Quantitative Diffusion - Weighted Magnetic Resonance Imaging in the Differential Diagnosis of Breast Lesions

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1 Quantitative Diffusion - Weighted Magnetic Resonance Imaging in the Differential Diagnosis of Breast Lesions Poster No.: C-0934 Congress: ECR 013 Type: Scientific Exhibit Authors: C. A. Ribeiro da Fonseca, A. Mesquita, A. Gaspar, M. D. L. G F. R. Orvalho ; Lisbon/PT, Lisboa/PT Keywords: Neoplasia, Cancer, Diagnostic procedure, MR, Breast DOI: /ecr013/C-0934 Any information contained in this pdf file is automatically generated from digital material submitted to EPOS by third parties in the form of scientific presentations. References to any names, marks, products, or services of third parties or hypertext links to thirdparty sites or information are provided solely as a convenience to you and do not in any way constitute or imply ECR's endorsement, sponsorship or recommendation of the third party, information, product or service. ECR is not responsible for the content of these pages and does not make any representations regarding the content or accuracy of material in this file. As per copyright regulations, any unauthorised use of the material or parts thereof as well as commercial reproduction or multiple distribution by any traditional or electronically based reproduction/publication method ist strictly prohibited. You agree to defend, indemnify, and hold ECR harmless from and against any and all claims, damages, costs, and expenses, including attorneys' fees, arising from or related to your use of these pages. Please note: Links to movies, ppt slideshows and any other multimedia files are not available in the pdf version of presentations. Page 1 of 18

2 Purpose The aim of this study is to evaluate the ADC utility in the differential diagnosis of breast benign and malignant lesions with correlation of morphological, dynamic and histological aspects. Methods and Materials 133 patients were studied at Ressonancia Magnetica de Caselas, aged between 5 and 81 years (mean 55 years) who had breast MR with contrast, between January and December 010. The parameters analyzed include: patient age, family history, topography and laterality of lesions, morphology, dynamic alterations, diffusion, histology and classification BI-RADS (Breast Imaging Reporting and Data System). Lesions measuring less than 7 mm, simple cysts, and examination with movement artifacts as well as patients submitted to radiotherapy and chemotherapy for a period under twelve months (which would increase the Apparent Diffusion Coefficient - ADC) were excluded. The main limitations of the study were related to the size of the lesions, the movement artifacts and the phase of menstrual cycle. Magnetic Resonance Image (MRI) data were acquired in a 1,5 Tesla scanner - MR SYSTEM - SIGNA HDX - GE Healthcare, with an 8 channel breast coil. The standard protocol consisted of an axial T1 weighted sequence, axial T weighted sequence FatSat and dynamic contrast-enhanced T1 weighted sequence in axial plane (gadolinium administration - 0,1 mmol/kg with automatic injector with a ml/s flow). Diffusion Weighted Imaging (DWI) was performed before the contrast administration, using a single-shot spin echo-planar imaging sequence and diffusion gradient b-values of 600 and 1000 s/mm with the following acquisition parameters: TR = ms; TE = 85,3 ms; slice thickness = 4 mm; slice gap = 1 mm; number of slices 34; number of excitations = 5; field of view (FOV) = 340x340 mm; image matrix 56x19. The duration of this sequence acquisition was approximately 3'30''. The ROI was always manually chosen in the ADC processing. The interpretation was based on the evaluation of the subtraction images, including MIP (maximum intensity projection), multiplanar reformatting, the curve signal intensity/time, diffusion and ADC map, (Fig. 1 and ). Page of 18

3 Images for this section: Fig. 1: Malignant lesion identified as an invasive ductal carcinoma (IDC), in a 39 years woman on the upper outer quadrant of the right breast (a) MIP (sagital plane); (b and c) Dynamic study with the corresponding curve signal intensity/time; (d) Contrast enhancement with gadolinium T1 weighted subtraction image (axial plane); (e) Diffusion - Weighted Image (DWI), b-1000 s/mm-(axial plane) and (f) ADC Maps Page 3 of 18

4 Fig. : Benign lesion identified as a Fibroadenoma, in a 47 years woman on the lower outer quadrant of the right breast (a) MIP (axial plane); (b and c) Dynamic study with the corresponding curve signal intensity/time; (d) T Weighted Image Fat Sat (axial plane); (e) Diffusion - Weighted Image (DWI), b-1000 s/mm-(axial plane) and (f) ADC Maps Page 4 of 18

