Murine orthotopic lung transplant models: A comprehensive overview of genetic mismatch degrees and histopathological insights into chronic lung allograft dysfunction.

Currently, lung transplantation outcome remains inferior compared to other solid organ transplantations. A major cause for limited survival after lung transplantation is chronic lung allograft dysfunction. Numerous animal models have been developed to investigate chronic lung allograft dysfunction to discover adequate treatments. The murine orthotopic lung transplant model has been further optimized over the last years. However, different degrees of genetic mismatch between donor and recipient mice have been used, applying a single, minor, moderate, and major genetic mismatch. This review aims to reassess the existing murine mismatch models and provide a comprehensive overview, with a specific focus on their eventual histopathological presentation. This will be crucial to leverage this model and tailor it according to specific research needs.

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.

Interobserver Variation in the Assessment of Immunohistochemistry Expression Levels in HER2-Negative Breast Cancer: Can We Improve the Identification of Low Levels of HER2 Expression by Adjusting the Criteria? An International Interobserver Study.

The classification of human epidermal growth factor receptor 2 (HER2) expression is optimized to detect HER2-amplified breast cancer (BC). However, novel HER2-targeting agents are also effective for BCs with low levels of HER2. This raises the question whether the current guidelines for HER2 testing are sufficiently reproducible to identify HER2-low BC. The aim of this multicenter international study was to assess the interobserver agreement of specific HER2 immunohistochemistry scores in cases with negative HER2 results (0, 1+, or 2+/in situ hybridization negative) according to the current American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) guidelines. Furthermore, we evaluated whether the agreement improved by redefining immunohistochemistry (IHC) scoring criteria or by adding fluorescent in situ hybridization (FISH). We conducted a 2-round study of 105 nonamplified BCs. During the first assessment, 16 pathologists used the latest version of the ASCO/CAP guidelines. After a consensus meeting, the same pathologists scored the same digital slides using modified IHC scoring criteria based on the 2007 ASCO/CAP guidelines, and an extra "ultralow" category was added. Overall, the interobserver agreement was limited (4.7% of cases with 100% agreement) in the first round, but this was improved by clustering IHC categories. In the second round, the highest reproducibility was observed when comparing IHC 0 with the ultralow/1+/2+ grouped cluster (74.3% of cases with 100% agreement). The FISH results were not statistically different between HER2-0 and HER2-low cases, regardless of the IHC criteria used. In conclusion, our study suggests that the modified 2007 ASCO/CAP criteria were more reproducible in distinguishing HER2-0 from HER2-low cases than the 2018 ASCO/CAP criteria. However, the reproducibility was still moderate, which was not improved by adding FISH. This could lead to a suboptimal selection of patients eligible for novel HER2-targeting agents. If the threshold between HER2 IHC 0 and 1+ is to be clinically actionable, there is a need for clearer, more reproducible IHC definitions, training, and/or development of more accurate methods to detect this subtle difference in protein expression levels.

Development of Training Materials for Pathologists to Provide Machine Learning Validation Data of Tumor-Infiltrating Lymphocytes in Breast Cancer.

The High Throughput Truthing project aims to develop a dataset for validating artificial intelligence and machine learning models (AI/ML) fit for regulatory purposes. The context of this AI/ML validation dataset is the reporting of stromal tumor-infiltrating lymphocytes (sTILs) density evaluations in hematoxylin and eosin-stained invasive breast cancer biopsy specimens. After completing the pilot study, we found notable variability in the sTILs estimates as well as inconsistencies and gaps in the provided training to pathologists. Using the pilot study data and an expert panel, we created custom training materials to improve pathologist annotation quality for the pivotal study. We categorized regions of interest (ROIs) based on their mean sTILs density and selected ROIs with the highest and lowest sTILs variability. In a series of eight one-hour sessions, the expert panel reviewed each ROI and provided verbal density estimates and comments on features that confounded the sTILs evaluation. We aggregated and shaped the comments to identify pitfalls and instructions to improve our training materials. From these selected ROIs, we created a training set and proficiency test set to improve pathologist training with the goal to improve data collection for the pivotal study. We are not exploring AI/ML performance in this paper. Instead, we are creating materials that will train crowd-sourced pathologists to be the reference standard in a pivotal study to create an AI/ML model validation dataset. The issues discussed here are also important for clinicians to understand about the evaluation of sTILs in clinical practice and can provide insight to developers of AI/ML models.

Circulating tumour cells and lung microvascular tumour cell retention in patients with metastatic breast and cervical cancer.

We have shown that in up to half of the patients with metastatic breast cancer (MBC), higher numbers of circulating tumour cells (CTCs) are present in the central venous blood (CVB) compared to the peripheral venous blood (PVB), suggesting that the lungs might retain a substantial number of CTCs. Here we report the presence of tumour cell emboli (TCE) in the microvasculature of the lungs in three out of eight patients with MBC and one patient with metastatic cervical carcinoma who had markedly elevated numbers of CTCs in the blood. All these patients suffered from symptomatic dyspnoea not easily attributable to other causes. No TCE were observed in five patients with MBC and elevated CTC counts and three patients with MBC who had low CTC counts (<5/7.5 ml). To investigate whether CTCs derived from CVB or PVB exhibit different transcriptional characteristics that might explain selective CTC retention, paired CTC samples from CVB and PVB of 12 patients with advanced breast cancer were subjected to gene expression analysis of 105 genes. No significant differences in CTC gene expression were observed. Together, these data suggest that potentially clinically relevant CTC retention in the microvasculature of the lung can occur in a subset of patients with advanced metastatic breast and cervical cancer, which seems to be transcriptionally non-selectively.