A group of researchers from several Chinese medical institutions has introduced a new noninvasive method to quantify tumor fibrosis in pancreatic cancer patients using preoperative enhanced CT scans. The technique allows for more precise assessment of the tumor microenvironment and could help doctors tailor chemotherapy regimens for individual patients.
The findings were reported by a team from Shenzhen University, Xiangya Hospital of Central South University, Zhujiang Hospital of Southern Medical University, and Wuxi People's Hospital Affiliated to Nanjing Medical University. Their study, "Noninvasive Computed Tomography-Based Quantification of Tumor Fibrosis Predicts Pancreatic Cancer Response to Gemcitabine/Nab-Paclitaxel," was published in Research (2025).
Pancreatic ductal adenocarcinoma (PDAC) is known for its poor prognosis and low five-year survival rate. Chemotherapy remains the primary treatment option for unresectable PDAC, but responses vary significantly between patients due to differences in tumor characteristics. There is a lack of reliable biomarkers to guide treatment decisions in clinical practice.
According to the research team, "Tumor fibrosis is a core pathological feature of PDAC. Dense stromal fibrosis not only affects tumor progression but also is closely related to the delivery and efficacy of chemotherapeutic drugs." Traditional assessments require invasive biopsies that can miss important details about the tumor's spatial heterogeneity.
Transcriptome analysis performed as part of the study confirmed that certain biological pathways, including those involved with collagen metabolism and cell-matrix interaction, are more active in highly fibrotic tumors. This supports the rationale behind quantifying fibrosis as a way to guide therapy choices.
The study used data from multiple cohorts and applied multi-dimensional analysis techniques. Researchers demonstrated that noninvasive quantification via CT imaging could predict how pancreatic cancer patients would respond to specific chemotherapy regimens such as gemcitabine/nab-paclitaxel (AG). The approach may assist with prognostic stratification for resectable cases as well as predicting outcomes for unresectable disease.
Looking ahead, researchers say that their CT-based model could be integrated into hospital imaging systems. "Preoperative routine CT examinations can quickly determine whether patients are suitable for AG therapy without additional invasive operations," they noted. The team expects this tool will help reduce unnecessary treatments and lower healthcare costs.
Further research aims to combine this fibrosis assessment with other therapies, including targeted treatments and immunotherapies. For tumors with high levels of fibrosis, matrix-targeted drugs might improve drug delivery efficiency when used alongside standard chemotherapy.
The team also plans technological improvements by integrating multimodal imaging methods such as MRI or PET-CT and artificial intelligence tools to refine predictions further. They hope this strategy can be adapted for use with other solid tumors characterized by significant stromal content, including breast and colorectal cancers.
