Cytology and cell-block immunohistochemistry of circulating tumour cells.

OBJECTIVE: The study set out to assess the feasibility of using ParsortixTM circulating tumour cell (CTC) extraction and CytoFoam Disc cell-block immunohistochemistry to diagnose metastatic carcinoma from blood samples in a National Health Service district general hospital. METHODS: Blood samples were taken from 50 patients with metastatic carcinoma and 50 healthy volunteers and processed, using a previously published method, to extract CTCs and collect them in a cell-block for routine formalin-fixed paraffin sectioning and immunohistochemistry. The extracted cells were compared with the patients' routine diagnostic samples. RESULTS: The samples from the 50 carcinoma patients showed cytokeratin-positive cells in 19 cases. In eight of these, the cytokeratin-positive cells had a similar immunoprofile to the carcinoma in the conventional biopsy or cytology specimen. Some carcinoma patients also had circulating cytokeratin-positive cells that were probably benign epithelial cells and circulating megakaryocytes. Both of these types of cells were also found in healthy volunteers. Processing and initial examination could be completed in 2 days. The full processing cost was approximately £316 per case. CONCLUSIONS: CTCs could be extracted from the blood of some patients with metastatic carcinoma and formed into a formalin-fixed cell-block for routine paraffin processing and immunohistochemistry. The specificity of this approach is constrained by the observation that some patients with metastatic carcinoma had circulating cytokeratin-positive cells that were probably benign, and these were also found in healthy volunteers. Circulating megakaryocytes were present in carcinoma patients and healthy volunteers.

Machine learning and machine teaching in histopathology.

An artificial intelligence (AI) platform was trained by a consultant histopathologist to classify whole slide images (WSIs) of large bowel biopsies. Six medical students viewed WSIs of five large bowel biopsy cases and assigned the WSIs to one of the nine diagnostic categories. Then the students compared their answers with those generated by the AI. This training was repeated for a total of six rounds of five cases, and the accuracy of the students was recorded for each round. Each case had one or more WSIs. The student with the best final accuracy was asked to describe the morphological features that they had deduced. All the students improved during their training, from a mean accuracy of 13.7% in the first round to a mean accuracy of 77.1% in the sixth round (p = 0.0011). The student-deduced diagnostic features were mainly accurate. Some students learned more quickly than others.

Artificial intelligence-based triage of large bowel biopsies can improve workflow.

Background: Large bowel biopsies are one of the commonest types of biopsy specimen. We describe a service evaluation study to test the feasibility of using artificial intelligence (AI) to triage large bowel biopsies from a reporting backlog and prioritize those that require more urgent reporting. Methods: The pathway was developed in the UK by National Health Service (NHS) laboratory staff working in a medium-sized general hospital.   The AI platform was interfaced with the slide scanner software and the reporting platform's software, so that pathologists could correct the AI label and reinforce the training set as they reported the cases. Results: he AI classifier achieved a sensitivity of 97.56% and specificity of 93.02% for the case-level-diagnosis of neoplasia (adenoma and adenocarcinoma) and for an AI diagnosis of any significant pathology (i.e., adenomas, adenocarcinomas, inflammation, hyperplastic polyps, and sessile serrated lesions) sensitivity was 95.65% and specificity 92.96%. The automated AI diagnostic classification pathway took approximately 175 s per slide to download and process the scanned whole slide image (WSI) and return an AI diagnostic classification. Biopsies with an AI diagnosis of neoplasia or inflammation were prioritized for reporting while the remainder followed the routine reporting pathway. The AI triaged pathway resulted in a significantly shorter reporting turnaround time for pathologist verified neoplastic cases (P < 0.001) and inflammation (P < 0.05). The project's costs amounted to  £14800, excluding laboratory staff salaries. More time and resources were spent on developing the interface between the AI platform and laboratory IT systems than on the development of the AI platform itself. Conclusions: NHS laboratory staff were able to implement an AI solution to accurately triage large bowel biopsies into several diagnostic classes and this improved reporting turnaround times for cases with neoplasia or with inflammation.

A Feasibility Study of Multisite Networked Digital Pathology Reporting in England.

BACKGROUND: The objective of the project was to evaluate the feasibility of introducing a single-networked digital histopathology reporting platform in the Southwest Peninsula region of England by allowing pathologists to experience the technology and recording their perceptions. This information was then used in planning future service development. The project was funded by the National Health Service (NHS) Peninsula Cancer Alliance and took place in 2020 during the COVID-19 pandemic. MATERIALS AND METHODS: Digital slides of 500 cases from Taunton were reported remotely in Truro, Plymouth, Exeter, Bristol, or Bath by using a single remote reporting platform located on the secure Health and Social Care Network (HSCN) that links NHS sites. These were mainly small gastrointestinal, skin, and gynecological specimens. The digital diagnoses were compared with the diagnoses issued on reporting the glass slides. At the end of the project, the pathologists completed a Google Forms questionnaire of their perceptions of digital pathology. The results were presented at a meeting with the funder and discussed. RESULTS: From the 500 cases there were nine cases of significant diagnostic discrepancy, seven of which involved the misrecognition of Helicobacter pylori in gastric biopsies. The questionnaire at the end of the project showed that there was a general agreement that the platform was easy to use, and the image quality was acceptable. It was agreed that extra work, such as deeper levels, was easy to request on the software platform. Most pathologists did not agree that digital reporting was quicker than glass slide reporting. Some were less confident in their digital diagnoses than glass diagnoses. They agreed that some types of specimens cannot easily be reported digitally. All users indicated that they would like to report at least half of their work digitally in the future if they could, and all strongly agreed that digital pathology would improve access to expert opinions, teaching, and multidisciplinary meetings. It was difficult to find pathologists with time to undertake remote digital reporting, in addition to their existing commitments. CONCLUSIONS: Overall, the pathologists developed a positive perception of digital pathology and wished to continue using it.