Table 1.
Radiomics for TNBC: the table includes the imaging method chosen for radiomic analysis, the type, the number of features used in creating the model including the radiomic score and nomograms, study objectives, authors and year of publication [58,62,63,64,65,66,67,68,69,70,71,72,73,74].
| Imaging Method | Radiomic Features/Features Number/Radiomic Signature | Study Objective | |
|---|---|---|---|
| US | optoacoustic imaging (OA) combined with gray-scale US | identify the differences between molecular subtype | Menezes et al. (2019) [62]. |
| MRI | first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient (GRA), autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry (GEO) | asessment of breast cancer receptor status and molecular subtypes. | Leithner et al. (2019) [64]. |
| MRI | 85 radiomic features (morphologic, densitometric, texture) | distinguish triple-negative cancers from other subtypes | Wang et al. (2015) [65]. |
| CT | radiomic signature based on preoperative CT | guidance in choosing the treatment | Feng et al. (2020) [66]. |
| MRI | 15 features | to differentiate triple-negative breast cancer (TNBC) and nontriple-negative breast cancer (non-TNBC). | Ma et al. (2021) [67]. |
| X-ray mammography | roundness, concavity, gray average and skewness | distinguish between TNBC and non-TNBC | Zhang et al. (2019) [68]. |
| US | 730 features (14 intensity-based features, 132 textural features and 584 wavelet-based features) | differential diagnosis between triple-negative breast cancer and fibroadenoma | Lee et al. (2018) [69]. |
| US | morphology, conventional texture, and multiresolution gray-scale invariant texture feature | distinguishing between TNBC and benign fibroadenomas | Moon et al. (2015) [70]. |
| MRI | both peritumoral and intratumoral features | prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). | Braman et al. (2015) [58]. |
| MRI | Rad-score | prediction of systemic recurrence | Koh et al. (2020) [71]. |
| US | Rad-score and radiomic nomogram | prediction of disease-free survival | Yu et al. (2021) [72]. |
| MRI | Three radiomic models based on pre- and post-NAC magnetic resonance images | prediction of systemic recurrence after NAC | Ma et al. (2022) [73]. |
| Mammography | radiomics nomogram that incorporates Rad-score | prediction of invasive disease-free survival | Jiang et al. (2020) [74]. |