|Table of Contents|

The value of radiomics nomogram based on thin-section CT images in identifying benign and malignant pulmonary nodules

Journal Of Modern Oncology[ISSN:1672-4992/CN:61-1415/R]

Issue:
2023 08
Page:
1502-1506
Research Field:
Publishing date:

Info

Title:
The value of radiomics nomogram based on thin-section CT images in identifying benign and malignant pulmonary nodules
Author(s):
SONG Xinyang1; SHEN Tianci1; HU Xiangyu2; WANG Yangyang1; YANG Feng1
1.Department of Radiology；2.Department of Respiratory,Xiangyang No.1 People's Hospital,Hubei University of Medicine，Hubei Xiangyang 441100,China.
Keywords:
X-ray computer; pulmonary nodules; imaging histology
PACS:
R734.2
DOI:
10.3969/j.issn.1672-4992.2023.08.021
Abstract:
Objective:To establish a clinical radiomics nomogram based on thin-section CT images to evaluate its clinical value for identifying benign and malignant pulmonary nodules.Methods:Pulmonary nodules undergoing thin-layer CT examination and pathologically confirmed between March 2018 and September 2020.There were 139 patients (65 benign and 74 malignant).Imaging histological features were extracted from the chest CT images of each patient.Data were downscaled using minimum absolute shrinkage and lasso regression to select useful features and construct an imaging histology feature model.Multiple logistic regression combined independent clinical risk factors with building imaging histology column line graphs.The accuracy and diagnostic efficacy of the column line graphs were evaluated in the training set and validated in the validation set.Finally,the clinical application value of the column line graphs was evaluated by decision curve analysis.Results:The traditional image feature model had poor diagnostic efficacy for benign and malignant pulmonary nodules in the training set (AUC=0.86,95%CI 0.79~0.93),validation set (AUC=0.79,95%CI 0.65~0.93).The hybrid model had poor diagnostic efficacy for benign and malignant pulmonary nodules in the training set (AUC=0.94,95%CI 0.90~0.99),validation set (AUC=0.94,95%CI 0.88~1.00) showed better discriminatory efficacy and pathological compliance,and the decision curves indicated that the inclusion of imaging histology was beneficial for patient prognosis.Conclusion:Clinical imaging radiomics nomogram line drawings based on thin-section CT images can facilitate accurate determination of the risk of malignancy of pulmonary nodules.

References:

［1］ SIEGEL RL,MILLER KD,FUCHS HE.Cancer statistics,2022 ［J］.Ca Cancer J Clin，2022，72(1):7-33.
［2］ SHEWALE JB,CORSINI EM,CORREA AM,et al.Time trends and predictors of survival in surgically resected early-stage non-small cell lung cancer patients［J］.Surg Oncol，2020，122(3):495-505.
［3］ GONZ LEZ M,CALVO V,REDONDO I,et al.Overall survival for early and locally advanced non-small-cell lung cancer from one institution:2000-2017［J］.Clin Transl Oncol，2021，23(7):1325-1333.
［4］POSTMUS PE,KERR KM,OUDKERK M,et al.Early and locally advanced non-small-cell lung cancer (NSCLC):ESMO clinical practice guidelines for diagnosis,treatment,and follow-up［J］.Ann Oncol,2017,28(4):1-21.
［5］ WU G,WOODRUFF HC,SANDULEANU S,et al.Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules:a multicenter study ［J］.European Radiology,2020,30(5):2680-2691.
［6］ GANESHAN B,GOH V,MANDEVILLE HC,et al.non-small cell lung cancer:histopathologic correlates for texture parameters at CT［J］.Radiology,2013,266:326-333.
［7］ HAWKINS SH,KORECKI JN,BALAGURUNATHAN Y,et al.Predicting outcomes of non-small cell lung cancer using CT image features［J］.IEEE Access，2014,2:1418-1426.
［8］ SHI L,HE Y,YUAN Z,et al.(2018) Radiomics for response and outcome assessment for non-small cell lung cancer［J/OL］.Technol Cancer Res Treat,17:1533033818782788［2018-01-26］ https://journals.sagepub.com/doi/epub/10.1177/1533033818782 788.
［9］LAMBIN P,RIOS-VELAZQUEZ E,LEIJENAAR R,et al.Radiomics:Extracting more information from medical images using advanced feature analysis［J］.European Journal of Cancer,2012,48(4):441-446.
［10］毕伟,丁长青,刘绪军，等.孤立性良恶性肺结节的临床及CT特点分析［J］.影像技术,2021,33(04):28-33,38. BI W,DING CQ,LIU XJ,et al.Analysis of clinical and CT features of isolated benign and malignant pulmonary nodules ［J］.Imaging Technology,2021,33(04):28-33,38.
［11］李镛,吴鹏亮,方珍菲，等.908例恶性孤立性肺小结节影像学征象的分析［J］.内科理论与实践,2021,16(03):193-196. LI Y,WU PL,FANG ZF,et al.Analysis of 908 cases of malignant isolated small pulmonary nodules with imaging signs［J］.Theory and Practice of Internal Medicine,2021,16(3):193-196.
［12］ AERTS HJWL,VELAZQUEZ ER,LEIJENAAR RTH,et al.Decoding tumor phenotype by noninvasive imaging using a quantitative radiomics approach［J/OL］.Nat Commun,2014,5(1)［2014-01-03］ https://doi.org/10.1038/ncomms5006.
［13］ WILSON R,DEVARAJ A.Radiomics of pulmonary nodules and lung cancer［J］.Transl Lung Cancer Res,2017,6(1):86-91.
［14］ KALPATHY-CRAMER J,MAMOMOV A,ZHAO B,et al.Radiomics of lung nodules:a multi-institutional study of robustness and agreement of quantitative imaging features［J］.Tomography,2016,2(4):430-437.
［15］LIU A,WANG Z,YANG Y,et al.Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram ［J］.Cancer Communications,2020,40(1):16-24.
［16］JING R,WANG J,LI J,et al.A wavelet features derived radiomics nomogram to predict malignant and benign early-stage lung nodules ［J/OL］.Scientific Reports,2021,11(1)［2021-10-21］ https://doi.org/10.1038/s41598-021-01470-5.
［17］ XU Y,LU L,SUN SH,et al.Effect of CT image acquisition parameters on the diagnostic performance of radiomics in predicting malignancy of pulmonary nodules of different sizes ［J］.Eur Radiol,2022,32(3):1517-1527.

Memo

Memo:
湖北医药学院研究生科技创新项目（编号：YC2022049）
Last Update: 1900-01-01