Development and validation of a nomogram to estimate the pretest probability of cancer in Chinese patients with solid solitary pulmonary nodules: A multi‐institutional study

She, Yunlang; Zhao, Lilan; Dai, Chenyang; Ren, Yijiu; Jiang, Gening; Xie, Huikang; Zhu, Huanhuan; Sun, Xuejun; Yang, Ping; Chen, Yongbing; Shi, Sixiang; Shi, Weirong; Yu, Bing; Xie, Dong; Chen, Chang

doi:10.1002/jso.24704

Cited by 58 publications

(141 citation statements)

References 27 publications

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“…Studies have shown that the performance of models is influenced by the composition of the study population. The Mayo 30,31 and Herder 31 models were successfully validated in an independent population in which the prevalence of malignant nodules was similar to that in the original…”

Section: F-fdg Pet/ct-based Clinical Prediction Modelmentioning

confidence: 99%

Development and Validation of a 18F-FDG PET/CT-Based Clinical Prediction Model for Estimating Malignancy in Solid Pulmonary Nodules Based on a Population With High Prevalence of Malignancy

Guo¹,

Lin

Huang³

et al. 2020

Clinical Lung Cancer

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Section: F-fdg Pet/ct-based Clinical Prediction Modelmentioning

confidence: 99%

Development and Validation of a 18F-FDG PET/CT-Based Clinical Prediction Model for Estimating Malignancy in Solid Pulmonary Nodules Based on a Population With High Prevalence of Malignancy

Guo¹,

Lin

Huang³

et al. 2020

Clinical Lung Cancer

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“…Growing use of chest computed tomography (CT) has resulted in the identification of over millions of solitary pulmonary nodules (SPNs) each year [1]. Although only approximately 30% of such SPNs are malignant, decisions to undergo surgical resection or pursue CT surveillance hinge on the probability of malignancy for the identified SPNs [2]. Tissue biopsy, radiographic imaging modalities, and serum biomarkers can help determine the malignancy.…”

Section: Introductionmentioning

confidence: 99%

Value of folate receptor-positive circulating tumour cells in the clinical management of indeterminate lung nodules: A non-invasive biomarker for predicting malignancy and tumour invasiveness

et al. 2019

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Background Non-invasive lung adenocarcinoma could benefit from limited resection, nonetheless, there is a lack of method to determine preoperative tumour invasiveness. We aimed to investigate whether folate receptor-positive circulating tumour cells (FR + -CTCs) in combination with maximum tumour diameter (MTD) determines, before surgery, the invasiveness of small-sized, indeterminate solitary pulmonary nodules (SPNs). Methods A total of 382 patients with suspicious lung adenocarcinoma on computed tomography who were expected to undergo lung resection were enrolled in this study at three participating institutes and randomly assigned into training and validation cohorts. Before surgery, 3 mL peripheral blood was collected from all participants. FR + -CTCs were analyzed using immunomagnetic leukocyte depletion and quantitated by ligand-targeted PCR method. After surgery, the resected tissues were diagnosed by pathologists according to IASLC/ATS/ERS classification. Findings FR + -CTC levels in the peripheral blood can differentiate benign from malignant nodules with a sensitivity of 78·6%–82·7% and a specificity of 68·8%–78·4%. Both FR + -CTC and MTD are independent predictive indices of invasive tumours for lung adenocarcinoma ≤2 cm based on multivariate analyses. Further, FR + -CTC count in combination with MTD can differentiate non-invasive cancers from invasive cancers with a sensitivity of 63·6%–81·8% and a specificity of 71·4%–89·7%. Interpretation Detection of FR + -CTC is a reliable method to differentiate malignancy of indeterminate SPNs. Combining of FR + -CTC count and MTD could possibly enhance preoperative determination of the invasiveness of lung nodules and guide surgeons to select limited lung resection and avoid overtreatment for patients with non-invasive lesions. Fund None.

