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Early prediction of tumor recurrence based on CT texture changes after stereotactic ablative radiotherapy (SABR) for lung cancer
Author(s) -
Mattonen Sarah A.,
Palma David A.,
Haasbeek Cornelis J. A.,
Senan Suresh,
Ward Aaron D.
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4866219
Subject(s) - sabr volatility model , ablative case , radiation therapy , radiosurgery , medicine , lung cancer , radiology , stereotactic radiotherapy , medical imaging , nuclear medicine , medical physics , oncology , mathematics , volatility (finance) , stochastic volatility , econometrics
Purpose: Benign computed tomography (CT) changes due to radiation induced lung injury (RILI) are common following stereotactic ablative radiotherapy (SABR) and can be difficult to differentiate from tumor recurrence. The authors measured the ability of CT image texture analysis, compared to more traditional measures of response, to predict eventual cancer recurrence based on CT images acquired within 5 months of treatment.Methods: A total of 24 lesions from 22 patients treated with SABR were selected for this study: 13 with moderate to severe benign RILI, and 11 with recurrence. Three‐dimensional (3D) consolidative and ground‐glass opacity (GGO) changes were manually delineated on all follow‐up CT scans. Two size measures of the consolidation regions (longest axial diameter and 3D volume) and nine appearance features of the GGO were calculated: 2 first‐order features [mean density and standard deviation of density (first‐order texture)], and 7 second‐order texture features [energy, entropy, correlation, inverse difference moment (IDM), inertia, cluster shade, and cluster prominence]. For comparison, the corresponding response evaluation criteria in solid tumors measures were also taken for the consolidation regions. Prediction accuracy was determined using the area under the receiver operating characteristic curve (AUC) and two‐fold cross validation (CV).Results: For this analysis, 46 diagnostic CT scans scheduled for approximately 3 and 6 months post‐treatment were binned based on their recorded scan dates into 2–5 month and 5–8 month follow‐up time ranges. At 2–5 months post‐treatment, first‐order texture, energy, and entropy provided AUCs of 0.79–0.81 using a linear classifier. On two‐fold CV, first‐order texture yielded 73% accuracy versus 76%–77% with the second‐order features. The size measures of the consolidative region, longest axial diameter and 3D volume, gave two‐fold CV accuracies of 60% and 57%, and AUCs of 0.72 and 0.65, respectively.Conclusions: Texture measures of the GGO appearance following SABR demonstrated the ability to predict recurrence in individual patients within 5 months of SABR treatment. Appearance changes were also shown to be more accurately predictive of recurrence, as compared to size measures within the same time period. With further validation, these results could form the substrate for a clinically useful computer‐aided diagnosis tool which could provide earlier salvage of patients with recurrence.