Table 4.
(a) Subtype classification (n = 35), (b) pCR prediction (n = 15), (c) lymph node metastasis detection (n = 7), (d) recurrence prediction (n = 6).
| Ref | MRI series | Study aim | Sub (V) 1 | Segmentation method | Radiomic features | Results |
|---|---|---|---|---|---|---|
| (a) | ||||||
| 17 | Multiparametric (1.5T) | Predict Ki67 status in breast cancer | 328(Y) | Manual (3D) | Higher‐order statistics 2 | Multiparametric model performed best with AUC 0.888, accuracy 0.971, sensitivity 0.993 and specificity 0.892 |
| 18 | Multiparametric (3T) | Predict Ki67 status in breast cancer | 144(N) | Semi‐automatic (2D) | Morphological; first‐ and second‐order statistics | Combining DWI and DCE (subtracted fifth post contrast image) produces best results with AUC 0.811 |
| 19 | DWI (3T) | Predict histological grade and Ki67 status using radiomic and deep learning methods | 322(Y) | Manual (2D) | Morphological | Prediction Ki67 expression had AUC 0.818 when using DWI and super resolution DWI images |
| 20 | Multiparametric (1.5T) | Differentiate molecular subtypes and predict Ki67 and HER2 status | 98(Y) | Semi‐automatic (3D) | First‐ and higher‐order statistics | Differentiated HER2+ and TNBC with AUC 0.97. Predicting Ki67 expression had AUC 0.81 |
| 21 | DCE (3T) | Predict Ki67 expression | 159(N) | Automatic (2D) | Morphological; first‐ and second‐order statistics | Classifying low and high Ki67 tumours occurred with statistical significance (P < 0.05) using a range of radiomic features |
| 22 | T2W images (1.5T) | Predict Ki67 status in breast cancer | 318(Y) | Manual (2D) | Morphological; first‐ and second‐order statistics | AUC, sensitivity, specificity and accuracy of the training set was 0.762, 0.72, 0.70 and 0.715 respectively |
| 23 | DWI (2 X 3T) | Develop a model for predicting Ki67 index | 128(Y) | Semi‐automatic (3D) | Morphological; first‐order statistics | Training cohort had AUC 0.75, accuracy 0.71, sensitivity 0.78 and specificity 0.72. Validation cohort had AUC 0.72, accuracy 0.7, sensitivity 0.71 and specificity 0.7 |
| 24 | DCE (1.5T) | Predict Ki67 status in breast cancer | 154(Y) | Manual (3D) | First‐ and second‐order statistics | Model produced AUC 0.785 in training and AUC 0.849 in validation cohort |
| 25 | DCE (3T) | Predict Ki67 status in breast cancer | 159(N) | Semi‐automatic (2D) | Morphological; first‐ and second‐order statistics | Texture features were most significant (P < 0.05). Model performed with AUC 0.773, accuracy 0.757, sensitivity 0.777 and specificity 0.769 |
| 26 | DCE (3T) | Predict Ki67 status in ER+ breast cancer patients based on heterogeneous tumour subregions | 77(N) | Semi‐automatic (3D) | Second‐order statistics | Model prediction based on the tumour subregion performed significantly (P < 0.01) better compared to the entire tumour |
| 27 | DCE (3T) | Predict HER2 and Ki67 status based on peritumoral and intratumoral regions | 351(Y) | Semi‐automatic (2D) | Morphological; first‐ and second‐order statistics 2 | Combined peritumoral and intratumoral model did not produce statistically significant improvements. AUC for predicting HER2 and Ki67 status were 0.713 and 0.749 respectively |
| 28 | DCE (1.5T) | Identify HER2 enriched tumours | 259(N) | Semi‐automatic (3D) | Morphological; first‐order statistics | HER2+ tumours had lower volume, smaller longest axial diameter, longest volumetric diameter, higher minimum signal intensity and lower entropy |
