Radiologists detect 'gist' of breast cancer before overt signs appear April 2018 PC Brennan* Z Gandomkar, Epko E, K Tapia, S Lewis, Georgian-Smith D*, Wolfe J* MIOPeG & BREAST, University of Sydney; Department of Radiology, Harvard Medical School The University of Sydney Page 1
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The GIST! The University of Sydney Page 3
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Architectural Distortion; 11 mm Detected by 98% of the readers in normal presentation Detected by all readers based on the gist response The University of Sydney Page 7
Background What do we know about Gist? 1. Initiates foveal checking 2. Above-chance on normal/abnormal classification 3. Above-chance performance in distinguishing mammograms from the contralateral breast from normal mammograms The University of Sydney Page 8
Aim Is a gist signal present in prior images of women who eventually develop breast cancer.. even when the diagnosis is normal! The University of Sydney Page 9
Method: Mammograms 200 craniocaudial mammograms, five categories, each with 40 images: 1) Cancer 2) Prior-Vis 3) Contra-lateral 4) Prior-Invis 5) Normal: Prior mammograms of normal cases The University of Sydney Page 10
Method: Experiment procedure A half-second A half-second A cross appears in the centre of the screen A half-second No time limit The University of Sydney Page 11
Method 23 RANZCR readers 2017 RANZCR Breast Imaging Group meeting The University of Sydney Page 12
Results Four pair-wise comparisons Normal category served as a baseline Cancer vs Normal Prior-Vis vs Normal Contra vs Normal Prior-Invis vs Normal The University of Sydney Page 13
Results Receiver Operating Characteristic (ROC) curves ROC curve for an average reader was calculated as suggested in: Chen, W, and F W Samuelson. The Average Receiver Operating Characteristic Curve in Multireader Multicase Imaging Studies. The British Journal of Radiology (2014). The University of Sydney Page 14
Results Receiver Operating Characteristic (ROC) curves (Best readers) 1 0.9 0.8 0.7 True Positive Rate 0.6 0.5 0.4 0.3 0.2 0.1 Cancer Prior-Vis Contra Prior-Invis Chance 0 0 0.2 0.4 0.6 0.8 1 False Positive Rate ROC curves for the best readers in each category The University of Sydney Page 15
Results Was this a density issue? Cancer category (P=0.27) Contra category (P=0.70) Prior-Vis (P=0.79) Prior-Invis (P=0.62) The University of Sydney Page 16
Results Could a few cases be driving performance? The University of Sydney Page 17
Results 0.9 0.8 0.7 0.6 True Positive Rate 0.5 0.4 0.3 0.2 0.1 Chance All After omiting 10 cases 0 0 0.2 0.4 0.6 0.8 1 F l P iti R t The University of Sydney Page 18
Implications Prior mammograms contain important information. even when no overt signs of cancer exist The University of Sydney Page 19
On-going works: two important questions remain 1. Which image features relate to the gist response? 2. Could the gist response be useful for risk assessment? Very early results.. The University of Sydney Page 20
Which image features relate to Gist? Feature Type (feature name) F1: First order statistics features (AVE, STD, skewness, kurtosis, Min, Max, 1 st, 5 th, 10 th, 15 th, 25 th, 50 th, 75 th, 80 th, 85 th, 95 th, 99 th percentile of intensity, Max-Min, 99 th percentile-1 st percentile, 95 th percentile-5 st percentile) F2: Second order statistics (Haralick features over four directionfor d= 1 and 3 pixels) (Contrast, Correlation, Cluster Prominence, Cluster Shade, Dissimilarity, Energy, Entropy, Homogeneity, Sum of squares, Sum average, Sum entropy, Difference variance, Difference entropy, Information measure of correlation 1 and 2, Inverse difference normalized, Inverse difference moment normalized) F3: Higher order statistics (Features from grey level run length matrix) (Short run emphasis, Long run emphasis, Grey level non-uniformity, Run percentage, Run length non-uniformity, Low grey level run emphasis, High grey level run emphasis) F4: Gabor-based features (AVE energy of filtered image using Gabor filter bank in one scale and six orientations) F5: Features based on Maximum Response (MR8) filters (AVE energy of in eight filtered images) F6: Textural features based on Neighbourhood Grey Tone Difference Matrix (Coarseness, contrast, busyness, complexity, strength) F7: Statistical Feature Matrix (Coarseness, contrast, period, roughness) F8: Laws Texture Energy Measures F9: Fractal Dimension Texture Analysis (FDTA) The University of Sydney Page 21
