Journal of Pathology Informatics最新文献

{"title":"PathVLM-Eval: Evaluation of open vision language models in histopathology","authors":"Nauman Ullah Gilal ,&nbsp;Rachida Zegour ,&nbsp;Khaled Al-Thelaya ,&nbsp;Erdener Özer ,&nbsp;Marco Agus ,&nbsp;Jens Schneider ,&nbsp;Sabri Boughorbel","doi":"10.1016/j.jpi.2025.100455","DOIUrl":"10.1016/j.jpi.2025.100455","url":null,"abstract":"<div><div>The emerging trend of vision language models (VLMs) has introduced a new paradigm in artificial intelligence (AI). However, their evaluation has predominantly focused on general-purpose datasets, providing a limited understanding of their effectiveness in specialized domains. Medical imaging, particularly digital pathology, could significantly benefit from VLMs for histological interpretation and diagnosis, enabling pathologists to use a complementary tool for faster morecomprehensive reporting and efficient healthcare service. In this work, we are interested in benchmarking VLMs on histopathology image understanding. We present an extensive evaluation of recent VLMs on the PathMMU dataset, a domain-specific benchmark that includes subsets such as PubMed, SocialPath, and EduContent. These datasets feature diverse formats, notably multiple-choice questions (MCQs), designed to aid pathologists in diagnostic reasoning and support professional development initiatives in histopathology. Utilizing VLMEvalKit, a widely used open-source evaluation framework—we bring publicly available pathology datasets under a single evaluation umbrella, ensuring unbiased and contamination-free assessments of model performance. Our study conducts extensive zero-shot evaluations of more than 60 state-of-the-art VLMs, including LLaVA, Qwen-VL, Qwen2-VL, InternVL, Phi3, Llama3, MOLMO, and XComposer series, significantly expanding the range of evaluated models compared to prior literature. Among the tested models, Qwen2-VL-72B-Instruct achieved superior performance with an average score of 63.97% outperforming other models across all PathMMU subsets. We conclude that this extensive evaluation will serve as a valuable resource, fostering the development of next-generation VLMs for analyzing digital pathology images. Additionally, we have released the complete evaluation results on our leaderboard PathVLM-Eval: <span><span>https://huggingface.co/spaces/gilalnauman/PathVLMs</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100455"},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596403","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 usability assessment of computer input devices for navigating digital whole slide images","authors":"John Rogers ,&nbsp;Yuvanesh Vedaraju ,&nbsp;Jim Hsu ,&nbsp;Jacob Kinskey ,&nbsp;S. Wesley Long ,&nbsp;Paul Christensen","doi":"10.1016/j.jpi.2025.100449","DOIUrl":"10.1016/j.jpi.2025.100449","url":null,"abstract":"<div><div>Labs worldwide are increasingly adopting digital pathology due to its ability to facilitate electronic slide distribution and sharing, integration with artificial intelligence tools, and the various workflow improvements enabled by a digital interface. The availability of efficient controls for navigating whole slide images is an important aspect of successful implementation. In this usability study of controller devices, we configured our whole slide image viewer to support navigation using 10 different methodologies, including standard click-and-drag mouse movement, keyboard panning, videogame controllers, and more. Thirty-eight practicing pathologists and trainees volunteered to use these devices and provide feedback. The videogame console gamepad, SpaceMouse Pro, and large trackball emerged as the most preferred devices. After testing each device, 63% of participants indicated that they would need an alternative to standard mouse click-and-drag for effective and efficient case sign-out. These results highlight non-traditional image navigation devices as valuable options in digital pathology implementation and suggest an opportunity for image management systems to differentiate themselves in a competitive marketplace.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100449"},"PeriodicalIF":0.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330065","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":"Advancing open-source visual analytics in digital pathology: A systematic review of tools, trends, and clinical applications","authors":"Zahoor Ahmad ,&nbsp;Mahmood Alzubaidi ,&nbsp;Khaled Al-Thelaya ,&nbsp;Corrado Calí ,&nbsp;Sabri Boughorbel ,&nbsp;Jens Schneider ,&nbsp;Marco Agus","doi":"10.1016/j.jpi.2025.100454","DOIUrl":"10.1016/j.jpi.2025.100454","url":null,"abstract":"<div><div>Histopathology is critical for disease diagnosis, and digital pathology has transformed traditional workflows by digitizing slides, enabling remote consultations, and enhancing analysis through computational methods. In this systematic review, we evaluated open-source visual analytics abilities in digital pathology by screening 254 studies and including 52 that met predefined criteria. Our analysis reveals that these solutions—comprising abilities (<em>n</em> = 29), software (<em>n</em> = 13), and frameworks (<em>n</em> = 10)—are predominantly applied in cancer research (e.g., breast, colon, ovarian, and prostate cancers) and primarily utilize whole slide images. Key contributions include advanced image analysis capabilities (as demonstrated by platforms such as QuPath and CellProfiler) and the integration of machine learning for diagnostic support, treatment planning, automated tissue segmentation, and collaborative research. Despite these promising advancements, challenges such as high computational demands, limited external validation, and difficulties integrating into clinical workflows remain. Future research should focus on establishing standardized validation frameworks, aligning with regulatory requirements, and enhancing user-centric designs to promote robust, interoperable solutions for clinical adoption.