Journal of Pathology Informatics最新文献

Digital mapping of resected cancer specimens: The visual pathology report 对切除的癌症标本进行数字绘图：可视病理报告

Journal of Pathology Informatics Pub Date : 2024-09-28 DOI: 10.1016/j.jpi.2024.100399

{"title":"Digital mapping of resected cancer specimens: The visual pathology report","authors":"","doi":"10.1016/j.jpi.2024.100399","DOIUrl":"10.1016/j.jpi.2024.100399","url":null,"abstract":"<div><h3>Background</h3><div>The current standard-of-care pathology report relies only on lengthy written text descriptions without a visual representation of the resected cancer specimen. This study demonstrates the feasibility of incorporating virtual, three-dimensional (3D) visual pathology reports to improve communication of final pathology reporting.</div></div><div><h3>Materials and methods</h3><div>Surgical specimens are 3D scanned and virtually mapped alongside the pathology team to replicate grossing. The 3D specimen maps are incorporated into a hybrid visual pathology report which displays the resected specimen and sampled margins alongside gross measurements, tumor characteristics, and microscopic diagnoses.</div></div><div><h3>Results</h3><div>Visual pathology reports were created for 10 head and neck cancer cases. Each report concisely communicated information from the final pathology report in a single page and contained significantly fewer words (293.4 words) than standard written pathology reports (850.1 words, <em>p</em> < 0.01).</div></div><div><h3>Conclusions</h3><div>We establish the feasibility of a novel visual pathology report that includes an annotated visual model of the resected cancer specimen in place of lengthy written text of standard of care head and neck cancer pathology reports.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A precise machine learning model: Detecting cervical cancer using feature selection and explainable AI 精确的机器学习模型：利用特征选择和可解释人工智能检测宫颈癌

Journal of Pathology Informatics Pub Date : 2024-09-26 DOI: 10.1016/j.jpi.2024.100398

{"title":"A precise machine learning model: Detecting cervical cancer using feature selection and explainable AI","authors":"","doi":"10.1016/j.jpi.2024.100398","DOIUrl":"10.1016/j.jpi.2024.100398","url":null,"abstract":"<div><div>Cervical cancer is a cancer that remains a significant global health challenge all over the world. Due to improper screening in the early stages, and healthcare disparities, a large number of women are suffering from this disease, and the mortality rate increases day by day. Hence, in these studies, we presented a precise approach utilizing six different machine learning models (decision tree, logistic regression, naïve bayes, random forest, k nearest neighbors, support vector machine), which can predict the early stage of cervical cancer by analysing 36 risk factor attributes of 858 individuals. In addition, two data balancing techniques—Synthetic Minority Oversampling Technique and Adaptive Synthetic Sampling—were used to mitigate the data imbalance issues. Furthermore, Chi-square and Least Absolute Shrinkage and Selection Operator are two distinct feature selection processes that have been applied to evaluate the feature rank, which are mostly correlated to identify the particular disease, and also integrate an explainable artificial intelligence technique, namely Shapley Additive Explanations, for clarifying the model outcome. The applied machine learning model outcome is evaluated by performance evaluation matrices, namely accuracy, sensitivity, specificity, precision, f1-score, false-positive rate and false-negative rate, and area under the Receiver operating characteristic curve score. The decision tree outperformed in Chi-square feature selection with outstanding accuracy with 97.60%, 98.73% sensitivity, 80% specificity, and 98.73% precision, respectively. During the data imbalance, DT performed 97% accuracy, 99.35% sensitivity, 69.23% specificity, and 97.45% precision. This research is focused on developing diagnostic frameworks with automated tools to improve the detection and management of cervical cancer, as well as on helping healthcare professionals deliver more efficient and personalized care to their patients.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ViCE: An automated and quantitative program to assess intestinal tissue morphology ViCE：自动定量评估肠道组织形态的程序

Journal of Pathology Informatics Pub Date : 2024-09-13 DOI: 10.1016/j.jpi.2024.100397

