Radiomics Multifactorial Biomarker for Pulmonary Nodules

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

COMPLETED

Total Enrollment

800 participants

Study Classification

OBSERVATIONAL

Study Start Date

2018-07-11

Study Completion Date

2019-02-01

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

The investigators aim to investigate the utility of radiomics to differentiate malignant nodules from benign nodules and invasive adenocarcinoma from non-invasive adenocarcinoma.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Multi-omics Detection Techniques for Differentiating Benign and Malignant Pulmonary Nodules

NCT07047573

Lung Cancer (Diagnosis) Pulmonary Nodule Persistent

RECRUITING

Application of Diffusion Weighted MRI Versus CT in Evaluation of the Effect of Treating Lung Cancer

NCT02320617

Lung Neoplasms

UNKNOWN NA

The Accuracy of Targeted Lymph Node Dissection of Non-small Cell Lung Cancer Patients According to Predictive Models

NCT06768853

Non-small Cell Lung Cancer

NOT_YET_RECRUITING NA

Monitoring the Changes of Tumor-related Biomarkers Before and After Pulmonary Nodule Biopsy.

NCT03397355

Lung Cancer

UNKNOWN

Diagnosis of Individuals With Pulmonary Nodules by Different Bronchoscopy Combination

NCT02268162

Lung Neoplasms Early Diagnosis Bronchoscopy

UNKNOWN NA

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

With the development of computed tomography (CT) equipment and the increasing use of lung cancer screening programs with low-dose CT, a growing number of early-stage lung cancers were detected so that a large number of patients have undergone surgery.

Although a number of radiological studies have been used morphological signs so-called semantic features to make a differential diagnosis, it is still hard to apply by clinician because pulmonary nodules especially ground-glass nodules and small size nodules have atypical radiology signs and have strong subjectivity from different observers. Recently, CT-based radiomics, extracting the quantitative high-throughput features from medical images and facilitating clinical decision-making system, showed a good performance to predict diagnosis and prognosis of diverse cancer.

Therefore, the proposed project aims to develop and validate radiomics models based on CT images to identify malignant nodules and then to discriminate the different types of lung adenocarcinoma in patients with pulmonary nodules.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Lung Neoplasms Carcinoma, Non-Small-Cell Lung Lung Diseases Neoplasms Pathology

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Training dataset

No interventions

radiomics

Intervention Type DIAGNOSTIC_TEST

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

External validation1

No interventions

radiomics

Intervention Type DIAGNOSTIC_TEST

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

External validation2

No interventions

radiomics

Intervention Type DIAGNOSTIC_TEST

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

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

radiomics

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

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

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

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No