniques because it learns discriminative features directly from the large training dataset for any given task[10]. Therefore, deep learning is increasingly applied for tissue analysis tasks[11]. With the approval to use the digitized whole-slide images (WSIs) for diagnostic purposes, the digitization of tissue slides has been explosively increasing, providing huge digitized tissue data[12]. Combining the routine digitization of tissue slides with deep learning, the computer-aided analysis of WSIs could be adopted to support the evaluation of molecular alterations in H&E-stained cancer tissues in the near future. Although deep learning-based tissue analysis is still in its early phase, few promising results have been published. For example, a recent study reported that deep learning-based molecular cancer subtyping can be performed directly from the standard H&E sections obtained from patients with colorectal cancers (CRCs)[13]. Microsatellite instability can also be predicted from the tissue slides[14]. Furthermore, positive results for the mutation prediction of specific genes from histopathologic images have been reported in patients with various cancer types[3,15-17].

    Motivated by these recent studies, we tried to predict the frequently occurring and clinically meaningful mutations from the H&E-stained CRC tissue WSIs with deep learning-based classifiers. Based on the frequency of mutation and prognostic values of the genes, we choseAPC,KRAS,PIK3CA,SMAD4, andTP53genes for the current study. The area under the curves (AUCs) for the receiver operating characteristic (ROC) curves ranged from 0.645 to 0.809 for The Cancer Genome Atlas (TCGA) datasets, showing the potential for deep learning-based mutation prediction in the CRC tissue slides. By combining two different datasets for training, the prediction performance can be enhanced with the expansion of datasets.

    MATERIALS AND METHODS

    Tests with TCGA WSI dataset

    TCGA program offers the opportunity to reveal the genotype-phenotype relationship because it provides extensive archives of digital pathology slides with multi-omics test results[18]. Both frozen section tissue slides and formalin-fixed paraffin-embedded (FFPE) diagnostic slides were provided by the program. The WSIs from the TCGACOAD (colon cancer) and TCGA-READ (rectal cancer) projects were combined in this study because colonic and rectal adenocarcinoma share similar molecular and histological features[18]. After removing the WSIs with poor quality, 629 patients were included in the present study. We chose to include the genetic alteration including frame shift insertion and deletion, missense mutations, and nonsense mutation. ForAPC,KRAS,PIK3CA,SMAD4, andTP53genes, 436, 249, 133, 74, and 340 patients were confirmed to have the mutations, respectively. Deep learning did not perform optimally when there was a huge imbalance between classes[19]. In a previous study, we failed to obtain the balanced performance in tissue classification tasks unless the dataset itself was forced to have similar numbers between the classes[20]. Thus, we limited the difference in patient numbers between the mutation group and wild-type group by less than 1.4 fold through a random sampling. To match this limitation, we selected 263 patients withAPCmutation as there were only 188 patients with theAPCwild-type gene in the cohorts. The final patient IDs with their respective mutations are listed in Supplementary Table 1.

    Various artifacts including air bubbles, compression artifacts, out-of-focus blur, pen markings, tissue folding, and white background are unavoidable in the WSIs. To make the prediction process fully automated, these artifacts should be automatically removed. Because it is impractical to analyze a WSI as a whole, small image patches are often sliced from a WSI and used for the analysis. Thus, we built a deep learningbased tissue/non-tissue classifier for 360 × 360 pixel image patches at 20 × magnification to remove all of these artifacts at once (Figure 1A). The classifier was a simple convolutional neural network (CNN) with 12 (5 × 5), 24 (5 × 5), and 24 (5 × 5) convolutional filters, each followed by a (2 × 2) max pooling layer. The tissue/nontissue classifier could filter out more than 99.9% of improper patches. Next, tumor tissues should be delineated to predict the mutational status of cancer cells. Because of the freezing process for frozen tissue preparation, the frozen and FFPE tissue WSIs can differ in their morphologic features. Thus, we built separate normal/tumor classifiers for the frozen and FFPE WSIs based on the 360 × 360 pixel tissue image patches using the Inception-v3 model, a widely used CNN architecture. To train the wildtype/mutation classifiers for each gene, frozen and FFPE tissue patches with tumor probability higher than 0.9 by each tumor classifier were collected (Figure 1B). We arbitrarily chose the tumor probability as 0.9 because we decided to only include tissues with prominent tumor features. Although each slide may contain mixed regions of wild-type and mutated tissues considering the tumor heterogeneity, we assigned the same label for all tumor tissue patches in a WSI based on the mutational status of the patients. This labeling strategy was inevitable since we had no methods to delineate the wild-type and mutated regions before the classifiers could be built. The classifiers for the five genes were separately trained and validated with a patient-level ten-fold cross-validation scheme for frozen and FFPE WSIs. The slide-level mutation probability was calculated as the average of the probabilities of all the tumor patches in the WSI. For the training of the Inception-v3 models, we used a mini-batch size of 128, and the cross entropy loss function was adopted as a loss function. Deep neural networks were implemented using the TensorFlow deep learning library (http://tensorflow.org). To minimize overfitting, data augmentation techniques, including random rotations by 90°, random horizontal/vertical flipping, and random perturbation of the contrast and brightness, were applied to the tissue patches during training. In addition, 10% of the training slides were used as a validation dataset for the early stopping of the training. At least five separate classifiers were trained for each gene and tissue modality, and the classifier with the best AUC on the test dataset was included in the results.

    Figure 1 Fully automated prediction of mutation with three consecutive classifiers. A: Proper tissue patches can be selected by the tissue/non-tissue classifier. The four insets in the middle panel demonstrated the tissue patches representing pen marking, blurry scanned area, background rich region, and tissue folding, clockwise from top left, all removed by the tissue/non-tissue classifiers. Then, the normal/tumor classifier delineates the tumor patches among the proper tissue patches; B: The wild-type/mutation classifiers are applied only for patches with tumor probability higher than 0.9. The patch-level probabilities of mutation are averaged to yield the slide-level probability.

    Tests on the external cohorts

    Patient cohort:A total of 142 patients with CRC who previously underwent surgical resection in Seoul St. Mary’s hospital between 2017 and 2019 were enrolled (SMH dataset). All cases were sporadic, without any familial history of CRCs. The clinicopathological parameters including age, sex, and tumor location were retrospectively reviewed from the medical records. The study was approved by the Institutional Review Board of the College of Medicine at the Catholic University of Korea, No. KC19SESI0787.

