This preprint is under consideration at Systematic Reviews. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Research
Marina Senent Valero
Miguel Hernandez University of Elche Faculty of Medicine: Universidad Miguel Hernandez de Elche Facultad de Medicina
Corresponding Author
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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. Facing clinical experience as a guide for decision-making in the management of SPN, the application of predictive models of malignancy in routine clinical practice would help to achieve better diagnostic management of SPN. For this, it is necessary to know and evaluate the current state of knowledge in relation to predictive models of malignancy of SPN in the general or low-risk population. The present systematic review was carried out with the purpose of critically assess studies aimed at developing predictive models of Solitary Pulmonary Nodule (SPN) malignancy from SPN incidentally detected in routine clinical practice.
Methods: we performed a systematic review through the following bibliographic databases: MEDLINE, SCOPUS and Cochrane Library. 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. We used The Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality of the selected articles. The Fleischner guidelines for the management of incidental SPN were used as reference to describe the variables included in the models.
Results: 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 malignancy included in the models were, in order of frequency: age, diameter, spiculated edge of the SPN, calcification, and smoking history. Variables such as race, growth rate of the SPN, emphysema, fibrosis, apical scarring and exposure to asbestos, uranium and radon among others 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 follow-up of the population, 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 International prospective register of systematic reviews: CRD42020161559.
Keywords
Solitary Pulmonary Nodule, prediction models, lung neoplasms, clinical setting, systematic review
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This is a list of supplementary files associated with this preprint. Click to download.
- Appendix.docx
Supplementary Information Additional file 1: Appendix A. Types of prediction models included in the revision according to TRIPOD Statement and their validations. Additional file 2: Appendix B. External validation of the included models by different authors from those who created the models. Additional file 3: Appendix C. Mathematical equations of the included models. Additional file 4: Appendix D. Items included in each domain of PROBAST quality.
- SupplementaryMaterial.docx
Supplementary Information Additional file 1: Appendix A. Types of prediction models included in the revision according to TRIPOD Statement and their validations. Additional file 2: Appendix B. External validation of the included models by different authors from those who created the models. Additional file 3: Appendix C. Mathematical equations of the included models. Additional file 4: Appendix D. Items included in each domain of PROBAST quality.
- Table2.docx
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CURRENT STATUS: Under Review
Version 1
Posted 01 Mar, 2021
No community comments so far
Editorial decision: Minor revision
On 15 Sep, 2021
Review #2 received
Received 14 Sep, 2021
Review #1 received
Received 02 Jun, 2021
Reviewer #2 agreed
On 15 May, 2021
Reviews received
Received 12 May, 2021
Reviewers invited
Invitations sent on 12 May, 2021
Reviewer #1 agreed
On 11 May, 2021
Editor assigned
On 11 May, 2021
Editor invited
On 10 May, 2021
Submission checks complete
On 23 Feb, 2021
First submitted
On 21 Feb, 2021
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Subject Areas
Systems Biology
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This preprint is under consideration at Systematic Reviews. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Marina Senent Valero
Miguel Hernandez University of Elche Faculty of Medicine: Universidad Miguel Hernandez de Elche Facultad de Medicina
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 01 Mar, 2021
No community comments so far
Editorial decision: Minor revision
On 15 Sep, 2021
Review #2 received
Received 14 Sep, 2021
Review #1 received
Received 02 Jun, 2021
Reviewer #2 agreed
On 15 May, 2021
Reviews received
Received 12 May, 2021
Reviewers invited
Invitations sent on 12 May, 2021
Reviewer #1 agreed
On 11 May, 2021
Editor assigned
On 11 May, 2021
Editor invited
On 10 May, 2021
Submission checks complete
On 23 Feb, 2021
First submitted
On 21 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Systems Biology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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. Facing clinical experience as a guide for decision-making in the management of SPN, the application of predictive models of malignancy in routine clinical practice would help to achieve better diagnostic management of SPN. For this, it is necessary to know and evaluate the current state of knowledge in relation to predictive models of malignancy of SPN in the general or low-risk population. The present systematic review was carried out with the purpose of critically assess studies aimed at developing predictive models of Solitary Pulmonary Nodule (SPN) malignancy from SPN incidentally detected in routine clinical practice.
Methods: we performed a systematic review through the following bibliographic databases: MEDLINE, SCOPUS and Cochrane Library. 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. We used The Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality of the selected articles. The Fleischner guidelines for the management of incidental SPN were used as reference to describe the variables included in the models.
Results: 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 malignancy included in the models were, in order of frequency: age, diameter, spiculated edge of the SPN, calcification, and smoking history. Variables such as race, growth rate of the SPN, emphysema, fibrosis, apical scarring and exposure to asbestos, uranium and radon among others 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 follow-up of the population, 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 International prospective register of systematic reviews: CRD42020161559.
Figure 1
Figure 2
Figure 3
This is a list of supplementary files associated with this preprint. Click to download.
- Appendix.docx
Supplementary Information Additional file 1: Appendix A. Types of prediction models included in the revision according to TRIPOD Statement and their validations. Additional file 2: Appendix B. External validation of the included models by different authors from those who created the models. Additional file 3: Appendix C. Mathematical equations of the included models. Additional file 4: Appendix D. Items included in each domain of PROBAST quality.
- SupplementaryMaterial.docx
Supplementary Information Additional file 1: Appendix A. Types of prediction models included in the revision according to TRIPOD Statement and their validations. Additional file 2: Appendix B. External validation of the included models by different authors from those who created the models. Additional file 3: Appendix C. Mathematical equations of the included models. Additional file 4: Appendix D. Items included in each domain of PROBAST quality.
- Table2.docx
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