Enhancing Imagistic Interstitial Lung Disease Diagnosis by Using Complex Networks
Ana Adriana Trușculescu,
Diana Luminița Manolescu,
Laura Broască,
Versavia Maria Ancușa,
Horia Ciocârlie,
Camelia Corina Pescaru,
Emanuela Vaștag,
Cristian Iulian Oancea
Affiliations
Ana Adriana Trușculescu
Pulmonology Department, ‘Victor Babes’ University of Medicine and Pharmacy, Eftimie Murgu Square 2, 300041 Timișoara, Romania
Diana Luminița Manolescu
Center for Research and Innovation in Precision Medicine of Respiratory Diseases (CRIPMRD), ‘Victor Babes’ University of Medicine and Pharmacy, 300041 Timișoara, Romania
Laura Broască
Department of Computer and Information Technology, Automation and Computers Faculty, “Politehnica” University of Timișoara, Vasile Pârvan Blvd. No. 2, 300223 Timișoara, Romania
Versavia Maria Ancușa
Department of Computer and Information Technology, Automation and Computers Faculty, “Politehnica” University of Timișoara, Vasile Pârvan Blvd. No. 2, 300223 Timișoara, Romania
Horia Ciocârlie
Department of Computer and Information Technology, Automation and Computers Faculty, “Politehnica” University of Timișoara, Vasile Pârvan Blvd. No. 2, 300223 Timișoara, Romania
Camelia Corina Pescaru
Pulmonology Department, ‘Victor Babes’ University of Medicine and Pharmacy, Eftimie Murgu Square 2, 300041 Timișoara, Romania
Emanuela Vaștag
Pulmonology Department, ‘Victor Babes’ University of Medicine and Pharmacy, Eftimie Murgu Square 2, 300041 Timișoara, Romania
Cristian Iulian Oancea
Pulmonology Department, ‘Victor Babes’ University of Medicine and Pharmacy, Eftimie Murgu Square 2, 300041 Timișoara, Romania
Background and Objectives: Diffuse interstitial lung diseases (DILD) are a heterogeneous group of over 200 entities, some with dramatical evolution and poor prognostic. Because of their overlapping clinical, physiopathological and imagistic nature, successful management requires early detection and proper progression evaluation. This paper tests a complex networks (CN) algorithm for imagistic aided diagnosis fitness for the possibility of achieving relevant and novel DILD management data. Materials and Methods: 65 DILD and 31 normal high resolution computer tomography (HRCT) scans were selected and analyzed with the CN model. Results: The algorithm is showcased in two case reports and then statistical analysis on the entire lot shows that a CN algorithm quantifies progression evaluation with a very fine accuracy, surpassing functional parameters’ variations. The CN algorithm can also be successfully used for early detection, mainly on the ground glass opacity Hounsfield Units band of the scan. Conclusions: A CN based computer aided diagnosis could provide the much-required data needed to successfully manage DILDs.