    Mutation prediction on SMH dataset:ForAPC,KRAS,PIK3CA,SMAD4, andTP53genes, 66, 75, 31, 23, and 98 patients were confirmed to have the mutations, respectively. The sequencing methods are described in Supplementary Methods. Because the SMH dataset was originally collected to extra-validate the model trained on the TCGA datasets, we did not adjust the patient numbers between the classes. The normal/tumor classifier for TCGA FFPE tissues was also used to discriminate the tumor tissue patches of SMH WSIs. The normal/tumor classification accuracy was reviewed by Lee SH and Song IH and was confirmed to be valid. Again, patches with tumor probability higher than 0.9 were collected for mutational status classification. Then, the SMH data were split into ten folds, and each training fold was mixed with TCGA training fold to build new classifiers trained on both datasets. The classification results of the new classifiers on TCGA or SMH datasets were compared with the TCGA-based classifiers to investigate the effects of the expanded training dataset.

    Statistical analysis

    The ROC curves and their AUCs for all classifiers were presented to demonstrate the performance of each classifier. We used a permutation test with 1000 iterations to compare the differences between the two paired or unpaired ROC curves when necessary[21]. APvalue of < 0.05 was considered significant.

    RESULTS

    This study aimed to investigate the feasibility of mutation prediction for the frequently occurring mutations in the CRC tissue WSIs. Since only tumor tissues would be meaningful for the prediction of the mutational status in the tissue slides, three different tissue patch classifiers were sequentially applied to discriminate between tissue/non-tissue, normal/tumor, and wild-type/mutation in order (Figure 1). Only proper tissue patches with high tumor probabilities were used to determine the mutational status (Figure 1B). Patient-level ten-fold cross validation was applied for both frozen and FFPE datasets to fully evaluate the properties of the TCGA CRC WSIs.

    From Figures 2 to 6, the classification results forAPC,KRAS,PIK3CA,SMAD4, andTP53genes are presented for both frozen (upper panels) and FFPE (lower panels) TCGA WSIs. In A and C of every figure, the representative binary heatmaps demonstrating the distribution of tissue patches classified as wild-type or mutation are presented. From left to right, WSIs with gene mutation correctly classified as mutation, with wild-type gene correctly classified as wild type, with gene mutation falsely classified as wild-type, and with wild-type gene falsely classified as mutation are presented, which were determined by the probability threshold set to 0.5. The sensitivity and specificity of a classifier can be much improved by setting the threshold appropriately. However, we set the threshold to 0.5 in the figures for simplicity because every classifier for different folds had different optimal thresholds. To demonstrate the differences in the performance between folds, slide-level ROC curves for folds with the lowest and highest AUCs were presented (left and middle ROC curves in the figures). Finally, the overall performance was inferred based on the slidelevel ROC curves drawn for the concatenated results from all ten folds (right ROC curves). For theAPCgene (Figure 2), the AUCs per fold ranged from 0.648 to 0.819 for the frozen tissues and from 0.655 to 0.880 for the FFPE tissues. The concatenated AUCs were 0.771 and 0.742 for the frozen and FFPE tissues, respectively. For theKRASgene (Figure 3), the performance was much better for the frozen tissues than for the FFPE tissues with a per fold AUC for the frozen tissues of 0.675-0.937 and a concatenated AUC of 0.778. For the FFPE tissues, the concatenated AUC was only 0.645, while the per fold AUCs ranged from 0.594 to 0.736. With regard to thePIK3CAgene (Figure 4), the lowest and highest AUCs per fold were 0.669 and 0.775 for the frozen tissues and 0.597 and 0.857 for the FFPE tissues. The concatenated AUCs were 0.713 and 0.690, respectively. For theSMAD4gene (Figure 5), AUCs per fold ranged from 0.619 to 0.849 for the frozen tissues and from 0.587 to 0.926 for the FFPE tissues, while the concatenated AUCs were 0.693 and 0.763, respectively. With regard to theTP53gene (Figure 6), the lowest and highest AUCs per fold were 0.707 and 0.963 for the frozen tissues and 0.737 and 0.805 for the FFPE tissues. The concatenated AUCs were 0.809 and 0.783, respectively. Overall, the wild-type/mutation classifiers for theTP53gene yielded the highest AUCs for both frozen and FFPE tissues of the TCGA datasets. Between the ROC curves of the frozen and FFPE tissues, classifiers for the frozen tissues yielded better results for theAPCandKRASgenes (P< 0.05,P< 0.001,P= 0.068,P= 0.057, andP= 0.115 between the frozen and FFPE classifiers forAPC,KRAS,PIK3CA,SMAD4, andTP53genes, respectively, by Venkatraman’s permutation test for unpaired ROC curves).

    The generalizability of a deep learning model for the external dataset is an important issue to be validated. Thus, we collected our own CRC FFPE WSIs with information on genetic mutation. The normal/tumor classifier for the TCGA FFPE tissues was applied to collect the tissue patches with high tumor probabilities. Then, the mutation classifiers for each gene trained on the TCGA FFPE tissues were applied to the tumor patches. The slide-level ROC curves for the five genes are presented in Supplementary Figure 1. The AUCs were 0.654, 0.581, 0.570, 0.652, and 0.775 forAPC,KRAS,PIK3CA,SMAD4, andTP53genes, respectively. For theAPC,KRAS, andPIK3CAgenes, the performance of the TCGA-based mutation classifiers on the SMH dataset were worse than that on the TCGA dataset (P< 0.01,P< 0.05,P< 0.05,P= 0.107, andP= 0.263 forAPC,KRAS,PIK3CA,SMAD4, andTP53genes, respectively, by Venkatraman’s permutation test for unpaired ROC curves). These results indicated that the mutation classifiers did not have an excellent generalizability when they were trained only with the TCGA WSI datasets. It remains unclear whether the performance could be improved when more data are used for the training. Thus, we combined the TCGA and SMH datasets to train new sets of mutation classifiers. Patient-level tenfold cross validation schemes were also used for the mixed dataset. The performance of the SMH dataset showed an obvious improvement, since the SMH data were included in the training data in this setting. The AUCs forAPCandKRASgenes increased to 0.812 and 0.832 (Figure 7,P< 0.01 andP< 0.001 compared with the TCGA-trained classifiers by Venkatraman’s permutation test for paired ROC curves). Improved results were also obtained forPIK3CA,SMAD4, andTP53with AUCs of 0.769, 0.782, and 0.845, respectively (Figure 8,P< 0.05,P< 0.01, andP< 0.05 by Venkatraman’s permutation test for paired ROC curves). More importantly, the performance of the TCGA data was also generally improved by the classifiers trained on both datasets (Supplementary Figure 2). The AUCs were 0.766, 0.694, 0.708, 0.791, and 0.822 for theAPC,KRAS,PIK3CA,SMAD4, andTP53genes, respectively (P= 0.072,P< 0.01,P= 0.091,P= 0.074, andP< 0.05 compared with the TCGA-trained classifiers). These results indicated that the deep learning-based classifiers for mutation prediction in tissue slides can yield better performance when more data are collected from various sources.

