Radiomics Combined With Frozen Section Prediction Model for Spread Through Air Space in Lung Adenocarcinoma

Sponsor

Wuhan Union Hospital, China (Other)

Overall Status

Not yet recruiting

CT.gov ID

NCT05400304

Collaborator

(none)

900

Enrollment

10.3

Anticipated Duration (Months)

Study Details

Study Description

Brief Summary

a multifactorial model combining radiomics with frozen section analysis is a potential biomarker for assessing Spread Through Air Space during surgery, which can provide decision-making support to therapeutic planning for early-stage lung adenocarcinomas.

Condition or Disease	Intervention/Treatment	Phase
Lung Adenocarcinoma	Diagnostic Test: radiomics

Detailed Description

Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. Its preoperative assessment could thus be useful to customize surgical treatment. Radiomics and frozen section haave been recently proposed to predict STAS in patients with lung adenocarcinoma. Radiomics-based Prediction Model is highly sensitive but not specific for STAS detection. While, frozen section is highly specific but not sensitive for STAS detection in early lung adenocarcinomas.

Therefore, the proposed project aims to develop and validate a multifactorial model combining radiomics with frozen section analysis to assesse Spread Through Air Space during surgery, which can provide decision-making support to therapeutic planning for early-stage lung adenocarcinomas.

Study Design

Study Type:

Observational

Anticipated Enrollment :

900 participants

Observational Model:

Cohort

Time Perspective:

Retrospective

Official Title:

Preoperative CT-based Radiomics Combined With Intraoperative Frozen Section is Predictive of Spread Through Air Space in Early Lung Adenocarcinoma

Anticipated Study Start Date :

Jul 1, 2022

Anticipated Primary Completion Date :

May 1, 2023

Anticipated Study Completion Date :

May 12, 2023

Arms and Interventions

Arm	Intervention/Treatment
Training dataset No interventions	Diagnostic Test: radiomics The high-throughput extraction of large amounts of quantitative image features from medical images
External validation1 No interventions	Diagnostic Test: radiomics The high-throughput extraction of large amounts of quantitative image features from medical images
External validation2 No interventions	Diagnostic Test: radiomics The high-throughput extraction of large amounts of quantitative image features from medical images

Arm

Intervention/Treatment

Training dataset

No interventions

Diagnostic Test: radiomics

The high-throughput extraction of large amounts of quantitative image features from medical images

External validation1

No interventions

Diagnostic Test: radiomics

The high-throughput extraction of large amounts of quantitative image features from medical images

External validation2

No interventions

Diagnostic Test: radiomics

The high-throughput extraction of large amounts of quantitative image features from medical images

Outcome Measures

Primary Outcome Measures

Sensitivity [24 hour before operation]
Testing the sensitivity of Radiomics to predict STAS using the area under receiver operating characteristic curve
Specificity [24 hour before operation]
Testing the specificity of Radiomics to predict STAS using the area under receiver operating characteristic curve

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years to 75 Years

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Inclusion Criteria:

intraoperative frozen section diagnosis and final pathology diagnosis are available
preoperative standard non-enhanced CT is available
Pathologically confirmed