Jocelyn Hoye1, Taylor Smith1, Ehsan Abadi2, Justin B Solomon3, Ehsan Samei4. 1. Carl E. Ravin Advanced Imaging Laboratories, 2424 Erwin Rd, Suite 302, Durham, North Carolina, 27705, USA; Duke University Medical Physics Graduate Program, 2424 Erwin Rd, Suite 302, Durham, North Carolina, 27705, USA; Duke University Department of Radiology, 2424 Erwin Rd, Suite 302, Durham, North Carolina, 27705, USA. 2. Carl E. Ravin Advanced Imaging Laboratories, 2424 Erwin Rd, Suite 302, Durham, North Carolina, 27705, USA; Duke University Department of Radiology, 2424 Erwin Rd, Suite 302, Durham, North Carolina, 27705, USA. 3. Duke University Medical Physics Graduate Program, 2424 Erwin Rd, Suite 302, Durham, North Carolina, 27705, USA; Duke Clinical Imaging Physics Group, 2424 Erwin Rd, Suite 302, Durham, North Carolina, 27705, USA. 4. Carl E. Ravin Advanced Imaging Laboratories, 2424 Erwin Rd, Suite 302, Durham, North Carolina, 27705, USA; Duke University Medical Physics Graduate Program, 2424 Erwin Rd, Suite 302, Durham, North Carolina, 27705, USA; Duke University Department of Radiology, 2424 Erwin Rd, Suite 302, Durham, North Carolina, 27705, USA; Duke Clinical Imaging Physics Group, 2424 Erwin Rd, Suite 302, Durham, North Carolina, 27705, USA; Duke University Department of Physics, 2424 Erwin Rd, Suite 302, Durham, North Carolina, 27705, USA; Duke University Department of Biomedical Engineering, 2424 Erwin Rd, Suite 302, Durham, North Carolina, 27705, USA; Duke University Department of Electrical and Computer Engineering, 2424 Erwin Rd, Suite 302, Durham, North Carolina, 27705, USA. Electronic address: ehsan.samei@duke.edu.
Abstract
RATIONALE AND OBJECTIVES: The accuracy of measured radiomics features is affected by CT imaging protocols. This study aims to ascertain if applying bias corrections can improve the classification performance of the radiomics features. MATERIALS AND METHODS: A cohort of 144 Non-Small Cell Lung Cancer patient CT images was used to calculate radiomics features for use in predictive models of patient pathological stage. Simulation models of the tumors, matched to patient lesion qualities of size, contrast, and degree of spiculation, were used to both create and assess protocol-specific correction factors. The usefulness of correction was first assessed by applying the corrections to simulated lesion phantoms with known properties using a corrected paired Student's t-test. The sensitivity of radiomics features to correction factors was assessed by applying a library of possible theoretical correction factors to the uncorrected radiomics from the patient data. The data were then used to assess the effect of the correction on prediction performance (AUC) from a logistic regression radiomics model across the patient cases. RESULTS: The correction factors were shown to reduce the bias of radiomics features, caused by protocols, provided that the correction factors were derived from lesion models with similar properties. The sensitivity of the radiomics features to changes due to protocol effects was on average 89% among all features. The corrections applied to patient data resulted in a small increase of 0.0074 in AUC that was not statistically significant (p=0.60). CONCLUSION: Protocol-specific correction factors can be applied to radiomics studies to control for biases introduced by different imaging protocols. The correction factors should ideally be lesion-specific, derived using lesion models that echo patient lesion characteristics in terms of size, contrast, and degree of spiculation. Small corrections in the 10% range offers only a small improvement in the predictability of radiomics.
RATIONALE AND OBJECTIVES: The accuracy of measured radiomics features is affected by CT imaging protocols. This study aims to ascertain if applying bias corrections can improve the classification performance of the radiomics features. MATERIALS AND METHODS: A cohort of 144 Non-Small Cell Lung Cancer patient CT images was used to calculate radiomics features for use in predictive models of patient pathological stage. Simulation models of the tumors, matched to patient lesion qualities of size, contrast, and degree of spiculation, were used to both create and assess protocol-specific correction factors. The usefulness of correction was first assessed by applying the corrections to simulated lesion phantoms with known properties using a corrected paired Student's t-test. The sensitivity of radiomics features to correction factors was assessed by applying a library of possible theoretical correction factors to the uncorrected radiomics from the patient data. The data were then used to assess the effect of the correction on prediction performance (AUC) from a logistic regression radiomics model across the patient cases. RESULTS: The correction factors were shown to reduce the bias of radiomics features, caused by protocols, provided that the correction factors were derived from lesion models with similar properties. The sensitivity of the radiomics features to changes due to protocol effects was on average 89% among all features. The corrections applied to patient data resulted in a small increase of 0.0074 in AUC that was not statistically significant (p=0.60). CONCLUSION: Protocol-specific correction factors can be applied to radiomics studies to control for biases introduced by different imaging protocols. The correction factors should ideally be lesion-specific, derived using lesion models that echo patient lesion characteristics in terms of size, contrast, and degree of spiculation. Small corrections in the 10% range offers only a small improvement in the predictability of radiomics.
Authors: Shaimaa Bakr; Olivier Gevaert; Sebastian Echegaray; Kelsey Ayers; Mu Zhou; Majid Shafiq; Hong Zheng; Jalen Anthony Benson; Weiruo Zhang; Ann N C Leung; Michael Kadoch; Chuong D Hoang; Joseph Shrager; Andrew Quon; Daniel L Rubin; Sylvia K Plevritis; Sandy Napel Journal: Sci Data Date: 2018-10-16 Impact factor: 6.444