    Figure 2 Classifiers to predict APC gene mutation for the Cancer Genome Atlas colorectal cancer tissue slides. A: Representative whole slide images (WSIs) of the frozen slides with APC gene mutation correctly classified as mutation, with wild-type gene correctly classified as wild-type, with gene mutation falsely classified as wild-type, and with wild-type gene falsely classified as mutation, from left to right; B: Receiver operating characteristic curves for the fold with lowest area under the curve (AUC), for the fold with highest AUC, and for the concatenated results of all ten folds, from left to right, obtained with the classifiers trained with the frozen tissues; C and D: Same as A and B, but the results were for the formalin-fixed paraffin-embedded WSIs. APC-M: APC mutated; APC-W: APC wild-type; AUC: Area under the curve; FFPE: Formalin-fixed paraffin-embedded.

    Figure 3 Classifiers to predict KRAS gene mutation for the Cancer Genome Atlas colorectal cancer tissue slides. A: Representative whole slide images (WSIs) of the frozen slides with KRAS gene mutation correctly classified as mutation, with wild-type gene correctly classified as wild-type, with gene mutation falsely classified as wild-type, and with wild-type gene falsely classified as mutation, from left to right; B: Receiver operating characteristic curves for the fold with lowest area under the curve (AUC), for the fold with highest AUC, and for the concatenated results of all ten folds, from left to right, obtained with the classifiers trained with the frozen tissues; C and D: Same as A and B, but the results were for the formalin-fixed paraffin-embedded WSIs. KRAS-M: KRAS mutated; KRAS-W: KRAS wild-type; AUC: Area under the curve; FFPE: Formalin-fixed paraffin-embedded.

    DISCUSSION

    Figure 4 Classifiers to predict PIK3CA gene mutation for the Cancer Genome Atlas colorectal cancer tissue slides. A: Representative whole slide images (WSIs) of the frozen slides with PIK3CA gene mutation correctly classified as mutation, with wild-type gene correctly classified as wild-type, with gene mutation falsely classified as wild-type, and with wild-type gene falsely classified as mutation, from left to right; B: Receiver operating characteristic curves for the fold with lowest area under the curve (AUC), for the fold with highest AUC, and for the concatenated results of all ten folds, from left to right, obtained with the classifiers trained with the frozen tissues; C and D: Same as A and B, but the results were for the formalin-fixed paraffin-embedded WSIs. PIK3CA-M: PIK3CA mutated; PIK3CA-W: PIK3CA wild-type; AUC: Area under the curve; FFPE: Formalin-fixed paraffin-embedded.

    In the present study, we selected theAPC,KRAS,PIK3CA,SMAD4, andTP53genes because they were frequently occurring in both TCGA and SMH CRC datasets and had prognostic values.APCis an important tumor suppressor known to play a role in CRC development. DeactivatingAPCleads to the constitutive activation of the Wnt signaling pathway, which may contribute to tumor progression[22]. The frequency ofAPCmutations was 47% for the SMH dataset, which is a slightly higher mutational rate compared with that in previous studies (24.2%-44.8%). The RAS proto-oncogenes (HRAS,KRAS, andNRAS) play a pivotal role in numerous basic cellular functions, such as control of cell growth, differentiation, and apoptosis, and regulate key signaling cascades including phosphoinositide 3-kinase (PI3K) and mitogen-activated protein kinase (MAPK) pathways[23,24]. Mutations in RAS family members are found in 20% of all human cancers, of whichKRASmutations account for 85%[25].KRASmutated in 30% to 50% of patients with CRCs[25]. In the SMH dataset, the frequency was 53%.KRASis a critical oncogene involved in the MAPK signaling pathway, andKRASmutations promote colorectal adenoma growth in the early phase of carcinogenesis[26]. The presence of activatingKRASandNRASmutations is a predictor of resistance to epidermal growth factor receptor (EGFR) inhibitors, such as cetuximab or panitumumab[27,28]. ThePIK3CAgene is responsible for coordinating various cellular processes, including proliferation, migration, and survival. ThePIK3CAmutation is associated with the activation of downstream PI3K/Akt signaling, which in turn deregulates other signaling pathways that contribute to oncogenic transformations[29]. ThePIK3CAmutation occurs in 10%-30% of patients with CRCs[30]. In the present study, the frequency of thePIK3CAmutation was observed to be 22%. Recent studies have shown thatPIK3CAmutations are associated with a worse clinical outcome and with a negative prediction for anti-EGFR targeted therapy[31].SMAD4is an essential intermediator in the TGFβ signaling pathway, exhibiting a pivotal role as a tumor suppressor gene in CRC[32].SMAD4mutations occur in 10%-20% of patients with CRC[32,33]. In the SMH dataset, the rate of theSMAD4mutation was 16%. Recent studies have demonstrated that somaticSMAD4mutations are more common in patients with advanced stages, and a decrease in the level ofSMAD4expression is associated with worse recurrence-free and overall survival in patients with CRC[32]. The tumor suppressor geneTP53regulates DNA repair mechanism and apoptosis. Loss ofTP53function is one of the major events in the development of CRC, which is thought to occur in the later stages of colon cancer progression[34]. TheTP53mutation rate in the SMH dataset was 69%, which is consistent with the frequencies reported in various studies (45%-84%)[35].

