Computer assistance for MR based diagnosis of breast cancer: Present and future challenges

Abstract

MR based methods have gained an important role for the clinical detection and diagnosis of breast cancer. Dynamic contrast-enhanced MRI of the breast has become a robust and successful method, especially for diagnosis of high-risk cases due to its higher sensitivity compared to X-ray mammography. The application of MR based imaging methods depends on various automated image processing routines. The combination of techniques for preprocessing, quantification and visualization of datasets is necessary to achieve fast and solid assessment of valuable parameters for diagnosis. In this paper, different aspects such as registration methods for the reduction of motion artifacts, segmentation issues, as well as morphologic and dynamic lesion analysis will be reviewed with a focus on breast MRI, MR spectroscopy and MR guided biopsies of the breast, their implications and technical challenges from a computer assistance point of view.

Introduction

Breast cancer is the second most prevalent cancer and the most common malignant disease in women in the industrialized countries. Besides common imaging techniques such as X-ray mammography and sonography used for screening and diagnosis of breast cancer, MR based methods such as dynamic contrast-enhanced MRI (DCE MRI or, more generally, breast MRI) or MR spectroscopy (MRS) have emerged as powerful diagnostic tools due to their high sensitivity. Other imaging techniques such as positron emission tomography or scintigraphy are used complementary in selected cases to further enhance specificity.

The use of breast MRI has a fundamental impact on the detection and diagnosis of breast cancer. Although X-ray mammography is the commonly used technique for screening and diagnosis in clinical practice, breast MRI has advantages due to its higher sensitivity and the absence of marring radiation.

Because of its ability to image dense breasts, MRI has a high potential for screening of high-risk cases. However, the reported specificities have displayed a very high range, varying from fairly low 20% to 100% [1], [2], [3]. Furthermore, microcalcifications are not visible on MR mammograms.

In addition, breast MRI, as is generally known, is associated with higher costs than X-ray mammography. Nevertheless, for women at high-familial risk of breast cancer, breast MRI might be a cost-effective screening modality [4].

The use of breast MRI as a screening modality is currently under investigation in several centers. In 1997 the MARIBS (magnetic resonance imaging in breast screening) study was initiated in the UK to evaluate breast MRI as a method of screening of premenopausal women with high risk of developing breast cancer [5]. Publications referring to the MARIBS study term that acceptable values of sensitivity and specificity were reached for MRI in these high-risk cases. A higher sensitivity of 77% was reported for MRI compared with 40% for X-ray [3]. Both methods combined resulted in a sensitivity of 94%. Specificity achieved only 81% for MR and 93% for X-ray mammography.

Breast MRI, however, is applied in selected cases. Added to standard imaging methods in cases of highly suspicious findings, breast MRI has shown to result in a change of treatment in many cases due to the assessment of further lesions in the contralateral breast or the assessment of multicentricity and multifocality [6], [7]. In combination with a better ability in the prognosis of tumor extent [7], [8], breast MRI is recommended for preoperative staging. Moreover, breast MRI should be applied in women with lymph node metastases and unknown primary cancer, as postoperative follow-up and for the examination of breast implants in patients suffering from symptoms, e.g. palpable findings or pain. Another important application of breast MRI is the monitoring of anti-cancer therapies for the assessment of drug effects on size of tumors and angiogenetic properties [9].

For the analysis of breast MRI data, both the importance of morphologic and the importance of kinetic parameter assessment was emphasized [10]. This has led to either a recommendation for a high spatial or a high temporal resolution of the MR sequences used. Recently, several studies have shown the importance of a combination for the discrimination of malignant from benign breast lesions [11], [12]. Moreover, the combination of morphologic and dynamic parameter assessment has proven to result in changes in recommended patient management and better treatment planning in many cases [6].

Combining different criteria, interpretation models (tree-shaped decision aids) have been evaluated in Refs. [12], [13] trying to differentiate malignant from benign disease. Furthermore, automated classification systems have been developed by several groups based on combinations of dynamic and morphologic features [14]. These systems as an addition to radiological reading were found to increase specificity and help in excluding benign lesions from further workup [15].

With the increasing amount of data in the acquired MR images, manual processing and interpretation becomes almost infeasible in a limited amount of time. There is an apparent need for computer assistance in preprocessing the data to extract and emphasize relevant information and present it to the radiologist. In addition, as the complexity of information increases, automated feature extraction for diagnostic purposes will become essential to improve the radiologists’ performance. Computer assistance for breast MRI has the potential to increase the specificity of the method. Deurloo et al. have stated that combining radiological reading with computer analysis has the potential to accurately exclude benign lesions from further workup [7]. Wiener et al. found that the implementation of computer-aided diagnosis for breast MRI will prove helpful [6]. An early system for computer assistance in DCE MRI in the breast was published by Meyer et al. [16].

In contrast to computerized analysis of X-ray mammograms, only a few investigations have pursued both the automated feature extraction and automated lesion classification. Most methods are based on the rating of features by radiologists followed by the merging of these ratings by an automated classifier [13]. Gilhuijs et al. developed a system for computerized delineation, rating and classification of breast lesions [14]. Chen et al. [17] evaluated a computerized interpretation of breast MRI using morphologic and kinetic features. Deurloo et al. [15] showed an improvement in lesion characterization rated indeterminate or suspicious by radiologists with a trained computer analysis system using a model that combined the results of the computerized analysis with those of clinical reading. Subramanian et al. [18] presented a system to identify, process, visualize and quantify breast lesions from MRI data.

The need for CAD is apparent when it comes to accurate diagnosis and regaining efficiency for radiologists. At present, there are two different meanings of “CAD” in its narrower sense: computer-aided detection and computer-aided diagnosis. The former denotes the detection of suspicious tissue and the latter denotes the differentiation of suspicious tissue into benign and malignant tissue. In computer-aided diagnosis, a computerized analysis serves as a second opinion for radiologists in the diagnosis of radiologic images. The goal is an estimate of the likelihood of malignancy based on computer-extracted features [19]. In computer-aided detection, a computer-based tool highlights suspicious areas to alert a human reader, thus trying to reduce the oversight error. In this article both aspects of CAD will be addressed.

Computer assistance in its wider sense additionally comprises automated or semi-automated procedures such as image preprocessing, image registration, image segmentation, color-overlay, signal processing for spectroscopic datasets and 3D rendering techniques.

In this paper the application of computerized methods for the evaluation of dynamic contrast-enhanced MRI of the breast will be discussed in more detail in Section 2 with an emphasis on registration methods for the reduction of motion artifacts, segmentation issues, as well as morphologic and dynamic lesion analyis. In the following sections the potentials of MR spectroscopy (Section 3) will be outlined and the application of MR guided biopsies (Section 4) is presented.