L’automatisation de la lecture p16/Ki67

Etude N Wentzensen et al

glandular epithelium squamous epithelium

productive infection

transforming infection

latent infection

persistent infection

Inactivation of pRB by HR-HPV E7 results in

marked overexpression of p16

E2FpRB E7 E2FpRB

Promoterp16INK4a Promoterp16INK4a

Persistent HPV infection

LSIL

Colposcopy,biopsy if lesion

M 12

+

Pap smear

-

p16/Ki67

Inca 2016 French guidelines after abnormal Pap smears

Test HR HPV

Negative Positive

Cytology p16 / Ki 6716/18

Positive

Colposcopy

Primary screening HPV with triage strategiesCombinations of different tests ? Interval between tests if negative?

Test HPV 5 years

NegativeNIIL ASC-us LSILPositiveNegative

Follow up? Follow up? Follow up?

HSIL

HPV 16/18 and p16/ki67 DUAL STAIN (DS) TRIAGE IN A LARGE ORGANIZED CANCER SCREENING PROGRAM of w HPV + (n = 3225)

• DS showed better risk stratification for CIN 3+ compared with cytology in HPV+ women irrespective of genotyping with 30% reduction of colposcopies

• Retesting interval in HPV 16/18 negative with negative p16/ki 67 can be safely extended to 3 years

Wentzensen N et al JAMA 2019

AUTOMATED EVALUATION OF p16/Ki-67 DUAL STAIN

Nicolas Wentzensen, MD, PhD, MSClinical Epidemiology UnitClinical Genetics Branch

Division of Cancer Epidemiology and Genetics

Sydney IPV 2018

Cytoreader

Niels GrabeUniversity Heidelberg

STCMED

niels.grabe@stcmed.com

• Artificial Intelligence

• Automated Microscopy

• Cloud technology

The Goals of “Artificial Intelligence”

12

Global outreachto customers

Higher medicalaccuracy

Automaticquality control

Load balancing(People,

infrastructure)

Higher efficiency

per lab

AI will assist not replace

MD potential without AI

MD potential with AI

Leverage: „Artificial Intelligence“ as an assistent

CYTOREADER Platform Components

Whole-Slide-Imaging (Digital Pathology)

Cloud-based Data access & Diagnostics

“Artificial Intelligence” Deep Learning

1.

2.

3.

Full microscopy in 60-120s per slideWhole-Slide-Imaging

(Digital Pathology)1.

Cooperation with Hamamatsu Photonics, Japan

Quality-controlledSingle Layer Focus for Cytology

Scanning with a single focus layer

Whole-Slide-Imaging (Digital Pathology)1.

Focus quality score [0-100%]

“Artificial Intelligence“deep learning neural network(CNN) for Quality Control

Cooperation with Hamamatsu Photonics, Japan

Local Infrastructure

AI-basedqualitycontrol

Web browser

Case management

AI-assisteddiagnostics

“Artificial Intelligence“deep learning network(CNN)

Cloud-based Data access & Diagnostics2.

“Use it from anywhere“

AI training of neural network = minimization of 3 image classification errors

Max Robustness(= minimize varianceon distorted images)

Max Sensitivity(=minimize false

negatives)

Max Specificity(= minimize false

positives)

“Artificial Intelligence” Deep Learning3.

Tiling in images (1 cell in median)3.1

3.2

“Artificial Intelligence” Deep Learning3.

Tiling in images (1 cell in median)3.1

Rank all tiles

with AI3.2

Comprehensive computation for each tile of a likelihood between 0 and 1 for precancer

“Artificial Intelligence” Deep Learning3.

Tiling in images (1 cell in median)3.1

Rank tiles with AI3.2

Assisted Diagnosis3.3

Automated detection of DS in Thinprep slides

• Deep Learning algorithm is superior to Support Vector Machine

• Equal sensitivity with increased specificity compared to manual evaluation

Evaluation Biopsy Study (CIN3+) Anal Cancer Screening Study (AIN2+)

AUC Sensitivityp-

valueSpecificity

p-

value

Youden’s

indexAUC Sensitivity

p-

valueSpecificity

p-

value

Youden’s

index

Manual 86.8% ref 40.6% ref 0.27 92.8% ref 36.1% ref 0.29

SVM0.73 83.0% 0.7 40.6% 1.0 0.24 0.75 91.3% 1.0 37.4% 0.7 0.29

CNN40.74 86.8% 1.0 45.7% 0.07 0.33 0.77 91.3% 1.0 46.1%

0.000

10.37

Performance of automated DS (Surepath, KPNC)

EvaluationColpo

referral

p-value

(cytology/

manual

DS)

Sensitivity

p-value

(cytology/

manual

DS)

Specificity

p-value

(cytology/

manual DS)

PPV

p-value

(cytology/

manual

DS)

NPV

p-value

(cytology

/ manual

DS)

Colposcopies

per CIN3+

detected

Pap cytology

(188 CIN3+)

1,860

(60.1%)ref

85.8%

(81.2-90.5)ref

41.9%

(40.1-43.7)Ref

10.1%

(8.7-11.5)Ref

97.5%

(96.6-

98.3)Ref 9.9

Manual DS

(197 CIN3+)

1,536

(50.4%)

<0.0001/

ref

90.0%

(86.0-93.9)

0.2 /

Ref

52.6%

(50.8-54.5)

<0.0001/

ref

12.6%

(11.0-14.3)

<0.0001/

Ref

98.6%

(98.0-

99.2)

0.02/

Ref7.8

Automated

DS >=2 cells

(192 CIN3+)

1,298

(41.9%)

<0.0001/

<0.0001

88.1%

(82.5-91.7)

0.6 /

0.4

61.5%

(59.7-63.3)

<0.0001/

<0.0001

14.8%

(12.9-16.8)<0.0001/

<0.0001

98.5%

(97.8-

99.0)

0.03/

0.86.8

Automated

DS >=1 cell

(201 CIN3+)

1,741

(56.3%)

0.007/

<0.0001

91.8%

(87.3-95.1)

0.05/

0.5

46.5%

(44.6-48.3)

0.06/

<0.0001

11.5%

(10.1-13.1)

0.002/

<0.0001

98.7%

(97.9-

99.2)

0.01/

0.58.7

ROC analysis of DS automation

Vote

•Do you consider assisted AI in Digital Pathology a threat or opportunity for labs in the next 3 years?

A. Threat

B. Opportunity

C. AI will not play an important role

D. None

A Growing NetworkKaiser Permanente, BerkeleyWalter Kinney, MDTom Lorey, MDKiranjit K. Grewal, Patricia E. Goldhoff, MD, Julie D. Kingery, MDDiane Tokugawa, MDNancy Poitras, Alex Locke M.D.

Albert Einstein College of Medicine, Bronx, NYPhilip Castle

Steinbeis Center forMedical Systems Biology HeidelbergBernd LahrmannNiels Grabe

University of OaklahomaRosemary Zuna, MDJoan Walker, MD

NCINicolas Wentzensen, MDMegan A. ClarkeMark Schiffman

UKHDAlexandra KrauthoffLiam BartelsNiels Grabe

Contact: niels.grabe@gmail.com

Cerba HealthcareChristine Bergeron, MD