    Figure 5 Classifiers to predict SMAD4 gene mutation for the Cancer Genome Atlas colorectal cancer tissue slides. A: Representative whole slide images (WSIs) of the frozen slides with SMAD4 gene mutation correctly classified as mutation, with wild-type gene correctly classified as wild-type, with gene mutation falsely classified as wild-type, and with wild-type gene falsely classified as mutation, from left to right; B: Receiver operating characteristic curves for the fold with lowest area under the curve (AUC), for the fold with highest AUC, and for the concatenated results of all ten folds, from left to right, obtained with the classifiers trained with the frozen tissues; C and D: Same as A and B, but the results were for the formalin-fixed paraffin-embedded WSIs. SMAD4-M: SMAD4 mutated; SMAD4-W: SMAD4 wild-type; AUC: Area under the curve; FFPE: Formalin-fixed paraffin-embedded.

    Figure 6 Classifiers to predict TP53 gene mutation for the Cancer Genome Atlas colorectal cancer tissue slides. A: Representative whole slide images (WSIs) of the frozen slides with TP53 gene mutation correctly classified as mutation, with wild-type gene correctly classified as wild-type, with gene mutation falsely classified as wild-type, and with wild-type gene falsely classified as mutation, from left to right; B: Receiver operating characteristic curves for the fold with lowest area under the curve (AUC), for the fold with highest AUC, and for the concatenated results of all ten folds, from left to right, obtained with the classifiers trained with the frozen tissues; C and D: Same as A and B, but the results were for the formalin-fixed paraffin-embedded WSIs. TP53-M: TP53 mutated; TP53-W: TP53 wild-type; AUC: Area under the curve; FFPE: Formalin-fixed paraffin-embedded.

    Figure 7 Mutation prediction of APC and KRAS genes for the Seoul St. Mary Hospital colorectal cancer tissue slides by the classifiers trained with both The Cancer Genome Atlas and Seoul St. Mary Hospital data. A: Representative whole slide images of the slides with APC gene mutation correctly classified as mutation, with wild-type gene correctly classified as wild-type, with gene mutation falsely classified as wild-type, and with wild-type gene falsely classified as mutation, from left to right; B: Receiver operating characteristic curves for the fold with lowest area under the curve (AUC), for the fold with highest AUC, and for the concatenated results of all ten folds, from left to right; C and D: Same as A and B, but the results were for the KRAS gene. SMH: Seoul St. Mary Hospital; APC-M: APC mutated; APC-W: APC wild-type; KRAS-M: KRAS mutated; KRAS-W: KRAS wild-type; AUC: Area under the curve; FFPE: Formalin-fixed paraffin-embedded.

    In general, theAPCmutation is thought to have no prognostic significance[36]. However, in a specific situation such as in a microsatellite stable proximal colon cancer, wild-typeAPChas been associated with poorer survival[37]. On the contrary,KRAS,PIK3CA,SMAD4, andTP53gene mutations were associated with poorer prognosis in CRCs[34,38-40]. Thus, information on the mutational status of these genes can be useful in making therapeutic decisions for CRC patients. On occasion, a specific gene mutation can be related to a specific visual characteristic in tissue histology. For example, thePIK3CAmutation often coincides with lymphovascular invasion, tumor budding, and a high number of poorly differentiated clusters in CRC tissues[39]. However, it is not always possible to discover the visually discernible features reflecting the mutation of a specific gene. Therefore, we adopted deep learning to predict the mutational status of the five genes because the discriminative features of the mutations can be automatically learned directly from the large training data of tissue images. To our knowledge, this is the first study to evaluate the mutation prediction capabilities of deep learning models for the frequently occurring mutations in the pathologic tissue slides of CRC patients.

    Figure 8 Mutation prediction of PIK3CA, SMAD4, and TP53 genes for the Seoul St. Mary Hospital colorectal cancer tissue slides by the classifiers trained with both The Cancer Genome Atlas and Seoul St. Mary Hospital data. A: Representative whole slide images of the slides with PIK3CA gene mutation correctly classified as mutation, with wild-type gene correctly classified as wild-type, with gene mutation falsely classified as wild-type, and with wild-type gene falsely classified as mutation, from left to right; B: Receiver operating characteristic curves for the fold with lowest area under the curve (AUC), for the fold with highest AUC, and for the concatenated results of all ten folds, from left to right; C and D: Same as A and B, but the results were for the SMAD4 gene; E and F: Same as A and B, but the results were for the TP53 gene. PIK3CA-M: PIK3CA mutated; PIK3CA-W: PIK3CA wild-type; SMAD4-M: SMAD4 mutated; SMAD4-W: SMAD4 wild-type; TP53-M: TP53 mutated; TP53-W: TP53 wild-type; AUC: Area under the curve; FFPE: Formalin-fixed paraffin-embedded.

    In all the mutation classifiers applied to the TCGA frozen and FFPE tissues, the slide-level discrimination capabilities were much better against chance performance (P< 0.001 for all five genes by permutation test). These results indicated that the Inception-v3 model learned valid features to discriminate the mutated tissue phenotypes of each gene. In the case ofAPCandKRASgenes, the classifiers for the frozen tissues yielded better results compared with the FFPE tissues, although the frozen sections generally showed poorer tissue quality than did the FFPE sections. It can be explained by the fact that the frozen sections provided the best representation of the tissue contents on which the genomic signatures were tested[18]. Since the FFPE sections can be taken far from the frozen tissue sections, the mutational status can be different between them, considering the heterogeneity of large tumors. When we validated the classifiers trained with the TCGA FFPE tissues on the SMH WSIs, the performance was generally poorer (Supplementary Figure 1). Deep learning operates well under a condition where both the training and test datasets come from the same distribution[41]. For the H&E-stained tissue slides, the quality may vary because they undergo multiple processes for preparation including formalin fixation, paraffin embedding, sectioning, and staining, which can be slightly different between institutes[42]. Furthermore, the ethnic difference between the TCGA and SMH datasets may also contribute to the difference in the performance. Although the difference can be negligible to human eye, deep learning can be very sensitive to the subtle difference in tissue conditions. Therefore, many researchers insisted on the necessity of using large multi-national and multi-institutional datasets to enhance the generalizability of the deep learning model[2,12]. Thus, we combined the two datasets to build new classifiers trained on both TCGA and SMH datasets. Naturally, the performance for the SMH data was greatly enhanced because the tissue features of the data were exposed to the classifiers in this setting. More importantly, the performance of the TCGA data was also enhanced by adding the WSIs from the SMH dataset for training. These results clearly demonstrated that multi-national and multi-institutional datasets can improve the performance of the mutation classifiers. However, it remains unclear how far the performance can be improved if much more data are supplied.

