medRxiv - Pathology最新文献

{"title":"Study Protocol: Development and Retrospective Validation of an Artificial Intelligence System for Diagnostic Assessment of Prostate Biopsies","authors":"Nita Mulliqi, Anders Blilie, Xiaoyi Ji, Kelvin Szolnoky, Henrik Olsson, Matteo Titus, Geraldine Martinez Gonzalez, Sol Erika Boman, Masi Valkonen, Einar Gudlaugsson, Svein R Kjosavik, Jose Asenjo, Marcello Gambacorta, Paolo Libretti, Marcin Braun, Radzislaw Kordek, Roman Lowicki, Kristina Hotakainen, Paivi Vare, Bodil Ginnerup Pedersen, Karina Dalsgaard Sorensen, Benedicte Parm Ulhoi, Mattias Rantalainen, Pekka Ruusuvuori, Brett Delahunt, Hemamali Samaratunga, Toyonori Tsuzuki, Emilius A.M. Janssen, Lars Egevad, Kimmo Kartasalo, Martin Eklund","doi":"10.1101/2024.07.04.24309948","DOIUrl":"https://doi.org/10.1101/2024.07.04.24309948","url":null,"abstract":"Histopathological evaluation of prostate biopsies using the Gleason scoring system is critical for prostate cancer diagnosis and treatment selection. However, grading variability among pathologists can lead to inconsistent assessments, risking inappropriate treatment. Similar challenges complicate the assessment of other prognostic features like cribriform cancer morphology and perineural invasion. Many pathology departments are also facing an increasingly unsustainable workload due to rising prostate cancer incidence and a decreasing pathologist workforce coinciding with increasing requirements for more complex assessments and reporting. Digital pathology and artificial intelligence (AI) algorithms for analysing whole slide images (WSI) show promise in improving the accuracy and efficiency of histopathological assessments. Studies have demonstrated AI's capability to diagnose and grade prostate cancer comparably to expert pathologists. However, external validations on diverse data sets have been limited and often show reduced performance. Historically, there have been no well-established guidelines for AI study designs and validation methods. Diagnostic assessments of AI systems often lack pre-registered protocols and rigorous external cohort sampling, essential for reliable evidence of their safety and accuracy. This study protocol covers the retrospective validation of an AI system for prostate biopsy assessment. The primary objective of the study is to develop a high-performing and robust AI model for diagnosis and Gleason scoring of prostate cancer in core needle biopsies, and at scale evaluate whether it can generalise to fully external data from independent patients, pathology laboratories, and digitalisation platforms. The secondary objectives cover AI performance in estimating cancer extent and in detecting cribriform prostate cancer and perineural invasion. This protocol outlines the steps for data collection, predefined partitioning of data cohorts for AI model training and validation, model development, and predetermined statistical analyses, ensuring systematic development and comprehensive validation of the system. The protocol adheres to TRIPOD+AI, PIECES, CLAIM, and other relevant best practices.","PeriodicalId":501528,"journal":{"name":"medRxiv - Pathology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141571350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plasma glycosaminoglycans and cell-free DNA to discriminate benign and malignant lung diseases","authors":"Alvida Qvick, Sinisa Bratulic, Jessica Carlsson, Bianca Stenmark, Christina Karlsson, Jens Nielsen, Francesco Gatto, Gisela Helenius","doi":"10.1101/2024.07.01.24309751","DOIUrl":"https://doi.org/10.1101/2024.07.01.24309751","url":null,"abstract":"We aimed to investigate the use of free glycosaminoglycan profiles (GAGomes) and cfDNA in plasma to differentiate between lung cancer and benign lung disease. GAGs were analyzed using the MIRAM(R) Free Glycosaminoglycan Kit with ultra-high-performance liquid chromatography and electrospray ionization triple-quadrupole mass spectrometry. We detected two GAGome features, 0S chondroitin sulfate (CS) and 4S CS, with cancer-specific changes. Based on the observed GAGome changes, we devised a model to predict lung cancer. The model, named the GAGome