Tumor-stroma ratio as a new prognosticator for pseudomyxoma peritonei: a comprehensive clinicopathological and immunohistochemical study

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.

Radiomics: A review of current applications and possibilities in the assessment of tumor microenvironment

With the recent success in the application of immunotherapy for treating various advanced cancers, the tumor microenvironment has rapidly become an important field of research. The tumor microenvironment is complex and its characteristics strongly influence disease biology and potentially responses to systemic therapy. Accurate preoperative assessment of tumor microenvironment is of great significance for the formulation of an immunotherapy strategy and evaluation of patient prognosis. As a research hotspot in medical image analysis technology, radiomics has been applied in the auxiliary diagnosis of the tumor microenvironment. This article reviews the current status of radiomics in the elective application on tumor microenvironment and discusses potential prospects.