Journal of Pathology Informatics最新文献

{"title":"Technical considerations during validation of the Genius® Digital Diagnostic System","authors":"Lakshmi Harinath ,&nbsp;Sarah Harrington ,&nbsp;Jonee Matsko ,&nbsp;Amy Colaizzi ,&nbsp;Esther Elishaev ,&nbsp;Samer Khader ,&nbsp;Rohit Bhargava ,&nbsp;Chengquan Zhao ,&nbsp;Liron Pantanowitz","doi":"10.1016/j.jpi.2025.100532","DOIUrl":"10.1016/j.jpi.2025.100532","url":null,"abstract":"<div><h3>Background</h3><div>The aim of this study was to document technical errors encountered during validation of the Genius Digital Diagnostics System (GDDS).</div></div><div><h3>Materials and methods</h3><div>A total of 909 cases of archived ThinPrep Pap slides with follow-up biopsies were retrieved. Slides were cleaned, relabeled, and scanned with GDDS. Digital imager errors, including slide events and imager errors, were documented and evaluated.</div></div><div><h3>Results</h3><div>Of the 909 slides scanned, 21 (2.3<!--> <!-->%) demonstrated slide events. For 5 cases, the slides had cell focus errors, 12 failed due to quality control (QC) errors, 2 had barcode issues, 1 showed an oversaturated frame, and 1 presented a problem because it was a duplicate. Some errors could be corrected, of which 8 cases with various diagnostic cytology interpretations were successfully rescanned. There were 13 (1.4%) cases that could not be scanned and thus were excluded from the study, predominantly because of focus QC errors due to scratched coverslips from long-term storage. There were 43 imager errors including failure of motor movement, cancellation of slide handling action, and failure to pick slides from the carrier station for which the scanning process had to be paused. Imager errors were solved by rebooting the system, correcting the positioning of the slide on the system, and technical help provided by the vendor.</div></div><div><h3>Conclusion</h3><div>Minor errors are to be expected when digitizing large volume of Pap slides. Total number of rescanned cases to address such technical problems were low in number and did not compromise the interpretation of Pap test slides using GDDS.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"20 ","pages":"Article 100532"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705589","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 pathology imaging artificial intelligence in cancer research and clinical trials: An NCI workshop report","authors":"Hala R. Makhlouf ,&nbsp;Miguel R. Ossandon ,&nbsp;Keyvan Farahani ,&nbsp;Irina Lubensky ,&nbsp;Lyndsay N. Harris","doi":"10.1016/j.jpi.2025.100531","DOIUrl":"10.1016/j.jpi.2025.100531","url":null,"abstract":"<div><div>Digital pathology imaging (DPI) is a rapidly advancing field with increasing relevance to cancer diagnosis, research, and clinical trials through large-scale image analysis and artificial intelligence (AI) integration. Despite these advances, regulatory adoption in digital pathology (DP) has lagged; to date, only three AI/ML Software as a Medical Device tool have received FDA clearance, highlighting a validation dataset gap rather than an absence of regulatory pathways. On March 6–7, 2024, the National Cancer Institute held a virtual workshop titled “Digital Pathology Imaging-Artificial Intelligence in Cancer Research and Clinical Trials,” bringing together experts in pathology, radiology, oncology, data science, and regulatory fields to assess current challenges, practical solutions, and future directions. This report summarizes expert opinions on key issues related to the use of DPI in cancer research and clinical trials, including data standardization, de-identification, and the application of Digital Imaging and Communication in Medicine (DICOM) standards. Key topics included data standardization, image quality assurance, validation strategies, AI applications, integration in clinical trials, biobanking, intellectual property, investigators' needs, and lessons from digital cytology and radiology domains. Solutions discussed included adoption of open standards such as DICOM, centralized imaging portals, and scalable cloud-based platforms. The expert consensus outlined in this report is intended to guide the development of DPI infrastructure, standardization, support AI validation, and align regulatory and data-sharing practices to advance precision oncology.