The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer.
OBJECTIVE
To investigative the predictive ability of radiomics signature for preoperative staging (I-IIvs.III-IV) of primary colorectal cancer (CRC).


METHODS
This study consisted of 494 consecutive patients (training dataset: n=286; validation cohort, n=208) with stage I-IV CRC. A radiomics signature was generated using LASSO logistic regression model. Association between radiomics signature and CRC staging was explored. The classification performance of the radiomics signature was explored with respect to the receiver operating characteristics(ROC) curve.


RESULTS
The 16-feature-based radiomics signature was an independent predictor for staging of CRC, which could successfully categorize CRC into stage I-II and III-IV (p <0.0001) in training and validation dataset. The median of radiomics signature of stage III-IV was higher than stage I-II in the training and validation dataset. As for the classification performance of the radiomics signature in CRC staging, the AUC was 0.792(95%CI:0.741-0.853) with sensitivity of 0.629 and specificity of 0.874. The signature in the validation dataset obtained an AUC of 0.708(95%CI:0.698-0.718) with sensitivity of 0.611 and specificity of 0.680.


CONCLUSIONS
A radiomics signature was developed and validated to be a significant predictor for discrimination of stage I-II from III-IV CRC, which may serve as a complementary tool for the preoperative tumor staging in CRC.
Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.|Graduate College, Southern Medical University, Guangzhou, 510515, China.
Cuishan
C
Liang
Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.|Graduate College, Southern Medical University, Guangzhou, 510515, China.
Yanqi
Y
Huang
Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.|School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510006, China.
Lan
L
He
Department of Radiology, The Affiliated Guangzhou First People' Hospital, Guangzhou Medical University, Guangzhou, 510180, China.
Xin
X
Chen
Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.|Graduate College, Southern Medical University, Guangzhou, 510515, China.
Zelan
Z
Ma
Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, 100190, China.
Di
D
Dong
Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, 100190, China.
Jie
J
Tian
Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
Changhong
C
Liang
Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
Zaiyi
Z
Liu
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015179:Colorectal Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging / Q000379:methods*; D057234:Preoperative Period; D012372:ROC Curve; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*; D055815:Young Adult
colorectal cancer; computed tomography; predictor; radiomics signature; stage