Breast mass classification based on opto-acoustic features using data mining

Transcription

1 Breast mass classification based on opto-acoustic features using data mining Poster No.: C-0926 Congress: ECR 2014 Type: Scientific Exhibit Authors: A. T. Stavros 1, P. Lavin 1, D. Schoenfeld 2, M. J. Ulissey 1 ; 1 San Antonio, TX/US, 2 Boston, MA/US Keywords: Breast, Ultrasound, Biopsy, Cancer DOI: /ecr2014/C-0926 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 7

2 Aims and objectives Imagio (Seno Medical Instruments, Inc.) is currently an investigational medical device being tested for FDA review. Imagio is a combination of opto-acoustics (OA) coregistered with ultrasound currently undergoing a clinical study to evaluate effectiveness. Imagio uses dual wavelength laser opto-acoustic imaging technology co-registered with conventional diagnostic ultrasound to gain both structural and functional imaging information of potentially suspicious breast masses without having to administer radioactive contrast agents or expose patients to radiation. This hybrid imaging technology has been previously introduced in abstracts and presentations at various medical societies [1, 2]. It may have the ability to increase both sensitivity and specificity of sonographically evident breast masses [1, 2]. The purpose of this study is to evaluate clinical applications of Imagio features to classify patients as either having a malignant or benign breast mass by examining morphological and functional features found within and outside the tumor mass, to ultimately spare biopsies. Methods and materials A prospective series of 66 cases included 37 histologically proven cancers and 29 histologically proven benign masses. Each patient had five specific features assessed by Imagio using a 0-5 ordinal scale. The internal tumor features included density of vascularity (DV), blood oxygen saturation (BO), and the total blood accumulation (BA) while external tumor features included total blood (TB) and peri-tumoral radiating vessels (RV); each were scored on a 0-5 ordinal scale and were summed to get a total internal score, total external score, and a total score. Five classification methods were used to classify between benign and malignant outcomes: Logistic Regression (LR) Support Vector Machines (SVM) Classification Trees (CT) Random Forests (RF), and K-Nearest Neighbors (KNN). Page 2 of 7

3 Ten-fold cross validation was used where the 66 cases were randomly divided into 10 groups. Each of the ten possible groups was removed in turn from the 66 observations and the classifier was trained on the remaining groups to develop a classification rule. This rule was then applied to the removed group. This process was repeated ten times until every observation was assigned by a classifier that had not previously been developed using that observation. A two-sided exact Jonckheere-Terpstra test was used to test the relationship between increasing scores (internal, external, total) and higher cancer grade. Results The study population consisted of 29 fibroadenomas (FAs) as well as 10 IDC 1, 11 IDC 2, and 18 IDC 3 cases; 66 cases were evaluated for the classification analyses. Results were consistent and favorable for Imagio; there was 100% sensitivity with 72% to 79% specificity (Table 1). KNN and SVM performed the best while LR performed the worst. Findings support the stand-alone biopsy-sparing potential for Imagio. In addition, the probability of malignancy for these methods decreased depending on the variables with negative coefficients and increased with those with positive ones. The coefficients for BA and DV were statistically significant. The importance of these coefficients for the other methods was consistent. The probability of malignancy further depended on the internal tumor features but not on the external features (Figure 1 and 2). OA Total (two-sided p=3x10-8) and OA Total Internal (two-sided p=6x10-9) were highly significant, but not OA Total External. OA Total and OA Total Internal Features were highly related to histology with lower scores for FAs increasing progressively to highest scores for IDC 3. All three internal OA features were highly significant: (1) DV (two-sided p=10-7), BO (two-sided p=3x10-8), and BA (two-sided p=5x10-6). The cumulative contribution of each internal feature to the total internal score was also assessed for each histopathology subgroup to confirm independent contribution to the OA Total Internal Score; pairwise correlations explained no more than 56% of the total variance. Page 3 of 7

4 Images for this section: Fig. 1: Displays an IDC grade 2 with numerous internal red vessels, internal red blush, increased internal blood and peri-tumoral radiating vessels (internal features highlighted by the blue arrow). Page 4 of 7

5 Fig. 2: Displays a Fibroadenoma with normal polar branching feeding and draining vessels, capsular vessels, and mostly green internal blush (internal features highlighted by the blue arrow). Page 5 of 7

6 Table 1: Depicts the five classification methods used to classify between benign and malignant outcomes. K-Nearest Neighbor and Support Vector Machine performed the best while Logistic Regression performed the worst. Page 6 of 7

7 Conclusion Preliminary data suggest that Imagio has the ability to achieve stand-alone clinically meaningful sensitivity and specificity in a diagnostic setting. The internal features are highly predictive consistent with the design of the co-registered methodologies. These features each carry prognosis. Prospective research to evaluate this technology is currently underway for regulatory review when it is complete. Personal information References 1. Pamela M. Otto, Kenneth Kist, et al. Feasibility of Co-registered Opto-Acoustic and Ultrasonic Imaging for Differentiation of Malignant From Benign Breast Tumors. Presented at: American Institute for Ultrasound in Medicine; 2012 Mar 31; Phoenix, AZ. 2. Pamela M. Otto, Kenneth Kist, et al. Clinical Feasibility Study of Combined Opto- Acoustic and Ultrasonic Imaging Modality Providing Co-registered Functional and Anatomical Maps of Breast Tumors. Presented at: Radiological Society of North America; 2011 Nov 28; Chicago, IL. Page 7 of 7