Name
Title | ||
|---|---|---|
Effect of diversity of patient population and acquisition systems on the use of radiomics and machine learning for classification of 2, 397 breast lesions. |
Abstract | ||
|---|---|---|
Radiomic features extracted from dynamic contrast-enhanced magnetic resonance (DCE-MR) images have been previously shown to be useful for classification of breast lesions as benign or malignant. In this study, we investigated the performance of radiomics in distinguishing between lesion molecular subtypes across two populations. Clinical DCE-MR images of 847 breast lesions in the United States and 1,550 breast lesions in China were collected under HIPAA and IRB compliance. The radiomics workstation automatically segmented lesions using a fuzzy C-means method and extracted thirty-eight radiomic features describing size, shape, morphology, kinetics, and texture, using previously reported methods. Binary classification pairs included benign versus malignant, benign versus each molecular subtype, and each molecular subtype versus the other molecular subtypes grouped together. Stepwise feature selection and linear discriminant analysis with five-fold cross-validation was used for each population in each classification task to determine the posterior probability of each lesion being in the positive class. The area under the receiver operating characteristic curve (AUC) was determined using the conventional binormal model. The AUC was also determined for each feature in each classification task. Classification performance for each task was compared between populations using superiority testing relative to the difference in AUC. Three out of nine classification tasks (benign versus luminal A (p = 0.008), non-luminal B versus luminal B (p = 0.048) and non-HER2-enriched versus HER2-enriched (p = 0.001)) demonstrated significant difference in performance between the two populations. Differences in classification performance and potential for harmonization may be affected by population biology (i.e., distribution of molecular subtypes) and scanner acquisition systems. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1117/12.2512507 | Proceedings of SPIE |
Field | DocType | Volume |
Population,Computer science,Artificial intelligence,Radiomics,Machine learning | Conference | 10950 |
ISSN | Citations | PageRank |
0277-786X | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heather M. Whitney | 1 | 0 | 1.35 |
| Yu Ji | 2 | 2 | 1.12 |
| Hui Li | 3 | 45 | 15.48 |
| Alexandra Edwards | 4 | 9 | 3.29 |
| john papaioannou | 5 | 7 | 4.34 |
| Peifang Liu | 6 | 0 | 0.68 |
| Maryellen L. Giger | 7 | 393 | 85.89 |