A computer-aided diagnosis (CAD) scheme for pretreatment prediction of pathological response to neoadjuvant therapy using dynamic contrast-enhanced MRI texture features

Abstract

Objective: To assess whether a computer-aided, diagnosis (CAD) system can predict pathological Complete Response (pCR) to neoadjuvant chemotherapy (NAC) prior to treatment using texture features.

Methods: Response to treatment of 44 patients was defined according to the histopatology of resected tumour and extracted axillary nodes in two ways: (a) pCR+ (Smith's Grade = 5) vs pCR- (Smith's Grade < 5); (b) pCRN+ (pCR+ and absence of residual lymph node metastases) vs pCRN - . A CAD system was developed to: (i) segment the breasts; (ii) register the DCE-MRI sequence; (iii) detect the lesion and (iv) extract 27 3D texture features. The role of individual texture features, multiparametric models and Bayesian classifiers in predicting patients' response to NAC were evaluated.

Results: A cross-validated Bayesian classifier fed with 6 features was able to predict pCR with a specificity of 72% and a sensitivity of 67%. Conversely, 2 features were used by the Bayesian classifier to predict pCRN, obtaining a sensitivity of 69% and a specificity of 61%.

Conclusion: A CAD scheme, that extracts texture features from an automatically segmented 3D mask of the tumour, could predict pathological response to NAC. Additional research should be performed to validate these promising results on a larger cohort of patients and using different classification strategies. Advances in knowledge: This is the first study assessing the role of an automatic CAD system in predicting the pathological response to NAC before treatment. Fully automatic methods represent the backbone of standardized analysis and may help in timely managing patients candidate to NAC.

Figure 1.

60 mm invasive ductal carcinoma in a 74 year-oldfemale. (a) Result of the breast segmentation algorithm superimposed to the first contrast-enhanced image subtracted to the precontrast one. (b) Normalized maximum intensity projection over time image of the breast region. (c) Tumour segmentation obtained by the computer-aided diagnosis scheme superimposed to the maximum intensity projection over time image. Once the segmentation has been obtained, the radiologist selected the tumour to exclude false positive findings (red box). (d) Three-dimensional render of the mask of the tumour multiplied for the subtracted first contrast-enhanced frame. The 2 most discriminative features of both grey-level co-occurrence matrices and grey-level run length method algorithm are reported for this tumour. Pathological response Grade = 3/5, estrogen receptorstatus = 99%, progesterone receptor status = 13% andKi67 = 11%, HER2 status= negative.

Figure 2.

26 mm invasive ductal carcinoma in a 43 year-oldfemale. (a) Result of the breast segmentation algorithm superimposed to the first contrastenhanced image subtracted to the precontrast one. (b) Normalized maximum intensity projection over time of the breast region. (c) Tumour segmentation obtained by the CAD scheme superimposed to the maximum intensity projection over timeimage. Once the segmentation has been obtained, the radiologist selected the tumour to exclude false positive findings (red box). (d) Three-dimensional render of the mask of the tumour multiplied for the subtracted first contrastenhanced frame. The 2 most discriminative features of both GLCM and GLRLM algorithm are reported for this tumour. Pathological Complete Response (5/5), estrogen receptorstatus = 20%, progesterone receptor = status 25%, Ki67 = 30%, HER2 status = positive.

Figure 3.

Receiver operating characteristic(ROC) curve of the logistic regression classifier compared with the ROC curves of the most discriminative individual features in predicting complete pathological response.