WHOLE SLIDE IMAGE SCANNER STUDIES
Academic Article: Journal of Pathology Informatics
Comparative evaluation of slide scanners, scan settings, and cytopreparations for digital urine cytology
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AIxURO URINE CYTOLOGY CLINICAL STUDIES
March 2025
USCAP 2025 Poster Presentation
Enhancing Bladder Cancer Diagnosis Through the Integration of an AI-Driven Cytology System
USCAP 2025 Poster Presentation
Enhancing Bladder Cancer Diagnosis Through the Integration of an AI-Driven Cytology System
STUDY DESIGN:
- De-identified ThinPrep urine cytology slides (200) were retrospectively selected. Two cytopathologists (CP) provided consensus diagnoses (ground truth, GT) for all cases: 100 Negative for High-Grade Urothelial Carcinoma (NHGUC), 35 Atypical Urothelial Cells (AUC), 32 Suspicious for HGUC (SHGUC), and 33 HGUC.
- Slides were digitized into WSIs utilizing Mikroscan SLxCyto and customized Huron WSI imagers and examined using AI-assisted WSI review (AIxURO)
- 1 cytopathologist (CP) and 2 cytologists (CT) blindly reviewed slides with a 2-week washout period between research arms:
o Arm 1- Microscopy only
o Arm 2- AIxURO Mikroscan SLxCyto’s WSI review
o Arm 3- AIxURO customized Huron’s WSI review
- Performance Metrics:
o Comparison of study diagnosis with ground truth diagnosis for 3 Arms using following thresholds:
(1) AUC+ (AUC, SHGUC and HGUC) cases as positive; NHGUC cases as negative
(2) SHGUC+ (SHGUC and HGUC) cases as positive; NHGUC and AUC cases as negative
o The slide evaluation time (SET) in each arm was documented and made comparison.
RESULTS:
- Diagnostic performance using the AUC+ threshold, AIxURO WSI review (Arm 2 and Arm 3) demonstrated higher sensitivity than microscopy (85.0% and 88.3% vs 79.3% overall). However, AIxURO WSI review exhibited lower specificity than microscopy (85.7% and 82.7% vs. 94.3%).
- When using the SHGUC+ threshold, AIxURO WSI review demonstrated higher overall sensitivity (74.9% and 86.2 vs 76.9%) and slightly lower specificity (96.0% and 92.3% vs 97.5% overall) compared to microscopy.
- AIxURO WSI review markedly reduced the SET versus microscopy (35.9s and 36.4s vs 102.6s). SETs for AUC+ are 45s and 43.6s vs 116.4s, while SET for negative cases are 26.5s and 29.1s vs 88.9s.
CONCLUSIONS:
AIxURO WSI review demonstrated higher sensitivity but lower specificity than microscopy for AUC+ and SHGUC+ thresholds. Notably, AIxURO WSI review reduced slide evaluation time by at least 64.5%, offering substantial efficiency gains. These findings highlight AIxURO’s potential to enhance workflow efficiency in settings withstaffing shortages while maintaining diagnostic performance.
THE PARIS SYSTEM FOR URINE CYTOLOGY NUCLEAR TO CYTOPLASMIC RATIO
March 2025
USCAP 2025 Oral Presentation
Quantitative Analysis of the Paris System Cytomorphological Criteria in Digital Urinary Cytology Using Artificial Intelligence Algorithm
USCAP 2025 Oral Presentation
Quantitative Analysis of the Paris System Cytomorphological Criteria in Digital Urinary Cytology Using Artificial Intelligence Algorithm
STUDY DESIGN:
- 296 urine cytology slides from confirmed biopsy-positive cases within six months (70 AUC, 65 SHGUC,161 HGUC)
- Slides digitized into WSIs with Leica AT2 scanner
- AI algorithm identified and categorized cells with high (suspicious) or low (atypical) malignancy risk based on The Paris System for Reporting Urinary Cytology (TPS) features; Top-24 category order-ranks 24 most abnormal cells
- Performance Metrics: Mean values of suspicious cells, atypical cells, and the top-24 cells (cells ranked by highest probability of malignancy) with TPS features compared across the three categories (HGUC, SHGUC, and AUC).
RESULTS:
- HGUC cases had significantly higher mean number of abnormal cells for top-24 cells (12.2 vs. 5.5 vs. 2.5), suspicious cells (91.6 vs. 13.1 vs. 2.7), and atypical cells (885.1 vs. 108.9 vs. 30.8) compared to SHGUC and AUC, respectively.
- Top-24 and suspicious cells displayed higher N:C ratio (0.65-0.66) and larger nuclear area (106-113 mm 2 ) than atypical cells (0.57 and 86-91 mm 2 ) across three categories.