    When we scrutinized the binary heatmaps of falsely classified WSIs, we recognized that the wild-type and mutated patches were generally aggregated rather than dispersed. The patterns implied the possibility that the tumor tissues in a tissue slide may have different mutational statuses between different regions. Large tumors can be molecularly heterogeneous, and the tumor heterogeneity can contribute to the resistance to treatment[43]. Therefore, tumor heterogeneity has been an important issue for both researchers and clinicians. To elucidate the spatial heterogeneity of a tumor, molecular methods with high spatial specificity such as multi-region sequencing and single-cell sequencing can be applied to examine a tissue sample. However, a random sampling of tissues for these molecular tests would be very inefficient. If possible regions of molecular heterogeneity in a tissue slide could be identified before the tests, molecular testing can be more specific and efficient. Furthermore, there are possibilities of false negative molecular tests because of the imprecise delineation of target regions in a tissue block[12]. Therefore, it is very important to objectively discriminate the tumor regions for the molecular evaluation of the tumor tissues. Thus, both normal/tumor and wild-type/mutation classifiers can be used to delineate the appropriate target sites for various molecular tests in cancer tissues. For example, Supplementary Figure 3 presents the heatmaps for the mutational status of all five genes in a TCGA frozen tissue slide, demonstrating how different regions of a slide can have different mutational statuses. When an overlaid probability map of mutation was drawn, areas with low and high mutational statuses can be recognized. It may not be easy to obtain this kind of information without the help of deep learning. Hence, molecular tests with high spatial specificity can be targeted to specific regions depending on the purpose of the tests. Therefore, these classifiers can make the selection of lesional regions for relevant multi-omics testing fully automated in the near future[2].

    Limitations also exist for the deep learning-based tissue classifiers. One of the limitations is the sensitive nature of deep learning to minute differences in the datasets. Because of the sensitive nature, classifiers applied to very subtly different conditions should be separately built. For example, classifiers for the frozen and FFPE tissues should be separately trained for the same tasks. It requires additional data collection and training overload. In clinical practice, pathologists should take an additional step to determine the kind of classifiers that should be applied for a specific specimen. It is currently inevitable to separately build classifiers to support various real-world tasks in the pathology laboratories. Therefore, manual selection of appropriate classifiers for target tasks is a necessary step that can limit the fully automated adoption of deep learning-based classifiers in the pathology workflows.

    In the current study, we used the high-throughput cancer panel to identify mutations in CRC tissues of the SMH dataset. This panel test approach makes it possible to identify diverse clinically actionable mutations in a single assay. However, it is quite expensive to prepare the equipment necessary to perform the test and to save a large number of data generated. This study demonstrated that a deep learningbased method could be a useful and effective tool for the prediction of actionable mutations from CRC WSIs. However, the interpretation of decision made by the deep learning-based classifier is unclear because of the black box nature of deep learning and should be further studied. Besides this aspect, the advantages and disadvantages between the mutation panel test (molecular test) and deep learning method were described in Table 1.

    Despite the limitation, with the increasing digitization of tissue slides, various computer-assisted methods will be introduced for histopathologic interpretation and clinical care. In the present study, we demonstrated the potential of deep learningbased classifiers to predict mutations in the CRC WSIs. Although the classifiers in this study are not yet enough to be used for predicting the genetic mutations in the clinic, deep learning-based methods have the potential to learn features for discriminating the wild-type tissues from the mutated tissues, which are not easily discernible to the human eye. Thus, deep learning will be increasingly adopted to discover new tissuebased biomarkers, which provide fundamental information for personalized medicine. With the accumulation of large sets of WSI data, deep learning-based tissue analyses will play important roles in the better characterization of cancer patients and will be an essential part of digital pathology in the era of precision medicine.

    CONCLUSION

    In the present study, we demonstrated that theAPC,KRAS,PIK3CA,SMAD4andTP53mutation can be predicted from H&E pathology images using the deep learningbased classifiers. Furthermore, by combining the TCGA and our datasets for training, the prediction performance was enhanced. Therefore, with the accumulation of tissue image data for training, deep learning can be used to supplement current molecular testing methods in the near future.

    Table 1 The advantages and disadvantages between the mutation panel test and deep learning-based method

