L' intelligenza artificiale nella diagnostica istologica degli adenomi: l'esperienza Torinese - L. BERTERO
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L’ intelligenza artificiale nella
diagnostica istologica degli
adenomi: l’esperienza
Torinese
L. BERTERO
Div. of Pathology, Dept. Medical Sciences
University of Turin, Italy
17 dicembre 2021
Computational
pathology
Feature
Deep learning
engineering
Explicit programming Automated learning
• Automated learning
• Predetermined
feature selection • Freely available
source codes of
• Multiple interactions Feature effective neural
Deep learning network
pathologists/informa engineering
ticians needed architectures
• Time consuming • Superior results in
most cases
Deep learning overall workflow:
Annotation
Collection of Model
(images, clinical Validation
WSI training
data,…)
ImageNet Large Scale • Since ~2010
Visual Recognition
• Efficacy of CNN (convolutional neural networks)
Competition (ILSVRC)
• Breast cancer metastases in
sentinel lymph nodes
CAMELYON challenge
• Dataset of 1399 manually
annotated WSI
Image
segmentation
Cell detection
and counting
Tumor detection,
• Reducing repetitive and classification and
time-consuming tasks grading Evalutation of
Computational
pathology IHC markers
• Lower interobserver
variability
Analysis of
kidnet transplant Mitosis
biopsies detection
Colorectal carcinoma
• Colorectal carcinoma (CRC) is
the second most deadly and
the third most common cancer
(Globocan 2020)
• Colorectal cancer screening
enables prompt detection of
early CRC or preinvasive
lesions, but represents a
significant workload for both
endoscopy and pathology units
Digital pathology for colorectal carcinoma
• Distinction between tumor tissue and stroma
(Kather JN et al. Sci Rep 2016)
• Outcome prediction (Bychkov D et al. Sci Rep
2018; Kather JN et al. PLoS Med 2019; Skrede
O et al. Lancet 2020)
• Molecular profile prediction (Yamashita R et
al., Lancet Oncol 2020; Sirinukunwattana K et
al. Gut 2021; Bilal M et al. Lancet Digit Health
2021)
• Adenoma classification…
Adenoma classification
Adenoma classification
Adenoma classification Limitations: • Lack of dysplasia grading • Lack of normal tissue • Lower performance during external testing
UniTOPatho
Dataset (WSI images)
• H&E slide acquired on the Hamamatsu Nanozoomer S210
scanner (200X)
• Manual annotation according to 6 classes:
• NORM: normal tissue
• HP: hyperplastic polyp
• TA.LG: tubular adenoma, low-grade dysplasia
• TA.HG: tubular adenoma, high-grade dyplasia
• TVA.LG: tubulo-villous adenoma, low-grade dysplasia
• TVA.HG: tubulo-villous adenoma, high-grade dysplasia
Perlo D. et al. MICAD 2021• CNN: ResNet-18
• Pre-training on the ImageNet classification task
• Data augmentation: one random operation between rotation,
equalization, solarization, inversion and contrast enhancing
Patches normalization: relevant features are
not embed in color, but in image texture and
signal strenght
Perlo D. et al. MICAD 2021Patches resolution:
• Achieved results are similar to those reported by
Denis B et al. (Eur J of Gastroenterol Hepatol 2009)
Perlo D. et al. MICAD 2021Dysplasia grading
• Poor results in
distinguishing
TA versus
TVA/VA
Perlo D et al. MICAD 2021Multi-resolution analysis
• Adenoma type and dysplasia
grade are best classified at
different scales
Barbano CA et al. IEEE ICIP 2021Multi-resolution analysis
Limitations:
• Some entities missing (serrated adenomas, invasive adenocarcinomas,…)
• Larger dataset is warranted
• Lack of external validation
Barbano CA et al. IEEE ICIP 2021Challenges
Combined H&E and IHC
for WSI annotation
• Collect large-scale Weakly
annotated datasets supervised
learning
(images and clinical
annotations)
Scailing up input
to whole WSIChallenges
Explainable/trust Data
Differences in technical
worthy AI augmentation
processing (different H&E, or
different slide scanner,…) normalization
• Generalizability and Differences in
validation of results patient
populations
Management of
Internal/external/diagnostic unexpected
/clinical validation findingsThank you!
• NTT Data Spain • AOU Città della Salute e
• Universitat Politecnica de Valencia (UPV) della Scienza di Torino
• Philips Medical Systems Netherland BV • Ecole Polytechnique
• Software Imagination & Vision SRL (SIMAVI) Federale de Lausanne
• Wings ICT Solutions Information & • Centre Hospitalier
• Prof.ssa P. Cassoni Communication Technologies IKE Universitarie Vaudois
• Dr. L. Bertero • Thales Six GTS France SAS • Tree Technology SA
• Dr. A. Gambella • Commissariat a l’energie atomique et aux • Otto Von Guericke
• Dr. E. Bottasso energies alternatives Universitat Magdeburg
• Barcelona Supercomputing Center • Stelar Security
• Pro Design Electronic GmbH Technology Law
• Karolinska Institutet Research
• Prof. M. Grangetto
• Fundacion para el fomento de la • Spitalul Clinic Prof. Dr.
• Dr. E. Tartaglione
investigacion sanitaria y biomedica de la Theodor Burghele
• Dr. D. Perlo
• Dr. C. A. Barbano comunitat valenciana (FISABIO) • Centro di Ricerca,
• Dr. A. Fiandrotti • Università degli Studi di Modena e Reggio Sviluppo e Studi
Emilia (UNIMORE) Superiori in Sardegna
• Stockholms lans landsting SRL (CRS4)You can also read
