Hongfei Yao

Background The prognostic prediction of pancreatic ductal adenocarcinoma (PDAC) remains challenging. This study aimed to develop a radiomics model to predict Ki-67 expression status in PDAC patients using radiomics features from dual-phase enhanced CT, and integrated clinical characteristics to create a radiomics-clinical nomogram for prognostic prediction. Methods In this retrospective study, data were collected from 124 PDAC patients treated surgically at a single center, from January 2017 to March 2023. Patients were categorized according to the Ki-67 expression rate. Radiomics features were extracted from arterial and portal venous phase CT images using 3D Slicer v5.0.3. A radiomics model was formulated and validated to predict the Ki-67 expression, and a nomogram combining clinical indicators and the radiomics model was developed to predict 1, 2 and 3 year overall survival (OS). Results The optimal Ki-67 expression rate cutoff was identified as 50%, with significant OS differences. The developed radiomics model showed good predictive ability with area under the curves of 0.806 and 0.801 in the training and validation groups, respectively. High radiomics score, elevated carbohydrate antigen 19-9 (CA19-9), and receipt of adjuvant chemotherapy were identified as independent prognostic factors for OS. The radiomics-clinical nomogram accurately predicted 1, 2 and 3 year OS in PDAC patients. Conclusions The radiomics-clinical nomogram provides a non-invasive and efficient method for predicting Ki-67 expression and overall survival in PDAC patients, which could potentially guide clinical decision-making and improve patient outcomes.

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Pancreatic adenocarcinoma (PAAD), one of the most malignant tumors, not only has abundant mesenchymal components, but is also characterized by an extremely high metastatic risk. The purpose of this study was to construct a model of stroma- and metastasis-associated prognostic signature, aiming to benefit the existing clinical staging system and predict the prognosis of patients. First, stroma-associated genes were screened from the TCGA database with the ESTIMATE algorithm. Subsequently, transcriptomic data from clinical tissues in the RenJi cohort were screened for metastasis-associated genes. Integrating the two sets of genes, we constructed a risk prognostic signature by Cox and LASSO regression analysis. We then obtained a risk score by a quantitative formula and divided all samples into high- and low-risk groups based on the scores. The results demonstrated that patients with high-risk scores have a worse prognosis than those with low-risk scores, both in the TCGA database and in the RenJi cohort. In addition, tumor mutation burden, chemotherapeutic drug sensitivity and immune infiltration analysis also exhibited significant differences between the two groups. In exploring the potential mechanisms of how stromal components affect tumor metastasis, we simulated different matrix stiffness in vitro to explore its effect on EMT key genes in PAAD cells. We found that cancer cells stimulated by high matrix stiffness may trigger EMT and promote PAAD metastasis.