Background Radiogenomics is an emerging field that integrates “Radiomics” and “Genomics”. In the current study, we aimed to predict the genetic information of pancreatic tumours in a simple, inexpensive, and non-invasive manner, using cancer imaging analysis and radiogenomics. We focused on p53 mutations, which are highly implicated in pancreatic ductal adenocarcinoma (PDAC), and PD-L1, a biomarker for immune checkpoint inhibitor-based therapies. Methods Overall, 107 patients diagnosed with PDAC were retrospectively examined. The relationship between p53 mutations as well as PD-L1 abnormal expression and clinicopathological factors was investigated using immunohistochemistry. Imaging features (IFs) were extracted from CT scans and were used to create prediction models of p53 and PD-L1 status. Results We found that p53 and PD-L1 are significant independent prognostic factors (P = 0.008, 0.013, respectively). The area under the curve for p53 and PD-L1 predictive models was 0.795 and 0.683, respectively. Radiogenomics-predicted p53 mutations were significantly associated with poor prognosis (P = 0.015), whereas the predicted abnormal expression of PD-L1 was not significant (P = 0.096). Conclusions Radiogenomics could predict p53 mutations and in turn the prognosis of PDAC patients. Hence, prediction of genetic information using radiogenomic analysis may aid in the development of precision medicine.

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Pancreatic cancer is an aggressive tumor associated with poor survival, and early detection is important to improve patient outcomes. In the present study, we examined MIR1246 expression as a biomarker of pancreatic cancer. Total RNA was extracted from serum, urine and saliva samples from healthy subjects (n = 30) and patients with pancreatic cancer (n = 41, stage 0-IV). The MIR1246 level in each fluid was analyzed by quantitative reverse transcription-polymerase chain reaction. Significantly higher MIR1246 expression in serum and urine was observed in patients with cancer than in healthy controls. A significant positive correlation was found between serum and urine MIR1246 expression (r = 0.34). Receiver operating characteristic curves were constructed for MIR1246 in all three body fluids. The area under the curve for serum MIR1246 was 0.87 (sensitivity, 92.3%; specificity, 73.3%), and that for urine MIR1246 was 0.90 (sensitivity, 90.2%; specificity, 83.3%). With a cutoff of the control group's mean plus twice the standard deviation, the sensitivities of MIR1246 in serum and urine for pancreatic cancer were 60.9 and 58.5%, respectively. Combining both serum and urine MIR1246 expression yielded a sensitivity of 85%. These results indicate that MIR246 may be a useful diagnostic biomarker for pancreatic cancer.

Background and Aim:We evaluated the clinicopathological and prognostic significance of serum p53 (s-p53-Abs) and serum NY-ESO-1 autoantibodies (s-NY-ESO-1-Abs) in esophageal squamous cell carcinoma (ESCC), gastric cancer and hepatocellular carcinoma (HCC). Patients and Methods:A total of 377 patients, 85 patients with ESCC, 248 patients with gastric cancer, and 44 patients with HCC were enrolled to measure s-p53-Abs and s-NY-ESO-1-Abs titers by the enzyme-linked immunosorbent assay before treatment. The clinicopathological significance and prognostic impact of the presence of autoantibodies were evaluated. Expression data based on the Cancer Genome Atlas and the prognostic impact of gene expression was also examined for discussion. Results:The positive rates of s-p53-Abs were 32.9% in ESCC, 15% in gastric cancer, and 4.5% in HCC. The positive rates of s-NY-ESO-1-Abs were 29.4% in ESCC, 9.7% in gastric cancer, and 13.6% in HCC. The presence of s-p53-Abs was not associated with tumor progression in these three cancer types. On the other hand, the presence of s-NY-ESO-1-Abs was significantly associated with tumor progression in ESCC and gastric cancer. The presence of s-p53-Abs and/or s-NY-ESO-1-Abs was significantly associated with poor prognosis in gastric cancer but not in ESCC nor HCC. Conclusions:The presence of s-p53-Abs and/or s-NY-ESO-1-Abs was associated with tumor progression in ESCC and gastric cancer. These autoantibodies might have poor prognostic impacts on gastric cancer (UMIN000014530).

Radiogenomics is a new field that provides clinically useful prognostic predictions by linking molecular characteristics such as the genetic aberrations of malignant tumors with medical images. the abnormal expression of serum microRNA-1246 (miR-1246) has been reported as a prognostic factor of esophageal squamous cell carcinoma (ESCC). To evaluate the power of the miR-1246 level predicted with radiogenomics techniques as a predictor of the prognosis of ESCC patients. The real miR-1246 expression (miR-1246 real) was measured in 92 ESCC patients. Forty-five image features (IFs) were extracted from tumor regions on contrast-enhanced computed tomography. A prediction model for miR-1246 real was constructed using linear regression with selected features identified in a correlation analysis of miR-1246 real and each IF. A threshold to divide the patients into two groups was defined according to a receiver operating characteristic analysis for miR-1246 real. Survival analyses were performed between two groups. Six IFs were correlated with miR-1246 real and were included in the prediction model. The survival curves of high and low groups of miR-1246 real and miR-1246 pred showed significant differences (p = 0.001 and 0.016). Both miR-1246 real and miR-1246 pred were independent predictors of overall survival (p = 0.030 and 0.035). miR-1246 pred produced by radiogenomics had similar power to miR-1246 real for predicting the prognosis of eScc.