Spiculated breast masses on MRI: Which category should we choose, 4 or 5?

Transcription

1 Spiculated breast masses on MRI: Which category should we choose, 4 or 5? Poster No.: C-1394 Congress: ECR 2015 Type: Scientific Exhibit Authors: N. Onishi, S. Kanao, M. Kataoka, M. Kawai, M. Iima, A. Ohashi, K. Togashi; Kyoto/JP Keywords: Breast, MR, Biopsy, Diagnostic procedure, Cancer DOI: /ecr2015/C-1394 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 25

2 Aims and objectives The aim of this study was to determine MRI findings and associated clinical characteristics of spiculated breast masses that are useful for accurate BI-RADS categorization, i.e. #95% probability of malignancy for category 5 ( Fig. 1 on page 3 )[1]. Spiculated margin of a breast mass is quite important as a highly suspicious finding for carcinoma in breast imaging, and its positive predictive value (PPV) for carcinoma is 81-91% on mammography[2, 3]. In breast imaging reporting and data system (BI-RADS) for MRI, "spiculated margin" of a mass is mentioned as a suspicious finding for cancer[1], and category 4 or 5 is generally given to such lesions with a strong recommendation for biopsy. It should be noted, however, that benign breast lesions with spiculation are occasionally encountered in the clinical practice. Sclerosing adenosis (SA), radial scar (RS) and complex sclerosing lesions (CSLs) are the examples of benign lesions that may present spiculated margin. These benign lesions mimic breast cancer in MRI just as in mammography and ultrasonography ( Fig. 2 on page 4, Fig. 7 on page 9) [4, 5], and nonmalignant results of biopsy on spiculated masses require clinicians to reassess images and choose repeat biopsy or follow-up. Though the current rationale for using a BI-RADS MRI category 5 is to identify lesions for which any nonmalignant percutaneous tissue diagnosis is considered discordant, resulting in a recommendation for repeat (usually surgical) biopsy, there are no definite MRI criteria for spiculated masses sufficiently predictive of malignancy to produce the #95% probability required for a category 5 assessment (Fig. 1 on page 3)[1]. Page 2 of 25

3 Fig. 2: Spiculated Breast Masses: Click Fig. 3-6 to see the detail and Fig.7 to check the answer. References: Department of Diagnostic Radiology, Kyoto University, Kyoto University Hospital - Kyoto/JP The aim of this study was to determine MRI findings and associated clinical characteristics of spiculated breast masses that are useful for accurate BI-RADS categorization, i.e. #95% probability of malignancy for category 5 ( Fig. 1 on page 3 )[1]. Images for this section: Page 3 of 25