Risk assessment? 1 1 0.8 0.8 True Positive Rate 0.6 0.4 True Positive Rate 0.6 0.4 0.2 Gist (AUC=0.81) Features (AUC =0.72) Gist+Features (AUC=0.84) Chance-level (AUC=0.5) 0 0 0.2 0.4 0.6 0.8 1 False Positive Rate Prior_Vis 0.2 Gist (AUC=0.65) Features (AUC=0.75) Gist+Features (AUC=0.81) Chance-level (AUC=0.5) 0 0 0.2 0.4 0.6 0.8 1 False Positive Rate Prior_Invis The University of Sydney Page 22
Conclusions from this preliminary work Image features can predict the gist response. Gist response with computer-extracted features can predict breast cancer risk. MY RISC (MammographY Radiologic InterrogationS and Calculations) The University of Sydney Page 23
Acknowledgements ACKNOWLEDGEMENTS All those wonderful clinicians and scientists Ziltron BAMC RANZCR committee Breast Imaging Group (Prof Mary Rickard; Prof Warwick Lee) Sectra Hologic Australian Synchrotron Universities and clinics National Breast Cancer Foundation, DoHA, RANZCR The University of Sydney Page 24 2
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Breast cancer risk-assessment models: parenchymal texture analysis Sample texture features from a recent study and the corresponding AUC values Texture features AUC Fractal dimension of image using thresholds from 75% 0.621 Haralick-Energy 0.621 Haralick-Entropy 0.62 Fractal dimension of image using thresholds from 70% 0.621 Fractal dimension of image using thresholds from 80% 0.62 Fractal dimension of image using thresholds from 10% 0.62 Kurtosis 0.62 Fractal dimension of image using thresholds from 65% 0.62 Fractal dimension, Minkowski method 0.621 NGTDM-Busyness 0.619 Haralick-Homogeneity 0.62 Haralick-Dissimilarity 0.62 Fractal dimension of image using thresholds from 60% 0.62 Fractal dimension of image using thresholds from 85% 0.619 Fractal dimension of image using thresholds from 15% 0.619 * Malkov et al. Breast Cancer Research (2016) 18:122. The University of Sydney Page 27
Results: Radiologists Expertise Characteristics Participants (n=23), n (%) Years reading mammograms 5 or less than 5 y 4 (17.39%) 6-10 y 5 (21.74%) 11-15 y 4 (17.39%) 16-20 y 4 (17.39%) More than 20 y 6 (26.09%) Screen reader Yes 20 (86.96%) No 3 (13.04%) Years since registration as breast screening radiologists 5 or less than 5 y 7 (30.43%) 6-10 y 4 (17.39%) 11-15 y 5 (21.74%) 16-20 y 4 (17.39%) More than 20 y 3 (13.04%) The University of Sydney Page 28
Results: Radiologists Expertise Characteristics Participants (n=23), n (%) Hours per week reading mammograms 4 or less than 4 h 5 (21.74%) 5-10 h 13 (56.52%) 11-15 h 2 (8.70%) 16-20 h 2 (8.70%) More than 20 h 1 (4.35%) Number of mammograms per week Less than 20 4 (17.39%) 20-59 1 (4.35%) 60-100 5 (21.74%) 101-150 2 (8.70%) 151-200 4 (17.39%) More than 200 7 (30.43%) Whether completed a breast fellowship Yes 7 (30.43%) No 16 (69.57%) The University of Sydney Page 29
1. Kundel, H. L. & Nodine, C. F. Interpreting chest radiographs without visual search. Radiology (1975). 2. Evans, K. K. et al. The gist of the abnormal: Above-chance medical decision making in the blink of an eye. Psychonomic bulletin & review (2013). 3. Evans, K. K. et al. A half-second glimpse often lets radiologists identify breast cancer cases even when viewing the mammogram of the opposite breast. Proceedings of the National Academy of Sciences (2016). The University of Sydney Page 30