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2023 Jan-Dec;22 15330338231175735.

The Differential Diagnostic Value of Radiomics Signatures Between Single-Nodule Pulmonary Metastases and Second Primary Lung Cancer in Patients with Colorectal Cancer.

Yu Yu, Jiaqing Tan, Yi Yang, Bin Zhang, Xiaodong Yao, Shibiao Sang, Shengming Deng
Author Information
  1. Yu Yu: Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China. ORCID
  2. Jiaqing Tan: Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China.
  3. Yi Yang: Department of Nuclear Medicine, the Affiliated Suzhou Science & Technology Town Hospital of Nanjing Medical University, Suzhou, China.
  4. Bin Zhang: Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China.
  5. Xiaodong Yao: Department of Medical Cosmetology, Department of Dermatology, Affiliated Hospital of Nantong University, Nantong, China.
  6. Shibiao Sang: Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China.
  7. Shengming Deng: Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China.
PMID: 37226476 DOI: 10.1177/15330338231175735

Abstract

BACKGROUND: Differential diagnosis of single-nodule pulmonary metastasis (SNPM) and second primary lung cancer (SPLC) in patients with colorectal cancer (CRC) prior to lung surgery is relatively complex. Radiomics is an emerging technique for image information analysis, while it has not yet been applied to construct a differential diagnostic model between SNPM and SPLC in patients with CRC. In the present study, we aimed to extract radiomics signatures from thin-section computed tomography (CT) images of the chest. These radiomics signatures were combined with clinical features to construct a composite differential diagnostic model.
METHOD: A total of 91 patients with CRC, including 66 patients with SNPM and 25 patients with SPLC, were enrolled in this study. Patients were randomly assigned to the training cohort (n  =  63) and validation cohort (n  =  28) at a ratio of 7 to 3. Moreover, 107 radiomics features were extracted from the chest thin-section CT images. The least absolute shrinkage and selection operator (LASSO) regression was used to filter these features, and clinical features were screened by univariate analysis. The screened radiomics and clinical features were combined to construct a multifactorial logistic regression composite model. The receiver operating characteristic (ROC) curves were adopted to evaluate the models, and the corresponding nomograms were created.
RESULTS: A series of 6 radiomics characteristics was screened by LASSO. After univariate logistic regression analysis, the composite model finally included 4 radiomics features and 4 clinical features. In the training cohort, the area under the curve scores of ROC curves were 0.912 (95% confidence interval [CI]: 0.813-0.969), 0.884 (95% CI: 0.778-0.951), and 0.939 (95% CI: 0.848-0.984) for models derived from radiomics, clinical, and combined features, respectively. Similarly, these values were 0.756 (95% CI: 0.558-0.897), 0.888 (95% CI: 0.711-0.975), and 0.950 (95% CI: 0.795-0.997) in the validation cohort, respectively.
CONCLUSIONS: We constructed a model for differential diagnosis of SNPM and SPLC in patients with CRC using radiomics and clinical features. Moreover, our findings provided a new assessment tool for patients with CRC in the future.

Keywords

CT images colorectal cancer machine learning radiomics second primary lung cancer single-nodule pulmonary metastasis

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MeSH Term

Humans
Lung Neoplasms
Diagnosis, Differential
Image Processing, Computer-Assisted
Neoplasms, Second Primary
Lung
Colorectal Neoplasms
Randomized Controlled Trial Journal Article Research Support, Non-U.S. Gov't
Article has an altmetric score of 2

Word Cloud

Created with Highcharts 10.0.00radiomicsfeaturespatientsclinical95%CRCmodelCI:SNPMcancerSPLCcohortlunganalysisconstructdifferentialCTimagescombinedcompositeregressionscreenedDifferentialdiagnosissingle-nodulepulmonarymetastasissecondprimarycolorectalRadiomicsdiagnosticstudysignaturesthin-sectionchestPatientstrainingvalidationMoreoverLASSOunivariatelogisticROCcurvesmodels4respectivelyCancerBACKGROUND:priorsurgeryrelativelycomplexemergingtechniqueimageinformationyetappliedpresentaimedextractcomputedtomographyMETHOD:total91including6625enrolledrandomlyassignedn  =  63n  =  28ratio73107extractedleastabsoluteshrinkageselectionoperatorusedfiltermultifactorialreceiveroperatingcharacteristicadoptedevaluatecorrespondingnomogramscreatedRESULTS:series6characteristicsfinallyincludedareacurvescores912confidenceinterval[CI]:813-0969884778-0951939848-0984derivedSimilarlyvalues756558-0897888711-0975950795-0997CONCLUSIONS:constructedusingfindingsprovidednewassessmenttoolfutureDiagnosticValueSignaturesSingle-NodulePulmonaryMetastasesSecondPrimaryLungColorectalmachinelearning

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