Long-Term Survival Among Histological Subtypes in Advanced Epithelial Ovarian Cancer: Population-Based Study Using the Surveillance, Epidemiology, and End Results Database
Bibliographic record
Abstract
BACKGROUND: Actual long-term survival rates for advanced epithelial ovarian cancer (EOC) are rarely reported. OBJECTIVE: This study aimed to assess the role of histological subtypes in predicting the prognosis among long-term survivors (≥5 years) of advanced EOC. METHODS: We performed a retrospective analysis of data among patients with stage III-IV EOC diagnosed from 2000 to 2014 using the Surveillance, Epidemiology, and End Results cancer data of the United States. We used the chi-square test, Kaplan-Meier analysis, and multivariate Cox proportional hazards model for the analyses. RESULTS: We included 8050 patients in this study, including 6929 (86.1%), 743 (9.2%), 237 (2.9%), and 141 (1.8%) patients with serous, endometrioid, clear cell, and mucinous tumors, respectively. With a median follow-up of 91 months, the most common cause of death was primary ovarian cancer (80.3%), followed by other cancers (8.1%), other causes of death (7.3%), cardiac-related death (3.2%), and nonmalignant pulmonary disease (3.2%). Patients with the serous subtype were more likely to die from primary ovarian cancer, and patients with the mucinous subtype were more likely to die from other cancers and cardiac-related disease. Multivariate Cox analysis showed that patients with endometrioid (hazard ratio [HR] 0.534, P<.001), mucinous (HR 0.454, P<.001), and clear cell (HR 0.563, P<.001) subtypes showed better ovarian cancer-specific survival than those with the serous subtype. Similar results were found regarding overall survival. However, ovarian cancer-specific survival and overall survival were comparable among those with endometrioid, clear cell, and mucinous tumors. CONCLUSIONS: Ovarian cancer remains the primary cause of death in long-term ovarian cancer survivors. Moreover, the probability of death was significantly different among those with different histological subtypes. It is important for clinicians to individualize the surveillance program for long-term ovarian cancer survivors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".