[Home ] [Archive]    
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
IJRR Information::
For Authors::
For Reviewers::
Subscription::
News & Events::
Web Mail::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
ISSN
Hard Copy 2322-3243
Online 2345-4229
..
Online Submission
Now you can send your articles to IJRR office using the article submission system.
..

AWT IMAGE

AWT IMAGE

:: Volume 21, Issue 2 (4-2023) ::
Int J Radiat Res 2023, 21(2): 267-274 Back to browse issues page
Development and validation of a stacking nomogram for predicting regional lymph node metastasis status in rectal cancer via deep learning and hand-crafted radiomics
J. Liu , L. Sun , X. Lu , Y. Geng , Z. Zhang
Department of Radiology, The fourth Affiliated Hospital of China Medical University, East Chongshan Road, Shenyang, 110000, Liaoning, China , liujiaxuan666666@163.com
Abstract:   (723 Views)
Background: Preoperative assessment of lymph node metastasis (LNM) status is the basis of individual treatment for rectal cancer (RC). However, conventional imaging methods are not accurate enough. Materials and Methods: We collected 282 RC patients who were divided into the training dataset (n=225) and the test dataset (n=57) with an 8:2 scale. A large number of deep learning (DL) features and hand-crafted radiomics (HCR) features of primary tumors were extracted from the arterial and venous phases of the computed tomography (CT) images. Three machine learning models, including support vector machine (SVM), k-nearest neighbor (KNN),and multi-layer perceptron (MLP) were utilized to predict LNM status in RC patients. A stacking nomogram was constructed by selecting optimal machine learning models for arterial and venous phases, respectively, combined with predictive clinical features. Results: The stacking nomogram performed well in predicting LNM status, with an area under the curve (AUC) of 0.914 [95% confidence interval (CI): 0.874-0.953] in the training dataset, and an AUC of 0.942 (95%CI: 0.886-0.997) in the test dataset. The AUC of the stacking nomogram were higher than those of CT_reported_N_status, ASVM, and VSVM model in the training dataset (P <0.05). However, in the test dataset, although the AUC of the stacking nomogram was higher than the VSVM, the difference was not obvious (P =0.1424). Conclusion: The developed deep learning radiomics stacking nomogram showed to be effective in predicting the preoperative LNM status in RC patients.
Keywords: Rectal cancer, lymph node metastasis, radiomics, deep learning, machine learning.
Full-Text [PDF 1371 kb]   (554 Downloads)    
Type of Study: Original Research | Subject: Radiation Biology
References
1. Sung H, Ferlay J, Siegel RL, Laversanne M, 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. [DOI:10.3322/caac.21660] [PMID]
2. Siegel RL, Miller KD, Fuchs HE, Jemal A (2021) Cancer statistics, 2021. CA Cancer J Clin, 71(1): 7-33. [DOI:10.3322/caac.21654] [PMID]
3. Ponnatapura J and Lalwani N(2021) Imaging of Colorectal Cancer: Screening, Staging, and Surveillance. Semin Roentgenol, 56(2): 128-139. [DOI:10.1053/j.ro.2020.07.005] [PMID]
4. Benson AB, Venook AP, Al-Hawary MM, et al. (2020) NCCN Guidelines Insights: Rectal Cancer, Version 6.2020. J Natl Compr Canc Netw, 18(7): 806-815. [DOI:10.6004/jnccn.2020.0032] [PMID]
5. Borgheresi A, De Muzio F, Agostini A, et al. (2022) Lymph Nodes Evaluation in Rectal Cancer: Where Do We Stand and Future Perspective. J Clin Med, 11(9): 2599. [DOI:10.3390/jcm11092599] [PMID] []
