Clinical trial NCT03872362: Radiomics Multifactorial Biomarker for Pulmonary Nodules
Official title: Radiomics and Clinical Variables Can Differentiate Malignant Nodules and Detect Invasive Adenocarcinoma in Pulmonary Nodules: a Multi-center Study
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Lung Neoplasms
Carcinoma, Non-Small-Cell Lung
Lung Diseases
Neoplasms
Pathology
Keywords: Lung cancer
CT
Frozen sections
Radiomics
Lung adenocarcinoma
Lead sponsor: Maastricht University
Sponsor class: OTHER
Collaborators: name: The Affiliated Zhongshan Hospital of Dalian University
name: The Second Affiliated Hospital of Dalian Medical University
name: The Fifth Hospital of Dalian
First posted: 2019-03-13
Enrollment: 800
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients with pulmonary nodules in the collaborating institutes.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Malignant nodules classifier
timeFrame: 30 days
measure: Invasive adenocarcinoma classifier
timeFrame: 30 days
Secondary outcomes:
Other outcomes:
Locations:
city: Dalian
country: China
facility: Affiliated Zhongshan Hospital of Dalian University
state: Liaoning
Study officials:
References:
citation: Wu G, Woodruff HC, Shen J, Refaee T, Sanduleanu S, Ibrahim A, Leijenaar RTH, Wang R, Xiong J, Bian J, Wu J, Lambin P. Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study. Radiology. 2020 Nov;297(2):451-458. doi: 10.1148/radiol.2020192431. Epub 2020 Aug 25.