{"title":"ViCE: An automated and quantitative program to assess intestinal tissue morphology","authors":"","doi":"10.1016/j.jpi.2024.100397","DOIUrl":"10.1016/j.jpi.2024.100397","url":null,"abstract":"<div><h3>Background and objective</h3><div>The tissue morphology of the intestinal surface is architecturally complex with finger-like projections called villi, and glandular structures called crypts. The ratio of villus height-to-crypt depth ratio (Vh:Cd) is used to quantitatively assess disease severity and response to therapy for intestinal enteropathies, such as celiac disease and is currently quantified manually. Given the time required, manual Vh:Cd measurements have largely been limited to clinical trials and are not used widely in clinical practice. We developed ViCE (Villus Crypt Evaluator), a user-friendly software that automatically quantifies histological parameters in standard hematoxylin and eosin-stained intestinal biopsies.</div></div><div><h3>Methods</h3><div>ViCE is based on mathematical morphology operations and is scale and staining agnostic. It evaluates tissue orientation, identifies geometrical structure, and outputs key tissue measurements.</div></div><div><h3>Results</h3><div>The output measurements of Vh:Cd are concordant with manual quantifications across multiple datasets.</div></div><div><h3>Conclusions</h3><div>The underlying mathematical morphological approach for ViCE is robust, and reproducible and easily adaptable for measurement of morphological features in other tissues.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep feature batch correction using ComBat for machine learning applications in computational pathology 利用 ComBat 对计算病理学中的机器学习应用进行深度特征批量校正

Journal of Pathology Informatics Pub Date : 2024-09-12 DOI: 10.1016/j.jpi.2024.100396

{"title":"Deep feature batch correction using ComBat for machine learning applications in computational pathology","authors":"","doi":"10.1016/j.jpi.2024.100396","DOIUrl":"10.1016/j.jpi.2024.100396","url":null,"abstract":"<div><h3>Background</h3><div>Developing artificial intelligence (AI) models for digital pathology requires large datasets from multiple sources. However, without careful implementation, AI models risk learning confounding site-specific features in datasets instead of clinically relevant information, leading to overestimated performance, poor generalizability to real-world data, and potential misdiagnosis.</div></div><div><h3>Methods</h3><div>Whole-slide images (WSIs) from The Cancer Genome Atlas (TCGA) colon (COAD), and stomach adenocarcinoma datasets were selected for inclusion in this study. Patch embeddings were obtained using three feature extraction models, followed by ComBat harmonization. Attention-based multiple instance learning models were trained to predict tissue-source site (TSS), as well as clinical and genetic attributes, using raw, Macenko normalized, and Combat-harmonized patch embeddings.</div></div><div><h3>Results</h3><div>TSS prediction achieved high accuracy (AUROC > 0.95) with all three feature extraction models. ComBat harmonization significantly reduced the AUROC for TSS prediction, with mean AUROCs dropping to approximately 0.5 for most models, indicating successful mitigation of batch effects (e.g., CCL-ResNet50 in TCGA-COAD: Pre-ComBat AUROC = 0.960, Post-ComBat AUROC = 0.506, <em>p <</em> 0.001). Clinical attributes associated with TSS, such as race and treatment response, showed decreased predictability post-harmonization. Notably, the prediction of genetic features like MSI status remained robust after harmonization (e.g., MSI in TCGA-COAD: Pre-ComBat AUROC = 0.667, Post-ComBat AUROC = 0.669, <em>p</em>=0.952), indicating the preservation of true histological signals.</div></div><div><h3>Conclusion</h3><div>ComBat harmonization of deep learning-derived histology features effectively reduces the risk of AI models learning confounding features in WSIs, ensuring more reliable performance estimates. This approach is promising for the integration of large-scale digital pathology datasets.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LVI-PathNet: Segmentation-classification pipeline for detection of lymphovascular invasion in whole slide images of lung adenocarcinoma LVI-PathNet：用于检测肺腺癌全切片图像中淋巴管侵犯的分割-分类管道

Journal of Pathology Informatics Pub Date : 2024-08-30 DOI: 10.1016/j.jpi.2024.100395

{"title":"LVI-PathNet: Segmentation-classification pipeline for detection of lymphovascular invasion in whole slide images of lung adenocarcinoma","authors":"","doi":"10.1016/j.jpi.2024.100395","DOIUrl":"10.1016/j.jpi.2024.100395","url":null,"abstract":"<div><p>Lymphovascular invasion (LVI) in lung cancer is a significant prognostic factor that influences treatment and outcomes, yet its reliable detection is challenging due to interobserver variability. This study aims to develop a deep learning model for LVI detection using whole slide images (WSIs) and evaluate its effectiveness within a pathologist's information system. Experienced pathologists annotated blood vessels and invading tumor cells in 162 WSIs of non-mucinous lung adenocarcinoma sourced from two external and one internal datasets. Two models were trained to segment vessels and identify images with LVI features. DeepLabV3+ model achieved an Intersection-over-Union of 0.8840 and an area under the receiver operating characteristic curve (AUC-ROC) of 0.9869 in vessel segmentation. For LVI classification, the ensemble model achieved a F1-score of 0.9683 and an AUC-ROC of 0.9987. The model demonstrated robustness and was unaffected by variations in staining and image quality. The pilot study showed that pathologists' evaluation time for LVI detecting decreased by an average of 16.95%, and by 21.5% in “hard cases”. The model facilitated consistent diagnostic assessments, suggesting potential for broader applications in detecting pathological changes in blood vessels and other lung pathologies.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000348/pdfft?md5=9a1e9217891b1539c144069b2cb2703f&pid=1-s2.0-S2153353924000348-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142238092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Globalization of a telepathology network with artificial intelligence applications in Colombia: The GLORIA program study protocol 哥伦比亚应用人工智能的远程病理网络全球化：GLORIA 计划研究协议

