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A clinical-radiomics nomogram based on spectral CT multi-parameter images for preoperative prediction of lymph node metastasis in colorectal cancer

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Abstract

To develop a clinical-radiomics nomogram based on spectral CT multi-parameter images for predicting lymph node metastasis in colorectal cancer. A total of 76 patients with colorectal cancer and 156 lymph nodes were included. The clinical data of the patients were collected, including gender, age, tumor location and size, preoperative tumor markers, etc. Three sets of conventional images in the arterial, venous, and delayed phases were obtained, and six sets of spectral images were reconstructed using the arterial phase spectral data, including virtual monoenergetic images (40 keV, 70 keV, 100 keV), iodine density maps, iodine no water maps, and virtual non-contrast images. Radiomics features of lymph nodes were extracted from the above images, respectively. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select features. A clinical model was constructed based on age and carcinoembryonic antigen (CEA) levels. The radiomics features selected were used to generate a composed radiomics signature (Com-RS). A nomogram was developed using age, CEA, and the Com-RS. The models’ prediction efficiency, calibration, and clinical application value were evaluated by the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis, respectively. The nomogram outperforms the clinical model and the Com-RS (AUC = 0.879, 0.824). It is well calibrated and has great clinical application value. This study developed a clinical-radiomics nomogram based on spectral CT multi-parameter images, which can be used as an effective tool for preoperative personalized prediction of lymph node metastasis in colorectal cancer.

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Data availability

The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.

References

  1. Sung H, Ferlay J, Siegel RL et al (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209–249. https://doi.org/10.3322/caac.21660

    Article  CAS  PubMed  Google Scholar 

  2. Ferlay J, Soerjomataram I, Dikshit R et al (2015) Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 136(5):E359-386. https://doi.org/10.1002/ijc.29210

    Article  CAS  PubMed  Google Scholar 

  3. Dekker E, Tanis PJ, Vleugels JLA, Kasi PM, Wallace MB (2019) Colorectal cancer. Lancet 394(10207):1467–1480. https://doi.org/10.1016/s0140-6736(19)32319-0

    Article  PubMed  Google Scholar 

  4. Hashiguchi Y, Muro K, Saito Y et al (2020) Japanese Society for Cancer of the Colon and Rectum (JSCCR) guidelines 2019 for the treatment of colorectal cancer. Int J Clin Oncol 25(1):1–42. https://doi.org/10.1007/s10147-019-01485-z

    Article  PubMed  Google Scholar 

  5. Sabbagh C, Mauvais F, Cosse C et al (2014) A lymph node ratio of 10% is predictive of survival in stage III colon cancer: a French regional study. Int Surg 99(4):344–353. https://doi.org/10.9738/intsurg-d-13-00052.1

    Article  PubMed  PubMed Central  Google Scholar 

  6. Tudyka V, Blomqvist L, Beets-Tan RG et al (2014) EURECCA consensus conference highlights about colon & rectal cancer multidisciplinary management: the radiology experts review. Eur J Surg Oncol 40(4):469–475. https://doi.org/10.1016/j.ejso.2013.10.029

    Article  CAS  PubMed  Google Scholar 

  7. de Vries FE, da Costa DW, van der Mooren K, van Dorp TA, Vrouenraets BC (2014) The value of pre-operative computed tomography scanning for the assessment of lymph node status in patients with colon cancer. Eur J Surg Oncol 40(12):1777–1781. https://doi.org/10.1016/j.ejso.2014.08.483

    Article  PubMed  Google Scholar 

  8. Zhao L, Liang M, Yang Y, Zhao X, Zhang H (2021) Histogram models based on intravoxel incoherent motion diffusion-weighted imaging to predict nodal staging of rectal cancer. Eur J Radiol 142:109869. https://doi.org/10.1016/j.ejrad.2021.109869

    Article  PubMed  Google Scholar 

  9. Gao Y, Li J, Ma X et al (2019) The value of four imaging modalities in diagnosing lymph node involvement in rectal cancer: an overview and adjusted indirect comparison. Clin Exp Med 19(2):225–234. https://doi.org/10.1007/s10238-019-00552-z

