Pharmacokinetic Tumor Heterogeneity as a Prognostic Biomarker for Classifying Breast Cancer Recurrence Risk

Abstract

Goal: Heterogeneity in cancer can affect response to therapy and patient prognosis. Histologic measures have classically been used to measure heterogeneity, although a reliable noninvasive measurement is needed both to establish baseline risk of recurrence and monitor response to treatment. Here, we propose using spatiotemporal wavelet kinetic features from dynamic contrast-enhanced magnetic resonance imaging to quantify intratumor heterogeneity in breast cancer.

Methods: Tumor pixels are first partitioned into homogeneous subregions using pharmacokinetic measures. Heterogeneity wavelet kinetic (HetWave) features are then extracted from these partitions to obtain spatiotemporal patterns of the wavelet coefficients and the contrast agent uptake. The HetWave features are evaluated in terms of their prognostic value using a logistic regression classifier with genetic algorithm wrapper-based feature selection to classify breast cancer recurrence risk as determined by a validated gene expression assay.

Results: Receiver operating characteristic analysis and area under the curve (AUC) are computed to assess classifier performance using leave-one-out cross validation. The HetWave features outperform other commonly used features (AUC = 0.88 HetWave versus 0.70 standard features). The combination of HetWave and standard features further increases classifier performance (AUCs 0.94).

Conclusion: The rate of the spatial frequency pattern over the pharmacokinetic partitions can provide valuable prognostic information.

Significance: HetWave could be a powerful feature extraction approach for characterizing tumor heterogeneity, providing valuable prognostic information.

Fig. 1.

First post-contrast breast DCE-MR images (first column) and corresponding PK heterogeneity partitioning (second column; teal, gold, and dark red partitions represent low, medium, and high maximum first contrast uptake) for (a), (b) low recurrence risk and (c), (d) high recurrence risk tumor examples.

Fig. 2.

Low (first row) and high (third row) recurrent tumor contrast images and the corresponding wavelet images from the first post-contrast DCE-MRI scan (second and fourth rows); (a)–(d), pre- and post-contrast images of the lesion; (e) approximate wavelet coefficients at DP1; (f) horizontal wavelet coefficients at DP1; (g) vertical wavelet coefficients at DP1; (h) diagonal wavelet coefficients at DP2; (i)–(p) are similar to the previous description but for a tumor of high risk of recurrence.

Fig. 3.

Block diagram of the HetWave feature extraction based on tumor heterogeneity partitioning and recurrence risk classification.

Fig. 4.

Classifier performance using ROC curves of HetWave features, standard features, and their combination based on PK partitioning.

Fig. 5.

Box plot of the most frequently selected features when the HetWave features are used in combination with standard features.

Fig. 6.

Heat-map showing intrinsic phenotypic heterogeneity patterns with rows representing the most frequently selected DCE-MRI features based on PK heterogeneity partitioning and columns representing tumors. The corresponding gene expression scores are shown in the colorbar.

Fig. 7.

Classifier performance using ROC curves of HetWave and ICA features.