- Percentage of cells with all 3 key TPS morphological features across 3 categories:
o Top-24 cells- 84%-95%
o Suspicious cells- 81%-91%
o Atypical cells- 22%-39%
- Critical distinguishing feature between suspicious and atypical was hyperchromasia
o 93% of suspicious cells
o 24%-40% of atypical cells.
- Percentage of top-24 / suspicious cells with all 3 TPS features:
o HGUC (95% / 91%)
o SHGUC (89% / 89%)
o AUC (84% / 81%)
CONCLUSIONS:
HGUC showed the highest numbers of abnormal urothelial cells compared to SHGUC and AUC. N/C ratio and nuclear sizes were larger in top-24 and suspicious cell categories compared to the atypical cell category. Hyperchromasia was the most important distinguishing morphologic feature between suspicious and atypical cells. HGUC had the highest % of top-24 and suspicious cells with all 3 TPS morphologic features, indicating that the AI algorithm correctly selected features predicting HGUC. These findings highlight AI’s potential to enhance accuracy and efficiency in bladder cancer diagnosis.
WHOLE SLIDE IMAGE SCANNER STUDIES
March 2025
USCAP 2025 Poster Presentation
Does Digital Scanner Selection Impact Urine Cytology Whole Slide Imaging? A Comparative Study of Five Commercial Digital Scanning Devices
USCAP 2025 Poster Presentation
Does Digital Scanner Selection Impact Urine Cytology Whole Slide Imaging? A Comparative Study of Five Commercial Digital Scanning Devices
STUDY DESIGN:
- Ten de-identified ThinPrep Urocyte slides with surgical concordance as gold standard/ground truth (4 NHGUC, 2 AUC, 1 SHGUC, 3 HGUC) digitized into WSIs using five digital imagers:
o Leica Aperio AT2
o Hamamatsu S360
o 3DHistech P1000
o Customized Huron WSI model
o Mikroscan SLxCyto
- AI algorithm was applied to the WSIs to detect suspicious cells (high-malignant risk) and atypical urothelial cells (low-malignant risk).
- 3 cytologists (CT) reported diagnosis on slides with 2-week washout period between arms:
o Arm 1- Microscopy only
o Arm 2- AI-assisted WSI review
- Performance Metrics:
o WSI conversion rate
o Comparison of binary (positive vs negative) diagnosis (AUC, SHGUC, and HGUC cases = positive) with ground truth diagnosis for 2 Arms
o Quantitative data analysis
RESULTS:
- All digital imagers achieved a 100% WSI conversion rate.
- In the binary diagnosis, AI-assisted WSI review demonstrated performance comparable to microscopy across all imagers
- Quantitative analysis revealed slight variations in key metrics, including suspicious/atypical cell numbers, nuclear-to-cytoplasmic (N:C) ratio, and nuclear area, among WSIs produced by different imagers
CONCLUSIONS:
First study to assess multiple digital pathology imagers for AI-assisted urine cytology. Results demonstrate that a disease-specific AI algorithm produces consistent diagnostic performance and quantitative analysis across different WSIs, supporting its potential to enhance diagnostic accuracy across imaging platforms.
November 2023
Comparative evaluation of slide scanners, scan settings, and cytopreparations for digital urine cytology
Hang JF, Ou YC, Yang WL et al. J Pathol Inform. 2023 Nov 4;15:100346. Doi: 10.1016/j.jpi.2023.100346. PMID: 38125926; PMCID: PMC10730371. AIxMED Technical Review Study, Stage I
STUDY DESIGN:
- 14 urine cytology specimens, equally divided and prepped into CytoSpin and ThinPrep specimens, were scanned for WSI by Leica Aperio AT2 and Hamamatsu NanoZoom S360 at 3 focus modes (default, semiautomatic, and manual) for single layer scanning and with a manual focus mode for 21 Z-layer scanning
- Performance metrics evaluated included scanning success rate, AI-algorithm-inferred atypical cell numbers and coverage rates (atypical cells in single or multiple Z-layers divided by total atypical cells), scanning time, and image file size
RESULTS:
Success Scanning Rates
- Default Mode Scanning: 85.7% (Cytospin, Leica) and 92.9% (ThinPrep, Leica; Cytospin and ThinPrep, Hamamatsu) success rates
- Semi-Auto Mode Scanning: 92.9% (Cytospin, Leica; Cytospin, Hamamatsu and ThinPrep, Hamamatsu) or 100% (ThinPrep, Leica) success
- Manual Scanning: 100% success (all preps with all scanners)
Scanning Times (median)
- Cytospin, Leica: 1010 seconds
- Cytospin, Hamamatsu: 275 seconds
- ThinPrep, Leica: 5357 seconds
- ThinPrep, Hamamatsu: 1429 seconds
File Image Sizes of WSI (using manual focus mode and 21 Z-layer scan settings)
- Cytospin, Leica: 2.0 GB
- Cytospin, Hamamatsu: 4.4 GB
- ThinPrep, Leica: 13.1 GB
- ThinPrep, Hamamatsu: 25.5 GB
CONCLUSIONS:
Semi-automatic and manual focus modes provide the most successful slide scanning with rates up to 100%. A minimum of 9-layer Z-stacking at 1 µm intervals is necessary to cover 80% of atypical cells. Cytospin preparations take less time to scan and result in smaller file sizes than do ThinPrep slides, regardless of the scanner. Z-stacking enhances AI inferred quality and coverage rates of atypical cells but with longer scanning times and larger image files.