    Article  PubMed  Google Scholar 

  10. Dighe S, Purkayastha S, Swift I et al (2010) Diagnostic precision of CT in local staging of colon cancers: a meta-analysis. Clin Radiol 65(9):708–719. https://doi.org/10.1016/j.crad.2010.01.024

    Article  CAS  PubMed  Google Scholar 

  11. Nerad E, Lahaye MJ, Maas M et al (2016) Diagnostic accuracy of CT for local staging of colon cancer: a systematic review and meta-analysis. AJR Am J Roentgenol 207(5):984–995. https://doi.org/10.2214/ajr.15.15785

    Article  PubMed  Google Scholar 

  12. Davnall F, Yip CS, Ljungqvist G et al (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3(6):573–589. https://doi.org/10.1007/s13244-012-0196-6

    Article  PubMed  PubMed Central  Google Scholar 

  13. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures. They are data. Radiology 278(2):563–577. https://doi.org/10.1148/radiol.2015151169

    Article  PubMed  Google Scholar 

  14. Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30(9):1234–1248. https://doi.org/10.1016/j.mri.2012.06.010

    Article  PubMed  PubMed Central  Google Scholar 

  15. Cheng Y, Yu Q, Meng W, Jiang W (2022) Clinico-radiologic nomogram using multiphase CT to predict lymph node metastasis in colon cancer. Mol Imaging Biol 24(5):798–806. https://doi.org/10.1007/s11307-022-01730-4

    Article  PubMed  Google Scholar 

  16. Li M, Zhang J, Dan Y et al (2020) A clinical-radiomics nomogram for the preoperative prediction of lymph node metastasis in colorectal cancer. J Transl Med 18(1):46. https://doi.org/10.1186/s12967-020-02215-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 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(18):2157–2164. https://doi.org/10.1200/jco.2015.65.9128

    Article  PubMed  Google Scholar 

  18. Wei Q, Yuan W, Jia Z et al (2023) Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma. Abdom Radiol 48(2):458–470. https://doi.org/10.1007/s00261-022-03731-x

    Article  Google Scholar 

  19. Simons D, Kachelriess M, Schlemmer HP (2014) Recent developments of dual-energy CT in oncology. Eur Radiol 24(4):930–939. https://doi.org/10.1007/s00330-013-3087-4

    Article  PubMed  Google Scholar 

  20. Fulwadhva UP, Wortman JR, Sodickson AD (2016) Use of dual-energy CT and iodine maps in evaluation of bowel disease. Radiographics 36(2):393–406. https://doi.org/10.1148/rg.2016150151

    Article  PubMed  Google Scholar 

  21. Goo HW, Goo JM (2017) Dual-energy CT: new horizon in medical imaging. Korean J Radiol 18(4):555–569. https://doi.org/10.3348/kjr.2017.18.4.555

    Article  PubMed  PubMed Central  Google Scholar 

  22. Liu H, Yan F, Pan Z et al (2015) Evaluation of dual energy spectral CT in differentiating metastatic from non-metastatic lymph nodes in rectal cancer: initial experience. Eur J Radiol 84(2):228–234. https://doi.org/10.1016/j.ejrad.2014.11.016

    Article  PubMed  Google Scholar 

  23. Yang YS, Feng F, Qiu YJ, Zheng GH, Ge YQ, Wang YT (2021) High-resolution MRI-based radiomics analysis to predict lymph node metastasis and tumor deposits respectively in rectal cancer. Abdom Radiol 46(3):873–884. https://doi.org/10.1007/s00261-020-02733-x

    Article  Google Scholar 

  24. Minicozzi AM, Conti G, Merigo G et al (2011) A new model of rectal cancer with regional lymph node metastasis allowing in vivo evaluation by imaging biomarkers. Biomed Pharmacother 65(6):401–406. https://doi.org/10.1016/j.biopha.2011.04.027

    Article  CAS  PubMed  Google Scholar 

  25. Cho EY, Kim SH, Yoon JH et al (2013) Apparent diffusion coefficient for discriminating metastatic from non-metastatic lymph nodes in primary rectal cancer. Eur J Radiol 82(11):e662-668. https://doi.org/10.1016/j.ejrad.2013.08.007

