| Title | Deep learning-assisted magnetic resonance imaging prediction of tumor response to chemotherapy in patients with colorectal liver metastases |
| Authors | Zhu, Hai-Bin Xu, Da Ye, Meng Sun, Li Zhang, Xiao-Yan Li, Xiao-Ting Nie, Pei Xing, Bao-Cai Sun, Ying-Shi |
| Affiliation | Peking Univ, Canc Hosp & Inst, Key Lab Carcinogenesis & Translat Res, Minist Educ Beijing,Dept Radiol, Beijing, Peoples R China Peking Univ, Canc Hosp & Inst, Key Lab Carcinogenesis & Translat Res, Minist Educ Beijing,Hepatopancreatobiliary Surg D, Beijing, Peoples R China Peking Univ, Canc Hosp & Inst, Key Lab Carcinogenesis & Translat Res, Minist Educ Beijing,Dept Pathol, Beijing, Peoples R China Qingdao Univ, Affiliated Hosp, Dept Radiol, Qingdao, Peoples R China |
| Keywords | NEURAL-NETWORKS CANCER SURVIVAL CRITERIA ADC MRI |
| Issue Date | Dec-2020 |
| Publisher | INTERNATIONAL JOURNAL OF CANCER |
| Abstract | Accurate evaluation of tumor response to preoperative chemotherapy is crucial for assigning appropriate patients with colorectal liver metastases (CRLM) to surgery or conservative therapy. However, there is no well-recognized method for predicting pathological response before surgery. Our study constructed and validated a deep learning algorithm using prechemotherapy and postchemotherapy magnetic resonance imaging (MRI) to predict pathological response in CRLM. CRLM patients from center one who had <= 5 lesions and were scheduled to receive preoperative chemotherapy followed by liver resection between January 2013 and November 2016, were included prospectively and chronologically divided into a training cohort (80% of patients) and a testing cohort (20% of patients). Patients from center two were included January 2017 and December 2018 as an external validation cohort. MRI-based models were constructed to discriminate according to pathology tumor regression grade (TRG) between the response (TRG1/2) and nonresponse (TRG3/4/5) groups at the lesion level. From center one, 155 patients (328 lesions) were included; chronologically, 101 (264 lesions) in the training cohort and 54 (64 lesions) in the testing cohort. The model achieved better accuracy (0.875 vs 0.578) and AUC (0.849 vs 0.615) than RECIST for discriminating response; it also distinguished the survival outcomes after hepatectomy better than the RECIST criteria. Evaluations of the external validation cohort (25 patients, 61 lesions) also showed good ability with an AUC of 0.833. In conclusion, the MRI-based deep learning model provided accurate prediction of pathological tumor response to preoperative chemotherapy in patients with CRLM and may inform individualized treatment. |
| URI | http://hdl.handle.net/20.500.11897/608283 |
| ISSN | 0020-7136 |
| DOI | 10.1002/ijc.33427 |
| Indexed | SCI(E) |
| Appears in Collections: | 北京肿瘤医院 |
