Jayasree Chakraborty1, Abhishek Midya1, Lior Gazit2, Marc Attiyeh1, Liana Langdon-Embry1, Peter J Allen1, Richard K G Do3, Amber L Simpson4. 1. Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA. 2. Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA. 3. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA. 4. Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA.
Abstract
PURPOSE: 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. METHODS: 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: 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). CONCLUSION: 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.
PURPOSE: 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. METHODS: 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: 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). CONCLUSION: 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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Authors: Camilo Correa-Gallego; Richard Do; Jennifer Lafemina; Mithat Gonen; Michael I D'Angelica; Ronald P DeMatteo; Yuman Fong; T Peter Kingham; Murray F Brennan; William R Jarnagin; Peter J Allen Journal: Ann Surg Oncol Date: 2013-09-18 Impact factor: 5.344
Authors: Kate A Harrington; Travis L Williams; Sharon A Lawrence; Jayasree Chakraborty; Mohammad A Al Efishat; Marc A Attiyeh; Gokce Askan; Yuting Chou; Alessandra Pulvirenti; Caitlin A McIntyre; Mithat Gonen; Olca Basturk; Vinod P Balachandran; T Peter Kingham; Michael I D'Angelica; William R Jarnagin; Jeffrey A Drebin; Richard K Do; Peter J Allen; Amber L Simpson Journal: J Med Imaging (Bellingham) Date: 2020-06-25
Authors: Lorraine Abel; Jakob Wasserthal; Thomas Weikert; Alexander W Sauter; Ivan Nesic; Marko Obradovic; Shan Yang; Sebastian Manneck; Carl Glessgen; Johanna M Ospel; Bram Stieltjes; Daniel T Boll; Björn Friebe Journal: Diagnostics (Basel) Date: 2021-05-19