Análise e validação de modelos probabilísticos de malignidade de nódulo pulmonar solitário em uma população no Brasil

Melo, Cromwell Barbosa de Carvalho; Perfeito, João Aléssio Juliano; Daud, Danilo Felix; Júnior, Altair da Silva Costa; Santoro, Ilka Lopes; Leão, Luiz Eduardo Villaça

doi:10.1590/s1806-37132012000500004

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…In the earliest studies, the Mayo Clinic model was examined alone or in comparison to other models in small, unscreened populations, nearly all of patients undergoing resection for lung nodules (malignancy prevalence: 44–84%) [64–68]. The Mayo Clinic model exhibited good discrimination in all studies except one [67] (AUCs, 0.67–0.80) and performed better than the VA model (0.68–0.73) [65, 66, 68], yet similar to the PKUPH model (0.81) [68]. However, calibration results, when reported, suggested that the Mayo Clinic model underestimated while the VA model overestimated the actual probability of malignancy [64, 66].…”

Section: Predicting Nodule Malignancy At Initial Detectionmentioning

confidence: 99%

Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions

et al. 2017

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Structured Abstract Purpose of review Risk prediction models may be useful for facilitating effective and high-quality decision-making at critical steps in the lung cancer screening process. This review provides a current overview of published lung cancer risk prediction models and their applications to lung cancer screening and highlights both challenges and strategies for improving their predictive performance and use in clinical practice. Recent findings Since the 2011 publication of the National Lung Screening Trial results, numerous prediction models have been proposed to estimate the probability of developing or dying from lung cancer or the probability that a pulmonary nodule is malignant. Respective models appear to exhibit high discriminatory accuracy in identifying individuals at highest risk of lung cancer or differentiating malignant from benign pulmonary nodules. However, validation and critical comparison of the performance of these models in independent populations are limited. Little is also known about the extent to which risk prediction models are being applied in clinical practice and influencing decision-making processes and outcomes related to lung cancer screening. Summary Current evidence is insufficient to determine which lung cancer risk prediction models are most clinically useful and how to best implement their use to optimize screening effectiveness and quality. To address these knowledge gaps, future research should be directed toward validating and enhancing existing risk prediction models for lung cancer and evaluating the application of model-based risk calculators and its corresponding impact on screening processes and outcomes.

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Section: Predicting Nodule Malignancy At Initial Detectionmentioning

confidence: 99%

Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions

et al. 2017

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“…We read with great interest the well-written manuscript by Melo et al,( 1 ) who analyzed clinical and radiological findings that influence the pathological diagnosis of solitary pulmonary nodule (SPN). They concluded that advanced age, greater maximum SPN diameter, and spiculated margins were significantly associated with the diagnosis of malignancy.…”

Section: To the Editormentioning

confidence: 99%

“…Lemos com grande interesse o bem elaborado artigo de Melo et al,( 1 ) que analisaram os achados clínicos e radiológicos que influenciam o diagnóstico patológico de nódulo pulmonar solitário (NPS). Concluíram que idade elevada, maior diâmetro do NPS e presença de margens espiculadas tiveram associações significativas com o diagnóstico de malignidade.…”

Section: Ao Editorunclassified

Intrapulmonary lymph node: a common and underrecognized tomography finding

Hochhegger

Irion

et al. 2013

J. bras. pneumol.

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“…The etiological diagnosis of pulmonary nodules remains a challenge. Imaging tests are important for diagnostic suspicion and risk estimation, but biopsy is necessary to confirm malignancy [Melo 2012, Snoeckx 2017]. There are protocols and algorithms to estimate the probability of malignancy of lung nodules, and the American College of Chest Physicians (ACCP) guidelines suggest performing biopsies when this probability is above 60% [Dale 2012, Ito 2018, Swensen 1997…”

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confidence: 99%

Minimally invasive interventions for pulmonary nodule biopsy: A systematic review protocol.

Miotto

Perfeito

Leite

et al. 2020

Preprint

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Background Lung cancer is the leading cause of cancer-related deaths worldwide, causing more than 1.6 million deaths annually, including 26,500 deaths in Brazil in 2015. The diagnosis of lung cancer in the early stages remains a challenge. Imaging tests are important for diagnostic suspicion and risk evaluation, but biopsy is needed to confirm malignancy. Minimally invasive methods for biopsy used include computed tomography-guided percutaneous biopsy (CTGB) and bronchoscopy transbronchial biopsy. The method of choice should have the best accuracy and the lowest complication rate. This systematic review is designed to map, critically evaluate and compare the effects (benefits and risks) of all lung nodule biopsy techniques.Methods We will conduct a systematic review according to the Cochrane Handbook for Systematic Reviews of Interventions recommendations.Discussion This study aims to compare the diagnostic yield and the safety of different techniques used for pulmonary nodule biopsy. We will include randomized clinical trials comparing tomography-guided percutaneous biopsy, nonguided transbronchial biopsy, fluoroscopy-guided transbronchial biopsy, transbronchial biopsy guided by endobronchial ultrasound with a radial probe and transbronchial biopsy guided by electromagnetic navigation. This study was approved by the research ethics committee of Universidade Federal de São Paulo (UNIFESP) (number 1344040918). The results of the completed protocol will be presented at any appropriate conference by the authors.Systematic review registration The protocol for this review was prospectively registered in the PROSPERO database (CRD42018092367).

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Análise e validação de modelos probabilísticos de malignidade de nódulo pulmonar solitário em uma população no Brasil

Abstract: Advanced age, greater maximum SPN diameter, and spiculated margins were significantly associated with the diagnosis of SPN malignancy. Our analysis shows that, although both mathematical models were effective in determining SPN malignancy in our population, the 1997 model was superior.

Cited by 6 publications

References 13 publications

Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions

Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions

Intrapulmonary lymph node: a common and underrecognized tomography finding

Minimally invasive interventions for pulmonary nodule biopsy: A systematic review protocol.

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