Systemic Therapy for Tumor Control in Metastatic Well-Differentiated Gastroenteropancreatic Neuroendocrine Tumors: ASCO Guideline
Bibliographic record
Abstract
PURPOSE: To develop recommendations for systemic therapy for well-differentiated grade 1 (G1) to grade 3 (G3) metastatic gastroenteropancreatic neuroendocrine tumors (GEP-NETs). METHODS: ASCO convened an Expert Panel to conduct a systematic review of relevant studies and develop recommendations for clinical practice. RESULTS: Eight randomized controlled trials met the inclusion criteria for the systematic review. RECOMMENDATIONS: Somatostatin analogs (SSAs) are recommended as first-line systemic therapy for most patients with G1-grade 2 (G2) metastatic well-differentiated GI-NETs. Observation is an option for patients with low-volume or slow-growing disease without symptoms. After progression on SSAs, peptide receptor radionuclide therapy (PRRT) is recommended as systematic therapy for patients with somatostatin receptor (SSTR)-positive tumors. Everolimus is an alternative second-line therapy, particularly in nonfunctioning NETs and patients with SSTR-negative tumors. SSAs are standard first-line therapy for SSTR-positive pancreatic (pan)NETs. Rarely, observation may be appropriate for asymptomatic patients until progression. Second-line systemic options for panNETs include PRRT (for SSTR-positive tumors), cytotoxic chemotherapy, everolimus, or sunitinib. For SSTR-negative tumors, first-line therapy options are chemotherapy, everolimus, or sunitinib. There are insufficient data to recommend particular sequencing of therapies. Patients with G1-G2 high-volume disease, relatively high Ki-67 index, and/or symptoms related to tumor growth may benefit from early cytotoxic chemotherapy. For G3 GEP-NETs, systemic options for G1-G2 may be considered, although cytotoxic chemotherapy is likely the most effective option for patients with tumor-related symptoms, and SSAs are relatively ineffective. Qualifying statements are provided to assist with treatment choice. Multidisciplinary team management is recommended, along with shared decision making with patients, incorporating their values and preferences, potential benefits and harms, and other characteristics and circumstances, such as comorbidities, performance status, geographic location, and access to care.Additional information is available at www.asco.org/gastrointestinal-cancer-guidelines.
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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) | 0.003 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".