Quantitative classification of invasive and noninvasive breast cancer using dynamic magnetic resonance imaging of the mammary gland

INTRODUCTION

Mammography and ultrasonography are the first-line diagnostic imaging methods for breast cancer. Magnetic resonance imaging (MRI) is also an important tool for precise preoperative examination of breast tumors because of its high spatial resolution and ability to evaluate blood flow, provide a qualitative diagnosis, and assess the spread of breast tumors. According to Berg et al. MRI detects malignancies with a sensitivity of over 95% (167/177), except in cases of special tumors and microscopic lesions; this detection rate was higher than that of mammography (68%; 120/177) and ultrasound (83%; 147/177).^[1]

The time-intensity curve (TIC) [Figure 1] plots the contrast enhancement effect of dynamic MRI over time and is expected to provide a qualitative diagnosis because it reflects the state of blood flow within the tumor.^[2] The diagnostic ability of the TIC to distinguish benign from malignant tumors has been demonstrated in several previous studies.^[3-10]

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Figure 1:

Overview of the time-intensity curve analysis.

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Malignant mammary gland tumors are divided into two types: invasive cancer, in which the cancer cells break through the ducts and lobules to invade the surrounding tissues in the breast, and noninvasive cancer, in which the cancer cells grow only in the ducts and lobules.^[11,12]

When cancer cells leave the ducts and lobules of the mammary gland, they may metastasize through the blood and lymphatic vessels, greatly affecting the treatment plan and prognosis for life.

Brennan et al. reported^[13] that 25.9% of cases diagnosed as noninvasive breast cancer were misdiagnosed, indicating that invasive breast cancer or other cancers were undetected postoperatively. In addition, Sagara et al. reported^[14] that in some “noninvasive cancers,” resection and nonresection revealed characteristics that did not significantly differ in survival rates. In other words, the importance of more accurate differentiation between “invasive” and “noninvasive” cancer as the initial management of breast cancer is extremely higher than in the two reports. However, in MRI, qualitative evaluation of the enhancement effect^[2] is the main method for differentiating between invasive and noninvasive lesions, and quantitative evaluation methods have not yet been established.

Therefore, in this study, to evaluate the ability of TIC analysis of dynamic MRI data (MRI-TIC) to distinguish between invasive and noninvasive breast cancers, we extracted TIC features and performed quantitative, pathology-based classification of invasive/noninvasive cancers. Furthermore, we compared the accuracy of MRI-TIC with ultrasonography, which can evaluate blood flow.^[15]

MATERIAL AND METHODS

Study design

This retrospective study included data for 429 cases of invasive (364 cases) and noninvasive (65 cases) breast cancers diagnosed and treated from April 2016 to March 2018. The inclusion criteria were as follows:^[1] dynamic MRI and ultrasonography were performed,^[2] no chemotherapy was administered, and^[3] a definite diagnosis of invasive or noninvasive cancer was established by pathological examination of the excised lesion.

A 1.5-Tesla MRI device (Siemens Magnetom Symphony; Erlangen, Germany) with a four-channel breast array coil was used to obtain dynamic MRI fat-suppressed T1-weighted images using the gadolinium contrast agent Magnevist IV (Schering Berlin, Germany) and the following parameters: matrix size, 512 × 256; pixel size, 0.6 × 0.8 mm; slice thickness, 1.0 mm; time to repeat, 5.42 s; time to echo, 2.11 s; flip angle, 20°; bandwidth, 300 Hz/pixel, and parallel imaging generalized autocalibrating partial parallel acquisition (GRAPPA; accel. factor PE 2 and ref. lines PE 50). A Sonic Shot GX device (Nemoto Kyorindo Co., Ltd, Tokyo, Japan) was used to inject the contrast medium. The contrast agent (0.2 mL/kg) was injected at a rate of 2.0 mL/s, and 20 mL of physiological saline was boosted at a rate of 2.0 mL/s.

Dynamic MRI was performed using four-time points: before injection of the contrast agent (prephase), immediately after injection of the contrast agent (injection phase), at 60 s (peak phase), and at 300 s (delay phase). The imaging time was 60 s per phase, and there were 96 images taken per phase (384 images total).

All personal information related to the clinical images used in this study was anonymized, except for the MRI, pathological diagnoses, and tumor locations. This study was conducted in accordance with the Declaration of Helsinki. The Ethics Review Committee of the National Hospital Organization Kyushu Cancer Center approved the utilization of data (Approval no. 2016-52) and waived the need for patient consent because of the retrospective nature of the study.

