Radiomics analysis of multicenter CT images for discriminating mucinous adenocarcinoma from nomucinous adenocarcinoma in rectal cancer and comparison with conventional CT values.

OBJECTIVE To investigate the value of CT-based radiomics signature for preoperatively discriminating mucinous adenocarcinoma (MA) from nomucinous adenocarcinoma (NMA) in rectal cancer and compare with conventional CT values. METHOD A total of 225 patients with histologically confirmed MA or NMA of rectal cancer were retrospectively enrolled. Radiomics features were computed from the entire tumor volume segmented from the post-contrast phase CT images. The maximum relevance and minimum redundancy (mRMR) and LASSO regression model were performed to select the best preforming features and build the radiomics models using a training cohort of 155 cases. Then, predictive performance of the models was validated using a validation cohort of 70 cases and receiver operating characteristics (ROC) analysis method. Meanwhile, CT values in post- and pre-contrast phase, as well as their difference (D-values) of tumors in two cohorts were measured by two radiologists. ROC curves were also calculated to assess diagnostic efficacies. RESULTS One hundred and sixty-three patients were confirmed by pathology as NMA and 62 cases were MA. The radiomics signature comprised 19 selected features and showed good discrimination performance in both the training and validation cohorts. The areas under ROC curves (AUC) are 0.93 (95% confidence interval [CI]: 0.89-0.98) in training cohort and 0.93 (95% CI: 0.87-0.99) in validation cohort, respectively. Three sets of CT values of MA in pre- and post-contrast phase, and their difference (D-value) (31±7.0, 51±12.6 and 20±9.3, respectively) were lower than those of NMA (37±5.6, 69±13.3 and 32±11.7, respectively). Comparing to the radiomics signature, using three sets of conventional CT values yielded relatively low diagnostic performance with AUC of 0.84 (95% CI: 0.78-0.88), 0.75 (95% CI: 0.69-0.81) and 0.78 (95% CI: 0.72-0.83), respectively. CONCLUSION This study demonstrated that CT radiomics features could be utilized as a noninvasive biomarker to identify MA patients from NMA of rectal cancer preoperatively, which is more accurate than using the conventional CT values.

[1]  M. A. van den Bosch,et al.  Diagnostic accuracy of computed tomography for colon cancer staging: A systematic review , 2011, Scandinavian journal of gastroenterology.

[2]  P. Villeneuve,et al.  Survival of patients diagnosed with either colorectal mucinous or non-mucinous adenocarcinoma: a population-based study in Canada. , 2009, International journal of oncology.

[3]  L. Wyrwicz,et al.  Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. , 2017, Annals of oncology : official journal of the European Society for Medical Oncology.

[4]  J. Burke,et al.  Mucinous Rectal Adenocarcinoma Is Associated with a Poor Response to Neoadjuvant Chemoradiotherapy: A Systematic Review and Meta-analysis , 2016, Diseases of the colon and rectum.

[5]  R. Kapoor,et al.  Mucinous adenocarcinoma of the rectum: a poor candidate for neo-adjuvant chemoradiation? , 2014, Journal of gastrointestinal oncology.

[6]  R. Thornhill,et al.  Quantitative CT texture and shape analysis: Can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer? , 2015, European Radiology.

[7]  Zhenhui Li,et al.  Role of CT scan in differentiating the type of colorectal cancer , 2017, OncoTargets and therapy.

[8]  Dimitris Visvikis,et al.  Potential Complementary Value of Noncontrast and Contrast Enhanced CT Radiomics in Colorectal Cancers. , 2019, Academic radiology.

[9]  K. Madbouly,et al.  Is it safe to omit neoadjuvant chemo-radiation in mucinous rectal carcinoma? , 2015, International journal of surgery.

[10]  Di Dong,et al.  CT-based radiomics signature for differentiating Borrmann type IV gastric cancer from primary gastric lymphoma. , 2017, European journal of radiology.

[11]  Yanqi Huang,et al.  Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[12]  Dina Muin,et al.  Texture-based classification of different gastric tumors at contrast-enhanced CT. , 2013, European journal of radiology.

[13]  C. Kunisaki,et al.  The clinicopathological features of colorectal mucinous adenocarcinoma and a therapeutic strategy for the disease , 2012, World Journal of Surgical Oncology.

[14]  Jun Wang,et al.  Radiomics study for differentiating gastric cancer from gastric stromal tumor based on contrast-enhanced CT images. , 2020, Journal of X-ray science and technology.

[15]  Di Dong,et al.  Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer? , 2018, European Radiology.

[16]  Hui-Hong Duan,et al.  Correlation between CT based radiomics features and gene expression data in non-small cell lung cancer. , 2019, Journal of X-ray science and technology.

[17]  I. Petkovska,et al.  Mucinous rectal cancer: concepts and imaging challenges , 2019, Abdominal Radiology.

[18]  Myeong-Jin Kim,et al.  Abstracts of selected papers from the current literature , 2003, Abdominal Imaging.

[19]  Andrés Larroza,et al.  Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study , 2018, European Radiology.

[20]  Di Dong,et al.  The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer , 2016, Oncotarget.

[21]  Guangtao Zhai,et al.  Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective , 2018, European Radiology.

[22]  I. Kamel,et al.  The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer , 2018, European Radiology.

[23]  Paul Kinahan,et al.  Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.

[24]  H. Ha,et al.  CT differentiation of mucinous and nonmucinous colorectal carcinoma. , 2007, AJR. American journal of roentgenology.

[25]  Yanqi Huang,et al.  CT-based Radiomics Signature to Discriminate High-grade From Low-grade Colorectal Adenocarcinoma. , 2018, Academic radiology.

[26]  Yan Xing,et al.  Clinicopathology and Outcomes for Mucinous and Signet Ring Colorectal Adenocarcinoma: Analysis from the National Cancer Data Base , 2012, Annals of Surgical Oncology.

[27]  Zongqiong Sun,et al.  A pilot study of radiomics technology based on X-ray mammography in patients with triple-negative breast cancer. , 2019, Journal of X-ray science and technology.

[28]  E. Outwater,et al.  Mucinous versus nonmucinous rectal carcinomas: differentiation with MR imaging. , 1999, Radiology.

[29]  Qiong Li,et al.  Radiomics signature: A potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology. , 2019, Lung cancer.