Because of its high aggressiveness and lethality, early detection and accurate characterization of lung cancer are among the most investigated challenges in the last years. Biomedical imaging techniques are an enabling tool for lung cancer assessment that strongly impacts on the decision-making process in daily clinical practice. Their role is a developing process that aims to provide predictive imaging biomarkers and a valid solution for personalized medicine. Within this context, radiomic approach, a relatively new solution that consists of a features-based characterization of the tumor, is showing promising results. It requires the mining of vast arrays of quantitative features derived from digital images and has opened to encouraging perspectives. Regardless of the interest in such a solution, a suboptimal standardization and lack of definitive results emerge. LuCIFEx presents the design and development of an automated pipeline for a non-invasive in-vivo identification and characterization of Non Small Cell Lung Cancer (NSCLC). It is devised to be a support for radiologists and physicians in the treatment decision phase and to speed up the diagnostic process. The developed pipeline exploits input data from routinely acquired biomedical images. From these images, we obtained a segmentation of the tumor lesion allowing the accurate textural features computation inside the Volume Of Interest (VOI), thus providing information for the characterization of lung lesion through Machine Learning algorithms.

1. LUNG CANCER IDENTIFICATION
THROUGH FEATURE EXTRACTION
Eleonora D’Arnese
eleonora.darnese@gmail.com
NGCX@San Francisco, 17-31 May

2. The Opportunity !2

3. The Opportunity !3
https://www.brmh.net/services/respiratory-care/lung-cancer-screening/

4. The Opportunity 3
Time consuming diagnostic process based on the analysis of
multiple images acquired over time

5. Cancer
Assessment
Diagnosis
Repeat
Therapy
Biopsy
Image
Processing
Current Diagnostic Process 4

6. LuCIFEx: a diagnostic support tool for lung cancer early detection and
characterisation, based on the combination of current imaging technologies and Machine
Learning techniques, with the potential to reduce identification time and misdiagnosis.
Our Solution 5

7. CT
PET
Diagnosis Therapy
Region Of
Interest
Segmentation
Radiomic
Features
Extraction
AI aided lesion
characterisation
Fully Automated Pipeline
LuCIFEx
Innovation 6
Metabolic information
Anatomical information
● Starting point for standardization of radiomic features
● Single tool for the entire process
● Decisional support for physicians in diagnosis
● Reduction of the need for invasive and expensive procedures (biopsy)

8. Key Asset
The validation test will be performed thanks to the important
support of a private hospital
7

9. Why Now
Interest and hype in using artificial intelligence techniques for diagnostic support.
Texture analysis of CT and PET images showed promising results in cancer
characterisation but an approach for data standardization is needed but still missing
8

10. Roadmap 9
Artificial
Intelligence aided
Lesion
Characterization
Learning model definition
Learning model optimization
Assembly of the
entire tool
Integration of the pipeline
Graphic User Interface
Radiomic Features
Extraction
Software prototype
Optimization (execution time)
Validation by the
private hospital
Region of Interest
Segmentation
Software prototype
Optimization (accuracy)
Optimization (execution time)

11. Why us 10
● ROI segmentation accuracy ~ 95% w.r.t. manual segmentation (ground truth)
● Discrimination accuracy between primary lung cancer tumors and lung
metastases ~ 90%
● Encouraging results on discriminating between cancer histological subtypes,
highlighting the need for accurate feature extraction.
Semi-automatic segmentation Our automatic segmentation

12. Eleonora
D’Arnese
Guido Walter Di
Donato
Marco Domenico
Santambrogio
Sara
Notargiacomo
Team
Technical advisor
Master student in
Biomedical
Engineering
Master student in
Biomedical
Engineering
11
Business advisor

13. Thanks for your attention
Eleonora D’Arnese, Guido Walter Di Donato, Sara Notargiacomo,
Marco D. Santambrogio
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano
eleonora.darnese@gmail.com