Influence of PET/CT Radiomic Features on the Outcome of Lung Cancer Patients
Study Details
Study Description
Brief 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.
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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
Outcome Measures
Primary Outcome Measures
- Overall survival (OS) of lung cancer patients [The patients were followed to December 31, 2019]
The time from the scan date to death for any reason
Eligibility Criteria
Criteria
Inclusion Criteria:
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Pathologically diagnosed as lung caner.
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Accepted PET/CT scans at the hospitals either affiliated to Shanxi Medical University or Anhui Medical University
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Both PET and CT serials can be obtained
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Can be followed for treatment modalities (including chemotherapy regimens, radiotherapy dose, and et al), survival time and status, and other related information.
Exclusion Criteria:
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Simultaneously suffering from the cancers from other tissues and organs
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Have a history of diabetes, chronic heart diseases, or chronic renal failure
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | First Affiliated Hospital of Anhui Medical University | Hefei | Anhui | China | 230022 |
| 2 | First Affiliated Hospital of Shanxi Medical University | Taiyuan | Shanxi | China | 030001 |
Sponsors and Collaborators
- The First Affiliated Hospital of Shanxi Medical University
- The First Affiliated Hospital of Anhui Medical University
Investigators
- Study Chair: Li Sijin, MD, First Affiliated Hospital of Shanxi Medical University
Study Documents (Full-Text)
None provided.More Information
Publications
- 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].
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- 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.
- 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.
- 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.
- 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.
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