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Authors: Ge Yan,1,* Xiujuan Wu,2,* Peiting Zhao,3 Feng Zhou,1 Shaoze Xu,1 Mengqian Yao,3 Yiyang Ji,3 Jiahui Li,3 Qiong Feng,1 Lin Zhao,1 Hua Du4,5

Abstract

Objective
The study aims to develop and validate a multi-omics model based on preoperative ultrasound (US) imaging results, intraoperative H&E- stained slides, and clinical features to predict lymph node metastasis (LNM) before lymph node dissection (LND) in ovarian cancer (OC) patients.

Methods
We analyzed 157 OC patients undergoing LND with definitive pathological confirmation of LNM status, comprising 91 patients in the training cohort, 38 in the internal validation cohort, and 28 in the external test cohort. US images were processed with PyRadiomics to extract radiomics features, while pathological WSIs were processed with deep learning (DL) algorithms and multi-instance learning(MIL) algorithms to extract pathomics features. Then, radiomics and pathomics models were developed using support vector machines (SVMs), logistic regression (LR), and extreme gradient boosting (XGBoost) after dimensionality reduction and feature selection. To create a powerful multi-omics model, clinical features were incorporated into the optimal radiomics and pathomics features. Performance of models was assessed by accuracy, AUC, 95% CI, sensitivity, specificity, PPV and NPV.

Results
A total of 11 features were used to build radiomics models out of a selection of 1561 radiomics features. The SVM_rad model demonstrated superior predictive performance (AUC: training=0.816, validation=0.760, test=0.775). In parallel, pathomics models were built using a refined set of 3 features selected from the original 206 pathomics features. Among these, the SVM_path model showed the highest predictive efficiency (AUC: training=0.983, validation=0.817, test=0.813). The multi-omics model showed the greatest discriminative power (AUC: training=0.988; validation=0.923; test cohort=0.862). The quality of the prediction model was demonstrated by the DeLong test, calibration curves, and decision curve analysis, which verified its high discrimination, calibration, and clinical usefulness.

Conclusion
The study’s findings indicate that the multi-omics model integrating the tumor-level radiological data, cellular-level pathological information, and patient-level clinical features can predict LNM before LND in OC and support rational treatment plans.

Read the full article: Development and Validation of a Multi-omics Model Integrating US-deriv | IJWH | Dove Medical Press

¹State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215000, People’s Republic of China

²Department of Ultrasound Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, People’s Republic of China

³School of Clinical Medicine, Soochow University, Suzhou, 215000, People’s Republic of China

⁴Department of Pathology, Basic Medical College, Inner Mongolia Medical University, Hohhot, 010000, People’s Republic of China

⁵Department of Pathology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, People’s Republic of China

*These authors contributed equally to this work