Signal enhancement ratio of multi-phase contrast-enhanced MRI: an imaging biomarker for survival in pancreatic adenocarcinoma
Creators
- 1. Cultivation and Construction Site of the State Key Laboratory of Intelligent Imaging and Interventional Medicine, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Dingjiaqiao Road, 210009, Nanjing, Jiangsu (China)
- 2. Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan (China)
- 3. Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing (China)
- 4. School of Instrument Science and Engineering, Southeast University, Nanjing (China)
- 5. MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai (China)
- 6. Department of Pathology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing (China)
- 7. Department of Radiology, The Xiangya Hospital of Central South University, Changsha (China)
- 8. Department of Medical Imaging, Subei People's Hospital, Medical School of Yangzhou University, Yangzhou (China)
- 9. Department of Radiology, Yijishan Hospital of Wannan Medical College, Wuhu (China)
- 10. Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou (China)
Description
To evaluate signal enhancement ratio (SER) for tissue characterization and prognosis stratification in pancreatic adenocarcinoma (PDAC), with quantitative histopathological analysis (QHA) as the reference standard. This retrospective study included 277 PDAC patients who underwent multi-phase contrast-enhanced (CE) MRI and whole-slide imaging (WSI) from three centers (2015-2021). SER is defined as (SI - SI)/(SI - SI), where SI, SI, and SI represent the signal intensity of the tumor in pre-contrast, early-, and late post-contrast images, respectively. Deep-learning algorithms were implemented to quantify the stroma, epithelium, and lumen of PDAC on WSIs. Correlation, regression, and Bland-Altman analyses were utilized to investigate the associations between SER and QHA. The prognostic significance of SER on overall survival (OS) was evaluated using Cox regression analysis and Kaplan-Meier curves. The internal dataset comprised 159 patients, which was further divided into training, validation, and internal test datasets (n = 60, 41, and 58, respectively). Sixty-five and 53 patients were included in two external test datasets. Excluding lumen, SER demonstrated significant correlations with stroma (r = 0.29-0.74, all p < 0.001) and epithelium (r = -0.23 to -0.71, all p < 0.001) across a wide post-injection time window (range, 25-300 s). Bland-Altman analysis revealed a small bias between SER and QHA for quantifying stroma/epithelium in individual training, validation (all within ± 2%), and three test datasets (all within ± 4%). Moreover, SER-predicted low stromal proportion was independently associated with worse OS (HR = 1.84 (1.17-2.91), p = 0.009) in training and validation datasets, which remained significant across three combined test datasets (HR = 1.73 (1.25-2.41), p = 0.001). SER of multi-phase CE-MRI allows for tissue characterization and prognosis stratification in PDAC. The signal enhancement ratio of multi-phase CE-MRI can serve as a novel imaging biomarker for characterizing tissue composition and holds the potential for improving patient stratification and therapy in PDAC.
Availability note (English)
Available from: http://dx.doi.org/10.1007/s00330-024-10746-zAdditional details
Identifiers
Publishing Information
- Journal Title
- European Radiology (Internet)
- Journal Volume
- 34
- Journal Issue
- 11
- Journal Page Range
- p. 7460-7470
- ISSN
- 1432-1084
- CODEN
- EURAE3
INIS
- Country of Publication
- Germany
- Country of Input or Organization
- Germany
- INIS RN
- 56000390
- Subject category
- S62: RADIOLOGY AND NUCLEAR MEDICINE;
- Descriptors DEI
- BIOLOGICAL MARKERS; CARCINOMAS; CONTRAST MEDIA; CORRELATIONS; DATA COMPILATION; EPITHELIUM; HISTOLOGY; IMAGE PROCESSING; MACHINE LEARNING; NMR IMAGING; PANCREAS; PATHOLOGY; REGRESSION ANALYSIS; RELAXATION TIME; SIGNAL-TO-NOISE RATIO; SURVIVAL CURVES; THERAPY; TRAINING; VALIDATION; WEIGHTING FUNCTIONS
- Descriptors DEC
- ALGORITHMS; ANIMAL TISSUES; ARTIFICIAL INTELLIGENCE; BODY; DATA; DATA PROCESSING; DIAGNOSTIC TECHNIQUES; DIGESTIVE SYSTEM; DIMENSIONLESS NUMBERS; DISEASES; EDUCATION; ENDOCRINE GLANDS; FUNCTIONS; GLANDS; INFORMATION; LEARNING; MATHEMATICAL LOGIC; MATHEMATICS; MEDICINE; NEOPLASMS; ORGANS; PROCESSING; STATISTICS; TESTING