4 Fig. 1: Assessment of Category in BI-RADS MRI 2013 (modified)[1] Page 4 of 25

5 Fig. 2: Spiculated Breast Masses: Click Fig. 3-6 to see the detail and Fig.7 to check the answer. Page 5 of 25

6 Fig. 3: Is this malignant? Please click Fig.7 to check the answer. Page 6 of 25

7 Fig. 4: Is this malignant? Please click Fig.7 to check the answer. Page 7 of 25

8 Fig. 5: Is this malignant? Please click Fig.7 to check the answer. Page 8 of 25

9 Fig. 6: Is this malignant? Please click Fig.7 to check the answer. Page 9 of 25

10 Fig. 7: Spiculated Breast Masses and Their Pathological Diagnoses Page 10 of 25

11 Methods and materials Patients Approval for this study was obtained from the Institutional Review Board of our institution, and informed consent was waived due to retrospective study design. Inclusion criteria of this study were spiculated breast mass lesions depicted by contrast enhanced breast MRI studies performed from June 2008 to March Lesions without pathological diagnosis by core needle biopsy (CNB), vacuum-assisted stereotactic CNB or surgery were excluded. Lesions after receiving neoadjuvant endocrine therapy (NAE) or neoadjuvant chemotherapy (NAC) were excluded. Male patients were excluded. A total of 136 female patients (mean age=60.1 years, age range=18-88 years) with 140 lesions were selected for this study. MRI Acquisition MRI was performed at a 3.0/1.5 T scanner (3.0T, 122 lesions; 1.5T, 18 lesions) with 16/4ch breast coil. Routinely, T2/T1/diffusion-weighted and fat-suppressed T1-weighted dynamic contrast-enhancied (DCE) images were obtained. Parameters were shown in Table 1 on page 13. Image Analysis Lesion size of a spiculated mass was measured at its central core. ADC values were calculated using the algorithm presented by the following equation: ADC = [1 / (b2-b1)] In [S1 / S2], where S1 and S2 are the signal intensities in the region of interest (ROI) 2 2 obtained by two gradient factors, b1 and b2 (b1= 0 sec/mm, b2=1000 sec/mm ). Inside the lesions, placing as many 3 3 mm circle ROIs as possible without overlapping areas, the minimum ADC (ADCmin) values were obtained. Statistical Analysis Patient's age, lesion size, ADCmin and BI-RADS descriptors were compared between malignant and benign lesions (Malignant group and Benign group, respectively). MannWhitney test was performed to evaluate the difference in patient's age, lesion size and ADCmin between the two groups. BI-RADS descriptors including shape, internal enhancement characteristics, initial phase kinetic curve assessment and delayed phase kinetic curve assessment were compared between the two groups using Fisher's exact test. Multivariate logistic regression analysis was performed to determine the factors useful for differentiating malignant from benign lesions. The variables included into the multivariate logistic regression analysis were selected based on the results obtained Page 11 of 25

12 by univariate logistic regression analysis. A p value of less than 0.05 was considered statistically significant. Using the results above, some criteria for proper classification of spiculated masses into BI-RADS category 4 or 5 were proposed. For convenience, C5M (number of lesions with category 5 that pathologically resulted in malignant diagnosis), C5B (number of lesions with category 5 that pathologically resulted in benign diagnosis), C4M (number of lesions with category 4 that pathologically resulted in malignant diagnosis) and C4B (number of lesions with category 4 that pathologically resulted in benign diagnosis) were defined ( Fig. 8 on page 13 ). Fig. 8: Contingency Table Comparing BI-RADS MRI Category and Pathological Diagnosis: PPVC5 was defined to identify how accurate category 5 assessments were in predicting malignancy. References: Department of Diagnostic Radiology, Kyoto University, Kyoto University Hospital - Kyoto/JP Page 12 of 25

13 The accuracy of the proposed criteria was evaluated by PPVC5, which was defined to identify how accurate category 5 assessments were in predicting malignancy ( Fig. 8 on page 13 ). PPVC5 and PPV were defined as follows: PPVC5 = (number of lesions with category 5 that pathologically resulted in malignant diagnosis: C5M)/(number of lesions categorized as category 5: C5M +C5B) PPV = (number of lesions that pathologically resulted in malignant diagnosis: C5M +C4M)/(number of all lesions: C5M+C5B+C4M+C4B). C4M and C5B were also calculated for the evaluation of the proposed criteria. Images for this section: Table 1: MRI Acquisition Page 13 of 25

14 Fig. 8: Contingency Table Comparing BI-RADS MRI Category and Pathological Diagnosis: PPVC5 was defined to identify how accurate category 5 assessments were in predicting malignancy. Page 14 of 25

15 Results Of all the 140 lesions, 131 lesions were pathologically diagnosed as malignant (Malignant group) and 9 lesions as benign (Benign group). The distribution of the final pathological diagnosis is shown in Table 2 on page 18. Patient's Age, Lesion Size and ADCmin Patient's age of the Malignant group was significantly higher than that of the Benign group (p=0.020, Fig. 9 on page 19a). In the Benign group, no patients were over 61 years old. Lesion size of the Malignant group was significantly larger than that of the Benign group (p<0.001, Fig. 9 on page 19b). ADC values were not available in 4 lesions (Malignant group, 3; Benign group, 1) because of the distortion of diffusion-weighted images. Total of 136 lesions (Malignant group, 128; Benign group, 8) were entered into the evaluation of ADCmin. ADCmin of the Malignant group was significantly lower than that of the Benign group (p=0.006, Fig. 9 on page 19c). Page 15 of 25

