Lung AdenocarcinomaNCT04893200
Radiomics-based Prediction Model of Tumor Spread Through Air Space in Lung Adenocarcinoma
An observational study of Lung Adenocarcinoma, run by University of Roma La Sapienza at 1 site in Italy.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lung Adenocarcinoma
- How many people
- 150 participants
- Where
- 1 siteItaly
- Study dates
- 1 Feb 2020to 1 Jun 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients undergoing lung cancer surgery at Policlinico Umberto I Hospital, Rome
Who may be able to join
- Patients with suspected or cito-histologically proven lung adenocarcinoma undergoing lung cancer surgery;
- Available preoperative CT images
- Age older than 18 years
Who cannot join
- Chest wall infiltration
- Induction radio or chemotherapy
- Incomplete surgical resection
Trial locations and contacts
1 site in 1 country.
Italy1 site
Dipartimento di chirurgia Generale e Specialistica "Paride Stefanini"
Roma, 00139
About this study
Official title: Could Radiomics Predict Tumor Spread Through Air Space in Lung Adenocarcinoma in All Computed Tomography Settings?
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.
- Conditions
- Lung Adenocarcinoma
- Keywords
- Spread Through Air Space; Radiomics
- Medical subject headings
- Adenocarcinoma of Lung
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Lung adenocarcinoma | Imaging from patients with surgically treated lung adenocarcinoma were collected and processed for the construction of the radiomics-based prediction model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity
Testing the sensitivity of Radiomics to predict STAS using the area under receiver operating characteristic curve
Measured: 24 hour before operation
Specificity
Testing the specificity of Radiomics to predict STAS using the area under receiver operating characteristic curve
Measured: 24 hour before operation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 May 2021
- Study start1 Feb 2020
- Primary completion1 Jul 2020
- Study completion1 Jun 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Lung AdenocarcinomaStudies of the same condition, open and under way.
- More trials run by University of Roma La SapienzaThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 150 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Roma La Sapienza
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Marco Anile, University of Roma La Sapienza
- Investigators
- Marco Anile, MD, Principal investigator, La Sapienza Università di Roma
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04893200
- Sponsor's protocol ID
- RADIOMICS
- Status last verified
- Sep 2021
- First submitted
- 6 May 2021
- Last updated
- 5 Sep 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 32112116Chen 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.
PubMed 32011674Zhuo 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.
PubMed 32622312Bassi M, Russomando A, Vannucci J, Ciardiello A, Dolciami M, Ricci P, Pernazza A, D'Amati G, Mancini Terracciano C, Faccini R, Mantovani S, Venuta F, Voena C, Anile M. Role of radiomics in predicting lung cancer spread through air spaces in a heterogeneous dataset. Transl Lung Cancer Res. 2022 Apr;11(4):560-571. doi: 10.21037/tlcr-21-895.
PubMed 35529792
Study at a glance
From the registry record, last updated by the sponsor on 5 Sep 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Could Radiomics Predict Tumor Spread Through Air Space in Lung Adenocarcinoma in All Computed Tomography Settings?
- ClinicalTrials.gov ID
- NCT04893200
- Acronym / sponsor ID
- RADIOMICS
- Conditions
- Lung Adenocarcinoma
- Keywords
- Spread Through Air Space; Radiomics
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 150 participants (actual)
- Lead sponsor
- University of Roma La SapienzaUniversity, hospital or other
- Study start
- 1 Feb 2020 (actual)
- Primary completion
- 1 Jul 2020 (actual)
- Study completion
- 1 Jun 2021 (actual)
- First posted
- 19 May 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Lung adenocarcinoma | – | –Imaging from patients with surgically treated lung adenocarcinoma were collected and processed for the construction of the radiomics-based prediction model |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients undergoing lung cancer surgery at Policlinico Umberto I Hospital, Rome
Key inclusion criteria (3 listed)
- Patients with suspected or cito-histologically proven lung adenocarcinoma undergoing lung cancer surgery;
- Available preoperative CT images
- Age older than 18 years
Key exclusion criteria (3 listed)
- Chest wall infiltration
- Induction radio or chemotherapy
- Incomplete surgical resection
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Sensitivity
Time frame: 24 hour before operation
Testing the sensitivity of Radiomics to predict STAS using the area under receiver operating characteristic curve
Specificity
Time frame: 24 hour before operation
Testing the specificity of Radiomics to predict STAS using the area under receiver operating characteristic curve
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Sep 2021.
- Background: PubMed 32112116Jiang 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.
- Background: PubMed 32011674Chen 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.
- Background: PubMed 32622312Zhuo 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.
- Derived from this study: PubMed 35529792Bassi M, Russomando A, Vannucci J, Ciardiello A, Dolciami M, Ricci P, Pernazza A, D'Amati G, Mancini Terracciano C, Faccini R, Mantovani S, Venuta F, Voena C, Anile M. Role of radiomics in predicting lung cancer spread through air spaces in a heterogeneous dataset. Transl Lung Cancer Res. 2022 Apr;11(4):560-571. doi: 10.21037/tlcr-21-895.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Marco Anile, MD principal investigator
La Sapienza Università di Roma
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Adenocarcinoma, run by University of Roma La Sapienza at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 5 Sep 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity (time frame: 24 hour before operation). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04893200, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Sep 2021.