Background: Women with colposcopy-directed biopsy results of low-grade or high-grade squamous intraepithelial lesion (LSIL/HSIL) who are subsequently found to have cervical cancer after surgery likely represent missed preoperative malignancy. This study aims to identify risk factors associated with these missed diagnoses and to develop a preoperative risk prediction model. Methods: Patients with LSIL/HSIL on colposcopy-directed biopsy who underwent cervical conization or hysterectomy at a single center were retrospectively included. Postoperative cervical cancer was the adverse outcome. Baseline characteristics were compared using chi-square tests. Predictors were identified by univariate and multivariable logistic regression to build a logistic regression model. Internal validation applied machine learning with five-fold cross-validation. Performance was evaluated by receiver operating characteristic (ROC) curve (AUC) and accuracy, with discrimination, calibration, and clinical utility assessed using ROC curves, calibration plots, and decision curve analysis (DCA). Results: A total of 1271 patients were included, and 79 were diagnosed with cervical cancer after surgery. Risk factors significantly associated with postoperative cervical cancer diagnosis included age (OR, 1.0; 95% CI, 1.0– 1.0, p=0.045), gravidity (OR, 1.2; 95% CI, 1.1– 1.3, p < 0.001), human papillomavirus 16 or 18 infection (OR, 2.6; 95% CI, 1.4– 4.6, p=0.002), colposcope biopsy site numbers ≥ 4 (OR, 0.2; 95% CI, 0.1– 0.3, p < 0.001), ThinPrep Cytologic Test indicates high-grade squamous intraepithelial lesion (OR, 3.1; 95% CI, 1.1– 8.5, p=0.028), and neutrophil-to-lymphocyte ratio (OR, 1.1; 95% CI, 1.0– 1.3, p=0.015). In five-fold cross-validation, the machine learning algorithms yielded an AUC of 0.72– 0.81 and an accuracy of 0.93– 0.94 for the model. ROC curves, calibration curves, and DCA analysis demonstrate the model’s excellent predictive capability and performance. Conclusion: This study developed and internally validated a clinical prediction model to estimate the preoperative risk of occult cervical cancer in patients with LSIL/HSIL on colposcopy-directed biopsy. This model may support risk-stratified surgical decision-making and help reduce missed diagnoses. Plain Language Summary: In clinical practice, a proportion of people with cervical cancer are still missed by colposcopy-guided biopsy. Some individuals receive a colposcopy-guided biopsy diagnosis of low-grade squamous intraepithelial lesion (LSIL) or high-grade squamous intraepithelial lesion (HSIL), but are subsequently found to have invasive cervical cancer only after cervical conization or hysterectomy. Such missed diagnoses may delay appropriate treatment and adversely affect prognosis. This study retrospectively analyzed 1271 patients whose colposcopy-guided biopsies suggested LSIL or HSIL and who later underwent loop electrosurgical excision procedure, cold knife conization, or hysterectomy at a tertiary center. Among these patients, 79 were diagnosed with cervical cancer. Baseline clinical characteristics were compared between those with and without cervical cancer, and logistic regression analysis was used to identify high-risk clinical factors and to construct a nomogram-based risk assessment model. Older age, higher gravidity, human papillomavirus infection, abnormal ThinPrep Cytologic Test results, increased neutrophil-to-lymphocyte ratio, and the number of colposcopic biopsy sites were associated with a higher likelihood of postoperative diagnosis of cervical cancer. These factors were integrated into a clinical prediction model that showed good ability to distinguish between individuals with and without occult cervical cancer, with favourable calibration and potential clinical utility. These findings suggest that colposcopy and biopsy result alone may be insufficient to reliably exclude early cervical cancer. Applying this risk prediction model alongside routine clinical and laboratory information may help clinicians to identify individuals at higher risk of occult cervical cancer, optimize preoperative surgical planning, reduce missed diagnoses, and improve management of cervical lesions. Keywords: colposcopy, missed diagnosis, cervical cancer, predictive model
Pan et al. (Sun,) studied this question.