Figure 2 shows the method for distinguishing between invasive and noninvasive breast cancers using MRI-TIC. We investigated the part of the lesion that showed a washout effect, which suggests malignancy. Figure 3 shows the process of extraction of the region with a washout effect in a breast cancer lesion. Automatic extraction was performed as previously reported.^[16]

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Figure 2:

Flowchart for histological classification of invasive/noninvasive cancer.

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Figure 3:

Extraction of the region with a washout effect in breast cancer using images processed by a computer-aided diagnosis program in dynamic magnetic resonance imaging Invasive breast cancer tissue from a 42-year-old woman with a 1.0 cm mass is shown.

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Invasive/noninvasive classification

Images of lesions with features suggestive of malignancy were automatically extracted from the imaged slices using a 2×2-pixel region of interest (ROI). We defined θ_{invasive/noninvasive} (θ_i/ni) as the angle between straight lines A and B in the TIC (A: a straight line parallel to the time axis and extending from the peak phase, B: a straight line passing through the two points of the peak phase and the delay phase) [Figure 4]. The highest θ value in each case was defined as θ_washout (θ_w), and the classification of invasiveness/noninvasiveness was made according to this value. For all 429 cases, θ_i/ni was calculated as the borderline value of θ_w where the pathological diagnosis was most consistent with the MRI-TIC-based classification by θ_w.

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Figure 4:

Definition of θwashout in invasive/noninvasive classification using TIC.

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RESULTS

Determination of θi/ni for distinguishing between invasive and noninvasive lesions

For all 429 cases, the signal intensity in the delay phase was reduced compared to that in the peak phase, and a plateau or washout was observed in MRI-TIC. The graph in Figure 5 shows the θ_w of cases classified as invasive or noninvasive based on the final pathological diagnosis. The θ_w for all cases ranged from 0.7° to 51.8°, with values of 11.2°-51.8° for invasive and 0.7°-32.1° for noninvasive cases. The relationship between the two was P = 9.7 × 10^-52.

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Figure 5:

Statistics of θwashout in pathological diagnosis (invasive/noninvasive).

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Figure 6 shows the relationship between the positive diagnosis rate at each θ_w value for cases classified as invasive or noninvasive from pathological diagnoses. For invasive cancer, the positive diagnosis rate showed a gradual decrease from 0° to 20°, after which the slope increased and the positive diagnosis rate decreased. For noninvasive cancer, the positive diagnosis rate reached 52% at 10°, 78% at 20°, and 96% at 30°. The graph determining the positive diagnosis rate for invasive and noninvasive cases revealed that the cut-off θ_i/ni value was 21.6° (invasive: θ_w > 21.6°, noninvasive: θ_w ≤ 21.6°).

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Figure 6:

Determination of θinvasive/noninvasive to classify invasive/noninvasive (Discriminant accuracy in θwashout).

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Effectiveness of MRI-TIC in distinguishing invasive and noninvasive breast carcinoma

Table 2 shows the classification of breast cancer tumors using the θ_i/ni cut-off value of 21.6° for invasiveness/noninvasiveness, which was the most consistent with the pathological diagnoses. The sensitivity, specificity, positive predictive value, and negative predictive value for each tissue are shown in Table 3; these values were 89.3%, 84.6%, 97.0%, and 58.5%, respectively, for invasive tumors and 84.6%, 89.3%, 58.5%, and 97.0%, respectively, for noninvasive tumors. The accuracy of discrimination between invasive and noninvasive cancers using MRI-TIC was 88.6%. Figure 7 shows the classification of invasive (θ_i/ni > 21.6°) and noninvasive (θ_i/ni ≤ 21.6°) breast carcinoma using MRI-TIC and 4-pixel pathological images (size of the ROI, 1.2×1.6 mm).

Table 2: Classification of invasive/noninvasive breast carcinoma using time-intensity curve analysis of magnetic resonance imaging data, compared to the diagnosis by pathology (n = 429).

Pathological diagnoses	θinvasive/noninvasive ≤ 21.6° (noninvasive)	θinvasive/noninvasive > 21.6° (invasive)
Invasive breast carcinoma: 364	39/364	325/364
Noninvasive breast carcinoma: 65	55/65	10/65

Table 3: Effectiveness of time-intensity curve analysis of magnetic resonance imaging data (θinvasive/noninvasive cut-off value = 21.6°) in distinguishing invasive and noninvasive breast carcinomas

	Sensitivity	Specificity	Positive predictive value	Negative predictive value	Discriminative predictive value
Invasive breast carcinoma: 364	89.3	84.6	97.0	58.5	88.6
Noninvasive breast carcinoma: 65	84.6	89.3	58.5	97.0

Values shown are percentages

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Figure 7:

Classification of invasive/noninvasive breast carcinoma using time-intensity curve analysis of magnetic resonance imaging data and pathological images The size of the region of interest (4 pixels:1.2 × 1.6 mm) is the same as the size of the pathology image.

