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A federated approach to identify women with early-stage cervical cancer at low risk of lymph node metastases

Wenzel, Hans H.B. ; Hardie, Anna N. ; Moncada-Torres, Arturo ; Høgdall, Claus K. ; Bekkers, Ruud L.M. ; Falconer, Henrik ; Jensen, Pernille T. ; Nijman, Hans W. ; van der Aa, Maaike A. and Martin, Frank , et al. (2023) In European Journal of Cancer 185. p.61-68
Abstract

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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Cervical cancer, Federated learning, Lymph node metastases, Risk factors
in
European Journal of Cancer
volume
185
pages
8 pages
publisher
Elsevier
external identifiers
  • scopus:85151402189
  • pmid:36965329
ISSN
0959-8049
DOI
10.1016/j.ejca.2023.02.021
language
English
LU publication?
yes
id
f71357f7-3b7c-4fa3-af1a-f76009efe00a
date added to LUP
2023-05-16 15:57:22
date last changed
2024-06-15 02:58:33
@article{f71357f7-3b7c-4fa3-af1a-f76009efe00a,
  abstract     = {{<p>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&gt;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.</p>}},
  author       = {{Wenzel, Hans H.B. and Hardie, Anna N. and Moncada-Torres, Arturo and Høgdall, Claus K. and Bekkers, Ruud L.M. and Falconer, Henrik and Jensen, Pernille T. and Nijman, Hans W. and van der Aa, Maaike A. and Martin, Frank and van Gestel, Anna J. and Lemmens, Valery E.P.P. and Dahm-Kähler, Pernilla and Alfonzo, Emilia and Persson, Jan and Ekdahl, Linnea and Salehi, Sahar and Frøding, Ligita P. and Markauskas, Algirdas and Fuglsang, Katrine and Schnack, Tine H.}},
  issn         = {{0959-8049}},
  keywords     = {{Cervical cancer; Federated learning; Lymph node metastases; Risk factors}},
  language     = {{eng}},
  pages        = {{61--68}},
  publisher    = {{Elsevier}},
  series       = {{European Journal of Cancer}},
  title        = {{A federated approach to identify women with early-stage cervical cancer at low risk of lymph node metastases}},
  url          = {{http://dx.doi.org/10.1016/j.ejca.2023.02.021}},
  doi          = {{10.1016/j.ejca.2023.02.021}},
  volume       = {{185}},
  year         = {{2023}},
}