Deep learning-assisted diagnosis of liver tumors using non-contrast magnetic resonance imaging: a multicenter study

Shihui Zhen^1,2*Peng Zhang¹Hanxiao Huang¹Zhiyu Jiang²Yankai Jiang¹Jihong Sun²Liqing Zhang³Mei Ruan³Qingqing Chen²Yujun Wang⁴Yubo Tao¹Weizhi Luo¹Ming Cheng¹Zhetuo Qi¹Wei Lu⁵Hai Lin¹Xiujun Cai²

• ¹Zhejiang University, Hangzhou, China
• ²sir run run shaw hospital ,zhejiang university, hangzhou, China
• ³Hangzhou First People's Hospital, Hangzhou, Zhejiang Province, China
• ⁴Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang Province, China
• ⁵Department of Medical Imaging, Ningbo No. 2 Hospital, Ningbo, Zhejiang Province, China

Objectives: Non-contrast MRI(NC-MRI) is an attractive option for liver tumors screening and followup. This study aims to develop and validate a deep convolutional neural network for the classification of liver lesions using non-contrast MRI.Methods: A total of 50418 enhanced MRI images from 1959 liver tumor patients across three centers were included. Inception-ResNet V2 was used to generate four models through transfer-learning for three-way lesion classification, which processed T2-weighted, diffusion-weighted (DWI) and multiphasic T1-weighted images. The models were then validated using one independent internal and two external datasets with 5172, 2916, and 1338 images, respectively. The efficacy of non-contrast models (T2,T2+DWI) in differentiating between benign and malignant liver lesions at the patient level was also evaluated and compared with radiologists. The performance of models was evaluated using the area under the receiver operating characteristic curve (AUC),sensitivity and specificity.Results: Similar to multi-sequence and enhanced image-based models, the non-contrast models showed comparable accuracy in classifying liver lesions as benign, primary malignant or metastatic.In the independent internal cohort, the T2+DWI model achieved AUC of 0.91(95% CI,0.888-0.932), 0.873(0.848-0.899), and 0.876(0.840-0.911) for three tumour categories, respectively. The sensitivities for distinguishing malignant tumors in three validation sets were 98.1%, 89.7%, and 87.5%%, with specificities over 70% in all three sets. Conclusions: Our deep-learning-based model yielded good applicability in classifying liver lesions in non-contrast MRI. It provides a potential alternative for screening liver tumors with the advantage of reducing costs, scanning time and contrast-agents risks. It is more suitable for benign tumours followup, surveillance of HCC and liver metastasis that need periodic repetitive examinations.