Early results in 1 breast cancer diagnosis

16.4.1 MRI

With a number of recent advances, contrast-enhanced MRI has emerged as a method with sensitivity approaching 100% for detection of invasive breast cancer [28, 29]. MRI is particularly valuable for detecting malignant lesions in mammographically dense breasts [30]. Studies are currently on going in the U.S., Canada, Germany, Netherlands, U.K., France and Italy to evaluate MRI as a possible screening method for women at high risk for breast cancer [13].

Since most invasive breast cancers are hypervascular, they typically show intense contrast enhancement. MRI for detecting breast cancer relies almost exclusively upon the presence of neovascularity. This vasculature is leaky and contains many arterio-venous shunts. Thus, breast cancerous tissue enhances rapidly and then washes out over the next several minutes, whereas normal breast parenchyma enhances more slowly. These washout time intensity curves are typical of most (but not all) cancers. With administration of contrast agents such as gadolinium, lesions can be well visualized, especially with fat suppressed, T weighted imaging [28].

However, false negatives have been reported for well-differentiated, invasive ductal carcinomas and for invasive lobular carcinomas. Moreover, the sensitivity of CE-MRI has been reported to be as low as 40% for ductal carcinoma in situ [28]. For more details on MRI findings in breast cancer, see Refs. [28-30].

While contrast-enhanced MRI has high spatial resolution and is more sensitive than mammography, it has limited specificity, thus sharing with mammography the problem of a high false positive rate (approximately 50%) [31].

Morris [28] cites the following as causes of false positive on MRI examinations of the breast:

• Fibroadenoma

• Ductal atypia

• Fibrocystic changes

• Sclerosing adenosis

• Ductal hyperplasia.

Lippman [2] includes MRS among the newer techniques that may improve primary diagnosis of breast cancer. :H MRS diagnostics based upon the presence or absence of a composite choline signal have been shown to increase the specificity of MRI with respect to the diagnosis of breast cancer [31].

Katz-Brull et al. [31] recently reviewed the five published clinical studies using single voxel in vivo 1H MRS, in which malignant and benign breast lesions were compared. They reported a sensitivity and specificity of 83% (95% CI = 73% - 89%) and 85% (95% CI = 71% - 93%), respectively, for identifying breast cancer in the 153 tumors examined, 53 of which were benign. Even better diagnostic accuracy was achieved among women aged 40 or younger, among whom there were 11 patients with breast carcinomas and 9 with benign breast lesions. The potential of 1H MRS for widespread application in breast cancer diagnostics was emphasized by these authors, provided that the factors limiting its diagnostic accuracy are overcome.

Lipid suppression is particularly important in applications of 1H MRS to the breast, due to its high lipid content. The current strategy has been to increase the echo time, which diminishes the overlap with the lipid peak, although this is achieved by a diminution in signal intensity [31]. As a consequence, a smaller number of compounds are visualized and heretofore, the focus has been upon the composite choline peak. Choline may also be observed in benign breast lesions, as well as in the normal breast during lactation. Choline is often undetected in small tumors that are then misclassified as benign.

As yet, the application of MRSI for breast cancer diagnostics has not been reported.

16.4.3 In vitro MRS findings

In contrast to in vivo 1H MRS breast examinations based mainly upon a single composite spectral entity (the total choline peak), the high resolution of in vitro MRS applied to extracted specimens provides a much greater insight into the metabolic activity of malignant breast tissue.

Biochemical pathways underlying the high choline in breast cancer

In vitro MR analysis of excised malignant breast tumors reveals that the composite choline peak contains a number of water-soluble metabolites such as phosphocholine, glycerophosphocholine, betaine and analogous compounds containing the ethanolamine head group and taurine, as well as choline itself. Milk, on the other hand, is comprised predominantly of choline compounds such as phosphatidylcholine as well as phosphocholine and free choline [31]. Katz-Brull et al. [32] applied in vitro analysis using tracer kinetics and 13C and 31P MRS to examine the biochemical pathways underlying the high levels of water-soluble choline metabolites seen in breast cancer. They identified two non-intersecting pathways: phosphorylation and oxidation of choline, to be augmented with malignant transformation of mammary cells, with increased synthesis of phosphocholine and betaine. They also found suppression of choline-derived ether lipids.

