Analysis and validation of probabilistic models for predicting malignancy in solitary pulmonary nodules in a population in Brazil

被引:0
作者
de Carvalho Melo, Cromwell Barbosa [1 ]
Juliano Perfeito, Joao Alessi
Daud, Danilo Felix [1 ]
Costa Junior, Altair da Silva [1 ]
Santoro, Ilka Lopes
Villaca Leao, Luiz Eduardo
机构
[1] Univ Fed Sao Paulo, Hosp Sao Paulo, Escola Paulista Med, UNIFESP, Sao Paulo, Brazil
关键词
Solitary Pulmonary Nodule; Risk Factors; Carcinoma; Non-Small-Cell Lung; LUNG-CANCER; MANAGEMENT; CT; GUIDELINES; SCANS;
D O I
暂无
中图分类号
R56 [呼吸系及胸部疾病];
学科分类号
摘要
Objective: To analyze clinical and radiographic findings that influence the pathological diagnosis of solitary pulmonary nodule (SPN) and to compare/validate two probabilistic models for predicting SPN malignancy in patients with SPN in Brazil. Methods: This was a retrospective study involving 110 patients diagnosed with SPN and submitted to resection of SPN at a tertiary hospital between 2000 and 2009. The clinical characteristics studied were gender, age, presence of systemic comorbidities, history of malignancy prior to the diagnosis of SPN, histopathological diagnosis of SPN, smoking status, smoking history, and time since smoking cessation. The radiological characteristics studied, in relation to the SPN, were presence of spiculated margins, maximum transverse diameter, and anatomical location. Two mathematical models, created in 1997 and 2007, respectively, were used in order to determine the probability of SPN malignancy. Results: We found that SPN malignancy was significantly associated with age (p = 0.006; OR = 5.70 for age > 70 years), spiculated margins (p = 0.001), and maximum diameter of SPN (p = 0.001; OR = 2.62 for diameters > 20 mm). The probabilistic model created in 1997 proved to be superior to that created in 2007 area under the ROC curve (AUC), 0.79 +/- 0.44 (95% Cl: 0.70-0.88) vs. 0.69 +/- 0.50 (95% Cl: 0.59-0.79). Conclusions: Advanced age, greater maximum SPN diameter, and spiculated margins were significantly associated with the diagnosis of SPN malignancy. Our analysis shows that, although both mathematical models were effective in determining SPN malignancy in our population, the 1997 model was superior.
引用
收藏
页码:559 / 565
页数:7
相关论文
共 50 条
  • [21] Predictive model for the probability of malignancy in solitary pulmonary nodules: a meta-analysis
    Gang Chen
    Tian Bai
    Li-Juan Wen
    Yu Li
    Journal of Cardiothoracic Surgery, 17
  • [22] Predictive model for the probability of malignancy in solitary pulmonary nodules: a meta-analysis
    Chen, Gang
    Bai, Tian
    Wen, Li-Juan
    Li, Yu
    JOURNAL OF CARDIOTHORACIC SURGERY, 2022, 17 (01)
  • [23] Establishment and validation of a prediction model for the probability of malignancy in solid solitary pulmonary nodules in northwest China
    Duan, Xue-Qin
    Wang, Xiao-Li
    Zhang, Li-Fen
    Liu, Xi-Zhi
    Zhang, Wen-Wen
    Liu, Yi-Hui
    Dong, Chun-Hui
    Zhao, Xin-Han
    Chen, Ling
    JOURNAL OF SURGICAL ONCOLOGY, 2021, 123 (04) : 1134 - 1143
  • [24] Development and validation of diagnostic prediction model for solitary pulmonary nodules
    Yonemori, Kan
    Tateishi, Ukihide
    Uno, Hajime
    Yonemori, Yoko
    Tsuta, Koji
    Takeuchi, Masahiro
    Matsuno, Yoshihiro
    Fujiwara, Yasuhiro
    Asamura, Hisao
    Kusumoto, Masahiko
    RESPIROLOGY, 2007, 12 (06) : 856 - 862
  • [25] Deep Learning Models for Predicting Malignancy Risk in CT-Detected Pulmonary Nodules: A Systematic Review and Meta-analysis
    Wulaningsih, Wahyu
    Villamaria, Carmela
    Akram, Abdullah
    Benemile, Janella
    Croce, Filippo
    Watkins, Johnathan
    LUNG, 2024, 202 (05) : 625 - 636
  • [26] Comparison of four models predicting the malignancy of pulmonary nodules: A single-center study of Korean adults
    Yang, Bumhee
    Jhun, Byung Woo
    Shin, Sun Hye
    Jeong, Byeong-Ho
    Um, Sang-Won
    Zo, Jae Il
    Lee, Ho Yun
    Sohn, Insoek
    Kim, Hojoong
    Kwon, O. Jung
    Lee, Kyungjong
    PLOS ONE, 2018, 13 (07):
  • [27] Establishing Assistant Diagnosis Models of Solitary Pulmonary Nodules Based on Intelligent Algorithms
    Zhao, Zhijun
    Chen, Jingtao
    Yin, Xiaoxiang
    Song, Huayong
    Wang, Xinchun
    Wang, Jing
    CELLULAR PHYSIOLOGY AND BIOCHEMISTRY, 2015, 35 (06) : 2463 - 2471
  • [28] The study of plain CT combined with contrast-enhanced CT-based models in predicting malignancy of solitary solid pulmonary nodules
    Zhang, Wenjia
    Cui, Xiaonan
    Wang, Jing
    Cui, Sha
    Yang, Jianghua
    Meng, Junjie
    Zhu, Weijie
    Li, Zhiqi
    Niu, Jinliang
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [29] A prediction model to evaluate the pretest risk of malignancy in solitary pulmonary nodules: evidence from a large Chinese southwestern population
    Wu, Zuohong
    Huang, Tingting
    Zhang, Shiqi
    Cheng, Deyun
    Li, Weimin
    Chen, Bojiang
    JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY, 2021, 147 (01) : 275 - 285
  • [30] Dynamic contrast-enhanced computed tomography for the diagnosis of solitary pulmonary nodules: a systematic review and meta-analysis
    Weir-McCall, Jonathan R.
    Joyce, Stella
    Clegg, Andrew
    MacKay, James W.
    Baxter, Gabrielle
    Dendl, Lena-Marie
    Rintoul, Robert C.
    Qureshi, Nagmi R.
    Miles, Ken
    Gilbert, Fiona J.
    EUROPEAN RADIOLOGY, 2020, 30 (06) : 3310 - 3323