November 2023
WHOLE SLIDE IMAGE SCANNER STUDIES
ASC Poster Presentation
Evaluating image quality and reproducibility of digital scanners for whole-slide imaging in urine cytology slides for artificial intelligence applications
Evaluating image quality and reproducibility of digital scanners for whole-slide imaging in urine cytology slides for artificial intelligence applications
ASC 2023 Poster Presentation / Abstract (JASC 2023; Vol12(5):S74-75). Yang WL, Hang JF, Ou YC et al.
STUDY DESIGN:
- 20 urine cytology slides, 5 types of preparation (cytospin, ThinPrep nonGYN, ThinPrep Urocyte, and BD CytoRich) were digitized using 3 digital scanners (Roch DP200, Roch DP600, and Hamamatsu Nanozoomer 360)
- Images were evaluated for quality, focus and color from the default, manual and advanced scanning modes by a senior cytologist
RESULTS:
- Roche DP200 and DP600 scanners achieved good quality WSI (30-50% in default mode and 40-65% in manual mode)
- Hamamatsu achieved 90% good quality imaging only in the manual mode, whereas the default quality dropped to 15%. It also had a lower coefficient of variation for average number of atypical cells across all preparation types
CONCLUSIONS:
Overall, Hamamatsu scanner showed better performance for image quality in the manual mode than Roche scanners, which performed better in the default mode. Hamamatsu scanners also had improved detection of atypical urothelial cells among triplicate images from the same samples compared to Roche scanners.
November 2023
AIxURO URINE CYTOLOGY CLINICAL STUDIES
ASC Oral Presentation
Evaluating the feasibility of a deep learning-based atypical cell gallery for clinical diagnosis in urine cytology
Evaluating the feasibility of a deep learning-based atypical cell gallery for clinical diagnosis in urine cytology
ASC 2023 Oral Platform/ Abstract (JASC 2023; Vol 12(5):S51-52) Liu TJ, Yang WC, Huang SM, et al.
STUDY DESIGN:
- 116 urine cytology slide scanned as WSI and analyzed by AI deep-learning software system and ranked as most suspicious or atypical urothelial cells based on The Paris System 2.0 (TPS 2.0) for Reporting Urine Cytology
- Cell image gallery displayed 14 top abnormal cells in viewing software for expert analysis
- 1 cytopathologist (1 CP) and 2 cytologists (CP) assigned a TPS 2.0 diagnosis to each case: NHGUC, AUC, SHGUC or HGUC; results were compared between reviewers
RESULTS:
- CP demonstrated the highest specificity, PPV, accuracy and agreement with the ground truth compared with the cytologists (CPs), who had greater sensitivity and NPV
- Both CP and CTs had reduced sensitivity, PPV and agreement in the diagnosis of AUC vs NHGUC
CONCLUSIONS:
Decreased sensitivity and PPV in differentiating AUC from NHGUC may be the result of the usual clinical workflow, where cytologists initially screen and mark suspicious regions as abnormal, but defer to CP for a more specific interpretation; it may also reflect less experience with digital cytology interpretation.
November 2023
SOFTWARE OPTIMIZATION & VALIDATION STUDIES
ASC Poster Presentation
Evaluating image quality and reproducibility of digital scanners for whole-slide imaging in urine cytology slides for artificial intelligence applications
Evaluating image quality and reproducibility of digital scanners for whole-slide imaging in urine cytology slides for artificial intelligence applications
ASC 2023 Poster Presentation / Abstract (JASC 2023; Vol12(5):S74-75). Yang WL, Hang JF, Ou YC et al.
