A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients
Baeza, Sonia (Universitat Autònoma de Barcelona. Departament de Medicina)
Gil, Debora (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Garcia-Olivé, Ignasi (Universitat Autònoma de Barcelona. Departament de Medicina)
Salcedo-Pujantell, Maite (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Deportós, Jordi (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Sánchez Ramos, Carles (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Torres, Guillermo (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Moragas, Gloria (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Rosell Gratacos, Antoni (Universitat Autònoma de Barcelona. Departament de Medicina)

Fecha:	2022
Resumen:	COVID-19 infection, especially in cases with pneumonia, is associated with a high rate of pulmonary embolism (PE). In patients with contraindications for CT pulmonary angiography (CTPA) or non-diagnostic CTPA, perfusion single-photon emission computed tomography/computed tomography (Q-SPECT/CT) is a diagnostic alternative. The goal of this study is to develop a radiomic diagnostic system to detect PE based only on the analysis of Q-SPECT/CT scans. This radiomic diagnostic system is based on a local analysis of Q-SPECT/CT volumes that includes both CT and Q-SPECT values for each volume point. We present a combined approach that uses radiomic features extracted from each scan as input into a fully connected classification neural network that optimizes a weighted cross-entropy loss trained to discriminate between three different types of image patterns (pixel sample level): healthy lungs (control group), PE and pneumonia. Four types of models using different configuration of parameters were tested. The proposed radiomic diagnostic system was trained on 20 patients (4,927 sets of samples of three types of image patterns) and validated in a group of 39 patients (4,410 sets of samples of three types of image patterns). In the training group, COVID-19 infection corresponded to 45% of the cases and 51. 28% in the test group. In the test group, the best model for determining different types of image patterns with PE presented a sensitivity, specificity, positive predictive value and negative predictive value of 75. 1%, 98. 2%, 88. 9% and 95. 4%, respectively. The best model for detecting pneumonia presented a sensitivity, specificity, positive predictive value and negative predictive value of 94. 1%, 93. 6%, 85. 2% and 97. 6%, respectively. The area under the curve (AUC) was 0. 92 for PE and 0. 91 for pneumonia. When the results obtained at the pixel sample level are aggregated into regions of interest, the sensitivity of the PE increases to 85%, and all metrics improve for pneumonia. This radiomic diagnostic system was able to identify the different lung imaging patterns and is a first step toward a comprehensive intelligent radiomic system to optimize the diagnosis of PE by Q-SPECT/CT. Artificial intelligence applied to Q-SPECT/CT is a diagnostic option in patients with contraindications to CTPA or a non-diagnostic test in times of COVID-19.
Ayudas:	Agencia Estatal de Investigación RTI2018-095209-B-C21 Agencia Estatal de Investigación PID2021-126776OB-C21
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Pulmonary embolism ; SPECT ; CT ; COVID-19 ; Radiomics ; Neural networks
Publicado en:	EJNMMI Physics, Vol. 9 (december 2022) , ISSN 2197-7364

DOI: 10.1186/s40658-022-00510-x
PMID: 36469151

17 p, 1.6 MB

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Documentos de investigación > Documentos de los grupos de investigación de la UAB > Centros y grupos de investigación (producción científica) > Ciencias de la salud y biociencias > Institut d'Investigació en Ciencies de la Salut Germans Trias i Pujol (IGTP)
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