Histogram Analysis and Visual Heterogeneity of Diffusion-Weighted Imaging with Apparent Diffusion Coefficient Mapping in the Prediction of Molecular Subtypes of Invasive Breast Cancers.

Joao V Horvat,Blanca Bernard-Davila,Dilip Giri,R Elena Ochoa-Albiztegui,Doris Leithner,Olivia M Sutton,Elizabeth A Morris,Aditya Apte,Danny F Martinez,Aditi Iyer,Sunitha B Thakur,Katja Pinker

doi:10.1155/2019/2972189

Abstract

Objective To investigate if histogram analysis and visually assessed heterogeneity of diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping can predict molecular subtypes of invasive breast cancers. Materials and Methods In this retrospective study, 91 patients with invasive breast carcinoma who underwent preoperative magnetic resonance imaging (MRI) with DWI at our institution were included. Two radiologists delineated a 2-D region of interest (ROI) on ADC maps in consensus. Tumors were also independently classified into low and high heterogeneity based on visual assessment of DWI. First-order statistics extracted through histogram analysis within the ROI of the ADC maps (mean, 10th percentile, 50th percentile, 90th percentile, standard deviation, kurtosis, and skewness) and visually assessed heterogeneity were evaluated for associations with tumor receptor status (ER, PR, and HER2 status) as well as molecular subtype. Results HER2-positive lesions demonstrated significantly higher mean (p=0.034), Perc50 (p=0.046), and Perc90 (p=0.040), with AUCs of 0.605, 0.592, and 0.652, respectively, than HER2-negative lesions. No significant differences were found in the histogram values for ER and PR statuses. Neither quantitative histogram analysis based on ADC maps nor qualitative visual heterogeneity assessment of DWI images was able to significantly differentiate between molecular subtypes, i.e., luminal A versus all other subtypes (luminal B, HER2-enriched, and triple negative) combined, luminal A and B combined versus HER2-enriched and triple negative combined, and triple negative versus all other types combined. Conclusion Histogram analysis and visual heterogeneity assessment cannot be used to differentiate molecular subtypes of invasive breast cancer.

Highlights

Breast cancer classification according to tumor molecular subtype is nowadays routinely performed and is used to predict cancer aggressiveness and to guide recommendations for systemic treatments
Firstorder statistics extracted through histogram analysis within the region of interest (ROI) of the apparent diffusion coefficient (ADC) maps and visually assessed heterogeneity were evaluated for associations with tumor receptor status (ER, progesterone receptor (PR), and HER2 status) as well as molecular subtype
Neither quantitative histogram analysis based on ADC maps nor qualitative visual heterogeneity assessment of diffusion-weighted imaging (DWI) images was able to signi cantly di erentiate between molecular subtypes, i.e., luminal A versus all other subtypes combined, luminal A and B combined versus human epidermal growth factor receptor 2- (HER2-)enriched and triple negative combined, and triple negative versus all other types combined

Summary

Introduction

Breast cancer classification according to tumor molecular subtype is nowadays routinely performed and is used to predict cancer aggressiveness and to guide recommendations for systemic treatments. Breast cancer can be classified into four molecular subtypes (luminal A, luminal B, human epidermal growth factor receptor 2- (HER2-) enriched, and triple negative) that present with distinctly different prognoses and treatment responses [1, 2]. More aggressive molecular subtypes such as triple negative and HER2-enriched cancers have a propensity for metastatic disease and require treatment with either cytotoxic chemotherapy or the combination of cytotoxic chemotherapy and targeted anti-HER2 treatment [4,5,6]. In addition to molecular subtypes, intratumoral heterogeneity, i.e., the presence of cell clones of different levels of aggressiveness within one lesion, has been linked to tumor aggressiveness and poor prognosis [7]. As more and more tumors are being treated with either neoadjuvant cytotoxic or endocrine treatment, it is increasingly important to have the ability to achieve an accurate assessment of tumor biology in the preoperative setting [8]

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