Radiomics Model Based on MR Images to Discriminate Pancreatic Ductal Adenocarcinoma and Mass-Forming Chronic Pancreatitis Lesions
Open accessAbstract
Background: It is difficult to identify pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions through conventional CT or MR examination. As an innovative image analysis method, radiomics may possess potential clinical value in identifying PDAC and MFCP. To develop and validate radiomics models derived from multiparametric MRI to distinguish pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions. Methods: This retrospective study included 119 patients from two independent institutions. Patients from one institution were used as the training cohort (51 patients with PDAC and 13 patients with MFCP), and patients from the other institution were used as the testing cohort (45 patients with PDAC and 10 patients with MFCP). All the patients had pathologically confirmed results, and preoperative MRI was performed. Four feature sets were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and the artery (A) and portal (P) phases of dynamic contrast-enhanced MRI, and the corresponding radiomics models were established. Several clinical characteristics were used to discriminate PDAC and MFCP lesions, and clinical model was established. The results of radiologists’ evaluation were compared with pathology and radiomics models. Univariate analysis and the least absolute shrinkage and selection operator algorithm were performed for feature selection, and a support vector machine was used for classification. The receiver operating characteristic (ROC) curve was applied to assess the model discrimination. Results: The areas under the ROC curves (AUCs) for the T1WI, T2WI, A and, P and clinical models were 0.893, 0.911, 0.958, 0.997 and 0.516 in the primary cohort, and 0.882, 0.902, 0.920, 0.962 and 0.649 in the validation cohort, respectively. All radiomics models performed better than clinical model and radiologists’ evaluation both in the training and testing cohorts by comparing the AUC of various models, all P<0.050. Good calibration was achieved. Conclusions: The radiomics models based on multiparametric MRI have the potential ability to classify PDAC and MFCP lesions.
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Radiomics models based on multiparametric MRI data have great potential for non-invasive differentiation of PDAC from MFCP.
“These findings suggest that radiomics models based on multiparametric MRI data have great potential for non-invasive differentiation of PDAC from MFCP [16].”
The portal phase post-contrast MR sequence showed the highest discriminative performance in the primary and validation group.
“The portal phase post-contrast MR sequence showed the highest discriminative performance in the primary and validation group [16].”
Deng and co-authors found that AUCs for each of four MR sequences were consistently higher than for clinical models and radiologists' evaluations.
“Comparing multiple models developed on T1W, T2W, arterial (A), and portal (P) phase MR images, the AUCs for each of four MR sequences were consistently higher than for clinical models and radiologists’ evaluations [16].”
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Self-citation
- Authors
- Deng, Y., Ming, B., Zhou, T., Wu, J.-L., Chen, Y., Liu, P., Zhang, J., Zhang, S.-Y., Chen, T.-W., & Zhang, X.-M.
- Affiliations
- North Sichuan Medical University (CN)
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Zhang, Xiao-Ming
- Venue
- Frontiers in Oncology
- Publisher
- Frontiers Media SA
- Published
- 2021-03-24
- Volume
- 11
- Pages
- 620981-620981
- ISSN
- 2234-943X (ISSN-L)
- PMC
- PMC8025779
- OpenAlex
- W3136300884
- Cited by
- 32
- References
- 35
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 4 · 2023: 6 · 2022: 10
- FWCI
- 2.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Oncology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Health Sciences › Medicine › Oncology › Pancreatic and Hepatic Oncology Research
- Keywords
- pancreatic ductal adenocarcinoma, radiomics, multiparametric MRI, differential diagnosis, dynamic contrast-enhanced MRI, T1-weighted imaging, T2-weighted imaging, support vector machine classification, LASSO feature selection
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