| 29 | Multiparametric (3T) | Predict HER2 status of patients with breast cancer | 306(Y) | Manual (3D) | Morphological; first‐ and second‐order statistics | Multiparametric model had the strongest results. AUC 0.8; sensitivity 88.7%; specificity 76.2% and accuracy 79.5% |
| 30 | DCE (3T) | Predict HER2 status based on tumour subregions | 76(N) | Semi‐automatic (2D) | First‐, second‐ and higher‐order statistics | Classifier performed best when analysing tumour subregions compared to the entire tumour, with AUC 0.929 and 0.847 respectively |
| 31 | DCE (3T) | Differentiate HER2 2+ status in breast tumours | 73(N) | Manual (2D) | First‐ and second‐order statistics | AUC 0.865, sensitivity 88.90%, specificity 73% and accuracy 81.06% |
| 32 | DCE (3T) | Predict HER2 2+ status | 92(N) | Semi‐automatic (2D) | Second‐order statistics | Best performance occurred with post‐contrast enhancement images with an AUC of 0.89 |
| 33 | DWI (3T) | Identify triple negative versus non‐triple negative tumours | 390(Y) | Manual (2D) | Morphological; First‐ and higher‐order statistics | Accuracy of discrimination between TNBC and non‐TNBC was 95.40% in training and 83.4% in validation cohort |
| 34 | DCE (3T) | Identify triple negative breast cancer | 84(N) | Semi‐automatic (3D) | First‐, second‐ and higher‐order statistics 2 | Classifying TNBC versus all others with a combined BPE and radiomics model improved prediction from AUC 0.782 to 0.878 |
| 35 | DCE (3T) | Identify ER positive breast tumours | 51(N) | Manual (2D) | Morphological; first‐order statistics | First‐order statistics features were significant for predicting ER+ tumours, with AUC >0.8 |
| 36 | Multiparametric (1.5T) | Identify characteristics of ER+ tumours | 75(N) | Manual (2D) | First‐ and higher‐order statistics | Higher grade tumours had significantly higher uniformity and lower entropy in T1W images, with the opposite true for T2W images |
| 37 | DWI (3T) | Evaluate breast cancer receptor status and molecular subtypes | 91(N) | Manual (2D) | First‐ and second‐order statistics | Accuracy of luminal B vs luminal A, HER2, TNBC and all others was 91.5%, 100%. 89.3% and 91.1% respectively |
| 38 | Multiparametric (3T) | Assess breast cancer receptor status and molecular subtypes | 91(Y 3 ) | Manual (2D) | Morphological; First‐ and second‐order statistics | Accuracy of luminal B vs luminal A, TNBC and all others was 84.2%, 83.9% and 89% respectively. Accuracy of HER2+ vs. all others was 81.3% |
| 39 | Multiparametric (1.5T) | Differentiate ER status in luminal A and luminal B tumours | 27 4 (N) | Manual (2D) | Second‐order statistics | Prediction of luminal B tumours had AUC 0.878, sensitivity 91.7% and specificity 86.7% |
| 40 | Multiparametric (1.5T & 3T) | Predict breast cancer molecular subtypes | 148(Y 5 ) | Manual (2D) | Second‐order statistics | Predictive model had an accuracy of 74.7% and AUC 0.816. In prospective validation cohort, accuracy was 72.5% and AUC 0.823 |
| 41 | Multiparametric (3T) | Differentiate TNBC from other subtypes | 134(N) | Semi‐automatic (3D) | First‐order statistics | Best performance noted when distinguishing between TNBC and HER2+, with AUC 0.763, sensitivity 86.4% and specificity 72.2% |
| 42 | DWI (3T) | Predict clinical‐pathological subtypes | 112(N) | Manual (3D) | First‐ and second‐order statistics | Accuracy for predicting individual subtypes is 97% luminal A, 100% luminal B (HER2−), 94% luminal B (HER2+), 92% HER2+ and 100% TNBC |