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“…30 Besides, another three models were also established for solid nodules based on a Chinese population (AUC=0.85~0.87). [31][32][33] Compared to these four models, the current solid-nodule models shared some similar risk factors, such as age, sex, history of malignancy, morphology, and serum CEA, but some novel markers like V25, RV/TLC, serum lymphocyte, CYFRA21-1, and NSE were also identified. As for model performance, these four were a little better than our models (AUC=0.82), which can be associated with some valuable predictors they enrolled, such as enhancement, VDT, as well as maximum uptake value of nodules.…”

Section: Discussionmentioning

confidence: 96%

“…Besides, one study also enrolled calcification in the model. 33 Anyway, all these solid-nodule models demonstrated good performance, and clinicians can choose the most appropriate one according to the variable availability and nodule similarity where models were derived.…”

Section: Discussionmentioning

confidence: 99%

<p>Predicting Lung Cancer Risk of Incidental Solid and Subsolid Pulmonary Nodules in Different Sizes</p>

Zhang¹,

Tian²,

Chen³

et al. 2020

CMAR

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Objective: Malignancy prediction models for pulmonary nodules are most accurate when used within nodules similar to those in which they were developed. This study was to establish models that respectively predict malignancy risk of incidental solid and subsolid pulmonary nodules of different size. Materials and Methods: This retrospective study enrolled patients with 5-30 mm pulmonary nodules who had a histopathologic diagnosis of benign or malignant. The median time to lung cancer diagnosis was 25 days. Four training/validation datasets were assembled based on nodule texture and size: subsolid nodules (SSNs) ≤15 mm, SSNs between 15 and 30 mm, solid nodules ≤15 mm and those between 15 and 30 mm. Univariate logistic regression was used to identify potential predictors, and multivariate analysis was used to build four models. Results: The study identified 1008 benign and 1813 malignant nodules from a single hospital, and by random selection 1008 malignant nodules were enrolled for further analysis. There was a much higher malignancy rate among SSNs than solid nodules (rate, 75% vs 39%, P<0.001). Four distinguishing models were respectively developed and the areas under the curve (AUC) in training sets and validation sets were 0.83 (0.78-0.88) and 0.70 (0.61-0.80) for SSNs ≤15 mm, 0.84 (0.74-0.93) and 0.72 (0.57-0.87) for SSNs between 15 and 30 mm, 0.82 (0.77-0.87) and 0.71 (0.61-0.80) for solid nodules ≤15 mm, 0.82 (0.79-0.85) and 0.81 (0.76-0.86) for solid nodules between 15 and 30 mm. Each model showed good calibration and potential clinical applications. Different independent predictors were identified for solid nodules and SSNs of different size. Conclusion: We developed four models to help characterize subsolid and solid pulmonary nodules of different sizes. The established models may provide decision-making information for thoracic radiologists and clinicians.

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Development and validation of a nomogram to estimate the pretest probability of cancer in Chinese patients with solid solitary pulmonary nodules: A multi‐institutional study

Abstract: We developed and validated a nomogram that can estimate the pretest probability of malignant solid SPNs, which can assist clinical physicians to select and interpret the results of subsequent diagnostic tests.

Cited by 58 publications

References 27 publications

Development and Validation of a 18F-FDG PET/CT-Based Clinical Prediction Model for Estimating Malignancy in Solid Pulmonary Nodules Based on a Population With High Prevalence of Malignancy

Development and Validation of a 18F-FDG PET/CT-Based Clinical Prediction Model for Estimating Malignancy in Solid Pulmonary Nodules Based on a Population With High Prevalence of Malignancy

Value of folate receptor-positive circulating tumour cells in the clinical management of indeterminate lung nodules: A non-invasive biomarker for predicting malignancy and tumour invasiveness

<p>Predicting Lung Cancer Risk of Incidental Solid and Subsolid Pulmonary Nodules in Different Sizes</p>

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