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100454"},"PeriodicalIF":0.0,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298275","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":"Automated determination of tumor cell percentages in whole slide images: A nuclear classification study for molecular pathology tests","authors":"Yunus Baran Kök,&nbsp;Işın Doğan Ekici,&nbsp;Ümit İnce","doi":"10.1016/j.jpi.2025.100451","DOIUrl":"10.1016/j.jpi.2025.100451","url":null,"abstract":"<div><div>Calculation of tumor cell percentage, a critical pre-analytical component in molecular pathology, is typically performed by pathologists estimating a ratio. This semiquantitative approach can lead to inter-observer variability, potentially adversely affecting patient management and treatment outcomes. In era of digital pathology, it became crucial to automate such assessments for more objective approach. This study aims to contribute to this process by developing a model for automated calculation of tumor cell percentage in high-grade serous carcinomas. Tumor containing hematoxylin-eosin slides from 100 patients were divided into training, validation, and test groups. Slides were digitalized and placed in QuPath platform. Image patches were obtained from WSIs of training and validation sets, and were stitched together to form digital microarrays by using ImageJ extension. Subsequently, nuclear detection and segmentation were performed using StarDist software, and tumor and non-tumor cell nuclei were classified using annotations. For binary classifier, random forest algorithm was selected. With hyperparameter tuning, many pre-models were assessed by cross-validation and most suitable pre-model was selected to apply to test set. Testing was performed on WSIs and criterion standard was based on corresponding immunohistochemistry (p53 or PAX8) slides which showed diffuse positivitity for tumor cells. Performance of model was measured using regression metrics. This study is designed to perform and assess a classifier in whole slide images to reflect real-world experience.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100451"},"PeriodicalIF":0.0,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696478","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":"Proceedings of the Association for Pathology Informatics Bootcamp 2023: Ethics, equity, and regulations","authors":"Michelle R. Stoffel ,&nbsp;Amrom E. Obstfeld ,&nbsp;Brian R. Jackson ,&nbsp;Vahid Azimi ,&nbsp;Samuel I. McCash ,&nbsp;Simone Arvisais-Anhalt ,&nbsp;Lisa-Jean Clifford ,&nbsp;Ronald Jackups","doi":"10.1016/j.jpi.2025.100452","DOIUrl":"10.1016/j.jpi.2025.100452","url":null,"abstract":"<div><div>The Pathology Informatics Bootcamp is held annually at the Pathology Informatics Summit and provides pathology trainees with knowledge about both core and emerging topics in the rapidly evolving field of Pathology Informatics. In 2023, the Bootcamp focused on the applications of ethics, equity, and regulations pertinent to pathology informatics, with emphasis on the importance of these topics in the rapidly evolving landscape of artificial intelligence in pathology and lab medicine practice. Session topics are mapped to Pathology Informatics Essentials for Residents outlines to highlight the significance of these topics in pathology practice overall, and more so within informatics practice. The curriculum included lectures on data use in the clinical lab and in digital pathology, equitable use of lab data in daily practice and downstream use, and practical application of regulations for data with clinical decision-support, accreditation, and management of patient results.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100452"},"PeriodicalIF":0.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243491","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":"Deep learning predicts the effect of neoadjuvant chemotherapy for patients with triple negative breast cancer","authors":"B. Sturm ,&nbsp;P. Lock ,&nbsp;D. Kumar ,&nbsp;W.A.M. Blokx ,&nbsp;J.A.W.M. van der Laak","doi":"10.1016/j.jpi.2025.100448","DOIUrl":"10.1016/j.jpi.2025.100448","url":null,"abstract":"<div><h3>Background</h3><div>Triple negative breast cancer (TNBC) is an aggressive subcategory of breast cancer with poor prognosis and high risk of recurrence after treatment. In a subset of cases systemic chemotherapy is offered before surgery, so called neoadjuvant chemotherapy (NAC), to downstage the disease resulting in 40–50% of cases to a pathological complete response. Meanwhile, patients receiving NAC suffer from toxic side effects and in a proportion of patients a significant amount of residual tumor remains. This study aims to predict the outcome of NAC with deep learning technology based on the microscopic morphological characteristics in whole slide images of hematoxylin and eosin (H&amp;E) slides from the pre-operative tumor biopsy before chemotherapy.