期刊
MEDICAL PHYSICS
卷 45, 期 11, 页码 5019-5029出版社
WILEY
DOI: 10.1002/mp.13159
关键词
image processing; intraductal papillary mucinous neoplasms; random forest classifier; risk stratification; texture analysis
资金
- NIH/NCI Cancer Center Support Grant [P30 CA008748]
- American Association of Cancer Research
- Pancreatic Cancer Action Network
- David M. Rubenstein Center for Pancreatic Research
Purpose Methods Intraductal papillary mucinous neoplasms (IPMNs) are radiographically visible precursor lesions of pancreatic cancer. Despite standard criteria for assessing risk, only 18% of cysts are malignant at resection. Thus, a large number of patients undergo unnecessary invasive surgery for benign disease. The ability to identify IPMNs with low or high risk of transforming into invasive cancer would optimize patient selection and improve surgical decision-making. The purpose of this study was to investigate quantitative CT imaging features as markers for objective assessment of IPMN risk. This retrospective study analyzed pancreatic cyst and parenchyma regions extracted from CT scans in 103 patients to predict IPMN risk. Patients who underwent resection between 2005 and 2015 with pathologically proven branch duct (BD)-IPMN and a preoperative CT scan were included in the study. Expert pathologists categorized IPMNs as low or high risk following resection as part of routine clinical care. We extracted new radiographically inspired features as well as standard texture features and designed prediction models for the categorization of high- and low-risk IPMNs. Five clinical variables were also combined with imaging features to design prediction models. Results Conclusion Using images from 103 patients and tenfold cross-validation technique, the novel radiographically inspired imaging features achieved an area under the receiver operating characteristic curve (AUC) of 0.77, demonstrating their predictive power. The combination of these features with clinical variables obtained the best performance (AUC = 0.81). The present study demonstrates that features extracted from pretreatment CT images can predict the risk of IPMN. Development of a preoperative model to discriminate between low-risk and high-risk IPMN will improve surgical decision-making.
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