AIxTHY THYROID FINE NEEDLE ASPIRATION CLINICAL STUDIES
Pathology Visions Oral Presentation
COMING SOON - Redefining Thyroid Cytology with AIxTHY: Improved Accuracy and Efficiency in FNAC Interpretation
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AIxTHY THYROID FINE NEEDLE ASPIRATION CLINICAL STUDIES
October 2025
Pathology Visions Oral Presentation
COMING SOON - Redefining Thyroid Cytology with AIxTHY: Improved Accuracy and Efficiency in FNAC Interpretation
Pathology Visions Oral Presentation
COMING SOON - Redefining Thyroid Cytology with AIxTHY: Improved Accuracy and Efficiency in FNAC Interpretation
STUDY DESIGN:
RESULTS:
CONCLUSIONS:
AIxURO URINE CYTOLOGY CLINICAL STUDIES
May 2025
ICC 2025 Oral Presentation
AIxURO: Enhancing Diagnostic Accuracy in Bladder Caner Detection with AI-Assisted Urine Cytology
ICC 2025 Oral Presentation
AIxURO: Enhancing Diagnostic Accuracy in Bladder Caner Detection with AI-Assisted Urine Cytology
STUDY DESIGN:
- Retrospective study using 200 ThinPrep urine cytology slides.
- Ground truth: NHGUC (n=100), AUC (n=35), SHGUC (n=32), HGUC (n=33).
- Slides digitized using: (1) Mikroscan SLxCyto( Mikroscan-WSIs); (2) Customized Huron scanner (Huron-WSIs).
- WSIs were analyzed using AIxURO, a disease-specific AI algorithm for bladder cancer.
- Three-arm blinded review by 1 cytopathologist and 2 cytologists, with 2-week washout periods:
- Arm 1: Conventional microscopy
- Arm 2: AIxURO with Mikroscan-WSIs
- Arm 3: AIxURO with Huron-WSIs
- Performance Metrics:
- Comparison of study diagnosis with urine cytology ground truth diagnosis (n=200) for 3 arms, using following threshold: AUC+(AUC, SHGUC and HGUC) cases as positive; NHGUC cases as negative.
- Assessment of agreement between study diagnoses and surgical pathology-confirmed bladder cancer (n=72) among the three arms.
- Evaluation of bladder cancer prediction in hematuria-indicated cases (n=16) across the three arms.
RESULTS:
- Urine cytology ground truth cases: Arm 2 and Arm3 exhibited higher sensitivity (85.0% and 88.3%) compared to Arm 1 (79.3%) with comparable accuracy across all arms (Arm 1: 86.8%, Arm 2: 85.3%, Arm 3: 85.3%).
- Biopsy-confirmed bladder cancer cases: Arm 2 and Arm 3 showed improved sensitivity (92.0% and 93.2%) and accuracy (82.9% and 82.4%) compared to Arm 1 (84.6% sensitivity, 78.2% accuracy).
- Hematuria cases: Arms 2 and 3 achieved superior sensitivity (96.7% and 100.0%) and accuracy (89.6% and 91.7%) compared to Arm 1 (90.0% sensitivity, 85.4% accuracy).
CONCLUSIONS:
AIxURO consistently enhanced diagnostic sensitivity and maintained accuracy across multiple scanners, outperforming microscopy in detecting bladder cancer. It demonstrated strong predictive performance in high-risk subgroups, supporting its utility in real-world clinical workflows and digital cytology integration.
AI SOFTWARE DEVELOPMENT
May 2025
ICC 2025 - Oral Presentation
Does AI performance in Thyroid Cancer Diagnosis Differ Between Single-Layer and Multi-Z-Stacked Imaging?
ICC 2025 - Oral Presentation
Does AI performance in Thyroid Cancer Diagnosis Differ Between Single-Layer and Multi-Z-Stacked Imaging?