score, could detect lung cancer with 41.2% sensitivity (95% CI: 9.2-54.2%) at 96.4% specificity (CI: 95.2-100.0%, n=113). Furthermore, we found that the GAGome score, when combined with a cfDNA test, could increase the sensitivity for lung cancer from 42.6% (95% CI: 31.7-60.6%, cfDNA alone) to 70.5% (CI: 57.4 - 81.5%) at 95% specificity (CI: 75.1-100%, n=74). Notably, the combined GAGome and cfDNA testing improved the sensitivity, especially in early stages, relative to the cfDNA alone. Our findings show that plasma GAGome profiles can enhance cfDNA testing performance, highlighting the applicability of a multiomics approach in lung cancer diagnostics.","PeriodicalId":501528,"journal":{"name":"medRxiv - Pathology","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison between 70% ethyl alcohol and 10% formalin as fixative mediums in surgical cooperation campaigns: a pilot study","authors":"Javier Arredondo Montero, Elena Carracedo Vega, Paula Ortola Fortes, Monica Bronte Anaut, Yerani Ruiz de Azua-Ciria, Adriana Fernandez-Ariza, Alejandra Moreno Iberico, Jessica Paulina Rodriguez, Carlos Bardaji Pascual, Rosa Guarch Troyas","doi":"10.1101/2024.06.19.24308894","DOIUrl":"https://doi.org/10.1101/2024.06.19.24308894","url":null,"abstract":"Background: The lack of adequate resources in international cooperation limits the study of anatomopathological specimens. The literature on potentially inexpensive and available fixation media is scarce.\u0000Material and methods: Our surgical team prospectively collected specimens during cooperation campaigns developed in Senegal. Lesions were fixed in parallel in 10% formalin (FF) and 70% ethyl alcohol (AF). Hematoxylin and eosin sections (HE) and immunohistochemistry (IHC) techniques were performed. Images were anonymized and assessed by two senior and two junior pathologists, who evaluated the quality of staining and diagnostic feasibility using an anonymized questionnaire.\u0000Results: Three surgical specimens were included: 1 lymph node (3 HE, 4 IHC), one seborrheic keratosis (2 HE, 5 IHC), and one branchial remnant (2 HE, 2 IHC). Fixation times were similar in all the specimens (10-13 days). All FF HE were diagnostic. AF H&E was 100% diagnostic in the 5/7 sections and 75% in the remaining sections. In most cases, pathologists preferred FF. CK7, P40, EMA, CKAE1/AE3, and TTF1 were 100% diagnostic in both groups. CD20, CD45, and EMA were 100% diagnostic (FF) and 75% diagnostic (AF). CD10 was 75% diagnostic (FF) and 25% diagnostic (AF). BCL6 was 75% diagnostic (FF) and 100% diagnostic (AF). IHC preferences were inconsistent.\u0000Conclusions: 70% ethyl alcohol has a worse fixation profile than 10% formalin but allows diagnosis in most cases. The immunoreactivity observed is variable depending on the tissue and the stain used. Based on these findings, it can be considered an inexpensive, readily available, and potentially helpful fixation medium for diagnosis in developing countries where surgical cooperation campaigns are conducted. Nevertheless, future studies of larger sample sizes and characterizing other histologic subtypes are needed to confirm these findings.","PeriodicalId":501528,"journal":{"name":"medRxiv - Pathology","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmarking Deep Learning-based Image Retrieval of Oral Tumor Histology","authors":"Ranny Rahaningrum Herdiantoputri, Daisuke Komura, Mieko Ochi, Yuki Fukawa, Kou Kayamori, Maiko Tsuchiya, Yoshinao Kikuchi, Tetsuo Ushiku, Tohru Ikeda, Shumpei Ishikawa","doi":"10.1101/2024.05.30.24308181","DOIUrl":"https://doi.org/10.1101/2024.05.30.24308181","url":null,"abstract":"Oral tumors necessitate a dependable computer-assisted pathological diagnosis system considering their rarity and diversity. A content-based image retrieval (CBIR) system using deep neural networks has been successfully devised for digital pathology. No CBIR system for oral pathology has been investigated because of the lack of an extensive image database and feature extractors tailored to oral pathology. This study uses a large CBIR database constructed from 30 categories of oral tumors to compare deep learning methods as feature extractors. The