Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection
Abstract Deep learning (DL) enabled liquid-based cytology has potential for cervical cancer screening or triage. Here, we develop a DL model using whole cytology slides from 17,397 women and test it on 10,826 additional cases through a three-stage process. The DL model achieves robust performance ac...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58883-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Deep learning (DL) enabled liquid-based cytology has potential for cervical cancer screening or triage. Here, we develop a DL model using whole cytology slides from 17,397 women and test it on 10,826 additional cases through a three-stage process. The DL model achieves robust performance across nine hospitals. In a multi-reader, multi-case study, it outperforms cytopathologists’ sensitivity by 9%. Reading time significantly decreases with DL assistance (218s vs 30s; p < 0.0001). In community-based organized screening, the DL model’s sensitivity matches that of senior cytopathologists (0.878 vs 0.854; p > 0.999), yet it has reduced specificity (0.831 vs 0.901; p < 0.0001). Notably, hospital-based opportunistic screening shows that junior cytopathologists with DL assistance significantly improve both their sensitivity and specificity (0.857 vs 0.657, 0.840 vs 0.737; both p < 0.0001). When triaging human papillomavirus-positive cases, DL assistance exhibits better performance than junior cytopathologists alone. These findings support using the DL model as an assistance tool in cervical screening and case triage. |
|---|---|
| ISSN: | 2041-1723 |