Article ; Online: CT-based radiomics prediction of complete response after stereotactic body radiation therapy for patients with lung metastases.
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al
2023 Volume 199, Issue 7, Page(s) 676–685
Abstract: Purpose: Stereotactic body radiotherapy (SBRT) is a key treatment modality for lung cancer patients. This study aims to develop a machine learning-based prediction model of complete response for lung oligometastatic cancer patients undergoing SBRT.: ... ...
| Abstract | Purpose: Stereotactic body radiotherapy (SBRT) is a key treatment modality for lung cancer patients. This study aims to develop a machine learning-based prediction model of complete response for lung oligometastatic cancer patients undergoing SBRT. Materials and methods: CT images of 80 pulmonary oligometastases from 56 patients treated with SBRT were analyzed. The gross tumor volumes (GTV) were contoured on CT images. Patients that achieved complete response (CR) at 4 months were defined as responders. For each GTV, 107 radiomic features were extracted using the Pyradiomics software. The concordance correlation coefficients (CCC) between the region of interest (ROI)-based radiomics features obtained by the two segmentations were calculated. Pairwise feature interdependencies were evaluated using the Spearman rank correlation coefficient. The association of clinical variables and radiomics features with CR was evaluated with univariate logistic regression. Two supervised machine learning models, the logistic regression (LR) and the classification and regression tree analysis (CART), were trained to predict CR. The models were cross-validated using a five-fold cross-validation. The performance of models was assessed by receiver operating characteristic curve (ROC) and class-specific accuracy, precision, recall, and F1-measure evaluation metrics. Results: Complete response was associated with four radiomics features, namely the surface to volume ratio (SVR; p = 0.003), the skewness (Skew; p = 0.027), the correlation (Corr; p = 0.024), and the grey normalized level uniformity (GNLU; p = 0.015). No significant relationship between clinical parameters and CR was found. In the validation set, the developed LR and CART machine learning models had an accuracy, precision, and recall of 0.644 and 0.750, 0.644 and 0.651, and 0.635 and 0.754, respectively. The area under the curve for CR prediction was 0.707 and 0.753 for the LR and CART models, respectively. Conclusion: This analysis demonstrates that radiomics features obtained from pretreatment CT could predict complete response of lung oligometastases following SBRT. |
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| MeSH term(s) | Humans ; Radiosurgery/methods ; Lung Neoplasms/diagnostic imaging ; Lung Neoplasms/radiotherapy ; Lung Neoplasms/pathology ; Lung/pathology ; ROC Curve ; Tomography, X-Ray Computed/methods |
| Language | English |
| Publishing date | 2023-05-31 |
| Publishing country | Germany |
| Document type | Journal Article |
| ZDB-ID | 84983-2 |
| ISSN | 1439-099X ; 0179-7158 ; 0039-2073 |
| ISSN (online) | 1439-099X |
| ISSN | 0179-7158 ; 0039-2073 |
| DOI | 10.1007/s00066-023-02086-6 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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