External validation of an MR-based radiomic model predictive of locoregional control in oropharyngeal cancer

Bos, Paula; Martens, Roland M.; Graaf, Pim de; Jasperse, Bas; Griethuysen, Joost J.M. van; Boellaard, Ronald; Leemans, C. René; Wiel, Mark A. van de; Brekel, M.W.M. van den; Castelijns, Jonas A.

doi:10.1007/s00330-022-09255-8

Cited by 3 publications

(3 citation statements)

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“…These findings highlight the dependency of perceived harmonization performance on the choice of classifier and may in part explain why some studies did not report improved classification performance following ComBat harmonization. 32 Contrary to binary classification, that is, separation of only 2 classes, such as benign and malignant lesions, 5,33 or prediction of locoregional spread or control, 17,18 treatment response or relapse at a given time-point, [19][20][21]30 for which ComBat has been successfully used, our use of 3 tissue types with visually similar signal intensity and homogeneity on MRI makes the classification task more complex. Classification was further made difficult by our choice of T1-weighted Dixon images for radiomic feature extraction, where signal intensities showed only minor visible differences between tissues of interest.…”

Section: Discussionmentioning

confidence: 99%

“…14,15 For clinical MRI data, ComBat has so far predominantly been used for binary classification tasks, that is, separation of just 2 tissue, lesion, or outcome classes. 3,5,6,[15][16][17][18][19][20][21] In the present study, our aim was therefore to determine the value of ComBat harmonization of clinical MRI radiomic data from 2 centers for nonbinary tissue classification by machine learning. Further, we aimed to compare the performances of 2 ComBat variants, as well as the effects of harmonization on radiomic features of different categories.…”

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confidence: 99%

“…ComBat, which is the most widely used harmonization technique at present, has been shown to remove, or at least decrease, the “batch effect”—for example, the influence of acquisition protocol differences between centers and scanners—from radiomic features, while preserving their underlying biological and pathophysiological associations 14,15 . For clinical MRI data, ComBat has so far predominantly been used for binary classification tasks, that is, separation of just 2 tissue, lesion, or outcome classes 3,5,6,15–21 …”

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ComBat Harmonization for MRI Radiomics

et al. 2023

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ObjectivesThe aims of this study were to determine whether ComBat harmonization improves multiclass radiomics-based tissue classification in technically heterogeneous MRI data sets and to compare the performances of 2 ComBat variants.Materials and MethodsOne hundred patients who had undergone T1-weighted 3D gradient echo Dixon MRI (2 scanners/vendors; 50 patients each) were retrospectively included. Volumes of interest (2.5 cm3) were placed in 3 disease-free tissues with visually similar appearance on T1 Dixon water images: liver, spleen, and paraspinal muscle. Gray-level histogram (GLH), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and gray-level size-zone matrix (GLSZM) radiomic features were extracted. Tissue classification was performed on pooled data from the 2 centers (1) without harmonization, (2) after ComBat harmonization with empirical Bayes estimation (ComBat-B), and (3) after ComBat harmonization without empirical Bayes estimation (ComBat-NB). Linear discriminant analysis with leave-one-out cross-validation was used to distinguish among the 3 tissue types, using all available radiomic features as input. In addition, a multilayer perceptron neural network with a random 70%:30% split into training and test data sets was used for the same task, but separately for each radiomic feature category.ResultsLinear discriminant analysis–based mean tissue classification accuracies were 52.3% for unharmonized, 66.3% for ComBat-B harmonized, and 92.7% for ComBat-NB harmonized data. For multilayer perceptron neural network, mean classification accuracies for unharmonized, ComBat-B–harmonized, and ComBat-NB–harmonized test data were as follows: 46.8%, 55.1%, and 57.5% for GLH; 42.0%, 65.3%, and 71.0% for GLCM; 45.3%, 78.3%, and 78.0% for GLRLM; and 48.1%, 81.1%, and 89.4% for GLSZM. Accuracies were significantly higher for both ComBat-B– and ComBat-NB–harmonized data than for unharmonized data for all feature categories (at P = 0.005, respectively). For GLCM (P = 0.001) and GLSZM (P = 0.005), ComBat-NB harmonization provided slightly higher accuracies than ComBat-B harmonization.ConclusionsComBat harmonization may be useful for multicenter MRI radiomics studies with nonbinary classification tasks. The degree of improvement by ComBat may vary among radiomic feature categories, among classifiers, and among ComBat variants.

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ComBat Harmonization for MRI Radiomics

et al. 2023

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The Use of MR-Guided Radiation Therapy for Head and Neck Cancer and Recommended Reporting Guidance

McDonald,

Dal Bello,

Fuller

et al. 2024

Seminars in Radiation Oncology

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Pretreatment multiparametric MRI radiomics-integrated clinical hematological biomarkers can predict early rapid metastasis in patients with nasopharyngeal carcinoma

Cao,

Wang,

Song

et al. 2024

BMC Cancer

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Background To establish and validate a predictive model combining pretreatment multiparametric MRI-based radiomic signatures and clinical characteristics for the risk evaluation of early rapid metastasis in nasopharyngeal carcinoma (NPC) patients. Methods The cutoff time was used to randomly assign 219 consecutive patients who underwent chemoradiation treatment to the training group (n = 154) or the validation group (n = 65). Pretreatment multiparametric magnetic resonance (MR) images of individuals with NPC were employed to extract 428 radiomic features. LASSO regression analysis was used to select radiomic features related to early rapid metastasis and develop the Rad-score. Blood indicators were collected within 1 week of pretreatment. To identify independent risk variables for early rapid metastasis, univariate and multivariate logistic regression analyses were employed. Finally, multivariate logistic regression analysis was applied to construct a radiomics and clinical prediction nomogram that integrated radiomic features and clinical and blood inflammatory predictors. Results The NLR, T classification and N classification were found to be independent risk indicators for early rapid metastasis by multivariate logistic regression analysis. Twelve features associated with early rapid metastasis were selected by LASSO regression analysis, and the Rad-score was calculated. The AUC of the Rad-score was 0.773. Finally, we constructed and validated a prediction model in combination with the NLR, T classification, N classification and Rad-score. The area under the curve (AUC) was 0.936 (95% confidence interval (95% CI): 0.901–0.971), and in the validation cohort, the AUC was 0.796 (95% CI: 0.686–0.905). Conclusions A predictive model that integrates the NLR, T classification, N classification and MR-based radiomics for distinguishing early rapid metastasis may serve as a clinical risk stratification tool for effectively guiding individual management.

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External validation of an MR-based radiomic model predictive of locoregional control in oropharyngeal cancer

Cited by 3 publications

References 43 publications

ComBat Harmonization for MRI Radiomics

ComBat Harmonization for MRI Radiomics

The Use of MR-Guided Radiation Therapy for Head and Neck Cancer and Recommended Reporting Guidance

Pretreatment multiparametric MRI radiomics-integrated clinical hematological biomarkers can predict early rapid metastasis in patients with nasopharyngeal carcinoma

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