The Journal of Practical Medicine >

2026 , Vol. 42 >Issue 1: 45 - 55

DOI: https://doi.org/10.3969/j.issn.1006-5725.2026.01.006

Oncology: Diagnosis, Treatment and Prevention

Predictive performance of an interpretable BPNN model for axillary lymph node burden in breast cancer patients with 1 ~ 2 sentinel lymph node positive

  • Sheng NONG ,
  • Zhanxiong LI ,
  • Qi ZHANG ,
  • Zhendong LU ,
  • Minping HONG ,
  • Wubiao CHEN ,
  • Zilin LIU
Expand
  • 1.Department of Radiology，the Second Affiliated Hospital of Guangdong Medical University，Zhanjiang 524000，Guangdong，China
    2.Department of Radiology，Affiliated Yangjiang Hospital of Guangdong Medical University，Yangjiang 529500，Guangdong，China
    3.Department of Radiology，the Seventh Affiliated Hospital，Southern Medical University，Shenzhen 518107，Guangdong，China
    4.Department of Radiology，Affiliated Hospital of Guangdong Medical University，Zhanjiang 524000，Guangdong，China
    5.Department of Radiology，Jiaxing Hospital of Traditional Chinese Medicine，Jiaxing 314001，Zhejiang，China

Received date: 2025-09-10

  Online published: 2026-01-14

Fold

Abstract

Objective To evaluate the accuracy of a backpropagation neural network （BPNN） model incorporating clinical and imaging features in predicting axillary lymph node burden among breast cancer patients with one to two positive sentinel lymph nodes. Methods We retrospectively analyzed clinical and imaging data from 386 female breast cancer patients who underwent axillary lymph node dissection at three medical centers between January 2021 and December 2024. Based on pathological findings， patients were categorized into a high axillary lymph node burden group （n = 155） and a low burden group （n = 231）. Data from Center 1 and Center 2 （Affiliated Hospital of Guangdong Medical University and Affiliated Yangjiang Hospital of Guangdong Medical University； n = 295） were randomly divided into a training set （n = 207） and an internal validation set （n = 88）， while data from Center 3 （Second Affiliated Hospital of Guangdong Medical University； n=91） served as the external validation cohort. Univariate and multivariate logistic regression analyses were conducted in the training cohort to identify independent risk factors. Four machine learning algorithms， including logistic regression， support vector machine （SVM）， random forest and BPNN were then used to construct predictive models， which were subsequently evaluated in the internal and external validation cohorts. The Shapley additive explanation （SHAP） method was applied to assess and visualize feature importance. Results Univariate and multivariate logistic regression analyses identified the neutrophil-to-lymphocyte ratio （NLR）， peritumoral edema， and axillary lymph node cortical thickening as independent predictors of nodal burden. The BPNN-based model demonstrated superior predictive performance， achieving an area under the receiver operating characteristic curve （AUC） of 0.793. SHAP analysis revealed that peritumoral edema contributed most significantly to model predictions， followed by lymph node cortical thickening and NLR. Conclusions An interpretable BPNN model integrating clinical and imaging characteristics provides reliable prediction of axillary lymph node burden in breast cancer patients. This approach offers valuable support for axillary management and personalized treatment planning.

Key words： breast cancer; axillary lymph node burden; 1 ~ 2 sentinel lymph node positivity; back propagation neural network; interpretability

Cite this article

Sheng NONG , Zhanxiong LI , Qi ZHANG , Zhendong LU , Minping HONG , Wubiao CHEN , Zilin LIU . Predictive performance of an interpretable BPNN model for axillary lymph node burden in breast cancer patients with 1 ~ 2 sentinel lymph node positive[J]. The Journal of Practical Medicine, 2026 , 42(1) : 45 -55 . DOI: 10.3969/j.issn.1006-5725.2026.01.006

