Application value of deep learning based on contrast-enhanced ultrasound for the diagnosis of liver malignant tumors
10.3760/cma.j.cn131148-20231013-00164
- VernacularTitle:基于超声造影的深度学习诊断肝恶性肿瘤的应用价值
- Author:
Shijie WANG
1
;
Jiaqi DENG
;
Rong KUANG
;
Yuxian WANG
;
Cao LI
;
Jing ZHOU
Author Information
1. 西南医科大学附属医院超声科，泸州 646000
- Keywords:
Contrast-enhanced ultrasound;
Deep learning;
Dynamic video;
Liver tumors
- From:
Chinese Journal of Ultrasonography
2024;33(2):112-118
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the clinical value of deep learning model based on contrast enhanced ultrasound (CEUS) video in the differential diagnosis of benign and malignant liver tumors.Methods:Between May 2010 and June 2022, 1 213 patients who underwent CEUS examination for liver masses in the Affiliated Hospital of Southwest Medical University were retrospectively collected, and the enrolled patients were divided into training and independent test cohorts with December 31, 2021 as the time cut-off. In the training cohort, the TimeSformer algorithm was used as the infrastructure, and multiple fixed-time segments were obtained from CEUS arterial videos by using the sliding window of the video, and the classification results of the entire video were obtained after fusing the features of multiple segments, so as to build a deep learning model based on CEUS videos. In the independent test cohort, ROC curves were used to verify the validity of the model and compared with three radiologists with different CEUS experience (R1, R2, and R3, with 3, 6, and 10 years of CEUS experience, respectively).Results:A total of 1 213 patients with liver masses were included in the study, including 1 066 patients in the training cohort (426 cases of malignancy) and 147 patients in the independent test cohort (50 cases of malignancy). The area under curve (AUC)value of deep learning model was 0.93±0.01 in the training cohort and 0.89±0.01 in the independent test cohort, and the accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 80.42%, 74.19%, 92.00%, 94.52% and 65.71%, respectively. Among the three radiologists, R1 had the lowest diagnostic performance, with accuracy, sensitivity, specificity, PPV and NPV of 67.83%, 51.61%, 98.00%, 97.96% and 52.13%, respectively, while the above indicators of R3 were 82.52%, 76.36%, 94.00%, 95.95% and 68.12%, respectively. McNemar′s test showed that the difference between R1 and the deep learning model was statistically significant ( P<0.001), while the differences between R2 and R3 and the deep learning model were not statistically significant ( P=0.720, 0.868). In addition, the analysis time of the model for a single case was (340.24±16.32)ms, while the average analysis time of radiologists was 62.9 s. Conclusions:The deep learning model based on CEUS can better identify benign and malignant liver masses, and may reach the diagnostic level of experienced radiologists.
