DCEMRI of breast lesions: Is kinetic analysis equally effective for both mass and nonmass-like enhancement?

Jansen, Sanaz A.; Fan, Xiaobing; Karczmar, Gregory S.; Abé, Hiroyuki; Schmidt, Robert A.; Giger, Maryellen L.; Newstead, Gillian M.

doi:10.1118/1.2936220

Cited by 62 publications

(42 citation statements)

References 43 publications

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“…Evaluating the enhancing patterns of focal mass lesions on dynamic study is useful in diŠeren-tiating benign and malignant lesions. [6][7][8][9][10][11] In contrast, such diŠerentiation is often di‹cult with nonmass-like enhancement because there is no standardized method of interpreting non-mass-like enhancement lesions. [6][7][8]10,[12][13][14] Several studies have reported the utility of evaluating apparent diŠusion coe‹cient (ADC) values derived from diŠusion-weighted imaging (DWI) for diŠerentiating benign and malignant lesions.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Performance of ADC for Non-mass-like Breast Lesions on MR Imaging

et al. 2010

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We assessed the usefulness and limitations of utilizing apparent diŠusion coe‹cient (ADC) values on diŠusion-weighted imaging (DWI) for the diŠerential diagnosis of benign and malignant non-mass-like breast lesions. We retrospectively reviewed 27 such lesions (16 malignant, 11 benign) detected on magnetic resonance (MR) imaging and analyzed the enhancing patterns of dynamic contrast-enhanced DCE-MRI (distribution and internal enhancement), kinetic curve patterns, and ADC values. All images were obtained with a 1.5-tesla MR unit, with patients supine. On DCE-MRI, malignant lesions tended to show either segmental or branching-ductal distribution, and when lesions with these patterns were considered malignant, sensitivity was 68.8z; speciˆcity, 63.6z; positive predictive value (PPV), 73.3z; negative predictive value (NPV), 58.3z; and accuracy, 66.7z. Kinetic curve analysis did not reliably diŠerentiate benign and malignant non-mass-like lesions. There was no signiˆcant diŠerence between the mean ADC value of the malignant lesions, 0.968×10 -3 mm 2 /s at b＝1000 s/mm 2 , and that of benign lesions, 1.207×10 -3 mm 2 /s (P ＝0.109). Receiver operating characteristic (ROC) analysis revealed the most eŠective threshold of ADC value for diŠerentiating tumors as 1.1×10 -3 mm 2 /s; values lower than this were observed more often in malignant than benign lesions (P＝0.054). Us of this threshold yielded sensitivity of 68.8z; speciˆcity, 72.7z; PPV, 78.6z; NPV, 61.5z; and accuracy, 70.4z. Combining the ADC value criteria with the analysis of DCE-MRI pattern increased sensitivity to 93.8z, negative predictive value (NPV) to 85.7z, and accuracy to 77.8z but decreased speciˆcity to 54.5z. Use of ADC values does not adequately improve DCE-MRI performance for diŠerential diagnosis of non-mass-like breast lesions, but adding the ADC value criteria to the DCE-MRI pattern analysis improves sensitivity, NPV, and accuracy.

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Section: Introductionmentioning

confidence: 99%

Diagnostic Performance of ADC for Non-mass-like Breast Lesions on MR Imaging

et al. 2010

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“…However, as there is not yet sufficient standardisation of quantitative methods, semi-quantitative approaches have been used because they could represent a compromise between qualitative and quantitative approaches. As a general concern, DCE-MRI can achieve very high sensitivity but moderate and highly varied specificity in detecting breast cancer: reported value range from 37% up to 90% [14,15].…”

Section: Discussionmentioning

confidence: 99%

“…The vascularisation of the malignant lesions resulting from neo-vessels, that are the basis of exponential tumour growth, is one of the major reasons that dynamic features of MRI have played a crucial role in breast cancer for a decade [7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…In fact lesions detection and characterization depend upon a combination of morphologic and kinetic observations [7,15].…”

Section: Introductionmentioning

confidence: 99%

“…Kinetic curve analysis can be performed qualitatively (visual inspection of the curve shape), semi-quantitatively (by means of empirical parameters of signal intensity changes as gradient of the upslope of enhancement curves, maximum signal intensity and wash-out gradient) or quantitatively through pharmacokinetic modelling techniques [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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Can semi-quantitative evaluation of uncertain (type II) time-intensity curves improve diagnosis in breast DCE-MRI?

