Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules
Abstract Purpose To develop a comprehensive multi-classification model that combines radiomics and clinic-radiological features to accurately predict the invasiveness and differentiation of pulmonary adenocarcinoma nodules. Methods A retrospective analysis was conducted on a cohort comprising 500 pa...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | BioMedical Engineering OnLine |
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| Online Access: | https://doi.org/10.1186/s12938-023-01180-1 |
| _version_ | 1797414476675284992 |
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| author | Haitao Sun Chunling Zhang Aimei Ouyang Zhengjun Dai Peiji Song Jian Yao |
| author_facet | Haitao Sun Chunling Zhang Aimei Ouyang Zhengjun Dai Peiji Song Jian Yao |
| author_sort | Haitao Sun |
| collection | DOAJ |
| description | Abstract Purpose To develop a comprehensive multi-classification model that combines radiomics and clinic-radiological features to accurately predict the invasiveness and differentiation of pulmonary adenocarcinoma nodules. Methods A retrospective analysis was conducted on a cohort comprising 500 patients diagnosed with lung adenocarcinoma between January 2020 and December 2022. The dataset included preoperative CT images and histological reports of adenocarcinoma in situ (AIS, n = 97), minimally invasive adenocarcinoma (MIA, n = 139), and invasive adenocarcinoma (IAC, n = 264) with well-differentiated (WIAC, n = 99), moderately differentiated (MIAC, n = 84), and poorly differentiated IAC (PIAC, n = 81). The patients were classified into two groups (IAC and non-IAC) for binary classification and further divided into three and five groups for multi-classification. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm to identify the most informative radiomics and clinic-radiological features. Eight machine learning (ML) models were developed using these features, and their performance was evaluated using accuracy (ACC) and the area under the receiver-operating characteristic curve (AUC). Results The combined model, utilizing the support vector machine (SVM) algorithm, demonstrated improved performance in the testing cohort, achieving an AUC of 0.942 and an ACC of 0.894 for the two-classification task. For the three- and five-classification tasks, the combined model employing the one versus one strategy of SVM (SVM-OVO) outperformed other models, with ACC values of 0.767 and 0.607, respectively. The AUC values for histological subtypes ranged from 0.787 to 0.929 in the testing cohort, while the Macro-AUC and Micro-AUC of the multi-classification models ranged from 0.858 to 0.896. Conclusions A multi-classification radiomics model combined with clinic-radiological features, using the SVM-OVO algorithm, holds promise for accurately predicting the histological characteristics of pulmonary adenocarcinoma nodules, which contributes to personalized treatment strategies for patients with lung adenocarcinoma. |
| first_indexed | 2024-03-09T05:33:40Z |
| format | Article |
| id | doaj.art-c2212838bf594ebabe29ece5b756ccd8 |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-03-09T05:33:40Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-c2212838bf594ebabe29ece5b756ccd82023-12-03T12:30:53ZengBMCBioMedical Engineering OnLine1475-925X2023-11-0122112110.1186/s12938-023-01180-1Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodulesHaitao Sun0Chunling Zhang1Aimei Ouyang2Zhengjun Dai3Peiji Song4Jian Yao5Medical Imaging Center, Central Hospital Affiliated to Shandong First Medical UniversityMedical Imaging Center, Central Hospital Affiliated to Shandong First Medical UniversityMedical Imaging Center, Central Hospital Affiliated to Shandong First Medical UniversityScientific Research Department of Huiying Medical Technology Co., LtdMedical Imaging Center, Central Hospital Affiliated to Shandong First Medical UniversityMedical Imaging Center, Shandong Provincial Hospital Affiliated to Shandong First Medical UniversityAbstract Purpose To develop a comprehensive multi-classification model that combines radiomics and clinic-radiological features to accurately predict the invasiveness and differentiation of pulmonary adenocarcinoma nodules. Methods A retrospective analysis was conducted on a cohort comprising 500 patients diagnosed with lung adenocarcinoma between January 2020 and December 2022. The dataset included preoperative CT images and histological reports of adenocarcinoma in situ (AIS, n = 97), minimally invasive adenocarcinoma (MIA, n = 139), and invasive adenocarcinoma (IAC, n = 264) with well-differentiated (WIAC, n = 99), moderately differentiated (MIAC, n = 84), and poorly differentiated IAC (PIAC, n = 81). The patients were classified into two groups (IAC and non-IAC) for binary classification and further divided into three and five groups for multi-classification. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm to identify the most informative radiomics and clinic-radiological features. Eight machine learning (ML) models were developed using these features, and their performance was evaluated using accuracy (ACC) and the area under the receiver-operating characteristic curve (AUC). Results The combined model, utilizing the support vector machine (SVM) algorithm, demonstrated improved performance in the testing cohort, achieving an AUC of 0.942 and an ACC of 0.894 for the two-classification task. For the three- and five-classification tasks, the combined model employing the one versus one strategy of SVM (SVM-OVO) outperformed other models, with ACC values of 0.767 and 0.607, respectively. The AUC values for histological subtypes ranged from 0.787 to 0.929 in the testing cohort, while the Macro-AUC and Micro-AUC of the multi-classification models ranged from 0.858 to 0.896. Conclusions A multi-classification radiomics model combined with clinic-radiological features, using the SVM-OVO algorithm, holds promise for accurately predicting the histological characteristics of pulmonary adenocarcinoma nodules, which contributes to personalized treatment strategies for patients with lung adenocarcinoma.https://doi.org/10.1186/s12938-023-01180-1Computed tomographyRadiomicsMachine learningPulmonary noduleMulti-classification |
| spellingShingle | Haitao Sun Chunling Zhang Aimei Ouyang Zhengjun Dai Peiji Song Jian Yao Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules BioMedical Engineering OnLine Computed tomography Radiomics Machine learning Pulmonary nodule Multi-classification |
| title | Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules |
| title_full | Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules |
| title_fullStr | Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules |
| title_full_unstemmed | Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules |
| title_short | Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules |
| title_sort | multi classification model incorporating radiomics and clinic radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules |
| topic | Computed tomography Radiomics Machine learning Pulmonary nodule Multi-classification |
| url | https://doi.org/10.1186/s12938-023-01180-1 |
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