References

[1] 陈基明，朱浩雨，高静，等. 基于临床病理及常规和功能MRI影像组学模型预测乳腺癌腋窝淋巴结转移［J］. 中国医学影像技术， 2021， 37（6）： 885-890.doi： 10.13929/j.issn.1003-3289. 2021.06.022 .
[2] CHANG J M， LEUNG J W T， MOY L， et al. Axillary Nodal Evaluation in Breast Cancer： State of the Art［J］. Radiology， 2020， 295（3）：500-515. doi： 10.1148/radiol.2020192534 .
[3] WU T， LONG Q， ZENG L， et al. Axillary lymph node metastasis in breast cancer： From historical axillary surgery to updated advances in the preoperative diagnosis and axillary management［J］. BMC Surg， 2025， 25（1）：81. doi： 10.1186/s12893-025-02802-2 .
[4] 丘海，归奕飞，刘媛. 术前腋窝超声正常的临床T1—2 N0乳腺癌患者发生前哨淋巴结转移的预测模型［J］. 实用医学杂志，2025，41（14）：2143-2151. doi： 10.3969/j.issn.1006-5725. 2025. 14.004 .
[5] GIULIANO A E， BALLMAN K V， MCCALL L， et al. Effect of Axillary Dissection vs No Axillary Dissection on 10-Year Overall Survival Among Women With Invasive Breast Cancer and Sentinel Node Metastasis： The ACOSOG Z0011 （Alliance） Randomized Clinical Trial［J］. JAMA， 2017， 318（10）：918-926. doi： 10.1001/jama.2017.11470 .
[6] 逯永晋，石志强，李彤，等. 乳腺癌前哨淋巴结阳性豁免腋窝清扫后区域淋巴结放疗的回顾性研究［J］. 中国癌症杂志， 2025， 35（2）： 228-236. doi： 10.19401/j.cnki.1007-3639.2025.02.010 .
[7] WEKKING D， PORCU M， DE SILVA P， et al. Breast MRI： Clinical Indications， Recommendations， and Future Applications in Breast Cancer Diagnosis［J］. Curr Oncol Rep， 2023， 25（4）： 257-267. doi： 10.1007/s11912-023-01372-x .
[8] CASTELLOTE-HUGUET P， RUIZ-ESPANA S， GALAN-AUGE C， et al. Breast Cancer Diagnosis Using Texture and Shape Features in MRI［J］. Annu Int Conf IEEE Eng Med Biol Soc， 2023， 2023：1-4. doi： 10.1109/EMBC40787.2023.10340385 .
[9] 赵杭菲， 姚劲草， 王立平， 等. 列线图预测T1-2期乳腺癌腋窝淋巴转移负荷的研究［J］. 中国超声医学杂志， 2023， 39（11）： 1224-1226. doi： 10.3969/j.issn.1002-0101.2023.11.009 .
[10] 吴怡雯， 周晓华， 陈菲， 等. 基于自动乳腺全容积扫查影像组学对预测乳腺癌腋窝淋巴结负荷的价值［J］. 中国超声医学杂志， 2023， 39（5）：499-502. doi： 10.3969/j.issn.1002-0101. 2023.05.006 .
[11] LIAO H， CHEN X， LU S， et al. MRI-Based Back Propagation Neural Network Model as a Powerful Tool for Predicting the Response to Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma［J］. J Magn Reson Imaging， 2022， 56（2）： 547-559. doi： 10.1002/jmri.28047 .
[12] SATAKE H， ISHIGAKI S， ITO R， et al. Radiomics in breast MRI： current progress toward clinical application in the era of artificial intelligence［J］. Radiol Med， 2022， 127（1）： 39-56. doi： 10.1002/jmri.28047 .
[13] VAN DER VELDEN B H M， KUIJF H J， GILHUIJS K G A， et al. Explainable artificial intelligence （XAI） in deep learning-based medical image analysis［J］. Med Image Anal， 2022， 79：102470. doi： 10.1016/j.media.2022.102470 .
[14] MARTAINDALE S R. Breast MR Imaging： Atlas of Anatomy， Physiology， Pathophysiology， and Breast Imaging Reporting and Data Systems Lexicon［J］. Magn Reson Imaging Clin N Am， 2018， 26 （2）：179-190. doi： 10.1016/j.mric.2017.12.001 .
[15] 杨光飞，常瑞姣，武雅婷，等. 超声与MRI对乳腺BI-RADS 3～5类肿物的诊断分类一致性比较［J］.中国医学影像学杂志， 2022， 30（10）： 991-995. doi： 10.3969/j.issn.1005-5185. 2022. 10.004 .
