Radiomics Combined With Frozen Section Prediction Model for Spread Through Air Space in Lung Adenocarcinoma
Preoperative CT-based Radiomics Combined With Intraoperative Frozen Section is Predictive of Spread Through Air Space in Early Lung Adenocarcinoma
Study Overview
Status
Conditions
Intervention / Treatment
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 Type
Enrollment (Anticipated)
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- intraoperative frozen section diagnosis and final pathology diagnosis are available
- preoperative standard non-enhanced CT is available
- Pathologically confirmed
Exclusion Criteria:
- with a previous history of radiation therapy, chemotherapy or biopsy
- the time interval between the CT examination and surgery was more than two weeks
Study Plan
How is the study designed?
Design Details
- Observational Models: Cohort
- Time Perspectives: Retrospective
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
|
Training dataset
No interventions
|
The high-throughput extraction of large amounts of quantitative image features from medical images
|
|
External validation1
No interventions
|
The high-throughput extraction of large amounts of quantitative image features from medical images
|
|
External validation2
No interventions
|
The high-throughput extraction of large amounts of quantitative image features from medical images
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Sensitivity
Time Frame: 24 hour before operation
|
Testing the sensitivity of Radiomics to predict STAS using the area under receiver operating characteristic curve
|
24 hour before operation
|
|
Specificity
Time Frame: 24 hour before operation
|
Testing the specificity of Radiomics to predict STAS using the area under receiver operating characteristic curve
|
24 hour before operation
|
Collaborators and Investigators
Sponsor
Study record dates
Study Major Dates
Study Start (Anticipated)
Primary Completion (Anticipated)
Study Completion (Anticipated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- RFSTAS
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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