Contrast-enhanced CT Radiomics Combined with Multiple Machine Learning Algorithms for Preoperative Identification of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma

Overview

Journal Front Oncol

Specialty Oncology

Date 2024 Sep 30

PMID 39346735

Authors

Yue Huang

Han Zhang

Lingfeng Chen

Qingzhu Ding

Dehua Chen

Guozhong Liu

Xiang Zhang

Qiang Huang

Denghan Zhang

Shangeng Weng

Affiliations

Soon will be listed here.

Abstract

Objectives: This research aimed to assess the value of radiomics combined with multiple machine learning algorithms in the diagnosis of pancreatic ductal adenocarcinoma (PDAC) lymph node (LN) metastasis, which is expected to provide clinical treatment strategies.

Methods: A total of 128 patients with pathologically confirmed PDAC and who underwent surgical resection were randomized into training (n=93) and validation (n=35) groups. This study incorporated a total of 13 distinct machine learning algorithms and explored 85 unique combinations of these algorithms. The area under the curve (AUC) of each model was computed. The model with the highest mean AUC was selected as the best model which was selected to determine the radiomics score (Radscore). The clinical factors were examined by the univariate and multivariate analysis, which allowed for the identification of factors suitable for clinical modeling. The multivariate logistic regression was used to create a combined model using Radscore and clinical variables. The diagnostic performance was assessed by receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA).

Results: Among the 233 models constructed using arterial phase (AP), venous phase (VP), and AP+VP radiomics features, the model built by applying AP+VP radiomics features and a combination of Lasso+Logistic algorithm had the highest mean AUC. A clinical model was eventually constructed using CA199 and tumor size. The combined model consisted of AP+VP-Radscore and two clinical factors that showed the best diagnostic efficiency in the training (AUC = 0.920) and validation (AUC = 0.866) cohorts. Regarding preoperative diagnosis of LN metastasis, the calibration curve and DCA demonstrated that the combined model had a good consistency and greatest net benefit.

Conclusions: Combining radiomics and machine learning algorithms demonstrated the potential for identifying the LN metastasis of PDAC. As a non-invasive and efficient preoperative prediction tool, it can be beneficial for decision-making in clinical practice.

References

Pai R, Beck A, Mitchem J, Linehan D, Chang D, Norton J . Pattern of lymph node involvement and prognosis in pancreatic adenocarcinoma: direct lymph node invasion has similar survival to node-negative disease. Am J Surg Pathol. 2011; 35(2):228-34. DOI: 10.1097/PAS.0b013e318206c37a. View

Xu X, Qu J, Zhang Y, Qian X, Chen T, Liu Y . Development and validation of an MRI-radiomics nomogram for the prognosis of pancreatic ductal adenocarcinoma. Front Oncol. 2023; 13:1074445. PMC: 9998897. DOI: 10.3389/fonc.2023.1074445. View

Mokdad A, Minter R, Zhu H, Augustine M, Porembka M, Wang S . Neoadjuvant Therapy Followed by Resection Versus Upfront Resection for Resectable Pancreatic Cancer: A Propensity Score Matched Analysis. J Clin Oncol. 2016; 35(5):515-522. DOI: 10.1200/JCO.2016.68.5081. View

Christodoulou E, Ma J, Collins G, Steyerberg E, Verbakel J, Van Calster B . A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019; 110:12-22. DOI: 10.1016/j.jclinepi.2019.02.004. View

Bian Y, Guo S, Jiang H, Gao S, Shao C, Cao K . Radiomics nomogram for the preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma. Cancer Imaging. 2022; 22(1):4. PMC: 8734356. DOI: 10.1186/s40644-021-00443-1. View

Swords D, Firpo M, Johnson K, Boucher K, Scaife C, Mulvihill S . Implications of inaccurate clinical nodal staging in pancreatic adenocarcinoma. Surgery. 2017; 162(1):104-111. PMC: 6402557. DOI: 10.1016/j.surg.2016.12.029. View

