The development and validation of a tumor-specific death predictive nomogram in patients with ovarian cancer: a cohort study
Bibliographic record
Abstract
Background: Ovarian cancer has a high mortality rate. Accurate identification of risk factors for mortality is crucial to improve treatment strategies. Regretfully, prognostication tools are limited. In recent years, quantitative parameters of contrast enhanced ultrasound have shown economic, reproducible, and highly accurate advantages in predicting the prognosis of ovarian cancer patients. The purpose of this study was to develop a nomogram prediction model for the oncological outcome of patients with ovarian cancer based on quantitative parameters of contrast-enhanced ultrasound. Methods: Data from 357 patients with ovarian cancer admitted to The Fourth People's Hospital of Zhenjiang from January 2018 to December 2019 were retrospectively collected and constructed the training set. Data from 153 cases admitted to The People's Hospital of Zhaoyuan during the same period were collected and constructed the validation set. All patients were treated with primary cytoreductive surgery, and were followed up for 5 years after surgery. The differences in clinical characteristics and quantitative parameters of contrast-enhanced ultrasound were compared between patients who passed away within 5 years and those which did not. Results: Peak systolic velocity (PSV), stage III, poor differentiation, and ascites were independent risk factors for tumor-specific mortality in patients with ovarian cancer, with their relative risk being 2.011 (95% confidence interval: 1.680-2.407), 13.480 (95% confidence interval: 4.540-40.022), 2.997 (95% confidence interval: 1.206-7.452), and 2.997 (95% confidence interval: 1.206-7.452), respectively. Time to peak (TTP) was a protective factor of tumor-specific mortality in patients with ovarian cancer, with a relative risk of 0.800 (95% confidence interval: 0.731-0.875). The area under the receiver operating characteristic (ROC) curve in the training set was 0.948 (95% confidence interval: 0.926-0.969), and the area under the ROC curve in the validation set was 0.860 (95% confidence interval: 0.802-0.917). Conclusions: The nomogram prediction model for prognosis of patients with ovarian cancer based on quantitative parameters of contrast-enhanced ultrasound has good efficacy and reliability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".