A New Challenge for Radiologists: Radiomics in Breast Cancer

Table 1

Specifications of radiomics studies included in this narrative review.

Reference	Study design	Patients; (No)	Diagnostic modality	Radiomics imaging features selected (No)	Prediction	Sensitivity	Specificity	Accurancy	AUC
Parekh et al. (2017) [13]	Retrospective	124	MRI (3T)	690 (RFMs)	Malignancy	93	85
Whitney et al. (2018) [14]	Retrospective	508	MRI (1.5 and 3 T)	38	Malignancy				0.846 (including size features); 0.848 (excluding size features)
Bickelhaupt et al. (2017) [15]	Retrospective	50	MRI (1.5 T)	188	Malignancy				0.842-0.851
Bickelhaupt et al. (2018) [16]	Retrospective	222	MRI (1.5 T)	359	Malignancy	98.4	69.7
Zhang et al. (2017)[17]	Retrospective	117	US	364	Malignancy	85.7	89.3
Tagliafico et al. (2018)[18]	Prospective	20	Mammography (DBT)	104	Malignancy				0.567
Braman et al. (2017)[19]	Retrospective	117	MRI (1.5 and 3 T)	99	NAC				0.78 (training dataset); 0.74(independent testing set); 0.83 (HR+, HER2−); 0.93 (TN/HER2+)
Dong et al. (2017)[20]	Retrospective	146	MRI (1.5 T)	25	Prognostic factors				0.847 (training set; model 10 T2-fat suppression); 0.770 (validation set; model 10 T2-fat suppression); 0.847 (training set; model 8 DWI); 0.787 (validation set; model 8); 0.863 (training set; model 10 joint T2-fat suppression/DWI); 0.805 (validation set; model 10 joint T2-fat suppression/DWI)
Obeid et al. (2016) [21]	Retrospective	63	MRI (1.5 and 3 T)	13	Prognostic factors	-	-	-	-
Ma et al. (2018)[22]	Retrospective	377	MRI (3 T)	56	Prognostic factors	77.7	76.9	0.757	0.773
Liang et al. (2018)[23]	Retrospective	318	MRI (1.5 T)	30	Prognostic factors				0.762; (training dataset); 0.740; (validation dataset)
Guo et al. (2015)[24]	Retrospective	91	MRI (1.5 T)	38	Molecular subtypes				0.877 (stage); 0.693 (lymph node); 0.789 (ER); 0.689 (PR); 0.641 (HER2)
Li et al. (2016) [4]	Retrospective	91	MRI (1.5 T)	38	Molecular subtypes				0.89 (ER+ vs ER−); 0.69 (PR+ vs PR-); 0.65 (HER”+ vs HER2-); 0.67 TN vs others)
Wang et al. (2015) [25]	Retrospective	84	MRI (3 T)	85	Molecular subtypes	57.0 (TN vs others); 62.0 (TN vs ER+); 53.0 (TN vs PR+); 49.5 (TN vs LumA); 69.5 (TN vs LumB)	94.7(TN vs others); 93.6 (TN vs ER+); 94.1 (TN vs PR+); 89.8(TN vs LumA); 90.0 (TN vs LumB)	90.0 (TN vs others); 89.4 (TN vs ER+); 87.8 (TN vs PR+); 81.8 (TN vs LumA); 84.3 (TN vs LumB)	0.878 (TN vs others); 0.883 (TN vs ER+); 0.859 (TN vs PR+); 0.814 (TN vs LumA); 0.789 (TN vs LumB)
Fan et al. (2017)[26]	Retrospective	60	MRI (1.5 T)	88	Molecular subtypes	88. 2 (LumA); 86.5 (LumB); 81.1 (HER2); 81.1 (basal-like)	76.9 (LumA); 62.5 (LumB); 100 (HER2); 100 (basal-like)		0.867 (LumA); 0.786 (LumB); 0.888 (HER2); 0.923 (basal-like)
Guo et al. (2017)[27]	Retrospective	215	US	463	Molecular subtypes				0.760
Ma et al. (2018)[28]	Retrospective	331	Mammography	39	Molecular subtypes				0.865 (TN vs non TN); 0.784 (HER2 vs non HER2); 0.752 (Lum vs non-Lum)
Li et al. (2016) [29]	Retrospective	84	MRI (1.5 and 3T)	38	Recurrence				0.88 (MammaPrint); 0.76 (Oncotype DX); 0.68 (PAM50 risk of relapse based on subtype); 0.55 (PAM50 risk of relapse based on subtype and proliferation)
Park et al. (2018)[30]	Retrospective	294	MRI (1.5 T)	156	Recurrence	-	-	-	-
Drukker et al. (2018)[31]	Retrospective	162	MRI (1.5 T)	1	Recurrence	-	-	-	-

(i) UC considering only radiomics models.
(ii) onsidering both tumor and BPE features.