16 Fig. 9: Distribution of Patient's Age, Lesion Size and ADCmin in Malignant Group and Benign Group: Patient's age, lesion size and ADCmin showed significant difference between the two groups (Mann-Whitney test: p=0.020, p<0.001 and p=0.006, respectively). References: Department of Diagnostic Radiology, Kyoto University, Kyoto University Hospital - Kyoto/JP Mass Descriptors Table 3 on page 20 shows the frequency of mass descriptors in the Malignant and the Benign group. All the descriptors including shape, internal enhancement characteristics, initial phase kinetic curve assessment and delayed phase kinetic curve assessment showed no significant difference in frequency between the two groups (Fisher's exact test, See Table 3 on page 20 ). In both groups, the majority of the masses showed "fast" kinetic curve in the initial phase (Malignant group, 98%; Benign group, 100%) and "washout" kinetic curve in the delayed phase (Malignant group, 76%; Benign group, 67%). Univariate and Multivariate Logistic Regression Analysis Page 16 of 25

17 Of all the 140 lesions, 4 lesions were excluded due to the missing ADC values, and the remaining 136 lesions were included in univariate and multivariate logistic regression analysis. By univariate logistic regression analysis, patient's age, lesion size and ADCmin were selected as the candidate predictive factors of malignancy and were included in multivariate logistic regression analysis. The univariate logistic regression analysis of "homogenous" showed p value less than 0.05, but was not included in multivariate logistic regression analysis due to very small number of masses that presented "homogenous" (Malignant group, 0; Benign group, 1). As a result, patient's age and lesion size turned out to be the significant predictive factors of malignancy, while ADCmin became non-significant. Results of the univariate and multivariate logistic regression analysis were summarized in Table 4 on page 21. Analysis of PPVC5 PPV of all the spiculated masses was 93.6% (131/140) and "spiculation" itself was not sufficiently predictive of cancer to produce the #95% probability required for a category 5 assessment. According to the result of multivariate logistic regression analysis, some thresholds on patient's age and lesion size were listed for a category 5 assessment. Listed thresholds and the consequent PPVC5, C4M and C5B were shown in Fig. 10 on page 22a,b & Fig. 11 on page 23. Both of thresholds "age>50years" and "size>9mm" for category 5 showed the consequent PPVC5#95% (96.0% and 96.8%, respectively), but at the expense of relatively large C4M (35 and 11 out of 131 malignant lesions, respectively) (Fig. 10 on page 22a,b). Combined thresholds on age and size decreased C4M, and a threshold "age>50 years or size>9 mm, or both" for category 5 showed the minimum C4M among all the thresholds that showed PPVC5#95% (Fig. 11 on page 23). If this threshold had been adopted as a threshold for category 5, consequent PPVC5 would have been 95.5% with 3 malignant lesions categorized as category 4(C4M) and 6 benign lesions categorized as category 5(C5B). Page 17 of 25

18 Fig. 10: Thresholds on a) Patient's Age and b) Lesion Size: Thresholds that showed PPVC5#95% are noted in red. a) The threshold "age>50years" for category 5 showed the consequent PPVC5=96%, but 35 out of 131 malignant lesions were missed. b) The threshold "size>9mm" for category 5 showed the consequent PPVC5=96.8%, but 11 out of 131 malignant lesions were missed. References: Department of Diagnostic Radiology, Kyoto University, Kyoto University Hospital - Kyoto/JP Fig. 11: Combined Thresholds on Patient's Age and Lesion Size: Thresholds that showed PPVC5#95% are noted in red. The threshold "age>50 years or size>9 mm, or both" for category 5 showed the minimum C4M (3 out of 131 malignant lesions) among all the thresholds that showed PPVC5#95%. References: Department of Diagnostic Radiology, Kyoto University, Kyoto University Hospital - Kyoto/JP Images for this section: Page 18 of 25

19 Table 2: Distribution of the Pathological Diagnosis Page 19 of 25

20 Fig. 9: Distribution of Patient's Age, Lesion Size and ADCmin in Malignant Group and Benign Group: Patient's age, lesion size and ADCmin showed significant difference between the two groups (Mann-Whitney test: p=0.020, p<0.001 and p=0.006, respectively). Page 20 of 25

