Improved pathology reporting in NAFLD/NASH for clinical trials

Complete digital pathology transition: A large multi-center experience

INTRODUCTION

Transitioning from glass slide pathology to digital pathology for primary diagnostics requires an appropriate laboratory information system, an image management system, and slide scanners; it also reinforces the need for sophisticated pathology informatics including synoptic reporting. Previous reports have discussed the transition itself and relevant considerations for it, but not the selection criteria and considerations for the infrastructure.

OBJECTIVE

To describe the process used to evaluate slide scanners, image management systems, and synoptic reporting systems for a large multisite institution.

METHODS

Six network hospitals evaluated six slide scanners, three image management systems, and three synoptic reporting systems. Scanners were evaluated based on the quality of image, speed, ease of operation, and special capabilities (including z-stacking, fluorescence and others). Image management and synoptic reporting systems were evaluated for their ease of use and capacity.

RESULTS

Among the scanners evaluated, the Leica GT450 produced the highest quality images, while the 3DHistech Pannoramic provided fluorescence and superior z-stacking. The newest generation of scanners, released relatively recently, performed better than slightly older scanners from major manufacturers Although the Olympus VS200 was not fully vetted due to not meeting all inclusion criteria, it is discussed herein due to its exceptional versatility. For Image Management Software, the authors believe that Sectra is, at the time of writing the best developed option, but this could change in the very near future as other systems improve their capabilities. All synoptic reporting systems performed impressively.

CONCLUSIONS

Specifics regarding quality and abilities of different components will change rapidly with time, but large pathology practices considering such a transition should be aware of the issues discussed and evaluate the most current generation to arrive at appropriate conclusions.

Natural Language Processing to extract SNOMED-CT codes from pathological reports

OBJECTIVE

The use of standardized structured reports (SSR) and suitable terminologies like SNOMED-CT can enhance data retrieval and analysis, fostering large-scale studies and collaboration. However, the still large prevalence of narrative reports in our laboratories warrants alternative and automated labeling approaches. In this project, natural language processing (NLP) methods were used to associate SNOMED-CT codes to structured and unstructured reports from an Italian Digital Pathology Department.

METHODS

Two NLP-based automatic coding systems (support vector machine, SVM, and long-short term memory, LSTM) were trained and applied to a series of narrative reports.

RESULTS

The 1163 cases were tested with both algorithms, showing good performances in terms of accuracy, precision, recall, and F1 score, with SVM showing slightly better performances as compared to LSTM (0.84, 0.87, 0.83, 0.82 vs 0.83, 0.85, 0.83, 0.82, respectively). The integration of an explainability allowed identification of terms and groups of words of importance, enabling fine-tuning, balancing semantic meaning and model performance.

CONCLUSIONS

AI tools allow the automatic SNOMED-CT labeling of the pathology archives, providing a retrospective fix to the large lack of organization of narrative reports.

A researcher's guide to preclinical mouse NASH models

Non-alcoholic fatty liver disease (NAFLD) and its inflammatory form, non-alcoholic steatohepatitis (NASH), have quickly risen to become the most prevalent chronic liver disease in the Western world and are risk factors for the development of hepatocellular carcinoma (HCC). HCC is not only one of the most common cancers but is also highly lethal. Nevertheless, there are currently no clinically approved drugs for NAFLD, and NASH-induced HCC poses a unique metabolic microenvironment that may influence responsiveness to certain treatments. Therefore, there is an urgent need to better understand the pathogenesis of this rampant disease to devise new therapies. In this line, preclinical mouse models are crucial tools to investigate mechanisms as well as novel treatment modalities during the pathogenesis of NASH and subsequent HCC in preparation for human clinical trials. Although, there are numerous genetically induced, diet-induced and toxin-induced models of NASH, not all of these models faithfully phenocopy and mirror the human pathology very well. In this Perspective, we shed some light onto the most widely used mouse models of NASH and highlight some of the key advantages and disadvantages of the various models with an emphasis on 'Western diets', which are increasingly recognized as some of the best models in recapitulating the human NASH pathology and comorbidities.