Nanjing Shihejiyin Technology, Inc.

This investigator-initiated study aims to evaluate the effectiveness of minimal residual disease (MRD) as a biomarker for guiding adjuvant therapy decisions in patients with Stage I non-small-cell lung cancer (NSCLC). The study will compare outcomes between an MRD-guided management group and a standard-of-care group, focusing on whether the use of MRD information can improve the 3-year disease-free survival rate compared to existing treatment protocols. Participants in the MRD-guided management group will receive targeted therapy, immunotherapy, or observation based on their postoperative MRD status, while those in the standard-of-care group will receive treatments or observation according to current clinical guidelines.

The purpose of this study is to enable non-invasive early detection of endometrial cancer in high-risk populations through the establishment of a multimodal machine learning model using plasma cell-free DNA fragmentomics. Plasma cell-free DNA from early stage endometrial cancer patients and healthy individuals will be subjected to whole-genome sequencing. Five different feature types, including Fragment Size Distribution, nucleosome features, SBS Signatures, BreakPoint Motif , and Copy Number Variation will be assessed to generate this model.

The purpose of this study is to enable non-invasive early detection of ovarian cancer in high-risk populations through the establishment of a multimodal machine learning model using plasma cell-free DNA fragmentomics. Plasma cell-free DNA from early stage ovarian cancer patients and healthy individuals will be subjected to whole-genome sequencing. Five diferent feature types, including Fragment Size Coverage (FSC), Fragment Size Distribution (FSD), EnD Motif (EDM), BreakPoint Motif (BPM), and Copy Number Variation (CNV) will be assessed to generate this model.