Feasibility Study of MRI and Multimodality CT/MRI Radiomics for Lung Nodule Classification

  • Anthony E. A. Jatobá UFAL
  • Marcelo C. Oliveira UFAL
  • Marcel Koenigkam-Santos USP
  • Paulo de Azevedo-Marques USP


O câncer de pulmão é o tipo mais frequente e letal de câncer e o seu diagnóstico precoce é crucial para a sobrevivência do paciente. A tomografia computadorizada (TC) é o padrão-ouro para o rastreio da doença; no entanto, apresenta a desvantagem de expor o paciente à radiação. Estudos recentes têm demonstrado o potencial da ressonância magnética (RM) no diagnóstico de nódulos pulmonares. Este trabalho busca avaliar a aplicação de características radiômicas de RM para a caracterização de nódulos pulmonares e se a combinação de atributos de TC e RM podem levar a melhores resultados que as modalidades individuais. Para tal, foram segmentados nódulos pulmonares em imagens de TC e RM de 33 pacientes com nódulos pulmonares; a partir de cada modalidade foram extraídos 89 características radiômicas, que foram combinadas em um conjunto de características multimodalidade. Estas características foram usadas para classificar os nódulos entre benignos e malignos por meio de algoritmos de aprendizagem de máquina, calculando a AUC em 30 iterações. Os resultados indicam que características radiômicas de RM são adequadas para a caracterização de lesões pulmonares, com valores de AUC até 17% maiores que seus equivalentes em TC e evidenciando RM enquanto modalidade de imagem para sistemas de suporte à decisão. No entanto, a abordagem multimodalidade não apresentou ganhos em desempenho, sugerindo que a concatenação de características pode não ser uma estratégia adequada para lidar com imagens médicas multimodalidade.


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JATOBÁ, Anthony E. A.; OLIVEIRA, Marcelo C.; KOENIGKAM-SANTOS, Marcel; AZEVEDO-MARQUES, Paulo de. Feasibility Study of MRI and Multimodality CT/MRI Radiomics for Lung Nodule Classification. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 21. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 200-211. ISSN 2763-8952. DOI: https://doi.org/10.5753/sbcas.2021.16065.