Explainable machine learning versus known nomogram for predicting non-sentinel lymph node metastases in breast cancer patients: A comparative study

被引：0
作者：
Fattahi, Asieh Sadat [1 ]
Hoseini, Maryam [2 ]
Dehghani, Toktam [3 ]
Nezhad Noor Nia, Raheleh Ghouchan [3 ]
Naseri, Zeinab [3 ]
Ebrahimzadeh, Amirali [4 ]
Mahri, Ali [5 ]
Eslami, Saeid [3 ,6 ,7 ]
机构：
[1] Department of Surgery, Faculty of Medicine, Endoscopic and Minimally Invasive Research Center, Mashhad University of Medical Sciences, Mashhad
[2] General Surgeon, Department of Surgery, Endoscopic and Minimally Invasive Research Center, Mashhad University of Medical Sciences, Mashhad
[3] Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad
[4] Computer Engineering, Sharif University of Technology, Tehran
[5] Endoscopic and Minimally Invasive Research Center, Mashhad University of Medical Sciences, Mashhad
[6] Pharmaceutical Research Center, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad
[7] Department of Medical Informatics, UMC-Location AMC, University of Amsterdam, Amsterdam
来源：
Computers in Biology and Medicine | 2025年 / 184卷
关键词：
Breast cancer; Explainable artificial intelligence (XAI); Machine learning; Memorial sloan kettering cancer center (MSKCC) nomogram; Non-sentinel lymph node metastases;
D O I：
10.1016/j.compbiomed.2024.109412
中图分类号：
学科分类号：
摘要：
Introduction: Axillary lymph node dissection (ALND) is the standard of care for breast cancer patients with positive sentinel lymph nodes (SLN), which are the first lymph nodes that drain the breast. However, many patients with positive SLNs may not have additional positive nodes, making the prediction of non-sentinel lymph node (NSLN) metastasis challenging. Reliable prognostic tools are essential for accurately assessing NSLN metastasis. The Memorial Sloan Kettering Cancer Center (MSKCC) nomogram has demonstrated effectiveness in this context, but it requires further evaluation within the Iranian breast cancer population. While ALND remains the gold standard, its unnecessary application in patients without evidence of additional positive nodes raises concerns due to potential complications such as lymphedema, nerve injury, and shoulder joint dysfunction. Furthermore, integrating Artificial Intelligence (AI) and Machine Learning (ML) techniques presents an opportunity to enhance the precision of NSLN metastasis predictions. Method: This study conducts an extensive comparative analysis between the MSKCC nomogram and various ML models to predict NSLN metastasis, utilizing a dataset of Iranian breast cancer patients. Employing eXplainable Artificial Intelligence (XAI) methodologies, we analyzed 16 clinical features across a cohort of 183 patients. Our methodology includes rigorous statistical evaluations and the training and validation of ML models to assess the precision and robustness of these models compared to the MSKCC nomogram. Results: Our analysis revealed that the Random Forest (RF) model outperformed the MSKCC nomogram, achieving an accuracy of 72.2 % and an AUC of 0.77, compared to the nomogram's AUC of 0.73. Logistic Regression (LR) also demonstrated competitive performance with an accuracy of 65 % and an AUC of 0.73. The RF model exhibited high sensitivity (75 %) and precision (73 %), effectively identifying critical predictors of NSLN metastasis, including the presence of ductal carcinoma in situ (DCIS) and tumor characteristics such as type and grade. Explainable AI techniques, particularly SHAP values, provided insights into feature importance, enhancing model interpretability. Conclusion: Our study offers a comprehensive comparison between ML models and the MSKCC nomogram for predicting NSLN metastasis among Iranian breast cancer patients. These findings contribute valuable insights to the discourse on personalized treatment approaches, emphasizing the need for tailored prognostic tools across diverse populations. The implications of this research extend to clinical decision-making, potentially improving the accuracy and efficacy of breast cancer management within the Iranian healthcare landscape. © 2024 Elsevier Ltd
引用
收藏
相关论文
共 24 条
  • [1] H S., J F., Rl S., M L., I S., A J., Et al., Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, Ca - Cancer J. Clin., 71, 3, (2021)
  • [2] Danko M.E., Bennett K.M., Zhai J., Marks J.R., Olson J.A., Improved staging in node-positive breast cancer patients using lymph node ratio: results in 1,788 patients with long-term follow-up, J. Am. Coll. Surg., 210, 5, (2010)
  • [3] Ahmed M., Purushotham A.D., Douek M., Novel techniques for sentinel lymph node biopsy in breast cancer: a systematic review, Lancet Oncol., 15, 8, pp. e351-e362, (2014)
  • [4] DeSantis C.E., Ma J., Gaudet M.M., Newman L.A., Miller K.D., Goding Sauer A., Et al., Breast cancer statistics, 2019, Ca - Cancer J. Clin., 69, 6, pp. 438-451, (2019)
  • [5] Chun J.W., Kim J., Chung I.Y., Ko B.S., Kim H.J., Lee J.W., Et al., Non-sentinel node metastasis prediction during surgery in breast cancer patients with one to three positive sentinel node(s) following neoadjuvant chemotherapy, Sci. Rep., 13, 1, (2023)
  • [6] Zeng X., Li Y., Sun C., Liu Z., Zhao J., Ma X., Et al., A new model for predicting nonsentinel lymph node metastasis in early-stage breast cancer using MMP15, JAMA Oncol., (2022)
  • [7] de Boniface J., Et al., Omitting axillary dissection in breast cancer with sentinel-node metastases, N. Engl. J. Med., 390, (2024)
  • [8] de Boniface J., Filtenborg Tvedskov T., Ryden L., Szulkin R., Reimer T., Kuhn T., Kontos M., Gentilini O.D., Olofsson Bagge R., Sund M., Lundstedt D., Omitting axillary dissection in breast cancer with sentinel-node metastases, N. Engl. J. Med., 390, 13, pp. 1163-1175, (2024)
  • [9] Windsor G.O., Bai H., Lourenco A.P., Jiao Z., Application of artificial intelligence in predicting lymph node metastasis in breast cancer, Front Radiol, (2023)
  • [10] Vrdoljak J., Kreso A., Kumric M., Martinovic D., Cvitkovic I., Grahovac M., Et al., The role of AI in breast cancer lymph node classification: a comprehensive review, Cancers, 15, 8, (2023)
123