    ARTICLE HIGHLIGHTS

    欧美亚洲日本最大视频资源| netflix在线观看网站| 在线观看免费午夜福利视频| 久久香蕉激情| 日本一区二区免费在线视频| x7x7x7水蜜桃| 国产熟女午夜一区二区三区| 在线观看免费日韩欧美大片| 国产精品久久视频播放| 欧美成狂野欧美在线观看| 在线观看免费午夜福利视频| 免费不卡黄色视频| 在线av久久热| 中出人妻视频一区二区| 久久久久国产一级毛片高清牌| 午夜成年电影在线免费观看| 看免费av毛片| 亚洲自偷自拍图片 自拍| 777久久人妻少妇嫩草av网站| 无遮挡黄片免费观看| 色94色欧美一区二区| 午夜精品久久久久久毛片777| 中文字幕最新亚洲高清| 成年人午夜在线观看视频| 精品人妻1区二区| 久久久国产精品麻豆| 一级作爱视频免费观看| 我的亚洲天堂| 日韩免费av在线播放| 亚洲视频免费观看视频| 国产精品偷伦视频观看了| 亚洲精品国产一区二区精华液| 免费在线观看视频国产中文字幕亚洲| 久久国产乱子伦精品免费另类| 日韩熟女老妇一区二区性免费视频| 亚洲熟女精品中文字幕| 美女福利国产在线| 午夜影院日韩av| 极品教师在线免费播放| 婷婷丁香在线五月| 777久久人妻少妇嫩草av网站| 国产精品香港三级国产av潘金莲| 男女下面插进去视频免费观看| 欧美黄色淫秽网站| 欧美激情久久久久久爽电影 | 欧美最黄视频在线播放免费 | 十八禁高潮呻吟视频| 成人亚洲精品一区在线观看| 久久久久视频综合| 国产精品1区2区在线观看. | 国产成人啪精品午夜网站| 欧美国产精品va在线观看不卡| 波多野结衣av一区二区av| 99精国产麻豆久久婷婷| 婷婷丁香在线五月| 国产精品98久久久久久宅男小说| 国产精品国产av在线观看| 国产精品久久视频播放| 精品国内亚洲2022精品成人 | 麻豆av在线久日| www.精华液| 亚洲午夜精品一区,二区,三区| 日本欧美视频一区| 国产伦人伦偷精品视频| 一级片'在线观看视频| 久久久久国产精品人妻aⅴ院 | 高潮久久久久久久久久久不卡| 成人特级黄色片久久久久久久| 欧美午夜高清在线| 99香蕉大伊视频| 日日夜夜操网爽| 午夜视频精品福利| 亚洲中文av在线| 女警被强在线播放| 高清欧美精品videossex| 男女午夜视频在线观看| 大型av网站在线播放| 亚洲综合色网址| 亚洲午夜精品一区,二区,三区| 午夜福利乱码中文字幕| 久久精品亚洲av国产电影网| 国精品久久久久久国模美| 三上悠亚av全集在线观看| 国产一区二区激情短视频| 在线观看免费午夜福利视频| 久久精品亚洲精品国产色婷小说| 一区二区日韩欧美中文字幕| 久久久国产成人免费| 男女高潮啪啪啪动态图| 黑人欧美特级aaaaaa片| 一夜夜www| 免费观看人在逋| 中国美女看黄片| 久久九九热精品免费| av一本久久久久| 精品卡一卡二卡四卡免费| 少妇 在线观看| 亚洲国产毛片av蜜桃av| 美女高潮到喷水免费观看| 国产精品偷伦视频观看了| 操出白浆在线播放| 99re6热这里在线精品视频| 国产欧美亚洲国产| 一个人免费在线观看的高清视频| 欧美在线一区亚洲| a级毛片在线看网站| 久久午夜亚洲精品久久| 精品久久久久久电影网| 国产人伦9x9x在线观看| 99久久99久久久精品蜜桃| 欧美人与性动交α欧美软件| 国产一区二区三区视频了| 啦啦啦免费观看视频1| 黄色视频不卡| 一边摸一边抽搐一进一小说 | www日本在线高清视频| 18禁观看日本| 人人妻人人爽人人添夜夜欢视频| 国产精品成人在线| 精品久久久精品久久久| 精品国产美女av久久久久小说| 久久精品亚洲av国产电影网| 黑人操中国人逼视频| 色在线成人网| 日韩人妻精品一区2区三区| 亚洲欧洲精品一区二区精品久久久| av免费在线观看网站| 丝袜在线中文字幕| 美女扒开内裤让男人捅视频| 欧美中文综合在线视频| 亚洲 欧美一区二区三区| 色老头精品视频在线观看| 国产欧美日韩一区二区三区在线| 亚洲欧美色中文字幕在线| 欧美激情极品国产一区二区三区| а√天堂www在线а√下载 | 丰满迷人的少妇在线观看| 亚洲自偷自拍图片 自拍| 妹子高潮喷水视频| 国产又色又爽无遮挡免费看| 国产精品国产av在线观看| 大陆偷拍与自拍| a在线观看视频网站| 亚洲人成伊人成综合网2020| 黄片播放在线免费| 一二三四在线观看免费中文在| 天天躁狠狠躁夜夜躁狠狠躁| 大香蕉久久网| 免费不卡黄色视频| 久久国产精品影院| 久久中文字幕一级| 淫妇啪啪啪对白视频| 亚洲全国av大片| 免费女性裸体啪啪无遮挡网站| 欧洲精品卡2卡3卡4卡5卡区| 亚洲精品在线美女| 香蕉丝袜av| 高清在线国产一区| av天堂在线播放| 啪啪无遮挡十八禁网站| 精品卡一卡二卡四卡免费| 国产又色又爽无遮挡免费看| 国产极品粉嫩免费观看在线| 午夜激情av网站| 天堂√8在线中文| 亚洲免费av在线视频| 色老头精品视频在线观看| 在线免费观看的www视频| 久久久精品国产亚洲av高清涩受| 激情在线观看视频在线高清 | 一区二区三区国产精品乱码| 国产真人三级小视频在线观看| 亚洲五月婷婷丁香| 啦啦啦视频在线资源免费观看| 久久国产乱子伦精品免费另类| 久久精品91无色码中文字幕| 黑人操中国人逼视频| 人妻 亚洲 视频| 亚洲一卡2卡3卡4卡5卡精品中文| 亚洲国产精品合色在线| 国产一区二区三区综合在线观看| 国产亚洲欧美精品永久| 欧美+亚洲+日韩+国产| 亚洲精品中文字幕一二三四区| 久久久久精品国产欧美久久久| 三上悠亚av全集在线观看| 国内毛片毛片毛片毛片毛片| aaaaa片日本免费| 老汉色av国产亚洲站长工具| 欧美激情极品国产一区二区三区| 国产极品粉嫩免费观看在线| 99精品在免费线老司机午夜| 一进一出抽搐gif免费好疼 | 人妻一区二区av| 在线观看午夜福利视频| 国产精品美女特级片免费视频播放器 | 高清av免费在线| 亚洲国产精品合色在线| 99re在线观看精品视频| 欧美激情高清一区二区三区| 