5 Results Patient age, family history, topography and laterality of lesions, morphology, dynamic alterations, diffusion and ADC (apparent diffusion coefficient), histology and Classification BI-RADS, were considered. From the 133 exams analyzed, adopting the classification of the American College of Radiology, BI-RADS: 10 were classified BI-RADS, 19 classified BI-RADS 3, 13 classified BI-RADS 4, 8 classified BI-RADS 5 and 63 exams BI-RADS 6. For each patient only the dominant lesion was analyzed. From 133 lesions analyzed, 9 malignant (four had two histological types, leading to 96 types) and 7 benign lesions confirmed by cytology/histology were found. Out of 7 benign lesions analyzed, there were 19 fibroadenomas, 1 cyst with an inflammatory process, fat necrosis 1 granuloma, 1 residual fibrosis after vacuum assisted biopsy, 1 ANDI (alterations in normal development and involution), 1 fibrosis, 1 negative histological finding for malignant cells (suspected recurrence not confirmed). Out of 9 malignant lesions, four had two histological types (leading to 96 histological types). The histological types were: 4 Ductal Carcinoma In Situ (DCIS), 83 Invasive Ductal Carcinoma (IDC) and 4 Invasive Lobular Carcinoma (ILC), Fig.. In the "others" malignant tumors were found: Necrotic Ductal Carcinoma, Papillary Carcinoma and 1 Mucinous Carcinoma. It was not possible to confirm the biopsy in 14 patients, approximately 10% of the total, Fig 3. Patients were grouped according to age (Fig. 4a), family history (Fig. 4b) and the preferential localization of the lesions (Fig. 5a and 5b). The laterality of the lesions was 81 unifocal, 36 multifocal, 1 multicentric and 4 bilateral. The morphological characteristics were grouped according to the border type (Fig. 6a) and dimensions (Fig. 6b). Fig. 7 summarizes the Dynamic changes, the BI-RADS Classification and the Histological types of all lesions. DWI was performed in 133 patients having been observed 98 lesions with diffusion restriction and 3 without restriction. In 3 patients it was not possible to evaluate the diffusion, Fig. 8. Page 5 of 18

6 For the lesions analyzed, morphology, dynamic and histology were correlated with diffusion and ADC values It was only possible to correlate the ADC values in patients with histological confirmation. It was not possible to determine the ADC in three lesions, two of them because of their small size (between 7 and 8 mm) and the other one for technical reasons. A correlation between the types of the lesions borders with ADC values was carried out. A total of 4 regular and 95 irregular borders gave an average ADC value of 1,043 x10 mm /s for the lesions with irregular borders and 1,479 x10 mm /s for the lesions with regular borders, Fig. 9. These morphological changes show a strong relationship with diffusion values. A correlation between the dynamic changes of the lesions - signal distribution and curve signal intensity/time - with ADC values was also carried out, Fig. 10 and 11. There is a consistent correlation between the signal distribution and ADC values, Fig. 10. Fig. 11 shows a weaker correlation between the curve signal intensity/time and ADC values. In Fig. 1 the average values of ADC and correspondent standard deviation are presented for each of the lesions borders, signal distribution, curve signal intensity/time and histological types. The histological types present ADC values with a strong differentiation. Well differentiated Average values are obtained with reduced standard deviations. The histological types present ADC values with a good differentiation, Fig. 13 and 14. The lesions borders and signal distribution types present well differentiated ADC values and they are also good indicators for the determination of the malignancy or benignancy of breast lesions, Fig. 9, 10 and 1. The Signal to Time Curve does not have so well differentiated ADC values, Fig11. It is consistent with existing literature, the abnormally high ADC values found in malignant cases included in the exceptions (presented in the figures 3 and 7 under the name of "others"): one carcinoma with necrosis (after chemotherapy) - 1,58 x 10 mm /s; one ulcerated carcinoma - 1,63 x 10 mm /s, one mucinous carcinoma - 1,38 x10 mm /s and two papillary carcinomas - 1,41 and 1,71 x10 mm /s. The evaluation of the ADC included the minimum, average maximum and standard deviation values for each histological type, Fig. 13. It was further carried out a correlation between the total lesions and ADC. Fig. 14 lists the distribution, from benign and malignant lesions percentage and ADC values. Page 6 of 18

7 The two curves (Fig. 14) are well separated and so the malignancy and benignity of the lesions have a strong relationship with the ADC. In this study, ADC values increased the specificity of MRI being the maximum ADC threshold for classification of malignant lesions 1,8x10 values for malignant lesions is 0,98x10 mm /s. The average ADC mm /s with a standard deviation of 0,16x10 mm /s. The average ADC values for benign lesions is 1,53x10 mm /s with a standard deviation of 0,16x10 mm /s. The determination of the ADC served as a strong indicator for malignancy versus benignity according to literature, Fig. 15. Images for this section: Fig. 3: Histological types Page 7 of 18

8 Fig. 4: (a) Age and (b) Family history Fig. 5: (a) Lesions distribution and (b) Lesions location (UOQ - upper outer quadrant; CR - central region; IQSL - inner quadrant separating line; UIQ - upper inner quadrant; BN - behind nipple; EQSL - external quadrant separating line; LIQ - lower inner quadrant; UQSL - upper quadrant separating line; LOQ - lower outer quadrant) Page 8 of 18

9 Fig. 6: (a) Lesions borders and (b) Lesions dimension Page 9 of 18

10 Page 10 of 18

11 Fig. 7: Dynamic changes, BI-RADS and Histological types Fig. 8: DWI determination Page 11 of 18

12 Fig. 9: Distribution of Irregular and Regular Borders with ADC values Fig. 10: Distribution of dynamic changes - signal distribution with ADC values Page 1 of 18

