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

Our plain-English summary of Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review, by M. Senent-Valero, J. Librero, M. Pastor-Valero (Systematic Reviews, 2021).

The study

• Authors: M. Senent-Valero, J. Librero, M. Pastor-Valero
• Journal: Systematic Reviews
• Published: 2021;10(1):308
• Type of article: Systematic review
• Cited by: 15 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

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.

© 2021. The Author(s).

In plain English

This was a systematic review, not a new study of patients. The authors searched PubMed, SCOPUS and Cochrane Central up to October 2020 for studies that built models to predict whether a solitary pulmonary nodule found during routine care was malignant. Such a nodule is a lung spot up to 30 mm across seen on a chest scan or X-ray. Fifteen studies met the criteria. All were retrospective, 11 were from China and Japan, and they had 55 to 1679 subjects. The share of malignant nodules ranged from 23 to 77.45%. The predictors the models identified most often were age (13 models), nodule diameter (9), spiculated edge (8), calcification (7) and smoking history (6). Only one study (Swensen et al., 1997) recorded asbestos exposure, in 6 subjects, and it did not identify asbestos as a predictor. No study included radon or uranium. Using a standard bias-rating tool (PROBAST), the authors rated all 15 studies at high risk of bias. The authors concluded that methodological weaknesses compromised the clinical applicability of the models. They noted that most studies involved high-risk populations and that only three databases were searched.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Systematic review of studies that developed predictive models of solitary pulmonary nodule malignancy, with risk-of-bias assessment using PROBAST; no meta-analysis
• Who was studied: 15 studies found in PubMed, SCOPUS and Cochrane Central up to October 2020. All were retrospective; 2 in the USA, 11 in China and Japan, 1 in Portugal and 1 in Spain. Study sizes ranged from 55 to 1679 subjects with a solitary pulmonary nodule seen in routine clinic settings.
• Exposure: Clinical and radiological predictor variables (and biomarkers in 6 studies). Asbestos, uranium and radon exposure were among the risk factors the authors checked for; only one study included asbestos exposure and none included radon or uranium.
• Main results:
  • Across the 15 studies, the prevalence of malignancy of the nodules ranged from 23 to 77.45%.
  • The most frequently identified independent predictors of malignancy in the models were age (13 models), SPN diameter (9), edge spiculation (8), nodule calcification (7) and smoking history (6).
  • In the one study that collected asbestos exposure (Swensen et al., 1997), 6 subjects with a solitary pulmonary nodule had been exposed to asbestos. That study did not identify asbestos as a predictor.
  • All 15 studies were rated as high risk of bias. The reasons were inadequate study designs, selection bias, insufficient population follow-up and lack of external validation.
  • Only 3 of the 15 models were validated by other authors. In those external validations the area under the curve (AUC) ranged from 0.53 to 0.89.
• The authors' conclusion: The authors concluded that the included studies had methodological weaknesses that compromised the clinical applicability of the predictive models they evaluated.
• Limitations the authors state:
  • The studies were too varied (heterogeneous) to combine in a meta-analysis.
  • Only English, Spanish and Portuguese articles were included; 6 articles in other languages that could have been relevant were discarded.
  • The final number of studies was limited to 15, and most involved high-risk populations, which limits extrapolation to routine clinical practice.
  • Only three databases were searched; Embase, for example, was not included.

Topics

The National Library of Medicine indexes this study under subjects our site covers: Lung cancer.

Medical terms in this study, explained in our Lexicon: Systematic Review, Prognosis, Emphysema, Fibrosis, Selection Bias.

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