STUDY DESIGN:
- 71 ThinPrep thyroid FNAC slides selected by consensus cytology diagnosis
- Cases included: TBS-II (n=35), TBS-IV (n=6), TBS-VI (n=30), confirmed by biopsy
- Slides digitized using 3DHistech scanner to create paired whole-slide images:
- S-WSI: single-layer WSI
- 7-WSI: seven-layer Z-stacked WSI
- AI-assisted review used an AIxTHY model to detect cancer cells and guide interpretation
- Three cytologists independently reviewed both S-WSI and 7-WSI sets
- Total of 213 reads (71 cases × 3 reviewers) analyzed
- Two diagnostic threshold:
- Threshold 1: Positive = TBS-V/VI; Negative = TBS-II
- Threshold 2: Positive = TBS-IV/V/VI; Negative = TBS-II
- Outcomes measured:
- Binary diagnostic sensitivity and specificity (under both thresholds)
- Agreement with consensus diagnoses
- Interobserver agreement (Cohen’s κ)
RESULTS:
- Threshold 1 (TBS-V/VI vs. TBS-II):
- Sensitivity: 7-WSI 88.9% vs. S-WSI 81.1%
- Specificity: 7-WSI 94.3% vs. S-WSI 96.2% (not significant)
- Threshold 2 (TBS-IV/V/VI vs. TBS-II):
- Sensitivity: 7-WSI 86.1% vs. S-WSI 80.6% (p < 0.05)
- Specificity: 7-WSI (%): All cases: 7-WSI 88.6% vs. S-WSI 91.4%
- Consensus Agreement (%): All cases: 7-WSI 74.6%vs. S-WSI 61.5%
- TBS-VI: 65.6% vs. 36.7%
- TBS-IV: 44.4% vs. 27.8%
- TBS-II: 87.6% vs. 88.6%
- Interobserver Agreement (Cohen’s κ): Overall: 0.366 vs. 0.351
- TBS-IV: 7-WSI 0.189 vs. S-WSI 0.075
- TBS-VI: 0.420 vs. 0.319
- TBS-II: 0.549 vs. 0.604
CONCLUSIONS:
Seven-layer Z-stacked WSIs (7-WSI) enhanced AI-assisted thyroid cancer diagnosis and improved interobserver agreement among cytologists, especially in indeterminate (TBS-IV) and malignant (TBS-VI) cases, supporting their clinical value over single-layer WSIs.
October 2025
COMING SOON - Redefining Thyroid Cytology with AIxTHY: Improved Accuracy and Efficiency in FNAC Interpretation
STUDY DESIGN:
RESULTS:
CONCLUSIONS:
May 2025
AIxURO URINE CYTOLOGY CLINICAL STUDIES
ICC 2025 Oral Presentation
AIxURO: Enhancing Diagnostic Accuracy in Bladder Caner Detection with AI-Assisted Urine Cytology
AIxURO: Enhancing Diagnostic Accuracy in Bladder Caner Detection with AI-Assisted Urine Cytology
STUDY DESIGN:
- Retrospective study using 200 ThinPrep urine cytology slides.
- Ground truth: NHGUC (n=100), AUC (n=35), SHGUC (n=32), HGUC (n=33).
- Slides digitized using: (1) Mikroscan SLxCyto( Mikroscan-WSIs); (2) Customized Huron scanner (Huron-WSIs).
- WSIs were analyzed using AIxURO, a disease-specific AI algorithm for bladder cancer.
- Three-arm blinded review by 1 cytopathologist and 2 cytologists, with 2-week washout periods:
- Arm 1: Conventional microscopy
- Arm 2: AIxURO with Mikroscan-WSIs
- Arm 3: AIxURO with Huron-WSIs
- Performance Metrics:
- Comparison of study diagnosis with urine cytology ground truth diagnosis (n=200) for 3 arms, using following threshold: AUC+(AUC, SHGUC and HGUC) cases as positive; NHGUC cases as negative.
- Assessment of agreement between study diagnoses and surgical pathology-confirmed bladder cancer (n=72) among the three arms.
- Evaluation of bladder cancer prediction in hematuria-indicated cases (n=16) across the three arms.
RESULTS:
- Urine cytology ground truth cases: Arm 2 and Arm3 exhibited higher sensitivity (85.0% and 88.3%) compared to Arm 1 (79.3%) with comparable accuracy across all arms (Arm 1: 86.8%, Arm 2: 85.3%, Arm 3: 85.3%).
