Development and validation of an ultrasound-based prediction model for differentiating between malignant and benign solid pancreatic lesions

被引:7
|
作者
Huang, Jiayan [1 ]
Yang, Jie [1 ]
Ding, Jianming [2 ]
Zhou, Jing [3 ]
Yang, Rui [1 ]
Li, Jiawu [1 ]
Luo, Yan [1 ]
Lu, Qiang [4 ]
机构
[1] Sichuan Univ, Dept Ultrasound, West China Hosp, Chengdu 610041, Peoples R China
[2] Tianjin Third Cent Hosp, Dept Ultrasound, Tianjin 300170, Peoples R China
[3] Affiliated Hosp Southwest Med Univ, Dept Ultrasound, Luzhou 646000, Sichuan, Peoples R China
[4] Sichuan Univ, West China Hosp, Lab Ultrasound Med, Dept Ultrasound, Chengdu 610041, Peoples R China
基金
中国国家自然科学基金;
关键词
Pancreatic neoplasms; Ultrasonography; Contrast media; Nomograms; CONTRAST-ENHANCED ULTRASOUND; DIAGNOSTIC PERFORMANCE; CANCER; TUMORS; MASS; ULTRASONOGRAPHY; MULTICENTER; SONOGRAPHY; PATTERNS; DUCT;
D O I
10.1007/s00330-022-08930-0
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Objective To identify the diagnostic ability of precontrast and contrast-enhanced ultrasound (CEUS) in differentiating between malignant and benign solid pancreatic lesions (MSPLs and BSPLs) and to develop an easy-to-use diagnostic nomogram. Materials and methods This study was approved by the institutional review board. Patients with pathologically confirmed solid pancreatic lesions were enrolled from one tertiary medical centre from March 2011 to June 2021 and in two tertiary institutions between January 2015 and June 2021. A prediction nomogram model was established in the training set by using precontrast US and CEUS imaging features that were independently associated with MSPLs. The performance of the prediction model was further externally validated. Results A total of 155 patients (mean age, 55 +/- 14.6 years, M/F = 84/71) and 78 patients (mean age, 59 +/- 13.4 years, M/F = 36/42) were included in the training and validation cohorts, respectively. In the training set, an ill-defined border and dilated main pancreatic duct on precontrast ultrasound, CEUS patterns of hypoenhancement in both the arterial and venous phases of CEUS, and hyperenhancement/isoenhancement followed by washout were independently associated with MSPLs. The prediction nomogram model developed with the aforementioned variables showed good performance in differentiating MSPLs from BSPLs with an area under the curve (AUC) of 0.938 in the training set and 0.906 in the validation set. Conclusion Hypoenhancement in all phases, hyperenhancement/isoenhancement followed by washout on CEUS, an ill-defined border, and a dilated main pancreatic duct were independent risk factors for MSPLs. The nomogram constructed based on these predictors can be used to diagnose MSPLs.
引用
收藏
页码:8296 / 8305
页数:10
相关论文
共 50 条
  • [1] Development and validation of an ultrasound-based prediction model for differentiating between malignant and benign solid pancreatic lesions
    Jiayan Huang
    Jie Yang
    Jianming Ding
    Jing Zhou
    Rui Yang
    Jiawu Li
    Yan Luo
    Qiang Lu
    European Radiology, 2022, 32 : 8296 - 8305
  • [2] Diagnostic value of a power Doppler ultrasound-based malignancy index for differentiating malignant and benign solid breast lesions
    Enshaei, Ali
    Mohammadi, Afshin
    Toomatari, Seyed Babak Moosavi
    Yekta, Zahra
    Toomatari, Seyed Ehsan Moosavi
    Ghasemi-Rad, Mohammad
    Shamspour, Saber Zafar
    Sarabi, Zahra Karimi
    Sepehrvand, Nariman
    INDIAN JOURNAL OF CANCER, 2020, 57 (01) : 44 - 48
  • [3] Ultrasound-based ADNEX model for differentiating between benign, borderline, and malignant epithelial ovarian tumours
    Xie, W.
    Zhang, Q.
    Wang, Y.
    Xiang, Z.
    Zeng, P.
    Huo, R.
    Du, Z.
    Tang, Lina
    CLINICAL RADIOLOGY, 2025, 81
  • [4] DEVELOPMENT OF AN ULTRASOUND PREDICTION MODEL TO DISCRIMINATE BETWEEN MALIGNANT AND BENIGN LIVER LESIONS
    Shen, Haolin
    Lv, Guorong
    Lin, Hanzong
    Huang, Ningjie
    Wu, Yifang
    Cheng, Hong
    Yang, Shuping
    ULTRASOUND IN MEDICINE AND BIOLOGY, 2020, 46 (04): : 952 - 958
  • [5] METHODOLOGICAL ISSUE IN "DEVELOPMENT OF AN ULTRASOUND PREDICTION MODEL TO DISCRIMINATE BETWEEN MALIGNANT AND BENIGN LIVER LESIONS"
    Pilangorgi, Sahar Souri
    Shahsavari, Soodeh
    ULTRASOUND IN MEDICINE AND BIOLOGY, 2020, 46 (11): : 3173 - 3173
  • [6] Endobronchial Ultrasound-Based Support Vector Machine Model for Differentiating between Benign and Malignant Mediastinal and Hilar Lymph Nodes
    Hu, Wenjia
    Wen, Feifei
    Zhao, Mengyu
    Li, Xiangnan
    Luo, Peiyuan
    Jiang, Guancheng
    Yang, Huizhen
    Herth, Felix J. F.
    Zhang, Xiaoju
    Zhang, Quncheng
    RESPIRATION, 2024, 103 (11) : 675 - 685
  • [7] Differentiating between benign and malignant breast lesions using dual-energy CT-based model: development and validation
    Xia, Han
    Chen, Yueyue
    Cao, Ayong
    Wang, Yu
    Huang, Xiaoyan
    Zhang, Shengjian
    Gu, Yajia
    INSIGHTS INTO IMAGING, 2024, 15 (01):
  • [8] Ultrasound-based radiomics analysis for discriminating between benign and malignant ovarian masses with solid ultrasound morphology
    Moro, F.
    Bernardini, F.
    Tran, H.
    Vagni, M.
    Boldrini, L.
    Ciccarone, F.
    Nero, C.
    Mascilini, F.
    Pozzati, F.
    Quagliozzi, L.
    Giannarelli, D.
    Scambia, G.
    Valentin, L.
    Testa, A. C.
    ULTRASOUND IN OBSTETRICS & GYNECOLOGY, 2023, 62 : 84 - 84
  • [9] Ultrasound-based radiomics analysis for differentiating benign and malignant breast lesions: From static images to CEUS video analysis
    Zhu, Jun-Yan
    He, Han-Lu
    Lin, Zi-Mei
    Zhao, Jian-Qiang
    Jiang, Xiao-Chun
    Liang, Zhe-Hao
    Huang, Xiao-Ping
    Bao, Hai-Wei
    Huang, Pin-Tong
    Chen, Fen
    FRONTIERS IN ONCOLOGY, 2022, 12
  • [10] Breast Ultrasound-Based Deep Learning Radiomics Model for Differentiating Malignancy in Low Malignant Risk Lesions
    Department of Oncology, Shengjing Hospital, China Medical University, Shenyang
    110004, China
    不详
    110004, China
    不详
    不详
    1600,