Nico Karssemeijer

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Nico Karssemeijer

  1. 1. Interactive Computer Aided Detection Nico Karssemeijer Maurice Samulski, Michiel Kallenberg, Rianne Hupse Carla Boetes, Roel Mus, Ard den Heeten Radboud University Nijmegen Medical Centre Radiology / LRCB
  2. 2. Overview presentation <ul><li>Mammography and CAD </li></ul><ul><li>CAD in practice </li></ul><ul><li>Observer studies with CAD </li></ul><ul><li>How can effect of CAD be optimized ? </li></ul>
  3. 3. Breast Cancer Facts <ul><li>Around 350,000 new breast cancer cases per year in Europe </li></ul><ul><li>26% of new cancer cases in women </li></ul><ul><li>Mortality: 130,000 / year </li></ul><ul><li>Breast cancer incidence still increases </li></ul><ul><li>Early detection by screening reduces mortality </li></ul>
  4. 4. X-ray Mammography <ul><li>Well established and highly optimized for screening and diagnosis </li></ul><ul><li>2D projection </li></ul><ul><li>Typically 2 views per breast are acquired (MLO and CC) </li></ul><ul><li>Digital mammography (FFDM) is replacing film </li></ul><ul><li>Mammography is low dose but there is a radiation risk </li></ul><ul><li>Poor performance in dense breasts </li></ul><ul><li>Computer aided detection (CAD) is widely used </li></ul>
  5. 9. Cancer missed by 10 out of 15 in observer study
  6. 11. Detection of masses
  7. 12. Detection of Masses: Effect of Training 940 TP mass regions and 38000 normal regions in training set 5 fold cross validation
  8. 13. Use of CAD prompts is intended to avoid oversights ImageChecker, R2 Technology / Hologic
  9. 14. Mammography CAD in practice <ul><li>Widely used: more than 10,000 systems installed </li></ul><ul><li>Reimbursement in the US </li></ul><ul><li>CAD increases efficiency of microcalcification detection </li></ul><ul><li>Most clinical studies confirm that in screening CAD leads to improved breast cancer detection </li></ul><ul><li>Large UK trial shows that CAD may replace double reading </li></ul>
  10. 15. Limitations of CAD <ul><li>Many radiologists have little confidence in CAD for masses due to its poor specificity </li></ul><ul><li>Current CAD systems only address problem of perception errors </li></ul><ul><li>Misinterpretation seems to be a more common cause of missing breast cancer in screening than perceptual oversights </li></ul><ul><li>CAD mark probabilities could be very valuable but remain invisible in current systems </li></ul><ul><li>CAD systems are not correlating information from different views </li></ul>
  11. 16. Manning et al., Categories of observer error from eye-tracking and AFROC data, SPIE Medical Imaging 2004, Proc SPIE Vol 5372 Why are cancers missed? Detection of lung nodules by 24 readers in 120 digitized chest radiographs
  12. 17. CAD (R2) and 10 experienced screening radiologists 115 visible mass priors, 250 normals Radiologists CAD
  13. 18. CAD performance on regions reported by readers 115 visible mass priors, 250 normals on regions marked by readers
  14. 19. Observer study with traditional CAD <ul><li>10 screening radiologists </li></ul><ul><li>Two levels of training with CAD </li></ul><ul><li>Series of 192 mammograms read twice, with and without mass CAD </li></ul><ul><ul><li>96 normals </li></ul></ul><ul><ul><li>96 prior mammograms of cancer cases showing visible signs of abnormality </li></ul></ul>
  15. 20. Results
  16. 21. Results
  17. 22. Effect of CAD on reader scores -1.26 (p=.54) -4.87 (p<.002) 48 +5.25 (p<.003) -3.05 (p<.05) +2.00 (p=.14) -3.96 (p<.001) 146 All TP marked by CAD (n=44) TP not marked by CAD (n=39) Training
  18. 23. Effect of CAD on reader scores +6.37 (p<.001) -1.51 (p<.001) 48 +2.75 (p<.001) -0.93 (p<.001) +4.56 (p<.001) -1.22 (p<.001) 146 All FP marked by CAD (n=75) FP not marked by CAD (n=244) Training
  19. 24. Independent Combination of Reader Scores with CAD <ul><li>* </li></ul>Single Reading Suspiciousness = S R Combined with CAD markers: Suspiciousness = (S R +S CAD )/2 *