</div></div><div><h3>Methods</h3><div>A convolutional neural network was trained on 221 H&amp;E-stained biopsies of carcinoma of no special type from 205 patients scanned at 40×. Cases were divided in three cohorts, with a good, moderate, or bad response to NAC based on the EUSOMA scoring according to the pathology report of the subsequent tumor surgery specimen. We defined good, moderate, and bad response as residual tumor &lt;10%, 10–50%, and &gt;50%, respectively. Manual segmentation of the tumor area was performed comprising invasive carcinoma with a small rim of surrounding benign tissue. The model was tested on 52 new biopsies of 50 patients. Because of the relative low number of moderate and bad responder cases, and to achieve a better discrimination for potential visual biomarkers, the moderate and bad response cohorts were merged.</div></div><div><h3>Results</h3><div>The predictive performance of the model was calculated by means of the area under the receiver operator curve (AUC ROC). 95% Confidence intervals (CIs) were calculated for better understanding of the range of values. In the test set, the AUC ROC performance score was 0.696 with a CI of 0.532–0.861.</div></div><div><h3>Conclusion</h3><div>This proof-of-concept study shows that H&amp;E pre-operative biopsies from TNBC, by means of deep learning technology, contain valuable information having predictive value for the outcome of NAC resulting in an AUC value of 0.696 outperforming a predictive AUC value of 0.63 based on structured clinical data of histological tumor grade, TILs, and ki-67 known from the literature.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100448"},"PeriodicalIF":0.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213221","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":"PD-L1 expression assessment in Angiosarcoma improves with artificial intelligence support","authors":"F.H. Reith ,&nbsp;A. Jarosch ,&nbsp;J.P. Albrecht ,&nbsp;F. Ghoreschi ,&nbsp;A. Flörcken ,&nbsp;A. Dörr ,&nbsp;S. Roohani ,&nbsp;F.M. Schäfer ,&nbsp;R. Öllinger ,&nbsp;S. Märdian ,&nbsp;K. Tielking ,&nbsp;P. Bischoff ,&nbsp;N. Frühauf ,&nbsp;F. Brandes ,&nbsp;D. Horst ,&nbsp;C. Sers ,&nbsp;D. Kainmüller","doi":"10.1016/j.jpi.2025.100447","DOIUrl":"10.1016/j.jpi.2025.100447","url":null,"abstract":"<div><div>Tumoral PD-L1 expression is assessed to weigh immunotherapy options in the treatment of various types of cancer. To determine PD-L1 expression, each tumor cell needs to be assessed to calculate the percentage of PD-L1 positive tumor cells, called tumor proportion score (TPS). Pathologists cannot evaluate each cell individually due to time constraints and thus need to approximate TPS, which has been shown to result in low concordance rates.</div><div>Decision quality could be improved by an AI-based TPS prediction tool which serves as a “second opinion”. Establishing such a tool requires a certain amount of training data, which manifests a bottleneck for rare cancer types such as Angiosarcoma.</div><div>To address this challenge, we developed and open sourced a pipeline that leverages pre-trained and generalist models to achieve strong TPS prediction performance on limited data. Pathologists were asked to reassess patients for which their TPS strongly disagreed with the AI's prediction. In many of these cases, pathologists updated their TPS score, improving their assessment, thus demonstrating the technical feasibility and practical value of AI-based TPS scoring assistance for rare cancers.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100447"},"PeriodicalIF":0.0,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166082","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":"Digital morphometry illustrates a relationship between percentage of ductal carcinoma in-situ in breast needle core biopsy and margin status at lumpectomy","authors":"Alexander R. Gross ,&nbsp;Gerald R. Hobbs Jr. ,&nbsp;Luis Samayoa ,&nbsp;Stell Santiago","doi":"10.1016/j.jpi.2025.100445","DOIUrl":"10.1016/j.jpi.2025.100445","url":null,"abstract":"<div><div>Candidates for breast conserving surgery are selected based on imprecise variables and there is uncertainty surrounding the risk of complicated margins. Earlier estimates made with light microscopy revealed the correlation between percentage of needle core biopsy ductal carcinoma in-situ and positive lumpectomy margin status. We now study this association utilizing the precision of digital pathology. One hundred and seventy-nine lumpectomy specimens of pure ductal neoplasia were identified and their pathological, clinical, and radiological parameters retrieved. Each lumpectomy had a corresponding needle core biopsy for histological review. Virtually all cases exhibited a luminal A phenotype. Eighty-three cases showed positive margins and ninety-six cases, negative margins. We used the 2019 American College of Breast Surgeons Lumpectomy Consensus Guidelines to define margin status. For each case, by analog microscopy, we selected a single needle core biopsy slide with the greatest absolute quantity of carcinoma in-situ; each selected slide was submitted for digital whole slide imaging. Digital images were manually annotated for carcinoma in-situ, invasive carcinoma, stroma, and fat strictly based on morphology. Morphometric