Comparison of radiomics models and dual-energy material decomposition to decipher abdominal lymphoma in contrast-enhanced CT

• Published in: International journal for computer assisted radiology and surgery 2023-10, Vol.18 (10), p.1829-1839
• Main Authors: Bernatz, Simon, Koch, Vitali, Dos Santos, Daniel Pinto, Ackermann, Jörg, Grünewald, Leon D., Weitkamp, Inga, Yel, Ibrahim, Martin, Simon S., Lenga, Lukas, Scholtz, Jan-Erik, Vogl, Thomas J., Mahmoudi, Scherwin
• Format: Article
• Language: English
• Subjects: Abdomen; Artificial intelligence; Computer Imaging; Computer Science; Correlation analysis; Decomposition; Health Informatics; Imaging; Imaging techniques; Lymphatic system; Lymphoma; Machine learning; Medicine; Medicine & Public Health; Original; Original Article; Pattern Recognition and Graphics; Permutations; Radiology; Radiomics; Surgery; Vision
• ISSN: 1861-6429; 1861-6410; 1861-6429
• DOI: 10.1007/s11548-023-02854-w

Purpose The radiologists’ workload is increasing, and computational imaging techniques may have the potential to identify visually unequivocal lesions, so that the radiologist can focus on equivocal and critical cases. The purpose of this study was to assess radiomics versus dual-energy CT (DECT) material decomposition to objectively distinguish visually unequivocal abdominal lymphoma and benign lymph nodes. Methods Retrospectively, 72 patients [ m , 47; age, 63.5 (27–87) years] with nodal lymphoma ( n  = 27) or benign abdominal lymph nodes ( n  = 45) who had contrast-enhanced abdominal DECT between 06/2015 and 07/2019 were included. Three lymph nodes per patient were manually segmented to extract radiomics features and DECT material decomposition values. We used intra-class correlation analysis, Pearson correlation and LASSO to stratify a robust and non-redundant feature subset. Independent train and test data were applied on a pool of four machine learning models. Performance and permutation-based feature importance was assessed to increase the interpretability and allow for comparison of the models. Top performing models were compared by the DeLong test. Results About 38% (19/50) and 36% (8/22) of the train and test set patients had abdominal lymphoma. Clearer entity clusters were seen in t-SNE plots using a combination of DECT and radiomics features compared to DECT features only. Top model performances of AUC = 0.763 (CI = 0.435–0.923) were achieved for the DECT cohort and AUC = 1.000 (CI = 1.000–1.000) for the radiomics feature cohort to stratify visually unequivocal lymphomatous lymph nodes. The performance of the radiomics model was significantly ( p  = 0.011, DeLong) superior to the DECT model. Conclusions Radiomics may have the potential to objectively stratify visually unequivocal nodal lymphoma versus benign lymph nodes. Radiomics seems superior to spectral DECT material decomposition in this use case. Therefore, artificial intelligence methodologies may not be restricted to centers with DECT equipment.