A nomogram prediction model of pseudomyxoma peritonei established based on new prognostic factors of HE stained pathological images analysis

Abstract

BackgroundPseudomyxoma peritonei (PMP) is a rare clinical malignant syndrome, and its rarity causes a lack of pathology research. This study aims to quantitatively analyze HE‐stained pathological images (PIs), and develop a new predictive model integrating digital pathological parameters with clinical information.MethodsNinety‐two PMP patients with complete clinic‐pathological information, were included. QuPath was used for PIs quantitative feature analysis at tissue‐, cell‐, and nucleus‐level. The correlations between overall survival (OS) and general clinicopathological characteristics, and PIs features were analyzed. A nomogram was established based on independent prognostic factors and evaluated.ResultsAmong the 92 PMP patients, there were 34 (37.0%) females and 58 (63.0%) males, with a median age of 57 (range: 31–76). A total of 449 HE stained images were obtained for QuPath analysis, which extracted 40 pathological parameters at three levels. Kaplan–Meier survival analysis revealed eight clinicopathological characteristics and 20 PIs features significantly associated with OS (p < 0.05). Partial least squares regression was used to screen the multicollinearity features and synthesize four new features. Multivariate survival analysis identified the following five independent prognostic factors: preoperative CA199, completeness of cytoreduction, histopathological type, component one at tissue‐level, and tumor nuclei circularity variance. A nomogram was established with internal validation C‐index 0.795 and calibration plots indicating improved prediction performance.ConclusionsThe quantitative analysis of HE‐stained PIs could extract the new prognostic information on PMP. A nomogram established by five independent prognosticators is the first model integrating digital pathological information with clinical data for improved clinical outcome prediction.