The importance of risk models for management of pulmonary nodules

被引:0
作者
Prosch, H. [1 ]
Baltzer, P. [1 ]
机构
[1] Med Univ Wien, Allgemeines Krankenhaus, Univ Radiol & Nukl Med, A-1090 Vienna, Austria
来源
RADIOLOGE | 2014年 / 54卷 / 05期
关键词
Malignancy; Computed tomography; X rays; Pre-test probability; Post-test probability; CLINICAL-PREDICTION MODEL; LUNG-CANCER; BAYESIAN-ANALYSIS; MALIGNANCY; VALIDATION; PROBABILITY; LIKELIHOOD;
D O I
10.1007/s00117-013-2600-8
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
Pulmonary nodules are a frequent finding in computed tomography (CT) investigations. Further diagnostic work-up of detected nodules mainly depends on the so-called pre-test probability, i.e. the probability that the nodule is malignant or benign. The pre-test probability can be calculated by combining all relevant information, such as the age and the sex of the patient, the smoking history, and history of previous malignancies, as well as the size and CT morphology of the nodule. If additional investigations are performed to further investigate the nodules, all results must be interpreted taking into account the pre-test probability and the test performance of the investigation in order to estimate the post-test probability. In cases with a low pre-test probability, a negative result from an exact test can exclude malignancies but a positive test cannot prove malignancy in such a setting. In cases with a high pre-test probability, a positive test result can be considered as proof of malignancy but a negative test result does not exclude malignancy.
引用
收藏
页码:449 / +
页数:5
相关论文
共 50 条
  • [21] Incidental pulmonary nodules - current guidelines and management
    Glandorf, Julian
    Vogel-Claussen, Jens
    [J]. ROFO-FORTSCHRITTE AUF DEM GEBIET DER RONTGENSTRAHLEN UND DER BILDGEBENDEN VERFAHREN, 2024, 196 (06): : 582 - 590
  • [22] Establishment and validation of multiclassification prediction models for pulmonary nodules based on machine learning
    Liu, Qiao
    Lv, Xue
    Zhou, Daiquan
    Yu, Na
    Hong, Yuqin
    Zeng, Yan
    [J]. CLINICAL RESPIRATORY JOURNAL, 2024, 18 (05)
  • [23] Management of solid pulmonary nodules
    Poschenrieder, F.
    Beyer, L.
    Rehbock, B.
    Diederich, S.
    Wormanns, D.
    Stroszczynski, C.
    Hamer, O. W.
    [J]. RADIOLOGE, 2014, 54 (05): : 436 - +
  • [24] Diagnosis and management of pulmonary nodules
    Krochmal, Rebecca
    Arias, Sixto
    Yarmus, Lonny
    Feller-Kopman, David
    Lee, Hans
    [J]. EXPERT REVIEW OF RESPIRATORY MEDICINE, 2014, 8 (06) : 677 - 691
  • [25] The Value of a Seven-Autoantibody Panel Combined with the Mayo Model in the Differential Diagnosis of Pulmonary Nodules
    Ling, Zhougui
    Chen, Jifei
    Wen, Zhongwei
    Wei, Xiaomou
    Su, Rui
    Tang, Zhenming
    Hu, Zhuojun
    [J]. DISEASE MARKERS, 2021, 2021
  • [26] Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study
    Wu, Wei
    Pierce, Larry A.
    Zhang, Yuzheng
    Pipavath, Sudhakar N. J.
    Randolph, Timothy W.
    Lastwika, Kristin J.
    Lampe, Paul D.
    Houghton, A. McGarry
    Liu, Haining
    Xia, Liming
    Kinahan, Paul E.
    [J]. EUROPEAN RADIOLOGY, 2019, 29 (11) : 6100 - 6108
  • [27] Evaluation and Management of Indeterminate Pulmonary Nodules
    Hodnett, Philip A.
    Ko, Jane P.
    [J]. RADIOLOGIC CLINICS OF NORTH AMERICA, 2012, 50 (05) : 895 - +
  • [28] Management strategy of solitary pulmonary nodules
    Zhan, Ping
    Xie, Haiyan
    Xu, Chunhua
    Hao, Keke
    Hou, Zhibo
    Song, Yong
    [J]. JOURNAL OF THORACIC DISEASE, 2013, 5 (06) : 824 - 829
  • [29] Incidental pulmonary nodules: characterization and management
    Trinidad Lopez, C.
    Delgado Sanchez-Gracian, C.
    Utrera Perez, E.
    Jurado Basildo, C.
    Sepulveda Villegas, C. A.
    [J]. RADIOLOGIA, 2019, 61 (05): : 357 - 369
  • [30] Comprehensive Analysis of Clinical Logistic and Machine Learning-Based Models for the Evaluation of Pulmonary Nodules
    Zhang, Kai
    Wei, Zihan
    Nie, Yuntao
    Shen, Haifeng
    Wang, Xin
    Wang, Jun
    Yang, Fan
    Chen, Kezhong
    [J]. JTO CLINICAL AND RESEARCH REPORTS, 2022, 3 (04):