Distinguishing radiation fibrosis from tumour recurrence after stereotactic ablative radiotherapy (SABR) for lung cancer: A quantitative analysis of CT density changes

Mattonen, Sarah A.; Palma, David A.; Haasbeek, Cornelis J.A.; Senan, Suresh; Ward, Aaron D.

doi:10.3109/0284186x.2012.731525

Cited by 47 publications

(33 citation statements)

References 23 publications

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“…Table 4 summarizes the literature regarding the use of texture analysis in NSCLC patients. 6–8, 18–22 It can be seen that significant heterogeneity exists in the literature in terms of study size, patient stage(s), imaging modality examined, and patient treatment. Prior work has established a relationship between tumor texture and patient outcomes post treatment; however, to the best of our knowledge this is the first study to examine this relationship in NSCLC patients of the same stage receiving chemoradiotherapy.…”

Section: Discussionmentioning

confidence: 99%

Prognostic Value and Reproducibility of Pretreatment CT Texture Features in Stage III Non-Small Cell Lung Cancer

Fried

Tucker

Zhou

et al. 2014

International Journal of Radiation Oncology*Biology*Physics

170

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Purpose To determine whether pretreatment CT texture features can improve patient risk stratification beyond conventional prognostic factors (CPFs) in stage III non-small cell lung cancer (NSCLC). Methods and Materials We retrospectively reviewed 91 patients with stage III NSCLC treated with definitive chemoradiation. All patients underwent a pretreatment diagnostic contrast enhanced CT (CE-CT) followed by a 4D-CT for treatment simulation. We used the average (average-CT) and expiratory (T50-CT) images from the 4D-CT along with the CE-CT for texture extraction. Histogram, gradient, co-occurrence, gray-tone difference, and filtration-based techniques were used for texture feature extraction. Penalized Cox regression implementing cross-validation was used for covariate selection and modeling. Models incorporating texture features from the 3 image types and CPFs were compared to models incorporating CPFs alone for overall survival (OS), local-regional control (LRC), and freedom from distant metastases (FFDM). Predictive Kaplan-Meier curves were generated using leave-one-out cross-validation. Patients were stratified based on their predicted outcome being above/below the median. Reproducibility of texture features was evaluated using test-retest scans from independent patients and quantified using concordance correlation coefficients (CCC). We compared models incorporating the reproducibility seen on test-retest scans to our original models and determined the classification reproducibility. Results Models incorporating both texture features and CPFs demonstrated a significant improvement in risk stratification compared to models using CPFs alone for OS (p=0.046), LRC (p=0.01), and FFDM (p=0.005). The average CCC was 0.89, 0.91, and 0.67 for texture features extracted from the average-CT, T50-CT, and CE-CT, respectively. Incorporating reproducibility within our models yielded 80.4 (SD=3.7), 78.3 (SD=4.0), and 78.8 (SD=3.9) percent classification reproducibility in terms of OS, LRC, and FFDM, respectively. Conclusions Pretreatment tumor texture may provide prognostic information beyond what is obtained from CPFs. Models incorporating feature reproducibility achieved classification rates of ~80%. External validation would be required to establish texture as a prognostic factor.

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Section: Discussionmentioning

confidence: 99%

Prognostic Value and Reproducibility of Pretreatment CT Texture Features in Stage III Non-Small Cell Lung Cancer

Fried

Tucker

Zhou

et al. 2014

International Journal of Radiation Oncology*Biology*Physics

170

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“…It is well known that distinguishing between recurrence and radiation-induced density changes in the lung after SABR is difficult. 34 However, highrisk features on serial CT scans are suggestive of recurrence such as enlarging opacity, sequential enlargement, bulging margin, linear margin disappearance, loss air bronchogram and craniocaudal growth. 26 In addition, 18 F-FDG-PET/CT, even though it needs further validations, could be of some utility to distinguish between recurrence and the radiation induced.…”

Section: Discussionmentioning

confidence: 99%

Cone-beam computed tomography in lung stereotactic ablative radiation therapy: predictive parameters of early response

Mazzola

Fiorentino

Ricchetti

et al. 2016

BJR

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“…Textural features showed a stronger correlation with local control rather than with locoregional failure, whereas IVH showed the opposite trend as reported by the authors. 41 Textural features were reported by Mattonen et al 42,43 to predict recurrence for patients with early stage NSCLC who underwent stereotactic ablative radiotherapy. The authors reported that SD [reported as the variation of Hounsfield units within the ground-glass opacity (GGO) regions, which are regions where the normal lung parenchyma density is increased with visible vessels] is able to significantly discriminate (p 5 0.0078) between patients with radiation-induced injury and patients with recurrence in a follow-up CT scan acquired at 9 months with an error of 26%.…”

Section: Texture Analysis In Ctmentioning

confidence: 99%

“…The authors reported that SD [reported as the variation of Hounsfield units within the ground-glass opacity (GGO) regions, which are regions where the normal lung parenchyma density is increased with visible vessels] is able to significantly discriminate (p 5 0.0078) between patients with radiation-induced injury and patients with recurrence in a follow-up CT scan acquired at 9 months with an error of 26%. 42 The authors explored the ability of second-order textural features to predict recurrence by generating GLCM from GGO for follow-up CT scans taken at 6 months for a group of 22 patients. The texture features calculated from the twodimensional (2D) averaged GLCM were energy, entropy, correlation, inverse difference moment, inertia (contrast), cluster shade and cluster prominence.…”

Section: Texture Analysis In Ctmentioning

confidence: 99%

The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning

Alobaidli

McQuaid²,

South³

et al. 2014

BJR

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Cite this article as: Alobaidli S, McQuaid S, South C, Prakash V, Evans P, Nisbet A. The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning. Br J Radiol 2014;87:20140369. REVIEW ARTICLEThe role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning ABSTRACTPredicting a tumour's response to radiotherapy prior to the start of treatment could enhance clinical care management by enabling the personalization of treatment plans based on predicted outcome. In recent years, there has been accumulating evidence relating tumour texture to patient survival and response to treatment. Tumour texture could be measured from medical images that provide a non-invasive method of capturing intratumoural heterogeneity and hence could potentially enable a prior assessment of a patient's predicted response to treatment. In this article, work presented in the literature regarding texture analysis in radiotherapy in relation to survival and outcome is discussed. Challenges facing integrating texture analysis in radiotherapy planning are highlighted and recommendations for future directions in research are suggested.

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Distinguishing radiation fibrosis from tumour recurrence after stereotactic ablative radiotherapy (SABR) for lung cancer: A quantitative analysis of CT density changes

Abstract: (2013) Distinguishing radiation fibrosis from tumour recurrence after stereotactic ablative radiotherapy (SABR) for lung cancer: A quantitative analysis of CT density changes, Acta Oncologica, 52:5, 910-918,

Cited by 47 publications

References 23 publications

Prognostic Value and Reproducibility of Pretreatment CT Texture Features in Stage III Non-Small Cell Lung Cancer

Prognostic Value and Reproducibility of Pretreatment CT Texture Features in Stage III Non-Small Cell Lung Cancer

Cone-beam computed tomography in lung stereotactic ablative radiation therapy: predictive parameters of early response

The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning

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