Ignite Creation Date:
2025-12-24 @ 11:53 PM
Ignite Modification Date:
2026-01-07 @ 10:09 AM
Study NCT ID:
NCT03648151
Status:
COMPLETED
Last Update Posted:
2020-07-23
First Post:
2018-08-21
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Influence of PET/CT Radiomic Features on the Outcome of Lung Cancer Patients
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D008175', 'term': 'Lung Neoplasms'}], 'ancestors': [{'id': 'D012142', 'term': 'Respiratory Tract Neoplasms'}, {'id': 'D013899', 'term': 'Thoracic Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D008171', 'term': 'Lung Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 1000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2010-01-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2020-07', 'completionDateStruct': {'date': '2019-12-31', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2020-07-22', 'studyFirstSubmitDate': '2018-08-21', 'studyFirstSubmitQcDate': '2018-08-23', 'lastUpdatePostDateStruct': {'date': '2020-07-23', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2018-08-27', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2019-12-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Overall survival (OS) of lung cancer patients', 'timeFrame': 'The patients were followed to December 31, 2019', 'description': 'The time from the scan date to death for any reason'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Radomic feature', 'PET', 'CT'], 'conditions': ['Lung Cancer', 'Image, Body']}, 'referencesModule': {'references': [{'pmid': '21943720', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '27660141', 'type': 'BACKGROUND', 'citation': 'Giesel 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.'}, {'pmid': '24892406', 'type': 'BACKGROUND', 'citation': 'Aerts 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.'}, {'pmid': '27688480', 'type': 'BACKGROUND', 'citation': 'Yip 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.'}, {'pmid': '27269645', 'type': 'BACKGROUND', 'citation': 'Yip 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.'}, {'pmid': '28336974', 'type': 'BACKGROUND', 'citation': 'Sollini 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.'}, {'pmid': '36884114', 'type': 'DERIVED', 'citation': 'Hongwei 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.'}]}, 'descriptionModule': {'briefSummary': '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.', 'detailedDescription': 'Radiomic Features:\n\nPET/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.\n\nPET Parameters:\n\nUsing 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.\n\nFeature Selection:\n\nData 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.\n\nModel Validation:\n\nThe 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'lung caner', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Pathologically diagnosed as lung caner.\n2. Accepted PET/CT scans at the hospitals either affiliated to Shanxi Medical University or Anhui Medical University\n3. Both PET and CT serials can be obtained\n4. Can be followed for treatment modalities (including chemotherapy regimens, radiotherapy dose, and et al), survival time and status, and other related information.\n\nExclusion Criteria:\n\n1. Simultaneously suffering from the cancers from other tissues and organs\n2. Have a history of diabetes, chronic heart diseases, or chronic renal failure'}, 'identificationModule': {'nctId': 'NCT03648151', 'briefTitle': 'Influence of PET/CT Radiomic Features on the Outcome of Lung Cancer Patients', 'organization': {'class': 'OTHER', 'fullName': 'The First Affiliated Hospital of Shanxi Medical University'}, 'officialTitle': 'Influence of PET/CT Radiomic Features on the Outcome of Lung Cancer Patients', 'orgStudyIdInfo': {'id': '20170501'}}, 'contactsLocationsModule': {'locations': [{'zip': '230022', 'city': 'Hefei', 'state': 'Anhui', 'country': 'China', 'facility': 'First Affiliated Hospital of Anhui Medical University', 'geoPoint': {'lat': 31.86389, 'lon': 117.28083}}, {'zip': '030001', 'city': 'Taiyuan', 'state': 'Shanxi', 'country': 'China', 'facility': 'First Affiliated Hospital of Shanxi Medical University', 'geoPoint': {'lat': 37.86944, 'lon': 112.56028}}], 'overallOfficials': [{'name': 'Li Sijin, MD', 'role': 'STUDY_CHAIR', 'affiliation': 'First Affiliated Hospital of Shanxi Medical University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'The First Affiliated Hospital of Shanxi Medical University', 'class': 'OTHER'}, 'collaborators': [{'name': 'The First Affiliated Hospital of Anhui Medical University', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Chief physician', 'investigatorFullName': 'Hongwei Si', 'investigatorAffiliation': 'The First Affiliated Hospital of Shanxi Medical University'}}}}