A comparison of the metabolic characteristics of breast cancer, fibroadenoma and normal breast tissue using in vitro 'H MRS

Gribbestad et al. [33] published a study using in vitro 'H MRS to compare fourteen extracts of malignant breast tissue and one fibroadenoma to non-involved breast from the same group of patients. We performed detailed pair-wise and logistic regression analysis of their data to ascertain the sensitivity and specificity of individual metabolite concentrations for identifying breast cancer [22].

• Lactate and alanine in breast cancer, fibroadenoma and non-involved breast tissue

In Figure '6.', the case-by-case lactate levels for normal versus malignant breast tissue are shown. It is clearly seen that lactate concentrations were much higher in the malignant lesions than in the normal tissue of each patient.

Figure 16.1: Case-by-case analysis of estimated [lactate] in normal versus cancerous breast tissue (From data by Gribbestad et al. [33])

Case 1 Case 3 Case 5 Case 7 Case 9 Case 11 Case 13 Case 15 Case 2 Case 4 Case 6 Case 8 Case 10 Case 12 Case 14 Case 16

Lac_N denotes [lactate] in non-involved breast tissue, Lac_LE denotes [lactate] in the

malignant breast lesion

Using logistic regression analysis, we calculated the sensitivity and specificity of the estimated metabolic concentrations. In Table '6.' we present the findings for lactate and alanine, comparing the malignant to the entirely normal extracts (denoted with (A)), and malignant versus benign breast tissue (denoted with (B)) and including 1 fibroadenoma). It is seen that lactate concentrations provided 100% diagnostic accuracy, for distinguishing benign versus malignant breast tissue, both when the fibroadenoma was excluded as well as when it was included.

Table 16.1

Diagnostic accuracy of estimated [lactate] and [alanine] for identifying breast cancer

(Data from Gribbestad et al. [33], calculations from Refs. [22])


Sensitivity (B)




[ 1.33 ppm ]





Alanine [ 1.47 ppm ]





(A) Denotes Malignant (N=14) versus non-infiltrated breast tissue (N= 12), fibroadenoma excluded

(B) Denotes Malignant (N=14)) versus benign breast tissue (N=13) (non-infiltrated or in 1 case fibroadenoma)

However, as shown in Figure 16.2, in the same patient [lactate] in the fibroadenoma was about 1.5 times greater than in the normal breast tissue from the same patient. The lactate concentration in the fibroadenoma was 2.6 Sd higher than the mean in normal breast tissue.

Figure 16.2: Estimated [lactate] in fibroadenoma and in normal breast tissue from the same patient (From data by Gribbestad et al. [33])






1 □ MicroM/g





Fibroadenoma Normal

Breast Tissue

• Total choline and its constituents in breast cancer, fibroadenoma and non-involved breast tissue

Next we examined the diagnostic accuracy of the estimated concentrations of total choline and its constituents for identifying breast cancer. As shown in Table 16.2, choline had 100% sensitivity only when the fibroadenoma was excluded. With inclusion of the fibroadenoma, there were two false negatives based upon choline concentrations. Each of the other constituents of total choline had less than perfect sensitivity, while phosphocholine had 100% specificity. Notably, total choline yielded marginally poorer results than most of the choline constituents, except for specificity when the fibroadenoma was included.

