Dataset Information

The radiomics nomogram predicts the prognosis of pancreatic cancer patients with hepatic metastasis after chemoimmunotherapy.

ABSTRACT:

Objective

To construct a prognostic model based on MR features and clinical data to evaluate the progression free survival (PFS), overall survival (OS) and objective response rate (ORR) of pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy.

Methods

105 pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy were assigned to the training set (n = 52), validation set (n = 22), and testing set (n = 31). Multi-lesion volume of interest were delineated, multi-sequence radiomics features were extracted, and the radiomics models for predicting PFS, OS and ORR were constructed, respectively. Clinical variables were extracted, and the clinical models for predicting PFS, OS and ORR were constructed, respectively. The nomogram was jointly constructed by radiomics model and clinical model.

Result

The ORR exhibits no significant correlation with either PFS or OS. The area under the curve (AUC) of nomogram for predicting 6-month PFS reached 0.847 (0.737-0.957), 0.786 (0.566-1.000) and 0.864 (0.735-0.994) in the training set, validation set and testing set, respectively. The AUC of nomogram for predicting 1-year OS reached 0.770 (0.635-0.906), 0.743 (0.479-1.000) and 0.818 (0.630-1.000), respectively. The AUC of nomogram for predicting ORR reached 0.914 (0.828-1.00), 0.938 (0.840-1.00) and 0.846 (0.689-1.00), respectively.

Conclusion

The prognostic models based on MR imaging features and clinical data are effective in predicting the PFS, OS and ORR of chemoimmunotherapy in pancreatic cancer patients with hepatic metastasis, and can be used to evaluate the prognosis of patients.

SUBMITTER: Lu W

PROVIDER: S-EPMC10981596 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Publications

The radiomics nomogram predicts the prognosis of pancreatic cancer patients with hepatic metastasis after chemoimmunotherapy.

Lu Wenxin W Wu Guangyu G Miao Xianyuan X Ma Jingyu J Wang Yanling Y Xu Haiyan H Shentu Daiyuan D Xue Shengbai S Xia Qing Q Wang Yu Y Wang Liwei L

Cancer immunology, immunotherapy : CII 20240330 5

<h4>Objective</h4>To construct a prognostic model based on MR features and clinical data to evaluate the progression free survival (PFS), overall survival (OS) and objective response rate (ORR) of pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy.<h4>Methods</h4>105 pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy were assigned to the training set (n = 52), validation set (n = 22), and testing set (n = 31). Multi-lesion volume of ...[more]

PMID: 38554161

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Project description:Pancreatic cancer is one of the most aggressive cancers worldwide with a high mortality rate. Prognosis remains poor even in this era of advanced medicine mainly due to early metastasis and invasion. The present study aimed to explore and validate predictors of distant metastasis and prognosis in pancreatic cancer. In our preliminary experiment, we established a novel metastatic pancreatic cancer cell line BxPC‑M8 from parent BxPC‑3 cells. Via whole genome sequencing, RT‑qPCR, western blotting, migration and invasion assays, we initially found that BxPC‑M8 shared similar biological characteristics to BxPC‑3, but only differed in enhanced metastatic and invasive capabilities with a significant increase in collagen type VI α1 chain (COL6A1) expression. Knockdown of COL6A1 via small interfering RNA led to a significant decrease in migration and invasion of BxPC‑M8 cells, suggesting suppressed epithelial‑mesenchymal transition. Furthermore, a significant increase in COL6A1 expression was observed in cancerous tissue compared with paracancerous tissue (40.7 vs 3.7, P=0.001). Additionally, its expression was observed to be significantly associated with distant metastasis and vascular invasion at the time of surgery. Multivariate analysis revealed that COL6A1 expression (hazard ratio 1.90, 95% confidence interval 1.04‑3.47, P=0.037) is an independent predictor of overall survival (OS). The median OS observed for COL6A1+ and COL6A1‑ patients was found to be 8±4 and 14±7 months (P=0.021), respectively. Of note, we identified that COL6A1 expression in tissue samples was associated with significantly reduced OS (P=0.001), demonstrating that COL6A1 may serve an important role in the metastatic process and could be considered as a predictor of poor outcomes in patients with pancreatic cancer. In addition, our findings suggest that COL6A1 could be an indicator of distant metastasis and a valid prognostic predictor in such patients; however, further investigation is required.

Project description:PURPOSE:To build and validate a radiomics-based nomogram for the prediction of pre-operation lymph node (LN) metastasis in esophageal cancer. PATIENTS AND METHODS:A total of 197 esophageal cancer patients were enrolled in this study, and their LN metastases have been pathologically confirmed. The data were collected from January 2016 to May 2016; patients in the first three months were set in the training cohort, and patients in April 2016 were set in the validation cohort. About 788 radiomics features were extracted from computed tomography (CT) images of the patients. The elastic-net approach was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to build the radiomics signature and another predictive nomogram model. The predictive nomogram model was composed of three factors with the radiomics signature, where CT reported the LN number and position risk level. The performance and usefulness of the built model were assessed by the calibration and decision curve analysis. RESULTS:Thirteen radiomics features were selected to build the radiomics signature. The radiomics signature was significantly associated with the LN metastasis (P<0.001). The area under the curve (AUC) of the radiomics signature performance in the training cohort was 0.806 (95% CI: 0.732-0.881), and in the validation cohort it was 0.771 (95% CI: 0.632-0.910). The model showed good discrimination, with a Harrell's Concordance Index of 0.768 (0.672 to 0.864, 95% CI) in the training cohort and 0.754 (0.603 to 0.895, 95% CI) in the validation cohort. Decision curve analysis showed our model will receive benefit when the threshold probability was larger than 0.15. CONCLUSION:The present study proposed a radiomics-based nomogram involving the radiomics signature, so the CT reported the status of the suspected LN and the dummy variable of the tumor position. It can be potentially applied in the individual preoperative prediction of the LN metastasis status in esophageal cancer patients.

Dataset Information

The radiomics nomogram predicts the prognosis of pancreatic cancer patients with hepatic metastasis after chemoimmunotherapy.

Objective

Methods

Result

Conclusion

Publications

The radiomics nomogram predicts the prognosis of pancreatic cancer patients with hepatic metastasis after chemoimmunotherapy.

Similar Datasets

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