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Radiomics Multifactorial Biomarker for Pulmonary Nodules

Radiomics and Clinical Variables Can Differentiate Malignant Nodules and Detect Invasive Adenocarcinoma in Pulmonary Nodules: a Multi-center Study

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT03872362
Acronym
RMBPN
Enrollment
800
Registered
2019-03-13
Start date
2018-07-11
Completion date
2019-02-01
Last updated
2019-03-13

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

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

Keywords

Lung cancer, CT, Frozen sections, Radiomics, Lung adenocarcinoma

Brief summary

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

Detailed description

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.

Interventions

DIAGNOSTIC_TESTradiomics

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

Sponsors

Affiliated Zhongshan Hospital of Dalian University
CollaboratorOTHER
The Second Affiliated Hospital of Dalian Medical University
CollaboratorOTHER
The Fifth Hospital of Dalian
CollaboratorUNKNOWN
Maastricht University
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

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

Design outcomes

Primary

MeasureTime frameDescription
Malignant nodules classifier30 daysModel based on Radiomic that can differentiate malignant nodules from benign nodules.
Invasive adenocarcinoma classifier30 daysModel based on Radiomic that can differentiate invasive adenocarcinoma from non-invasive adenocarcinoma.

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 17, 2026