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"20 ","pages":"Article 100531"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705637","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 of digital displays for efficient diagnosis and accuracy","authors":"Monika Lamba Saini ,&nbsp;Amanda Hemmerich ,&nbsp;Anna Banach-Ovens ,&nbsp;Hannah Manssens ,&nbsp;Robbie Dougan ,&nbsp;Tom Kimpe ,&nbsp;John D. Cochran","doi":"10.1016/j.jpi.2026.100544","DOIUrl":"10.1016/j.jpi.2026.100544","url":null,"abstract":"<div><div>Digital pathology is transforming diagnostic workflows by enabling the interpretation of whole-slide images on digital displays, offering advantages in remote diagnostics, efficiency, and integration with computational tools. However, diagnostic reliability depends not only on scanner and software quality but also on the performance of the display systems used for image review, as well as on environmental and ergonomic factors. This study evaluated the impact of display quality on diagnostic accuracy by comparing a medical-grade, professional-grade, and consumer-grade display against conventional light microscopy. Nineteen hematoxylin and eosin (H&amp;E) and 19 immunohistochemistry (IHC) slides were assessed by two board-certified pathologists across all modalities. The medical-grade display achieved perfect concordance with microscopy for H&amp;E slides and was rated highest for image clarity, color fidelity, and luminance stability. In contrast, the professional- and consumer-grade displays showed lower concordance (85–95%) and received inferior subjective evaluation. For IHC slides, both observers achieved 100% concordance for HER2 and AMACR markers across all displays. One discordant MLH1 case was noted on the medical-grade display for one observer out of five cases. Although MLH1 is consistently analyzed alongside other MSI markers, this isolated discrepancy precludes firm conclusions about display performance for this marker. These findings highlight the importance of clinically validated displays with automated calibration and quality assurance mechanisms to ensure consistent diagnostic performance. As digital pathology becomes increasingly integrated with remote workflows, establishing minimum performance standards for display systems will be essential to safeguard diagnostic accuracy, reproducibility, and patient safety in clinical practice.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"20 ","pages":"Article 100544"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188858","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":"MuCoSA: Multi-contextual similarity assessment for histopathology image search","authors":"Gyu Yeong Kim ,&nbsp;Yongjun Jeon ,&nbsp;Hoyeon Jeong ,&nbsp;Seungkyun Lee ,&nbsp;Hyungbin Kim ,&nbsp;Boram Lee ,&nbsp;Kyu-Hwan Jung ,&nbsp;David Joon Ho ,&nbsp;Yoon-La Choi","doi":"10.1016/j.jpi.2025.100533","DOIUrl":"10.1016/j.jpi.2025.100533","url":null,"abstract":"<div><div>Histological diagnosis in pathology often begins with visual identification of morphological patterns that resemble known disease entities. Whereas digital pathology and augmented intelligence have enabled reverse image search in histopathology, most existing frameworks rely on single-magnification image patches and slide-level labels, limiting their ability to capture tissue morphologies with at various levels of granularity and scale. To address this, we propose Progressive Regional Image Sequence by Magnification (PRISM), a sequence of images that captures contextual information across multiple magnifications. Building on this, we introduce Multi-Contextual Similarity Assessment (MuCoSA), a PRISM-based image retrieval framework that leverages pre-trained feature encoders without additional fine-tuning. To evaluate MuCoSA's performance, we constructed reference and query PRISM datasets utilizing histological patterns of lung adenocarcinoma. Whole-slide image (WSIs) from Samsung Medical Center were used as reference and internal query, whereas those from The Cancer Genome Atlas served as external query. For each WSI, representative regions of interest were selected and converted into PRISM structure. Similarity between two PRISMs was calculated by averaging the cosine similarities of corresponding patches at the same magnification level across all magnifications. We conducted a comprehensive evaluation across different feature encoders, magnification levels, receptive field sizes, and both internal/external query conditions. As a result, MuCoSA with a multi-magnification outperformed single-magnification methods. For instance, MuCoSA using UNI2-h encoder with eight magnifications achieved an F1-score of 0.8051 (95% CI: 0.7843–0.8267), mAP@5 of 0.7065 (95% CI: 0.6893–0.7250), and mMV@5 of 0.8232 (95% CI: 0.8038–0.8434), all significantly higher than the single-magnification baseline (<em>p</em> &lt; 0.0001). Furthermore, confusion matrices and visual inspection confirmed that search results were closely aligned with the morphological perceptions of pathologists. In conclusion, our study demonstrates that using a simple and efficient framework like MuCoSA with multi-magnification PRISM without fine-tuning can significantly improve image search in histopathology. We anticipate that MuCoSA can assist pathologists in making more accurate and consistent diagnoses, thereby reducing observer variability in histopathological interpretation.