Ultrasomics differentiation of malignant and benign focal liver lesions based on contrast-enhanced ultrasound

To establish a nomogram for differentiating malignant and benign focal liver lesions (FLLs) using ultrasomics features derived from contrast-enhanced ultrasound (CEUS). 527 patients were retrospectively enrolled. On the training cohort, ultrasomics features were extracted from CEUS and b-mode ultrasound (BUS). Automatic feature selection and model development were performed using the Ultrasomics-Platform software, outputting the corresponding ultrasomics scores. A nomogram based on the ultrasomics scores from artery phase (AP), portal venous phase (PVP) and delayed phase (DP) of CEUS, and clinical factors were established. On the validation cohort, the diagnostic performance of the nomogram was assessed and compared with seniorexpert and resident radiologists. In the training cohort, the AP, PVP and DP scores exhibited better differential performance than BUS score, with area under the curve (AUC) of 84.1-85.1% compared with the BUS (74.6%, P < 0.05). In the validation cohort, the AUC of combined nomogram and expert was significantly higher than that of the resident (91.4% vs. 89.5% vs. 79.3%, P < 0.05). The combined nomogram had a comparable sensitivity with the expert and resident (95.2% vs. 98.4% vs. 97.6%), while the expert had a higher specificity than the nomogram and the resident (80.6% vs. 72.2% vs. 61.1%, P = 0.205). A CEUS ultrasomics based nomogram had an expert level performance in FLL characterization.

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Publication Details

Journal
BMC Medical Imaging
Published
2024-09-16
DOI
https://doi.org/10.1186/s12880-024-01426-x
Citations
2
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.81

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article

Ultrasomics differentiation of malignant and benign focal liver lesions based on contrast-enhanced ultrasound

Ming Kuang, Si‐Min Ruan, Xin-Xin Lin, Erjiao Xu et al.
2 citations
BMC Medical Imaging
Radiomics and Machine Learning in Medical Imaging
0.81
article

Ultrasomics differentiation of malignant and benign focal liver lesions based on contrast-enhanced ultrasound

Ming Kuang, Si‐Min Ruan, Xin-Xin Lin, Erjiao Xu, Ming-De Lu, Xiaoyan Xie, Ming‐De Li, Shanshan Wu, Wei Wang, Jianchao Zhang, Hai-Yu Kang
article en
2 citations

Abstract

To establish a nomogram for differentiating malignant and benign focal liver lesions (FLLs) using ultrasomics features derived from contrast-enhanced ultrasound (CEUS). 527 patients were retrospectively enrolled. On the training cohort, ultrasomics features were extracted from CEUS and b-mode ultrasound (BUS). Automatic feature selection and model development were performed using the Ultrasomics-Platform software, outputting the corresponding ultrasomics scores. A nomogram based on the ultrasomics scores from artery phase (AP), portal venous phase (PVP) and delayed phase (DP) of CEUS, and clinical factors were established. On the validation cohort, the diagnostic performance of the nomogram was assessed and compared with seniorexpert and resident radiologists. In the training cohort, the AP, PVP and DP scores exhibited better differential performance than BUS score, with area under the curve (AUC) of 84.1-85.1% compared with the BUS (74.6%, P < 0.05). In the validation cohort, the AUC of combined nomogram and expert was significantly higher than that of the resident (91.4% vs. 89.5% vs. 79.3%, P < 0.05). The combined nomogram had a comparable sensitivity with the expert and resident (95.2% vs. 98.4% vs. 97.6%), while the expert had a higher specificity than the nomogram and the resident (80.6% vs. 72.2% vs. 61.1%, P = 0.205). A CEUS ultrasomics based nomogram had an expert level performance in FLL characterization.

BMC Medical ImagingVol. 24(1)
Sun Yat-sen University (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), Puer University (CN), Eighth Affiliated Hospital of Sun Yat-sen University
National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province
Good health and well-being
Openalex Percentile: Top 26%
Radiomics and Machine Learning in Medical Imaging
0.81
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