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Preoperative differentiation of pancreatic mucinous cystic neoplasm from macrocystic serous cystic adenoma using radiomics: Preliminary findings and comparison with radiological model.
Eur J Radiol 2020; 122:108747EJ

Abstract

PURPOSE

To develop a radiomics model in the preoperative differentiation of mucinous cystic neoplasm (MCN) and macrocystic serous cystadenoma (MaSCA) and to compare its diagnostic performance with conventional radiological model.

METHODS

57 Patients (MCN = 31, MaSCA = 26) with preoperative multidetector computed tomography (MDCT) scans were retrospectively included in this study. A radiological model was constructed from radiological features evaluated by radiologists. A radiomics model was constructed with high-dimensional quantitative features extracted from manually segmented volume of interests (VOIs). A combined model was constructed using both radiomics features and radiological features. The diagnostic performance of three models were assessed by the area under the receiver-operating characteristic curve (AUC), sensitivity, specificity, accuracy, and the calibration curves.

RESULTS

The radiological model yielded an AUC of 0.775, sensitivity of 74.2 %, specificity of 80.8, and accuracy of 77.2 %. The radiomics model yielded an AUC of 0.989, sensitivity of 93.6 %, specificity of 96.2 %, and accuracy of 94.7 %. The combined model yielded an AUC of 0.994, sensitivity of 96.8 %, specificity of 100 %, and accuracy of 98.2 %. Both combined model and radiomics model showed higher AUC, sensitivity, and accuracy than radiological model (all P < .05). The combined model showed higher AUC than radiomics model, though no significant difference was found (P = .41). The combined model showed better calibration than radiomics model (P = .91 vs. P < .001).

CONCLUSIONS

Combined model which contained both radiomics features and radiological features outperformed radiomics model and radiological model in the preoperative differentiation of MCN and MaSCA.

Authors+Show Affiliations

Department of Radiology, Peking University First Hospital, Beijing, China. Electronic address: xiehuihui1030@163.com.Department of Radiology, Peking University First Hospital, Beijing, China. Electronic address: mashuai316@126.com.Department of Radiology, Peking University First Hospital, Beijing, China. Electronic address: Guoxiaochao1985@163.com.Department of Radiology, Peking University First Hospital, Beijing, China. Electronic address: zhxd2009@gmail.com.Department of Radiology, Peking University First Hospital, Beijing, China. Electronic address: wangxiaoying@bjmu.edu.cn.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31760275

Citation

Xie, Huihui, et al. "Preoperative Differentiation of Pancreatic Mucinous Cystic Neoplasm From Macrocystic Serous Cystic Adenoma Using Radiomics: Preliminary Findings and Comparison With Radiological Model." European Journal of Radiology, vol. 122, 2020, p. 108747.
Xie H, Ma S, Guo X, et al. Preoperative differentiation of pancreatic mucinous cystic neoplasm from macrocystic serous cystic adenoma using radiomics: Preliminary findings and comparison with radiological model. Eur J Radiol. 2020;122:108747.
Xie, H., Ma, S., Guo, X., Zhang, X., & Wang, X. (2020). Preoperative differentiation of pancreatic mucinous cystic neoplasm from macrocystic serous cystic adenoma using radiomics: Preliminary findings and comparison with radiological model. European Journal of Radiology, 122, p. 108747. doi:10.1016/j.ejrad.2019.108747.
Xie H, et al. Preoperative Differentiation of Pancreatic Mucinous Cystic Neoplasm From Macrocystic Serous Cystic Adenoma Using Radiomics: Preliminary Findings and Comparison With Radiological Model. Eur J Radiol. 2020;122:108747. PubMed PMID: 31760275.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Preoperative differentiation of pancreatic mucinous cystic neoplasm from macrocystic serous cystic adenoma using radiomics: Preliminary findings and comparison with radiological model. AU - Xie,Huihui, AU - Ma,Shuai, AU - Guo,Xiaochao, AU - Zhang,Xiaodong, AU - Wang,Xiaoying, Y1 - 2019/11/14/ PY - 2019/06/11/received PY - 2019/10/19/revised PY - 2019/11/12/accepted PY - 2019/11/25/pubmed PY - 2019/11/25/medline PY - 2019/11/25/entrez KW - Computed tomography KW - Diagnostic performance KW - Macrocystic serous cystadenoma KW - Mucinous cystic neoplasm KW - Radiomics SP - 108747 EP - 108747 JF - European journal of radiology JO - Eur J Radiol VL - 122 N2 - PURPOSE: To develop a radiomics model in the preoperative differentiation of mucinous cystic neoplasm (MCN) and macrocystic serous cystadenoma (MaSCA) and to compare its diagnostic performance with conventional radiological model. METHODS: 57 Patients (MCN = 31, MaSCA = 26) with preoperative multidetector computed tomography (MDCT) scans were retrospectively included in this study. A radiological model was constructed from radiological features evaluated by radiologists. A radiomics model was constructed with high-dimensional quantitative features extracted from manually segmented volume of interests (VOIs). A combined model was constructed using both radiomics features and radiological features. The diagnostic performance of three models were assessed by the area under the receiver-operating characteristic curve (AUC), sensitivity, specificity, accuracy, and the calibration curves. RESULTS: The radiological model yielded an AUC of 0.775, sensitivity of 74.2 %, specificity of 80.8, and accuracy of 77.2 %. The radiomics model yielded an AUC of 0.989, sensitivity of 93.6 %, specificity of 96.2 %, and accuracy of 94.7 %. The combined model yielded an AUC of 0.994, sensitivity of 96.8 %, specificity of 100 %, and accuracy of 98.2 %. Both combined model and radiomics model showed higher AUC, sensitivity, and accuracy than radiological model (all P < .05). The combined model showed higher AUC than radiomics model, though no significant difference was found (P = .41). The combined model showed better calibration than radiomics model (P = .91 vs. P < .001). CONCLUSIONS: Combined model which contained both radiomics features and radiological features outperformed radiomics model and radiological model in the preoperative differentiation of MCN and MaSCA. SN - 1872-7727 UR - https://www.unboundmedicine.com/medline/citation/31760275/Preoperative_differentiation_of_pancreatic_mucinous_cystic_neoplasm_from_macrocystic_serous_cystic_adenoma_using_radiomics:_Preliminary_findings_and_comparison_with_radiological_model L2 - https://linkinghub.elsevier.com/retrieve/pii/S0720-048X(19)30397-3 DB - PRIME DP - Unbound Medicine ER -