日韩一卡2卡3卡4卡2021年| 在线观看免费高清a一片| 飞空精品影院首页| 亚洲av美国av| 在线观看免费日韩欧美大片| 精品一区二区三区av网在线观看| 超碰97精品在线观看| 天堂√8在线中文| 久久午夜亚洲精品久久| 亚洲va日本ⅴa欧美va伊人久久| 99精品欧美一区二区三区四区| 在线av久久热| 欧美亚洲日本最大视频资源| 一级,二级,三级黄色视频| 国产亚洲欧美精品永久| 亚洲色图综合在线观看| 亚洲五月色婷婷综合| 日本欧美视频一区| 亚洲欧美一区二区三区黑人| 亚洲成国产人片在线观看| 亚洲自偷自拍图片 自拍| 91麻豆精品激情在线观看国产 | 青草久久国产| 成人18禁高潮啪啪吃奶动态图| 超碰97精品在线观看| 日本黄色日本黄色录像| 欧美色视频一区免费| 淫妇啪啪啪对白视频| 午夜精品国产一区二区电影| 丝袜人妻中文字幕| 99国产精品一区二区三区| 久久久久精品人妻al黑| 91大片在线观看| 精品亚洲成国产av| 午夜老司机福利片| 一边摸一边做爽爽视频免费| 男女免费视频国产| 午夜91福利影院| 老司机福利观看| 999精品在线视频| 大型av网站在线播放| 在线观看免费高清a一片| 在线永久观看黄色视频| 欧美一级毛片孕妇| 亚洲精品在线观看二区| 如日韩欧美国产精品一区二区三区| 欧美人与性动交α欧美软件| 午夜福利在线免费观看网站| 看免费av毛片| 日韩欧美三级三区| 日韩欧美在线二视频 | 国产亚洲欧美精品永久| 无遮挡黄片免费观看| 午夜精品在线福利| 午夜两性在线视频| 最近最新免费中文字幕在线| 又黄又爽又免费观看的视频| 成人免费观看视频高清| 人人妻人人添人人爽欧美一区卜| 91麻豆精品激情在线观看国产 | 韩国av一区二区三区四区| 午夜影院日韩av| 极品教师在线免费播放| 欧美丝袜亚洲另类 | 一级毛片精品| 身体一侧抽搐| videosex国产| 18禁裸乳无遮挡动漫免费视频| 一边摸一边抽搐一进一出视频| 午夜免费观看网址| 香蕉国产在线看| 日日爽夜夜爽网站| 久久人人97超碰香蕉20202| 久久中文字幕一级| 亚洲精品国产一区二区精华液| 女同久久另类99精品国产91| 19禁男女啪啪无遮挡网站| 亚洲成a人片在线一区二区| 岛国在线观看网站| 天堂√8在线中文| 成人18禁在线播放| 丁香欧美五月| 免费不卡黄色视频| 人人妻,人人澡人人爽秒播| 后天国语完整版免费观看| 国产精品免费视频内射| 亚洲国产欧美一区二区综合| www日本在线高清视频| 黑丝袜美女国产一区| 精品国内亚洲2022精品成人 | 成熟少妇高潮喷水视频| 午夜影院日韩av| 国产欧美日韩综合在线一区二区| 悠悠久久av| 中国美女看黄片| 岛国在线观看网站| 在线看a的网站| 黑人操中国人逼视频| 99在线人妻在线中文字幕 | 久久久国产精品麻豆| 黄色a级毛片大全视频| 少妇被粗大的猛进出69影院| 欧美中文综合在线视频| 国产主播在线观看一区二区| 成熟少妇高潮喷水视频| 亚洲美女黄片视频| 亚洲成a人片在线一区二区| 纯流量卡能插随身wifi吗| 精品免费久久久久久久清纯 | 亚洲欧美激情综合另类| 人妻丰满熟妇av一区二区三区 | 国产一区二区激情短视频| 日本撒尿小便嘘嘘汇集6| 欧美日韩国产mv在线观看视频| videos熟女内射| 亚洲aⅴ乱码一区二区在线播放 | 色精品久久人妻99蜜桃| 男女高潮啪啪啪动态图| 少妇粗大呻吟视频| 香蕉丝袜av| 国产片内射在线| 18禁观看日本| 国产成人精品久久二区二区91| 欧美大码av| 黑人巨大精品欧美一区二区蜜桃| 精品免费久久久久久久清纯 | 国产精品二区激情视频| 女人精品久久久久毛片| 手机成人av网站| 深夜精品福利| 国产亚洲欧美98| 一边摸一边做爽爽视频免费| 亚洲性夜色夜夜综合| 高清在线国产一区| 好男人电影高清在线观看| 国产欧美日韩一区二区精品| 一进一出抽搐gif免费好疼 | netflix在线观看网站| 女性生殖器流出的白浆| 真人做人爱边吃奶动态| 成人亚洲精品一区在线观看| 免费一级毛片在线播放高清视频 | 国产91精品成人一区二区三区| 免费av中文字幕在线| 精品一品国产午夜福利视频| 成人18禁在线播放| 亚洲精品一二三| 亚洲欧洲精品一区二区精品久久久| 国产主播在线观看一区二区| 久久久久精品国产欧美久久久| 亚洲片人在线观看| 国产精品 欧美亚洲| 久久狼人影院| 国产亚洲欧美精品永久| 人妻 亚洲 视频| √禁漫天堂资源中文www| 久久狼人影院| 高清在线国产一区| 亚洲av美国av| 亚洲色图综合在线观看| 久久香蕉国产精品| netflix在线观看网站| 人人妻人人澡人人爽人人夜夜| 中文字幕av电影在线播放| 黄色视频不卡| 亚洲午夜理论影院| 狠狠婷婷综合久久久久久88av| 黄色视频不卡| 亚洲av片天天在线观看| 俄罗斯特黄特色一大片| 成人永久免费在线观看视频| 一夜夜www| 99国产精品一区二区三区| 18禁美女被吸乳视频| 亚洲一区二区三区不卡视频| 18禁美女被吸乳视频| 欧美精品啪啪一区二区三区| 国产精品 国内视频| 国产精品免费视频内射| 久久性视频一级片| 国产精品综合久久久久久久免费 | 一个人免费在线观看的高清视频| 女人爽到高潮嗷嗷叫在线视频| 99国产综合亚洲精品| netflix在线观看网站| 99国产综合亚洲精品| 欧美乱色亚洲激情| 满18在线观看网站| 变态另类成人亚洲欧美熟女 | 亚洲av日韩在线播放| 亚洲一区中文字幕在线| 这个男人来自地球电影免费观看| 精品国产超薄肉色丝袜足j| 国产精品偷伦视频观看了| 国产无遮挡羞羞视频在线观看| 国产色视频综合| 国产成人欧美| 99久久国产精品久久久| 精品久久久久久,| 777米奇影视久久| avwww免费| 丰满人妻熟妇乱又伦精品不卡| 国产av一区二区精品久久| 高清在线国产一区| 精品人妻熟女毛片av久久网站| 国产激情欧美一区二区| 午夜福利免费观看在线| 超碰成人久久| 亚洲精品自拍成人| 