Assessing the impact of CNN architectures for whole organ segmentation on predictive models of organ toxicity

Segmentation of disease and critical structures from medical images is a critical task that enables development of predictive models of treatment response and treatment-related toxicities. Convolutional neural networks (CNN) are often used for this task. However, the impact assessment of CNN segmentation model architectures on predictive models’ performance is incipient. Here, we perform such assessment on a ¹⁸F-FDG PET histogram metrics-based model for predicting organ inflammation.

Two CNN architectures (DeepMedic, nnUNet) were employed to segment bowel, lungs and thyroid on the CT scans of melanoma cancer patients; from which the PET signal indicative of organ inflammation was extracted. This signal was used to predict organ toxicity via classical statistical and machine learning models. Model performance was assessed using area under the receiver operating characteristic curve. Model’s sensitivity to CNN architecture was analyzed.

Dice similarity coefficient of organ segmentation was 0.96±0.06 (mean±sd) in bowel, 0.87±0.07 in lungs and 0.61±0.16 in thyroid accounting for differences in different CNN architectures. Different CNN architectures had no significant impact on prediction of organ toxicities (z-test, p>0.05).

Our findings suggest that PET-derived, segmentation-based organ toxicity biomarkers are robust against different CNN architectures.