    Article  PubMed  Google Scholar 

  26. Kato T, Uehara K, Ishigaki S et al (2015) Clinical significance of dual-energy CT-derived iodine quantification in the diagnosis of metastatic LN in colorectal cancer. Eur J Surg Oncol 41(11):1464–1470. https://doi.org/10.1016/j.ejso.2015.08.154

    Article  CAS  PubMed  Google Scholar 

  27. Beets-Tan RGH, Lambregts DMJ, Maas M et al (2018) Magnetic resonance imaging for clinical management of rectal cancer: updated recommendations from the 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus meeting. European Radiol 28(4):1465–1475. https://doi.org/10.1007/s00330-017-5026-2

    Article  Google Scholar 

  28. Jiang C, Luo Y, Yuan J et al (2020) CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma. Eur Radiol 30(7):4050–4057. https://doi.org/10.1007/s00330-020-06694-z

    Article  PubMed  Google Scholar 

  29. Wang R, Li J, Fang MJ, Dong D, Liang P, Gao JB (2020) The value of spectral CT-based radiomics in preoperative prediction of lymph node metastasis of advanced gastric cancer. Zhonghua Yi Xue Za Zhi 100(21):1617–1622. https://doi.org/10.3760/cma.j.cn112137-20191113-02468

    Article  CAS  PubMed  Google Scholar 

  30. Molodianovitch K, Faraggi D, Reiser B (2006) Comparing the areas under two correlated ROC curves: parametric and non-parametric approaches. Biom J 48(5):745–757. https://doi.org/10.1002/bimj.200610223

    Article  PubMed  Google Scholar 

  31. Peduzzi P, Concato J, Feinstein AR, Holford TR (1995) Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol 48(12):1503–1510. https://doi.org/10.1016/0895-4356(95)00048-8

    Article  CAS  PubMed  Google Scholar 

  32. Vittinghoff E, McCulloch CE (2007) Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol 165(6):710–718. https://doi.org/10.1093/aje/kwk052

    Article  PubMed  Google Scholar 

  33. Ali K, Cho S, Jang HJ, Kim K, Jheon S (2019) Predictive factors of thoracic lymph node metastasis accompanying pulmonary metastasis from colorectal cancer. Thorac Cardiovasc Surg 67(8):683–687. https://doi.org/10.1055/s-0038-1642602

    Article  PubMed  Google Scholar 

  34. Choi JY, Jung SA, Shim KN et al (2015) Meta-analysis of predictive clinicopathologic factors for lymph node metastasis in patients with early colorectal carcinoma. J Korean Med Sci 30(4):398–406. https://doi.org/10.3346/jkms.2015.30.4.398

    Article  PubMed  PubMed Central  Google Scholar 

  35. Wu XZ, Ma F, Wang XL (2010) Serological diagnostic factors for liver metastasis in patients with colorectal cancer. World J Gastroenterol 16(32):4084–4088. https://doi.org/10.3748/wjg.v16.i32.4084

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Oh JR, Park B, Lee S et al (2019) Nomogram development and external validation for predicting the risk of lymph node metastasis in T1 colorectal cancer. Cancer Res Treat 51(4):1275–1284. https://doi.org/10.4143/crt.2018.569

    Article  PubMed  PubMed Central  Google Scholar 

  37. Liu Z, Huang C, Tian H, Liu Y, Huang Y, Zhu Z (2022) Establishment of a dynamic nomogram for predicting the risk of lymph node metastasis in T1 stage colorectal cancer. Front Surg 9:845666. https://doi.org/10.3389/fsurg.2022.845666

    Article  PubMed  PubMed Central  Google Scholar 

  38. Hu DY, Cao B, Li SH, Li P, Zhang ST (2019) Incidence, risk factors, and a predictive model for lymph node metastasis of submucosal (T1) colon cancer: a population-based study. J Dig Dis 20(6):288–293. https://doi.org/10.1111/1751-2980.12754