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"20 ","pages":"Article 100533"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926432","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":"Applications and challenges of utilizing digital pathology and AI-enabled workflows in clinical trials","authors":"Manu Sebastian ,&nbsp;Harsh Batra ,&nbsp;Monika Lamba Saini ,&nbsp;Staci Kearney ,&nbsp;Lorcan Sherry ,&nbsp;Serge Alexanian ,&nbsp;Michael Cohen ,&nbsp;William Weber ,&nbsp;Joe Lennerz ,&nbsp;Anil V. Parwani","doi":"10.1016/j.jpi.2025.100542","DOIUrl":"10.1016/j.jpi.2025.100542","url":null,"abstract":"<div><div>This is a comprehensive review on current utilization and challenges of digital pathology adoption in clinical trials and aims to provide a broad view on its impact on pathology review processes in clinical trials. It provides an overview of current pathology review practices in clinical trials and unique advantages digital pathology adoption can offer. The key areas including existing workflows, use case scenarios in different disease areas in clinical trials, including but not limited to patient identification and pre-screening, and regulatory aspects have been described with relevance. In addition, the review delves into the integration of genomics, AI, image analysis, radiology, and advanced computational pathology, to propose measures to enhance clinical trial outcomes. The current regulatory landscape around digital pathology adoption and potential future advancements in this field are also discussed as appropriate.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"20 ","pages":"Article 100542"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037903","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":"Leveraging sequence-to-sequence models for semantic annotation of Dutch pathology reports","authors":"M. Siepel ,&nbsp;G.T.N. Burger ,&nbsp;Q.J.M. Voorham ,&nbsp;R. Cornet ,&nbsp;I. Calixto ,&nbsp;I. Vagliano","doi":"10.1016/j.jpi.2025.100534","DOIUrl":"10.1016/j.jpi.2025.100534","url":null,"abstract":"<div><div>Palga Foundation is responsible for indexing Dutch pathology data across the Netherlands, which relies on annotations of pathology reports. These annotations, derived from the conclusion text, consist of codes from the Palga thesaurus, serving patient care and scientific research. However, manual annotation by pathologists is both labor-intensive and prone to errors. Therefore, in this study, we seek to leverage sequence-to-sequence transformer models, particularly Text-To-Text Transfer Transformer (T5)-based models, to generate these annotations. Additionally, we investigate a constrained decoding (CD) approach that encodes domain knowledge. We compare a standard multilingual T5 model (mT5) with our own T5 model (PaTh5.NL) pre-trained using Palga data with the goal of better aligning the model's learned representations with the specific structure, terminology, and annotation conventions used in Dutch pathology reports. We fine-tune both pre-trained models using default (DD) and CD and compare both decoding strategies. Performance is assessed using Bilingual Evaluation Understudy (BLEU) scores for quantitative evaluation and case-based evaluations for qualitative assessment, where we use the generated codes to retrieve patients from the Palga database. Quantitative evaluations indicated that our two fine-tuned PaTh5.NL models significantly outperformed the fine-tuned mT5 model, particularly for shorter histology and cytology reports, but performance of all models declined on longer or complex reports. The case-based evaluation revealed that, despite higher BLEU scores, the PaTh5.NL models did not consistently outperform the mT5 model in retrieving relevant patients. This study demonstrates that fine-tuned T5-based models can enhance the annotation process for Dutch pathology reports, though challenges remain regarding complex conclusion texts, especially in histology and autopsy reports. Future research should focus on expanding gold-standard datasets and developing post-processing algorithms to improve annotations' generalization.