Perspectives on Managing Solitary Pulmonary Nodules: A Survey of Primary Care Physicians

Wu, Geena X.; Consunji, Martin; Nelson, Rebecca A.; Yeung, Kenny; Sun, Can‐Lan; Kim, Jae Y.; Raz, Dan J.

doi:10.1053/j.semtcvs.2017.05.012

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…In this context, FGP-NET stratified 41.7% of lung cancer with PPV at 96.3% in routine diagnostic population, which enabled examinations of 'lung cancer' to be moved ahead in the image interpretation workflow. Patients with lung cancer could receive timely diagnoses and treatment, even in primary hospitals where primary care providers are with low confidence and preference for referral [38][39][40] . In the second step, further stratification of ambiguous nodules was performed to aid clinical decision making.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a clinically applicable deep learning strategy (HONORS) for pulmonary nodule classification at CT: A retrospective multicentre study

Yang

Zhou

et al. 2021

Lung Cancer

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Section: Discussionmentioning

confidence: 99%

Development and validation of a clinically applicable deep learning strategy (HONORS) for pulmonary nodule classification at CT: A retrospective multicentre study

Yang

Zhou

et al. 2021

Lung Cancer

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“…Perhaps unsurprisingly, patients who had been seen by a pulmonologist were more likely to have guideline-concordant follow-up than those seen only by a PCP. In prior studies, PCPs have reported low confidence in their ability to manage nodules, preferring to defer diagnostic evaluation to specialists [8]. Additionally, PCPs often feel they lack the necessary information and resources to counsel patients and assure appropriate nodule follow-up [9].…”

Section: Discussionmentioning

confidence: 99%

Patient characteristics associated with adherence to pulmonary nodule guidelines

Iaccarino

Steiling

Slatore

et al. 2020

Respiratory Medicine

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Section: Discussionmentioning

confidence: 99%

Clinically Applicable Deep Learning Strategy for Pulmonary Nodule Risk Prediction: Insights into HONORS

Yang

Zhou

et al. 2020

Preprint

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Background and Purpose: Limited optimization was clinically applicable for reducing missed diagnosis, misdiagnosis and inter-reader variability in pulmonary nodule diagnosis. We aimed to propose a deep learning-based algorithm and a practical strategy to better stratify the risk of pulmonary nodules, thus reducing medical errors and optimizing the clinical workflow. Materials and Methods: A total of 2,348 pulmonary nodules (1,215 with lung cancer) containing screened nodules from National Lung Cancer Screening Trial (NLST) and incidentally detected nodules from Jinling Hospital (JLH) were used to train and evaluate a deep learning algorithm, Filter-guided pyramid network (FGP-NET). Internal and external test of FGP-NET were performed on two independent datasets (n=542). The performance of FGP-NET at Youden point which maximizing the Youden index was compared with 126 board-certificated radiologists. We further proposed Hierarchical Ordered Network ORiented Strategy (HONORS), which manipulates the emphasis either on sensitivity or specificity to target risk-stratified clinical scenarios, directly making decisions for some patients. Results: FGP-NET achieved a high area under the curve (AUC) of 0.969 and 0.855 for internal and external testing, and was comparable or even outperformed the radiologists when considering sensitivity. HONORS-guided FGP-NET identified benign nodules with a high sensitivity (95.5%) in the screening scenario, and demonstrated satisfactory performance for the rest ambiguous nodules with 0.945 of AUC by the Youden point. FGP-NET also detected lung cancer with a high specificity of 94.5% in routine diagnostic scenario; an AUC of 0.809 was achieved for the rest nodules. Conclusion: The combination of HONORS and FGP-NET provides well-organized stratification for pulmonary nodules and also offers the potential for reducing medical errors.

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Perspectives on Managing Solitary Pulmonary Nodules: A Survey of Primary Care Physicians

Cited by 4 publications

References 19 publications

Development and validation of a clinically applicable deep learning strategy (HONORS) for pulmonary nodule classification at CT: A retrospective multicentre study

Development and validation of a clinically applicable deep learning strategy (HONORS) for pulmonary nodule classification at CT: A retrospective multicentre study

Patient characteristics associated with adherence to pulmonary nodule guidelines

Clinically Applicable Deep Learning Strategy for Pulmonary Nodule Risk Prediction: Insights into HONORS

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