Nondiagnostic, radial‐probe endobronchial ultrasound‐guided biopsy for peripheral lung lesions: The added value of radiomics from ultrasound imaging for predicting malignancy
Abstract Objectives This study investigated whether radiomic features extracted from radial‐probe endobronchial ultrasound (radial EBUS) images can assist in decision‐making for subsequent clinical management in cases with indeterminate pathologic results. Methods A total of 494 patients who underwe...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Thoracic Cancer |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/1759-7714.14730 |
| _version_ | 1797955237910151168 |
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| author | Jihwan Choi Sungmin Zo Jong Hoon Kim You Jin Oh Joong Hyun Ahn Myoungkyoung Kim Kyungjong Lee Ho Yun Lee |
| author_facet | Jihwan Choi Sungmin Zo Jong Hoon Kim You Jin Oh Joong Hyun Ahn Myoungkyoung Kim Kyungjong Lee Ho Yun Lee |
| author_sort | Jihwan Choi |
| collection | DOAJ |
| description | Abstract Objectives This study investigated whether radiomic features extracted from radial‐probe endobronchial ultrasound (radial EBUS) images can assist in decision‐making for subsequent clinical management in cases with indeterminate pathologic results. Methods A total of 494 patients who underwent radial EBUS biopsy for lung nodules between January 2017 and December 2018 were allocated to our training set. For the validation set, 229 patients with radial EBUS biopsy results from January 2019 to April 2020 were used. A multivariate logistic regression analysis was used for feature selection and prediction modeling. Results In the training set, 157 (67 benign and 90 malignant) of 212 patients pathologically diagnosed as indeterminate were analyzed. In the validation set, 213 patients were diagnosed as indeterminate, and 158 patients (63 benign and 95 malignant) were included in the analysis. The performance of the radiomics‐added model, which considered satellite nodules, linear arc, shape, patency of vessels and bronchi, echogenicity, spiculation, C‐reactive protein, and minimum histogram, was 0.929 for the training set and 0.877 for the validation set, whereas the performance of the model without radiomics was 0.910 and 0.891, respectively. Conclusion Although the next diagnostic step for indeterminate lung biopsy results remains controversial, integrating various factors, including radiomic features from radial EBUS, might facilitate decision‐making for subsequent clinical management. |
| first_indexed | 2024-04-10T23:31:06Z |
| format | Article |
| id | doaj.art-cdfa45861e4549ac929dad9f9bd01258 |
| institution | Directory Open Access Journal |
| issn | 1759-7706 1759-7714 |
| language | English |
| last_indexed | 2024-04-10T23:31:06Z |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Thoracic Cancer |
| spelling | doaj.art-cdfa45861e4549ac929dad9f9bd012582023-01-12T07:01:02ZengWileyThoracic Cancer1759-77061759-77142023-01-0114217718510.1111/1759-7714.14730Nondiagnostic, radial‐probe endobronchial ultrasound‐guided biopsy for peripheral lung lesions: The added value of radiomics from ultrasound imaging for predicting malignancyJihwan Choi0Sungmin Zo1Jong Hoon Kim2You Jin Oh3Joong Hyun Ahn4Myoungkyoung Kim5Kyungjong Lee6Ho Yun Lee7Department of Digital Health SAIHST, Sungkyunkwan University Seoul South KoreaDivision of Pulmonary and Critical Care Medicine, Department of Medicine Samsung Medical Center, Sungkyunkwan University School of Medicine Seoul South KoreaIndustrial Biomaterial Research Center, Korea Research Institute of Bioscience and Biotechnology Daejeon South KoreaDepartment of Health Sciences and Technology SAIHST, Sungkyunkwan University Seoul South KoreaBiomedical Statistics Center, Data Science Research Institute, Research Institute for Future Medicine, Samsung Medical CenterDepartment of Radiology and Center for Imaging Science Samsung Medical Center, Sungkyunkwan University School of Medicine Seoul South KoreaDivision of Pulmonary and Critical Care Medicine, Department of Medicine Samsung Medical Center, Sungkyunkwan University School of Medicine Seoul South KoreaDepartment of Health Sciences and Technology SAIHST, Sungkyunkwan University Seoul South KoreaAbstract Objectives This study investigated whether radiomic features extracted from radial‐probe endobronchial ultrasound (radial EBUS) images can assist in decision‐making for subsequent clinical management in cases with indeterminate pathologic results. Methods A total of 494 patients who underwent radial EBUS biopsy for lung nodules between January 2017 and December 2018 were allocated to our training set. For the validation set, 229 patients with radial EBUS biopsy results from January 2019 to April 2020 were used. A multivariate logistic regression analysis was used for feature selection and prediction modeling. Results In the training set, 157 (67 benign and 90 malignant) of 212 patients pathologically diagnosed as indeterminate were analyzed. In the validation set, 213 patients were diagnosed as indeterminate, and 158 patients (63 benign and 95 malignant) were included in the analysis. The performance of the radiomics‐added model, which considered satellite nodules, linear arc, shape, patency of vessels and bronchi, echogenicity, spiculation, C‐reactive protein, and minimum histogram, was 0.929 for the training set and 0.877 for the validation set, whereas the performance of the model without radiomics was 0.910 and 0.891, respectively. Conclusion Although the next diagnostic step for indeterminate lung biopsy results remains controversial, integrating various factors, including radiomic features from radial EBUS, might facilitate decision‐making for subsequent clinical management.https://doi.org/10.1111/1759-7714.14730bronchoscopydiagnostic imaginglung neoplasmpathologyultrasound |
| spellingShingle | Jihwan Choi Sungmin Zo Jong Hoon Kim You Jin Oh Joong Hyun Ahn Myoungkyoung Kim Kyungjong Lee Ho Yun Lee Nondiagnostic, radial‐probe endobronchial ultrasound‐guided biopsy for peripheral lung lesions: The added value of radiomics from ultrasound imaging for predicting malignancy Thoracic Cancer bronchoscopy diagnostic imaging lung neoplasm pathology ultrasound |
| title | Nondiagnostic, radial‐probe endobronchial ultrasound‐guided biopsy for peripheral lung lesions: The added value of radiomics from ultrasound imaging for predicting malignancy |
| title_full | Nondiagnostic, radial‐probe endobronchial ultrasound‐guided biopsy for peripheral lung lesions: The added value of radiomics from ultrasound imaging for predicting malignancy |
| title_fullStr | Nondiagnostic, radial‐probe endobronchial ultrasound‐guided biopsy for peripheral lung lesions: The added value of radiomics from ultrasound imaging for predicting malignancy |
| title_full_unstemmed | Nondiagnostic, radial‐probe endobronchial ultrasound‐guided biopsy for peripheral lung lesions: The added value of radiomics from ultrasound imaging for predicting malignancy |
| title_short | Nondiagnostic, radial‐probe endobronchial ultrasound‐guided biopsy for peripheral lung lesions: The added value of radiomics from ultrasound imaging for predicting malignancy |
| title_sort | nondiagnostic radial probe endobronchial ultrasound guided biopsy for peripheral lung lesions the added value of radiomics from ultrasound imaging for predicting malignancy |
| topic | bronchoscopy diagnostic imaging lung neoplasm pathology ultrasound |
| url | https://doi.org/10.1111/1759-7714.14730 |
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