Analysis of MRI and CT-based radiomics features for personalized treatment in locally advanced rectal cancer and external validation of published radiomics models

• Published in: Scientific reports Vol. 12; no. 1; p. 10192
• Main Authors: Shahzadi, Iram, Zwanenburg, Alex, Lattermann, Annika, Linge, Annett, Baldus, Christian, Peeken, Jan C., Combs, Stephanie E., Diefenhardt, Markus, Rödel, Claus, Kirste, Simon, Grosu, Anca-Ligia, Baumann, Michael, Krause, Mechthild, Troost, Esther G. C., Löck, Steffen
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17-06-2022; Nature Publishing Group; Nature Portfolio
• Subjects: 692/4028/67/1059/485; 692/4028/67/1504/1885/1777; 692/4028/67/2321; 692/53/2422; Chemoradiotherapy; Colorectal cancer; Computed tomography; Humanities and Social Sciences; Magnetic resonance imaging; Metastases; multidisciplinary; Patients; Predictions; Radiomics; Rectum; Science; Science (multidisciplinary); Standardization; Tumors
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-13967-8

Radiomics analyses commonly apply imaging features of different complexity for the prediction of the endpoint of interest. However, the prognostic value of each feature class is generally unclear. Furthermore, many radiomics models lack independent external validation that is decisive for their clinical application. Therefore, in this manuscript we present two complementary studies. In our modelling study, we developed and validated different radiomics signatures for outcome prediction after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) based on computed tomography (CT) and T2-weighted (T2w) magnetic resonance (MR) imaging datasets of 4 independent institutions (training: 122, validation 68 patients). We compared different feature classes extracted from the gross tumour volume for the prognosis of tumour response and freedom from distant metastases (FFDM): morphological and first order (MFO) features, second order texture (SOT) features, and Laplacian of Gaussian (LoG) transformed intensity features. Analyses were performed for CT and MRI separately and combined. Model performance was assessed by the area under the curve (AUC) and the concordance index (CI) for tumour response and FFDM, respectively. Overall, intensity features of LoG transformed CT and MR imaging combined with clinical T stage (cT) showed the best performance for tumour response prediction, while SOT features showed good performance for FFDM in independent validation (AUC = 0.70, CI = 0.69). In our external validation study, we aimed to validate previously published radiomics signatures on our multicentre cohort. We identified relevant publications on comparable patient datasets through a literature search and applied the reported radiomics models to our dataset. Only one of the identified studies could be validated, indicating an overall lack of reproducibility and the need of further standardization of radiomics before clinical application.