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

NCT04893200 | Completed

• 1 other identifier: RADIOMICS
• Study Type: observational
• Target: 150
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Lung Adenocarcinoma

Keywords

Spread Through Air Space; Radiomics

Outcome Measures

Primary Outcomes (2)

• 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

Study Arms (1)

Lung adenocarcinoma

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

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

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

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• University of Roma La Sapienza: lead

Study Sites (1)

Dipartimento di chirurgia Generale e Specialistica "Paride Stefanini"

Roma, 00139, Italy

Location

Related Publications (4)

• 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.

  PMID: 32112116 BACKGROUND

• 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.

  PMID: 32011674 BACKGROUND

• 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.

  PMID: 32622312 BACKGROUND

• 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.

  PMID: 35529792 DERIVED

MeSH Terms

Conditions

Adenocarcinoma of Lung

Condition Hierarchy (Ancestors)

Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Lung Neoplasms; Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site

Study Officials

• Marco Anile, MD

  La Sapienza Università di Roma

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: May 6, 2021
• First Posted: May 19, 2021
• Study Start: February 1, 2020
• Primary Completion: July 1, 2020
• Study Completion: June 1, 2021
• Last Updated: September 5, 2021

Record last verified: 2021-09

Locations

IT (1)