ArticleESMO real world data and digital oncology2026
Using machine learning to estimate time-to-progression in ovarian cancer based on cancer registry and administrative health data.
Article in ESMO real world data and digital oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Cancer progression and recurrence are not routinely registered in Belgium. A machine-learning algorithm was developed using administrative data to retrospectively estimate time-to-progression (including recurrence) of epithelial ovarian cancer. The algorithm was validated against a multicentric clinical reference dataset. Patients and methods: From the Belgian Cancer Registry, we extracted patients diagnosed with epithelial ovarian cancer during 2014-2018 and treated for their primary tumor in seven participating centers in Belgium. The reference dataset was constructed through a manual review of hospital records for the selected patients. Using a unique personal identification number, the cancer registry data and administrative records were linked, including reimbursed medical procedures and medication, vital status, and causes of death. The reference dataset was split into a training set and a test set. To estimate time-to-progression, we developed and validated a machine-learning algorithm (XGBoost classifier), framing the task as a discrete-time classification problem at a patient-month level. Results: Of the 588 patients in the reference dataset, 61.6% (stage IV: 73.2%) progressed during a median follow-up of 52.5 months (interquartile range: 36.3 months). The algorithm achieved excellent discrimination between pre- and postprogression months (area under the receiving operators curve: 0.99) in the held-out test set, translating into high patient-level classification performance for identifying progression [sensitivity: 98% (95% confidence interval: 96-100), specificity: 91% (84-97), positive predictive value: 95% (90-98)] and a mean absolute timing error of 1.5 months for true positive cases. Conclusions: In the absence of systematic registration of progression in Belgium, automated data mining of population-level administrative records offers a valuable alternative to estimate the timing of ovarian cancer progression.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.