Table 16.2

Diagnostic accuracy of estimated concentrations of total choline and its constituents for identifying breast cancer

(Data from Gribbestad et al. [33], calculations from Refs. [22])



Choline 100%

[ 3.21 ppm ]




Phosphocholine 92.9% [ 3.22 ppm ]




Glycerophosphocholine 85.7% [ 3.23 ppm ]




Total Choline 92.9% (C+PC+GPC)




(A) Denotes Malignant (N=14) versus non-infiltrated breast tissue (N= 12), fibroadenoma excluded

(B) Denotes Malignant (N=14)) versus benign breast tissue (N=13) (non-infiltrated or in 1 case fibroadenoma)

Moreover, as shown in Figure 16.3, in the same patient [total choline] in the fibroadenoma was also about 1.5 times greater than in the normal breast tissue from the same patient. The total choline concentration in the fibroadenoma was 1.81 Sd higher than the mean in normal breast tissue.

Figure 16.3: Estimated [total choline] in fibroadenoma and in normal breast tissue from the same patient (From data by Gribbestad et al. [33])


Normal Breast Tissue

• Phosphethanolamine, p-glucose, taurine and myoinositol in breast cancer, fibroadenoma and non-involved breast tissue

Finally, we present the analysis of phosphethanolamine, p-glucose, taurine and myoinositol concerning diagnostic accuracy in distinguishing breast cancer, fibroadenoma and non-involved breast tissue. As shown in Table 16.3, none of these metabolites provided perfect sensitivity, and only phosphoethanolamine and taurine yielded 100% specificity with and without inclusion of the fibroadenoma.

Table 16.3

Diagnostic accuracy of estimated concentrations of phospho-ethanolamine, P-glucose, taurine and myoinositol for identifying breast cancer

(Data from Gribbestad et al. [33], calculations from Refs. [22])


Specificity (B)

Phosphoethanolamine [ 3.22 ppm ]





ß-Glucose [ 3.25 ppm ]





Taurine [ 3.27 ppm ]





Myoinositol [ 3.28 ppm ]





(A) Denotes Malignant (N=14, except for p-Glucose N=6 and for myoinositol N=9) ' infiltrated breast tissue (N=12), fibroadenoma excluded

(B) Denotes Malignant (N=14, except for p-Glucose N=6 and for myoinositol N=9)) breast tissue (N=13) (non-infiltrated or in 1 case fibroadenoma)

versus non-versus benign

Compared to most of the other metabolites, myoinositol did not have very high overall diagnostic accuracy. However, myoinositol did offer some diagnostic insight that the other metabolites failed to provide. Namely, the calculated concentration of myoinositol was nearly the same (0.465 and 0.448) for the fibroadenoma and for the non-infiltrated tissue, respectively, from the same patient and showed the lowest difference from the mean for normal breast tissue (+ 0.52 Sd) (see Figure 16.4).

Figure 16.4: Estimated [myoinositol] in fibroadenoma and in normal breast tissue from the same patient, and mean [myoinositol] in malignant breast tissue and in normal breast (From data by Gribbestad et al. [33])

□ Fibroadenoma

□ Normal Tissue

□ Mean Malignant Breast

□ Mean Normal Tissue

Overall assessment

On the basis of these data from a fairly small sample (with substantial missing data for a few metabolite concentrations in malignant tissues), definitive conclusions cannot be drawn about which metabolites are optimal for detecting the presence of breast cancer and distinguishing this from normal breast tissue or from benign lesions. Nevertheless, several metabolites (lactate, in particular) showed promise with respect to diagnostic accuracy. On the other hand, total choline, upon which most in vivo MRS diagnoses are based, had marginally lower sensitivity and specificity than several other metabolites. Furthermore, metabolites such as myoinositol also provided potentially important insights, even though the calculated sensitivity and specificity were less than optimal. Viewed together, these analyses corroborate this group of authors [33] that a very rich "window" of information is provided by in vitro :H MRS analysis of metabolite concentrations in malignant versus non-cancerous breast tissue. This should justify exploration of how in vivo :H MRS might tap into this rich source of information for improved primary breast cancer diagnostics.


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Part C

Future Perspectives for MRS and MRSI in Cancer Diagnostics



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