Lung CancerNCT03648151
Influence of PET/CT Radiomic Features on the Outcome of Lung Cancer Patients
An observational study of Lung Cancer and Image, Body, run by The First Affiliated Hospital of Shanxi Medical University at 2 sites in China.
Trial summary
Radiomics is an attractive field in objectively quantifying image features, and may overcome the subjectivity of visually interpreting computed tomography (CT), or positron emission tomography (PET). Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Lung Cancer, Image, Body
- How many people
- 1,000 participants
- Where
- 2 sitesChina
- Study dates
- 1 Jan 2010to 31 Dec 2019
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: lung caner
Who may be able to join
- Pathologically diagnosed as lung caner.
- Accepted PET/CT scans at the hospitals either affiliated to Shanxi Medical University or Anhui Medical University
- Both PET and CT serials can be obtained
- Can be followed for treatment modalities (including chemotherapy regimens, radiotherapy dose, and et al), survival time and status, and other related information.
Who cannot join
- Simultaneously suffering from the cancers from other tissues and organs
- Have a history of diabetes, chronic heart diseases, or chronic renal failure
Trial locations and contacts
2 sites in 1 country.
China2 sites
First Affiliated Hospital of Anhui Medical University
Hefei, Anhui, 230022
First Affiliated Hospital of Shanxi Medical University
Taiyuan, Shanxi, 030001
About this study
Radiomics is an attractive field in objectively quantifying image features, and may overcome the subjectivity of visually interpreting computed tomography (CT), or positron emission tomography (PET). It is reported that the features related to treatment response, outcomes, tumor staging, tissue identification, and cancer genetics. Therefore, the investigators try to explore the key features for the outcome of lung cancer patients.
Read the detailed description
Radiomic Features:
PET/CT images, including other kinds of CT serials, were transported into a personal computer. Using the open source software of 3D-Slicer, volumes of interest (VOIs) for primary tumor, or even lymph nodes, was semi-automatically or manually segmented. And then, radiomic features were extracted.
PET Parameters:
Using combined CT VOIs, corresponding PET standard uptake value (SUV, no unit) were measured. For a foci (either tumor, or lymph node), mean, sum and maximum SUV were documented, and were used for training and validating models alongside radiomic features.
Feature Selection:
Data were analyzed by deep learning or random forests method, and top 20 variables were scored by their contribution to the regression (variable importance, VIMP). The generalized features were identified as the same ones between two kinds of image serials (for example, ordinary and thin-section CT, or PET and CT). Additionally, when three or more features met the criterion, a lower value of Akaike information criterion (AIC) which measures the relative quality of statistical models was used to find appropriate features with lower overfitting possibility.
Model Validation:
The developed model was validated internally and externally. The internal indices for independent continuous variable were accuracy (bias and absolute bias) and precision (correlation coefficient and R square), and that for independent classified or survival variable was c-index. The patients enrolled from another medical center were used for external validation.
- Conditions
- Lung Cancer, Image, Body
- Keywords
- Radomic feature, PET, CT
- Medical subject headings
- Lung Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
Overall survival (OS) of lung cancer patients
The time from the scan date to death for any reason
Measured: The patients were followed to December 31, 2019
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Aug 2018
- Study start1 Jan 2010
- Primary completion31 Dec 2019
- Study completion31 Dec 2019
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- The First Affiliated Hospital of Shanxi Medical University
- Sponsor type
- University, hospital or other
- Collaborators
- The First Affiliated Hospital of Anhui Medical University
- Responsible party
- Principal investigator, Hongwei Si, The First Affiliated Hospital of Shanxi Medical University
- Investigators
- Li Sijin, MD, Study chair, First Affiliated Hospital of Shanxi Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03648151
- Sponsor's protocol ID
- 20170501
- Status last verified
- Jul 2020
- First submitted
- 21 Aug 2018
- Last updated
- 23 Jul 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Ganeshan B, Skogen K, Pressney I, Coutroubis D, Miles K. Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol. 2012 Feb;67(2):157-64. doi: 10.1016/j.crad.2011.08.012. Epub 2011 Sep 23.
PubMed 21943720Giesel FL, Schneider F, Kratochwil C, Rath D, Moltz J, Holland-Letz T, Kauczor HU, Schwartz LH, Haberkorn U, Flechsig P. Correlation Between SUVmax and CT Radiomic Analysis Using Lymph Node Density in PET/CT-Based Lymph Node Staging. J Nucl Med. 2017 Feb;58(2):282-287. doi: 10.2967/jnumed.116.179648. Epub 2016 Sep 22.
PubMed 27660141Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014 Jun 3;5:4006. doi: 10.1038/ncomms5006.
PubMed 24892406Yip SS, Kim J, Coroller TP, Parmar C, Velazquez ER, Huynh E, Mak RH, Aerts HJ. Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non-Small Cell Lung Cancer. J Nucl Med. 2017 Apr;58(4):569-576. doi: 10.2967/jnumed.116.181826. Epub 2016 Sep 29.
PubMed 27688480Yip SS, Aerts HJ. Applications and limitations of radiomics. Phys Med Biol. 2016 Jul 7;61(13):R150-66. doi: 10.1088/0031-9155/61/13/R150. Epub 2016 Jun 8.
PubMed 27269645Sollini M, Cozzi L, Antunovic L, Chiti A, Kirienko M. PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology. Sci Rep. 2017 Mar 23;7(1):358. doi: 10.1038/s41598-017-00426-y.
PubMed 28336974Hongwei S, Xinzhong H, Huiqin X, Shuqin X, Ruonan W, Li L, Jianzhong C, Sijin L. Standard deviation of CT radiomic features among malignancies in each individual: prognostic ability in lung cancer patients. J Cancer Res Clin Oncol. 2023 Aug;149(10):7165-7173. doi: 10.1007/s00432-023-04649-7. Epub 2023 Mar 8.
