Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity / Análise radiômica do câncer de pulmão para avaliação prognóstica do paciente e da heterogeneidade intratumoral

Abstract Objective: To determine whether the radiomic features of lung lesions on computed tomography correlate with overall survival in lung cancer patients. Materials and Methods: This was a retrospective study involving 101 consecutive patients with malignant neoplasms confirmed by biopsy or surgery. On computed tomography images, the lesions were submitted to semi-automated segmentation and were characterized on the basis of 2,465 radiomic variables. The prognostic assessment was based on Kaplan-Meier analysis and log-rank tests, according to the median value of the radiomic variables. Results: Of the 101 patients evaluated, 28 died (16 dying from lung cancer), and 73 were censored, with a mean overall survival time of 1,819.4 days (95% confidence interval [95% CI]: 1,481.2-2,157.5). One radiomic feature (the mean of the Fourier transform) presented a difference on Kaplan-Meier curves (p < 0.05). A high-risk group of patients was identified on the basis of high values for the mean of the Fourier transform. In that group, the mean survival time was 1,465.4 days (95% CI: 985.2-1,945.6), with a hazard ratio of 2.12 (95% CI: 1.01-4.48). We also identified a low-risk group, in which the mean of the Fourier transform was low (mean survival time of 2,164.8 days; 95% CI: 1,745.4-2,584.1). Conclusion: A radiomic signature based on the Fourier transform correlates with overall survival, representing a prognostic biomarker for risk stratification in patients with lung cancer.

Resumo Objetivo: Associar características radiômicas de lesões pulmonares em imagens de tomografia computadorizada com a sobrevida global de pacientes com câncer de pulmão. Materiais e Métodos: Estudo retrospectivo composto por 101 pacientes consecutivos com neoplasia maligna confirmada por biópsia/cirurgia. As lesões foram semiautomaticamente segmentadas e caracterizadas por 2.465 variáveis radiômicas. A avaliação prognóstica foi baseada na análise de Kaplan-Meier e no teste log-rank, de acordo com a mediana dos valores das variáveis. Resultados: Vinte e oito pacientes faleceram (16 por câncer de pulmão) e 73 foram censurados, com tempo médio de sobrevida de 1.819,4 dias (intervalo de confiança 95% [IC 95%]: 1.481,2-2.157,5). Uma característica radiômica (média de Fourier) apresentou diferença nas curvas de Kaplan-Meier (p < 0,05). Um grupo de pacientes de maior risco foi identificado a partir de valores altos da variável: sobrevida de 1.465,4 dias (IC 95%: 985,2-1.945,6) e razão de risco de 2,12 (IC 95%: 1,01-4,48). Um grupo de menor risco foi identificado a partir de valores baixos da variável (sobrevida de 2.164,8 dias; IC 95%: 1.745,4-2.584,1). Conclusão: Este estudo apresentou uma assinatura radiômica em imagens de tomografia computadorizada, baseada na transformada de Fourier, correlacionada com a sobrevida global de pacientes com câncer de pulmão, representando assim um biomarcador prognóstico.

Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging

Abstract Background: Currently, magnetic resonance imaging (MRI) is used to evaluate active inflammatory sacroiliitis related to axial spondyloarthritis (axSpA). The qualitative and semiquantitative diagnosis performed by expert radiologists and rheumatologists remains subject to significant intrapersonal and interpersonal variation. This encouraged us to use machine-learning methods for this task. Methods: In this retrospective study including 56 sacroiliac joint MRI exams, 24 patients had positive and 32 had negative findings for inflammatory sacroiliitis according to the ASAS group criteria. The dataset was randomly split with ∼ 80% (46 samples, 20 positive and 26 negative) as training and ∼ 20% as external test (10 samples, 4 positive and 6 negative). After manual segmentation of the images by a musculoskeletal radiologist, multiple features were extracted. The classifiers used were the Support Vector Machine, the Multilayer Perceptron (MLP), and the Instance-Based Algorithm, combined with the Relief and Wrapper methods for feature selection. Results: Based on 10-fold cross-validation using the training dataset, the MLP classifier obtained the best performance with sensitivity = 100%, specificity = 95.6% and accuracy = 84.7%, using 6 features selected by the Wrapper method. Using the test dataset (external validation) the same MLP classifier obtained sensitivity = 100%, specificity = 66.7% and accuracy = 80%. Conclusions: Our results show the potential of machine learning methods to identify SIJ subchondral bone marrow edema in axSpA patients and are promising to aid in the detection of active inflammatory sacroiliitis on MRI STIR sequences. Multilayer Perceptron (MLP) achieved the best results.(AU)

Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine / Inteligência artificial, aprendizado de máquina, diagnóstico auxiliado por computador e radiômica: avanços da imagem rumo à medicina de precisão

Abstract The discipline of radiology and diagnostic imaging has evolved greatly in recent years. We have observed an exponential increase in the number of exams performed, subspecialization of medical fields, and increases in accuracy of the various imaging methods, making it a challenge for the radiologist to "know everything about all exams and regions". In addition, imaging exams are no longer only qualitative and diagnostic, providing now quantitative information on disease severity, as well as identifying biomarkers of prognosis and treatment response. In view of this, computer-aided diagnosis systems have been developed with the objective of complementing diagnostic imaging and helping the therapeutic decision-making process. With the advent of artificial intelligence, "big data", and machine learning, we are moving toward the rapid expansion of the use of these tools in daily life of physicians, making each patient unique, as well as leading radiology toward the concept of multidisciplinary approach and precision medicine. In this article, we will present the main aspects of the computational tools currently available for analysis of images and the principles of such analysis, together with the main terms and concepts involved, as well as examining the impact that the development of artificial intelligence has had on radiology and diagnostic imaging.

Resumo A disciplina de radiologia e diagnóstico por imagem evoluiu sobremaneira nos últimos anos. Temos observado o aumento exponencial do número de exames realizados, a subespecialização das disciplinas médicas e a maior acurácia dos métodos, tornando um desafio para o médico radiologista "saber tudo sobre todos exames e regiões". Além disso, os exames de imagem deixaram de ser somente qualitativos e diagnósticos e passaram a fornecer informações quantitativas e de gravidade de doença, identificando biomarcadores prognósticos e de resposta ao tratamento. Diante disso, sistemas computadorizados de auxílio diagnóstico vêm sendo desenvolvidos com o objetivo dar suporte ao diagnóstico por imagem e à decisão terapêutica. Com o advento da inteligência artificial, do big data e do aprendizado de máquina, caminhamos para a rápida expansão do uso dessas ferramentas no dia-a-dia dos médicos, tornando cada paciente único, levando a radiologia ao encontro do conceito de abordagem multidisciplinar e medicina de precisão. Neste artigo serão abordados os principais aspectos das ferramentas computacionais atualmente disponíveis para análise das imagens médicas, apresentando os princípios de análise das imagens, os principais termos e conceitos envolvidos nesses processos, assim como o impacto do desenvolvimento da inteligência artificial na radiologia e diagnóstico por imagem.

Avaliação computadorizada de sacroiliíte em imagens de ressonância magnética / Computer evaluation of sacroiliitis in magnetic resonance imaging

OBJETIVOS: avaliar e classificar a atividade inflamatória nas articulações sacroilíacas de pacientes com espondiloartrite em imagens de ressonância magnética, utilizando atributos de textura e de histograma de níveis de cinza. MÉTODOS: imagens de 51 pacientes foram avaliadas retrospectivamente e segmentadas manualmente por um radiologista. Trinta e nove atributos de brilho e de textura foram utilizados para caracterizar a presença ou ausência de processo inflamatório. A classificação foi realizada utilizando-se diferentes classificadores e avaliada por um método de validação cruzada com 10-fold. RESULTADOS: uma rede neural multicamadas, utilizando o conjunto total de atributos, alcançou o melhor desempenho no estudo, obtendo 0,915 de área sob a curva ROC, 0,864 de sensibilidade e 0,724 de especificidade. CONCLUSÕES: o processamento computadorizado implementado possui bom potencial como base para o desenvolvimento de uma ferramenta de auxílio ao diagnóstico de processo inflamatório de articulações sacroilíacas de pacientes com espondiloartrites.

GOAL: to evaluate and classify the inflammatory process in sacroiliac joints of patients with spondyloarthritis in magnetic resonance imaging using attributes of texture and gray-level histogram. METHODS: images from 51 patients were retrospectively evaluated and manually segmented by a radiologist. Thirty nine attributes of histogram and texture were used to characterize the presence or absence of the inflammatory process. Classification was performed by several classifiers and evaluated with a 10-fold cross-validation. RESULTS: a multilayer neural network and all extracted attributes obtained highest diagnostic performance in the study with 0.915 of area under the ROC curve, 0.864 of sensitivity and 0.724of specificity. CONCLUSIONS: the implemented computerized processing presents good potential as a starting point for the development of a tool to aid the diagnosis of inflammatory process of sacroiliac joints of patients with spondyloarthritis.