Table 4.

Studies on Prediction of Tumor Response to Chemotherapy in Breast Cancer.

First Author, Year	Study Design	Number of Patients	MRI Modality	Magnetic Field	Radiomics Features	Studies Directions	Outcomes
Ahmed et al (2013)36	Retrospective	100	DCE-MRI	3.0 T	Texture features	To predict response to chemotherapy in a cohort of patients with breast cancer.	The selected features showed significant differences between responders and partial responders of chemotherapy.
Braman et al (2017)37	Retrospective	117	DCE-MRI	1.5 T	First-order statistics, Gabor features, Haralick features, CoLlAGe features	In this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer DCE-MRI to predict pCR to NAC.	A combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 ± 0.030 within the training set and 0.74 within the independent testing set. Receptor status-specific feature discovery and classification enabled improved prediction of pCR, yielding maximum AUCs of 0.83 ± 0.025 within the HR+, HER2− group using DLDA and 0.93 ± 0.018 within the TN/HER2+ group using a naive Bayes classifier.
Cain et al (2019)38	Retrospective	288	DCE-MRI	Not mentioned	Texture features	To predict pathologic complete response (pCR) to NAT in patients with breast cancer.	The AUC values for predicting pCR in TN/HER+ patients who received NAT were significant (AUC = 0.707)
Chamming’s et al (2018)39	Retrospective	85	T1WI, T2WI	1.5 T	Texture features	To evaluate whether texture features of breast cancers were associated with pCR after NAC.	T1-weighted kurtosis showed good performance for the identifcation of triple-negative breast cancer (AUC = 0.834).
Fan et al (2017)40	Retrospective	57	DCE-MRI	1.5 T/3.0 T	Morphologic features, texture features, first-order statistical features and dynamic features.	To predict NAC in breast cancer.	The classifier based on the features yield a LOOCV-AUC of 0.910 and 0.874 for the main and the reproducibility study cohort.
Henderson et al (2017)41	Not mentioned	88	T2WI	3.0 T	Texture features	To investigate whether interim changes in hetereogeneity (measured using entropy features) were associated with pathological residual cancer burden in patients receiving NAC for primary breast cancer.	Association of ultimate pCR with coarse entropy changes between baseline/interim MRI across all lesions yielded 85.2% accuracy (area under ROC curve: 0.845). Excellent sensitivity/specificity was obtained for pCR prediction within each immunophenotype: ER+: 100%/100%; HER2+: 83.3%/95.7%, TNBC: 87.5%/80.0%.
Liu et al (2019)42	Retrospective	586	T2WI, DWI, T1 + C	Not mentioned	Shape- and size-based features; first 2 order statistical features; textural features; wavelet features.	To predict pCR to NAC in breast cancer.	Radiomic signature combining multiparametric MRI achieved an AUC of 0.79
Michoux et al (2015)43	Not mentioned	69	T2-WI, DWI, 3D gradient echo axial T1-weighted sequence with fat suppression (SPAIR).	1.5 T	Texture features	To predict tumor response to NAC in breast cancer.	A model based on 4 pre-NAC parameters (inverse difference moment, GLN, LRHGE, washin) and k-means clustering as statistical classifier identified nonresponders with 84% sensitivity.
Panzeri et al (2018)44	Not mentioned	69	DWI-MRI, DCE-MRI	Not mentioned	First-order texture kinetics	To assess correlations between volumetric first-order texture parameters on baseline MRI and pathological response after NAC for locally advanced breast cancer.	Higher levels of AUC max (P value = .0338), AUC range (P value = .0311) and TME75 (P value = .0452) and lower levels of washout 10 (P value = .0417), washout 20 (P value = .0138), washout 25 (P = .0114), washout 30 (P value = .05) were predictive of noncomplete response.
Teruel et al (2014)45	Retrospective	58	DCE-MRI	3.0 T	Texture features	To investigate the potential of texture analysis to predict the clinical and pathological response to NAC in patients with locally advanced breast cancer (LABC) before NAC is started.	The most significant feature yielding an area under the curve (AUC) of 0.77 for response prediction for stable disease versus complete responders after 4 cycles of NAC.
Wu et al (2016)46	Not mentioned	35	DCE-MRI	3.0 T	Texture features	To predict pathological response of breast cancer to NAC.	In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (P = .002) in leave-one-out cross-validation. This improved upon conventional imaging predictors such as tumor volume (AUC = 0.53) and texture features based on whole-tumor analysis (AUC = 0.65)
Banerjee et al (2018)47	Retrospective	96	DCE-MRI	Not mentioned	Riesz, first-order statistical features, gray-level co-occurrence	To predict treatment response, specifically the residual tumor (RT) status and pathological complete response (pCR), in response to neoadjuvant chemotherapy.	The most efficient models are based on first-order statistics and Riesz wavelets, which predicted RT with an AUC value of 0.85 and pCR with an AUC value of 0.83, improving results reported in a previous study by approximately 13%.

Abbreviations: ADC, apparent diffusion coefficient; AUC, area under the curve; CoLlAGe, Co-occurrence of Local Anisotropic Gradient Orientations; DCE, dynamic contrast-enhanced; DWI, diffusion-weighted imaging; ER, estrogen receptor; HER-2, human epidermal growth factor receptor 2; MRI, magnetic resonance imaging; NAC, neoadjuvant chemotherapy; NAT, neoadjuvant therapy; pCR, pathological complete response; PR, progesterone receptor; ROC, receive operating characteristics; TN, triple negative; T1WI, T1-weighted image; T2WI, T2-weighted image; HR+: hormone receptor postive; LOOCV, leave-one-out cross-validation; TNBC, triple negative breast cancer; GLN, Gray-Level Nonuniformity; LRHGE, Long Run High Gray-Level Emphasis; LABC, locally advanced breast cancer.