- Biopsy-confirmed bladder cancer cases: Arm 2 and Arm 3 showed improved sensitivity (92.0% and 93.2%) and accuracy (82.9% and 82.4%) compared to Arm 1 (84.6% sensitivity, 78.2% accuracy).
- Hematuria cases: Arms 2 and 3 achieved superior sensitivity (96.7% and 100.0%) and accuracy (89.6% and 91.7%) compared to Arm 1 (90.0% sensitivity, 85.4% accuracy).
CONCLUSIONS:
AIxURO consistently enhanced diagnostic sensitivity and maintained accuracy across multiple scanners, outperforming microscopy in detecting bladder cancer. It demonstrated strong predictive performance in high-risk subgroups, supporting its utility in real-world clinical workflows and digital cytology integration.
May 2025
AI SOFTWARE DEVELOPMENT
ICC 2025 - Oral Presentation
Does AI performance in Thyroid Cancer Diagnosis Differ Between Single-Layer and Multi-Z-Stacked Imaging?
Does AI performance in Thyroid Cancer Diagnosis Differ Between Single-Layer and Multi-Z-Stacked Imaging?
STUDY DESIGN:
- 71 ThinPrep thyroid FNAC slides selected by consensus cytology diagnosis
- Cases included: TBS-II (n=35), TBS-IV (n=6), TBS-VI (n=30), confirmed by biopsy
- Slides digitized using 3DHistech scanner to create paired whole-slide images:
- S-WSI: single-layer WSI
- 7-WSI: seven-layer Z-stacked WSI
- AI-assisted review used an AIxTHY model to detect cancer cells and guide interpretation
- Three cytologists independently reviewed both S-WSI and 7-WSI sets
- Total of 213 reads (71 cases × 3 reviewers) analyzed
- Two diagnostic threshold:
- Threshold 1: Positive = TBS-V/VI; Negative = TBS-II
- Threshold 2: Positive = TBS-IV/V/VI; Negative = TBS-II
- Outcomes measured:
- Binary diagnostic sensitivity and specificity (under both thresholds)
- Agreement with consensus diagnoses
- Interobserver agreement (Cohen’s κ)
RESULTS:
- Threshold 1 (TBS-V/VI vs. TBS-II):
- Sensitivity: 7-WSI 88.9% vs. S-WSI 81.1%
- Specificity: 7-WSI 94.3% vs. S-WSI 96.2% (not significant)
- Threshold 2 (TBS-IV/V/VI vs. TBS-II):
- Sensitivity: 7-WSI 86.1% vs. S-WSI 80.6% (p < 0.05)
- Specificity: 7-WSI (%): All cases: 7-WSI 88.6% vs. S-WSI 91.4%
- Consensus Agreement (%): All cases: 7-WSI 74.6%vs. S-WSI 61.5%
- TBS-VI: 65.6% vs. 36.7%
- TBS-IV: 44.4% vs. 27.8%
- TBS-II: 87.6% vs. 88.6%
- Interobserver Agreement (Cohen’s κ): Overall: 0.366 vs. 0.351
- TBS-IV: 7-WSI 0.189 vs. S-WSI 0.075
- TBS-VI: 0.420 vs. 0.319
- TBS-II: 0.549 vs. 0.604
CONCLUSIONS:
Seven-layer Z-stacked WSIs (7-WSI) enhanced AI-assisted thyroid cancer diagnosis and improved interobserver agreement among cytologists, especially in indeterminate (TBS-IV) and malignant (TBS-VI) cases, supporting their clinical value over single-layer WSIs.
May 2025
THE PARIS SYSTEM FOR URINE CYTOLOGY NUCLEAR TO CYTOPLASMIC RATIO
Academic Article: Cancer Cytopathology
Does Artificial Intelligence Redefine Nuclear-to-Cytoplasmic Ratio Threshold for Diagnosing High-Grade Urothelial Carcinoma?
Does Artificial Intelligence Redefine Nuclear-to-Cytoplasmic Ratio Threshold for Diagnosing High-Grade Urothelial Carcinoma?
