Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer
PurposeThis study aimed to create a nomogram model (NM) that combines clinical-radiological factors with radiomics features of both intra- and peritumoral regions extracted from pretherapy dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, in order to establish a reliable method...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
|
| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1561599/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849736146002640896 |
|---|---|
| author | Yun Zhu Shuni Zhang Wei Wei Li Yang Lingling Wang Ying Wang Ye Fan Haitao Sun Zongyu Xie |
| author_facet | Yun Zhu Shuni Zhang Wei Wei Li Yang Lingling Wang Ying Wang Ye Fan Haitao Sun Zongyu Xie |
| author_sort | Yun Zhu |
| collection | DOAJ |
| description | PurposeThis study aimed to create a nomogram model (NM) that combines clinical-radiological factors with radiomics features of both intra- and peritumoral regions extracted from pretherapy dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, in order to establish a reliable method for early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with breast cancer.MethodsA total of 214 patients were randomly divided into a training set (n=149) and a test set (n=65) in a ratio of 7:3. Radiomics features were extracted from intratumoral region and 2-mm, 4-mm, 6-mm, 8-mm peritumoral regions on DCE-MRI images, and selected the optimal peritumoral region. The intratumoral radiomics model (IRM), 2-mm, 4-mm, 6-mm, 8-mm peritumoral radiomics model (PRM), the combined intra- and the optimal peritumoral radiomics model (CIPRM) were constructed based on five machine learning algorithms, and then the radiomics scores (Rad-score) were obtained. Independent risk factors for clinical-radiological features were obtained by univariate and multivariate logistic regression analysis, and clinical model (CM) was constructed. Finally, the CIPRM Rad-score combined with clinical-radiological factors was used to construct a NM. The performance of different models were evaluated by receiver operating characteristic curve (ROC) analysis, calibration curve analysis, and decision curve analysis (DCA).ResultsIn our study, the 6-mm peritumoral size was considered to be the optimal peritumoral region. The CM is constructed based on three independent risk factors: estrogen receptor (ER), Ki-67, and breast edema score (BES). Incorporating ER, Ki-67, BES, and CIPRM Rad-score (combined intra- and 6-mm peritumoral) into the nomogram achieved a reliable predictive performance. And the area under the curve (AUC), sensitivity, specificity, and accuracy of the NM was 0.911, 0.848, 0.831, 0.826 for the training set and 0.897, 0.893, 0.784, 0.815 for the test set, respectively.ConclusionThe NM has a good value for early prediction of pCR after NAC in breast cancer patients. |
| format | Article |
| id | doaj-art-10b3380f97e44ea490e6c4f49d91d252 |
| institution | DOAJ |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-10b3380f97e44ea490e6c4f49d91d2522025-08-20T03:07:21ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-06-011510.3389/fonc.2025.15615991561599Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancerYun Zhu0Shuni Zhang1Wei Wei2Li Yang3Lingling Wang4Ying Wang5Ye Fan6Haitao Sun7Zongyu Xie8Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, ChinaDepartment of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, ChinaDepartment of Radiology, Anhui No.2 Provincial People’s Hospital, Hefei, ChinaDepartment of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, ChinaDepartment of Medical Imaging Diagnostics, Bengbu Medical University, Bengbu, ChinaDepartment of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, ChinaDepartment of Clinical Medicine, Bengbu Medical University, Bengbu, Anhui, ChinaZhongshan Hospital, Fudan University, Shanghai, ChinaDepartment of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, ChinaPurposeThis study aimed to create a nomogram model (NM) that combines clinical-radiological factors with radiomics features of both intra- and peritumoral regions extracted from pretherapy dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, in order to establish a reliable method for