Deep learning driven diagnosis of malignant soft tissue tumors based on dual-modal ultrasound images and clinical indexes

作者：Haiqin Xie, Yudi Zhang, Yudi Zhang, Licong Dong, Heng Lv, Xuechen Li, Chenyang Zhao, Yun Tian, Lu Xie, Wangjie Wu, Qi Yang, Li Liu, Desheng Sun, Qiu Li, Linlin Shen, Yusen Zhang, Yusen Zhang · 发表于：Frontiers in Oncology · 年份：2024 · DOI：10.3389/fonc.2024.1361694 · 被引用次数：5 · 研究领域：Sarcoma Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、Ultrasound Imaging and Elastography

Background Soft tissue tumors (STTs) are benign or malignant superficial neoplasms arising from soft tissues throughout the body with versatile pathological types. Although Ultrasonography (US) is one of the most common imaging tools to diagnose malignant STTs, it still has several drawbacks in STT diagnosis that need improving. Objectives The study aims to establish this deep learning (DL) driven Artificial intelligence (AI) system for predicting malignant STTs based on US images and clinical indexes of the patients. Methods We retrospectively enrolled 271 malignant and 462 benign masses to build the AI system using 5-fold validation. A prospective dataset of 44 malignant masses and 101 benign masses was used to validate the accuracy of system. A multi-data fusion convolutional neural network, named ultrasound clinical soft tissue tumor net (UC-STTNet), was developed to combine gray scale and color Doppler US images and clinic features for malignant STTs diagnosis. Six radiologists (R1-R6) with three experience levels were invited for reader study. Results The AI system achieved an area under receiver operating curve (AUC) value of 0.89 in the retrospective dataset. The diagnostic performance of the AI system was higher than that of one of the senior radiologists (AUC of AI vs R2: 0.89 vs. 0.84, p =0.022) and all of the intermediate and junior radiologists (AUC of AI vs R3, R4, R5, R6: 0.89 vs 0.75, 0.81, 0.80, 0.63; p < 0.01). The AI system also achieved an AUC of 0....