Establishment and validation of prognostic nomograms integrating histopathological features in patients with invasive endocervical adenocarcinoma

Abstract Background To develop and verify pathological models using pathological features basing on hematoxylin and eosin (H&E) images to predict postoperative survival in patients with invasive endocervical adenocarcinoma (ECA). Method: There were 289 patients with ECA classified into training and validation cohorts. A histological signature was produced in 191 patients and verified in the validation group. Histological models combining the histological features were built. They showed increased value compared to the conventional model in terms of individualized prognosis estimation. Results Our model included five selected histological characteristics and was significantly related to overall survival (OS). In the training cohort, it had AUC values of 0.862 and 0.955, respectively, for predicting 3- and 5-year survival; in the validation cohort, the equivalent values were 0.891 and 0.801. In the training cohort, it showed better OS evaluation (C-index: 0.832; 95% confidence interval [CI] = 0.751–0.913) than both the FIGO staging system (C-index: 0.648; 95% CI = 0.542–0.753) and treatment (C-index: 0.687; 95% CI = 0.605–0.769), with advanced efficiency for classifying survival outcomes. In both cohorts, a risk stratification system was built that could precisely stratify patients with stage I and II ECA into high-risk and low-risk subpopulations with significantly different prognoses. Conclusion Our nomogram with five histological signatures had better accuracy in the prediction of OS in patients with ECA. This may contribute to the development of precision medicine in such patients.