Types and frequency of whole slide imaging scan failures in a clinical high throughput digital pathology scanning laboratory

Patel, Ankush; Shaker, Nada; Erck, Savannah; Kellough, David A.; Palermini, Erin; Lujan, Giovanni; Satturwar, Swati; Parwani, Anil V.

doi:10.1016/j.jpi.2022.100112

Cited by 8 publications

(6 citation statements)

References 30 publications

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“…A recent study from a large centre with established DP practice reported an overall scan failure rate in a series of over two million slides of 1.19% [27], mostly attributed to machine error such as skipped tissue and failed region of interest detection, with contribution to this figure also from quality issues affecting GS. A similar scan error rate of <1.5% is reported by Ardon et al [14], with missing tissue and out-of-focus errors accounting together for around 60% of cases.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of ‘PathProfiler’ in a Diagnostic Pathology Setting

Browning,

Jesus,

Malacrino

et al. 2024

Diagnostics

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Digital pathology continues to gain momentum, with the promise of artificial intelligence to aid diagnosis and for assessment of features which may impact prognosis and clinical management. Successful adoption of these technologies depends upon the quality of digitised whole-slide images (WSI); however, current quality control largely depends upon manual assessment, which is inefficient and subjective. We previously developed PathProfiler, an automated image quality assessment tool, and in this feasibility study we investigate its potential for incorporation into a diagnostic clinical pathology setting in real-time. A total of 1254 genitourinary WSI were analysed by PathProfiler. PathProfiler was developed and trained on prostate tissue and, of the prostate biopsy WSI, representing 46% of the WSI analysed, 4.5% were flagged as potentially being of suboptimal quality for diagnosis. All had concordant subjective issues, mainly focus-related, 54% severe enough to warrant remedial action which resulted in improved image quality. PathProfiler was less reliable in assessment of non-prostate surgical resection-type cases, on which it had not been trained. PathProfiler shows potential for incorporation into a digitised clinical pathology workflow, with opportunity for image quality improvement. Whilst its reliability in the current form appears greatest for assessment of prostate specimens, other specimen types, particularly biopsies, also showed benefit.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of ‘PathProfiler’ in a Diagnostic Pathology Setting

Browning,

Jesus,

Malacrino

et al. 2024

Diagnostics

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“…The lack of ADS determines that the operator should interfere with the scanning process to improve the capture of the image, conducting to multiple attempts to re-scan the glass slide and decreasing the efficiency. Reported re-scan rates/rates of scanning errors in highly successful workflows may be as low as 1.0%-1.19% 3,7 which is one of the reasons that warrants the rapid sample management in the laboratory.…”

Section: Discussionmentioning

confidence: 99%

Inking cell blocks improves scanner detection for diagnosis in pathology

Eloy,

Neves,

Vale

et al. 2023

Diagnostic Cytopathology

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Cell blocks may be hard to be totally automatically detected by the scanner (ADS), generating incomplete whole slide images (WSIs), with areas that are not scanned, leading to possible false negative diagnosis. The aim of this study is to test if inking the cell blocks helps increasing ADS. Test 1: 15 cell blocks were sectioned, one half inked black (1HB) and the other inked green (1HG). Each of the halves was individually processed to generate a WSI stained by the H&E. 1HBs and 1HGs had similar scanning time (median 59 s vs. 65 s, p = .126) and file sizes (median 382 Mb vs. 381 Mb, p = .567). The black ink interfered less in the observation (2.2% vs. 44.4%; p < .001) than in the green one. Test 2: 15 cell blocks were sectioned, one half inked black (2HB) and the other left unstained/null (2HN). Each of the halves was individually processed to generate three WSIs—one HE, one periodic‐acid Schiff (PAS), and one immunostained by cytokeratin AE1&AE3 (CKAE1AE3). HE and PAS WSIs from both 2HN and 2HB groups were all totally ADS and had similar scanning times and file sizes. Concerning immunostaining with CKAE1AE3: ADS (46.7% vs. 93.3%; p = .014), median time for scanning (57 s vs. 83 s; p < .001) and file size (178 Mb vs. 338 Mb; p < .001) were reduced significantly in the 2HN group in comparison with the 2HB. Although increasing scanning time and file size, inking the cell blocks helps increasing ADS after immunostaining, improving the safety and efficiency of the digital pathology workflow.

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“…Factors that should be considered arise when developing a DL model (71)(72)(73)(74)(75). Awareness of these factors is crucial to ensure a successful development process.…”

Section: Elements Impacting Model Developmentmentioning

confidence: 99%

Digital Pathology, Deep Learning, and Cancer: A Narrative Review

Williams,

Graifman,

Hussain

et al. 2024

Preprint

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Background and Objective: Cancer is a leading cause of morbidity and mortality worldwide. The emergence of digital pathology and deep learning technologies signifies a transformative era in healthcare. These technologies can enhance cancer detection, streamline operations, and bolster patient care. A substantial gap exists between the development phase of deep learning models in controlled laboratory environments and their translations into clinical practice. This narrative review evaluates the current landscape of deep learning and digital pathology, analyzing the factors influencing model development and implementation into clinical practice. Methods: We searched multiple databases, including Web of Science, Arxiv, MedRxiv, BioRxiv, Embase, PubMed, DBLP, Google Scholar, IEEE Xplore, and Cochrane, targeting articles on whole slide imaging and deep learning published from 2014 and 2023. Out of 776 articles identified based on inclusion criteria, we selected 36 papers for the analysis. Key Content and Findings: Most articles in this review focus on the in-laboratory phase of deep learning model development, a critical stage in the deep learning lifecycle. Challenges arise during model development and their integration into clinical practice. Notably, lab performance metrics may not always match real-world clinical outcomes. As technology advances and regulations evolve, we expect more clinical trials to bridge this performance gap and validate deep learning models' effectiveness in clinical care. High clinical accuracy is vital for informed decision-making throughout a patient's cancer care. Conclusions: Deep learning technology can enhance cancer detection, clinical workflows, and patient care. Challenges may arise during model development. The deep learning lifecycle involves data preprocessing, model development, and clinical implementation. Achieving health equity requires including diverse patient groups and eliminating bias during implementation. While model development is integral, most articles focus on the pre-deployment phase. Future longitudinal studies are crucial for validating models in real-world settings post-deployment. A collaborative approach among computational pathologists, technologists, industry, and healthcare providers is essential for driving adoption in clinical settings. Keywords: Artificial Intelligence, Deep Learning, Digital Pathology, Computational Pathology, Cancer

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Types and frequency of whole slide imaging scan failures in a clinical high throughput digital pathology scanning laboratory

Cited by 8 publications

References 30 publications

Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of ‘PathProfiler’ in a Diagnostic Pathology Setting

Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of ‘PathProfiler’ in a Diagnostic Pathology Setting

Inking cell blocks improves scanner detection for diagnosis in pathology

Digital Pathology, Deep Learning, and Cancer: A Narrative Review

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