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

Lung Adenocarcinoma Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04893200
• Other study ID #: RADIOMICS
• Status: Completed
• Start date: February 1, 2020
• Est. completion date: June 1, 2021

Study information

• Verified date: September 2021
• Source: University of Roma La Sapienza
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 150
• Est. completion date: June 1, 2021
• Est. primary completion date: July 1, 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Adenocarcinoma
• Adenocarcinoma of Lung
• Lung Adenocarcinoma

Locations

Country	Name	City	State
Italy	Dipartimento di chirurgia Generale e Specialistica "Paride Stefanini"	Roma

Sponsors (1)

Lead Sponsor	Collaborator
University of Roma La Sapienza

Country where clinical trial is conducted

Italy, 

References & Publications (3)

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. — View Citation

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Sensitivity	Testing the sensitivity of Radiomics to predict STAS using the area under receiver operating characteristic curve	24 hour before operation
Primary	Specificity	Testing the specificity of Radiomics to predict STAS using the area under receiver operating characteristic curve	24 hour before operation