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LUNG CANCER IDENTIFICATION THROUGH FEATURE EXTRACTION Eleonora D’Arnese eleonora.darnese@gmail.com NGCX@San Francisco, 1...
The Opportunity !2
The Opportunity !3 https://www.brmh.net/services/respiratory-care/lung-cancer-screening/
The Opportunity 3 Time consuming diagnostic process based on the analysis of multiple images acquired over time
Cancer Assessment Diagnosis Repeat Therapy Biopsy Image Processing Current Diagnostic Process 4
LuCIFEx: a diagnostic support tool for lung cancer early detection and characterisation, based on the combination of curre...
CT PET Diagnosis Therapy Region Of Interest Segmentation Radiomic Features Extraction AI aided lesion characterisation Ful...
Key Asset The validation test will be performed thanks to the important support of a private hospital 7
Why Now Interest and hype in using artificial intelligence techniques for diagnostic support. Texture analysis of CT and ...
Roadmap 9 Artificial Intelligence aided Lesion Characterization Learning model definition Learning model optimization Asse...
Why us 10 ● ROI segmentation accuracy ~ 95% w.r.t. manual segmentation (ground truth) ● Discrimination accuracy between p...
Eleonora D’Arnese Guido Walter Di Donato Marco Domenico Santambrogio Sara Notargiacomo Team Technical advisor Master s...
Thanks for your attention Eleonora D’Arnese, Guido Walter Di Donato, Sara Notargiacomo, Marco D. Santambrogio Dipartimento...
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19 mai 2019

LuCIFEx: Lung Cancer Identification through Feature Extraction

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.

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LuCIFEx: Lung Cancer Identification through Feature Extraction

  1. 1. LUNG CANCER IDENTIFICATION THROUGH FEATURE EXTRACTION Eleonora D’Arnese eleonora.darnese@gmail.com NGCX@San Francisco, 17-31 May
  2. 2. The Opportunity !2
  3. 3. The Opportunity !3 https://www.brmh.net/services/respiratory-care/lung-cancer-screening/
  4. 4. The Opportunity 3 Time consuming diagnostic process based on the analysis of multiple images acquired over time
  5. 5. Cancer Assessment Diagnosis Repeat Therapy Biopsy Image Processing Current Diagnostic Process 4
  6. 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. 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. 8. Key Asset The validation test will be performed thanks to the important support of a private hospital 7
  9. 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. 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. 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. 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. 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

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