Imaging texture analysis for automated prediction of lung cancer recurrence after stereotactic radiotherapy
| Autor: | David A. Palma, Alexander V. Louie, Suresh Senan, Shyama Tetar, Aaron D. Ward, Sarah A. Mattonen |
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| Přispěvatelé: | Radiation Oncology, CCA - Innovative therapy |
| Rok vydání: | 2015 |
| Předmět: |
medicine.medical_specialty
Receiver operating characteristic business.industry medicine.medical_treatment Image segmentation Stereotactic radiation therapy Lung injury SABR volatility model medicine.disease Radiation therapy Special Section on Radiomics and Imaging Genomics medicine Radiology Nuclear Medicine and imaging Segmentation Radiology Lung cancer business |
| Zdroj: | Journal of Medical Imaging, 2(4):041010. SPIE Mattonen, S A, Tetar, S U, Palma, D A, Louie, A V & Senan, S 2015, ' Imaging texture analysis for automated prediction of lung cancer recurrence after stereotactic radiotherapy ', Journal of Medical Imaging, vol. 2, no. 4, 041010 . https://doi.org/10.1117/1.JMI.2.4.041010 |
| ISSN: | 2329-4302 |
| Popis: | Benign radiation-induced lung injury (RILI) is not uncommon following stereotactic ablative radiotherapy (SABR) for lung cancer and can be difficult to differentiate from tumor recurrence on follow-up imaging. We previously showed the ability of computed tomography (CT) texture analysis to predict recurrence. The aim of this study was to evaluate and compare the accuracy of recurrence prediction using manual region-of-interest segmentation to that of a semiautomatic approach. We analyzed 22 patients treated for 24 lesions (11 recurrences, 13 RILI). Consolidative and ground-glass opacity (GGO) regions were manually delineated. The longest axial diameter of the consolidative region on each post-SABR CT image was measured. This line segment is routinely obtained as part of the clinical imaging workflow and was used as input to automatically delineate the consolidative region and subsequently derive a periconsolidative region to sample GGO tissue. Texture features were calculated, and at two to five months post-SABR, the entropy texture measure within the semiautomatic segmentations showed prediction accuracies [areas under the receiver operating characteristic curve (AUC): 0.70 to 0.73] similar to those of manual GGO segmentations (AUC: 0.64). After integration into the clinical workflow, this decision support system has the potential to support earlier salvage for patients with recurrence and fewer investigations of benign RILI. |
| Databáze: | OpenAIRE |
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