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AI-powered computational pathology for liver diseasesOscar Carrasco-Zevallos, PhDSenior Scientific Program ManagerPathAI

Interpretation of liver histology is prone to error and current scoring systems do not fully capture disease heterogeneity

Interpretation of liver histology is prone to error and current scoring systems do not fully capture disease heterogeneity

1 David E. Kleiner et al. “Design and validation of histological scoring system for nonalcoholic fatty liver disease.” Hepatology 20052 Zach Goodman, et al. “Grading and staging systems for inflammation and fibrosis in chronic liver diseases.” Journal of Hepatology 2007

Ishak scoring system CRN NAFLD activity scoring system

Histologic scoring systems have limited reproducibility

1 Beth A. Davison, et al. “Liver biopsies in nonalcoholic steatohepatitis (NASH) clinical trials.” Hepatology 20202 Zach Goodman, et al. “Grading and staging systems for inflammation and fibrosis in chronic liver diseases.” Journal of Hepatology 20073 PathAI Analysis: AASLD 2019, median interval between biopsy re-reads, 16 weeks (range 9, 20).

Intra-observer discordance for grading key NASH features grading is high3

(Particularly for lobular inflammation and ballooning, 22–47% of cases)

Number of Biopsies

Steatosis Lobular Inflammation Ballooning

KappaCases with

Discordance KappaCases with

Discordance KappaCases with

Discordance166 0.69 22% 0.38 42% 0.66 22%

162 0.50 29% 0.29 43% 0.43 36%

149 0.59 26% 0.42 39% 0.29 47%

◆ Published literature has shown only moderate levels of inter- and intra-reader concordance for grading key features of chronic hepatitis and NASH

• Inter-reader kappas were 0.61, 0.48, 0.33, and 0.52 for steatosis, fibrosis, lobular inflammation, and ballooning, respectively1

• Inter-reader kappas were 0.4-0.6 for portal inflammation, interface hepatitis and parenchymal injury and inflammation2

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AI-powered pathology for HBV and NASH◆Machine learning (ML) models trained

to interpret liver histology with 100% reproducibility

◆Designed for rigorous quantification of key histologic features

◆ Elucidate associations of ML histologic features with disease progression, clinical outcomes and response to therapy

ML model development for automated assessment and quantitation of liver histopathology

Pokkalla H, et al., Oral presentation #187, AASLD 2019Juyal D, et al., Poster presentation LBP31, EASL ILC 2020 (Late-breaker abstract submission)

ML model development for automated assessment and quantitation of liver histopathology

Pokkalla H, et al., Oral presentation #187, AASLD 2019Juyal D, et al., Poster presentation LBP31, EASL ILC 2020 (Late-breaker abstract submission)

Tissue regionsPortal inflammationLobular inflammationInterface hepatitisBallooningSteatosisMicrovesicular steatosisBile duct

ML-based quantification of histologic features of chronic inflammation correlate with expert pathologist assessment1

Juyal D, et al., Poster presentation LBP31, EASL ILC 2020 (Late-breaker presentation)1: Pathologist reads followed clinical trial protocol

Portal inflammationInterface hepatitis

Ishak periportal necrosis grade

ML

inte

rface

hep

atiti

s ar

ea, %

M

L po

rtal i

nfla

mm

atio

n ar

ea, %

Ishak portal inflammation grade

ML-based quantification of histologic features of NASH correlates with consensus pathologist assessment1

Pokkalla H, et al., Oral presentation #187, AASLD 20191: Consensus pathologist reads obtained for research purposes

SteatosisBallooningLob inflam

Pathologist consensus grade

ML

stea

tosi

s ar

ea, %

10

0

20

40

30

50

3n=4

0 1 2n=22 n=101 n=37

r=0.86; p <0.001

Mod

el S

core

,%

Consensusmedian grade

1

3

2

4 r=0.47; p <0.001

Mod

el S

core

,%

00 1 2 3

n=25 n=80 n=47 n=12Consensus

median grade

STEATOSIS LOBULAR INFLAMMTION

ML

lob

infla

mar

ea, %

ML

ballo

onin

g ar

ea, %

Pathologist consensus gradePathologist consensus grade

50 r=0.86; p <0.001

10

0

20

40

30

50

3n=4

0 1 2n=22 n=101 n=37

r=0.86; p <0.001

Mod

el S

core

,%

Consensusmedian grade

1

3

2

4 r=0.47; p <0.001

Mod

el S

core

,%

00 1 2 3

n=25 n=80 n=47 n=12Consensus

median grade

STEATOSIS LOBULAR INFLAMMTION

F0 F1 F2 F3 F4 F5 F6

ML Ishak fibrosis patterns

ML fibrosis score quantifies heterogeneity and correlates with expert pathologist assessment1