variables were compiled and compared to the corresponding lumpectomy margin status. Increases in percent ductal carcinoma in-situ are associated with greater odds of positive lumpectomy margins (<em>P</em> &lt; 0.05). Above 10% carcinoma in-situ all but one case showed positive margins. This prediction was more precise compared to the association between pre-operative radiological studies and margin status, particularly in cases of pure ductal carcinoma in-situ. Our work suggests that needle core biopsy percentage of ductal carcinoma in-situ maybe clinically useful in assessing the risk of a positive lumpectomy margin in select patients. A larger, multi-institutional study can further elucidate if pathological reporting of needle core biopsies with pure ductal breast neoplasia should include a percentage needle core biopsy ductal carcinoma in-situ.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100445"},"PeriodicalIF":0.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144155024","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":"Technical note: Impact of tissue section thickness on accuracy of cell classification with a deep learning network","authors":"Ida Skovgaard Christiansen ,&nbsp;Rasmus Hartvig ,&nbsp;Thomas Hartvig Lindkær Jensen","doi":"10.1016/j.jpi.2025.100440","DOIUrl":"10.1016/j.jpi.2025.100440","url":null,"abstract":"<div><h3>Introduction</h3><div>We are currently developing a cell classification system intended for routine histopathology. During observation, cells of interest are added to a deep learning (DL) network, which after training classifies the remaining cells of interest with high and immediately validatable accuracy. In this study, we identify the optimal histological microsection thickness for this process and describe in high detail the morphological differences introduced by variation in microsection thickness.</div></div><div><h3>Method</h3><div>From HE-stained digitized sections of liver cut manually at 5 thicknesses and on an automated microtome (DS), hepatocytes and non-hepatocytes were manually annotated and loaded into a DL convolutional neural network (ResNet). The network was trained at different settings to identify the thickness with optimal relation between number of training cells and validation accuracy. To shed interpretable light on the impact of thickness, exhaustive morphological details of the annotated cells were quantified and the differences between hepatocytes and non-hepatocytes were analyzed with random forest.</div></div><div><h3>Results</h3><div>Classifying hepatocytes from DS sections clearly resulted in highest validation accuracy with least number of cells and for the remaining thicknesses a trend towards thin sections being more efficient was observed. Random forest analysis generally identified variations in nuclear granularity as the most important features in distinguishing cells. In DS and the thinner tissue sections, nuclear granularity features were more distinguished.</div></div><div><h3>Conclusion</h3><div>Microsections cut with DS in particular and thin sections in general are better suited for the intended cell classification system.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"17 ","pages":"Article 100440"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874460","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":"Fully automatic HER2 tissue segmentation for interpretable HER2 scoring","authors":"Mathias Öttl ,&nbsp;Jana Steenpass ,&nbsp;Frauke Wilm ,&nbsp;Jingna Qiu ,&nbsp;Matthias Rübner ,&nbsp;Corinna Lang-Schwarz ,&nbsp;Cecilia Taverna ,&nbsp;Francesca Tava ,&nbsp;Arndt Hartmann ,&nbsp;Hanna Huebner ,&nbsp;Matthias W. Beckmann ,&nbsp;Peter A. Fasching ,&nbsp;Andreas Maier ,&nbsp;Ramona Erber ,&nbsp;Katharina Breininger","doi":"10.1016/j.jpi.2025.100435","DOIUrl":"10.1016/j.jpi.2025.100435","url":null,"abstract":"<div><div>Breast cancer is the most common cancer in women, with HER2 (human epidermal growth factor receptor 2) overexpression playing a critical role in regulating cell growth and division. HER2 status, assessed according to established scoring guidelines, offers important information for treatment selection. However, the complexity of the task leads to variability in human rater assessments. In this work, we propose a fully automated, interpretable HER2 scoring pipeline based on pixel-level semantic segmentations, designed to align with clinical guidelines. Using polygon annotations, our method balances annotation effort with the ability to capture fine-grained details and larger structures, such as non-invasive tumor tissue.</div><div>To enhance HER2 segmentation, we propose the use of a Wasserstein Dice loss to model class relationships, ensuring robust segmentation and HER2 scoring performance. Additionally, based on observations of pathologists' behavior in clinical practice, we propose a calibration step to the scoring rules, which positively impacts the accuracy and consistency of automated HER2 scoring. Our approach achieves an F1 score of 0.832 on HER2 scoring, demonstrating its effectiveness. This work establishes a potent segmentation pipeline that can be further leveraged to analyze HER2 expression in breast cancer tissue.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"17 ","pages":"Article 100435"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739022","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}