Radiomics-based Prediction Model of Tumor Spread Through Air Space in Lung Adenocarcinoma

Overview

Spread through air space (STAS) has been reported as a negative prognostic factor in patients with lung cancer undergone sublobar resection. Its preoperative assessment could thus be useful to customize surgical treatment. Radiomics has been recently proposed to predict STAS in patients with lung adenocarcinoma. However, all the studies have strictly selected both imaging and patients, leading to results hardly applicable to daily clinical practice. The aim of this study is to test a radiomics-based prediction model of STAS in practice-based dataset and verify its validity and translational potentials. Radiological and clinical data from 100 consecutive patients with resected lung adenocarcinoma were retrospectively collected for the training section. As in common clinical practice, preoperative CT images were acquired independently by different physicians and from different hospitals. Therefore, our dataset presents high variance in model and manufacture of scanner, acquisition and reconstruction protocol, endovenous contrast phase and pixel size. To test the effect of normalization in highly varying data, preoperative CT images and tumor region of interest were preprocessed with four different pipelines. Features were extracted using pyradiomics and selected considering both separation power and robustness within pipelines. After that, a radiomics-based prediction model of STAS were created using the most significant associated features. This model were than validated in a group of 50 patients prospectively enrolled as external validation group to test its efficacy in STAS prediction.

Full Title of Study: “Could Radiomics Predict Tumor Spread Through Air Space in Lung Adenocarcinoma in All Computed Tomography Settings?”

Study Type

  • Study Type: Observational
  • Study Design
    • Time Perspective: Prospective
  • Study Primary Completion Date: July 1, 2020

Arms, Groups and Cohorts

  • Lung adenocarcinoma
    • Imaging from patients with surgically treated lung adenocarcinoma were collected and processed for the construction of the radiomics-based prediction model

Clinical Trial Outcome Measures

Primary Measures

  • Sensitivity
    • Time Frame: 24 hour before operation
    • Testing the sensitivity of Radiomics to predict STAS using the area under receiver operating characteristic curve
  • Specificity
    • Time Frame: 24 hour before operation
    • Testing the specificity of Radiomics to predict STAS using the area under receiver operating characteristic curve

Participating in This Clinical Trial

Inclusion Criteria

  • Patients with suspected or cito-histologically proven lung adenocarcinoma undergoing lung cancer surgery; – Available preoperative CT images – Age older than 18 years Exclusion Criteria:

  • Chest wall infiltration – Induction radio or chemotherapy – Incomplete surgical resection

Gender Eligibility: All

Minimum Age: 18 Years

Maximum Age: N/A

Are Healthy Volunteers Accepted: No

Investigator Details

  • Lead Sponsor
    • University of Roma La Sapienza
  • Provider of Information About this Clinical Study
    • Principal Investigator: Marco Anile, Principal Investigator – University of Roma La Sapienza
  • Overall Official(s)
    • Marco Anile, MD, Principal Investigator, La Sapienza Università di Roma

References

Jiang C, Luo Y, Yuan J, You S, Chen Z, Wu M, Wang G, Gong J. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma. Eur Radiol. 2020 Jul;30(7):4050-4057. doi: 10.1007/s00330-020-06694-z. Epub 2020 Feb 28.

Chen D, She Y, Wang T, Xie H, Li J, Jiang G, Chen Y, Zhang L, Xie D, Chen C. Radiomics-based prediction for tumour spread through air spaces in stage I lung adenocarcinoma using machine learning. Eur J Cardiothorac Surg. 2020 Jul 1;58(1):51-58. doi: 10.1093/ejcts/ezaa011.

Zhuo Y, Feng M, Yang S, Zhou L, Ge D, Lu S, Liu L, Shan F, Zhang Z. Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma. Transl Oncol. 2020 Oct;13(10):100820. doi: 10.1016/j.tranon.2020.100820. Epub 2020 Jul 1.

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