STUDY DESIGN:
- 20 urine cytology slides, 5 types of preparation (cytospin, ThinPrep nonGYN, ThinPrep Urocyte, and BD CytoRich) were digitized using 3 digital scanners (Roch DP200, Roch DP600, and Hamamatsu Nanozoomer 360)
- Images were evaluated for quality, focus and color from the default, manual and advanced scanning modes by a senior cytologist
RESULTS:
- Roche DP200 and DP600 scanners achieved good quality WSI (30-50% in default mode and 40-65% in manual mode)
- Hamamatsu achieved 90% good quality imaging only in the manual mode, whereas the default quality dropped to 15%. It also had a lower coefficient of variation for average number of atypical cells across all preparation types
CONCLUSIONS:
Overall, Hamamatsu scanner showed better performance for image quality in the manual mode than Roche scanners, which performed better in the default mode. Hamamatsu scanners also had improved detection of atypical urothelial cells among triplicate images from the same samples compared to Roche scanners.
October 2023
AI SOFTWARE DEVELOPMENT
ASC Poster Presentation
Assessing an artificial intelligence-driven digital cytology tool for efficient diagnosis of upper tract urothelial carcinoma in upper urinary tract cytology
Assessing an artificial intelligence-driven digital cytology tool for efficient diagnosis of upper tract urothelial carcinoma in upper urinary tract cytology
ASC 2023 Poster Presentation / Abstract (JASC 2023; Vol 12(5):S73-75) Wei-Lei Yang; JF Hang et al.
STUDY DESIGN:
- 60 paired cytospin and CytoRich slides from 30 pts scanned into WSI and inferred using AIxURO AI-driven software, ranking into most top 24 suspicious cells/groups
- 3 senior cytologists with variable digital interpretive experience (A= over 1 year; B = 6 mo-1 yr; C = less than one month) evaluated interface gallery of images to render a diagnosis
RESULTS:
- Dx performance was consistent for the 2 preparation types, with cytologists A & B showing excellent performance
- All reviewers spent similar amounts of time on review per preparation type, with cytologist C taking the least amount of time.
CONCLUSIONS:
The AI-assisted tool allowed for excellent performance to aid interpretation of upper GU tract urothelial carcinoma
October 2023
AIxURO URINE CYTOLOGY CLINICAL STUDIES
USCAP Poster Presentation
Assessing the practicability of artificial intelligence-assisted digital urine cytology in diagnosing bladder cancer in clinical practice
Assessing the practicability of artificial intelligence-assisted digital urine cytology in diagnosing bladder cancer in clinical practice
DPA PathVysions 2023 Poster Presentation / Abstract (J Pathol Inform 2023) Liu TJ, Yang WC, Huang SM, et al. Show Chwan Memorial Hospital
STUDY DESIGN:
- 116 urine cytology slides digitized into WSI and analyzed by AI-assisted software to identify and categorize abnormal cells into suspicious (likely SHGUC or HGUC) or atypical (AUC) categories
- 1 cytopathologist (CP) and 2 cytologists (CP) reviewed all slides microscopically (Arm 1), as a WSI (Arm 2) and with AI-assistance presenting the top 24 most abnormal cells in a gallery display (Arm 3), with a 2-week wash-out period between reviews
- Performance Metrics: Comparison of expert panel consensus of the glass slide (86 negative, 30 positive) to the review diagnoses to calculate sensitivity, specificity, PPV and NPV, along with the time spent examining the slide for each arm per individual
RESULTS:
- AI-assisted software (AIxURO) improved the overall sensitivity (from 82.2% to 92.2%) and NPV (from 93.4% to 96.5%) but decreased specificity (from 87.2% to 75.6%), PPV (from 69.2% to 56.8%) and accuracy (from 85.9% to 79.9%) compared to microscopy. The WSI performance was the worst arm.
- Time for slide review was significantly reduced overall (from 159.9 min for microscopy to 106.3 minutes for AI-assisted), as well as per individual slide (from 1.38 min to 0.92 min)
CONCLUSIONS:
AI-assistance improves the sensitivity and NPV of urine cytology review, but at the expense of a decrease in specificity, PPV and accuracy. This may indicate that AI-assistance will facilitate the detection of abnormal urothelial cells while lowering the time required for slide review.
