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

Abstract

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.

Keywords

Rectal cancer mucinous adenocarcinoma radiomics CT

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References

Horvat

, Hope

T.A.

and Pickhardt

P.J.

, et al., Mucinous rectal cancer: Concepts and imaging challenges, Abdominal radiology (2019).

Hyngstrom

J.R.

, Hu

C.T.

and Xing

, et al., Clinicopathology and outcomes for mucinous and signet ring colorectal adenocarcinoma: Analysis from the National Cancer Data Base, Ann Surg Oncol 19(9) (2012), 2814–2821.

Xie

, Villeneuve

P.J.

and Shaw

, Survival of patients diagnosed with either colorectal mucinous or non-mucinous adenocarcinoma: A population-based study in Canada, International Journal of Oncology 34(4) (2009), 1109–1115.

Numata

, Shiozawa

and Watanabe

, et al., The clinicopathological features of colorectal mucinous adenocarcinoma and a therapeutic strategy for the disease, World Journal of Surgical Oncology 10(1) (2012).

McCawley

, Clancy

and O’Neill

B.D.

, et al., Mucinous rectal adenocarcinoma is associated with a poor response to neoadjuvant chemoradiotherapy: A systematic review and meta-analysis, Diseases of the Colon and Rectum 59(12) (2016), 1200–1208.

Simha

, Kapoor

and Gupta

, et al., Mucinous adenocarcinoma of the rectum: A poor candidate for neo-adjuvant chemoradiation? Journal of Gastrointestinal Oncology 5(4) (2014), 276–279.

Numata

, Shiozawa

and Watanabe

, et al., The clinicopathological features of colorectal mucinous adenocarcinoma and a therapeutic strategy for the disease, World Journal of Surgical Oncology 10 (2012), 109.

Madbouly

K.M.

, Mashhour

A.N.

and Omar

, Is it safe to omit neoadjuvant chemo-radiation in mucinous rectal carcinoma? International Journal of Surgery 23(Pt A) (2015), 120–127.

Leufkens

A.M.

, van den Bosch

M.A.

and van

Leeuwen M.S.

, et al., Diagnostic accuracy of computed tomography for colon cancer staging: A systematic review, Scand J Gastroenterol 46(7–8) (2011), 887–894.

10.

Gillies

R.J.

, Kinahan

P.E.

and Hricak

, Radiomics: Images are more than pictures, they are data, Radiology 278(2) (2016), 151169.

11.

Ba-Ssalamah

, Muin

and Schernthaner

, et al., Texture-based classification of different gastric tumors at contrast-enhanced CT, European Journal of Radiology 82(10) (2013), e537–543.

12.

, Fang

and Huang

, et al., CT-based radiomics signature for differentiating Borrmann type IV gastric cancer from primary gastric lymphoma, Eur J Radiol 91 (2017), 142–147.

13.

Bayanati

, Thornhill

R.E.

and Souza

C.A.

, et al., Quantitative CT texture and shape analysis: Can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer? European Radiology 25(2) (2015), 480–487.

14.

Zhang

, Sun

, Cheng

and Mao

, A pilot study of radiomics technology based on X-ray mammography in patients with triple-negative breast cancer, Journal of X-ray Science and Technology 27(3) (2019), 485–492.

15.

Wang

, Gong

and Duan

, et al., Correlation between CT based radiomics features and gene expression data in non-small cell lung cancer, Journal of X-ray Science and Technology 27(5) (2019), 773–803.

16.

Z.C.

, Zhai

and Zhang

, 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, European radiology 29 (2019), 3996–4007.

17.

Sun

Z.Q.

, Hu

S.D.

and Li

, et al., Radiomics study for differentiating gastric cancer from gastric stromal tumor based on contrast-enhanced CT images, Journal of X-ray Science and Technology 27(6) (2019), 1021–1031.

18.

, Sun

and Fan

, et al., Radiomics signature: A potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology, Lung Cancer 132 (2019), 28–35.

19.

Huang

, Cheng

and Huang

, et al., CT-based radiomics signature to discriminate high-grade from low-grade colorectal adenocarcinoma, Academic radiology 25(10) (2018), 1285–1297.

20.

Liang

, Huang

and He

, 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, Oncotarget 7(21) (2014), 31401–31412.

21.

Huang

Y.Q.

, Liang

and He

, et al., Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer, Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 34(18) (2016), 2157–2164.

22.

Ortiz-Ramon

, Larroza

and Ruiz-Espana

, et al., Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: A feasibility study, European Radiology 28(11) (2018), 4514–4523.

23.

Z.H.

, You

D.Y.

and Gao

D.P.

, et al., Role of CT scan in differentiating the type of colorectal cancer, OncoTargets and Therapy 10 (2017), 2297–2303.

24.

E.Y.

, Ha

H.K.

and Kim

A.Y.

, et al., CT differentiation of mucinous and nonmucinous colorectal carcinoma, AJR. American Journal of Roentgenology 188(3) (2007), 785–791.

25.

Hussain

S.M.

, Outwater

E.K.

and Siegelman

E.S.

, Mucinous versus nonmucinous rectal carcinomas: Differentiation with MR imaging, Radiology 213(1) (1999), 79–85.

26.

Kim

M.J.

, Park

J.S.

and Park

S.I.

, et al., Accuracy in differentiation of mucinous and nonmucinous rectal carcinoma on MR imaging, Journal of Computer Assisted Tomography 27(1) (2003), 48–55.

27.

Glynne-Jones

, Tiret

and Brown

, et al., Rectal cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up, Annals of Oncology 28 (2017), iv22–iv40.

28.

Yang

, Dong

and Fang

, et al., Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer? European Radiology 28(5) (2018), 2058–2067.

29.

Badic

, Desseroit

M.C.

and Hatt

, et al., Potential complementary value of noncontrast and contrast enhanced CT radiomics in colorectal cancers, Academic Radiology 26(4) (2019), 469–479.

30.

, Shen

and Qin

, et al., The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer, European Radiology 29(2) (2019), 906–914.