Talking To Your Hepatologists & Colleagues About LI-RADS. Joseph Yacoub MD & Claude Sirlin MD SCBT-MR 2017 Workshop

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1 Talking To Your Hepatologists & Colleagues About LI-RADS Joseph Yacoub MD & Claude Sirlin MD SCBT-MR 2017 Workshop

2 What is LI-RADS? System for standardizing technique, interpretation, and reporting For patients at high risk of HCC Algorithm + lexicon + atlas Multidisciplinary and international collaboration As simple as possible, but no simpler

3 What is LI-RADS not? Not a management guideline Doesn t replace, but rather complements management guidelines and transplant policy Not a mandated policy

4 Why did you create LI-RADS? It started with a complaint We cannot understand your reports

5 How long have you been using LI- RADS and why did you start? 4 years ago informal, personal use 2.5 years ago formal, division, MD conference

6 What challenges did you have starting LI-RADS at your institution? From my colleagues NONE! From my fellow radiologists MAKE IT SIMPLER!

7 How did you implement LI-RADS at your institution? Personal use to gain familiarity and experience Formally introduce to colleagues (PPT, Q&A) 3-month trial period with MDD Gradual acceptance

8 Any Advice? Post LR at reading stations Educate trainees (Catalysts for change) Templates

9 Any Challenges? Multiple other systems available and in use Lack of receptiveness for change

10 How has LI-RADS helped? How has LI-RADS hurt? The positive Increased uniformity MDD more efficient/streamlined Great resource for trainee education The negative Inconsistencies in applying it Lack of templates and reporting aids

11 COMMON QUESTIONS ABOUT LI-RADS

12 Why so many categories? What s up with LR-2, -3, -4, -M? Other systems The Truth Ignore Benign Biopsy Indeterminate Spectrum Treat as HCC HCC

13 Why so many categories? What s up with LR-2, -3, -4, -M? Other systems LI-RADS Ignore Benign Benign Ignore Probably Benign Followed Biopsy Indeterminate Intermediate Followed closely Probably HCC MDD, biopsy? Treat as HCC HCC HCC Treat as HCC Malignant: ICC? MDD, biopsy?

14 LI-RADS allows individualized management, research, refinement Other systems LI-RADS Ignore Benign Benign Ignore Probably Benign Followed Biopsy Indeterminate Intermediate Followed closely Probably HCC MDD, biopsy? Treat as HCC HCC HCC Treat as HCC Malignant: ICC? MDD, biopsy?

15 Not LR-5, but you know it is HCC: What do you do? Suggested reporting: LR-4 by LI-RADS criteria but has features highly suggestive of HCC. Suggested next step: MD discussion Remember that many HCCs do not meet LR-5 criteria LR-4 does not exclude HCC

16 All these ancillary features! Are they worth the time? LI-RADS 2017: ancillary features now optional AFs enhance knowledge, confidence New rule: up 1, down 1, stay the same

17 probable and intermediate probability are imprecise. Show me the percentages! No short answer, lets divide into series of questions I will summarize results of small, single-center, retrospective studies No high-quality evidence yet

Ehman EC, et al. Abdom. Radiol. 2016;41:963 9. b. Zhang Y-D et al. Acad. Radiol. 2016;23:344 52.

18 About ½ of HCCs Meet LR-5 Criteria 114 histologically proven HCCs with imaging a LR-4 37% LR-3 9% LR-5 53% LR-2 1% 3 other studies 11, 21 % b 57.3 % c 63, 64% d a. Ehman EC, et al. Abdom. Radiol. 2016;41: b. Zhang Y-D et al. Acad. Radiol. 2016;23: c. Choi SH et al. Invest. Radiol. 2016;51: d. Joo I et al. J. Magn. Reson. Imaging. 2016;

19 Over 95% of LR-5s are HCC 95% Choi SH et al. Invest. Radiol. 2016;51: % (analysis of extracted data) Darnell A et al. Radiology. 2015;275: Caution: LR-5 does not actually have 100% positive predictive value as intended

20 82% About 80% of LR-4s are HCCs Choi SH et al. Invest. Radiol. 2016;51: % (analysis of extracted data) Darnell A et al. Radiology. 2015;275:

21 Natural History Of LR-4 & LR-4 observations 69 LR-3 observations Downgraded 29% Upgraded (LR-5) 31% Regressed 14% Progressed (LR-5&4) 6% Stable (LR- 4) 40% Stable (LR- 3) 80% Burke LMB et al. Abdom. Radiol. 2016;41: Choi J-YY Am. J. Roentgenol. 2013;201:

22 Natural History Of LR- 4, 3 & 2 63 LR-2 observations 166 LR-3 observations 52 LR-4 observations Tanabe M, et al. Radiology. 2016;281:

23 Natural History Of LR- 4, 3 & 2 68% 44% 63 LR-2 observations 166 LR-3 observations 52 LR-4 observations 23% 4% 5% 38% Tanabe M, et al. Radiology. 2016;281:

24 LR-4 is embolized. Shows APHE on angio? Is it an HCC? OPTN: transplant decision are based on the pretreatment category. Suggested reporting: LR-TR Nonviable (pretreatment LR-4, 18 mm, highly likely to be HCC combining all information) OR LR-TR Viable 10 mm (pretreatment LR-4, 18 mm, highly likely to be HCC combining all information) MDD should consider transplant implication when treating LR-4

25 What do we know about the inter-observer agreement of LI-RADS?

26 Inter-reader agreement Ten blinded readers; 100 liver MR. Substantial Moderate Fair OPTN 5 LI-RADS 5 Arterial enhancement Washout Pseudocapsule LI-RADS 1 AASLD definite HCC Over all OPTN LI-RADS 4 Over all LI-RADS Over all AASLD Davenport MS et al. Radiology. 2014;272(1):

27 3 other studies Inter-reader agreement Moderate inter-reader agreement on major feature Moderate inter-reader agreement on LI-RADS Excellent agreement on diameter Barth BK et al. Acad. Radiol. 2016;23: Ehman EC et al. Abdom. Radiol. 2016;41: Zhang YD et al.. J. Magn. Reson. Imaging. 2016;43:

28 Inter-system agreement OPTN LI-RADS 200 nodules; 3 radiologist. Very Strong Strong Moderate MELD exception points OPTN 5 LI-RADS 5 Inter-reader agreement on imaging features and nodule classification Bashir MR et al. J. Magn. Reson. Imaging. 2015;42:305 14

29 Inter-modality agreement CT MRI Potential substantial discordance between CT and MRI MRI improved detection of APHE, washout and capsule Zhang Y-D et al. J Magn Reson Imaging 43: Relative to CT: MRI Upgrades MRI Downgrades Corwin et al. 42.5% 8.8% Hope et al. 30% 41% Corwin MT et al. Am. J. Roentgenol. 2016;206: Hope TA et al. J. Comput. Assist. Tomogr. 2016;

30 Busy radiologist: Impractical in daily practice to remember and apply such complex criteria! LI-RADS contains lots of information, but the algorithm is straightforward. Precise feature definitions and tie breaking rules reduce the guesswork and deliberation In the near future: LI-RADS will be integrated into PACS and reporting systems.

Management. Chapter 11. Primary Author. Contributing Authors. University of Toronto & University of Ottawa. Illustrators & figure contributors

Management. Chapter 11. Primary Author. Contributing Authors. University of Toronto & University of Ottawa. Illustrators & figure contributors Chapter 11 Primary Author Donald G. Mitchell Thomas Jefferson University Contributing Authors Victoria Chernyak Ania Z. Kielar Yuko Kono Claude B. Sirlin Montefiore Medical Center University of Toronto