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MRI of Lung Cancer

The recent report of the National Lung Screening Trial showed a 20% reduction in mortality due to the early detection of lung cancer (1). The recent review entitled “Screening and Early Detection of Lung Cancer” by van’t Westeinde and van Klaveren (2) illustrates some of the common findings across a number of these low dose CT based screening trials. Some of these are also discussed by Blanchon et al (3). These findings, primarily the relatively high rate of detection of non-calcified nodules (NCN) of about 45% and a true incidence of cancer of about 1.5%. Moreover, as pointed out by van’t Westeinde and van Klaveren (2) even with very stringent follow up algorithms by the NELSON trial (4), the rate of false positive findings at surgery was still quite high. This concern has also been published by the MAYO trial (5). A number of imaging examinations have been evaluated as secondary characterization methods to reduce this number of false positive results at surgery. These include F-18 fluoro-deoxyglucose positron emission tomography (FDG-PET), dynamic contrast CT (DCECT), 99m-Tc-depreotide single photon emission tomography (SPECT), and dynamic contrast enhanced MRI (DCEMRI). The diagnostic accuracies of these methods have been compared in a meta-analysis by Cronin et al. (6, 7). The results are complied below in Table 1.

 

Table 1. The Diagnostic Performances of Four Imaging Tests for the Characterization of Solitary Pulmonary Nodules (adapted from Cronin et al. (7)).

Test Sensitivity Specificity PPV NPV
DCECT 0.99 0.66 0.90 0.91
DCEMRI 0.99 0.62 0.83 0.95
SPECT 0.98 0.61 0.80 0.96
FDG-PET 0.99 0.67 0.91 0.90

The rationale for employing DCEMRI in evaluating suspicious SPN’s is that tumors are more angiogenic than non-malignant tissues (8). The rationale for employing FDG-PET is based on the premise that malignant nodules will exhibit increased glucose uptake and metabolism than benign nodules. This phenomenon is the well-known “Warburg Effect” (see for example (9)).

DCECT can measure significant aspects of angiogenesis in lung nodules. For example,(10-12) the use of DCECT in evaluating patients with non-small cell lung cancer (NSCLC has recently been reviewed by Ng and Goh (13). In patients with operable NSCLC, CT measurements of peak enhancement, blood flow (BF), and relative blood volume (BV) were significantly higher in VEGF-positive compared with VEGF-negative tumors (11, 12, 14). DCECT derived values of peak enhancement, BF, BV, and the permeability surface product (PS also kep) were also shown to correlate significantly with micro-vascular density (MVD) (10-12). In patients with solitary pulmonary nodules, peak enhancement correlated significantly with both MVD and VEGF expression (15). These correlations may have both diagnostic and prognostic value since tumor angiogenic factors have been shown to have potential prognostic significance in lung cancer. Both VEGF expression and MVD have been associated with poor prognosis albeit with some conflicting findings reported in the literature (13).

Following the early report of Schaefer et al. (16), there have been a number of studies evaluation both the diagnostic capability of DCEMRI in distinguishing benign from malignant nodules and the correlation of DCEMRI derived parameters and angiogenic factors (see for examples (17-20)). As shown in the meta-analysis (6, 7) there is a great deal of diagnostic potential in DCEMRI. This was confirmed in the report by Zou et al. (20), who also showed that there was a good correlation between the steepest slope of enhancement and the MVD. One of the limitations of this report was the lack of any pharmacokinetic fitting of the DCEMRI data to give reliable perfusion parameters.

Although there have been relatively few reports of DWI in SPN’s, the early results are encouraging (21-23) for two reasons. First DWI has shown the ability to discriminate between benign and malignant lesions. Liu et al. (21) have reported that although the signal intensities of pulmonary malignant tumors and solid benign lesions were not significantly different, but the ADC value of benign lesions was statistically higher than that of malignant tumors (p = 0.001). Razek et al. (22) have proposed that the ADC value of the lung cancer can be considered as a new prognostic parameter. They found that a lower ADC value of the malignant nodule is associated with higher pathological tumor grade and metastatic lymph nodes.

An additional application of DWI is in the evaluation of one particular subset of these pulmonary nodules detected by CT, called “subsolid” nodules, however, presents particular challenges to the current diagnostic paradigms based on growth assessed on longitudinal CT examination (24, 25). On one hand, subsolid nodules are common, reaching an incidence of 20% among all lung cancers, and have a 40%-50% malignancy rate (24). On the other hand, given their specific growth pattern, the growth of these nodules is difficult or even impossible to detect on CT(24). As opposed to the eccentric growth of solid pulmonary nodules, subsolid pulmonary nodules have a unique scale-like “lepidic” histological growth pattern along alveolar septa without stromal invasion and with an indolent clinical course. While the nodules increase in mass, their lepidic growth simultaneously causes either a complete lack or at least a markedly reduced increase in visible size on serial follow-up CT examinations (24).

 

 

LL_MRI_LC_FIG1

 

Figure 1. Subsolid nodule in the right upper lobe.

Baseline CT examination (A) and follow-up CT examination (B) 48 months later. The increase in size of the subsolid nodule (arrow) is not visible. At the time of the follow-up CT examination (B), the patient suffered from cerebral metastases and was considered clinically incurable. Histology of the nodule was bronchoalveolar carcinoma.

Therefore, the traditional concept that lack of growth on CT follow-up examinations indicates a benign etiology does not apply to subsolid pulmonary nodules (24). Albeit its high sensitivity in the detection of subsolid pulmonary nodules, the limited specificity of CT in determining the diagnosis of these nodules often causes prolonged CT follow-up periods, resulting in increased numbers of CT examinations, increased patient irradiation, increased health care costs, increased patient anxiety, and a delayed clinical diagnosis (24). Given these specific shortfalls, the overall limitations of CT in the assessment of subsolid pulmonary nodules have been recognized, emphasizing the need for an alternative diagnostic approach (24, 26).

There are two overall goals of this project. The first is to is to assess several very promising imaging biomarkers that can reflect either the physiological or metabolic status of these nodules in order to develop more accurate imaging algorithms for follow-up that are either less invasive or do not use ionizing radiation or both. Based on our experience with other cancers (27, 28) and our preliminary results in lung cancer (29), we have two potential MR imaging approaches that we believe have the potential to result in validated “imaging biomarkers” that can either individually, or in combination, characterize malignancies. Since tumors tend to exhibit angiogenesis and altered vascular permeability (see for example (8)), we and others (27, 28, 30-32), have found that analyses of dynamic contrast enhanced MRI (DCEMRI) can be employed as “imaging biomarkers” for malignancy. Tumors often exhibit higher cellularity than benign, or normal tissue, suggesting that pixel-by-pixel ADC values derived from diffusion weighted MRI could useful imaging biomarkers (33-37).

Our second goal is to probe the metabolism and its heterogeneity by measuring alterations in metabolic fluxes by using image guided tissue procurement in patients undergoing surgery. Prior to surgery these patients are infused with pathway specific C-13 labeled compounds, a technique pioneered here at the AIRC in UT Southwestern (38-40). The analysis of the C-13 spectra obtained from tissue extracts has shown the capability of providing metabolic information about the relative rates of glycolysis and oxidative metabolism in the tumor. By using MR imaging to guide tissue procurement, we have been able to probe the metabolic heterogeneity of glucose metabolism in these tumors. Our experience, to date is summarized in the presentation that can be downloaded at the end of this section.

 

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