Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble research
Abstract The aim to investigate the predictive efficacy of automatic breast volume scanner (ABVS), clinical and serological features alone or in combination at model level for predicting HER2 status. The model weighted combination method was developed to identify HER2 status compared with single dat...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-02-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-55838-4 |
| _version_ | 1797274720910966784 |
|---|---|
| author | Hui Wang Wei Chen Shanshan Jiang Ting Li Fei Chen Junqiang Lei Ruixia Li Lili Xi Shunlin Guo |
| author_facet | Hui Wang Wei Chen Shanshan Jiang Ting Li Fei Chen Junqiang Lei Ruixia Li Lili Xi Shunlin Guo |
| author_sort | Hui Wang |
| collection | DOAJ |
| description | Abstract The aim to investigate the predictive efficacy of automatic breast volume scanner (ABVS), clinical and serological features alone or in combination at model level for predicting HER2 status. The model weighted combination method was developed to identify HER2 status compared with single data source model method and feature combination method. 271 patients with invasive breast cancer were included in the retrospective study, of which 174 patients in our center were randomized into the training and validation sets, and 97 patients in the external center were as the test set. Radiomics features extracted from the ABVS-based tumor, peritumoral 3 mm region, and peritumoral 5 mm region and clinical features were used to construct the four types of the optimal single data source models, Tumor, R3mm, R5mm, and Clinical model, respectively. Then, the model weighted combination and feature combination methods were performed to optimize the combination models. The proposed weighted combination models in predicting HER2 status achieved better performance both in validation set and test set. For the validation set, the single data source model, the feature combination model, and the weighted combination model achieved the highest area under the curve (AUC) of 0.803 (95% confidence interval [CI] 0.660–947), 0.739 (CI 0.556,0.921), and 0.826 (95% CI 0.689,0.962), respectively; with the sensitivity and specificity were 100%, 62.5%; 81.8%, 66.7%; 90.9%,75.0%; respectively. For the test set, the single data source model, the feature combination model, and the weighted combination model attained the best AUC of 0.695 (95% CI 0.583, 0.807), 0.668 (95% CI 0.555,0.782), and 0.700 (95% CI 0.590,0.811), respectively; with the sensitivity and specificity were 86.1%, 41.9%; 61.1%, 71.0%; 86.1%, 41.9%; respectively. The model weighted combination was a better method to construct a combination model. The optimized weighted combination models composed of ABVS-based intratumoral and peritumoral radiomics features and clinical features may be potential biomarkers for the noninvasive and preoperative prediction of HER2 status in breast cancer. |
| first_indexed | 2024-03-07T15:03:20Z |
| format | Article |
| id | doaj.art-65f629a81cf2413b918949a0fa8e1959 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:03:20Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-65f629a81cf2413b918949a0fa8e19592024-03-05T19:02:08ZengNature PortfolioScientific Reports2045-23222024-02-0114111410.1038/s41598-024-55838-4Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble researchHui Wang0Wei Chen1Shanshan Jiang2Ting Li3Fei Chen4Junqiang Lei5Ruixia Li6Lili Xi7Shunlin Guo8Department of Ultrasound, The First Hospital of Lanzhou UniversityDepartment of Ultrasound, The Ningxia Hui Autonomous Region People’s HospitalDepartment of Advanced Technical Support, Clinical and Technical Support, Philips HealthcareDepartment of Ultrasound, The Ningxia Hui Autonomous Region People’s HospitalDepartment of Ultrasound, The First Hospital of Lanzhou UniversityDepartment of Radiology, The First Hospital of Lanzhou UniversityDepartment of Ultrasound, The First Hospital of Lanzhou UniversityDepartment of Pharmacologic Bases, The First Hospital of Lanzhou UniversityDepartment of Radiology, The First Hospital of Lanzhou UniversityAbstract The aim to investigate the predictive efficacy of automatic breast volume scanner (ABVS), clinical and serological features alone or in combination at model level for predicting HER2 status. The model weighted combination method was developed to identify HER2 status compared with single data source model method and feature combination method. 