Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
| Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis | |
Zhao, Qin-xian1; He, Xue-lei2,3; Wang, Kun3 ; Cheng, Zhi-gang1; Han, Zhi-yu1; Liu, Fang-yi1; Yu, Xiao-ling1; Hui, Zhong4; Yu, Jie1; Chao, An1; Liang, Ping1
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| Source Publication | EUROPEAN RADIOLOGY
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| ISSN | 0938-7994 |
| 2022-11-24 | |
| Pages | 11 |
| Corresponding Author | Yu, Jie(jiemi301@163.com) ; Chao, An(anchao-1983@163.com) ; Liang, Ping(liangping301@126.com) |
| Abstract | 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. |
| Keyword | Thermal ablation Colorectal neoplasms Ultrasound Deep learning Recurrence |
| DOI | 10.1007/s00330-022-09203-6 |
| WOS Keyword | HEPATOCELLULAR-CARCINOMA ; MICROWAVE ABLATION ; RISK-FACTORS ; INTRAHEPATIC RECURRENCE ; RESECTION ; EXPERIENCE ; MANAGEMENT ; EFFICACY ; PATTERNS ; SAFETY |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[81971625,91859201] ; National Natural Science Foundation of China[82102076] ; National Natural Science Foundation of China[82030047] |
| Funding Organization | National Natural Science Foundation of China |
| WOS Research Area | Radiology, Nuclear Medicine & Medical Imaging |
| WOS Subject | Radiology, Nuclear Medicine & Medical Imaging |
| WOS ID | WOS:000886855000004 |
| Publisher | SPRINGER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/51288 |
| Collection | 中国科学院分子影像重点实验室 |
| Corresponding Author | Yu, Jie; Chao, An; Liang, Ping |
| Affiliation | 1.Chinese Peoples Liberat Army Gen Hosp, Dept Intervent Ultrasound, Med Ctr 5, 28 Fuxing Rd, Beijing 100853, Peoples R China 2.Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Shaanxi, Peoples R China 3.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 4.Minist Educ, Key Lab Biomed Informat Engn, Xian, Peoples R China |
| Recommended Citation GB/T 7714 | Zhao, Qin-xian,He, Xue-lei,Wang, Kun,et al. Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis[J]. EUROPEAN RADIOLOGY,2022:11. |
| APA | Zhao, Qin-xian.,He, Xue-lei.,Wang, Kun.,Cheng, Zhi-gang.,Han, Zhi-yu.,...&Liang, Ping.(2022).Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis.EUROPEAN RADIOLOGY,11. |
| MLA | Zhao, Qin-xian,et al."Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis".EUROPEAN RADIOLOGY (2022):11. |
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