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

University of Roma La Sapienza

Status: Completed

Conditions: Lung Adenocarcinoma

Study type: Observational

Funder types: Other

Identifiers

NCT04893200

RADIOMICS

Details and patient eligibility

About

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.

Enrollment: 150 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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

Trial design

150 participants in 1 patient group

Lung adenocarcinoma

Description:

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