Preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast: development of a machine learning-based predictive model with a web-based calculator
Abstract Background Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer characterized by a high risk of lymph node metastasis (LNM). The study aimed to identify predictors of LNM and to develop a machine learning (ML)-based risk prediction model for patients with breast IMPC....
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| Format: | Article |
| Language: | English |
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BMC
2025-04-01
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| Series: | World Journal of Surgical Oncology |
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| Online Access: | https://doi.org/10.1186/s12957-025-03807-0 |
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| author | Yan Zhang Nan Wang Yuxin Qiu Yingxiao Jiang Peiyan Qin Xiaoxiao Wang Yang Li Xiangdi Meng Furong Hao |
| author_facet | Yan Zhang Nan Wang Yuxin Qiu Yingxiao Jiang Peiyan Qin Xiaoxiao Wang Yang Li Xiangdi Meng Furong Hao |
| author_sort | Yan Zhang |
| collection | DOAJ |
| description | Abstract Background Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer characterized by a high risk of lymph node metastasis (LNM). The study aimed to identify predictors of LNM and to develop a machine learning (ML)-based risk prediction model for patients with breast IMPC. Methods We retrospectively analyzed a cohort of 229 patients diagnosed with breast IMPC between 2019 and 2021. Patients were randomly assigned to training and test sets in a 7:3 ratio. Independent risk factors for LNM were identified using univariable and multivariable logistic regression analyses. Thirteen ML algorithms were trained and compared to determine the optimal model. Model performance was evaluated using the area under the curve (AUC), calibration plots, and decision curve analysis. Internal validation was performed using 100 iterations of tenfold cross-validation. Results LNM was present in 158 patients (69%). Tumor size, histological grade, progesterone receptor staining intensity, and lymphovascular invasion were identified as independent predictors of LNM (all p < 0.05). Among the 13 ML models, logistic regression (LR) demonstrated the best performance, achieving an AUC of 0.88 in the test set. A nomogram based on the LR model was constructed to facilitate clinical application, showing excellent calibration, clinical utility, and a classification accuracy of 76% (95% confidence interval: 70%–82%). The median AUC across cross-validation iterations was 0.83 (interquartile range: 0.76–0.91). Conclusions This study identified key predictors of LNM in breast IMPC and developed a well-calibrated nomogram to support individualized treatment decision-making. |
| format | Article |
| id | doaj-art-ea4c10ef532b421083e2588c2e5b0cee |
| institution | DOAJ |
| issn | 1477-7819 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | World Journal of Surgical Oncology |
| spelling | doaj-art-ea4c10ef532b421083e2588c2e5b0cee2025-08-20T03:14:03ZengBMCWorld Journal of Surgical Oncology1477-78192025-04-0123111110.1186/s12957-025-03807-0Preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast: development of a machine learning-based predictive model with a web-based calculatorYan Zhang0Nan Wang1Yuxin Qiu2Yingxiao Jiang3Peiyan Qin4Xiaoxiao Wang5Yang Li6Xiangdi Meng7Furong Hao8School of Clinical Medicine, Shandong Second Medical UniversityDepartment of Radiation Oncology, Weifang People’s HospitalSchool of Clinical Medicine, Shandong Second Medical UniversityDepartment of Radiation Oncology, Weifang People’s HospitalDepartment of Radiation Oncology, Weifang People’s HospitalDepartment of Radiation Oncology, Weifang People’s HospitalDepartment of Radiation Oncology, Weifang People’s HospitalDepartment of Radiation Oncology, Weifang People’s HospitalDepartment of Radiation Oncology, Weifang People’s HospitalAbstract Background Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer characterized by a high risk of lymph node metastasis (LNM). The study aimed to identify predictors of LNM and to develop a machine learning (ML)-based risk prediction model for patients with breast IMPC. Methods We retrospectively analyzed a cohort of 229 patients diagnosed with breast IMPC between 2019 and 2021. Patients were randomly assigned to training and test sets in a 7:3 ratio. Independent risk factors for LNM were identified using univariable and multivariable logistic regression analyses. Thirteen ML algorithms were trained and compared to determine the optimal model. Model performance was evaluated using the area under the curve (AUC), calibration plots, and decision curve analysis. Internal validation was performed using 100 iterations of tenfold cross-validation. Results LNM was present in 158 patients (69%). Tumor size, histological grade, progesterone receptor staining intensity, and lymphovascular invasion were identified as independent predictors of LNM (all p < 0.05). Among the 13 ML models, logistic regression (LR) demonstrated the best performance, achieving an AUC of 0.88 in the test set. A nomogram based on the LR model was constructed to facilitate clinical application, showing excellent calibration, clinical utility, and a classification accuracy of 76% (95% confidence interval: 70%–82%). The median AUC across cross-validation iterations was 0.83 (interquartile range: 0.76–0.91). Conclusions This study identified key predictors of LNM in breast IMPC and developed a well-calibrated nomogram to support individualized treatment decision-making.https://doi.org/10.1186/s12957-025-03807-0Invasive micropapillary carcinomaBreast cancerLymph node metastasisMachine learningPrediction modelNomogram |
| spellingShingle | Yan Zhang Nan Wang Yuxin Qiu Yingxiao Jiang Peiyan Qin Xiaoxiao Wang Yang Li Xiangdi Meng Furong Hao Preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast: development of a machine learning-based predictive model with a web-based calculator World Journal of Surgical Oncology Invasive micropapillary carcinoma Breast cancer Lymph node metastasis Machine learning Prediction model Nomogram |
| title | Preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast: development of a machine learning-based predictive model with a web-based calculator |
| title_full | Preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast: development of a machine learning-based predictive model with a web-based calculator |
| title_fullStr | Preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast: development of a machine learning-based predictive model with a web-based calculator |
| title_full_unstemmed | Preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast: development of a machine learning-based predictive model with a web-based calculator |
| title_short | Preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast: development of a machine learning-based predictive model with a web-based calculator |
| title_sort | preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast development of a machine learning based predictive model with a web based calculator |
| topic | Invasive micropapillary carcinoma Breast cancer Lymph node metastasis Machine learning Prediction model Nomogram |
| url | https://doi.org/10.1186/s12957-025-03807-0 |
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