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"20 ","pages":"Article 100534"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841456","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":"Iris RESTful Server and IrisTileSource: An Iris implementation for existing OpenSeaDragon viewers","authors":"Ryan Erik Landvater,&nbsp;Navin Kathawa,&nbsp;Mustafa Yousif,&nbsp;Ulysses Balis","doi":"10.1016/j.jpi.2025.100530","DOIUrl":"10.1016/j.jpi.2025.100530","url":null,"abstract":"<div><div>The Iris File Extension (IFE) is a low overhead performance-oriented whole-slide image (WSI) file format designed to improve the image rendering experience for pathologists and simplify image management for system administrators. However, static hypertext transfer protocol (HTTP) file servers cannot natively stream subregions of high-resolution image files, such as the IFE. The majority of contemporary WSI viewer systems are designed as browser-based web applications and leverage OpenSeaDragon as the tile-based rendering framework. These systems convert WSI files to Deep Zoom Images (DZI) for compatibility with simple static HTTP file servers. To address this limitation, we have developed the Iris RESTful Server, a low-overhead HTTP server with a RESTful application programming interface (API) that is natively compatible with the DICOMweb WADO-RS API. Written in C++ with Boost Beast HTTP and Asio networking libraries atop the public IFE libraries, the server offers both security and high performance. Testing shows that a single Raspberry Pi equivalent system can handle a peak of 5061 req./s (average 3883 req./s) with a median latency of 21 ms on a private (i.e., hospital) network. We also developed and merged a new OpenSeaDragon TileSource, compatible with the Iris RESTful API, into the next OpenSeaDragon release, enabling simple and immediate drop-in replacement of DZI images within WSI viewer stacks. Designed as a secure cross-origin resource sharing microservice, this architecture includes detailed deployment instructions for new or existing WSI workflows, and the public <span><span>examples.restful.irisdigitalpathology.org</span><svg><path></path></svg></span> subdomain is provided as a development tool to accelerate WSI web viewer development. All relevant Iris software is available under the open-source MIT software license.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"20 ","pages":"Article 100530"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977811","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":"An integrated system from microscopy to AI for real-time object detection in endometrial cytology","authors":"Mika Terasaki ,&nbsp;Shun Tanaka ,&nbsp;Ichito Shimokawa ,&nbsp;Etsuko Toda ,&nbsp;Shoichiro Takakuma ,&nbsp;Yusuke Kajimoto ,&nbsp;Shinobu Kunugi ,&nbsp;Akira Shimizu ,&nbsp;Yasuhiro Terasaki","doi":"10.1016/j.jpi.2025.100541","DOIUrl":"10.1016/j.jpi.2025.100541","url":null,"abstract":"<div><div>Endometrial cytology, which is minimally invasive and available as an outpatient procedure, is widely used in Japan for early detection of endometrial cancer, but its diagnostic process is time-consuming and requires expert diagnosticians. We developed a real-time artificial intelligence (AI)-assisted system using a standard microscope, a charge-coupled device (CCD) camera, and the You-Only-Look-Once version 5x (YOLOv5x) (a well-established object detection model) to support endometrial cytology screening in resource-limited settings. A total of 146 pre-operative cytology cases were collected, and the model was trained to detect abnormal cell clusters. The system was evaluated in real-time using a CCD camera, and its diagnostic performance was compared with that of three pathologists and four medical students. In an independent test of 20 cases, the AI model achieved an accuracy of 85%, showing promising performance comparable to the average accuracy of 75% among human evaluators. Furthermore, the median diagnostic time was reduced by approximately 45% with AI assistance. The impact of AI support varied by user expertise, with notable improvements among non-specialists. This proof-of-concept study demonstrates the feasibility and potential of affordable, real-time AI support for endometrial cytology using widely available equipment. Further validation with larger, multicenter datasets is warranted to confirm the generalizability and clinical utility of this approach.