Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics

Abstract

Background The advent of next-generation computed tomography (CT)- and magnetic resonance imaging (MRI) opened many new perspectives in the evaluation of tumor characteristics. An increasing body of evidence suggests the incorporation of quantitative imaging biomarkers into clinical decision-making to provide mineable tissue information. The present study sought to evaluate the diagnostic and predictive value of a multiparametric approach involving radiomics texture analysis, dual-energy CT-derived iodine concentration (DECT-IC), and diffusion-weighted MRI (DWI) in participants with histologically proven pancreatic cancer. Methods In this study, a total of 143 participants (63 years ± 13, 48 females) who underwent third-generation dual-source DECT and DWI between November 2014 and October 2022 were included. Among these, 83 received a final diagnosis of pancreatic cancer, 20 had pancreatitis, and 40 had no evidence of pancreatic pathologies. Data comparisons were performed using chi-square statistic tests, one-way ANOVA, or two-tailed Student’s t-test. For the assessment of the association of texture features with overall survival, receiver operating characteristics analysis and Cox regression tests were used. Results Malignant pancreatic tissue differed significantly from normal or inflamed tissue regarding radiomics features (overall P < .001, respectively) and iodine uptake (overall P < .001, respectively). The performance for the distinction of malignant from normal or inflamed pancreatic tissue ranged between an AUC of ≥ 0.995 (95% CI, 0.955–1.0; P < .001) for radiomics features, ≥ 0.852 (95% CI, 0.767–0.914; P < .001) for DECT-IC, and ≥ 0.690 (95% CI, 0.587–0.780; P = .01) for DWI, respectively. During a follow-up of 14 ± 12 months (range, 10–44 months), the multiparametric approach showed a moderate prognostic power to predict all-cause mortality (c-index = 0.778 [95% CI, 0.697–0.864], P = .01). Conclusions Our reported multiparametric approach allowed for accurate discrimination of pancreatic cancer and revealed great potential to provide independent prognostic information on all-cause mortality.

Unmasking pancreatic cancer: Advanced biomedical imaging for its detection in native versus arterial dual‐energy computed tomography (DECT) scans

“…8 Consistent with previous radiomics research numerous GLCM features were chosen by the model to differentiate between pancreatic cancer and normal pancreatic tissue. 8,11,24,25 This selection reflects the distinctive heterogeneity of pancreatic tumor tissue as opposed to normal pancreatic tissue. 8 Currently, CT scans are commonly included in the standard diagnostic protocol for suspected pancreatic cancer to enhance the detection of pancreatic lesions due to the subtle nature of pancreatic cancer's imaging characteristics.…”

“…29,30 Recent studies have demonstrated that radiomics can serve as a complementary diagnostic tool for the improved detection of pancreatic cancer. 11 Koch et al revealed that a multiparametric approach, combining DECT, MRI and radiomics, enhances the discrimination between malignant lesions and inflamed or normal pancreatic parenchyma with greater diagnostic accuracy than each modality alone. Additionally, these studies indicate that radiomics has the potential to provide prognostic information for these patients.…”

“…Note: A subcohort of the current study has been previously reported. 11 Abbreviations: CEA, carcinoembryonic antigen; CRP, C-reactive protein; GFR, glomerular filtration rate; LDH, lactate dehydrogenase; SD, standard deviation.…”

“…[8][9][10] Its ability to analyze a multitude of image features has sparked interest in utilizing radiomics for pancreatic cancer detection. 11 In this study, we aimed to conduct a comprehensive comparative analysis of radiomics applied to non-contrast and arterial-enhanced dual-energy CT (DECT) scans for pancreatic carcinoma detection. By harnessing the power of radiomics, we aspire to unveil the diagnostic capabilities of this cutting-edge approach in distinguishing between malignant pancreatic lesions and normal pancreas tissue.…”

Advanced biomedical imaging for accurate discrimination and prognostication of mediastinal masses

“…Altogether, the findings of our studies are in line with previous studies that investigated a multiparametric approach involving radiomic features, DECT parameters and clinical parameters to discriminate between tumours of different degrees of malignancy. 18,31 Considering the broad range of anterior mediastinal lesions, our methodology offers a comprehensive framework to improve the characterization and classification of mediastinal tumours.…”