STUDY DESIGN:
- 106 urine cytology slides (from 46 lower and 60 upper urinary tract HGUC/CIS cases) digitized and analyzed using AIxURO, an AI-based WSI tool.
- AI quantified atypical and suspicious urothelial cells, their nuclear-to-cytoplasmic(N/C) ratios, and nuclear areas.
- Morphologic data were correlated with biopsy-confirmed HGUC/CIS diagnoses. Statistical comparisons were conducted using Kruskal–Wallis tests.
RESULTS:
- Suspicious vs Atypical Cells (Total):
- Fewer suspicious cells detected (median 20.5 vs 242.0, p<.001)
- Higher median N/C ratio: 0.66 vs 0.58 (p<.001)
- Larger nuclear area: 102.3 µm² vs 85.7 µm² (p<.001)
- By Cytology Category (AUC, SHGUC, HGUC):
- Suspicious cells consistently had higher N/C ratios and nuclear areas than atypical cells.
- Median N/C ratio of suspicious cells: 0.66~0.67 and atypical cells: 0.58~0.59 across groups
- Median nuclear areas of suspicious cells: AUC 108.9 µm², SHGUC 99.2 µm², HGUC 101.6 µm² and atypical cells: AUC 88.5 µm², SHGUC 86.8 µm², HGUC 83.5 µm²
- Upper vs Lower Tract:
- No significant difference in N/C ratios
- Nuclear areas were smaller in UUT vs LUT cases (e.g., suspicious: 98.0 µm² vs 108.0 µm², p<.001)
- No significant difference in N/C ratios
- Biopsy Correlation:
- CIS had the largest nuclear areas among suspicious (116.3 µm²) and atypical (101.5 µm²) cells
- Cell number and N/C ratio did not differ significantly across CIS, CIS-HGUC, and HGUC biopsy categories
CONCLUSIONS:
AIxURO provides objective measurement of N/C ratio and nuclear area in urine cytology, challenging the current TPS threshold (>0.7) for diagnosing HGUC. The study suggests that a revised N/C ratio cutoff of 0.66 may be more appropriate for SHGUC/HGUC categorization using AI. Findings also support the use of consistent thresholds across upper and lower urinary tract cases. Nuclear area offers additional discriminative value, particularly for differentiating CIS from HGUC.
March 2025
AIxURO URINE CYTOLOGY CLINICAL STUDIES
USCAP 2025 Poster Presentation
Enhancing Bladder Cancer Diagnosis Through the Integration of an AI-Driven Cytology System
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.
March 2025
THE PARIS SYSTEM FOR URINE CYTOLOGY NUCLEAR TO CYTOPLASMIC RATIO
USCAP 2025 Oral Presentation
Quantitative Analysis of the Paris System Cytomorphological Criteria in Digital Urinary Cytology Using Artificial Intelligence Algorithm
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.
March 2025
WHOLE SLIDE IMAGE SCANNER STUDIES
USCAP 2025 Poster Presentation
Does Digital Scanner Selection Impact Urine Cytology Whole Slide Imaging? A Comparative Study of Five Commercial Digital Scanning Devices
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.
March 2025
AIxTHY THYROID FINE NEEDLE ASPIRATION CLINICAL STUDIES
USCAP 2025 Oral Presentation
Assessing Role of Artificial Intelligence in Thyroid Cytology: A Feasibility Study in Clinical Practice
Assessing Role of Artificial Intelligence in Thyroid Cytology: A Feasibility Study in Clinical Practice
STUDY DESIGN:
• 100 thyroid ThinPrep slides categorized for ground truth diagnosis (The Bethesda System for Reporting Thyroid Cytopathology) using a consensus of the original cytology report interpretation and the surgical pathology findings. All cases had a surgical result except Nondiagnostic cases.
