Low-Dose CT Screening for Lung Cancer: Computer-Aided Detection of Missed Lung Cancers

M. Liang, et al.

Journal Club 1/31/17

Jason Hostetter, MD

  • Low-dose CT screening shown to decrease mortality → early diagnosis
  • 85% of cancers found on screening clinical stage I, 10 yr survival 88%
  • 75% of diagnosed cancers present on earlier scan

Introduction

  • CAD approved for use as second reader
  • Little prior investigation into sens/spec of CAD with regard to previously missed cancers
  • Interval improvement in technology and increased screening

Introduction

  • International Early Lung Cancer Action Program (I-ELCAP)
  • 1994-2003
  • solid nodules only (more aggressive, CAD for solid nodules more mature)

Methods

  • 50 cancers
    • Path proven
    • Missed on prior scan
    • Slice thickness <= 1.25mm
    • Solid nodules only
  • Time 1: First detected by radiologist
  • Time 0: Prior screening round, retrospectively detected

Methods

  • Independently reviewed by 2 radiologists (6 & 10 years experience)
  • Differences resolved by consensus or by 3rd radiologist tiebreaker

Methods - Image review

  • 4 CAD systems
    • CAD 1: Lung VCAR (GE)
      • Size limit: 3mm
    • CAD 2: ImageChecker CT (R2 Technologies)
      • Limit: 4mm
    • CAD 3: Syngovia Via Va 20 (Siemens)
      • Limit: 3mm
    • CAD 4: Cornell Via (Cornell U)
      • Limit: 3mm (manually set)

Methods - CAD systems

  • All CAD annotated images reviewed by 2 radiologists for true and false positives
    • True nodules:
      • Nodules found by initial interpreting radiologist
      • CAD found nodules confirmed by reviewers
      • Additional nodules found by reviewers

Methods - CAD performance

  • Interobserver agreement: κ statistic
  • Nodule diameter: paired t-test
  • CAD agreement on detection and FP rates: Multirater κ statistics
    • ​Fair: κ = 0.2 - 0.4
    • Moderate: κ = 0.41 - 0.6
    • Substantial: κ = 0.61 - 0.8
    • Very good: κ > 0.8

Statistics

Results - Cancer characteristics

Reviewers: moderate - substantial agreement

Avg 4.8 → 11.4 mm

Results - CAD detection

Results - CAD detection of actionable nodules at time 0

Of 37 actionable cancers:

CAD 1: 62%

CAD 2: 84%

CAD 3: 70%

CAD 4: 70%

Results - False positives at time 0

  • Percent of identified nodules accepted
    • 34.5% - 85.4%
  • Avg # rejected marks per scan
    • 0.6 - 7.4 (CAD 2, 1)

Results - CAD Detection of Cancers at time 1

  • Fair agreement between systems
  • Range from 74-82% (CAD 1, (2,3))
  • None identified all that interpreting radiologist identified

Results - CAD Matchup

System Time 0 Sens Time 1 Sens FP
CAD 1 56% 74% 7.4/scan
CAD 2 70% 82% 1.7/scan
CAD 3 68% 82% 0.6/scan
CAD 4 60% 78% 4.5/scan

Discussion

  • CAD detected 56 - 70% of missed cancers
  • All were smaller at time 0, suggesting size as factor for human reader performance
  • CAD also performs much better when nodules > 3 mm (69-78% vs 0-17%)
  • 3 of 4 CAD systems better with 3-6mm nodules than >6 mm
    • >6 mm category triggers add'nl workup on baseline scan
  • At time 1, CAD missed up to 26% of cancers (not ready as primary/concurrent reader)

Limitations

  • Relatively small n (50)
  • Only analyzed performance with missed cancers rather than all cancers

Conclusion

  • Most appropriate role of CAD is as a second reader
  • Capability of the CAD system to detect at least some missed cancers is compelling

My thoughts

  • What strategies do these algorithms use?
  • Could they be combined into a super-CAD?
  • Illustrates how far CAD still has to go before our jobs are at risk
    • Few more ideal problems for CAD/CV in radiology, esp cross sectional imaging
  • What is the threshold for acceptance of CAD?
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