CT-Based Radiomics Analysis for Preoperative Diagnosis of Pancreatic Mucinous Cystic Neoplasm and Atypical Serous Cystadenomas

Tiansong Xie; Xuanyi Wang; Zehua Zhang; Zhengrong Zhou

doi:10.3389/fonc.2021.621520

CT-Based Radiomics Analysis for Preoperative Diagnosis of Pancreatic Mucinous Cystic Neoplasm and Atypical Serous Cystadenomas

Front Oncol. 2021 Jun 11:11:621520. doi: 10.3389/fonc.2021.621520. eCollection 2021.

Authors

Tiansong Xie^{1

2}, Xuanyi Wang^{2

3}, Zehua Zhang^{1

2}, Zhengrong Zhou^{1

4}

Affiliations

¹ Department of Radiology, Shanghai Cancer Center, Fudan University, Shanghai, China.
² Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China.
³ Department of Radiation Oncology, Shanghai Cancer Center, Fudan University, Shanghai, China.
⁴ Minhang Branch, Shanghai Cancer Center, Fudan University, Shanghai, China.

Abstract

Objectives: To investigate the value of CT-based radiomics analysis in preoperatively discriminating pancreatic mucinous cystic neoplasms (MCN) and atypical serous cystadenomas (ASCN).

Methods: A total of 103 MCN and 113 ASCN patients who underwent surgery were retrospectively enrolled. A total of 764 radiomics features were extracted from preoperative CT images. The optimal features were selected by Mann-Whitney U test and minimum redundancy and maximum relevance method. The radiomics score (Rad-score) was then built using random forest algorithm. Radiological/clinical features were also assessed for each patient. Multivariable logistic regression was used to construct a radiological model. The performance of the Rad-score and the radiological model was evaluated using 10-fold cross-validation for area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy.

Results: Ten screened optimal features were identified and the Rad-score was then built based on them. The radiological model was built based on four radiological/clinical factors. In the 10-fold cross-validation, the Rad-score was proved to be robust and reliable (average AUC: 0.784, sensitivity: 0.847, specificity: 0.745, PPV: 0.767, NPV: 0.849, accuracy: 0.793). The radiological model performed slightly less well in classification (average AUC: average AUC: 0.734 sensitivity: 0.748, specificity: 0.705, PPV: 0.732, NPV: 0.798, accuracy: 0.728.

Conclusions: The CT-based radiomics analysis provided promising performance for preoperatively discriminating MCN from ASCN and showed good potential in improving diagnostic power, which may serve as a novel tool for guiding clinical decision-making for these patients.

Keywords: comput(eriz)ed tomography; cystademoma; diagnosis; pancreas; radiomic analysis.