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Research Article
Hong Yin
Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi
Corresponding Author
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Background: Non-small cell lung cancer (NSCLC) is treatable when caught early, yet limited non-invasive methods exist for grading NSCLC patients. In the present study, we aimed to examine the diagnostic utility of multi-sequence magnetic resonance imaging (MRI) radiomics and clinical features for grading NSCLC.
Methods: In this retrospective study, 148 patients with postoperative pathologically-confirmed NSCLC were recruited. Both preoperative T2-weighted imaging (T2WI) and multi-b-value diffusion-weighted imaging (DWI) were performed on a 1.5 T MRI scanner. A total of 2775 radiomics features were extracted from the T2WI, DWI, and the corresponding apparent diffusion coefficient (ADC) maps of patients. The least absolute shrinkage and selection operator (LASSO) and stepwise regression method were used for feature selection using the training cohort (n=110). Next, these features were further evaluated assessed in the two cohorts using a non-linear support vector machine (SVM) classifier. Lastly, a Radscore model was used to develop the radiomics-clinical nomogram.
Results: Favorable discrimination performance was obtained for five of the optimal features using both cohorts, as demonstrated by the area under the curves (AUC) of 0.761 and 0.753. In addition, the radiomics-clinical nomogram, which integrated the Radscore with four independent clinical predictors, showed higher discriminative power, with AUCs of 0.814 and 0.767 for the X.T cohort and H.Y cohort, respectively. The nomogram showed excellent predictive performance and potential clinical utility for grading NSCLC.
Conclusions: Multi-sequence MRI radiomics features can stratify NSCLC tumor grades noninvasively. The radiomics features can be integrated with the clinical features to improve its predictive performance.
Keywords
Lung cancer, lung adenocarcinoma, non-small cell lung cancer, multi-sequence MRI, nomogram, radiomics features
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A new preprint design is coming!
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research Article
Hong Yin
Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 02 Dec, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Non-small cell lung cancer (NSCLC) is treatable when caught early, yet limited non-invasive methods exist for grading NSCLC patients. In the present study, we aimed to examine the diagnostic utility of multi-sequence magnetic resonance imaging (MRI) radiomics and clinical features for grading NSCLC.
Methods: In this retrospective study, 148 patients with postoperative pathologically-confirmed NSCLC were recruited. Both preoperative T2-weighted imaging (T2WI) and multi-b-value diffusion-weighted imaging (DWI) were performed on a 1.5 T MRI scanner. A total of 2775 radiomics features were extracted from the T2WI, DWI, and the corresponding apparent diffusion coefficient (ADC) maps of patients. The least absolute shrinkage and selection operator (LASSO) and stepwise regression method were used for feature selection using the training cohort (n=110). Next, these features were further evaluated assessed in the two cohorts using a non-linear support vector machine (SVM) classifier. Lastly, a Radscore model was used to develop the radiomics-clinical nomogram.
Results: Favorable discrimination performance was obtained for five of the optimal features using both cohorts, as demonstrated by the area under the curves (AUC) of 0.761 and 0.753. In addition, the radiomics-clinical nomogram, which integrated the Radscore with four independent clinical predictors, showed higher discriminative power, with AUCs of 0.814 and 0.767 for the X.T cohort and H.Y cohort, respectively. The nomogram showed excellent predictive performance and potential clinical utility for grading NSCLC.
Conclusions: Multi-sequence MRI radiomics features can stratify NSCLC tumor grades noninvasively. The radiomics features can be integrated with the clinical features to improve its predictive performance.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
This is a list of supplementary files associated with this preprint. Click to download.
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