国产一区二区三区视频了| 操出白浆在线播放| 久久久国产欧美日韩av| 亚洲国产中文字幕在线视频| 在线免费观看的www视频| 人妻丰满熟妇av一区二区三区 | 久久久久久久久免费视频了| 岛国毛片在线播放| 国产成+人综合+亚洲专区| 午夜福利在线免费观看网站| 精品一区二区三区av网在线观看| 最近最新中文字幕大全电影3 | 国产高清国产精品国产三级| 自线自在国产av| 男人操女人黄网站| 在线观看免费视频日本深夜| 国产一区二区激情短视频| 夫妻午夜视频| 精品亚洲成a人片在线观看| 国产熟女午夜一区二区三区| 热99国产精品久久久久久7| 男女之事视频高清在线观看| 欧美 亚洲 国产 日韩一| videosex国产| av国产精品久久久久影院| 高清视频免费观看一区二区| 国产单亲对白刺激| 日本五十路高清| 日韩有码中文字幕| 日韩免费av在线播放| 美女扒开内裤让男人捅视频| 欧美+亚洲+日韩+国产| 视频区图区小说| 成人av一区二区三区在线看| 欧美在线一区亚洲| 9热在线视频观看99| 天天添夜夜摸| av国产精品久久久久影院| 18禁国产床啪视频网站| 国产aⅴ精品一区二区三区波| 亚洲第一青青草原| 两个人看的免费小视频| 在线观看免费视频网站a站| 午夜老司机福利片| 精品人妻1区二区| 久99久视频精品免费| 国产欧美日韩一区二区三区在线| 国产极品粉嫩免费观看在线| 伊人久久大香线蕉亚洲五| 欧美成狂野欧美在线观看| 黄色成人免费大全| 亚洲人成77777在线视频| av电影中文网址| 国产成人精品在线电影| 亚洲av成人一区二区三| 视频区欧美日本亚洲| 在线国产一区二区在线| 久久精品人人爽人人爽视色| 色播在线永久视频| 国产精品av久久久久免费| 咕卡用的链子| 成人亚洲精品一区在线观看| xxx96com| av欧美777| 欧美国产精品一级二级三级| 久久亚洲精品不卡| 亚洲免费av在线视频| 国产一区有黄有色的免费视频| 黄色片一级片一级黄色片| 中文字幕精品免费在线观看视频| 久久中文看片网| 黄片播放在线免费| 久久久精品免费免费高清| 午夜福利在线观看吧| 岛国在线观看网站| 男女之事视频高清在线观看| 色老头精品视频在线观看| 色精品久久人妻99蜜桃| 国产精品亚洲一级av第二区| 国产黄色免费在线视频| 曰老女人黄片| 黄频高清免费视频| 欧美激情久久久久久爽电影 | 欧美+亚洲+日韩+国产| 视频区图区小说| 国产99白浆流出| 欧美亚洲 丝袜 人妻 在线| 久久久久久免费高清国产稀缺| 一级作爱视频免费观看| 久久精品国产a三级三级三级| 国产极品粉嫩免费观看在线| 国产成人欧美| 波多野结衣一区麻豆| 国产高清视频在线播放一区| 国产欧美日韩一区二区精品| 亚洲熟女毛片儿| tocl精华| 好男人电影高清在线观看| e午夜精品久久久久久久| 欧美精品高潮呻吟av久久| 免费久久久久久久精品成人欧美视频| 国产在线观看jvid| 18禁裸乳无遮挡动漫免费视频| 极品少妇高潮喷水抽搐| 国产成人一区二区三区免费视频网站| 三上悠亚av全集在线观看| 欧美成狂野欧美在线观看| 一区二区三区激情视频| 夜夜爽天天搞| 色婷婷久久久亚洲欧美| 久久精品国产亚洲av高清一级| 午夜福利影视在线免费观看| 午夜福利在线免费观看网站| 黄色毛片三级朝国网站| 在线天堂中文资源库| 50天的宝宝边吃奶边哭怎么回事| 亚洲,欧美精品.| 国产精品欧美亚洲77777| 国产不卡一卡二| 国产成人啪精品午夜网站| 丰满的人妻完整版| 黑人欧美特级aaaaaa片| 深夜精品福利| 国产激情欧美一区二区| 天天操日日干夜夜撸| 女人被躁到高潮嗷嗷叫费观| 成熟少妇高潮喷水视频| 岛国毛片在线播放| 国产精品99久久99久久久不卡| 国产国语露脸激情在线看| 久热这里只有精品99| 在线观看免费高清a一片| 日韩人妻精品一区2区三区| 成人国产一区最新在线观看| 日韩欧美在线二视频 | 在线观看舔阴道视频| 国产精品久久视频播放| 亚洲精品在线观看二区| 成人18禁高潮啪啪吃奶动态图| 久久中文字幕一级| 热re99久久精品国产66热6| 欧美黄色淫秽网站| 午夜福利,免费看| 无限看片的www在线观看| tocl精华| av欧美777| 亚洲成人手机| 嫁个100分男人电影在线观看| 十八禁高潮呻吟视频| 久久国产精品人妻蜜桃| 精品免费久久久久久久清纯 | 久久久久国产一级毛片高清牌| 天堂√8在线中文| 欧美不卡视频在线免费观看 | 欧美日韩国产mv在线观看视频| 超色免费av| 色在线成人网| 一级毛片女人18水好多| 亚洲av欧美aⅴ国产| 日韩欧美国产一区二区入口| 成人18禁高潮啪啪吃奶动态图| 国产亚洲精品久久久久5区| 丝袜在线中文字幕| www.精华液| 天天添夜夜摸| 国产无遮挡羞羞视频在线观看| 亚洲综合色网址| 亚洲中文av在线| 在线观看免费视频日本深夜| 首页视频小说图片口味搜索| 国产一区二区三区综合在线观看| 水蜜桃什么品种好| e午夜精品久久久久久久| 99re6热这里在线精品视频| 欧美不卡视频在线免费观看 | 国产免费男女视频| 99香蕉大伊视频| 少妇被粗大的猛进出69影院| 国产在视频线精品| 亚洲一卡2卡3卡4卡5卡精品中文| 国产av一区二区精品久久| 国产亚洲精品一区二区www | 欧美av亚洲av综合av国产av| 国产激情欧美一区二区| 免费看十八禁软件| 国产一区有黄有色的免费视频| 中文字幕av电影在线播放| 这个男人来自地球电影免费观看| 欧美av亚洲av综合av国产av| 黄色毛片三级朝国网站| 亚洲人成电影观看| 99re在线观看精品视频| 色播在线永久视频| 如日韩欧美国产精品一区二区三区| 成年动漫av网址| 精品久久久精品久久久| 亚洲一卡2卡3卡4卡5卡精品中文| 少妇被粗大的猛进出69影院| 国产免费男女视频| bbb黄色大片| 国产不卡av网站在线观看| 欧美乱色亚洲激情| 女警被强在线播放| 亚洲,欧美精品.| 欧美日韩av久久| 国产精品亚洲av一区麻豆|