6. You YN, Hardiman KM, Bafford A, et al. (2020) The American Society of Colon and Rectal Surgeons Clinical Practice Guidelines for the Management of Rectal Cancer. Dis Colon Rectum, 63(9): 1191-1222. [DOI:10.1097/DCR.0000000000001762] [PMID]
7. Lambregts DMJ, Bogveradze N, Blomqvist LK, et al. (2022) Current controversies in TNM for the radiological staging of rectal cancer and how to deal with them: results of a global online survey and multidisciplinary expert consensus. Eur Radiol, 32(7): 4991-5003. [DOI:10.1007/s00330-022-08591-z] [PMID] []
8. Bates DDB, Homsi ME, Chang KJ, et al. (2022) MRI for Rectal Cancer: Staging, mrCRM, EMVI, Lymph Node Staging and Post-Treatment Response. Clin Colorectal Cancer, 21(1): 10-18. [DOI:10.1016/j.clcc.2021.10.007] [PMID] []
9. Kijima S, Sasaki T, Nagata K, et al. (2014) Preoperative evaluation of colorectal cancer using CT colonography, MRI, and PET/CT. World J Gastroenterol, 20(45): 16964-75. [DOI:10.3748/wjg.v20.i45.16964] [PMID] []
10. Cianci R, Cristel G, Agostini A, et al. (2020) MRI for Rectal Cancer Primary Staging and Restaging After Neoadjuvant Chemoradiation Therapy: How to Do It During Daily Clinical Practice. Eur J Radiol, 131: 109238. [DOI:10.1016/j.ejrad.2020.109238] [PMID]
11. Zhuang Z, Zhang Y, Wei M, et al. (2021) Magnetic resonance imaging evaluation of the accuracy of various lymph node staging criteria in rectal cancer: A systematic review and meta-analysis. Front Oncol, 11: 709070. [DOI:10.3389/fonc.2021.709070] [PMID] []
12. Fritz S, Killguss H, Schaudt A, et al. (2021) Preoperative versus pathological staging of rectal cancer-challenging the indication of neoadjuvant chemoradiotherapy. Int J Colorectal Dis, 36(1): 191-194. [DOI:10.1007/s00384-020-03751-3] [PMID]
13. Reali C, Bocca G, Lindsey I, et al. (2022) Influence of incorrect staging of colorectal carcinoma on oncological outcome: are we playing safely? Updates Surg, 74(2): 591-597. [DOI:10.1007/s13304-021-01095-3] [PMID] []
14. Lafata KJ, Wang Y, Konkel B, et al. (2021) Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol, 47: 2986-3002. [DOI:10.1007/s00261-021-03254-x] [PMID]
15. Attanasio S, Forte SM, Restante G, et al. (2020) Artificial intelligence, radiomics and other horizons in body composition assessment. Quant Imaging Med Surg, 10(8): 1650-1660. [DOI:10.21037/qims.2020.03.10] [PMID] []
16. Avanzo M, Wei L, Stancanello J, et al.(2020)Machine and deep learning methods for radiomics. Med Phys, 47(5): e185-e202. [DOI:10.1002/mp.13678] [PMID] []
17. Jiang Y, Yang M, Wang S, et al. (2020) Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun (Lond), 40(4): 154-166. [DOI:10.1002/cac2.12012] [PMID] []
18. Kim M, Yun J, Cho Y, et al. (2019)Deep Learning in Medical Imaging. Neurospine, 16(4): 657-668. [DOI:10.14245/ns.1938396.198] [PMID] []
19. Wainberg M, Merico D, Delong A, Frey BJ (2018) Deep learning in biomedicine. Nat Biotechnol, 36(9): 829-838. [DOI:10.1038/nbt.4233] [PMID]
20. Dong D, Fang MJ, Tang L, et al. (2020) Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study. Ann Oncol, 31(7): 912-920. [DOI:10.1016/j.annonc.2020.04.003] [PMID]
21. Li J, Zhou Y, Wang P, et al. (2021) Deep transfer learning based on magnetic resonance imaging can improve the diagnosis of lymph node metastasis in patients with rectal cancer. Quant Imaging Med Surg, 11(6): 2477-2485. [DOI:10.21037/qims-20-525] [PMID] []