• Urine slides were digitized as WSIs and analyzed with AI-assistance software (AIxTHY)
• 1 cytopathologist (CP) and 2 cytologists (CT) evaluated slides with a 2-week washout period between research arms:
o Arm 1- Microscopy only
o Arm 2- WSI only
o Arm 3- WSI with AI-assistance
• Performance Metrics: Comparison of study diagnosis with ground truth diagnosis for 3 Arms, using following cut-off values:
o Threshold 1: Negative = TBS II; Positive = V or VI (III and IV are indeterminate)
o Threshold 2: Negative = TBS II; Positive = IV, V, VI (III excluded)
o Threshold 3: Negative = TBS II (Benign); Positive = III, IV, V, VI (Atypia of undetermined significance, Follicular neoplasm, Suspicious for malignancy, Malignant)
o All TBS I (Nondiagnostic) cases were excluded from the study
RESULTS:
- Threshold 1 (TBS V+ as positive) showed highest accuracy and consensus with biopsy and cytology (Arm 1 = 100%; Arm 2 = 95.7%;Arm 3 = 100%)
- Threshold 3 (TBS III+ as positive) showed accuracy of Arm 1 = 75.8%; Arm 2 = 72.7%; Arm 3 = 72.9%
- For AI-assistance (Arm 3), Threshold TBS iV+ had higher Sensitivity (93.5%), Specificity (96.2%), PPV (96.6% and NPV (92.6%)than including atypia (TBS III+)(Sensitivity = 94.3%; Specificity = 78.9%; PPV= 83.2%; NPV = 92.6%; accuracy 87.0%)
- Using consensus cytology+biopsy reports to determine accuracy yielded higher accuracy, compared to lower accuracy with cytology diagnosis alone
CONCLUSIONS:
The accuracy of AI-assisted thyroid cytology diagnosis closelyapproximates microscopic accuracy, but dependent on the cut-off point for“negative” and “positive” results. The best diagnostic review concordance occurswhen both cytology and biopsy results serve as the “ground truth” for thestudy.
November 2024
THE PARIS SYSTEM FOR URINE CYTOLOGY NUCLEAR TO CYTOPLASMIC RATIO
Pathology Visions Oral Presentation
AI-Driven Comparative Study of Cell Characteristics in Urine Cytology with Biopsy Correlation
AI-Driven Comparative Study of Cell Characteristics in Urine Cytology with Biopsy Correlation
DPA 2024 Oral Presentation Crothers BA et al. Tungs’ Taichung MetroHarbor Hospital, Show Chwan Memorial Hospital, Taipei Veteran’s General Hospital, Taichung Veteran’s General Hospital
STUDY DESIGN:
- 242 urine cytology slides (74 AUC, 56 SHGUC, 112 HGUC) from patients with biopsy-confirmed HGUC within a 6-month window
- 162 (67%) Cytospin, 60 (25%) TP-UroCyte, 20 (8%) BD CytoRich
- 124,980 abnormal cells inferred by AI-assisted digital software system (AIxURO)
- 54% of biopsy proven CIS or HGUC cases had a cytology interpretation of AUC or SHGUC; 46% cytology interpretation of HGUC
- Evaluation of N/C ratios and nuclear sizes for the top 24 most abnormal cells, and for the categories of suspicious cells vs atypical cells, with statistical significance
RESULTS:
- Average N/C ratios:some text
- Top 24 abnormal cells = 0.66 (95% CI; 0.65-0.66)
- Suspicious cell category = 0.65 (95% CI; 0.64-0.65)
- Atypical cell category = 0.57 (95% CI; 0.57-0.57)(p < 0.0001)
- Average nuclear size:some text
- Top 24 abnormal cells = 108.1-116.8 µm2
- Atypical cell category = 86.3-87.7 µm2
- Significantly larger nuclear sizes occurred in the top 24 abnormal and suspicious cell categories than in atypical cells category
- Nuclear size of biopsy:CIS cells was significantly larger than those of biopsy:HGUC cells for all AI-categories
- Slightly more HGUC biopsy cases were cytologically interpreted as AUC or SHGUC than as HGUC
CONCLUSIONS:
AI-assistance demonstrates a predictable quantitative advantage for assessment of nuclear size and N/C ratio when assessing atypical and suspicious cells using The Paris System. The average N/C ratio is lower (0.66) than that suggested for HGUC/SHGUC (0.70) in TPS.
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November 2024
American Society of Cytopathology (ASC)
Dr. Barbara Crothers is the recipient of the ASC’s highest award- 2024 Papanicolaou AwardApril 2024
