A federated approach to identify women with early-stage cervical cancer at low risk of lymph node metastases
Research output: Contribution to journal › Journal article › Research › peer-review
Objective: Lymph node metastases (pN+) in presumed early-stage cervical cancer negatively impact prognosis. Using federated learning, we aimed to develop a tool to identify a group of women at low risk of pN+, to guide the shared decision-making process concerning the extent of lymph node dissection. Methods: Women with cervical cancer between 2005 and 2020 were identified retrospectively from population-based registries: the Danish Gynaecological Cancer Database, Swedish Quality Registry for Gynaecologic Cancer and Netherlands Cancer Registry. Inclusion criteria were: squamous cell carcinoma, adenocarcinoma or adenosquamous carcinoma; The International Federation of Gynecology and Obstetrics 2009 IA2, IB1 and IIA1; treatment with radical hysterectomy and pelvic lymph node assessment. We applied privacy-preserving federated logistic regression to identify risk factors of pN+. Significant factors were used to stratify the risk of pN+. Results: We included 3606 women (pN+ 11%). The most important risk factors of pN+ were lymphovascular space invasion (LVSI) (odds ratio [OR] 5.16, 95% confidence interval [CI], 4.59–5.79), tumour size 21–40 mm (OR 2.14, 95% CI, 1.89–2.43) and depth of invasion>10 mm (OR 1.81, 95% CI, 1.59–2.08). A group of 1469 women (41%)—with tumours without LVSI, tumour size ≤20 mm, and depth of invasion ≤10 mm—had a very low risk of pN+ (2.4%, 95% CI, 1.7–3.3%). Conclusion: Early-stage cervical cancer without LVSI, a tumour size ≤20 mm and depth of invasion ≤10 mm, confers a low risk of pN+. Based on an international privacy-preserving analysis, we developed a useful tool to guide the shared decision-making process regarding lymph node dissection.
Original language | English |
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Journal | European Journal of Cancer |
Volume | 185 |
Pages (from-to) | 61-68 |
Number of pages | 8 |
ISSN | 0959-8049 |
DOIs | |
Publication status | Published - 2023 |
Bibliographical note
Publisher Copyright:
© 2023 Elsevier Ltd
- Cervical cancer, Federated learning, Lymph node metastases, Risk factors
Research areas
ID: 366987715