highest average area under the receiver operating curve (AUC) was achieved by models trained on database images using self-supervised learning (SSL) methods (0.900 with SimCLR; 0.897 with TiCo). The generalizability of the models was validated using query images from the same cases taken with smartphones. When smartphone images were tested as queries, both models yielded the highest mean AUC (0.871 with SimCLR and 0.857 with TiCo). We ensured the retrieved image result would be easily observed by evaluating the top-10 mean accuracy and checking for an exact diagnostic category and its differential diagnostic categories. Therefore, training deep learning models with SSL methods using image data specific to the target site is beneficial for CBIR tasks in oral tumor histology to obtain histologically meaningful results and high performance. This result provides insight into the effective development of a CBIR system to help improve the accuracy and speed of histopathology diagnosis and advance oral tumor research in the future.","PeriodicalId":501528,"journal":{"name":"medRxiv - Pathology","volume":"310 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141256793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analytical and Clinical Validation of AIM-NASH: A Digital Pathology Tool for Artificial Intelligence-based Measurement of Nonalcoholic Steatohepatitis Histology","authors":"Hanna Pulaski, Stephen A. Harrison, Shraddha S. Mehta, Arun J Sanyal, Marlena C. Vitali, Laryssa C. Manigat, Hypatia Hou, Susan P. Madasu Christudoss, Sara M. Hoffman, Adam Stanford-Moore, Robert Egger, Jonathan Glickman, Murray Resnick, Neel Patel, Cristin E. Taylor, Robert P. Myers, Chuhan Chung, Scott D. Patterson, Anne-Sophie Sejling, Anne Minnich, Vipul Baxi, G. Mani Subramaniam, Quentin M. Anstee, Rohit Loomba, Vlad Ratziu, Michael C Montalto, Andrew H Beck, Katy Wack","doi":"10.1101/2024.05.29.24308109","DOIUrl":"https://doi.org/10.1101/2024.05.29.24308109","url":null,"abstract":"Metabolic-dysfunction associated steatohepatitis (MASH) is a major cause of liver-related morbidity and mortality, yet treatment options are limited. Manual scoring of liver biopsies, currently the gold standard for clinical trial enrollment and endpoint assessment, suffers from high reader variability. This study represents the most comprehensive multi-site analytical and clinical validation of an AI-based pathology system, Artificial Intelligence-based Measurement of Nonalcoholic Steatohepatitis (AIM-NASH), to assist pathologists in MASH trial histology scoring. AIM-NASH demonstrated high repeatability and reproducibility compared to manual scoring. AIM-NASH-assisted reads by expert MASH pathologists were superior to unassisted reads in accurately assessing inflammation, ballooning, NAS >= 4 with >=1 in each score category, and MASH resolution, while maintaining non-inferiority in steatosis and fibrosis assessment. These findings suggest AIM-NASH could mitigate reader variability, providing a more reliable assessment of therapeutics in MASH clinical trials.","PeriodicalId":501528,"journal":{"name":"medRxiv - Pathology","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141194923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Study of Explainability Methods for Whole Slide Classification of Lymph Node Metastases using Vision Transformers","authors":"Jens Rahnfeld, Mehdi Naouar, Gabriel Kalweit, Joschka Boedecker, Estelle Dubruc, Maria Kalweit","doi":"10.1101/2024.05.07.24306815","DOIUrl":"https://doi.org/10.1101/2024.05.07.24306815","url":null,"abstract":"Recent advancements in deep learning (DL), such as transformer networks, have shown promise in enhancing the performance of medical image analysis. In pathology, automated whole slide imaging (WSI) has transformed clinical workflows by streamlining routine tasks and diagnostic and prognostic support. However, the lack of transparency of DL models, often described as “black boxes”, poses a significant barrier to their clinical adoption. This necessitates the use of explainable AI methods (xAI) to clarify the decision-making processes of the models. Heatmaps can provide clinicians visual