External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma

Leijenaar, Ralph T.H.; Carvalho, Sara; Hoebers, Frank; Aerts, Hugo J.W.L.; Elmpt, Wouter J. C. van; Huang, Shao Hui; Chan, Ben; Waldron, John; O’Sullivan, Brian; Lambin, Philippe

doi:10.3109/0284186x.2015.1061214

Cited by 194 publications

(150 citation statements)

References 34 publications

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“…This result is consistent with previously published studies investigating local control [12] and overall survival [13,15] as endpoints. To understand the underlying biological processes of these findings, it would be of great interest to investigate if radiomic features corresponding to heterogeneity correlate with histopathological stainings of cell proliferation, microvessel density or hypoxia.…”

Section: Discussionsupporting

confidence: 93%

“…On the level of anatomical imaging, pre-treatment CT radiomics was found to be prognostic for local tumor control [12] and overall survival [11,13,14,15] in head and neck cancer, significantly stratifying patients into low-and high-risk groups. Those studies showed that tumors, which are heterogeneous in terms of CT density are associated with a worse prognosis.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma

et al. 2017

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(2017). Material and methodsData from HNSCC patients (n=121) treated with definitive radiochemotherapy were used for model training. In total, 569 radiomic features were extracted from both contrast enhanced CT and 18F-FDG PET images in the primary tumor region.CT, PET and combined PET/CT radiomic models to assess local tumor control were trained separately. Five feature selection and three classification methods were implemented. The performance of the models was quantified using concordance index (CI) in 5-fold cross-validation in the training cohort. The best models, per image modality, were compared and verified in the independent validation cohort (n=51). The difference in CI was investigated using bootstrapping. Additionally, the observed and radiomics-based estimated probabilities of local tumor control were compared between two risk groups. ResultsThe feature selection using principal component analysis and the classification based on the multivariabale Cox regression with backward selection of the variables resulted in the best models for all image modalities (CICT=0.72, CIPET=0.74, CIPET/CT=0.77). Tumors more homogenous in CT density (decreased GLSZMsize_zone_entropy) and with a focused region of high FDG uptake (higher GLSZMSZLGE) indicated better prognosis. No significant difference in the performance of the models in the validation cohort was observed (CICT=0.73, CIPET=0.71, CIPET/CT=0.73). However, the CT radiomics based model overestimated the probability of tumor control in the poor prognostic group (predicted=68%, observed=56%). ConclusionBoth CT and PET radiomics showed equally good discriminative power for local tumor control modeling in HNSCC. However, CT-based predictions overestimated the local control rate in the poor prognostic validation cohort and thus we recommend to base the local control modeling on the 18F-FDG PET.

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma

et al. 2017

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“…In CT images, especially in the head and neck region, textural features can be affected by the presence of metal streaks due to dental fillings. 41 There is the possibility to reduce metal artefacts using some promising techniques, 42 but as texture analysis relies on extracting meaningful information from medical images, they could introduce artificial texture by modifying image information.…”

Section: Ct Imagesmentioning

confidence: 99%

“…In fact, these studies reported high values of accuracy, considering the radiomic signature estimated in a selected population and applied on an independent one from another institute, or from another body region, reporting high values of accuracy. 4,41,107 If this kind of validation is not feasible, some other evaluations should be performed, for example, using test and training sets, which should be taken from a separated cohort or at least considering leave-one-out cross-validation. 9 Even if texture analysis was carried out in the most rigorous way and if significant models that could predict tissue response to radiation or provide a characterization of tumour heterogeneity were found, the biological interpretation of these parameters remains one of the major questions about texture analysis.…”

Section: Limitations Of Texture Analysismentioning

confidence: 99%

Texture analysis of medical images for radiotherapy applications

Scalco¹,

Rizzo²

2017

The British Journal of Radiology

134

108

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The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.

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“…Radiomics involves the extraction of additional quantitative data from medical images using advanced imaging processing and analysis tools (98). These quantitative data extend beyond what is visible to the human eye and can be powerfully correlated with patient outcomes (99,100).…”

Section: Imaging For Treatment Response Assessment After Completion Omentioning

confidence: 99%

Anatomic, functional and molecular imaging in lung cancer precision radiation therapy: treatment response assessment and radiation therapy personalization

MacManus

Everitt

Schimek-Jasch

et al. 2017

Transl. Lung Cancer Res.

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This article reviews key imaging modalities for lung cancer patients treated with radiation therapy (RT) and considers their actual or potential contributions to critical decision-making. An international group of researchers with expertise in imaging in lung cancer patients treated with RT considered the relevant literature on modalities, including computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET). These perspectives were coordinated to summarize the current status of imaging in lung cancer and flag developments with future implications.Although there are no useful randomized trials of different imaging modalities in lung cancer, multiple prospective studies indicate that management decisions are frequently impacted by the use of complementary imaging modalities, leading both to more appropriate treatments and better outcomes. This is especially true of 18 F-fluoro-deoxyglucose (FDG)-PET/CT which is widely accepted to be the standard imaging modality for staging of lung cancer patients, for selection for potentially curative RT and for treatment planning. PET is also more accurate than CT for predicting survival after RT. PET imaging during RT is also correlated with survival and makes response-adapted therapies possible. PET tracers other than FDG have potential for imaging important biological process in tumors, including hypoxia and proliferation. MRI has superior accuracy in soft tissue imaging and the MRI Linac is a rapidly developing technology with great potential for online monitoring and modification of treatment. The role of imaging in RT-treated lung cancer patients is evolving rapidly and will allow increasing personalization of therapy according to the biology of both the tumor and dose limiting normal tissues.

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External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma

Cited by 194 publications

References 34 publications

Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma

Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma

Texture analysis of medical images for radiotherapy applications

Anatomic, functional and molecular imaging in lung cancer precision radiation therapy: treatment response assessment and radiation therapy personalization

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