22. Kim C, You SC, Reps JM, et al. (2021) Machine-learning model to predict the cause of death using a stacking ensemble method for observational data. J Am Med Inform Assoc, 28(6): 1098-1107. [DOI:10.1093/jamia/ocaa277] [PMID] []
23. Yang Y, Wei L, Hu Y, et al. (2021) Classification of Parkinson's disease based on multi-modal features and stacking ensemble learning. J Neurosci Methods, 350: 109019. [DOI:10.1016/j.jneumeth.2020.109019] [PMID]
24. Lokuhetty N, Seneviratne SL, Rahman FA, Marapana T, Niloofa R, De Zoysa I. (2021)Radiological staging of rectal cancer in a resource limited setting. BMC Res Notes. 13(1): 479. [DOI:10.1186/s13104-020-05327-4] [PMID] []
25. 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. [DOI:10.1007/s11307-022-01730-4] [PMID]
26. Forghani R, Savadjiev P, Chatterjee A, Muthukrishnan N, Reinhold C, Forghani B. (2019)Radiomics and Artificial Intelligence for Biomarker and Prediction Model Development in Oncology. Comput Struct Biotechnol J. 17: 995-1008. [DOI:10.1016/j.csbj.2019.07.001] [PMID] []
27. Gardin I, Grégoire V, Gibon D, Kirisli H, Pasquier D, Thariat J, et al. (2019) Radiomics: Principles and radiotherapy applications. Crit Rev Oncol Hematol. 138: 44-50. [DOI:10.1016/j.critrevonc.2019.03.015] [PMID]
28. Litvin AA, Burkin DA, Kropinov AA, Paramzin FN. (2021) Radiomics and Digital Image Texture Analysis in Oncology (Review). Sovrem Tekhnologii Med. 13(2): 97-104. [DOI:10.17691/stm2021.13.2.11] [PMID] []
29. Eun NL, Kang D, Son EJ, Park JS, Youk JH, Kim JA, et al. (2020) Texture Analysis with 3.0-T MRI for Association of Response to Neoadjuvant Chemotherapy in Breast Cancer. Radiology. 294(1): 31-41. [DOI:10.1148/radiol.2019182718] [PMID]
30. Oh JE, Kim MJ, Lee J, Hur BY, Kim B, Kim DY, et al. (2020)Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer. Cancer Res Treat. 52(1): 51-59. [DOI:10.4143/crt.2019.050] [PMID] []
31. Bo L, Zhang Z, Jiang Z, Yang C, Huang P, Chen T, et al. (2021) Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features. Front Med (Lausanne), 8: 748144. [DOI:10.3389/fmed.2021.748144] [PMID] []
32. Gao R, Zhao S, Aishanjiang K, Cai H, Wei T, Zhang Y, et al. (2021) Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced CT and clinical data. J Hematol Oncol, 14(1): 154. [DOI:10.1186/s13045-021-01167-2] [PMID] []
33. Liu H, Yin H, Li J, Dong X, Zheng H, Zhang T, et al. (2022) A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Evaluation of KRAS Mutation in Rectal Cancer. J Magn Reson Imaging, 56(6):1659-1668. [DOI:10.1002/jmri.28237] [PMID]
34. Zhao X, Xie P, Wang M, Li W, Pickhardt PJ, Xia W, et al. (2020) Deep learning-based fully automated detection and segmentation of lymph nodes on multiparametric-mri for rectal cancer: A multicentre study. EBioMedicine, 56: 102780. [DOI:10.1016/j.ebiom.2020.102780] [PMID] []
35. Zhao J, Wang H, Zhang Y, Wang R, Liu Q, Li J, et al. (2022) Deep learning radiomics model related with genomics phenotypes for lymph node metastasis prediction in colorectal cancer. Radiother Oncol, 167: 195-202. [DOI:10.1016/j.radonc.2021.12.031] [PMID]
36. He K, Liu X, Li M, Li X, Yang H, Zhang H (2020) Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging. BMC Med Imaging, 20(1): 59. https://doi.org/10.21203/rs.3.rs-29491/v1 https://doi.org/10.21203/rs.2.22851/v1 [DOI:10.1186/s12880-020-00457-4]
37. Chen T, Liu S, Li Y, Feng X, Xiong W, Zhao X, et al. (2019) Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning. EBioMedicine, 39: 272-279. [DOI:10.1016/j.ebiom.2018.12.028] [PMID] []
