Prediction of Local Failure after Stereotactic Radiotherapy in Melanoma Brain Metastases Using Ensemble Learning on Clinical, Dosimetric, and Radiomic Data
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Hartong, Nanna E. Sachpazidis, Ilias Blanck, Oliver Etzel, Lucas Peeken, Jan C. Combs, Stephanie E. Urbach, Horst Zaitsev, Maxim Baltas, Dimos Popp, Ilinca Grosu, Anca-Ligia Fechter, Tobias |
| author_facet | Hartong, Nanna E. Sachpazidis, Ilias Blanck, Oliver Etzel, Lucas Peeken, Jan C. Combs, Stephanie E. Urbach, Horst Zaitsev, Maxim Baltas, Dimos Popp, Ilinca Grosu, Anca-Ligia Fechter, Tobias |
| contents | Background: This study aimed to predict lesion-specific outcomes after stereotactic radiotherapy (SRT) in patients with brain metastases from malignant melanoma (MBM), using clinical, dosimetric, and pretherapeutic MRI data. Methods: In this multicenter retrospective study, 517 MBM from 130 patients treated with single-fraction or hypofractionated SRT at three centers were analyzed. From contrast-enhanced T1-weighted MRI, 1576 radiomic features (RF) were extracted per lesion - 788 from the gross tumor volume (GTV) and 788 from a 3 mm peritumoral margin. Clinical, dosimetric and RF data from one center were used for feature selection and model development via nested cross-validation employing an ensemble learning approach; external validation used data from the other two centers. Results: Local failure occurred in 72/517 lesions (13.9%). Predictive models based on clinical data, RF, or a combination of both achieved c-indices of 0.60 +/- 0.15, 0.65 +/- 0.11, and 0.65 +/- 0.12, respectively. RF-based models outperformed the clinical models; dosimetric data alone were not predictive. Most predictive RF originated from the peritumoral margin (92%) versus GTV (76%). On the first external dataset, all models performed similarly (c-index: 0.60-0.63), but generalization was poor on the second (c-index < 0.50), likely due to differences in patient characteristics and imaging protocols. Conclusions: Pretherapeutic MRI features, particularly from the peritumoral region, show promise for predicting lesion-specific outcomes in MBM after SRT. Their consistent contribution suggests biologically relevant information that may support individualized treatment planning. Combined with clinical data, these markers offer prognostic insight, though generalizability remains limited by data heterogeneity. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_20825 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prediction of Local Failure after Stereotactic Radiotherapy in Melanoma Brain Metastases Using Ensemble Learning on Clinical, Dosimetric, and Radiomic Data Hartong, Nanna E. Sachpazidis, Ilias Blanck, Oliver Etzel, Lucas Peeken, Jan C. Combs, Stephanie E. Urbach, Horst Zaitsev, Maxim Baltas, Dimos Popp, Ilinca Grosu, Anca-Ligia Fechter, Tobias Medical Physics Machine Learning Background: This study aimed to predict lesion-specific outcomes after stereotactic radiotherapy (SRT) in patients with brain metastases from malignant melanoma (MBM), using clinical, dosimetric, and pretherapeutic MRI data. Methods: In this multicenter retrospective study, 517 MBM from 130 patients treated with single-fraction or hypofractionated SRT at three centers were analyzed. From contrast-enhanced T1-weighted MRI, 1576 radiomic features (RF) were extracted per lesion - 788 from the gross tumor volume (GTV) and 788 from a 3 mm peritumoral margin. Clinical, dosimetric and RF data from one center were used for feature selection and model development via nested cross-validation employing an ensemble learning approach; external validation used data from the other two centers. Results: Local failure occurred in 72/517 lesions (13.9%). Predictive models based on clinical data, RF, or a combination of both achieved c-indices of 0.60 +/- 0.15, 0.65 +/- 0.11, and 0.65 +/- 0.12, respectively. RF-based models outperformed the clinical models; dosimetric data alone were not predictive. Most predictive RF originated from the peritumoral margin (92%) versus GTV (76%). On the first external dataset, all models performed similarly (c-index: 0.60-0.63), but generalization was poor on the second (c-index < 0.50), likely due to differences in patient characteristics and imaging protocols. Conclusions: Pretherapeutic MRI features, particularly from the peritumoral region, show promise for predicting lesion-specific outcomes in MBM after SRT. Their consistent contribution suggests biologically relevant information that may support individualized treatment planning. Combined with clinical data, these markers offer prognostic insight, though generalizability remains limited by data heterogeneity. |
| title | Prediction of Local Failure after Stereotactic Radiotherapy in Melanoma Brain Metastases Using Ensemble Learning on Clinical, Dosimetric, and Radiomic Data |
| topic | Medical Physics Machine Learning |
| url | https://arxiv.org/abs/2405.20825 |