| 43 | DCE (1.5T) | Distinguish between molecular subtypes using a multi‐institutional data set | 91 4 (N) | Semi‐automatic (3D) | Morphological; first‐ and second‐order statistics | Model distinguished between ER+ vs ER‐ with AUC 0.89, PR+ vs PR‐ with AUC 0.69, HER2+ vs. HER2− with AUC 0.65 and TNBC vs all other with AUC 0.67 |
| 44 | DCE (1.5T) | Predict molecular subtype | 96(Y) | Semi‐automatic (3D) | Morphological; first‐ and second‐order statistics; kinetic 2 | Model had overall prediction AUC of 0.869. Discrimination among luminal A, luminal B, HER2 and TNBC subtypes had AUC 0.867, 0.786, 0.888 and 0.923 respectively |
| 45 | DCE (1.5T & 3T) | Identify relationship between radiomic features and molecular subtypes | 275(N) | Semi‐automatic (2D) | Morphological; first‐order statistics; kinetic | Model predicted luminal A (P = 0.0007) and luminal B (P = 0.0063) subtypes with statistical significance |
| 46 | DCE (1.5T & 3T) | Association of imaging phenotypes with molecular subtypes | 922 4 (Y) | Semi‐automatic (3D) | Morphological; first‐order statistics | Statistically significant results for distinguishing luminal A vs. all others (AUC 0.697) and TNBC vs. all others (AUC 0.654) |
| 13 | DCE (3T) | Predict histological outcomes and molecular phenotypes | 49(N) | Semi‐automatic (3D) | Morphological; first‐ and second‐order statistics | Discriminating between subtypes produced AUC >0.8 for all categories. Best results for PR+/PR− with AUC 0.875 |
| 47 | Multiparametric (3T) | Classify IHC subtypes based on machine learning analysis | 134(Y) | Semi‐automatic (3D) | Morphological; second‐order statistics | Accuracy of predicting TNBC vs non‐TNBC ranged from 72.4–91% |
| 48 | DCE (1.5T & 3T) | Distinguish between molecular subtypes using deep learning models | 270(N) | Semi‐automatic (2D) | Second‐order statistics | Deep learning model performed better than training from scratch and transfer learning, with AUC's of 0.65, 0.58 and 0.6 respectively |
| 49 | DCE (1.5T) | Classify IHC subtypes | 60(N) | Semi‐automatic (2D) | Morphological; first‐ and second‐order statistics | Model classified luminal A, HER2+ and TNBC with statistical significance (P < 0.05) |
| 50 | Multiparametric (1.5T & 3T) | Classify molecular subtypes | 107(N) | Manual (2D) | Morphological; first‐ and second‐order statistics | Accuracy of classification was best on the 3T scanner with 86.4% on contrast enhanced T1W and 88.6% on DWI |
| (b) | ||||||
| 51 | DCE (3T) | Predict pCR following NAC in HER2+ tumours | 311(Y) | Manual (3D) | Second‐order statistics 2 | Sensitivity 86.5%; specificity 80%; accuracy 84.9% |
| 28 | DCE (1.5T) | Predict pCR following NAC | 259(N) | Semi‐automatic (3D) | Morphological; second‐order statistics | Model predicted pCR in TNBC with AUC of 0.974 |
| 6 | DCE (1.5) | Predict DFS following NAC in HER2+ tumours | 127(Y) | Manual (2D) | Morphological; second‐order statistics 2 | AUC 0.974 in combined radiomic‐clinicopathological model |
| 52 | DCE (1.5T & 3T) | Pre‐treatment prediction of response to NAC | 158(N) | Semi‐automatic (4D) | Morphological; first‐ & second‐order statistics | Two individual features could predict pCR in pre‐NAC images with AUC of 0.82 and 0.73, which are maximum voxel value and surface area to volume ratio respectively |
| 53 | DCE (3T) | Explore relationship between tumour shrinkage and initial tumour enhancement with pCR in HER2+ breast cancer | 51(N) | Semi‐automatic (2D) | Morphological | Tumour shrinkage pattern was associated with pCR with an AUC 0.778 |