October 2023
AI SOFTWARE DEVELOPMENT
Pathology Visions Poster Presentation
Utilizing artificial intelligence-assisted heuristic scanning to enhancing efficiency of urine cytology slides scanning
Utilizing artificial intelligence-assisted heuristic scanning to enhancing efficiency of urine cytology slides scanning
DPA PathVysions 2023 Poster Presentation / Abstract (J Path Inform 2023) Liu TJ, Hang JF, Ou YC et al. TVGHTE and Tung’s Taichung MetroHospital
STUDY DESIGN:
- 52 urine cytology slides from bladder cancer patients (24 cytospin, 16 ThinPrep, 12 Cytorich) were digitally scanned with Leica AT2 using conventional Z-stacking (Z = 0 to Z = +10 layers above and below at 1 µm intervals) and compared with using a heuristic scanning simulation method with AI to determine regions of interest (ROI) for scanning, whereby the software determines the ideal number of layers to scan, from 3 layers (Z = 0+1) to 21 layers (Z = +10).
- Performance Metrics: Total number of suspicious cells; coverage rate (ratio of suspicious cells in single vs multiple Z-layers to the total suspicious cells in 21 Z-layers); scanning time (seconds); and image file size for storage (in gigabytes).
RESULTS:
- Heuristic scanning was comparable to Z-stacking with similar average numbers of suspicious cell coverage rates (79.3% cytospin; 85.9% TP; 78.3% CytoRich) to those of Z-stacking (81.9% in 5 layers for cytospin, 87.1% in 9 layers for TP, 82.9% in 7 layers for CytoRich).
- Heuristic scanning significantly reduced scanning time (65-72%) and image file size (by 46-64%).
- Heuristic scanning in low cellularity slides was more accurate (7/7 correct diagnoses) than using single Z-layer WSI (4/7 correct diagnoses)
CONCLUSIONS:
Heuristic scanning is an effective alternative to conventional Z-stacking to identify suspicious cells on urine cytology slides, with the advantage of reducing scanning time and image file size.
August 2023
SOFTWARE OPTIMIZATION & VALIDATION STUDIES
Japanese Society of Digital Pathology (JSDP) 21st Annual Meeting
Detection of urothelial carcinoma in upper urinary tract cytology using a digital artificial intelligence-assisted tool
Detection of urothelial carcinoma in upper urinary tract cytology using a digital artificial intelligence-assisted tool
2023 Japanese Society of Digital Pathology Oral Platform/ Abstract Liu TJ, Yang WL, Hsu SW, Yeh CH, Hang JF. Taipei Veterans General Hospital (VGHTE); Nat’l Yang Ming Chiao Tung University
STUDY DESIGN:
- 100 paired cytospin and CytoRich urine cytology slides from 50 bladder cancer patients digitized to create WSI and analyzed with AI-assisted software that identifies the most atypical urothelial cells and displays them in a gallery of the top 24
- 3 cytologists with variable digital pathology experience (A= over 12 months; B= 6-12 months; C= less than one month) reviewed the AI-assisted images and interpreted them using The Paris System categories
RESULTS:
- No significant difference in diagnostic performance of cytologists between preparations (cytospin vs CytoRich)
- Digital pathology experience improved performance, with C having the poorest performance
- Sensitivity = 84-96%; Specificity = 92- 96%; PPV = 91.3-95.7%; NPV = 85.2-95.8%; accuracy = 88-94%
- Cytologist C took the least time reviewing slides (median 29.5-30 sec) compared to the other 2 (median 63.5-71.5 sec)
CONCLUSIONS:
AI-assistance markedly improves efficiency for the interpretation of upper urinary tract cytology. Experience using the software system enhances overall performance and diagnostic concordance.
May 2023
AIxURO URINE CYTOLOGY CLINICAL STUDIES
iSMIT Conference Oral Presentation
A novel and effective deep-learning algorithm for risk stratification of urothelial carcinoma
A novel and effective deep-learning algorithm for risk stratification of urothelial carcinoma
International Society for Medical Innovation and Technology 2023 Oral Platform / Abstract (Min Inv Ther Allied Technol)
STUDY DESIGN:
- 116 urine cytology WSI (76 cytospin, 40 CytoRich) scanned with Leica AT2 at 20X and analyzed and ranked “top 24” by AI-deep learning algorithm for most abnormal urothelial cells
- 1 cytopathologist (CP) and 2 cytologists (CT) evaluated WSI with AI-assistance and diagnosed slides as NHGUC, AUC, SHGUC or HGUC according to The Paris System 2.0.
- Performance Metrics: Concordance with original cytologic diagnosis and time to diagnosis compared to conventional microscopy
RESULTS:
- AI-assistance increased sensitivity for all 2 reviewers (from 83.3-100%) and improved time efficiency from 159.9 mins to 106.3 min)
- Specificity was reduced for the CTs, but not the CP
CONCLUSIONS:
AI-assistance using a deep learning algorithm to identify and rank abnormal cells improves overall clinical sensitivity for the detection of urothelial carcinoma while improving clinical efficiency through time-savings.
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