Influence of PET/CT Radiomic Features on the Outcome of Lung Cancer Patients

Lung Cancer Clinical Trial

Official title:

Influence of PET/CT Radiomic Features on the Outcome of Lung Cancer Patients

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03648151
• Other study ID #: 20170501
• Status: Completed
• Start date: January 1, 2010
• Est. completion date: December 31, 2019

Study information

• Verified date: July 2020
• Source: The First Affiliated Hospital of Shanxi Medical University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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). 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 1000
• Est. completion date: December 31, 2019
• Est. primary completion date: December 31, 2019
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

1. Pathologically diagnosed as lung caner.

2. Accepted PET/CT scans at the hospitals either affiliated to Shanxi Medical University or Anhui Medical University

3. Both PET and CT serials can be obtained

4. Can be followed for treatment modalities (including chemotherapy regimens, radiotherapy dose, and et al), survival time and status, and other related information.

Exclusion Criteria:

1. Simultaneously suffering from the cancers from other tissues and organs

2. Have a history of diabetes, chronic heart diseases, or chronic renal failure

Related Conditions & MeSH terms

• Image, Body
• Lung Cancer
• Lung Neoplasms

Locations

Country	Name	City	State
China	First Affiliated Hospital of Anhui Medical University	Hefei	Anhui
China	First Affiliated Hospital of Shanxi Medical University	Taiyuan	Shanxi

Sponsors (2)

Lead Sponsor	Collaborator
The First Affiliated Hospital of Shanxi Medical University	The First Affiliated Hospital of Anhui Medical University

Country where clinical trial is conducted

China, 

References & Publications (6)

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. Erratum in: Nat Commun. 2014;5:4644. Cavalho, Sara [corrected to Carvalho, Sara]. — View 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. — View 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. — View 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. Review. — View 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. Review. — View 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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Overall survival (OS) of lung cancer patients	The time from the scan date to death for any reason	The patients were followed to December 31, 2019