Xu L, Yang P, Liang W, Liu W, Wang W, Luo C . A radiomics approach based on support vector machine using MR images for preoperative lymph node status evaluation in intrahepatic cholangiocarcinoma. Theranostics. 2019; 9(18):5374-5385. PMC: 6691572. DOI: 10.7150/thno.34149. View

Tempero M, Malafa M, Al-Hawary M, Behrman S, Benson A, Cardin D . Pancreatic Adenocarcinoma, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2021; 19(4):439-457. DOI: 10.6004/jnccn.2021.0017. View

Hoi-Yee Li G, Cheung C, Choon-Beng Tan K, Kung A, Kwok T, Lau W . Development and validation of sex-specific hip fracture prediction models using electronic health records: a retrospective, population-based cohort study. EClinicalMedicine. 2023; 58:101876. PMC: 9989633. DOI: 10.1016/j.eclinm.2023.101876. View

10.

Martinez-Useros J, Garcia-Foncillas J . Can Molecular Biomarkers Change the Paradigm of Pancreatic Cancer Prognosis?. Biomed Res Int. 2016; 2016:4873089. PMC: 5023838. DOI: 10.1155/2016/4873089. View

11.

Zhang T, Xiang Y, Wang H, Yun H, Liu Y, Wang X . Radiomics Combined with Multiple Machine Learning Algorithms in Differentiating Pancreatic Ductal Adenocarcinoma from Pancreatic Neuroendocrine Tumor: More Hands Produce a Stronger Flame. J Clin Med. 2022; 11(22). PMC: 9697420. DOI: 10.3390/jcm11226789. View

12.

Park W, Chawla A, OReilly E . Pancreatic Cancer: A Review. JAMA. 2021; 326(9):851-862. PMC: 9363152. DOI: 10.1001/jama.2021.13027. View

13.

Imai H, Doi R, Kanazawa H, Kamo N, Koizumi M, Masui T . Preoperative assessment of para-aortic lymph node metastasis in patients with pancreatic cancer. Int J Clin Oncol. 2010; 15(3):294-300. DOI: 10.1007/s10147-010-0066-5. View

14.

Ye Y, Xiong Y, Zhou Q, Wu J, Li X, Xiao X . Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study. J Diabetes Res. 2020; 2020:4168340. PMC: 7306091. DOI: 10.1155/2020/4168340. View

15.

Iwai N, Okuda T, Sakagami J, Harada T, Ohara T, Taniguchi M . Neutrophil to lymphocyte ratio predicts prognosis in unresectable pancreatic cancer. Sci Rep. 2020; 10(1):18758. PMC: 7599218. DOI: 10.1038/s41598-020-75745-8. View

16.

Fang W, Li X, Zhu H, Miao F, Qian X, Pan Z . Resectable pancreatic ductal adenocarcinoma: association between preoperative CT texture features and metastatic nodal involvement. Cancer Imaging. 2020; 20(1):17. PMC: 7011565. DOI: 10.1186/s40644-020-0296-3. View

17.

Shen Q, Shan Y, Hu Z, Chen W, Yang B, Han J . Quantitative parameters of CT texture analysis as potential markersfor early prediction of spontaneous intracranial hemorrhage enlargement. Eur Radiol. 2018; 28(10):4389-4396. DOI: 10.1007/s00330-018-5364-8. View

18.

Kurahara H, Takao S, Shinchi H, Maemura K, Mataki Y, Sakoda M . Significance of lymphangiogenesis in primary tumor and draining lymph nodes during lymphatic metastasis of pancreatic head cancer. J Surg Oncol. 2010; 102(7):809-15. DOI: 10.1002/jso.21744. View

19.

Gillies R, Kinahan P, Hricak H . Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2015; 278(2):563-77. PMC: 4734157. DOI: 10.1148/radiol.2015151169. View

20.

Li X, Lin H, Ouyang R, Yang Y, Peng J . Prognostic significance of the systemic immune-inflammation index in pancreatic carcinoma patients: a meta-analysis. Biosci Rep. 2021; 41(8). PMC: 8329648. DOI: 10.1042/BSR20204401. View