Heterogeneity in intratumoral regions with rapid gadolinium washout correlates with estrogen receptor status and nodal metastasis

Abstract

Purpose To evaluate heterogeneity within tumor subregions or “habitats” via textural kinetic analysis on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the classification of two clinical prognostic features; 1) estrogen receptor (ER)-positive from ER-negative tumors, and 2) tumors with four or more viable lymph node metastases after neoadjuvant chemotherapy from tumors without nodal metastases. Materials and Methods Two separate volumetric DCE-MRI datasets were obtained at 1.5T, comprised of bilateral axial dynamic 3D T1-weighted fat suppressed gradient recalled echo-pulse sequences obtained before and after gadolinium-based contrast administration. Representative image slices of breast tumors from 38 and 34 patients were used for ER status and lymph node classification, respectively. Four tumor habitats were defined based on their kinetic contrast enhancement characteristics. The heterogeneity within each habitat was quantified using textural kinetic features, which were evaluated using two feature selectors and three classifiers. Results Textural kinetic features from the habitat with rapid delayed washout yielded classification accuracies of 84.44% (area under the curve [AUC] 0.83) for ER and 88.89% (AUC 0.88) for lymph node status. The texture feature, information measure of correlation, most often chosen in cross-validations, measures heterogeneity and provides accuracy approximately the same as with the best feature set. Conclusion Heterogeneity within habitats with rapid washout is highly predictive of molecular tumor characteristics and clinical behavior.

DCE‐MRI texture analysis with tumor subregion partitioning for predicting Ki‐67 status of estrogen receptor‐positive breast cancers

“…Present studies of intratumoral analyses mainly focus on discriminating between patients who are responsive or nonresponsive to pathological neoadjuvant chemotherapy . Few studies have examined radiomic features inside tumors for molecular subtype classification . Whether a regional analysis within the tumor could be more informative than an analysis of the entire tumor remains unclear.…”

“…However, studies have suggested that intratumoral regions display distinct dynamic patterns, which reflect differing fundamental biological processes and differing prognostic potentials, 12 and could also possess valuable information that is not effectively captured by radiomic analysis of the entire tumor. [25][26][27] Present studies of intratumoral analyses mainly focus on discriminating between patients who are responsive or nonresponsive to pathological neoadjuvant chemotherapy. [27][28][29] Few studies have examined radiomic features inside tumors for molecular subtype classification.…”

DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response

“…In addition, the following 5 semiquantitative metrics were quantified from the DCE-MRI signal time-course data, and voxel-based parametric maps were generated (41, 54, 55): Initial enhancement (WashIn) describes the initial signal increase from the precontrast value to the postcontrast value, as defined by the following equation: $$\text{italicWashIn}=\frac{s_{\mathit{\text{max}}}-s_0}{s_0}$$ where s 0 is the average of the precontrast baseline signal intensity and s max is the postcontrast signal intensity ~90 seconds after completion of CA injection.Signal enhancement ratio (SER) characterizes the postcontrast signal curve shape, as defined by the following equation: $$\text{italicSER}=\frac{s_{\mathit{\text{max}}}-s_0}{s_{\mathit{\text{last}}}-s_0}$$ where s last is the last signal value.After initial enhancement (WashOut), the signal curve behavior is described after the initial phase of contrast enhancement, as follows: $$\text{italicWashOut}=\frac{s_{\mathit{\text{last}}}-s_{\mathit{\text{max}}}}{s_{\text{italicmax}}}$$Wash-in slope (SlopeIn) is a measure of the CA uptake rate, and is calculated using the following equation: $$\text{italicSlopeIn}=\frac{s_{\mathit{\text{max}}}-s_0}{t_{\text{italicmax}}}$$ where t max is the time when s max was measured.Area under the curve (AUC), the signal integral value, is defined using the following equation: $$\text{italicAUC}=true{\int }_{t_0}^{t_{\text{italiclast}}}s\left(t\right)dt$$ where t 0 and t last are the first and last time points, respectively.…”

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

“…174 DCE-MRI can be analyzed quantitatively by fitting the data to ad hoc or pharmacokinetic models that measure the time course of the presence of Cancer December 15, 2018 gadolinium within the tissues. 145,179,180 However, relevant to habitat imaging is that the lack of perfusion would be expected to correlate to hypoxia only in areas with relatively high cell density. 160,174 These parameters have been related to tumor angiogenesis and hypoxia by histological coregistration.…”