0 1 2 3 4 5 60

2

4

6

Ishak Stage by Central Reader

Slid

e-le

vel M

L Is

hak

Scor

e 𝜌=0.60p <0.001n=456

F0 F1 F2 F3 F4 F5 F6

F0 0.8%F2 3%

F314%

F418%

F552%

F612%

Manual Ishak Stage 6 ML Ishak Score 4.54

Juyal D, et al., Poster presentation LBP31, EASL ILC 2020 (Late-breaker presentation)1: Pathologist reads followed clinical trial protocol

In subjects whose cirrhosis regressed at year 5, fibrosis improvement by year 1 is evident only on ML Ishak score

n=22 n=8 n=22 n=8 n=22 n=80

2

4

6

ML

Isha

k Sc

ore

p=0.039 p=0.043p=0.146

Isha

k St

age

byC

entra

l Rea

der

p=0.235 p<0.001p=0.156

p<0.001

p=0.120

No cirrhosis at Year 5Cirrhosis at Year 5

◆22/30 HBV subjects achieved cirrhosis regression after 5 years of therapy

◆Subjects who achieved cirrhosis regression at year 5 had significant reduction in ML Ishak score from baseline to year 1

◆Fibrosis improvement was not evident on manual histology by year 1

Juyal D, et al., Poster presentation LBP31, EASL ILC 2020 (Late-breaker presentation)

Baseline Year 1 Year 5

ML-based histologic features are predictive of progression to cirrhosis in subjects with bridging fibrosis due to NASH

Pokkalla H, et al., Poster presentation #2497, EASL ILC 2020 (Selected for “Best of ILC Summary”)

Surv

ival

free

of

dise

ase

prog

ress

ion

,%

Baseline ML CRN fibrosis score

113/755 of subjects had progression to cirrhosis

Baseline ML hepatocellular ballooning area, %

Surv

ival

free

of

dise

ase

prog

ress

ion

,%

◆ Progression to cirrhosis was associated with higher ML CRN fibrosis score at baseline (HR 2.66 [95% CI: 1.82, 3.90])

◆ Progression to cirrhosis was associated with higher ML ballooning proportionate area at baseline (HR 1.87 [95% CI: 1.20, 2.91])

Ratio of portal/lobular inflammation is associated with increased risk of clinical disease progression in NASH

Pokkalla H, et al., Oral presentation #187, AASLD 2019

No ClinicalEvent n=653

ClinicalEvent n=21

0

50

100

150

200

0

20

40

60

80

100

Even

t-Fre

e Su

rviv

al,%

p <0.001

Rat

io o

f por

tal t

o lo

bula

rin

flam

mat

ion

0 3 6 9 12 15 18 21 24Month

Portal Inflammation

Log-rank p <0.001Hazard ratio 4.50(95% CI 1.86, 10.85)

Event-free Survival According to Ratio of Portal

to Lobular Inflammation

<40

≥40

Boxes depict median (IQR); whiskers based on Tukey method.Richardson MM, et al. Gastroenterology 2007;133:80-90; Gadd VI, et al. Hepatology 2014;59:1393-1405; Brunt EM, et al. Hepatology 2019;70:522-31.

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Conclusions◆ PathAI ML models enabled reproducible

and quantitative assessment of liver histology beyond that afforded by manual scoring

◆ In research studies, ML read-outs:

• Revealed treatment-associated histologic improvement not evident by manual scoring

• Were predictive of disease progression and liver-related clinical events

Acknowledgments

We extend our thanks to the patients, their families, and all participating investigators.

These studies were funded by Gilead Sciences, Inc.

Gilead SciencesVithika Suri

Anuj GaggarJohn F. Flaherty

G. Mani SubramanianLing Han

Catherine JiaRyan S. Huss

Chuhan ChungRobert P. Myers

PathAIDinkar Juyal

Chinmay ShuklaHarsha Pokkalla

Zahil ShanisQuang Le

Maryam PouryahyaAmaro Taylor-Weiner

Benjamin GlassKishalve PethiaMurray Resnick

Michael MontaltoIlan WapinskiAditya KhoslaAndrew Beck

CollaboratorsIra JacobsonEdward Gane

Maria ButiStephen A. HarrisonZachary Goodman

Arun J. SanyalZobair M. Younossi

Thank You

Oscar Carrasco-Zevallos, PhDemail: oscar.zevallos@pathai.com