TABLE 3.
Review of Machine-Learning Studies Predicting Response to Neoadjuvant Chemotherapy
| Study | Patients (N, subgroups) | MR sequences | Features | Machine-learning classifier | Results |
|---|---|---|---|---|---|
| Braman (2017) | 117 HR+, TN, HER2+ | DCE, pretherapy | Intratumoral and peritumoral texture | LDA, DLDA,* quadratic discriminant analysis, naive Bayes,* SVM | AUC 0.78 all patients; AUC 0.93 for TN/HER2+ |
| Cain (2019) | 288 HR+, TN/HER2+ | 1st postcontrast subtraction, pretherapy | Fibroglandular tissue (nontumor) and tumor volume, | enhancement, texture | Logistic regression,* SVM |
| AUC 0.707 in TN/HER2+ Tahmassebi (2019) | 38 | T2, DCE, DWI, pretherapy | BIRADS descriptors, | ||
| pharmacokinetic, ADC values | SVM, LDA, logistic regression, random forests, stochastic gradient descent, decision tree, adaptive boosting, XGBoost* | AUC 0.86 for RCB class | |||
| Machireddy (2019) | 55 | DCE, pretherapy and after 1st cycle | Texture, | multiresolution fractal analysis | SVM |
| AUC 0.91 Banerjee (2018) | 53, TN | DCE, pre and posttherapy | Intensity, texture, shape, Riesz wavelets | Lasso, SVM | AUC 0.83 |
| Johansen (2007) | 24 | DCE, pre and after 1st cycle of therapy | Pre and | posttreatment change in signal intensity | Probabilistic neural network and Kohonen neural network |
| Significant difference between pCR and non-pCR groups | |||||
| Aghaei (2015) | 68 | DCE, pretherapy | Kinetics of necrotic and enhancing tumor, background parenchyma | ANN | AUC 0.96 |
| Giannini (2017) | 44 | 1st postcontrast subtraction, pretherapy | Texture | Bayesian | 70% accuracy |
| Wu (2016) | 35 | DCE, before and after first cycle of chemo | Texture within tumor subregions | LASSO and logistic regression | AUC 0.79 |
| Liu (2019) | 414, HR+, TN, HER2+ | T2, DWI, postcontrast, pretherapy | Morphology, texture, wavelet | SVM | AUC 0.79 |
| Braman (2019) | 209, HR+, TN, HER2+ | DCE, pretherapy | Intratumoral and peritumoral texture | DLDA | AUC 0.89 |
| Aghaei (2016) | 151 | DCE, pretherapy | Global kinetic (both breasts) | ANN | AUC 0.83 |
| Fan (2017) | 57 | DCE, pretherapy | Morphology, dynamic, texture | Wrapper Subset Evaluator | AUC 0.874 |
| Ha (2018) | 141, HR+, triple positive, TN, HER2+ | First postcontrast T1, pretherapy | (unsupervised learning) | CNN | 88% accuracy |
| Ravichandran (2018) | 168, HER2 status | Pre and postcontrast, pretherapy | (unsupervised learning) | CNN | AUC 0.85 |
HR = hormone receptor; LDA = linear discriminant analysis; DLDA = diagonal linear discriminant analysis; SVM = support vector machine; CNN = convolutional neural network; TN = triple negative.
*
Better-performing classifier.