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DISCUSSION

In this study, we developed and validated an algorithm for distinguishing between invasive and noninvasive breast cancers using quantitative MRI-TIC for 429 cases. Classification using MRI-TIC was based on θ_i/ni, with a cut-off value of 21.6°.

We found that θ_i/ni > 21.6° indicated invasive cancer and θ_i/ni ≤ 21.6° indicated noninvasive cancer. They are related by the degree of contrast agent loss after the peak signal intensity, similar to the findings of Kamitani et al.^[17] and indicate that the post-peak TIC has a steep trend for invasive cancer and a gradual trend for noninvasive cancer. The difference in θ_i/ni for invasive vs. noninvasive tumors in this study may be the result of differences in histological construction.

In cases of invasive cancer, as the blood concentration of the contrast agent increases, the contrast agent seeps from the blood vessels into the interstitium, resulting in a strong thick stain; this is due to the high vascular density^[18] and increased vascular permeability.^[19] The contrast agent that has leaked into the extravascular interstitium is readily expelled into the vasculature as its blood concentration decreases, and its concentration within the tumor decreases. Because there is little interstitium,^[11,12] the concentration of contrast agent in the tumor tends to decrease quickly and is likely to exhibit a steep washout curve.

In noninvasive cancers, tumor vessels with increased vascular permeability rarely grow.^[19,20] The periductal stroma is wide, and it takes time for the concentration of the contrast agent to increase,^[21] resulting in a poor contrast effect early in the imaging process (slow pattern). The periductal stroma gradually gains contrast, but the contrast agent temporarily pools and drains slowly. Largely because of tumor angiogenesis in the interstitium,^[20] washout is less likely to occur within the examination time, and a plateau or slow washout is seen. In brief, the differences in TIC characteristics are related to the density and permeability of the tumor vessels (noninvasive < invasive) and the size of the stroma (invasive < noninvasive).

Using the pathological diagnoses as a reference, the accuracy of MRI-TIC in distinguishing between invasive and noninvasive breast cancers in this study (364 invasive and 65 noninvasive breast cancers) was 88.6%. Thus, the TIC results did not completely agree with the pathological diagnoses, possibly because data were collected at only four-time points (prephase, injection phase, peak phase, and delay phase). More time points were not used because the timing technique was not fine-tuned, and this may have caused a discrepancy between the exact peak point and the values at the 2-minute point, which is assumed to be the peak point.^[2] The same can also be said for the washout point; this issue should be investigated further in future research.

Another reason for the lack of complete agreement between MRI-TIC and pathological results was that the invasive/noninvasive tissue classification was determined by observation. It is difficult to observe the entirety of the malignant tissues of a lesion, and judgments based on partial observation may lead to bias among observers and discrepancies in diagnostic results. However, the method of classification of invasive/noninvasive cancers in this study did achieve good quantitative results and compensated for possible subjectivity by scanning all imaging data (396 images: 96 images × 4 phases) and extracting the washout areas in the tumors. The usefulness of our method is demonstrated by the fact that MRI-TIC-based invasive/noninvasive classifications were consistent with the pathological diagnoses in 88.6% of the 429 cases.

There was a large difference of more than 15.0% in the discrimination accuracy between ultrasound examination, where the first and second differential diagnoses were considered, and our MRI-TIC method. We believe that there are three reasons for this difference. First, ultrasonography findings are affected by the skill of the operator. Second, it is difficult to image the entire lesion. Third, it is difficult to evaluate the lesion quantitatively.

Accurate diagnosis of invasive/noninvasive breast cancer is thought to play a significant role not only in the evaluation of possible metastasis but also in surgical decisions.^[13,14] Since the most accurate information possible from multiple examinations is essential for the diagnosis of breast cancer, this study demonstrates the validity of the quantitative evaluation of invasion/noninvasion in MRI of breast cancer.

We believe that this will help improve pathological diagnosis and will be of great utility in clinical practice.

CONCLUSION

The findings of this study suggest that quantitative MRI-TIC of the mammary glands is effective in distinguishing between invasive and noninvasive breast cancers. We believe that this method can assist physicians in determining the most appropriate treatments for patients with malignant carcinomas.