21 Table 3: Frequency of Breast Mass Descriptors in Malignant and Benign Group: No BIRADS descriptors showed significant difference in frequency between the two groups. The majority of the Benign group showed "fast" kinetic curve in the initial phase and "washout" kinetic curve in the delayed phase, the curves generally known for malignant pattern [1, 6]. Page 21 of 25

22 Table 4: Results of Univariate & Multivariate Logistic Regression Analysis: *Factors statistically significant (p<0.05) by univariate logistic regression analysis and included in multivariate logistic regression analysis; **The univariate logistic regression analysis of "homogenous" showed p<0.05, but was not included in multivariate logistic regression analysis due to very small number of masses that presented "homogenous" (Malignant group, 0; Benign group, 1); ***Factors statistically significant (p<0.05) by multivariate logistic regression analysis. Page 22 of 25

23 Fig. 10: Thresholds on a) Patient's Age and b) Lesion Size: Thresholds that showed PPVC5#95% are noted in red. a) The threshold "age>50years" for category 5 showed the consequent PPVC5=96%, but 35 out of 131 malignant lesions were missed. b) The threshold "size>9mm" for category 5 showed the consequent PPVC5=96.8%, but 11 out of 131 malignant lesions were missed. Fig. 11: Combined Thresholds on Patient's Age and Lesion Size: Thresholds that showed PPVC5#95% are noted in red. The threshold "age>50 years or size>9 mm, or both" for category 5 showed the minimum C4M (3 out of 131 malignant lesions) among all the thresholds that showed PPVC5#95%. Page 23 of 25

24 Conclusion 1.) Patient's age and lesion size are useful for differentiating malignant from benign spiculated masses. 2.) Benign masses with spiculated margin may present "malignant pattern" kinetic curves. We have demonstrated that patient's age and lesion size are useful for differentiating malignant from benign spiculated masses. The threshold of "age>50 years or size>9 mm, or both" for category 5 categorized spiculated masses with 95.5% of accuracy. DCE MRI information including shape, internal enhancement characteristics, and initial/ delayed phase kinetic curve assessment was not useful for differentiating malignant from benign spiculated masses. It is especially notable that the majority of benign spiculated masses showed "fast" kinetic curve in the initial phase and "washout" kinetic curve in the delayed phase, the curves generally known for malignant pattern[1, 6]. This result shows that kinetic curve assessment may cause overdiagnosis of spiculated breast masses. Radiologists need to keep it in mind that benign masses with spiculated margin may present "fast" and "washout" kinetic curves. In BI-RADS assessment of spiculated masses on MRI, patient's age and lesion size are useful for accurate categorization. Personal information Natsuko Onishi Dept. of Diagnostic Imaging and Nuclear Medicine Kyoto University, Kyoto, JAPAN natsucom@kuhp.kyoto-u.ac.jp References Page 24 of 25

25 Morris EA, Comstock CE, Lee CH, et al. ACR BI-RADS Magnetic ResonanceImaging. In: ACR BI-RADS Atlas, Breast Imaging Reporting and DataSystem. Reston, VA: American College of Radiology, 2013 Liberman L, Abramson AF, Squires FB, Glassman JR, Morris EA, Dershaw DD. The breast imaging reporting and data system: positive predictive value of mammographic features and final assessment categories. AJR Am J Roentgenol 1998; 171:35-40 Nunes LW, Schnall MD, Orel SG. Update of breast MR imaging architectural interpretation model. Radiology 2001; 219: Linda A, Zuiani C, Londero V, Cedolini C, Girometti R, Bazzocchi M. Magnetic resonance imaging of radial sclerosing lesions (radial scars) of the breast. Eur J Radiol 2012; 81: Shaheen R, Schimmelpenninck CA, Stoddart L, Raymond H, Slanetz PJ. Spectrum of diseases presenting as architectural distortion on mammography: multimodality radiologic imaging with pathologic correlation. Semin Ultrasound CT MR 2011; 32: Kuhl C. The current status of breast MR imaging. Part I. Choice of technique, image interpretation, diagnostic accuracy, and transfer to clinical practice. Radiology 2007; 244: Page 25 of 25