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

Background: Pseudomyxoma 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.

Methods: Ninety-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.

Results: Among 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.

Conclusions: The 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.