Journal of Pathology Informatics Pub Date : 2024-08-15 DOI: 10.1016/j.jpi.2024.100394

{"title":"Globalization of a telepathology network with artificial intelligence applications in Colombia: The GLORIA program study protocol","authors":"","doi":"10.1016/j.jpi.2024.100394","DOIUrl":"10.1016/j.jpi.2024.100394","url":null,"abstract":"<div><p>In Colombia, cancer is recognized as a high-cost pathology by the national government and the Colombian High-Cost Disease Fund. As of 2020, the situation is most critical for adult cancer patients, particularly those under public healthcare and residing in remote regions of the country. The highest lag time for a diagnosis was observed for cervical cancer (79.13 days), followed by prostate (77.30 days), and breast cancer (70.25 days). Timely and accurate histopathological reporting plays a vital role in the diagnosis of cancer. In recent years, digital pathology has been globally implemented as a technological tool in two main areas: telepathology (TP) and computational pathology. TP has been shown to improve rapid and timely diagnosis in anatomic pathology by facilitating interaction between general laboratories and specialized pathologists worldwide through information and telecommunication technologies. Computational pathology provides diagnostic and prognostic assistance based on histopathological patterns, molecular, and clinical information, aiding pathologists in making more accurate diagnoses. We present the study protocol of the GLORIA digital pathology network, a pioneering initiative, and national grant-approved program aiming to design and pilot a Colombian digital pathology transformation focused on TP and computational pathology, in response to the general needs of pathology laboratories for diagnosing complex malignant tumors. The study protocol describes the design of a TP network to expand oncopathology services across all Colombian regions. It also describes an artificial intelligence proposal for lung cancer, one of Colombia's most prevalent cancers, and a freely accessible national histopathological image database to facilitate image analysis studies.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000336/pdfft?md5=861e86fc08dee64d7bef49370be8286b&pid=1-s2.0-S2153353924000336-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142075800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-diagnostic time in digital pathology: An empirical study over 10 years 数字病理学的非诊断时间：十年实证研究

Journal of Pathology Informatics Pub Date : 2024-08-05 DOI: 10.1016/j.jpi.2024.100393

引用次数: 0

Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency 工程特征嵌入与深度学习的结合：改善骨髓细胞分类和模型透明度的新策略

Journal of Pathology Informatics Pub Date : 2024-07-03 DOI: 10.1016/j.jpi.2024.100390

{"title":"Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency","authors":"","doi":"10.1016/j.jpi.2024.100390","DOIUrl":"10.1016/j.jpi.2024.100390","url":null,"abstract":"<div><p>Cytomorphology evaluation of bone marrow cell is the initial step to diagnose different hematological diseases. This assessment is still manually performed by trained specialists, who may be a bottleneck within the clinical process. Deep learning algorithms are a promising approach to automate this bone marrow cell evaluation. These artificial intelligence models have focused on limited cell subtypes, mainly associated to a particular disease, and are frequently presented as black boxes. The herein introduced strategy presents an engineered feature representation, the region-attention embedding, which improves the deep learning classification performance of a cytomorphology with 21 bone marrow cell subtypes. This embedding is built upon a specific organization of cytology features within a squared matrix by distributing them after pre-segmented cell regions, i.e., cytoplasm, nucleus, and whole-cell. This novel cell image representation, aimed to preserve spatial/regional relations, is used as input of the network. Combination of region-attention embedding and deep learning networks (Xception and ResNet50) provides local relevance associated to image regions, adding up interpretable information to the prediction. Additionally, this approach is evaluated in a public database with the largest number of cell subtypes (21) by a thorough evaluation scheme with three iterations of a 3-fold cross-validation, performed in 80% of the images (<em>n</em> = 89,484), and a testing process in an unseen set of images composed by the remaining 20% of the images (<em>n</em> = 22,371). This evaluation process demonstrates the introduced strategy outperforms previously published approaches in an equivalent validation set, with a f1-score of 0.82, and presented competitive results on the unseen data partition with a f1-score of 0.56.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000294/pdfft?md5=87a5b2e97447248282a9f8d40bb281e3&pid=1-s2.0-S2153353924000294-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Validation of AI-assisted ThinPrep® Pap test screening using the GeniusTM Digital Diagnostics System 使用 GeniusTM 数字诊断系统对人工智能辅助 ThinPrep® Pap 测试筛查进行验证

