Development and Validation of KN-DIOC: A Novel Preoperative Diagnostic Index Using Ultrasound, Complete Blood Count, and Cancer Antigen 125 for Ovarian Cancer
Bibliographic record
Abstract
Background: Ovarian cancer, particularly epithelial ovarian cancer (EOC), is one of the deadliest gynecological malignancies due to nonspecific early symptoms and late diagnosis. Current diagnostic tools, while useful, often do not account for regional variations in disease presentation, particularly in Asian populations. This study aimed to develop and validate a new preoperative diagnostic index tailored to the Thai population by integrating complete blood count (CBC), tumor markers, and ultrasound features. Methods: This retrospective cohort study included patients with pathologic pelvic or adnexal masses scheduled for surgery at Vajira Hospital from April 2022 to October 2024. Clinical data, CBC, cancer antigen 125 (CA125) levels, and ultrasound findings were analyzed to develop and validate a diagnostic index (KMUTT-NMU Diagnostic Index for Ovarian Cancer (KN-DIOC)). The model's performance was compared against established indices like Risk of Malignancy Index (RMI), Risk of Ovarian Malignancy Algorithm (ROMA), and Rajavithi-Ovarian Cancer Predictive Score (R-OPS) through multivariate logistic regression, focusing on key predictors. Results: The study comprised 195 patients divided into 151 for the development dataset and 44 for the validation dataset. The KN-DIOC showed high discriminative ability with an area under curve (AUC) of 0.866, indicating very good capability in differentiating between benign and malignant ovarian masses. The index achieved a sensitivity of 93.75% and a specificity of 78.57%, demonstrating superior performance to traditional diagnostic tools, especially in the validation dataset. Conclusion: The novel diagnostic index (KN-DIOC), incorporating CBC, ultrasound features, and tumor markers, provides a robust tool for preoperative assessment of ovarian tumors in Thai patients. It offers significant improvements in sensitivity and specificity over existing models, suggesting its potential for broader application in similar settings. This index supports enhanced decision-making in gynecological oncology, potentially leading to better patient outcomes through timely and accurate diagnosis.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".