Radiomic Quantification for MRI Assessment of Sacroiliac Joints of Patients with Spondyloarthritis.

Spondyloarthritis (SpA) is a group of diseases primarily involving chronic inflammation of the spine and peripheral joints, as evaluated by magnetic resonance imaging (MRI). Considering the complexity of SpA, we performed a retrospective study to discover quantitative/radiomic MRI-based features correlated with SpA. We also investigated different fat-suppression MRI techniques to develop detection models for inflammatory sacroiliitis. Finally, these model results were compared with those of experienced musculoskeletal radiologists, and the concordance level was evaluated. Examinations of 46 consecutive patients were obtained using SPAIR (spectral attenuated inversion recovery) and STIR (short tau inversion recovery) MRI sequences. Musculoskeletal radiologists manually segmented the sacroiliac joints for further extraction of 230 MRI features from gray-level histogram/matrices and wavelet filters. These features were associated with sacroiliitis, SpA, and the current biomarkers of ESR (erythrocyte sedimentation rate), CRP (C-reactive protein), BASDAI (Bath Ankylosing Spondylitis Activity Index), BASFI (Bath Ankylosing Spondylitis Functional Index), and MASES (Maastricht Ankylosing Spondylitis Enthesis Score). The Mann-Whitney U test showed that the radiomic markers from both MRI sequences were associated with active sacroiliitis and with SpA and its axial and peripheral subtypes (p < 0.05). Spearman's coefficient also identified a correlation between MRI markers and data from clinical practice (p < 0.05). Fat-suppression MRI models yielded performances that were statistically equivalent to those of specialists and presented strong concordance in identifying inflammatory sacroiliitis. SPAIR and STIR acquisition protocols showed potential for the evaluation of sacroiliac joints and the composition of a radiomic model to support the clinical assessment of SpA.

Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity.

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.

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.

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.

A study of MRI-based radiomics biomarkers for sacroiliitis and spondyloarthritis.

PURPOSE: To evaluate the performance of texture-based biomarkers by radiomic analysis using magnetic resonance imaging (MRI) of patients with sacroiliitis secondary to spondyloarthritis (SpA). RELEVANCE: The determination of sacroiliac joints inflammatory activity supports the drug management in these diseases. METHODS: Sacroiliac joints (SIJ) MRI examinations of 47 patients were evaluated. Thirty-seven patients had SpA diagnoses (27 axial SpA and ten peripheral SpA) which was established previously after clinical and laboratory follow-up. To perform the analysis, the SIJ MRI was first segmented and warped. Second, radiomics biomarkers were extracted from the warped MRI images for associative analysis with sacroiliitis and the SpA subtypes. Finally, statistical and machine learning methods were applied to assess the associations of the radiomics texture-based biomarkers with clinical outcomes. RESULTS: All diagnostic performances obtained with individual or combined biomarkers reached areas under the receiver operating characteristic curves ≥ 0.80 regarding SpA related sacroiliitis and and SpA subtypes classification. Radiomics texture-based analysis showed significant differences between the positive and negative SpA groups and differentiated the axial and peripheral subtypes (P < 0.001). In addition, the radiomics analysis was also able to correctly identify the disease even in the absence of active inflammation. CONCLUSION: We concluded that the application of the radiomic approach constitutes a potential noninvasive tool to aid the diagnosis of sacroiliitis and for SpA subclassifications based on MRI of sacroiliac joints.

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

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.

CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms.

PURPOSE: As some of the most important factors for treatment decision of lung cancer (which is the deadliest neoplasm) are staging and histology, this work aimed to associate quantitative contrast-enhanced computed tomography (CT) features from malignant lung tumors with distant and nodal metastases (according to clinical TNM staging) and histopathology (according to biopsy and surgical resection) using radiomics assessment. METHODS: A local cohort of 85 patients were retrospectively (2010-2017) analyzed after approval by the institutional research review board. CT images acquired with the same protocol were semiautomatically segmented by a volumetric segmentation method. Tumors were characterized by quantitative CT features of shape, first-order, second-order, and higher-order textures. Statistical and machine learning analyses assessed the features individually and combined with clinical data. RESULTS: Univariate and multivariate analyses identified 40, 2003, and 45 quantitative features associated with distant metastasis, nodal metastasis, and histopathology (adenocarcinoma and squamous cell carcinoma), respectively. A machine learning model yielded the highest areas under the receiver operating characteristic curves of 0.92, 0.84, and 0.88 to predict the same previous patterns. CONCLUSION: Several radiomic features (including wavelet energies, information measures of correlation and maximum probability from co-occurrence matrix, busyness from neighborhood intensity-difference matrix, directionalities from Tamura's texture, and fractal dimension estimation) significantly associated with distant metastasis, nodal metastasis, and histology were discovered in this work, presenting great potential as imaging biomarkers for pathological diagnosis and target therapy decision.

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)

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.