Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models.

This work puts forth and demonstrates the utility of a reporting framework for collecting and evaluating annotations of medical images used for training and testing artificial intelligence (AI) models in assisting detection and diagnosis. AI has unique reporting requirements, as shown by the AI extensions to the Consolidated Standards of Reporting Trials (CONSORT) and Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklists and the proposed AI extensions to the Standards for Reporting Diagnostic Accuracy (STARD) and Transparent Reporting of a Multivariable Prediction model for Individual Prognosis or Diagnosis (TRIPOD) checklists. AI for detection and/or diagnostic image analysis requires complete, reproducible, and transparent reporting of the annotations and metadata used in training and testing data sets. In an earlier work by other researchers, an annotation workflow and quality checklist for computational pathology annotations were proposed. In this manuscript, we operationalize this workflow into an evaluable quality checklist that applies to any reader-interpreted medical images, and we demonstrate its use for an annotation effort in digital pathology. We refer to this quality framework as the Collection and Evaluation of Annotations for Reproducible Reporting of Artificial Intelligence (CLEARR-AI).

Training pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research.

A growing body of research supports stromal tumour-infiltrating lymphocyte (TIL) density in breast cancer to be a robust prognostic and predicive biomarker. The gold standard for stromal TIL density quantitation in breast cancer is pathologist visual assessment using haematoxylin and eosin-stained slides. Artificial intelligence/machine-learning algorithms are in development to automate the stromal TIL scoring process, and must be validated against a reference standard such as pathologist visual assessment. Visual TIL assessment may suffer from significant interobserver variability. To improve interobserver agreement, regulatory science experts at the US Food and Drug Administration partnered with academic pathologists internationally to create a freely available online continuing medical education (CME) course to train pathologists in assessing breast cancer stromal TILs using an interactive format with expert commentary. Here we describe and provide a user guide to this CME course, whose content was designed to improve pathologist accuracy in scoring breast cancer TILs. We also suggest subsequent steps to translate knowledge into clinical practice with proficiency testing.

Use of Artificial Intelligence Chatbots in Interpretation of Pathology Reports.

Importance: Anatomic pathology reports are an essential part of health care, containing vital diagnostic and prognostic information. Currently, most patients have access to their test results online. However, the reports are complex and are generally incomprehensible to laypeople. Artificial intelligence chatbots could potentially simplify pathology reports. Objective: To evaluate the ability of large language model chatbots to accurately explain pathology reports to patients. Design, Setting, and Participants: This cross-sectional study used 1134 pathology reports from January 1, 2018, to May 31, 2023, from a multispecialty hospital in Brooklyn, New York. A new chat was started for each report, and both chatbots (Bard [Google Inc], hereinafter chatbot 1; GPT-4 [OpenAI], hereinafter chatbot 2) were asked in sequential prompts to explain the reports in simple terms and identify key information. Chatbot responses were generated between June 1 and August 31, 2023. The mean readability scores of the original and simplified reports were compared. Two reviewers independently screened and flagged reports with potential errors. Three pathologists reviewed the flagged reports and categorized them as medically correct, partially medically correct, or medically incorrect; they also recorded any instances of hallucinations. Main Outcomes and Measures: Outcomes included improved mean readability scores and a medically accurate interpretation. Results: For the 1134 reports included, the Flesch-Kincaid grade level decreased from a mean of 13.19 (95% CI, 12.98-13.41) to 8.17 (95% CI, 8.08-8.25; t = 45.29; P < .001) by chatbot 1 and 7.45 (95% CI, 7.35-7.54; t = 49.69; P < .001) by chatbot 2. The Flesch Reading Ease score was increased from a mean of 10.32 (95% CI, 8.69-11.96) to 61.32 (95% CI, 60.80-61.84; t = -63.19; P < .001) by chatbot 1 and 70.80 (95% CI, 70.32-71.28; t = -74.61; P < .001) by chatbot 2. Chatbot 1 interpreted 993 reports (87.57%) correctly, 102 (8.99%) partially correctly, and 39 (3.44%) incorrectly; chatbot 2 interpreted 1105 reports (97.44%) correctly, 24 (2.12%) partially correctly, and 5 (0.44%) incorrectly. Chatbot 1 had 32 instances of hallucinations (2.82%), while chatbot 2 had 3 (0.26%). Conclusions and Relevance: The findings of this cross-sectional study suggest that artificial intelligence chatbots were able to simplify pathology reports. However, some inaccuracies and hallucinations occurred. Simplified reports should be reviewed by clinicians before distribution to patients.

Bridging the Clinical-Computational Transparency Gap in Digital Pathology.

CONTEXT.­: Computational pathology combines clinical pathology with computational analysis, aiming to enhance diagnostic capabilities and improve clinical productivity. However, communication barriers between pathologists and developers often hinder the full realization of this potential. OBJECTIVE.­: To propose a standardized framework that improves mutual understanding of clinical objectives and computational methodologies. The goal is to enhance the development and application of computer-aided diagnostic (CAD) tools. DESIGN.­: The article suggests pivotal roles for pathologists and computer scientists in the CAD development process. It calls for increased understanding of computational terminologies, processes, and limitations among pathologists. Similarly, it argues that computer scientists should better comprehend the true use cases of the developed algorithms to avoid clinically meaningless metrics. RESULTS.­: CAD tools improve pathology practice significantly. Some tools have even received US Food and Drug Administration approval. However, improved understanding of machine learning models among pathologists is essential to prevent misuse and misinterpretation. There is also a need for a more accurate representation of the algorithms' performance compared to that of pathologists. CONCLUSIONS.­: A comprehensive understanding of computational and clinical paradigms is crucial for overcoming the translational gap in computational pathology. This mutual comprehension will improve patient care through more accurate and efficient disease diagnosis.