13 Fig. 11: Distribution of dynamic changes - curve signal intensity/time with ADC values Page 13 of 18

14 Fig. 1: ADC average values and standard deviation related to the lesions borders, dynamic changes and histological types Fig. 13: Relationship of ADC values with histological type Page 14 of 18

15 Fig. 14: Distribution of benign and malignant breast lesions with ADC values Fig. 15: Summary of some studies illustrating the ADC values Page 15 of 18

16 Conclusion ADC determination shows a correlation of benign and malignant lesions with the morphological and dynamic characteristics and increases the MR accuracy. It can be a valuable tool in the differential diagnosis on MR. Clinical validation studies with larger numbers are needed. The diffusion- weighted sequence improves specificity of breast MR. It does not increase significantly the time of examination and can be performed without intravenous contrast. References Savannah C. Partridge, Wendy De Martini, Brenda Kurland, Peter R. Eby e al; Quantitative Diffusion-Weighted Imaging as an adjunct to Conventional Breast MRI for Improved Positive Predictive Value; AJR 009; 193: Savannah C. Partridge, Revathi S. Murthyb, Ali Ziadloob, Steven W. Whitec, Kimberly H. Allisond, Constance D. Lehmana; Diffusion tensor magnetic resonance imaging of the normal breast; Magnetic Resonance Imaging 010, 8:308 Savannah C Partridge, Wendy De Martini, Brenda Kurland e al; Differencial diagnosis of mammographically and clinically occult breast lesions on diffusion-weighted MRI; J M.R.I 010;31: Nicky Peters, Koen Vincken, Maurice Van den Bosch e al; Quantitative Difusion Weighted imaging for differentiation of benig and malignant breast lesions:the influence of choice of B values; J.M.R.I.010; 31: Wolfgang Bogner, MSc, Stephan Gruber, PhD, Katja Pinker,MD, Gu nther Grabner, MSc, e al; Diffusion-weighted MR for Differentiation of Breast Lesions at 3.0 T: How Does Selection of Diffusion Protocols Affect Diagnosis?; Radiology; Nov 009 Volume 53: Number 4151 Reiko Woodhams1,,3,4, Satoko Kakita1, Hirofumi Hata1, Keiichi Iwabuchi1, Shigeaki Umeoka, Carolyn E. Mountford3, Hiroto Hatabu; Diffusion-Weighted Imaging of Page 16 of 18

17 Mucinous Carcinoma of the Breast: Evaluation of Apparent Diffusion Coefficient and Signal Intensity in Correlation With Histologic Finding; AJR 009; 193:60-66 Mami Lima, Denis Le Bihan, Ryousuke Okada e al; Aparent difusion coeficient as an MR imaging biomarker of low risk ductal carcinoma in situ; Radiology; August 011, 60, n-6471 Nicky Peters, KoenVincken, Maurice Bosh e al; Quantitative Diffusion Weighted Imaging for Differentiation of Benig and Malignant Breast Lesions :the influence of the choice of b-values; JMRI; 010, 31, 5: Fernanda Pereira, Gabriela Martins, Eduardo Figueiredo, Marisa Domingues e al; Assessment of breast leasions with diffusio-weighted MRI:comparing the use of diferent b values; AJR 009;193: Valência King,Jennifer Brooks, Jonine Bernstein, Anne Reiner e al; Background parenchymal enhancement at breast MR Imaging and brestt câncer risk; Radiology; july 011; 60,n1:50-60 Abramovici, Martha Mainiero, Screening Breast Imaging: comparison of interpretation of baseline and annual follow up Studies; Radiology; 011,jully-V59,1:85-91 Yanming Yo, Quang Jiang, Yanwei Miao, Jun Li e al; Quantitrative analysis of clinical dynamic contrast-enhanced MR imaging for evaluation treatment response in human breast cancer; Radiology 010,57,n1:47-55 Matthias Schabel, 010,31: Glen Morrel, Karen Oh, Cheryl Walczak e al; J.MRI Sang Hee, Woo Kyung Moon, Nariya Cho e al; Diffusion -Weighted MR Imaging,Pretreatement prediction of response to neoadjuvant chemotherapy in patients with breast cancer; Radiology-October 010,57,n1:56-63 Sonia li, Andreas Makris, Mark Beresford, Jane Taylor e al; Use dynamc contrast - enhanced MR imaging to predict survival in patients withprimary breast cancer undergoing neodjuvant chemotherapy; Radiology; july 011,vol 60,n1:68-77 Page 17 of 18

18 Fangberg, Nilsen Hole, Holmen e al; Neoadjuvant chemoterapy in breast cancerresponse evaluation and prediction of response to treatment using; Eur Radiol; 011, 1: Mitsuhiro Tozaki1, Eisuke Fukuma, 1H MR Spectroscopy and Diffusion-Weighted Imaging of the Breast: Are They Useful Tools for Characterizing Breast Lesions Before Biopsy?, AJR 009; 193: Personal Information cristinarf@netcabo.pt Page 18 of 18