representations that highlight areas of interest or concern for the prediction of the specific model. Generating them from deep neural networks, especially from vision transformers, is non-trivial, as typically their self-attention mechanisms can lead to overconfident artifacts. The aim of this work is to evaluate current xAI methods for transformer models in order to assess which yields the best heatmaps in the histopathological context. Our study undertakes a comparative analysis for classifying a publicly available dataset comprising of N=400 WSIs of lymph node metastases of breast cancer patients. Our findings indicate that heatmaps calculated from Attention Rollout and Integrated Gradients are limited by artifacts and in quantitative performance. In contrast, removal-based methods like RISE and ViT-Shapley exhibit better qualitative attribution maps, showing better results in the well-known interpretability metrics for insertion and deletion. In addition, ViT-Shapley shows faster runtime and the most promising, reliable and practical heatmaps. Incorporating the heatmaps generated from approximate Shapley values in pathology reports could help to integrate xAI in the clinical workflow and increase trust in a scalable manner.","PeriodicalId":501528,"journal":{"name":"medRxiv - Pathology","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140940091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adoption of AI-Powered Chatbots with Large Language Models by Pathologists","authors":"Andrey Bychkov, Thiyaphat Laohawetwanit, Daniel Gomes Pinto","doi":"10.1101/2024.04.05.24305405","DOIUrl":"https://doi.org/10.1101/2024.04.05.24305405","url":null,"abstract":"<strong>Aims</strong> The study aimed to investigate the adoption and perception of artificial intelligence (AI) chatbots, particularly those powered by large language models (LLMs), among pathologists worldwide. It explored the extent of their engagement with these technologies, identifying potential impacts on their professional practices.","PeriodicalId":501528,"journal":{"name":"medRxiv - Pathology","volume":"119 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140590361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estrogen Receptor Gene Expression Prediction from H&E Whole Slide Images","authors":"Anvita A. Srinivas, Ronnachai Jaroensri, Ellery Wulczyn, James H. Wren, Elaine E. Thompson, Niels Olson, Fabien Beckers, Melissa Miao, Yun Liu, Po-Hsuan Cameron Chen, David F. Steiner","doi":"10.1101/2024.04.05.24302951","DOIUrl":"https://doi.org/10.1101/2024.04.05.24302951","url":null,"abstract":"Gene expression profiling (GEP) provides valuable information for the care of breast cancer patients. However, the test itself is expensive and can take a long time to process. In contrast, microscopic examination of hematoxylin and eosin (H&amp;E) stained tissue is inexpensive, fast, and integrated into the standard of care. This work explores the possibility of predicting <em>ESR1</em> gene expression from H&amp;E images, and its use in predicting clinical variables and patient outcomes. We utilized a weakly supervised method to train a deep learning model to predict <em>ESR1</em> expression from whole slide images, and achieved 0.57 [95% CI: 0.46, 0.67] Pearson’s correlation with the ground truth value. Our <em>ESR1</em> expression prediction achieved an AUROC of 0.81 [0.74, 0.87] in predicting clinical ER status obtained using an immunohistochemistry staining technique, and a c-index of 0.59 [0.52, 0.65] in predicting progression-free interval for the patients in our cohort. This work further demonstrates the potential to infer gene expression from H&amp;E stained images in a manner that shows meaningful associations with clinical variables. Because obtaining H&amp;E stained images is substantially easier and faster than genetic testing, the capability to derive molecular genetic information from these images may increase access to this type of information for patient risk stratification and provide research insights into molecular-morphological associations.","PeriodicalId":501528,"journal":{"name":"medRxiv - Pathology","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140590721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Overexpression of Fibroblast Activation Protein (FAP) in stroma of proliferative inflammatory atrophy (PIA) and primary adenocarcinoma of the prostate","authors":"Fernanda Caramella-Pereira, Qizhi Zheng, Jessica L. Hicks, Sujayita Roy, Tracy Jones, Martin Pomper, Lizamma Antony, Alan K. Meeker, Srinivasan Yegnasubramanian, Angelo M. De Marzo, W. Nathaniel Brennen","doi":"10.1101/2024.04.04.24305338","DOIUrl":"https://doi.org/10.1101/2024.04.04.24305338","url":null,"abstract":"Fibroblast activation protein (FAP) is a serine protease upregulated at sites of tissue remodeling and cancer that represents a promising therapeutic and molecular imaging target. In prostate cancer, studies of FAP expression using tissue microarrays are conflicting, such that its clinical potential is unclear. Furthermore, little is known regarding FAP expression in benign prostatic tissues. Here we demonstrated, using a novel iterative multiplex IHC assay in standard tissue sections, that FAP was nearly absent in normal regions, but was increased consistently in regions of proliferative inflammatory atrophy (PIA). In carcinoma, FAP was expressed in all cases, but was highly heterogeneous. High FAP levels were associated with increased pathological stage and cribriform morphology. We verified that FAP levels in cancer correlated with CD163+ M2 macrophage density. In this first report to quantify FAP protein in benign prostate and primary tumors, using standard large tissue sections, we clarify that FAP is present in all primary prostatic carcinomas, supporting its potential clinical relevance. The finding of high levels of FAP within PIA supports the injury/regeneration model for its pathogenesis and suggests that it harbors a protumorigenic stroma. Yet, high levels of FAP in benign regions could lead to false positive FAP-based molecular imaging results in clinically localized prostate cancer.","PeriodicalId":501528,"journal":{"name":"medRxiv - Pathology","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140590336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel AI-based Score for Assessing the Prognostic Value of Intra-Epithelial Lymphocytes in Oral Epithelial Dysplasia","authors":"Adam J Shephard, Hanya Mahmood, Shan E Ahmed Raza, Syed Ali Khurram, Nasir M Rajpoot","doi":"10.1101/2024.03.27.24304967","DOIUrl":"https://doi.org/10.1101/2024.03.27.24304967","url":null,"abstract":"Oral epithelial dysplasia (OED) poses a significant clinical challenge due to its potential for malignant transformation and the lack of reliable prognostic markers. Current grading systems for OED may not be reliable for prediction of malignant transformation and suffer from considerable inter- and intra-rater variability, potentially leading to suboptimal treatment decisions. Recent studies have highlighted the potential prognostic significance of peri-epithelial lymphocytes (PELs) in malignant transformation, with suggestions that intra-epithelial lymphocytes (IELs) may also play a role. In this study, we propose a novel artificial intelligence (AI) based IEL score from Haematoxylin and Eosin (H&amp;E) stained Whole Slide Images (WSIs) of OED tissue slides. We further determine the prognostic value of our IEL score on a large digital dataset of 219 OED WSIs (acquired using three different scanners), compared to pathologist-led clinical grading. Notably, despite IELs not being incorporated into the current WHO grading system for OED, our findings suggest that IEL scores carry significant prognostic value that were shown to further improve both the Binary/WHO grading systems in multivariate analyses. This underscores the potential importance of IELs, and by extension our IEL score, as prognostic indicators in OED. Further validation through prospective multi-centric studies is warranted to confirm the clinical utility of the proposed IEL score and its integration into existing grading systems for OED.","PeriodicalId":501528,"journal":{"name":"medRxiv - Pathology","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140324397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}