  20. 25. Independent Combination of Reader Scores with CAD 10 radiologists, 192 cases with cancers missed in screening
  21. 26. <ul><li>Can radiologists use CAD marks interactively to help with interpretation </li></ul><ul><li>Opposite to current use of CAD: </li></ul><ul><ul><li>Change decisions using CAD </li></ul></ul><ul><ul><li>No display of traditional marks after reading a case </li></ul></ul><ul><li>Compare results with independent combination of reader scores with CAD </li></ul>Interactive use of CAD for decision support
  22. 27. Mammographic workstation design <ul><li>Hanging protocols </li></ul><ul><li>Display of priors </li></ul><ul><li>30 inch Eizo Flexscan display (2560x1600) </li></ul><ul><li>Reporting tool for localized findings </li></ul><ul><li>0-100 rating scale </li></ul>
  23. 35. Reader Study <ul><li>120 cases read with and without CAD </li></ul><ul><ul><li>80 normals, 40 abnormal </li></ul></ul><ul><ul><li>Mammograms from screening, displayed with priors </li></ul></ul><ul><ul><li>All positives were cancer cases missed in screening </li></ul></ul><ul><ul><li>Abnormalities were masses visible in retrospect </li></ul></ul><ul><ul><li>Read in batches of 60 cases </li></ul></ul><ul><li>Counterbalanced design, 4 sessions </li></ul><ul><li>Participants: </li></ul><ul><ul><li>3 screening radiologists </li></ul></ul><ul><ul><li>5 non-radiologists with mammogram reading experience </li></ul></ul>
  24. 36. Reader Study (2) <ul><li>Non-radiologists were trained with a series of 60 cases </li></ul><ul><li>True positive = cancer correctly localized </li></ul><ul><li>Case based analysis </li></ul><ul><li>Area under LROC at high specificity used for evaluation </li></ul><ul><ul><li>TPF10 : Mean True Positive Fraction for recall rate < 10% </li></ul></ul><ul><li>LROC: Localization Receiver Operating Characteristic </li></ul>
  25. 37. Mean LROC performance of readers
  26. 38. Mean sensitivity in interval FPF < 10% non radiologists radiologists
  27. 39. Mean reading time per case non radiologists radiologists seconds
  28. 40. Summary of results Non radiologists Radiologists Unaided CAD Unaided CAD TPF10 (%) 26.5 39.2 26.5 28.9 Reading time per case (s) 126 112 56 64
  29. 41. Conclusion <ul><li>Readers performed better with interactive CAD in comparison to unaided reading (p = 0.012) </li></ul><ul><li>Results of reading with CAD were comparable to independent combination of reader scores with CAD </li></ul><ul><li>Non-radiologists had more benefit from CAD than radiologists </li></ul><ul><ul><li>Non-radiologists had some training with CAD </li></ul></ul><ul><ul><li>Radiologists were not familiar with numeric scale </li></ul></ul><ul><li>CAD had no significant effect on reading time </li></ul>
  30. 42. Future work <ul><li>Improve CAD by case based mammogram analysis </li></ul><ul><ul><li>Temporal comparison </li></ul></ul><ul><ul><li>CC/MLO correlation </li></ul></ul><ul><li>Make use of large databases that become available with digitization of screening programs </li></ul><ul><li>Standalone CAD should become as good or better than expert </li></ul>
  31. 43. Independent combination of reader scores
  32. 44. Future work: Other Modalities <ul><li>Application to lung cancer detection in chest x-rays </li></ul>
  33. 45. Mean number of regions probed for CAD per case non radiologists radiologists
  34. 46. Review of interval cancers in the Netherlands 2nd site visit (1999-2003) 0 20 40 60 80 100 120 A2 B2 C2 D2 E2 F2 G2 H2 I2 J2 K2 L2 M2 N2 O2 P2 Q2 R2 U2 V2 W2 X2 Z2 Mean no abnormality minimal signs sign lesion
  35. 47. CAD in digital environment CAD server Mammography system PACS Review station Processed (Raw)‏ CAD dicom SR Processed Raw
  36. 48. Breast Cancer <ul><li>Almost always originates from ductal and lobular cells </li></ul><ul><li>In situ cancer is contained inside the ducts and cannot metastasize </li></ul><ul><li>Invasive cancers have grown through the basement membrane </li></ul>A: normal duct cells B: basement membrane C: lumen (centre of duct)‏

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