DIMED – University of Padova

BRAF mutation prediction from metastatic melanoma cytological smear

using deep learning: an ongoing project

Nicolè Lorenzo1, Rocco Cappellesso 1, Samir Suweis 2, Filippo Cappello 1, Kathrin Ludwig1 and Fassina Ambrogio1

1Department of Medicine, Surgical Pathology & Cytopathology Unit, University of Padova, Padova, Italy.2 Physics and Astronomy Department, University of Padova, Padova, Italy.

Background - Malignant Melanoma -

Melanoma in Veneto (2015)

1318/year (696M;622F)

Source: Veneto Tumor Registry -- https://www.registrotumoriveneto.it/en/

Molecular AnalysisTherapyFollow-up

Redirect to a Third level Structure

In our cytopathology service we detect about 100/year of metastatic melanomas by FNAB

Background - Deep Learning -

Increasing interest for Image recognition

Smears digitization

Background – Deep learning in pathology -

Aim

Set up of a deep learning computational classifier able to classify BRAF mutated melanoma vs wild

type melanoma in cytologic smears.

Deep learning: …Dark side of pathology…

Materials & Methods –Methodology –

Cases selections Pathologist review

Patches extraction

Class attribution

CNN training

Retrain CNN after pathologist review and correct predicted results

Trained CNNs

Trained CNNs

Testing CohortCytological/molecular

confirmed Classified cases

BRAFm

FIRST STEP

SECOND STEP

• BRAFm• BRAFwt

Misclassification

Correct classification

STEP 3

STEP 2

BRAFwt

Trained CNNs

New cases

Concordance

Predicted mutation/WT

THIRD STEP

Confirmed mutation/WT

Conventional Workflow

Materials & Methods

Cases retrievaland review

Digitalization

273 cases (2016-2018)

901 Smears

111 MMG Smearsincluded

(94 cases: 64wt/30m)

Virtual Slides(Maximum magnification 40x)

Smear segmentation

Tiling: 100x100um tilesAbout 50.000 tiles/slide

About 6000000 tiles automatically exportedas single TIFF files

80% of tiles >>> training 20% >>> validation

Informative Non Informative

Nuclear segmentation

Not malignant

Removed from input files by post segmentation analysis

Input for CNN training

Qupath: Openslides –Smear segmentation -Tiling

Python: Tilies Screening –Nuclear segmentation –Post processing

Docker: Deep learningEnvironment

Tensorflow: Classification

Youtube tutorial: learn all above

Project timeline

Start01/10/2018

FinishSpring 2020

TARGET3M input tiles for each

class

Training Input

Acc

ura

cy

Predicted on results assuming logaritmiccorrelation between Input & Accuracy