Abstract
Objectives
To investigate the value of MRI radiomics based on T2-weighted (T2W) images in predicting preoperative synchronous distant metastasis (SDM) in patients with rectal cancer.
Methods
This retrospective study enrolled 177 patients with histopathology-confirmed rectal adenocarcinoma (123 patients in the training cohort and 54 in the validation cohort). A total of 385 radiomics features were extracted from pretreatment T2W images. Five steps, including univariate statistical tests and a random forest algorithm, were performed to select the best preforming features for predicting SDM. Multivariate logistic regression analysis was conducted to build the clinical and clinical-radiomics combined models in the training cohort. The predictive performance was validated by receiver operating characteristics curve (ROC) analysis and clinical utility implementing a nomogram and decision curve analysis.
Results
Fifty-nine patients (33.3%) were confirmed to have SDM. Six radiomics features and four clinical characteristics were selected for predicting SDM. The clinical-radiomics combined model performed better than the clinical model in both the training and validation datasets. A threshold of 0.44 yielded an area under the ROC (AUC) value of 0.827 (95% confidence interval (CI), 0.6963–0.9580), a sensitivity of 72.2%, a specificity of 94.4%, and an accuracy of 87.0% in the validation cohort for the combined model. A clinical-radiomics nomogram and decision curve analysis confirmed the clinical utility of the combined model.
Conclusions
Our proposed clinical-radiomics combined model could be utilized as a noninvasive biomarker for identifying patients at high risk of SDM, which could aid in tailoring treatment strategies.
Key Points
• T2WI-based radiomics analysis helps predict synchronous distant metastasis (SDM) of rectal cancer.
• The clinical-radiomics combined model could be utilized as a noninvasive biomarker for predicting SDM.
• Personalized treatment can be carried out with greater confidence based on the risk stratification for SDM in rectal cancer.
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Abbreviations
- AUC:
-
Area under the curve
- CA199:
-
Carbohydrate antigen 199
- CEA:
-
Carcinoembryonic antigen
- DKI:
-
Diffusion kurtosis imaging
- DWI:
-
Diffusion-weighted imaging
- GLCM:
-
Gray-level co-occurrence matrix
- GLRLM:
-
Gray-level run-length matrix
- ICC:
-
Intraclass correlation coefficient
- LN:
-
Lymph node
- NPV:
-
Negative predictive value
- PPV:
-
Positive predictive value
- ROC:
-
Receiver operating characteristic curve
- SDM:
-
Synchronous distant metastasis
- VOI:
-
Volume of interest
References
Bosset JF, Collette L, Calais G et al (2006) Chemotherapy with preoperative radiotherapy in rectal cancer. N Engl J Med 355:1114–1123
Ho-Pun-Cheung A, Assenat E, Bascoul-Mollevi C et al (2011) EGFR and HER3 mRNA expression levels predict distant metastases in locally advanced rectal cancer. Int J Cancer 128:2938–2946
Lee WS, Yun SH, Chun HK et al (2008) Clinical outcomes of hepatic resection and radiofrequency ablation in patients with solitary colorectal liver metastasis. J Clin Gastroenterol 42:945–949
Butte JM, Gonen M, Ding P et al (2012) Patterns of failure in patients with early onset (synchronous) resectable liver metastases from rectal cancer. Cancer 118:5414–5423
Fossum CC, Alabbad JY, Romak LB et al (2017) The role of neoadjuvant radiotherapy for locally-advanced rectal cancer with resectable synchronous metastasis. J Gastrointest Oncol 8:650–658
Gaitanidis A, Alevizakos M, Tsaroucha A, Tsalikidis C, Pitiakoudis M (2018) Predictive nomograms for synchronous distant metastasis in rectal cancer. J Gastrointest Surg 22:1268–1276
Hur H, Ko YT, Min BS et al (2009) Comparative study of resection and radiofrequency ablation in the treatment of solitary colorectal liver metastases. Am J Surg 197:728–736
Kanas GP, Taylor A, Primrose JN et al (2012) Survival after liver resection in metastatic colorectal cancer: review and meta-analysis of prognostic factors. Clin Epidemiol 4:283–301
Sohn B, Lim JS, Kim H et al (2015) MRI-detected extramural vascular invasion is an independent prognostic factor for synchronous metastasis in patients with rectal cancer. Eur Radiol 25:1347–1355
Sun Y, Lin H, Lu X et al (2017) A nomogram to predict distant metastasis after neoadjuvant chemoradiotherapy and radical surgery in patients with locally advanced rectal cancer. J Surg Oncol 115:462–469
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577
Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446
Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248
Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762