early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with breast cancer.MethodsA total of 214 patients were randomly divided into a training set (n=149) and a test set (n=65) in a ratio of 7:3. Radiomics features were extracted from intratumoral region and 2-mm, 4-mm, 6-mm, 8-mm peritumoral regions on DCE-MRI images, and selected the optimal peritumoral region. The intratumoral radiomics model (IRM), 2-mm, 4-mm, 6-mm, 8-mm peritumoral radiomics model (PRM), the combined intra- and the optimal peritumoral radiomics model (CIPRM) were constructed based on five machine learning algorithms, and then the radiomics scores (Rad-score) were obtained. Independent risk factors for clinical-radiological features were obtained by univariate and multivariate logistic regression analysis, and clinical model (CM) was constructed. Finally, the CIPRM Rad-score combined with clinical-radiological factors was used to construct a NM. The performance of different models were evaluated by receiver operating characteristic curve (ROC) analysis, calibration curve analysis, and decision curve analysis (DCA).ResultsIn our study, the 6-mm peritumoral size was considered to be the optimal peritumoral region. The CM is constructed based on three independent risk factors: estrogen receptor (ER), Ki-67, and breast edema score (BES). Incorporating ER, Ki-67, BES, and CIPRM Rad-score (combined intra- and 6-mm peritumoral) into the nomogram achieved a reliable predictive performance. And the area under the curve (AUC), sensitivity, specificity, and accuracy of the NM was 0.911, 0.848, 0.831, 0.826 for the training set and 0.897, 0.893, 0.784, 0.815 for the test set, respectively.ConclusionThe NM has a good value for early prediction of pCR after NAC in breast cancer patients.https://www.frontiersin.org/articles/10.3389/fonc.2025.1561599/fullbreast cancerneoadjuvant chemotherapypathological complete responseintratumoralperitumoralradiomics |
| spellingShingle | Yun Zhu Shuni Zhang Wei Wei Li Yang Lingling Wang Ying Wang Ye Fan Haitao Sun Zongyu Xie Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer Frontiers in Oncology breast cancer neoadjuvant chemotherapy pathological complete response intratumoral peritumoral radiomics |
| title | Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer |
| title_full | Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer |
| title_fullStr | Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer |
| title_full_unstemmed | Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer |
| title_short | Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer |
| title_sort | intra and peritumoral radiomics nomogram based on dce mri for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer |
| topic | breast cancer neoadjuvant chemotherapy pathological complete response intratumoral peritumoral radiomics |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1561599/full |
| work_keys_str_mv | AT yunzhu intraandperitumoralradiomicsnomogrambasedondcemrifortheearlypredictionofpathologicalcompleteresponsetoneoadjuvantchemotherapyinbreastcancer AT shunizhang intraandperitumoralradiomicsnomogrambasedondcemrifortheearlypredictionofpathologicalcompleteresponsetoneoadjuvantchemotherapyinbreastcancer AT weiwei intraandperitumoralradiomicsnomogrambasedondcemrifortheearlypredictionofpathologicalcompleteresponsetoneoadjuvantchemotherapyinbreastcancer AT liyang intraandperitumoralradiomicsnomogrambasedondcemrifortheearlypredictionofpathologicalcompleteresponsetoneoadjuvantchemotherapyinbreastcancer AT linglingwang intraandperitumoralradiomicsnomogrambasedondcemrifortheearlypredictionofpathologicalcompleteresponsetoneoadjuvantchemotherapyinbreastcancer AT yingwang intraandperitumoralradiomicsnomogrambasedondcemrifortheearlypredictionofpathologicalcompleteresponsetoneoadjuvantchemotherapyinbreastcancer AT yefan intraandperitumoralradiomicsnomogrambasedondcemrifortheearlypredictionofpathologicalcompleteresponsetoneoadjuvantchemotherapyinbreastcancer AT haitaosun intraandperitumoralradiomicsnomogrambasedondcemrifortheearlypredictionofpathologicalcompleteresponsetoneoadjuvantchemotherapyinbreastcancer AT zongyuxie intraandperitumoralradiomicsnomogrambasedondcemrifortheearlypredictionofpathologicalcompleteresponsetoneoadjuvantchemotherapyinbreastcancer |