Neuroendocrine Neoplasms in Pregnancy: A Narrative Review of Clinical Challenges and Therapeutic Limitations in the Absence of Established Safe Treatments
Bibliographic record
Abstract
Cancer during pregnancy is a rare but complex clinical scenario that affects approximately 0.1% of pregnant individuals and is associated with increased maternal morbidity. With the trend of delayed childbearing, the incidence of pregnancy-associated cancers is expected to rise. Neuroendocrine neoplasms (NENs), although rare in pregnancy, present unique diagnostic and therapeutic challenges due to their hormonal activity, histological diversity, and limited data on management in the gestational context. Objectives: This manuscript reviews the current evidence on the diagnosis, staging, and management of NENs during pregnancy, focusing on maternal–fetal safety, therapeutic limitations, and multidisciplinary care strategies. Methods: A comprehensive narrative review was conducted using relevant case reports, retrospective studies, clinical guidelines, and expert consensus documents addressing cancer in pregnancy and NEN-specific management. Results: Pregnancy complicates the evaluation and treatment of NENs due to overlapping symptoms, contraindications to standard imaging and systemic therapies, and unreliable biomarkers such as chromogranin A and 5-HIAA. Most systemic therapies for NENs, including somatostatin analogs, tyrosine kinase inhibitors, and peptide receptor radionuclide therapy, are contraindicated or lack safety data in pregnancy. Surgical interventions and supportive care require careful planning. Decisions regarding pregnancy continuation or termination must be individualized and supported by a multidisciplinary team. Conclusions: The management of NENs during pregnancy demands a highly individualized approach, coordinated among oncology, maternal–fetal medicine, and supportive care teams. Given the paucity of robust data, future research is essential to establish evidence-based guidelines and improve outcomes for both mother and fetus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".