Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL).
Catharina Silvia LissonChristoph Gerhard LissonSherin AchillesMarc Fabian MezgerDaniel WolfStefan Andreas SchmidtWolfgang M ThaissJohannes BloehdornAmbros J BeerStephan StilgenbauerMeinrad BeerMichael GötzPublished in: Cancers (2022)
The study's primary aim is to evaluate the predictive performance of CT-derived 3D radiomics for MCL risk stratification. The secondary objective is to search for radiomic features associated with sustained remission. Included were 70 patients: 31 MCL patients and 39 control subjects with normal axillary lymph nodes followed over five years. Radiomic analysis of all targets ( n = 745) was performed and features selected using the Mann Whitney U test; the discriminative power of identifying "high-risk MCL" was evaluated by receiver operating characteristics (ROC). The four radiomic features, "Uniformity", "Entropy", "Skewness" and "Difference Entropy" showed predictive significance for relapse ( p < 0.05)-in contrast to the routine size measurements, which showed no relevant difference. The best prognostication for relapse achieved the feature "Uniformity" (AUC-ROC-curve 0.87; optimal cut-off ≤0.0159 to predict relapse with 87% sensitivity, 65% specificity, 69% accuracy). Several radiomic features, including the parameter "Short Axis," were associated with sustained remission. CT-derived 3D radiomics improves the predictive estimation of MCL patients; in combination with the ability to identify potential radiomic features that are characteristic for sustained remission, it may assist physicians in the clinical management of MCL.
Keyphrases
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- lymph node
- prognostic factors
- computed tomography
- high resolution
- primary care
- squamous cell carcinoma
- magnetic resonance imaging
- magnetic resonance
- systemic lupus erythematosus
- patient reported outcomes
- radiation therapy
- mass spectrometry
- lymph node metastasis
- deep learning
- disease activity
- positron emission tomography
- climate change
- rectal cancer
- patient reported
- human health
- locally advanced