Multicentre external validation of the BIMC model for solid solitary pulmonary nodule malignancy prediction

Soardi, Gian Alberto; Perandini, Simone; Larici, Anna Rita; Ciello, Annemilia del; Rizzardi, Giovanna; Solazzo, Antonio; Mancino, Laura; Bernhart, Marco; Motton, Massimiliano; Montemezzi, Stefania

doi:10.1007/s00330-016-4538-5

Cited by 24 publications

(13 citation statements)

References 12 publications

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“… 36 Italy Mayo, b Gurney, b PKU, b BIMC Pathologically confirmed SPNs HP 285 55 15.36 20 Soardi et al. 37 Italy BIMC, Mayo b SPNs from three medical centers HP 200 54 15.89 21 Chen et al. 38 People’s Republic of China Mayo, b PKU b Pathologically confirmed PNs after surgery HP 41 76 — 22 Yang et al.…”

Section: Resultsmentioning

confidence: 99%

Comprehensive Analysis of Clinical Logistic and Machine Learning-Based Models for the Evaluation of Pulmonary Nodules

Zhang

Wei

Nie

et al. 2022

JTO Clinical and Research Reports

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Section: Resultsmentioning

confidence: 99%

Comprehensive Analysis of Clinical Logistic and Machine Learning-Based Models for the Evaluation of Pulmonary Nodules

Zhang

Wei

Nie

et al. 2022

JTO Clinical and Research Reports

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“…Currently the American College of Chest Physicians guidelines suggest using the Mayo Clinic prediction model based on patient categorisation into low (>5%), intermediate (5-65%) and high risk (>65%) of malignancy [5], while the BTS guidelines suggest the use of the Brock and Herder models [2]. More recently, the Bayesian inference malignancy calculator model proved to be an accurate tool for characterising pulmonary nodules by guiding lesion-tailored diagnostic and interventional procedures during work-up [138].…”

Section: Integrating Clinical and Nodule Characteristics: Risk Predicmentioning

confidence: 99%

Lung nodules: size still matters

et al. 2017

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The incidence of indeterminate pulmonary nodules has risen constantly over the past few years. Determination of lung nodule malignancy is pivotal, because the early diagnosis of lung cancer could lead to a definitive intervention. According to the current international guidelines, size and growth rate represent the main indicators to determine the nature of a pulmonary nodule. However, there are some limitations in evaluating and characterising nodules when only their dimensions are taken into account. There is no single method for measuring nodules, and intrinsic errors, which can determine variations in nodule measurement and in growth assessment, do exist when performing measurements either manually or with automated or semi-automated methods. When considering subsolid nodules the presence and size of a solid component is the major determinant of malignancy and nodule management, as reported in the latest guidelines. Nevertheless, other nodule morphological characteristics have been associated with an increased risk of malignancy. In addition, the clinical context should not be overlooked in determining the probability of malignancy. Predictive models have been proposed as a potential means to overcome the limitations of a sized-based assessment of the malignancy risk for indeterminate pulmonary nodules.

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“…In Additional file 2 , according to the TRIPOD classification [ 14 ], the 17 studies were type 4. Validations were carried out in the USA [ 30 – 34 ], Asia (China, Japan, Korea) [ 35 – 40 ], one in the UK [ 41 ], one in Brazil [ 42 ], one in the Netherlands [ 43 ] and another in Italy [ 44 ], and in 2, it was not specified [ 45 , 46 ]. The prevalence of malignancy ranged from 25 to 85.6%, with one study not providing this information, and the prevalence of current or past smokers ranged from 11.8 to 89%, although this was not reported in 4 studies [ 39 , 40 , 42 , 46 ].…”

Section: Resultsmentioning

confidence: 99%

Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review

2021

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Background Solitary pulmonary nodule (SPN) is a common finding in routine clinical practice when performing chest imaging tests. The vast majority of these nodules are benign, and only a small proportion are malignant. The application of predictive models of nodule malignancy in routine clinical practice would help to achieve better diagnostic management of SPN. The present systematic review was carried out with the purpose of critically assessing studies aimed at developing predictive models of solitary pulmonary nodule (SPN) malignancy from SPN incidentally detected in routine clinical practice. Methods We performed a search of available scientific literature until October 2020 in Pubmed, SCOPUS and Cochrane Central databases. The inclusion criteria were observational studies carried out in low-risk population from 35 years old onwards aimed at constructing predictive models of malignancy of pulmonary solitary nodule detected incidentally in routine clinical practice. Studies had to be published in peer-reviewed journals, either in Spanish, Portuguese or English. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches (such as radiomics). We used The Transparent Reporting of a multivariable Prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, to describe the type of predictive model included in each study, and The Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality of the selected articles. Results A total of 186 references were retrieved, and after applying the exclusion/inclusion criteria, 15 articles remained for the final review. All studies analysed clinical and radiological variables. The most frequent independent predictors of SPN malignancy were, in order of frequency, age, diameter, spiculated edge, calcification and smoking history. Variables such as race, SPN growth rate, emphysema, fibrosis, apical scarring and exposure to asbestos, uranium and radon were not analysed by the majority of the studies. All studies were classified as high risk of bias due to inadequate study designs, selection bias, insufficient population follow-up and lack of external validation, compromising their applicability for clinical practice. Conclusions The studies included have been shown to have methodological weaknesses compromising the clinical applicability of the evaluated SPN malignancy predictive models and their potential influence on clinical decision-making for the SPN diagnostic management. Systematic review registration PROSPERO CRD42020161559

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Multicentre external validation of the BIMC model for solid solitary pulmonary nodule malignancy prediction

Abstract: • The BIMC model can accurately discriminate malignancies in the clinical setting • The BIMC model showed ROC AUC of 0.880 in this multicentre study • The BIMC model compares favourably with the Mayo Clinic model.

Cited by 24 publications

References 12 publications

Comprehensive Analysis of Clinical Logistic and Machine Learning-Based Models for the Evaluation of Pulmonary Nodules

Comprehensive Analysis of Clinical Logistic and Machine Learning-Based Models for the Evaluation of Pulmonary Nodules

Lung nodules: size still matters

Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review

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