Non-Hodgkin's lymphoma classification using 3D Radiomics Machine Learning Models for Precision Imaging in Oncology

Zielsetzung To apply quantitative imaging analysis for noninvasive classification of the most frequent subtypes of Non-Hodgkin Lymphoma (NHL) as a basis for a clinical imaging genomic model to support therapeutic monitoring and clinical decision making.

Material und Methoden In this single-center study, 201 treatment-naïve patients with biopsy-proven NHL (50 diffuse large B-cell lymphoma [DLBCL], 51 mantle cell lymphoma [MCL], 49 follicular lymphoma [FL], and 51 chronic lymphocytic leukemia [CLL]) and 39 treatment-naïve non-small cell lung cancer patients with positron emission tomography (PET)/computed tomography (CT)-confirmed healthy axillary lymph nodes (LNs) were retrospectively analyzed. Three-dimensional (3D) segmentation and radiomic analysis of pathologically enlarged nodes (n=1,628) were performed on contrast-enhanced CT scans, including healthy LNs as references. Feature selection was performed using a random forest (RF) classifier. Multiclass Classifier was performed using a Light Gradient Boosting Machine (LGBM) classifier for lymphoma subtype classification.

Ergebnisse Performance to classify lymphoma from non-lymphoma and lymphoma subtypes was as follows: malignant lymphoma vs. non-lymphoma: area under the curve (AUC)=0.998; MCL vs. other NHL: AUC=0.996; DLBCL vs. other NHL: AUC=0.983; CLL vs. other NHL: AUC=0.964; FL vs. other NHL: AUC=0.914.

Schlussfolgerungen Our study demonstrates the potential of a radiomics-based machine learning framework for distinguishing lymphoma from non-lymphoma and classifying the most common NHL subtypes. Coupled with the ability to non-invasively assess multiple LNs at different sites in the body for therapy monitoring and the potential to identify early progression to a more aggressive disease course, our findings may help to expand the applicability of radiomics in precision oncology.

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Artikel online veröffentlicht:
25. März 2025