Journal of Pathology Informatics Pub Date : 2024-07-02 DOI: 10.1016/j.jpi.2024.100391

{"title":"Validation of AI-assisted ThinPrep® Pap test screening using the GeniusTM Digital Diagnostics System","authors":"","doi":"10.1016/j.jpi.2024.100391","DOIUrl":"10.1016/j.jpi.2024.100391","url":null,"abstract":"<div><p>Advances in whole-slide imaging and artificial intelligence present opportunities for improvement in Pap test screening. To date, there have been limited studies published regarding how best to validate newer AI-based digital systems for screening Pap tests in clinical practice. In this study, we validated the Genius™ Digital Diagnostics System (Hologic) by comparing the performance to traditional manual light microscopic diagnosis of ThinPrep<strong>®</strong> Pap test slides. A total of 319 ThinPrep<strong>®</strong> Pap test cases were prospectively assessed by six cytologists and three cytopathologists by light microscopy and digital evaluation and the results compared to the original ground truth Pap test diagnosis. Concordance with the original diagnosis was significantly different by digital and manual light microscopy review when comparing across: (i) exact Bethesda System diagnostic categories (62.1% vs 55.8%, respectively, <em>p</em> = 0.014), (ii) condensed diagnostic categories (76.8% vs 71.5%, respectively, <em>p</em> = 0.027), and (iii) condensed diagnoses based on clinical management (71.5% vs 65.2%, respectively, <em>p</em> = 0.017). Time to evaluate cases was shorter for digital (M = 3.2 min, SD = 2.2) compared to manual (M = 5.9 min, SD = 3.1) review (t(352) = 19.44, <em>p</em> < 0.001, Cohen's d = 1.035, 95% CI [0.905, 1.164]). Not only did our validation study demonstrate that AI-based digital Pap test evaluation had improved diagnostic accuracy and reduced screening time compared to light microscopy, but that participants reported a positive experience using this system.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000300/pdfft?md5=f678b76ba4ddf0bb5fbfba56b65df94c&pid=1-s2.0-S2153353924000300-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141639228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An explainable AI-based blood cell classification using optimized convolutional neural network 利用优化的卷积神经网络实现基于人工智能的可解释血细胞分类

Journal of Pathology Informatics Pub Date : 2024-07-02 DOI: 10.1016/j.jpi.2024.100389

{"title":"An explainable AI-based blood cell classification using optimized convolutional neural network","authors":"","doi":"10.1016/j.jpi.2024.100389","DOIUrl":"10.1016/j.jpi.2024.100389","url":null,"abstract":"<div><p>White blood cells (WBCs) are a vital component of the immune system. The efficient and precise classification of WBCs is crucial for medical professionals to diagnose diseases accurately. This study presents an enhanced convolutional neural network (CNN) for detecting blood cells with the help of various image pre-processing techniques. Various image pre-processing techniques, such as padding, thresholding, erosion, dilation, and masking, are utilized to minimize noise and improve feature enhancement. Additionally, performance is further enhanced by experimenting with various architectural structures and hyperparameters to optimize the proposed model. A comparative evaluation is conducted to compare the performance of the proposed model with three transfer learning models, including Inception V3, MobileNetV2, and DenseNet201.The results indicate that the proposed model outperforms existing models, achieving a testing accuracy of 99.12%, precision of 99%, and F1-score of 99%. In addition, We utilized SHAP (Shapley Additive explanations) and LIME (Local Interpretable Model-agnostic Explanations) techniques in our study to improve the interpretability of the proposed model, providing valuable insights into how the model makes decisions. Furthermore, the proposed model has been further explained using the Grad-CAM and Grad-CAM++ techniques, which is a class-discriminative localization approach, to improve trust and transparency. Grad-CAM++ performed slightly better than Grad-CAM in identifying the predicted area's location. Finally, the most efficient model has been integrated into an end-to-end (E2E) system, accessible through both web and Android platforms for medical professionals to classify blood cell.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000282/pdfft?md5=357d6d2314681f04709e94998615c5a1&pid=1-s2.0-S2153353924000282-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141708134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0