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"20 ","pages":"Article 100541"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037908","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":"ADPv2: A hierarchical histological tissue type-annotated dataset for potential biomarker discovery of colorectal disease","authors":"Zhiyuan Yang ,&nbsp;Kai Li ,&nbsp;Sophia Ghamoshi Ramandi ,&nbsp;Patricia Brassard ,&nbsp;Abdelhakim Khellaf ,&nbsp;Vincent Quoc-Huy Trinh ,&nbsp;Jennifer Zhang ,&nbsp;Lina Chen ,&nbsp;Corwyn Rowsell ,&nbsp;Sonal Varma ,&nbsp;Kostas Plataniotis ,&nbsp;Mahdi S. Hosseini","doi":"10.1016/j.jpi.2025.100537","DOIUrl":"10.1016/j.jpi.2025.100537","url":null,"abstract":"<div><div>Computational pathology (CPath) leverages histopathology images to enhance diagnostic precision and reproducibility in clinical pathology. However, publicly available datasets for CPath that are annotated with extensive histological tissue type (HTT) taxonomies at a granular level remain scarce due to the significant expertise and high annotation costs required. Existing datasets, such as the Atlas of Digital Pathology (ADP), address this by offering diverse HTT annotations generalized to multiple organs, but limit the capability for in-depth studies on specific organ diseases. Building upon this foundation, we introduce ADPv2, a novel dataset focused on gastrointestinal histopathology. Our dataset comprises 20,004 image patches derived from healthy colon biopsy slides, annotated according to a hierarchical taxonomy of 32 distinct HTTs of 3 levels. Furthermore, we train a multilabel representation learning model following a two-stage training procedure on our ADPv2 dataset. By leveraging the VMamba model architecture, we achieve a mean average precision of 0.88 in multilabel colon HTT classification.. Finally, we show that our dataset is capable of an organ-specific in-depth study for potential biomarker discovery by analyzing the model's prediction behavior on tissues affected by different colon diseases, which reveals statistical patterns that confirm the two pathological pathways of colon cancer development. Our dataset is publicly available here: Part 1, Part 2, and Part 3.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"20 ","pages":"Article 100537"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077926","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":"PANDA-PLUS: Improved dataset of prostate whole slide images from PANDA Challenge with pixel-level expert annotations","authors":"Spencer Hopson,&nbsp;Carson Mildon,&nbsp;Corbyn Kubalek,&nbsp;Joshua Ebbert,&nbsp;Ryan Vance,&nbsp;Lauren Laverty,&nbsp;Paul Urie,&nbsp;Dennis Della Corte","doi":"10.1016/j.jpi.2025.100540","DOIUrl":"10.1016/j.jpi.2025.100540","url":null,"abstract":"<div><div>Artificial intelligence (AI)-based prostate cancer detection through whole slide images (WSIs) offers promising potential to address the global pathologist shortage while improving clinical consistency. Digital slides and improving image analysis methods encourage the creation of tools to aid in WSI classification. Despite promising advances, these tools are still limited by available training data. Current publicly available datasets, such as Kaggle's PANDA Challenge, while large in scale, rely on slide-level labels that may introduce noise and limit model reliability. Others contain detailed annotations, but are smaller in size due to manual processing efforts. In this work, we introduce PANDA-PLUS, a 546-image dataset derived from PANDA images with improved pixel-level annotations, as well as an accompanying annotation pipeline that reduces pathologists' time commitment. We present a detailed comparative analysis between PANDA-PLUS and PANDA using Gleason score and ISUP grade, supported by agreement values, κ, and PABAK under multiple weighting schemes. The results demonstrate consistently lower grading in PANDA-PLUS, with disagreement patterns especially pronounced at higher grades. We also demonstrate through single rater grading of various annotation granularities how slide- and patch-level labels may distort grading proportions and alter image scores. PANDA-PLUS not only improves annotation granularity and reduces label noise but also exposes potential grading errors in the original PANDA dataset. We present PANDA-PLUS's annotations as an improved alternative to the PANDA labels and conclude that it represents a step forward in the development of higher-quality public datasets for clinical AI applications in prostate cancer pathology.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"20 ","pages":"Article 100540"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037910","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}