| 54 | DWI (1.5T) | Predict non‐response to NAC | 69(N) | Manual (2D) | Morphological; second‐order statistics; kinetic | Texture (second‐order statistics) and kinetic features could best predict non‐response to NAC, with accuracy, sensitivity and specificity all measuring 74% |
| 55 | DCE (1.5T & 3T) | Predict pCR following NAC by using peritumoral and intratumoral regions | 117(Y) | Manual (2D) | First‐ and second‐order statistics; kinetic | For all subtypes, combined peritumoral and intratumoral model had best results with AUC 0.78 |
| 56 | DCE (1.5T & 3T) | Predict pCR to neoadjuvant therapies | 288(Y) | Manual (3D) | Morphological; first‐ and second‐order statistics | Statistically significant (P < 0.002) results found for predicting pCR in TNBC and HER2+ patients, with AUC 0.707 |
| 57 | DCE (1.5T & 3T) | Characterise HER2+ tumours and estimate response to neoadjuvant therapy | 209 4 (Y) | Manual (2D) | First‐, second‐ and higher‐order statistics | Combined peritumoral and intratumoral model could identify HER2+ tumours and predict response to treatment with AUC 0.89 and 0.80 respectively |
| 58 | Multiparametric (3T) | Predict pCR to NAC | 91(Y) | Manual (3D) | Morphological; first‐ and second‐order statistics | Combined ADC and DCE model had best predictive power with AUC 0.931, accuracy 0.825, specificity 0.766 and sensitivity 1.0 |
| 59 | Multiparametric | Predict pCR to pre‐treatment NAC | 414 4 (Y 3 ) | Manual (2D) | Morphological; first‐ and second‐order statistics | Multiparametric model had highest AUC of 0.79, compared to other models that all had AUC <0.7. Predicting TNBC had AUC 0.96 |
| 60 | DCE (1.5T & 3T) | Predict breast cancers insensitive to NAC | 125(Y) | Manual (2D) | First‐ and second‐order statistics 2 | Combined radiomic‐clinicopathological model produced the best results of AUC 0.986 |
| 61 | Multiparametric (1.5T & 3T) | Predict breast cancer regression patterns following NAC | 144(Y) | Manual (2D) | Morphological; first‐order statistics 2 | Combined radiomic‐clinicopathological model produced the best results of AUC 0.902 |
| 62 | DCE (3T) | Predict efficacy of NAC based on machine learning techniques | 158(Y) | Manual (2D) | First‐ and second‐order statistics 2 | Combined radiomic‐clinicopathological model produced best results with AUC 0.888, sensitivity 86.96% and specificity 79.31% |
| 63 | T2W (3T) | Predict RCB following NAC | 88(N) | Manual (2D) | First‐ and second‐order statistics | Predicting pCR across all lesions had an accuracy of 85.2% |
| (c) | ||||||
| 64 | DCE (3T) | Determine the level of tumour infiltrating lymphocytes | 172(Y) | Semi‐automatic (2D) | First‐ and second‐order statistics 2 | Combining the radiomic and clinical model improved detection of infiltrating lymphocytes from AUC 0.742 to 0.800 |
| 65 | DCE (3T) | Predict axillary sentinel lymph node metastasis | 62(Y) | Manual (3D) | Morphological; first‐, second‐and higher‐order statistics | AUC 0.82; accuracy 0.76; sensitivity 0.75; specificity 0.76 |
| 66 | DCE (3T) | Predict axillary lymph node metastasis | 329(Y) | Manual (3D) | Morphological; first‐ and second‐order statistics 2 | Combined radiomic‐clinicopathological model performed best. AUC 0.894; sensitivity 81.25%, specificity 82.08%; accuracy 81.78% |
