Combined Radiomic and Visual Assessment for Improved Detection of Lung Adenocarcinoma Invasiveness on Computed Tomography Scans: A Multi-Institutional Study

Pranjal Vaidya, Kaustav Bera, Philip A. Linden, Amit Gupta, Prabhakar Shantha Rajiah, David R. Jones, Matthew Bott, Harvey Pass, Robert Gilkeson, Frank Jacono, Kevin Li Chun Hsieh, Gong Yau Lan, Vamsidhar Velcheti, Anant Madabhushi

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Objective: The timing and nature of surgical intervention for semisolid abnormalities are dependent upon distinguishing between adenocarcinoma-in-situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (INV). We sought to develop and evaluate a quantitative imaging method to determine invasiveness of small, ground-glass lesions on computed tomography (CT) chest scans. Methods: The study comprised 268 patients from 4 institutions with resected (<=3 cm) semisolid lesions with confirmed histopathological diagnosis of MIA/AIS or INV. A total of 248 radiomic texture features from within the tumor nodule (intratumoral) and adjacent to the nodule (peritumoral) were extracted from manually annotated lung nodules of chest CT scans. The datasets were randomly divided, with 40% of patients used for training and 60% used for testing the machine classifier (Training DTrain, N=106; Testing, DTest, N=162). Results: The top five radiomic stable features included four intratumoral (Laws and Haralick feature families) and one peritumoral feature within 3 to 6 mm of the nodule (CoLlAGe feature family), which successfully differentiated INV from MIA/AIS nodules with an AUC of 0.917 [0.867-0.967] on DTrain and 0.863 [0.79-0.931] on DTest. The radiomics model successfully differentiated INV from MIA cases (<1 cm AUC: 0.76 [0.53-0.98], 1-2 cm AUC: 0.92 [0.85-0.98], 2-3 cm AUC: 0.95 [0.88-1]). The final integrated model combining the classifier with the radiologists’ score gave the best AUC on DTest (AUC=0.909, p<0.001). Conclusions: Addition of advanced image analysis via radiomics to the routine visual assessment of CT scans help better differentiate adenocarcinoma subtypes and can aid in clinical decision making. Further prospective validation in this direction is warranted.

Original languageEnglish
Article number902056
JournalFrontiers in Oncology
Volume12
DOIs
Publication statusPublished - May 30 2022

Keywords

  • ct scan (CT)
  • integrated model analysis
  • invasive adenocarcinoma (IA)
  • minimally invasive adenocarcinoma (MIA)
  • radiologists interpretation
  • radiomics

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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