Clinical Trials Logo

Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT04893200
Other study ID # RADIOMICS
Secondary ID
Status Completed
Phase
First received
Last updated
Start date February 1, 2020
Est. completion date June 1, 2021

Study information

Verified date September 2021
Source University of Roma La Sapienza
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

Spread through air space (STAS) has been reported as a negative prognostic factor in patients with lung cancer undergone sublobar resection. Its preoperative assessment could thus be useful to customize surgical treatment. Radiomics has been recently proposed to predict STAS in patients with lung adenocarcinoma. However, all the studies have strictly selected both imaging and patients, leading to results hardly applicable to daily clinical practice. The aim of this study is to test a radiomics-based prediction model of STAS in practice-based dataset and verify its validity and translational potentials. Radiological and clinical data from 100 consecutive patients with resected lung adenocarcinoma were retrospectively collected for the training section. As in common clinical practice, preoperative CT images were acquired independently by different physicians and from different hospitals. Therefore, our dataset presents high variance in model and manufacture of scanner, acquisition and reconstruction protocol, endovenous contrast phase and pixel size. To test the effect of normalization in highly varying data, preoperative CT images and tumor region of interest were preprocessed with four different pipelines. Features were extracted using pyradiomics and selected considering both separation power and robustness within pipelines. After that, a radiomics-based prediction model of STAS were created using the most significant associated features. This model were than validated in a group of 50 patients prospectively enrolled as external validation group to test its efficacy in STAS prediction.


Recruitment information / eligibility

Status Completed
Enrollment 150
Est. completion date June 1, 2021
Est. primary completion date July 1, 2020
Accepts healthy volunteers No
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria: - Patients with suspected or cito-histologically proven lung adenocarcinoma undergoing lung cancer surgery; - Available preoperative CT images - Age older than 18 years Exclusion Criteria: - Chest wall infiltration - Induction radio or chemotherapy - Incomplete surgical resection

Study Design


Locations

Country Name City State
Italy Dipartimento di chirurgia Generale e Specialistica "Paride Stefanini" Roma

Sponsors (1)

Lead Sponsor Collaborator
University of Roma La Sapienza

Country where clinical trial is conducted

Italy, 

References & Publications (3)

Chen D, She Y, Wang T, Xie H, Li J, Jiang G, Chen Y, Zhang L, Xie D, Chen C. Radiomics-based prediction for tumour spread through air spaces in stage I lung adenocarcinoma using machine learning. Eur J Cardiothorac Surg. 2020 Jul 1;58(1):51-58. doi: 10.1093/ejcts/ezaa011. — View Citation

Jiang C, Luo Y, Yuan J, You S, Chen Z, Wu M, Wang G, Gong J. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma. Eur Radiol. 2020 Jul;30(7):4050-4057. doi: 10.1007/s00330-020-06694-z. Epub 2020 Feb 28. — View Citation

Zhuo Y, Feng M, Yang S, Zhou L, Ge D, Lu S, Liu L, Shan F, Zhang Z. Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma. Transl Oncol. 2020 Oct;13(10):100820. doi: 10.1016/j.tranon.2020.100820. Epub 2020 Jul 1. — View Citation

Outcome

Type Measure Description Time frame Safety issue
Primary Sensitivity Testing the sensitivity of Radiomics to predict STAS using the area under receiver operating characteristic curve 24 hour before operation
Primary Specificity Testing the specificity of Radiomics to predict STAS using the area under receiver operating characteristic curve 24 hour before operation
See also
  Status Clinical Trial Phase
Recruiting NCT02898857 - Chemoresistance and Involvement of the NOTCH Pathway in Patients With Lung Adenocarcinoma N/A
Completed NCT02127359 - Whole-Exome Sequencing (WES) of Cancer Patients
Recruiting NCT01249066 - Expression of AMP-activated Protein Kinase (AMPK) Protein in Lung Adenocarcinoma N/A
Not yet recruiting NCT04482829 - TCM in the Treatment of Lung Adenocarcinoma N/A
Recruiting NCT04929041 - Testing the Addition of Radiation Therapy to Immunotherapy for Stage IV Non-Small Cell Lung Cancer Patients Who Are PD-L1 Negative Phase 2/Phase 3
Terminated NCT04691375 - A Study of PY314 in Subjects With Advanced Solid Tumors Phase 1
Terminated NCT02621333 - Chemotherapy Combined Autologous Cytokine-induced Killer Cells in Naive Stage IV EGFR-wild Type Lung Adenocarcinoma Phase 2
Active, not recruiting NCT02282267 - Blood Detection of EGFR Mutation For Iressa Treatment N/A
Not yet recruiting NCT01942629 - Prognostic Value of the Marker P63 in Adenocarcinoma of Lung, Breast, and Pancreas N/A
Recruiting NCT01482585 - Study of Early-stage Lung Adenocarcinoma for Early Detection and Effective Treatment Strategy N/A
Recruiting NCT03376737 - Study of Apatinib as the Maintenance Therapy in Advanced Lung Adenocarcinoma Phase 2
Recruiting NCT05537922 - I3LUNG: Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care With Immunotherapy
Recruiting NCT04937283 - Segmentectomy Versus Lobectomy for Lung Adenocarcinoma ≤ 2cm N/A
Recruiting NCT06255197 - Characteristics, Treatment Patterns and Outcomes for Patients With Surgically Resected Lung Cancers
Completed NCT02093000 - A Study Examining Maintenance Bevacizumab (Avastin®) Monotherapy in Participants With Advanced Lung Adenocarcinoma N/A
Recruiting NCT04119024 - Gene Modified Immune Cells (IL13Ralpha2 CAR T Cells) After Conditioning Regimen for the Treatment of Stage IIIC or IV Melanoma or Metastatic Solid Tumors Phase 1
Terminated NCT04682431 - A Phase 1a/1b FIH Study of PY159 and in Combination With Pembrolizumab in Subjects With Advanced Solid Tumors Phase 1
Recruiting NCT05736991 - Deep Learning Signature for Predicting the Novel Grading System of Clinical Stage I Lung Adenocarcinoma
Enrolling by invitation NCT05136014 - Evaluation of the Response to Tyrosine Kinase Inhibitors in Localized Non-small Cell Lung Cancer (NSCLC) Patients With EGFR Mutation in a Patient-derived Organoid Model
Terminated NCT05012397 - Milademetan in Advanced/Metastatic Solid Tumors Phase 2