Construction and Validation of a Nomogram Based on Radiomics and Clinical Features for Discerning Malignant Soft...

This study aimed to extract radiomic features from ultrasound (US) images of soft tissue tumors (STTs) and develop a diagnostic model for STTs using radiomic and clinical patient data.

Three hundred and sixty-nine patients were recruited as the training group, with 249 benign and 120 malignant STTs, and 127 patients as the validation group, with 93 benign and 34 malignant STTs. We extracted the radiomic features of the US images using an open-source Python package. We selected the most relevant features using the least absolute shrinkage and selection operator (LASSO) regression. Then we used a combination of clinical indexes, radiomic features, and color-Doppler US to construct a diagnostic model for STTs. The diagnostic performance of the model was evaluated by measuring its sensitivity, specificity, area under the receiver operating curve (AUC), and calibration.

We selected 20 radiomic features of the US images. The model based on the clinical indexes, radiomic features, and color-Doppler scores showed good diagnostic performances on both the training [AUC: 0.97 (0.95-0.98)] and validation datasets [AUC: 0.93 (0.86-0.99)]. The model also presented good calibration with the original results.

We extracted radiomic features of ultrasound images of patients with STTs and constructed a clinical-imaging model for differentiating malignant and benign STT lesions. A nomogram displayed a clinical-imaging model, which showed good diagnostic efficacy and calibration in both the training and validation datasets. The clinical-imaging model has potential value for clinical use.

The diagnostic model based on clinical, US radiomic, and imaging features presented a high diagnostic performance in STTs, which can have potential value in further clinical utilization.