Pathologic categorization of lung nodules: Radiomic descriptors of CT attenuation distribution patterns of solid and subsolid nodules in low-dose CT

Eur J Radiol. 2020 Aug:129:109106. doi: 10.1016/j.ejrad.2020.109106. Epub 2020 May 31.


Purpose: Develop a quantitative image analysis method to characterize the heterogeneous patterns of nodule components for the classification of pathological categories of nodules.

Materials and methods: With IRB approval and permission of the National Lung Screening Trial (NLST) project, 103 subjects with low dose CT (LDCT) were used in this study. We developed a radiomic quantitative CT attenuation distribution descriptor (qADD) to characterize the heterogeneous patterns of nodule components and a hybrid model (qADD+) that combined qADD with subject demographic data and radiologist-provided nodule descriptors to differentiate aggressive tumors from indolent tumors or benign nodules with pathological categorization as reference standard. The classification performances of qADD and qADD + were evaluated and compared to the Brock and the Mayo Clinic models by analysis of the area under the receiver operating characteristic curve (AUC).

Results: The radiomic features were consistently selected into qADDs to differentiate pathological invasive nodules from (1) preinvasive nodules, (2) benign nodules, and (3) the group of preinvasive and benign nodules, achieving test AUCs of 0.847 ± 0.002, 0.842 ± 0.002 and 0.810 ± 0.001, respectively. The qADD + obtained test AUCs of 0.867 ± 0.002, 0.888 ± 0.001 and 0.852 ± 0.001, respectively, which were higher than both the Brock and the Mayo Clinic models.

Conclusion: The pathologic invasiveness of lung tumors could be categorized according to the CT attenuation distribution patterns of the nodule components manifested on LDCT images, and the majority of invasive lung cancers could be identified at baseline LDCT scans.

Keywords: LDCT; Lung cancer screening; Lung nodule; Pathologic categorization; Radiomic.

MeSH terms

  • Aged
  • Area Under Curve
  • Diagnosis, Differential
  • Female
  • Humans
  • Lung / diagnostic imaging
  • Lung / pathology
  • Lung Neoplasms / diagnostic imaging*
  • Lung Neoplasms / pathology*
  • Male
  • Middle Aged
  • Multiple Pulmonary Nodules / diagnostic imaging*
  • Multiple Pulmonary Nodules / pathology*
  • ROC Curve
  • Radiation Dosage
  • Tomography, X-Ray Computed / methods*