A retrospective validation of the FocalPoint GS slide profiler NFR technology by analysis of interval disease outcomes compared with manual cytology

Nuttall, David; Hillier, Sharon L.; Clayton, Helen; Savage, Amanda J.; Martin, Cara; O’Leary, John

doi:10.1002/cncy.22109

Cited by 4 publications

(14 citation statements)

References 16 publications

(20 reference statements)

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“…Studies that evaluated segmentation and/or classification algorithms rather than the automated system available for implementation were excluded. Table 1 presents the relevant information from the reviewed articles 48‐81 …”

Section: Resultsmentioning

confidence: 99%

“…Studies that evaluated the FocalPoint System 50,65 did not report the number of slides/hour or day but determined that productivity increased by an average of 17.5%. The ThinPrep System 48,60,64,71 showed an ~75% productivity improvement.…”

Section: Resultsmentioning

confidence: 99%

“…The motorized stage of the microscope enables a review of these fields on the slides. If a cell is considered suspicious by the professional, the slide must be manually reviewed 50‐54,59,62,65,68,69,81 …”

Section: Discussionmentioning

confidence: 99%

“…This scenario is demonstrated in the increases in laboratory productivity with improvement in the laboratory's overall accuracy 48‐50,60,64‐66,71,73,74,80 …”

Section: Discussionmentioning

confidence: 99%

“…AMs resulted in fewer FNRs 48‐50,65,66,75 . Studies have indicated that the system was effective in quality control (QC) in cytology, showing a great discriminatory capacity to identify altered cases 51,53,59,63,68,77 .…”

Section: Discussionmentioning

confidence: 99%

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Cervical cancer: Automation of Pap test screening

Rezende

Bianchi

Carneiro

2021

Diagnostic Cytopathology

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Background Cervical cancer progresses slowly, increasing the chance of early detection of pre‐neoplastic lesions via Pap exam test and subsequently preventing deaths. However, the exam presents both false‐negatives and false‐positives results. Therefore, automatic methods (AMs) of reading the Pap test have been used to improve the quality control of the exam. We performed a literature review to evaluate the feasibility of implementing AMs in laboratories. Methods This work reviewed scientific publications regarding automated cytology from the last 15 years. The terms used were “Papanicolaou test” and “Automated cytology screening” in Portuguese, English, and Spanish, in the three scientific databases (SCIELO, PUBMED, MEDLINE). Results Of the resulting 787 articles, 34 were selected for a complete review, including three AMs: ThinPrep Imaging System, FocalPoint GS Imaging System and CytoProcessor. In total, 1 317 148 cytopathological slides were evaluated automatically, with 1 308 028 (99.3%) liquid‐based cytology slides and 9120 (0.7%) conventional cytology smears. The AM diagnostic performances were statistically equal to or better than those of the manual method. AM use increased the detection of cellular abnormalities and reduced false‐negatives. The average sample rejection rate was ≤3.5%. Conclusion AMs are relevant in quality control during the analytical phase of cervical cancer screening. This technology eliminates slide‐handling steps and reduces the sample space, allowing professionals to focus on diagnostic interpretation while maintaining high‐level care, which can reduce false‐negatives. Further studies with conventional cytology are needed. The use of AM is still not so widespread in cytopathology laboratories.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…This scenario is demonstrated in the increases in laboratory productivity with improvement in the laboratory's overall accuracy 48‐50,60,64‐66,71,73,74,80 …”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Cervical cancer: Automation of Pap test screening

Rezende

Bianchi

Carneiro

2021

Diagnostic Cytopathology

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Calibration of the Becton Dickinson FocalPoint GS slide profiler is essential for optimal performance: Avoiding the algorithm supersaturation cascade effect pitfall

Nuttall

Anderson²,

Savage

et al. 2023

Cancer Cytopathology

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BACKGROUND The MAVARIC study supported the use of the FocalPoint GS (FPGS) imaging system “No Further Review” (NFR) technology for cervical screening and recommended further investigation. A validation study (Nuttall et al.) was performed by Cervical Screening Wales before implementing the NFR slide reporting technology within the cervical screening program in Wales, United Kingdom. METHOD A total of 45,317 SurePath liquid‐based cytology cervical screening samples were submitted for FPGS scanning within four Welsh cytology laboratories between 2006 and 2011. The study, Computer Assisted Evaluation, Screening and Reporting, involved scanning the slides using the FPGS and comparing the results with manual screening performed under established Cervical Screening Wales protocols. RESULTS An increased number of abnormal cases presented in the NFR reporting category, significantly greater than that previously encountered. This anomaly resulted in higher false‐negative rates with potentially life‐changing consequences for the screening participant. Subsequent investigation determined that this increase in cases created an algorithm cascade or “sump” effect, which resulted in an unprecedented increased number of samples categorized as NFR. This exceeded the calibration parameters set for the FPGS and was thought to be caused by an increase in the number of younger women attending for screening following the death of a young reality television celebrity from cervical cancer. CONCLUSION Adequate and timely calibration of FPGS technology is vital for quality assurance of the results produced, particularly following events that may impact on cervical precancer incidence rates. Failure to do so can result in potentially catastrophic screening incidents that are avoidable.

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The digital revolution in pathology: Towards a smarter approach to research and treatment

Tucci,

Laurinavicius,

Kather

et al. 2024

Tumori

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Artificial intelligence (AI) applications in oncology are at the forefront of transforming healthcare during the Fourth Industrial Revolution, driven by the digital data explosion. This review provides an accessible introduction to the field of AI, presenting a concise yet structured overview of the foundations of AI, including expert systems, classical machine learning, and deep learning, along with their contextual application in clinical research and healthcare. We delve into the current applications of AI in oncology, with a particular focus on diagnostic imaging and pathology. Numerous AI tools have already received regulatory approval, and more are under active development, bringing clear benefits but not without challenges. We discuss the importance of data security, the need for transparent and interpretable models, and the ethical considerations that must guide AI development in healthcare. By providing a perspective on the opportunities and challenges, this review aims to inform and guide researchers, clinicians, and policymakers in the adoption of AI in oncology.

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A retrospective validation of the FocalPoint GS slide profiler NFR technology by analysis of interval disease outcomes compared with manual cytology

Cited by 4 publications

References 16 publications

Cervical cancer: Automation of Pap test screening

Cervical cancer: Automation of Pap test screening

Calibration of the Becton Dickinson FocalPoint GS slide profiler is essential for optimal performance: Avoiding the algorithm supersaturation cascade effect pitfall

The digital revolution in pathology: Towards a smarter approach to research and treatment

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