In European radiology ; h5-index 62.0
OBJECTIVES : To develop and validate a deep learning (DL) model based on quantitative analysis of contrast-enhanced ultrasound (CEUS) images that predicts early recurrence (ER) after thermal ablation (TA) of colorectal cancer liver metastasis (CRLM).
METHODS : Between January 2010 and May 2019, a total of 207 consecutive patients with CRLM with 13,248 slice images at three dynamic phases who received CEUS within 2 weeks before TA were retrospectively enrolled in two centres (153 for the training cohort (TC), 32 for the internal test cohort (ITC), and 22 for the external test cohort (ETC)). Clinical and CEUS data were used to develop and validate the clinical model, DL model, and DL combining with clinical (DL-C) model to predict ER after TA. The performance of these models was compared by the receiver operating characteristic curve (ROC) with the DeLong test.
RESULTS : After a median follow-up of 56 months, 49% (99/207) of patients experienced ER. Three key clinical features (preoperative chemotherapy (PC), lymph node metastasis of the primary colorectal cancer (LMPCC), and T stage) were used to develop the clinical model. The DL model yielded better performance than the clinical model in the ETC (AUC: 0.67 for the clinical model, 0.76 for the DL model). The DL-C model significantly outperformed the clinical model and DL model (AUC: 0.78 for the DL-C model in the ETC; both, p < 0.001).
CONCLUSIONS : The model based on CEUS can achieve satisfactory prediction and assist physicians during the therapeutic decision-making process in clinical practice.
KEY POINTS : • This is an exploratory study in which ablation-related contrast-enhanced ultrasound (CEUS) data from consecutive patients with colorectal cancer liver metastasis (CRLM) were collected simultaneously at multiple institutions. • The deep learning combining with clinical (DL-C) model provided desirable performance for the prediction of early recurrence (ER) after thermal ablation (TA). • The DL-C model based on CEUS provides guidance for TA indication selection and making therapeutic decisions.
Zhao Qin-Xian, He Xue-Lei, Wang Kun, Cheng Zhi-Gang, Han Zhi-Yu, Liu Fang-Yi, Yu Xiao-Ling, Hui Zhong, Yu Jie, Chao An, Liang Ping
2022-Nov-24
Colorectal neoplasms, Deep learning, Recurrence, Thermal ablation, Ultrasound
