Article ; Online: A priori prediction of breast cancer response to neoadjuvant chemotherapy using quantitative ultrasound, texture derivative and molecular subtype.
2023 Volume 13, Issue 1, Page(s) 22687
Abstract: The purpose of this study was to investigate the performances of the tumor response prediction prior to neoadjuvant chemotherapy based on quantitative ultrasound, tumour core-margin, texture derivative analyses, and molecular parameters in a large cohort ...
| Abstract | The purpose of this study was to investigate the performances of the tumor response prediction prior to neoadjuvant chemotherapy based on quantitative ultrasound, tumour core-margin, texture derivative analyses, and molecular parameters in a large cohort of patients (n = 208) with locally advanced and earlier-stage breast cancer and combined them to best determine tumour responses with machine learning approach. Two multi-features response prediction algorithms using a k-nearest neighbour and support vector machine were developed with leave-one-out and hold-out cross-validation methods to evaluate the performance of the response prediction models. In a leave-one-out approach, the quantitative ultrasound-texture analysis based model attained good classification performance with 80% of accuracy and AUC of 0.83. Including molecular subtype in the model improved the performance to 83% of accuracy and 0.87 of AUC. Due to limited number of samples in the training process, a model developed with a hold-out approach exhibited a slightly higher bias error in classification performance. The most relevant features selected in predicting the response groups are core-to-margin, texture-derivative, and molecular subtype. These results imply that that baseline tumour-margin, texture derivative analysis methods combined with molecular subtype can potentially be used for the prediction of ultimate treatment response in patients prior to neoadjuvant chemotherapy. |
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| MeSH term(s) | Humans ; Female ; Breast Neoplasms/diagnostic imaging ; Breast Neoplasms/drug therapy ; Breast Neoplasms/pathology ; Neoadjuvant Therapy/methods ; Chemotherapy, Adjuvant ; Ultrasonography ; Algorithms ; Retrospective Studies |
| Language | English |
| Publishing date | 2023-12-19 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 2615211-3 |
| ISSN | 2045-2322 ; 2045-2322 |
| ISSN (online) | 2045-2322 |
| ISSN | 2045-2322 |
| DOI | 10.1038/s41598-023-49478-3 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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