PubMed 36884114
Study at a glance
From the registry record, last updated by the sponsor on 23 Jul 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Influence of PET/CT Radiomic Features on the Outcome of Lung Cancer Patients
- ClinicalTrials.gov ID
- NCT03648151
- Acronym / sponsor ID
- 20170501
- Conditions
- Lung Cancer, Image, Body
- Keywords
- Radomic feature, PET, CT
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,000 participants (actual)
- Lead sponsor
- The First Affiliated Hospital of Shanxi Medical UniversityUniversity, hospital or other
- Collaborators
- The First Affiliated Hospital of Anhui Medical University
- Study start
- 1 Jan 2010 (actual)
- Primary completion
- 31 Dec 2019 (actual)
- Study completion
- 31 Dec 2019 (actual)
- First posted
- 27 Aug 2018 (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.
Radiomics is an attractive field in objectively quantifying image features, and may overcome the subjectivity of visually interpreting computed tomography (CT), or positron emission tomography (PET). It is reported that the features related to treatment response, outcomes, tumor staging, tissue identification, and cancer genetics. Therefore, the investigators try to explore the key features for the outcome of lung cancer patients.
Detailed description
Radiomic Features:
PET/CT images, including other kinds of CT serials, were transported into a personal computer. Using the open source software of 3D-Slicer, volumes of interest (VOIs) for primary tumor, or even lymph nodes, was semi-automatically or manually segmented. And then, radiomic features were extracted.
PET Parameters:
Using combined CT VOIs, corresponding PET standard uptake value (SUV, no unit) were measured. For a foci (either tumor, or lymph node), mean, sum and maximum SUV were documented, and were used for training and validating models alongside radiomic features.
Feature Selection:
Data were analyzed by deep learning or random forests method, and top 20 variables were scored by their contribution to the regression (variable importance, VIMP). The generalized features were identified as the same ones between two kinds of image serials (for example, ordinary and thin-section CT, or PET and CT). Additionally, when three or more features met the criterion, a lower value of Akaike information criterion (AIC) which measures the relative quality of statistical models was used to find appropriate features with lower overfitting possibility.
Model Validation:
The developed model was validated internally and externally. The internal indices for independent continuous variable were accuracy (bias and absolute bias) and precision (correlation coefficient and R square), and that for independent classified or survival variable was c-index. The patients enrolled from another medical center were used for external validation.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- lung caner
Key inclusion criteria (4 listed)
- Pathologically diagnosed as lung caner.
- Accepted PET/CT scans at the hospitals either affiliated to Shanxi Medical University or Anhui Medical University
- Both PET and CT serials can be obtained
- Can be followed for treatment modalities (including chemotherapy regimens, radiotherapy dose, and et al), survival time and status, and other related information.
Key exclusion criteria (2 listed)
- Simultaneously suffering from the cancers from other tissues and organs
- Have a history of diabetes, chronic heart diseases, or chronic renal failure
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
Overall survival (OS) of lung cancer patients
Time frame: The patients were followed to December 31, 2019
The time from the scan date to death for any reason
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 23 Jul 2020.
- Background: PubMed 21943720Ganeshan B, Skogen K, Pressney I, Coutroubis D, Miles K. Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol. 2012 Feb;67(2):157-64. doi: 10.1016/j.crad.2011.08.012. Epub 2011 Sep 23.
- Background: PubMed 27660141Giesel FL, Schneider F, Kratochwil C, Rath D, Moltz J, Holland-Letz T, Kauczor HU, Schwartz LH, Haberkorn U, Flechsig P. Correlation Between SUVmax and CT Radiomic Analysis Using Lymph Node Density in PET/CT-Based Lymph Node Staging. J Nucl Med. 2017 Feb;58(2):282-287. doi: 10.2967/jnumed.116.179648. Epub 2016 Sep 22.
- Background: PubMed 24892406Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014 Jun 3;5:4006. doi: 10.1038/ncomms5006.
- Background: PubMed 27688480Yip SS, Kim J, Coroller TP, Parmar C, Velazquez ER, Huynh E, Mak RH, Aerts HJ. Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non-Small Cell Lung Cancer. J Nucl Med. 2017 Apr;58(4):569-576. doi: 10.2967/jnumed.116.181826. Epub 2016 Sep 29.
- Background: PubMed 27269645Yip SS, Aerts HJ. Applications and limitations of radiomics. Phys Med Biol. 2016 Jul 7;61(13):R150-66. doi: 10.1088/0031-9155/61/13/R150. Epub 2016 Jun 8.
- Background: PubMed 28336974Sollini M, Cozzi L, Antunovic L, Chiti A, Kirienko M. PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology. Sci Rep. 2017 Mar 23;7(1):358. doi: 10.1038/s41598-017-00426-y.
- Derived from this study: PubMed 36884114Hongwei S, Xinzhong H, Huiqin X, Shuqin X, Ruonan W, Li L, Jianzhong C, Sijin L. Standard deviation of CT radiomic features among malignancies in each individual: prognostic ability in lung cancer patients. J Cancer Res Clin Oncol. 2023 Aug;149(10):7165-7173. doi: 10.1007/s00432-023-04649-7. Epub 2023 Mar 8.
- Individual participant dataSharing planned: No
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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.
Li Sijin, MD study chair
First Affiliated Hospital of Shanxi Medical University
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer and Image, Body, run by The First Affiliated Hospital of Shanxi Medical University at 2 sites in China.
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 4 inclusion and 2 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 23 Jul 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China. 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?
Overall survival (OS) of lung cancer patients (time frame: The patients were followed to December 31, 2019). 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 NCT03648151, which also lists 7 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 23 Jul 2020.