Management of pulmonary nodules according to the 2015 British Thoracic Society guidelines. Key messages for clinical practice

Baldwin, David R

doi:10.20452/pamw.3379

Cited by 10 publications

(5 citation statements)

References 32 publications

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“…Therefore, more effective imaging techniques or the construction of multifactor predictive models are needed to distinguish between benign and malignant GGNs. Among the many models, only the Brock model [ 37 ] considers the characteristics of GGNs and adjusts the correlation coefficient according to the nodule type. However, this model's application is complicated, and its modeling data came from the initially screened patients whose malignancy rate was low (5.5%).…”

Section: Discussionmentioning

confidence: 99%

Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

et al. 2021

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Purpose This work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (18F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs). Methods We retrospectively analyzed patients with suspicious GGNs who underwent 18F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data were randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation were used for the training and validation (fivefold cross-validation) of the three CNNs (PET, CT, and PET/CT networks). Results A total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the training set, the performance of PET network (accuracy, sensitivity, and specificity of 0.92 ± 0.02, 0.97 ± 0.03, and 0.76 ± 0.15) was better than the CT network (accuracy, sensitivity, and specificity of 0.84 ± 0.03, 0.90 ± 0.07, and 0.62 ± 0.16) (especially accuracy was significant, P-value was 0.001); in the testing set, the performance of both networks declined. However, the accuracy and sensitivity of PET network were still higher than that of CT network (0.76 vs. 0.67; 0.85 vs. 0.70). For dual-stream PET/CT network, its performance was almost the same as PET network in the training set (P-value was 0.372–1.000), while in the testing set, although its performance decreased, the accuracy and sensitivity (0.85 and 0.96) were still higher than both CT and PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [physician 1 (3-year experience): 0.70 and physician 2 (10-year experience): 0.73]. Conclusion The 3D-CNN based on 18F-FDG PET/CT can be used to distinguish benign lesions and IAC in GGNs, and the performance is better when both CT and PET images are used together.

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Section: Discussionmentioning

confidence: 99%

Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

et al. 2021

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“…Therefore, more effective imaging techniques or the construction of multi-factor predictive models are needed to distinguish between benign and malignant GGNs. Among the many models, only the Brock model [35] considers the characteristics of GGNs and adjusts the correlation coe cient according to the nodule type. However, this model's application is complicated, and its modeling data came from the initially screened patients whose malignancy rate was low (5.5%).…”

Section: Discussionmentioning

confidence: 99%

Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

Shao¹,

Niu²,

Shao³

et al. 2021

Preprint

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Purpose: This work aims to train, validate and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on Fluorine 18 (18F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs).Methods: We retrospectively analyzed patients with suspicious GGNs who underwent 18F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data was randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation was used for the training and validation (5-fold cross-validation) of the three CNNs (PET, CT, and PET/CT networks).Results: A total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the validation set, the performance of PET network (accuracy of 0.92 ± 0.02; sensitivity and specificity of 0.97 ± 0.03 and 0.94 ± 0.04) were better than CT network (accuracy of 0.84 ± 0.03; sensitivity and specificity of 0.90 ± 0.07 and 0.90 ± 0.04); in the testing set, the performance of both networks declined. However, when the specificity was close, the accuracy and sensitivity of PET network (0.79 ± 0.05 and 0.90 ± 0.05) were still higher than CT network (0.72 ± 0.05 and 0.79 ± 0.08). For the dual-stream PET/CT network, its performance was almost the same as PET network in the validation set; while in testing set, although its performance decreased (accuracy was 0.81 ± 0.03; sensitivity and specificity were 0.92 ± 0.03 and 0.86 ± 0.00), it was still higher than CT or PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [Physician 1 (3-year experience): 0.70 and Physician 2 (10-year experience): 0.73]. Conclusion: The 3D-CNN based on 18F-FDG PET/CT can be used to distinguish benign lesions and IAC in GGNs, and the performance is better when both CT and PET images are used together.

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“…Las recomendaciones de la Sociedad Fleischner para el manejo de los nódulos pulmonares del año 2017, como regla general, la biopsia por aguja transtorácica en un enfoque efectivo en manos expertas, pero tiene importantes limitaciones en casos de nódulos muy pequeños y lesiones en vidrio esmerilado debido a problemas potenciales de obtención de muestras inadecuadas y resultados falsos negativos (13).…”

Section: Discussionunclassified

Punción transparietal de masas intratorácicas pulmonares con aguja gruesa o tru-cut guiada por tomografía computada como parte del algoritmo diagnóstico: un estudio descriptivo

Araujo-Cuauro

2019

biosalud

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La biopsia transparietal pulmonar con aguja de corte tipo Tru-cut de núcleo rápido Quick-Core ®, guiada por tomografía computada, es una técnica útil para el estudio histológico de lesiones de masas intratorácicas pulmonares, sospechosas de malignidad. Objetivo: Describir nuestra experiencia en el uso diagnóstico de la biopsia transparietal torácica pulmonar con aguja Trucut guiada por tomografía computada como parte del algoritmo diagnóstico de las masas intratorácicas pulmonares. Materiales y métodos: Se realizó un análisis transversal prospectivo, descriptivo de biopsias transparietal guiadas por tomografía de pulmón, realizadas entre enero del 2016 y noviembre del 2018. Resultados: El promedio de edad de los pacientes fue 50,8 años, con predominio del sexo masculino en el 70% casos. El 60% de las lesiones pulmonares se localizaron en el pulmón derecho y 40% en el pulmón izquierdo. En el 90% de las biopsias obtenidas se obtuvo suficiente muestra para estudio histológico. El diagnóstico histológico de las biopsias realizadas reportó que el 67% eran tumores primarios de pulmón; adenocarcinoma, carcinoma escamoso o epidermoide, adenocarcinoma bronquioloalveolar, liposarcoma mixoide de bajo grado de malignidad; mientras que el 33% de los pacientes tenían antecedente de tumores secundarios o enfermedad metastásica. Conclusión: la biopsia transparietal con aguja Tru-cut guiada por tomografía en el diagnóstico de masas intratorácicas pulmonares, es un método seguro y preciso debido a su carácter no invasivo, baja tasa de complicaciones y menor costo para hacer un diagnóstico histopatológico, por lo que se debe integrar como parte del algoritmo diagnóstico.

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Management of pulmonary nodules according to the 2015 British Thoracic Society guidelines. Key messages for clinical practice

Cited by 10 publications

References 32 publications

Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

Punción transparietal de masas intratorácicas pulmonares con aguja gruesa o tru-cut guiada por tomografía computada como parte del algoritmo diagnóstico: un estudio descriptivo

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