New Paper - A Federated Approach to Identify Women with Early-stage Cervical Cancer

After more than two years of work, I am happy to share that our latest paper is out! Here, we used federated learning (more specifically, the logistic regression flavor of our previously published GLM model) to identify women at low risk of pN+ cervical cancer. This work has the potential to be used as a guide in shared decision-making process when considering the extent of lymph node dissection.

The abstract is as follows:

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.

This was a project with a lot of moving parts. Moreover, a big part of its development took place during the pandemic. I have to say that this makes seeing the final product much more satisfactory.

If you are interested, you can find the paper here (and its corresponding BibTeX citation here).

If you have any comments, questions or feedback, leave them in the comments below or drop me a line on Twitter (@amoncadatorres).