Global variation in patterns of care and time to initial treatment for breast, cervical, and ovarian cancer from 2015 to 2018 (VENUSCANCER): a secondary analysis of individual records for 275 792 women from 103 population-based cancer registries in 39 countries and territories
Bibliographic record
Abstract
BACKGROUND: Cancers of the breast, cervix, and ovary are a major public health problem worldwide. Evaluating the consistency with clinical guidelines for treatment by use of individual high-resolution data from population-based cancer registries is a powerful tool to help interpretation of global inequalities in cancer survival. The VENUSCANCER project aims to assess the worldwide variation in patterns of care and time to initial treatment for women diagnosed with one of these three common cancers. METHODS: In this secondary analysis of anonymised individual records from population-based cancer registries (VENUSCANCER), 103 registries from 39 countries worldwide contributed high-resolution data for women diagnosed with cancer of the breast, cervix, or ovary for a single year of incidence during 2015-18. High-resolution data included cancer stage at diagnosis; staging procedures; tumour grade; biomarkers (ER, PR, and HER2); and the first course of each treatment modality (surgery, radiotherapy, chemotherapy, endocrine treatment, or anti-HER2 therapy) and related dates. We examined prognostic factors, key indicators of consistency with international clinical guidelines for treatment (ESMO, ASCO, and NCCN), and median time between diagnosis and treatment, by country or territory. We analysed the odds of women receiving treatment consistent with guidelines in high-income countries (HICs) and low-income and middle-income countries (LMICs), controlling for age and tumour subtype. FINDINGS: We received 275 792 anonymised individual records for women diagnosed with a cancer of the breast (214 111 [77·6%]), cervix (44 468 [16·1%], including in situ), or ovary (17 213 [6·2%]). In HICs, early-stage, node-negative cancers comprised over 40% of breast and cervical cancers, but less than 20% of ovarian cancers. By contrast, in LMICs, these proportions were generally below 20% for all three cancers, but higher in Cuba (30% for breast), and Russia (36% for cervix and 27% for ovary). Consistency with main international guidelines was highly variable, particularly for surgery and radiotherapy in early-stage breast cancer (from 13% in Georgia to 82% in France), chemotherapy in advanced cervical cancer (from 18% in Mongolia to 90% in Canada), and surgery plus chemotherapy in metastatic ovarian cancer (from 9% in Cuba to 53% in the USA). Some type of surgery was offered to 78% of women in HICs and 56% of women in LMICs, but initial treatment that is consistent with clinical guidelines for early-stage tumours was followed more uniformly for cervical and ovarian cancer than for breast cancer. Older women (aged 70-99 years) had lower odds of receiving initial treatment consistent with clinical guidelines than women aged 50-69 years in both HICs and LMICs. The median time between diagnosis and treatment for early-stage cancers was less than 1 month in several HICs, but up to 4 months for cervical cancer in Mongolia and ovarian cancer in Ecuador, and up to 1 year for breast cancer in Mongolia. INTERPRETATION: The VENUSCANCER project provides the first global picture of patterns of care for three of the most common cancers in women. These findings offer crucial real-world evidence to support the implementation and monitoring of global initiatives on cancer control such as WHO's Global Breast Cancer Initiative and Cervical Cancer Elimination Initiative. Although guideline-consistent treatment has become more accessible for women diagnosed with early-stage tumours in LMICs, the proportion of these women diagnosed early remains far too low. FUNDING: European Research Council Consolidator Grant.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".