The SIOP PARC Committee is pleased to announce that Dr. Jinghao Yan has been awarded the 2026 PARC Early Career Award, made possible through the generous support from the Rally Foundation for Childhood Cancer Research and the NaiKong and Irene Cheung Family.
Nephron-sparing surgery (NSS) is the standard of care for bilateral Wilms tumor (BWT), yet clinical practice lacks an objective, preoperative tool to predict surgical complexity. This gap directly affects critical decisions, including preoperative chemotherapy intensification, referral to high-volume centers, and the choice between simultaneous and staged procedures.
To address this unmet need, we propose a multicenter observational cohort study with a two-stage design: retrospective modeling followed by prospective validation. Leveraging an established national BWT cohort under the Society of Pediatric Surgery, Chinese Medical Association (CMA), we will enroll patients aged ≤18 years with pathologically confirmed BWT, standardized preoperative chemotherapy, and planned NSS. The primary outcome, “high-complexity NSS,” is defined by intraoperative tumor rupture, positive surgical margins, conversion to radical nephrectomy, gross residual tumor, failure of lymph node sampling, or a postoperative decline in renal function of ≥25%.
We will extract radiomics features (morphological, first-order, texture, and wavelet) from preoperative contrast-enhanced CT images and combine them with clinical variables. After feature selection using ICC consistency and LASSO regression, multiple machine learning algorithms (regularized logistic regression, random forest, SVM, and XGBoost) will be trained on 70% of the cohort and validated on the remaining 30%, with external validation using an independent dataset.
This research will strengthen the CMA cooperative group in five ways. First, it introduces state-of-the-art radiomics and machine learning methods to CMA, building local capacity in image segmentation, feature extraction, and predictive-tool development. Second, as the first pediatric oncology center from western China—a low-income region—to participate in this CMA cohort, our site will contribute real-world data that helps close the current gap in regional representation. Third, the resulting predictive model will serve as a practical triage tool for resource-limited centers, enabling timely referral of high-complexity cases to high-volume institutions and thereby improving patient safety and outcomes. Fourth, the project establishes a standardized surgical-complexity assessment framework—using predefined perioperative endpoints—that can be adopted across all CMA centers for uniform outcome reporting in future surgical studies. Finally, the mentorship and collaboration model developed through this project will create a replicable template for future multicenter research, strengthening CMA’s research infrastructure and fostering sustained partnerships between eastern and western China.
Bio
Dr. Jinghao Yan is an attending surgeon specializing in neonatal surgery and surgical oncology at Xinjiang Children’s Hospital, and a recent MD graduate of Capital Medical University, where he completed clinical training at its affiliated Beijing Children’s Hospital. His research focuses on bilateral Wilms tumor, integrating radiomics and machine learning to predict surgical complexity. He has published multiple peer-reviewed articles on Wilms tumor outcomes and is a core member of a national multicenter BWT cohort. As the first surgeon from western China—a low-income region—to join this cohort, he is committed to bridging regional disparities and advancing data-driven surgical decision-making.
