Critique of Al-Ameri et al. (2015) – Risk of malignancy in pulmonary nodules: A validation study of four prediction models

Perandini, Simone; Soardi, Gian Alberto; Motton, Massimiliano; Montemezzi, Stefania

doi:10.1016/j.lungcan.2015.05.015

Cited by 8 publications

(4 citation statements)

References 3 publications

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“…We thank Perandini et al for their comments regarding our recently published data and the interest they have shown in our work [1,2]. We agree with some of the points they raise, although disagree with some of their comments regarding our conclusions, and also in their interpretation of the American College of Chest Physicians (ACCP) 2013 guidelines [3] for the management of pulmonary nodules.…”

Section: Contents Lists Available At Sciencedirectmentioning

confidence: 62%

Authors’ response—Risk of malignancy in pulmonary nodules: a validation study of four prediction models

et al. 2015

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Section: Contents Lists Available At Sciencedirectmentioning

confidence: 62%

Authors’ response—Risk of malignancy in pulmonary nodules: a validation study of four prediction models

et al. 2015

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“…A ROC curve is designed as a discriminator, to illustrate the diagnostic ability of a binary classifier system and return an optimal threshold value (25). However, pulmonary nodule risk assessment is not a simple binary classification; often a three-class classifying system, based on malignancy risk thresholds, is recommended (26). Based on the result of using thresholds, only 28 (13.5%), 2 (1.0%), and 32 (15.5%) out of 207 malignancies were predicted above the surgical threshold using the Mayo, Brock and VA models, respectively.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population

Cui¹,

Heuvelmans²,

Han³

et al. 2019

Transl. Lung Cancer Res.

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Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population . (2019). Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population. Translational lung cancer research, 8(5), 605-613. Background: Several classification models based on Western population have been developed to help clinicians to classify the malignancy probability of pulmonary nodules. However, the diagnostic performance of these Western models in Chinese population is unknown. This paper aimed to compare the diagnostic performance of radiologist evaluation of malignancy probability and three classification models (Mayo Clinic, Veterans Affairs, and Brock University) in Chinese clinical pulmonology patients. Methods: This single-center retrospective study included clinical patients from Tianjin Medical University Cancer Institute and Hospital with new, CT-detected pulmonary nodules in 2013. Patients with a nodule with diameter of 4-25 mm, and histological diagnosis or 2-year follow-up were included. Analysis of area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and threshold of decision analysis was used to evaluate the diagnostic performance of radiologist diagnosis and the three classification models, with histological diagnosis or 2-year follow-up as the reference. Results: In total, 277 patients (286 nodules) were included. Two hundred and seven of 286 nodules (72.4%) in 203 patients were malignant. AUC of the Mayo model (0.77; 95% CI: 0.72-0.82) and Brock model (0.77; 95% CI: 0.72-0.82) were similar to radiologist diagnosis (0.78; 95% CI: 0.73-0.83; P=0.68, P=0.71, respectively). The diagnostic performance of the VA model (AUC: 0.66) was significantly lower than that of radiologist diagnosis (P=0.003). A three-class classifying threshold analysis and DCA showed that the radiologist evaluation had higher discriminatory power for malignancy than the three classification models. Conclusions: In a cohort of Chinese clinical pulmonology patients, radiologist evaluation of lung nodule malignancy probability demonstrated higher diagnostic performance than Mayo, Brock, and VA classification models. To optimize nodule diagnosis and management, a new model with more radiological characteristics could be valuable. of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population. Transl Lung Cancer Res 2019;8(5):605-613.

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“…Our study has limitations of which the most important one refers to the use of ROC statistics to evaluate the performance of clinical decision rules. As pointed out by Perandini et al, referring to the validation study of four prediction rules, ROC analysis is designed to estimate the performance of a binary classifier system and to determine an optimal threshold value to be used as discriminator [ 19 ]. The risk estimation systems tested in our study, however, use either a continuous scale (the PanCan model) or multiple categorical thresholds (Lung-RADS and NCCN) to balance a nodule's risk to represent a malignancy and the likelihood of being benign.…”

Section: Discussionmentioning

confidence: 99%

Malignancy risk estimation of screen-detected nodules at baseline CT: comparison of the PanCan model, Lung-RADS and NCCN guidelines

et al. 2017

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ObjectivesTo compare the PanCan model, Lung-RADS and the 1.2016 National Comprehensive Cancer Network (NCCN) guidelines for discriminating malignant from benign pulmonary nodules on baseline screening CT scans and the impact diameter measurement methods have on performances.MethodsFrom the Danish Lung Cancer Screening Trial database, 64 CTs with malignant nodules and 549 baseline CTs with benign nodules were included. Performance of the systems was evaluated applying the system's original diameter definitions: Dlongest-C (PanCan), DmeanAxial (NCCN), both obtained from axial sections, and Dmean3D (Lung-RADS). Subsequently all diameter definitions were applied uniformly to all systems. Areas under the ROC curves (AUC) were used to evaluate risk discrimination.ResultsPanCan performed superiorly to Lung-RADS and NCCN (AUC 0.874 vs. 0.813, p = 0.003; 0.874 vs. 0.836, p = 0.010), using the original diameter specifications. When uniformly applying Dlongest-C, Dmean3D and DmeanAxial, PanCan remained superior to Lung-RADS (p < 0.001 – p = 0.001) and NCCN (p < 0.001 – p = 0.016). Diameter definition significantly influenced NCCN’s performance with Dlongest-C being the worst (Dlongest-C vs. Dmean3D, p = 0.005; Dlongest-C vs. DmeanAxial, p = 0.016).ConclusionsWithout follow-up information, the PanCan model performs significantly superiorly to Lung-RADS and the 1.2016 NCCN guidelines for discriminating benign from malignant nodules. The NCCN guidelines are most sensitive to nodule size definition.Key Points• PanCan model outperforms Lung-RADS and 1.2016 NCCN guidelines in identifying malignant pulmonary nodules.• Nodule size definition had no significant impact on Lung-RADS and PanCan model.• 1.2016 NCCN guidelines were significantly superior when using mean diameter to longest diameter.• Longest diameter achieved lowest performance for all models.• Mean diameter performed equivalently when derived from axial sections and from volumetry.

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Critique of Al-Ameri et al. (2015) – Risk of malignancy in pulmonary nodules: A validation study of four prediction models

Cited by 8 publications

References 3 publications

Authors’ response—Risk of malignancy in pulmonary nodules: a validation study of four prediction models

Authors’ response—Risk of malignancy in pulmonary nodules: a validation study of four prediction models

Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population

Malignancy risk estimation of screen-detected nodules at baseline CT: comparison of the PanCan model, Lung-RADS and NCCN guidelines

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