Fusco¹,

Filice²,

Granata³

et al. 2013

JBiSE

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Objective/Background: Qualitative assessment of uncertain (type II) time-intensity curves (TICs) in breast DCE-MRI is problematic and operator dependent. The aim of this work is to evaluate if a semi-quantitative assessment of uncertain TICs could improve overall diagnostic performance. Methods: In this study 49 lesions from 44 patients were retrospectively analysed. Per each lesion one region-of-interest (ROI)-averaged TIC was qualitatively evaluated by two radiologists in consensus: all the ROIs resulted in type II (uncertain) TIC. The same TICs were semi-quantitatively re-classified on the basis of the difference between the signal intensities of the last-time-point and of the peak: this difference was classified according to two different cut-off ranges (±5% and ±3%). All patients were cytological or histological biopsy proven. Fisher test and McNemar test were performed to evaluate if results were statistically significant (p < 0.05). Results: Using ±5% cut-off 16 TICs were reclassified as type III and 12 as type I while 21 were reclassified again as type II. Using ±3% 22 TICs were reclassified as type III and 16 as type I while 11 were reclassified again as type II. The semi-quantitative classification was compared to the histologicalcytological results: the sensitivity, specificity, positive and negative predictive values obtained with ±3% were 77%, 91%, 91% and 78% respectively while using ±5% were 58%, 96%, 94% and 68% respectively. Using the ±5% cut-off 26/28 (93%) TICs were correctly reclassified while using the ±3% cut-off 34/38 (90%) TICs were correctly reclassified (p < 0.05). Conclusions: Semi-quantitative methods in kinetic curve assessment on DCE-MRI could improve classification of qualitatively uncertain TICs, leading to a more accurate classification of suspicious breast lesions.

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Sigmoid model analysis of breast dynamic contrast‐enhanced MRI: Distinguishing between benign and malignant breast masses and breast cancer subtype prediction

Koori

Miyati

Ohno

et al. 2022

J Applied Clin Med Phys

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Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) is performed to distinguish between benign and malignant lesions by evaluating the changes in signal intensity of the acquired image (kinetic curve). This study aimed to verify whether the existing breast DCE‐MRI analyzed by the sigmoid model can accurately distinguish between benign and invasive ductal carcinoma (IDC) and predict the subtype. A total of 154 patients who underwent breast MRI for detailed breast mass examinations were included in this study (38 with benign masses and 116 with IDC. The sigmoid model involved the acquisition of images at seven timepoints in 1‐min intervals to determine the change in signal intensity before and after contrast injection. From this curve, the magnitude of the increase in signal intensity in the early phase, the time to reach the maximum increase, and the slopes in the early and late phases were calculated. The Mann–Whitney U‐test was used for the statistical analysis. The IDC group exhibited a significantly larger and faster signal increase in the early phase and a significantly smaller rate of increase in the late phase than the benign group (P < 0.001). The luminal A‐like group demonstrated a significantly longer time to reach the maximum signal increase rate than other IDC subtypes (P < 0.05). The sigmoid model analysis of breast DCE‐MRI can distinguish between benign lesions and IDC and may also help in predicting luminal A‐like breast cancer.

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DCEMRI of breast lesions: Is kinetic analysis equally effective for both mass and nonmass-like enhancement?

Cited by 62 publications

References 43 publications

Diagnostic Performance of ADC for Non-mass-like Breast Lesions on MR Imaging

Diagnostic Performance of ADC for Non-mass-like Breast Lesions on MR Imaging

Can semi-quantitative evaluation of uncertain (type II) time-intensity curves improve diagnosis in breast DCE-MRI?

Sigmoid model analysis of breast dynamic contrast‐enhanced MRI: Distinguishing between benign and malignant breast masses and breast cancer subtype prediction

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