Zhe Zhou, Zhanguo Sun, Fan Zhao, Yuan Jin, Yueqin Chen, Laimin Zhu, Yanji Guo, Weiwei Wang
{"title":"基于形态学图的临床病理生物标志物联合多参数MRI对乳腺癌肿瘤浸润性淋巴细胞表达的预测价值。","authors":"Zhe Zhou, Zhanguo Sun, Fan Zhao, Yuan Jin, Yueqin Chen, Laimin Zhu, Yanji Guo, Weiwei Wang","doi":"10.1016/j.acra.2025.09.017","DOIUrl":null,"url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To investigate the value of clinicopathological features and multiparametric magnetic resonance imaging (MRI) in predicting tumour-infiltrating lymphocyte (TIL) levels in breast cancer.</p><p><strong>Materials and methods: </strong>A total of 171 patients diagnosed with invasive ductal carcinoma who underwent preoperative MRI (2023-2025) were included. The analysis focused on the clinicopathological characteristics alongside conventional MRI features and a range of quantitative parameters. Multiple logistic regression analysis identified independent predictors of high and low TIL levels. A nomogram was constructed based on the multivariable logistic regression model results.</p><p><strong>Results: </strong>Logistic regression analysis identified histological grade, D, D*, Ktrans, and Kep as independent factors in the training cohort. The nomogram's C-index was 0.944 in the training cohort and 0.964 in the validation cohort. The area under the curve (AUC) of the nomogram model was 0.954 (85.1% sensitivity, 91.1% specificity, and 87.4% accuracy) in the training cohort and 0.974 (96.7% sensitivity, 92.1% specificity, and 92.6% accuracy) in the validation cohort, both significantly higher than those of the individual models in the corresponding cohorts (Z=3.018-6.653, all P<0.05 and Z=2.546-5.668, all P<0.05).</p><p><strong>Conclusion: </strong>Combining clinicopathological characteristics with multiparametric MRI parameters significantly improves prediction accuracy for TIL levels in breast cancer. This integrated model holds considerable clinical potential, providing robust support for personalised treatment strategies.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predictive Value of Nomogram-Based Clinicopathological Biomarkers Combined with Multiparametric MRI for Tumour-Infiltrating Lymphocyte Expression in Breast Cancer.\",\"authors\":\"Zhe Zhou, Zhanguo Sun, Fan Zhao, Yuan Jin, Yueqin Chen, Laimin Zhu, Yanji Guo, Weiwei Wang\",\"doi\":\"10.1016/j.acra.2025.09.017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Rationale and objectives: </strong>To investigate the value of clinicopathological features and multiparametric magnetic resonance imaging (MRI) in predicting tumour-infiltrating lymphocyte (TIL) levels in breast cancer.</p><p><strong>Materials and methods: </strong>A total of 171 patients diagnosed with invasive ductal carcinoma who underwent preoperative MRI (2023-2025) were included. The analysis focused on the clinicopathological characteristics alongside conventional MRI features and a range of quantitative parameters. Multiple logistic regression analysis identified independent predictors of high and low TIL levels. A nomogram was constructed based on the multivariable logistic regression model results.</p><p><strong>Results: </strong>Logistic regression analysis identified histological grade, D, D*, Ktrans, and Kep as independent factors in the training cohort. The nomogram's C-index was 0.944 in the training cohort and 0.964 in the validation cohort. The area under the curve (AUC) of the nomogram model was 0.954 (85.1% sensitivity, 91.1% specificity, and 87.4% accuracy) in the training cohort and 0.974 (96.7% sensitivity, 92.1% specificity, and 92.6% accuracy) in the validation cohort, both significantly higher than those of the individual models in the corresponding cohorts (Z=3.018-6.653, all P<0.05 and Z=2.546-5.668, all P<0.05).</p><p><strong>Conclusion: </strong>Combining clinicopathological characteristics with multiparametric MRI parameters significantly improves prediction accuracy for TIL levels in breast cancer. This integrated model holds considerable clinical potential, providing robust support for personalised treatment strategies.</p>\",\"PeriodicalId\":50928,\"journal\":{\"name\":\"Academic Radiology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Academic Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.acra.2025.09.017\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Academic Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.acra.2025.09.017","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Predictive Value of Nomogram-Based Clinicopathological Biomarkers Combined with Multiparametric MRI for Tumour-Infiltrating Lymphocyte Expression in Breast Cancer.
Rationale and objectives: To investigate the value of clinicopathological features and multiparametric magnetic resonance imaging (MRI) in predicting tumour-infiltrating lymphocyte (TIL) levels in breast cancer.
Materials and methods: A total of 171 patients diagnosed with invasive ductal carcinoma who underwent preoperative MRI (2023-2025) were included. The analysis focused on the clinicopathological characteristics alongside conventional MRI features and a range of quantitative parameters. Multiple logistic regression analysis identified independent predictors of high and low TIL levels. A nomogram was constructed based on the multivariable logistic regression model results.
Results: Logistic regression analysis identified histological grade, D, D*, Ktrans, and Kep as independent factors in the training cohort. The nomogram's C-index was 0.944 in the training cohort and 0.964 in the validation cohort. The area under the curve (AUC) of the nomogram model was 0.954 (85.1% sensitivity, 91.1% specificity, and 87.4% accuracy) in the training cohort and 0.974 (96.7% sensitivity, 92.1% specificity, and 92.6% accuracy) in the validation cohort, both significantly higher than those of the individual models in the corresponding cohorts (Z=3.018-6.653, all P<0.05 and Z=2.546-5.668, all P<0.05).
Conclusion: Combining clinicopathological characteristics with multiparametric MRI parameters significantly improves prediction accuracy for TIL levels in breast cancer. This integrated model holds considerable clinical potential, providing robust support for personalised treatment strategies.
期刊介绍:
Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.