Development and validation of a nomogram for predicting histologic subtypes of subpleural non-small cell lung cancer using ultrasound parameters and clinical data
AimsTo develop and validate an individualized nomogram for differentiating the histologic subtypes (adenocarcinoma and squamous cell carcinoma) of subpleural non-small cell lung cancer (NSCLC) based on ultrasound parameters and clinical data.MethodsThis study was conducted retrospectively between Ma...
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1477450/full |
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| author | Feng Mao Feng Mao Mengjun Shen Yi Zhang Hongwei Chen Yang Cong Huiming Zhu Chunhong Tang Shengmin Zhang Yin Wang |
| author_facet | Feng Mao Feng Mao Mengjun Shen Yi Zhang Hongwei Chen Yang Cong Huiming Zhu Chunhong Tang Shengmin Zhang Yin Wang |
| author_sort | Feng Mao |
| collection | DOAJ |
| description | AimsTo develop and validate an individualized nomogram for differentiating the histologic subtypes (adenocarcinoma and squamous cell carcinoma) of subpleural non-small cell lung cancer (NSCLC) based on ultrasound parameters and clinical data.MethodsThis study was conducted retrospectively between March 2018 and December 2019. Patients were randomly assigned to a development cohort (DC, n=179) and a validation cohort (VC, n=77). A total of 7 clinical parameters and 16 ultrasound parameters were collected. Least absolute shrinkage and selection operator regression analysis was employed to identify the most significant predictors utilizing a 10-fold cross-validation. The multivariate logistic regression model was applied to investigate the relevant factors. An individualized nomogram was then developed. Receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA) were applied for model validation in both DC and VC.ResultsFollowing the final regression analysis, gender, serum carcinoembryonic antigen, lesion size and perfusion defect in contrast-enhanced ultrasound were entered into the nomogram. The model showed moderate predictive ability, with an area under the ROC curve of 0.867 for DC and 0.838 for VC. The calibration curves of the model showed good agreement between actual and predicted probabilities. The ROC and DCA curves demonstrated that the nomogram exhibited a good predictive performance.ConclusionWe developed a nomogram that can predict the histologic subtypes of subpleural NSCLC. Both internal and external validation revealed optimal discrimination and calibration, indicating that the nomogram may have clinical utility. This model has the potential to assist clinicians in making treatment recommendations. |
| format | Article |
| id | doaj-art-b9424abb842d4ca1ad8dfd9bf22ae63a |
| institution | OA Journals |
| issn | 2234-943X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-b9424abb842d4ca1ad8dfd9bf22ae63a2025-08-20T02:13:07ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2024-11-011410.3389/fonc.2024.14774501477450Development and validation of a nomogram for predicting histologic subtypes of subpleural non-small cell lung cancer using ultrasound parameters and clinical dataFeng Mao0Feng Mao1Mengjun Shen2Yi Zhang3Hongwei Chen4Yang Cong5Huiming Zhu6Chunhong Tang7Shengmin Zhang8Yin Wang9Department of Medical Ultrasound, The First Affiliated Hospital of Ningbo University, Ningbo, ChinaDepartment of Ultrasound, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Ultrasound, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Ultrasound, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Ultrasound, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Ultrasound, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Ultrasound, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Ultrasound, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Medical Ultrasound, The First Affiliated Hospital of Ningbo University, Ningbo, ChinaDepartment of Ultrasound, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, ChinaAimsTo develop and validate an individualized nomogram for differentiating the histologic subtypes (adenocarcinoma and squamous cell carcinoma) of subpleural non-small cell lung cancer (NSCLC) based on ultrasound parameters and clinical data.MethodsThis study was conducted retrospectively between March 2018 and December 2019. Patients were randomly assigned to a development cohort (DC, n=179) and a validation cohort (VC, n=77). A total of 7 clinical parameters and 16 ultrasound parameters were collected. Least absolute shrinkage and selection operator regression analysis was employed to identify the most significant predictors utilizing a 10-fold cross-validation. The multivariate logistic regression model was applied to investigate the relevant factors. An individualized nomogram was then developed. Receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA) were applied for model validation in both DC and VC.ResultsFollowing the final regression analysis, gender, serum carcinoembryonic antigen, lesion size and perfusion defect in contrast-enhanced ultrasound were entered into the nomogram. The model showed moderate predictive ability, with an area under the ROC curve of 0.867 for DC and 0.838 for VC. The calibration curves of the model showed good agreement between actual and predicted probabilities. The ROC and DCA curves demonstrated that the nomogram exhibited a good predictive performance.ConclusionWe developed a nomogram that can predict the histologic subtypes of subpleural NSCLC. Both internal and external validation revealed optimal discrimination and calibration, indicating that the nomogram may have clinical utility. This model has the potential to assist clinicians in making treatment recommendations.https://www.frontiersin.org/articles/10.3389/fonc.2024.1477450/fullnon-small cell lung cancersubpleural pulmonary lesionnomogramultrasoundcontrast-enhanced ultrasound |
| spellingShingle | Feng Mao Feng Mao Mengjun Shen Yi Zhang Hongwei Chen Yang Cong Huiming Zhu Chunhong Tang Shengmin Zhang Yin Wang Development and validation of a nomogram for predicting histologic subtypes of subpleural non-small cell lung cancer using ultrasound parameters and clinical data Frontiers in Oncology non-small cell lung cancer subpleural pulmonary lesion nomogram ultrasound contrast-enhanced ultrasound |
| title | Development and validation of a nomogram for predicting histologic subtypes of subpleural non-small cell lung cancer using ultrasound parameters and clinical data |
| title_full | Development and validation of a nomogram for predicting histologic subtypes of subpleural non-small cell lung cancer using ultrasound parameters and clinical data |
| title_fullStr | Development and validation of a nomogram for predicting histologic subtypes of subpleural non-small cell lung cancer using ultrasound parameters and clinical data |
| title_full_unstemmed | Development and validation of a nomogram for predicting histologic subtypes of subpleural non-small cell lung cancer using ultrasound parameters and clinical data |
| title_short | Development and validation of a nomogram for predicting histologic subtypes of subpleural non-small cell lung cancer using ultrasound parameters and clinical data |
| title_sort | development and validation of a nomogram for predicting histologic subtypes of subpleural non small cell lung cancer using ultrasound parameters and clinical data |
| topic | non-small cell lung cancer subpleural pulmonary lesion nomogram ultrasound contrast-enhanced ultrasound |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1477450/full |
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