| 67 | Multiparametric (1.5T) | Predict sentinel lymph node metastasis | 146(Y) | Manual (3D) | Second‐order statistics | Multiparametric model produced the best results. AUC 0.863; sensitivity 0.663; specificity 0.816 |
| 68 | DCE (1.5T) | Predict lymph node metastasis based on existing dataset | 91 4 (N) | Semi‐automatic (2D) | Morphological; second‐order statistics; kinetic 2 | Genomic features performed best compared to the radiomics model with AUC 0.916 and 0.775 respectively |
| 69 | Multiparametric (3T) | Predict axillary lymph node metastasis | 120(N) | Manual (2D) | Morphological; first‐, second‐and higher‐order statistics; kinetic | Addition of kinetic features improved model performance to AUC 0.91 and accuracy 86.37 |
| 70 | DCE (3T) | Predict axillary lymph node metastasis | 102(N) | Semi‐automatic (2D) | Morphological; second‐order statistics | Combining morphological and texture features improved results. Accuracy 89.54%; sensitivity 94.5%; specificity 80.06% |
| (d) | ||||||
| 28 | DCE (3T) | Predict RCB in patients treated with NAC | 259(N) | Semi‐automatic (2D) | Morphological; first‐order statistics | In TNBC, RCB was associated with mean, minimum, maximum and standard deviation of signal intensity |
| 71 | DCE | Identify imaging phenotypes of heterogeneity and its prognostic ability to predict 10 year recurrence | 95(Y 3 ) | Semi‐automatic (3D) | Morphological; first‐ and second‐order statistics 2 | Nomogram consisting of radiomic features and heterogeneity phenotype improved prediction from c‐statistic 0.55 to 0.73 |
| 11 | DCE (1.5T) | Predict recurrence prognosis in ER+ tumours | 78(N) | Manual (3D) | First‐order statistics; Kinetic | Kurtosis in delayed enhancement phase significantly indicates high recurrence (P = 0.0116). Volume ratio of slow persistent was associated with the low‐risk group (P = 0.041) |
| 72 | DCE (1.5) | Estimate disease free survival and recurrence rates | 294(Y) | Manual (2D) | Morphological; second‐ & higher‐order statistics 2 | Combined radiomic‐clinicopathological model predicted recurrence with c‐index of 0.76 |
| 73 | DCE (1.5T) | Predict recurrence free survival | 162 4 (N) | Semi‐automatic (3D) | Morphological | High METV was predictive of recurrence and statistically significant in the HR+/HER2− (P = 0.012) and HER2+ (P = 0.036) subtypes |
| 74 | DCE (3T) | Predict cancer angiogenesis for tumour biology and patient outcomes | 27(N) | Semi‐automatic (3D) | Morphological; first‐ and second‐order statistics; kinetic 2 | Cancers with increased micro‐vessel density had higher peak signal ratio enhancement (AUC 0.79). Second‐order statistics features could determine angiogenesis with AUC >0.83 |
Note: Predicting Ki67 (red), HER2 (yellow), TNBC (blue), ER and PR (white) and Subtype (green). Assumption is made that when P < 0.05 the null hypothesis is rejected and therefore the results are statistically significant.
Abbreviations: ADC, apparent diffusion coefficient maps; AUC, area under the curve; BPE, background parenchymal enhancement; DCE, dynamic contrast enhanced; DFS, disease‐free survival; DWI, diffusion weighted imaging; ER, oestrogen receptor; HER2, human epidermal growth factor 2; METV, most enhancing tumour volume; pCR, pathologically complete response; PR, progesterone receptor; RCB, residual cancer burden; T, Tesla; TNBC, triple negative breast cancer.
1
Number of subjects (whether conducted a validation on external set).
2
Clinical features used.
3
External validation cohort.
4
Multicentre study.
5
Prospective validation cohort.