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.

Radiomics-based features for pattern recognition of lung cancer histopathology and metastases.

BACKGROUND AND OBJECTIVES: lung cancer is the leading cause of cancer-related deaths in the world, and its poor prognosis varies markedly according to tumor staging. Computed tomography (CT) is the imaging modality of choice for lung cancer evaluation, being used for diagnosis and clinical staging. Besides tumor stage, other features, like histopathological subtype, can also add prognostic information. In this work, radiomics-based CT features were used to predict lung cancer histopathology and metastases using machine learning models. METHODS: local image datasets of confirmed primary malignant pulmonary tumors were retrospectively evaluated for testing and validation. CT images acquired with same protocol were semiautomatically segmented. Tumors were characterized by clinical features and computer attributes of intensity, histogram, texture, shape, and volume. Three machine learning classifiers used up to 100 selected features to perform the analysis. RESULTS: radiomics-based features yielded areas under the receiver operating characteristic curve of 0.89, 0.97, and 0.92 at testing and 0.75, 0.71, and 0.81 at validation for lymph nodal metastasis, distant metastasis, and histopathology pattern recognition, respectively. CONCLUSIONS: the radiomics characterization approach presented great potential to be used in a computational model to aid lung cancer histopathological subtype diagnosis as a "virtual biopsy" and metastatic prediction for therapy decision support without the necessity of a whole-body imaging scanning.

Bronchial fistula closure with negative pressure wound therapy: a feasible and cost-effective treatment.

Treatment of bronchial fistula (BF) after pulmonary lobectomy is a challenge. Often, patients require long hospital stay, have recurrent empyema and pneumonia, are susceptible to sepsis, often need broad-spectrum antibiotics, as well as various surgical approaches. With the advent and growing evidence of the benefits of negative pressure therapy (NPT), its use in some patients with BF has been reported with encouraging results concerning its feasibility and cost-effectiveness. The aim of this study was to demonstrate the application of NPT as a resource for BF treatment and comparatively analyze the overall cost of treatment. RESUMO O tratamento de fístula brônquica (FB) após lobectomia pulmonar é um desafio. Muitas vezes, o paciente demanda longo tempo de internação, apresenta recidivas de empiema e pneumonia, pode evoluir para sepse, frequentemente necessita de antibioticoterapia de amplo espectro, bem como de várias abordagens cirúrgicas. Com o advento e acúmulo de evidências dos benefícios da terapia por pressão negativa (TPN), seu uso em alguns pacientes com FB tem sido relatado com resultados animadores relativos à sua viabilidade e ao seu custo-efetividade. O objetivo deste estudo foi demonstrar a aplicação de TPN como recurso para tratamento da FB e analisar comparativamente o custo global do seu tratamento.

A survey among Brazilian thoracic surgeons about the use of preoperative 2D and 3D images.

BACKGROUND: Describe the characteristics of how the thoracic surgeon uses the 2D/3D medical imaging to perform surgical planning, clinical practice and teaching in thoracic surgery and check the initial choice and the final choice of the Brazilian Thoracic surgeon as the 2D and 3D models pictures before and after acquiring theoretical knowledge on the generation, manipulation and interactive 3D views. METHODS: A descriptive research type Survey cross to data provided by the Brazilian Thoracic Surgeons (members of the Brazilian Society of Thoracic Surgery) who responded to the online questionnaire via the internet on their computers or personal devices. RESULTS: Of the 395 invitations visualized distributed by email, 107 surgeons completed the survey. There was no statically difference when comparing the 2D vs. 3D models pictures for the following purposes: diagnosis, assessment of the extent of disease, preoperative surgical planning, and communication among physicians, resident training, and undergraduate medical education. Regarding the type of tomographic image display routinely used in clinical practice (2D or 3D or 2D-3D model image) and the one preferred by the surgeon at the end of the questionnaire. Answers surgeons for exclusive use of 2D images: initial choice =50.47% and preferably end =14.02%. Responses surgeons to use 3D models in combination with 2D images: initial choice =48.60% and preferably end =85.05%. There was a significant change in the final selection of 3D models used together with the 2D images (P<0.0001). CONCLUSIONS: There is a lack of knowledge of the 3D imaging, as well as the use and interactive manipulation in dedicated 3D applications, with consequent lack of uniformity in the surgical planning based on CT images. These findings certainly confirm in changing the preference of thoracic surgeons of 2D views of technologies for 3D images.

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.