Proper detection and accurate characterization of Non-Small Cell Lung Cancer (NSCLC) are an open challenge in the imaging field. Biomedical imaging is fundamental in lung cancer assessment and offers the possibility of calculating predictive biomarkers impacting patients' management. Within this context, radiomics, which consists of extracting quantitative features from digital images, shows encouraging results for clinical applications, but the sub-optimal standardization of the procedure and the lack of definitive results are still a concern in the field. For these reasons, this work proposes the design and development of LuCIFEx, a fully-automated pipeline for non-invasive in-vivo characterization of NSCLC, aiming to speed up the analysis process and enable an early diagnosis of the tumor.LuCIFEx pipeline relies on routinely acquired [18F]FDG-PET/CT images for the automatic segmentation of the cancer lesion, allowing the computation of accurate radiomic features, then employed for cancer characterization through Machine Learning algorithms. The proposed multi-stage segmentation process can identify the lesion with a mean accuracy of 94.2±5.0%. Finally, the proposed data analysis pipeline demonstrates the potential of PET/CT features for the automatic recognition of lung metastases and NSCLC histological subtypes, while highlighting the main current limitations of the radiomic approach.

On the Automation of Radiomics-Based Identification and Characterization of NSCLC

D'Arnese, Eleonora;Donato, Guido Walter Di;Sozzo, Emanuele Del;Sciuto, Donatella;Santambrogio, Marco Domenico
2022-01-01

Abstract

Proper detection and accurate characterization of Non-Small Cell Lung Cancer (NSCLC) are an open challenge in the imaging field. Biomedical imaging is fundamental in lung cancer assessment and offers the possibility of calculating predictive biomarkers impacting patients' management. Within this context, radiomics, which consists of extracting quantitative features from digital images, shows encouraging results for clinical applications, but the sub-optimal standardization of the procedure and the lack of definitive results are still a concern in the field. For these reasons, this work proposes the design and development of LuCIFEx, a fully-automated pipeline for non-invasive in-vivo characterization of NSCLC, aiming to speed up the analysis process and enable an early diagnosis of the tumor.LuCIFEx pipeline relies on routinely acquired [18F]FDG-PET/CT images for the automatic segmentation of the cancer lesion, allowing the computation of accurate radiomic features, then employed for cancer characterization through Machine Learning algorithms. The proposed multi-stage segmentation process can identify the lesion with a mean accuracy of 94.2±5.0%. Finally, the proposed data analysis pipeline demonstrates the potential of PET/CT features for the automatic recognition of lung metastases and NSCLC histological subtypes, while highlighting the main current limitations of the radiomic approach.
File in questo prodotto:
File Dimensione Formato  
JBHI.pdf

Accesso riservato

: Publisher’s version
Dimensione 865.45 kB
Formato Adobe PDF
865.45 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1217929
Citazioni
  • ???jsp.display-item.citation.pmc??? 2
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 3
social impact