We have located links that may give you full text access.
Deep neural network-based approach to improving radiomics analysis reproducibility in liver cancer: effect on image resampling.
Physics in Medicine and Biology 2021 July 23
OBJECTIVES: To test the effect of traditional up-sampling slice thickness (ST) methods on the reproducibility of CT radiomics features of liver tumors and investigate the improvement using a deep neural network (DNN) scheme.
METHODS: CT images with ≤ 1 mm ST in the public dataset were converted to low resolution (3 mm, 5 mm) CT images. A DNN model was trained for the conversion from 3 mm-ST and 5 mm-ST to 1 mm-ST and compared with conventional interpolation-based methods (cubic, linear, nearest) using structural similarity (SSIM) and peak-signal-to-noise-ratio (PSNR). Radiomics features were extracted from the tumor and tumor ring regions. The reproducibility of features from images converted using DNN and interpolation schemes were assessed using the concordance correlation coefficients (CCC) with the cutoff of 0.85. The paired t-test and Mann-Whitney U test were used to compare the evaluation metrics, where appropriate.
RESULTS: CT images of 108 patients were used for training (n=63), validation (n=11) and testing (n=34). The DNN method showed significantly higher PSNR and SSIM values (p < 0.05) than interpolation-based methods. The DNN method also showed a significantly higher CCC value than interpolation-based methods. For features in the tumor region, compared with the cubic interpolation approach, the reproducible features increased from 393 (82%) to 422(88%) for the conversion of 3mm-to-1mm, and from 305(64%) to 353(74%) for the conversion of 5mm-to-1mm. For features in the tumor ring region, the improvement was from 395 (82%) to 431 (90%) and from 290 (60%) to 335 (70%), respectively.
CONCLUSIONS: The DNN based ST up-sampling approach can improve the reproducibility of CT radiomics features in liver tumors, promoting the standardization of CT radiomics studies in liver cancer.
METHODS: CT images with ≤ 1 mm ST in the public dataset were converted to low resolution (3 mm, 5 mm) CT images. A DNN model was trained for the conversion from 3 mm-ST and 5 mm-ST to 1 mm-ST and compared with conventional interpolation-based methods (cubic, linear, nearest) using structural similarity (SSIM) and peak-signal-to-noise-ratio (PSNR). Radiomics features were extracted from the tumor and tumor ring regions. The reproducibility of features from images converted using DNN and interpolation schemes were assessed using the concordance correlation coefficients (CCC) with the cutoff of 0.85. The paired t-test and Mann-Whitney U test were used to compare the evaluation metrics, where appropriate.
RESULTS: CT images of 108 patients were used for training (n=63), validation (n=11) and testing (n=34). The DNN method showed significantly higher PSNR and SSIM values (p < 0.05) than interpolation-based methods. The DNN method also showed a significantly higher CCC value than interpolation-based methods. For features in the tumor region, compared with the cubic interpolation approach, the reproducible features increased from 393 (82%) to 422(88%) for the conversion of 3mm-to-1mm, and from 305(64%) to 353(74%) for the conversion of 5mm-to-1mm. For features in the tumor ring region, the improvement was from 395 (82%) to 431 (90%) and from 290 (60%) to 335 (70%), respectively.
CONCLUSIONS: The DNN based ST up-sampling approach can improve the reproducibility of CT radiomics features in liver tumors, promoting the standardization of CT radiomics studies in liver cancer.
Full text links
Related Resources
Trending Papers
Challenges in Septic Shock: From New Hemodynamics to Blood Purification Therapies.Journal of Personalized Medicine 2024 Februrary 4
Molecular Targets of Novel Therapeutics for Diabetic Kidney Disease: A New Era of Nephroprotection.International Journal of Molecular Sciences 2024 April 4
Perioperative echocardiographic strain analysis: what anesthesiologists should know.Canadian Journal of Anaesthesia 2024 April 11
The 'Ten Commandments' for the 2023 European Society of Cardiology guidelines for the management of endocarditis.European Heart Journal 2024 April 18
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app