Cancer in Pregnancy in Indonesia: A Global Review and 2022–2025 Cohort Analysis of Maternal and Neonatal Outcomes
Bibliographic record
Abstract
Background: Cancer during pregnancy is rare but presents serious challenges, especially in low- and middle-income countries like Indonesia. Limited national data, delayed diagnosis, and lack of standardized care make management difficult. Global awareness is growing, but regional differences in outcomes remain poorly understood. This study aims to provide a comprehensive overview of cancer during pregnancy, including its clinical characteristics and maternal-fetal outcomes both in Indonesian and global data. Subjects and Method: This systematic review was conducted following PRISMA guidelines from databases of PubMed, EMBASE, Scopus, and additional search, published between 2022 and April 2025. The included studies reported the global depiction of pregnancy-associated cancer. Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the quality of observational included studies, while the Joanna Briggs Institute checklists for assessment of case reports. Results: A total of 14 studies were included based on the criteria, with a total population of 29,403 pregnant women associated with cancer. From this systematic review, the most found cancers during pregnancy were breast cancer, cervical cancer, and ovarian cancer, both from Indonesian data and global studies. Compared to global studies, obstetric complications were more prevalent in Indonesia, including preterm birth (64% vs 52%, respectively); very preterm birth (22% vs 15%, respectively); caesarean delivery (76% vs 65%, respectively); preeclampsia (18% vs 12%, respectively); and postpartum haemorrhage (15% vs 10%, respectively). Conclusion: The global literature shows wide variation in cancer types, gestational timing, and outcomes. Indonesian cohort data show higher rates of preterm birth, low birth weight, and maternal complications compared to global averages. Delays in diagnosis and limited access to integrated cancer-obstetric care may explain these differences. The findings support the urgent need for national guidelines, early detection programs, and multidisciplinary care models for managing cancer in pregnancy in resource-limited settings.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".