38. Warin K, Limprasert W, Suebnukarn S, Jinaporntham S, Jantana P. (2022) Performance of deep convolutional neural network for classification and detection of oral potentially malignant disorders in photographic images. Int J Oral Maxillofac Surg, 51(5): 699-704. [DOI:10.1016/j.ijom.2021.09.001] [PMID]
39. Yu H, Li J, Zhang L, Cao Y, Yu X, Sun J (2021) Design of lung nodules segmentation and recognition algorithm based on deep learning. BMC Bioinformatics, 22(Suppl 5): 314. [DOI:10.1186/s12859-021-04234-0] [PMID] []
40. Huang YQ, Liang CH, He L, Tian J, Liang CS, Chen X, 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): 2157-64. [DOI:10.1200/JCO.2015.65.9128] [PMID]
41. Li M, Zhang J, Dan Y, Yao Y, Dai W, Cai G, et al. (2020) A clinical-radiomics nomogram for the preoperative prediction of lymph node metastasis in colorectal cancer. J Transl Med, 18(1): 46. [DOI:10.1186/s12967-020-02215-0] [PMID] []
42. 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 (NY), 46(3): 873-884. [DOI:10.1007/s00261-020-02733-x] [PMID]
43. He J, Wang Q, Zhang Y, Wu H, Zhou Y, Zhao S (2021) Preoperative prediction of regional lymph node metastasis of colorectal cancer based on 18F-FDG PET/CT and machine learning. Ann Nucl Med, 35(5): 617-627. [DOI:10.1007/s12149-021-01605-8] [PMID]
44. Liu X, Yang Q, Zhang C, Sun J, He K, Xie Y, et al. (2021) Multiregional-Based Magnetic Resonance Imaging Radiomics Combined With Clinical Data Improves Efficacy in Predicting Lymph Node Metastasis of Rectal Cancer. Front Oncol, 10: 585767. [DOI:10.3389/fonc.2020.585767] [PMID] []
45. Shen D, Wang X, Wang H, Xu G, Xie Y, Zhuang Z, et al. (2022) Current Surveillance After Treatment is Not Sufficient for Patients With Rectal Cancer With Negative Baseline CEA. J Natl Compr Canc Netw, 20(6):653. [DOI:10.6004/jnccn.2021.7101] [PMID]
46. Huang R, Meng T, Zha Q, Cheng K, Zhou X, Zheng J, et al. (2022) The predicting roles of carcinoembryonic antigen and its underlying mechanism in the progression of coronavirus disease 2019. Crit Care, 25(1): 234. [DOI:10.1186/s13054-021-03661-y] [PMID] []
47. Hao C, Zhang G, Zhang L (2019) Serum CEA levels in 49 different types of cancer and noncancer diseases. Prog Mol Biol Transl Sci, 162: 213-227. [DOI:10.1016/bs.pmbts.2018.12.011] [PMID]
48. Su Y, Zhao H, Liu P, Zhang L, Jiao Y, Xu P, Lyu Z, Fu P (2022) A nomogram model based on MRI and radiomic features developed and validated for the evaluation of lymph node metastasis in patients with rectal cancer. Abdominal Radiology (New York), 47(12): 4103-4114. [DOI:10.1007/s00261-022-03672-5] [PMID]
49. Bedrikovetski S, Dudi-Venkata NN, Kroon HM, Seow W, Vather R, Carneiro G, et al. (2021) Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis. BMC Cancer, 21(1): 1058. [DOI:10.1186/s12885-021-08773-w] [PMID] []
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA



XML     Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Liu J, Sun L, Lu X, Geng Y, Zhang Z. Development and validation of a stacking nomogram for predicting regional lymph node metastasis status in rectal cancer via deep learning and hand-crafted radiomics. Int J Radiat Res 2023; 21 (2) :267-274
URL: http://ijrr.com/article-1-4723-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 21, Issue 2 (4-2023) Back to browse issues page
International Journal of Radiation Research
Persian site map - English site map - Created in 0.05 seconds with 50 queries by YEKTAWEB 4652