271 patients with invasive breast cancer were included in the retrospective study, of which 174 patients in our center were randomized into the training and validation sets, and 97 patients in the external center were as the test set. Radiomics features extracted from the ABVS-based tumor, peritumoral 3 mm region, and peritumoral 5 mm region and clinical features were used to construct the four types of the optimal single data source models, Tumor, R3mm, R5mm, and Clinical model, respectively. Then, the model weighted combination and feature combination methods were performed to optimize the combination models. The proposed weighted combination models in predicting HER2 status achieved better performance both in validation set and test set. For the validation set, the single data source model, the feature combination model, and the weighted combination model achieved the highest area under the curve (AUC) of 0.803 (95% confidence interval [CI] 0.660–947), 0.739 (CI 0.556,0.921), and 0.826 (95% CI 0.689,0.962), respectively; with the sensitivity and specificity were 100%, 62.5%; 81.8%, 66.7%; 90.9%,75.0%; respectively. For the test set, the single data source model, the feature combination model, and the weighted combination model attained the best AUC of 0.695 (95% CI 0.583, 0.807), 0.668 (95% CI 0.555,0.782), and 0.700 (95% CI 0.590,0.811), respectively; with the sensitivity and specificity were 86.1%, 41.9%; 61.1%, 71.0%; 86.1%, 41.9%; respectively. The model weighted combination was a better method to construct a combination model. The optimized weighted combination models composed of ABVS-based intratumoral and peritumoral radiomics features and clinical features may be potential biomarkers for the noninvasive and preoperative prediction of HER2 status in breast cancer.https://doi.org/10.1038/s41598-024-55838-4 |
| spellingShingle | Hui Wang Wei Chen Shanshan Jiang Ting Li Fei Chen Junqiang Lei Ruixia Li Lili Xi Shunlin Guo Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble research Scientific Reports |
| title | Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble research |
| title_full | Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble research |
| title_fullStr | Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble research |
| title_full_unstemmed | Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble research |
| title_short | Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble research |
| title_sort | intra and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of her2 status in breast cancer a model ensemble research |
| url | https://doi.org/10.1038/s41598-024-55838-4 |
| work_keys_str_mv | AT huiwang intraandperitumoralradiomicsfeaturesbasedonmulticenterautomaticbreastvolumescannerfornoninvasiveandpreoperativepredictionofher2statusinbreastcanceramodelensembleresearch AT weichen intraandperitumoralradiomicsfeaturesbasedonmulticenterautomaticbreastvolumescannerfornoninvasiveandpreoperativepredictionofher2statusinbreastcanceramodelensembleresearch AT shanshanjiang intraandperitumoralradiomicsfeaturesbasedonmulticenterautomaticbreastvolumescannerfornoninvasiveandpreoperativepredictionofher2statusinbreastcanceramodelensembleresearch AT tingli intraandperitumoralradiomicsfeaturesbasedonmulticenterautomaticbreastvolumescannerfornoninvasiveandpreoperativepredictionofher2statusinbreastcanceramodelensembleresearch AT feichen intraandperitumoralradiomicsfeaturesbasedonmulticenterautomaticbreastvolumescannerfornoninvasiveandpreoperativepredictionofher2statusinbreastcanceramodelensembleresearch AT junqianglei intraandperitumoralradiomicsfeaturesbasedonmulticenterautomaticbreastvolumescannerfornoninvasiveandpreoperativepredictionofher2statusinbreastcanceramodelensembleresearch AT ruixiali intraandperitumoralradiomicsfeaturesbasedonmulticenterautomaticbreastvolumescannerfornoninvasiveandpreoperativepredictionofher2statusinbreastcanceramodelensembleresearch AT lilixi intraandperitumoralradiomicsfeaturesbasedonmulticenterautomaticbreastvolumescannerfornoninvasiveandpreoperativepredictionofher2statusinbreastcanceramodelensembleresearch AT shunlinguo intraandperitumoralradiomicsfeaturesbasedonmulticenterautomaticbreastvolumescannerfornoninvasiveandpreoperativepredictionofher2statusinbreastcanceramodelensembleresearch |