Understanding the financial aspects of digital pathology: A dynamic customizable return on investment calculator for informed decision-making.

Background: The adoption of digital pathology has transformed the field of pathology, however, the economic impact and cost analysis of implementing digital pathology solutions remain a critical consideration for institutions to justify. Digital pathology implementation requires a thorough evaluation of associated costs and should identify and optimize resource allocation to facilitate informed decision-making. A dynamic cost calculator to estimate the financial implications of deploying digital pathology systems was needed to estimate the financial effects on transitioning to a digital workflow. Methods: A systematic approach was used to comprehensively assess the various components involved in implementing and maintaining a digital pathology system. This consisted of: (1) identification of key cost categories associated with digital pathology implementation; (2) data collection and analysis of cost estimation; (3) cost categorization and quantification of direct and indirect costs associated with different use cases, allowing customization of each factor based on specific intended uses and market rates, industry standards, and regional variations; (4) opportunities for savings realized by digitization of glass slides and (5) integration of the cost calculator into a unified framework for a holistic view of the financial implications associated with digital pathology implementation. The online tool enables the user to test various scenarios specific to their institution and provides adjustable parameters to assure organization specific relatability. Results: The Digital Pathology Association has developed a web-based calculator as a companion tool to provide an exhaustive list of the necessary concepts needed when assessing the financial implications of transitioning to a digital pathology system. The dynamic return on investment (ROI) calculator successfully integrated relevant cost and cost-saving components associated with digital pathology implementation and maintenance. Considerations include factors such as digital pathology infrastructure, clinical operations, staffing, hardware and software, information technology, archive and retrieval, medical-legal, and potential reimbursements. The ROI calculator developed for digital pathology workflows offers a comprehensive, customizable tool for institutions to assess their anticipated upfront and ongoing annual costs as they start or expand their digital pathology journey. It also offers cost-savings analysis based on specific user case volume, institutional geographic considerations, and actual costs. In addition, the calculator also serves as a tool to estimate number of required whole slide scanners, scanner throughput, and data storage (TB). This tool is intended to estimate the potential costs and cost savings resulting from the transition to digital pathology for business plan justifications and return on investment calculations. Conclusions: The digital pathology online cost calculator provides a comprehensive and reliable means of estimating the financial implications associated with implementing and maintaining a digital pathology system. By considering various cost factors and allowing customization based on institution-specific variables, the calculator empowers pathology laboratories, healthcare institutions, and administrators to make informed decisions and optimize resource allocation when adopting or expanding digital pathology technologies. The ROI calculator will enable healthcare institutions to assess the financial feasibility and potential return on investment on adopting digital pathology, facilitating informed decision-making and resource allocation.

A Genomics-Driven Artificial Intelligence-Based Model Classifies Breast Invasive Lobular Carcinoma and Discovers CDH1 Inactivating Mechanisms.

Artificial intelligence (AI)-systems can improve cancer diagnosis, yet their development often relies on subjective histological features as ground truth for training. Here, we developed an AI-model applied to histological whole-slide images (WSIs) using CDH1 bi-allelic mutations, pathognomonic for invasive lobular carcinoma (ILC) in breast neoplasms, as ground truth. The model accurately predicted CDH1 bi-allelic mutations (accuracy=0.95) and diagnosed ILC (accuracy=0.96). A total of 74% of samples classified by the AI-model as having CDH1 bi-allelic mutations but lacking these alterations displayed alternative CDH1 inactivating mechanisms, including a deleterious CDH1 fusion gene and non-coding CDH1 genetic alterations. Analysis of internal and external validation cohorts demonstrated 0.95 and 0.89 accuracy for ILC diagnosis, respectively. The latent features of the AI-model correlated with human-explainable histopathologic features. Taken together, this study reports the construction of an AI-algorithm trained using a genetic rather than histologic ground truth that can robustly classify ILCs and uncover CDH1 inactivating mechanisms, providing the basis for orthogonal ground truth utilization for development of diagnostic AI-models applied to WSI.

The 1000 Mitoses Project: A Consensus-Based International Collaborative Study on Mitotic Figures Classification.

Introduction. The identification of mitotic figures is essential for the diagnosis, grading, and classification of various different tumors. Despite its importance, there is a paucity of literature reporting the consistency in interpreting mitotic figures among pathologists. This study leverages publicly accessible datasets and social media to recruit an international group of pathologists to score an image database of more than 1000 mitotic figures collectively. Materials and Methods. Pathologists were instructed to randomly select a digital slide from The Cancer Genome Atlas (TCGA) datasets and annotate 10-20 mitotic figures within a 2 mm2 area. The first 1010 submitted mitotic figures were used to create an image dataset, with each figure transformed into an individual tile at 40x magnification. The dataset was redistributed to all pathologists to review and determine whether each tile constituted a mitotic figure. Results. Overall pathologists had a median agreement rate of 80.2% (range 42.0%-95.7%). Individual mitotic figure tiles had a median agreement rate of 87.1% and a fair inter-rater agreement across all tiles (kappa = 0.284). Mitotic figures in prometaphase had lower percentage agreement rates compared to other phases of mitosis. Conclusion. This dataset stands as the largest international consensus study for mitotic figures to date and can be utilized as a training set for future studies. The agreement range reflects a spectrum of criteria that pathologists use to decide what constitutes a mitotic figure, which may have potential implications in tumor diagnostics and clinical management.