    Article  PubMed  Google Scholar 

  39. Longlian D, Haibin S, Enri-Letu, Enri-Letu W (2023) Research progresses of nomogram in predicting lymph node metastasis of colorectal cancer. J Clin Med Practice 27(5):143–148. https://doi.org/10.7619/jcmp.20223328

    Article  Google Scholar 

  40. Sato K, Morohashi H, Tsushima F et al (2019) Dual energy CT is useful for the prediction of mesenteric and lateral pelvic lymph node metastasis in rectal cancer. Mol Clin Oncol 10(6):625–630. https://doi.org/10.3892/mco.2019.1834

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Liu J, Liu Y, Li L-X et al (2011) Preliminary study of spectral CT imaging in the differential diagnosis of metastatic lymphadenopathy due to various tumors. Chinese J Radiol 45:731–735. https://doi.org/10.3760/CMA.J.ISSN.1005-1201.2011.08.006

    Article  Google Scholar 

  42. Fernandez LM, Parlade AJ, Wasser EJ et al (2019) How reliable is CT scan in staging right colon cancer? Dis Colon Rectum 62(8):960–964. https://doi.org/10.1097/dcr.0000000000001387

    Article  PubMed  Google Scholar 

  43. Iannicelli E, Di Renzo S, Ferri M et al (2014) Accuracy of high-resolution MRI with lumen distention in rectal cancer staging and circumferential margin involvement prediction. Korean J Radiol 15(1):37–44. https://doi.org/10.3348/kjr.2014.15.1.37

    Article  PubMed  PubMed Central  Google Scholar 

  44. Meng X, Xia W, Xie P et al (2019) Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer. Eur Radiol 29(6):3200–3209. https://doi.org/10.1007/s00330-018-5763-x

    Article  PubMed  Google Scholar 

  45. Eresen A, Li Y, Yang J et al (2020) Preoperative assessment of lymph node metastasis in colon cancer patients using machine learning: a pilot study. Cancer Imaging 20(1):30. https://doi.org/10.1186/s40644-020-00308-z

    Article  PubMed  PubMed Central  Google Scholar 

  46. Jin M, Frankel WL (2018) Lymph node metastasis in colorectal cancer. Surg Oncol Clin N Am 27(2):401–412. https://doi.org/10.1016/j.soc.2017.11.011

    Article  PubMed  Google Scholar 

  47. Nakarai C, Osawa K, Akiyama M et al (2015) Expression of AKR1C3 and CNN3 as markers for detection of lymph node metastases in colorectal cancer. Clin Exp Med 15(3):333–341. https://doi.org/10.1007/s10238-014-0298-1

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors appreciate the training and guidance provided by Philips engineers.

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No funding was received for conducting this study.

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Author notes

  1. Qian Li and Rui Hong should be considered as co-first authors.

Authors and Affiliations

  1. Department of Radiology, The Third Hospital of Hebei Medical University, Ziqiang Road, Shijiazhuang, 050000, Hebei, China

    Qian Li, Rui Hong, Ping Zhang, Liting Hou, Hailun Bao, Lin Bai & Jian Zhao

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  1. Qian Li
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Contributions

Conceptualization: Qian Li, Rui Hong, Jian Zhao; Methodology: Qian Li, Rui Hong, Ping Zhang; Formal analysis and investigation: Qian Li, Liting Hou; Writing—original draft preparation: Qian Li, Rui Hong; Review: Ping Zhang, Hailun Bao, Lin Bai; Writing—review and editing: Jian Zhao, Liting Hou, Ping Zhang; Resources: Rui Hong; Lin Bai, Hailun Bao; Interpretation of data: Qian Li, Rui Hong; Supervision: Jian Zhao, Lin Bai.

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Correspondence to Lin Bai or Jian Zhao.

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The study was conducted with approval from the Ethics Committee of the Third Hospital of Hebei Medical University, and the requirement for written informed consent was waived. This data analysis was performed in accordance with the Declaration of Helsinki.

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Li, Q., Hong, R., Zhang, P. et al. A clinical-radiomics nomogram based on spectral CT multi-parameter images for preoperative prediction of lymph node metastasis in colorectal cancer. Clin Exp Metastasis (2024). https://doi.org/10.1007/s10585-024-10293-3

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