Literature DB >> 27900458

Utility of whole-lesion ADC histogram metrics for assessing the malignant potential of pancreatic intraductal papillary mucinous neoplasms (IPMNs).

David H Hoffman1, Justin M Ream2, Christina H Hajdu3, Andrew B Rosenkrantz2.   

Abstract

PURPOSE: To evaluate whole-lesion ADC histogram metrics for assessing the malignant potential of pancreatic intraductal papillary mucinous neoplasms (IPMNs), including in comparison with conventional MRI features.
METHODS: Eighteen branch-duct IPMNs underwent MRI with DWI prior to resection (n = 16) or FNA (n = 2). A blinded radiologist placed 3D volumes-of-interest on the entire IPMN on the ADC map, from which whole-lesion histogram metrics were generated. The reader also assessed IPMN size, mural nodularity, and adjacent main-duct dilation. Benign (low-to-intermediate grade dysplasia; n = 10) and malignant (high-grade dysplasia or invasive adenocarcinoma; n = 8) IPMNs were compared.
RESULTS: Whole-lesion ADC histogram metrics demonstrating significant differences between benign and malignant IPMNs were: entropy (5.1 ± 0.2 vs. 5.4 ± 0.2; p = 0.01, AUC = 86%); mean of the bottom 10th percentile (2.2 ± 0.4 vs. 1.6 ± 0.7; p = 0.03; AUC = 81%); and mean of the 10-25th percentile (2.8 ± 0.4 vs. 2.3 ± 0.6; p = 0.04; AUC = 79%). The overall mean ADC, skewness, and kurtosis were not significantly different between groups (p ≥ 0.06; AUC = 50-78%). For entropy (highest performing histogram metric), an optimal threshold of >5.3 achieved a sensitivity of 100%, a specificity of 70%, and an accuracy of 83% for predicting malignancy. No significant difference (p = 0.18-0.64) was observed between benign and malignant IPMNs for cyst size ≥3 cm, adjacent main-duct dilatation, or mural nodule. At multivariable analysis of entropy in combination with all other ADC histogram and conventional MRI features, entropy was the only significant independent predictor of malignancy (p = 0.004).
CONCLUSION: Although requiring larger studies, ADC entropy obtained from 3D whole-lesion histogram analysis may serve as a biomarker for identifying the malignant potential of IPMNs, independent of conventional MRI features.

Entities:  

Keywords:  Apparent diffusion coefficient; Diffusion-weighted imaging; Intraductal papillary mucinous neoplasm; MRI; Pancreas

Mesh:

Year:  2017        PMID: 27900458     DOI: 10.1007/s00261-016-1001-7

Source DB:  PubMed          Journal:  Abdom Radiol (NY)


  12 in total

1.  Preoperative risk prediction for intraductal papillary mucinous neoplasms by quantitative CT image analysis.

Authors:  Marc A Attiyeh; Jayasree Chakraborty; Lior Gazit; Liana Langdon-Embry; Mithat Gonen; Vinod P Balachandran; Michael I D'Angelica; Ronald P DeMatteo; William R Jarnagin; T Peter Kingham; Peter J Allen; Richard K Do; Amber L Simpson
Journal:  HPB (Oxford)       Date:  2018-08-07       Impact factor: 3.647

2.  CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas.

Authors:  Jayasree Chakraborty; Abhishek Midya; Lior Gazit; Marc Attiyeh; Liana Langdon-Embry; Peter J Allen; Richard K G Do; Amber L Simpson
Journal:  Med Phys       Date:  2018-09-27       Impact factor: 4.071

3.  Multimodal radiomics and cyst fluid inflammatory markers model to predict preoperative risk in intraductal papillary mucinous neoplasms.

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

Review 4.  Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions.

Authors:  Shiva Rangwani; Devarshi R Ardeshna; Brandon Rodgers; Jared Melnychuk; Ronald Turner; Stacey Culp; Wei-Lun Chao; Somashekar G Krishna
Journal:  Biomimetics (Basel)       Date:  2022-06-14

Review 5.  Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions.

Authors:  Jorge D Machicado; Eugene J Koay; Somashekar G Krishna
Journal:  Diagnostics (Basel)       Date:  2020-07-21

6.  Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study.

Authors:  Sijia Cui; Tianyu Tang; Qiuming Su; Yajie Wang; Zhenyu Shu; Wei Yang; Xiangyang Gong
Journal:  Cancer Imaging       Date:  2021-03-09       Impact factor: 3.909

7.  Prediction of overall survival in patients with pancreatic ductal adenocarcinoma: histogram analysis of ADC value and correlation with pathological intratumoral necrosis.

Authors:  Yoshifumi Noda; Hiroyuki Tomita; Takuma Ishihara; Yoshiki Tsuboi; Nobuyuki Kawai; Masaya Kawaguchi; Tetsuro Kaga; Fuminori Hyodo; Akira Hara; Avinash R Kambadakone; Masayuki Matsuo
Journal:  BMC Med Imaging       Date:  2022-02-08       Impact factor: 1.930

8.  3D quantitative analysis of diffusion-weighted imaging for predicting the malignant potential of intraductal papillary mucinous neoplasms of the pancreas.

Authors:  Takao Igarashi; Megumi Shiraishi; Ken Watanabe; Kazuyoshi Ohki; Shinsuke Takenaga; Hirokazu Ashida; Hiroya Ojiri
Journal:  Pol J Radiol       Date:  2021-05-19

9.  Diagnostic Performance of Diffusion-Weighted Imaging for Differentiating Malignant From Benign Intraductal Papillary Mucinous Neoplasms of the Pancreas: A Systematic Review and Meta-Analysis.

Authors:  Fan Xu; Yingying Liang; Wei Guo; Zhiping Liang; Liqi Li; Yuchao Xiong; Guoxi Ye; Xuwen Zeng
Journal:  Front Oncol       Date:  2021-07-05       Impact factor: 6.244

10.  Multiphase computed tomography radiomics of pancreatic intraductal papillary mucinous neoplasms to predict malignancy.

Authors:  Stuart L Polk; Jung W Choi; Melissa J McGettigan; Trevor Rose; Abraham Ahmed; Jongphil Kim; Kun Jiang; Yoganand Balagurunathan; Jin Qi; Paola T Farah; Alisha Rathi; Jennifer B Permuth; Daniel Jeong
Journal:  World J Gastroenterol       Date:  2020-06-28       Impact factor: 5.742

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