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

Could Radiomics Predict Tumor Spread Through Air Space in Lung Adenocarcinoma in All Computed Tomography Settings?

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.

Study Overview

• Status: Completed
• Conditions: Lung Adenocarcinoma

• Study Type: Observational
• Enrollment (Actual): 150

Contacts and Locations

Study Locations

• Italy
  • Roma, Italy, 00139
    • Dipartimento di chirurgia Generale e Specialistica "Paride Stefanini"

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

Patients undergoing lung cancer surgery at Policlinico Umberto I Hospital, Rome

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

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

What is the study measuring?

Primary Outcome Measures

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

Collaborators and Investigators

• Sponsor: University of Roma La Sapienza
• Investigators: Principal Investigator: Marco Anile, MD, La Sapienza Università di Roma

Publications and helpful links

General Publications

• 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.
• Bassi M, Russomando A, Vannucci J, Ciardiello A, Dolciami M, Ricci P, Pernazza A, D'Amati G, Mancini Terracciano C, Faccini R, Mantovani S, Venuta F, Voena C, Anile M. Role of radiomics in predicting lung cancer spread through air spaces in a heterogeneous dataset. Transl Lung Cancer Res. 2022 Apr;11(4):560-571. doi: 10.21037/tlcr-21-895.

Study record dates

Study Major Dates

• Study Start (ACTUAL): February 1, 2020
• Primary Completion (ACTUAL): July 1, 2020
• Study Completion (ACTUAL): June 1, 2021

Study Registration Dates

• First Submitted: May 6, 2021
• First Submitted That Met QC Criteria: May 18, 2021
• First Posted (ACTUAL): May 19, 2021

Study Record Updates

• Last Update Posted (ACTUAL): September 5, 2021
• Last Update Submitted That Met QC Criteria: September 3, 2021
• Last Verified: September 1, 2021

More Information

Terms related to this study

• Keywords: Spread Through Air Space; Radiomics
• Additional Relevant MeSH Terms: Neoplasms by Histologic Type; Neoplasms; Neoplasms by Site; Carcinoma; Neoplasms, Glandular and Epithelial; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms; Adenocarcinoma; Adenocarcinoma of Lung
• Other Study ID Numbers: RADIOMICS

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No