Ginsburg SB, Algohary A, Pahwa S et al (2017) Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: preliminary findings from a multi-institutional study. J Magn Reson Imaging 46:184–193
Corino VDA, Montin E, Messina A et al (2018) Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high-grade lesions. J Magn Reson Imaging 47:829–840
Tian Q, Yan LF, Zhang X et al (2018) Radiomics strategy for glioma grading using texture features from multiparametric MRI. J Magn Reson Imaging. https://doi.org/10.1002/jmri.26010
Zhang Y, Oikonomou A, Wong A, Haider MA, Khalvati F (2017) Radiomics-based prognosis analysis for non-small cell lung cancer. Sci Rep 7:46349
Zhu X, Dong D, Chen Z et al (2018) Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. Eur Radiol 28:2772–2778
Algohary A, Viswanath S, Shiradkar R et al (2018) Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: preliminary findings. J Magn Reson Imaging. https://doi.org/10.1002/jmri.25983
Ding J, Xing Z, Jiang Z et al (2018) CT-based radiomic model predicts high grade of clear cell renal cell carcinoma. Eur J Radiol 103:51–56
Hou Z, Li S, Ren W, Liu J, Yan J, Wan S (2018) Radiomic analysis in T2W and SPAIR T2W MRI: predict treatment response to chemoradiotherapy in esophageal squamous cell carcinoma. J Thorac Dis 10:2256–2267
Meng Y, Zhang Y, Dong D et al (2018) Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer. J Magn Reson Imaging. https://doi.org/10.1002/jmri.25968
Park H, Lim Y, Ko ES et al (2018) Radiomics signature on magnetic resonance imaging: association with disease-free survival in patients with invasive breast cancer. Clin Cancer Res. https://doi.org/10.1158/1078-0432.CCR-17-3783
Sun Y, Hu P, Wang J et al (2018) Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: preliminary findings. J Magn Reson Imaging. https://doi.org/10.1002/jmri.25969
Liu Z, Zhang XY, Shi YJ et al (2017) Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res 23:7253–7262
Horvat N, Veeraraghavan H, Khan M et al (2018) MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology 287:833–843
Nie K, Shi L, Chen Q et al (2016) Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res 22:5256–5264
Jhaveri KS, Hosseini-Nik H (2015) MRI of rectal cancer: an overview and update on recent advances. AJR Am J Roentgenol 205:W42–W55
Kim YC, Kim JK, Kim MJ, Lee JH, Kim YB, Shin SJ (2016) Feasibility of mesorectal vascular invasion in predicting early distant metastasis in patients with stage T3 rectal cancer based on rectal MRI. Eur Radiol 26:297–305
Liu H, Cui Y, Shen W et al (2016) Pretreatment magnetic resonance imaging of regional lymph nodes with carcinoembryonic antigen in prediction of synchronous distant metastasis in patients with rectal cancer. Oncotarget 7:27199–27207
Yu J, Huang DY, Li Y, Dai X, Shi HB (2016) Correlation of standard diffusion-weighted imaging and diffusion kurtosis imaging with distant metastases of rectal carcinoma. J Magn Reson Imaging 44:221–229
Zhu L, Pan Z, Ma Q et al (2016) Diffusion kurtosis imaging study of rectal adenocarcinoma associated with histopathologic prognostic factors: preliminary findings. Radiology 284:66–76
Huang YQ, Liang CH, He L et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34:2157–2164
Taylor FG, Quirke P, Heald RJ et al (2014) Preoperative magnetic resonance imaging assessment of circumferential resection margin predicts disease-free survival and local recurrence: 5-year follow-up results of the MERCURY study. J Clin Oncol 32:34–43
Funding
This study has received funding from the National Key Research and Development Program of China (No. 2017YFC0109003) and Special Research Program of Shanghai Municipal Commission of Heath and Family Planning on medical intelligence (No. 2018ZHYL0108).
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Guarantor
The scientific guarantor of this publication is Dengbin Wang, MD, PhD, the chief of department of radiology, Xinhua hospital affiliated to Shanghai Jiao Tong University School of Medicine.
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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and biometry
Shaofeng Duan kindly provided statistical advice for the manuscript.
Informed consent
Written informed consent was waived by the Institutional Review Board.
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Institutional Review Board approval was obtained.
Methodology
• retrospective
• diagnostic or prognostic study
• performed at one institution
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Liu, H., Zhang, C., Wang, L. et al. MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer. Eur Radiol 29, 4418–4426 (2019). https://doi.org/10.1007/s00330-018-5802-7
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DOI: https://doi.org/10.1007/s00330-018-5802-7