The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer

Objectives 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.

[1]  Mitchell H Katz,et al.  Multivariable Analysis: A Primer for Readers of Medical Research , 2003, Annals of Internal Medicine.

[2]  I. Narabayashi,et al.  Preoperative Evaluation of Local Invasion and Metastatic Lymph Nodes of Colorectal Cancer and Mesenteric Vascular Variations Using Multidetector-Row Computed Tomography Before Laparoscopic Surgery , 2007, Journal of computer assisted tomography.

[3]  Shao Hui Huang,et al.  External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma , 2015, Acta oncologica.

[4]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[5]  Preoperative computed tomography staging of nonmetastatic colon cancer predicts outcome: implications for clinical trials , 2007, British Journal of Cancer.

[6]  A. Jemal,et al.  Cancer treatment and survivorship statistics, 2016 , 2016, CA: a cancer journal for clinicians.

[7]  Yong Yin,et al.  Three-dimensional positron emission tomography image texture analysis of esophageal squamous cell carcinoma: relationship between tumor 18F-fluorodeoxyglucose uptake heterogeneity, maximum standardized uptake value, and tumor stage , 2013, Nuclear medicine communications.

[8]  L. Bonomo,et al.  Preoperative T and N staging of colorectal cancer: accuracy of contrast-enhanced multi-detector row CT colonography--initial experience. , 2004, Radiology.

[9]  C. Compton,et al.  The Staging of Colorectal Cancer: 2004 and Beyond , 2004, CA: a cancer journal for clinicians.

[10]  B. Acunaş,et al.  Preoperative CT staging of colon carcinoma (excluding the recto-sigmoid region). , 1990, European journal of radiology.

[11]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[12]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[13]  B. Jiang,et al.  The accuracy of computed tomography in the pretreatment staging of colorectal cancer. , 2014, Hepato-gastroenterology.

[14]  Balaji Ganeshan,et al.  Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage , 2010, Cancer imaging : the official publication of the International Cancer Imaging Society.

[15]  A. Jemal,et al.  Global Cancer Statistics , 2011 .

[16]  M. Okada,et al.  Radiologic findings to predict low-grade malignant tumour among clinical T1bN0 lung adenocarcinomas: lessons from histological subtypes. , 2015, Japanese journal of clinical oncology.

[17]  Michael A. Blake,et al.  ACR Appropriateness Criteria pretreatment staging of colorectal cancer. , 2012, Journal of the American College of Radiology : JACR.

[18]  Vicky Goh,et al.  Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? , 2013, European journal of radiology.

[19]  Alejandro Munoz del Rio,et al.  CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes , 2015, Abdominal Imaging.

[20]  V. Goh,et al.  Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. , 2013, Radiology.

[21]  K. Miles,et al.  Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. , 2012, Clinical radiology.

[22]  A. Darzi,et al.  Diagnostic precision of CT in local staging of colon cancers: a meta-analysis. , 2010, Clinical radiology.

[23]  V. Goh,et al.  Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. , 2013, Radiology.

[24]  R. Tibshirani.  The lasso method for variable selection in the Cox model. , 1997, Statistics in medicine.

[25]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[26]  Shu Zheng,et al.  Preoperatively molecular staging with CM10 ProteinChip and SELDI-TOF-MS for colorectal cancer patients , 2006, Journal of Zhejiang University SCIENCE B.