[16] XU Z， DING Y， ZHAO K， et al. MRI characteristics of breast edema for assessing axillary lymph node burden in early-stage breast cancer： A retrospective bicentric study［J］. Eur Radiol， 2022， 32（12）： 8213-8225. doi： 10.1007/s00330-022-08896-z .
[17] 杨森，贾志莺，范清，等. 列线图可预测T1期乳腺癌前哨淋巴结转移：基于超声特征联合临床病理指标［J］. 分子影像学杂志， 2025， 48（7）： 848-854. doi： 10.12122/j.issn.1674-4500. 2025. 07.09 .
[18] HARADA T L， UEMATSU T， NAKASHIMA K， et al. Evaluation of breast edema findings at T2-weighted breast MRI is useful for diagnosing occult inflammatory breast cancer and can predict prognosis after neoadjuvant chemotherapy［J］. Radiology， 2021， 299（1）： 53-62. doi： 10.1148/radiol.2021202604 .
[19] MALHAIRE C， SELHANE F， SAINT-MARTIN M J， et al. Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy［J］. Eur Radiol， 2023， 33（11）： 8142-8154. doi： 10.1007/s00330-023-09797-5 .
[20] KAISER C G， HEROLD M， KRAMMER J， et al. Prognostic value of “prepectoral edema” in MR-mammography［J］. Anticancer Res， 2017， 37（4）： 1989-1995. doi： 10.21873/anticanres. 11542 .
[21] CHEON H， KIM H J， KIM T H， et al. Invasive breast cancer： Prognostic value of peritumoral edema identified at preoperative MR imaging［J］. Radiology， 2018， 287（1）： 68-75. doi： 10.1148/radiol.2017171157 .
[22] PARK N J Y， JEONG J Y， PARK J Y， et al. Peritumoral edema in breast cancer at preoperative MRI： An interpretative study with histopathological review toward understanding tumor microenvironment［J］. Sci Rep， 2021， 11（1）： 12992. doi： 10.1038/s41598-021-92283-z .
[23] YILMAZ R， AKPINAR Y， OZYAVUZ I， et al. Synchronous metastatic leiomyosarcoma and primer invasive ductal carcinoma tumors in the same breast： Mammography， ultrasonography， and magnetic resonance imaging findings［J］. Breast J， 2019， 25（2）： 310-311. doi： 10.1111/tbj.13211 .
[24] 冯其柱，卢曼曼，孙杰，等. 新型全身性炎症指标对急性胰腺炎早期病情严重程度的预测价值［J］. 实用医学杂志， 2024， 40（14）：1963-1968. doi： 10.3969/j.issn.1006-5725.2024.14.011 .
[25] KAWAGUCHI S， KINOWAKI K， TAMURA N， et al. High-accuracy prediction of axillary lymph node metastasis in invasive lobular carcinoma using focal cortical thickening on magnetic resonance imaging［J］. Breast Cancer， 2023， 30 （4）： 637-646. doi： 10.1007/s12282-023-01457-2 .
[26] 史娜，王爽，马兆丽，等. 超声联合免疫炎症指标构建列线图预测早期浸润性导管癌淋巴结转移［J］.中国超声医学杂志，2025，41（3）：265-269. doi： 10.3969/j.issn.1002-0101. 2025. 03.006 .
[27] 张伟娜，钟李长，师琳，等. 超声影像组学特征联合miRNA-34a表达水平对乳腺癌新辅助化疗患者病理完全缓解的预测能力［J］. 新医学，2025，56（7）：645-653. doi： 10.12464/j.issn.0253-9802.2025-0023 .
[28] BENEDETTO U， DIMAGLI A， SINHA S， et al. Machine learning improves mortality risk prediction after cardiac surgery： Systematic review and meta-analysis［J］. J Thorac Cardiovasc Surg， 2022， 163（6）： 2075-2087. doi： 10.1016/j.jtcvs.2020.07.105 .
[29] BELLE V， PAPANTONIS I. Principles and Practice of Explainable Machine Learning［J］. Front Big Data， 2021， 4：688969. doi： 10.3389/fdata.2021.688969 .
Options
Outlines

/

〈 〉