Radiomics of diffusion-weighted MRI compared to conventional measurement of apparent diffusion-coefficient for...

Diffusion-weighted imaging (DWI) is proven useful to differentiate benign and malignant soft tissue tumors (STTs). Radiomics utilizing a vast array of extracted imaging features has a potential to uncover disease characteristics. We aim to assess radiomics using DWI can outperform the conventional DWI for STT differentiation. In 151 patients with 80 benign and 71 malignant tumors, ADC_mean and ADC_min were measured on solid portion within the mass by two different readers. For radiomics approach, tumors were segmented and 100 original radiomic features were extracted on ADC map. Eight radiomics models were built with training set (n = 105), using combinations of 2 different algorithms-multivariate logistic regression (MLR) and random forest (RF)-and 4 different inputs: radiomics features (R), R + ADC_min (I), R + ADC_mean (E), R + ADC_min and ADC_mean (A). All models were validated with test set (n = 46), and AUCs of ADC_mean, ADC_min, MLR-R, RF-R, MLR-I, RF-I, MLR-E, RF-E, MLR-A and RF-A models were 0.729, 0.753 0.698, 0.700, 0.773, 0.807, 0.762, 0.744, 0.773 and 0.807, respectively, without statistically significant difference. In conclusion, radiomics approach did not add diagnostic value to conventional ADC measurement for differentiating benign and malignant STTs.