CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer

Huynh, Elizabeth; Coroller, Thibaud P.; Narayan, Vivek; Agrawal, Vishesh; Hou, Ying; Romano, John; Franco, Idalid; Mak, Raymond H.; Aerts, Hugo J.W.L.

doi:10.1016/j.radonc.2016.05.024

Cited by 173 publications

(140 citation statements)

References 42 publications

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“…Previously, our group investigated the potential application of radiomics for lung cancer patients treated with SBRT using pre-treatment FB images and found that FB images did contain some prognostic information for predicting DM[9]. Comparatively, our current study uses a similar cohort of patients (112 patients in our current study vs. 113 patients previously), however the radiomics feature extraction and analysis is different.…”

Section: Discussionmentioning

confidence: 97%

Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT

et al. 2017

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Radiomics aims to quantitatively capture the complex tumor phenotype contained in medical images to associate them with clinical outcomes. This study investigates the impact of different types of computed tomography (CT) images on the prognostic performance of radiomic features for disease recurrence in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT). 112 early stage NSCLC patients treated with SBRT that had static free breathing (FB) and average intensity projection (AIP) images were analyzed. Nineteen radiomic features were selected from each image type (FB or AIP) for analysis based on stability and variance. The selected FB and AIP radiomic feature sets had 6 common radiomic features between both image types and 13 unique features. The prognostic performances of the features for distant metastasis (DM) and locoregional recurrence (LRR) were evaluated using the concordance index (CI) and compared with two conventional features (tumor volume and maximum diameter). P-values were corrected for multiple testing using the false discovery rate procedure. None of the FB radiomic features were associated with DM, however, seven AIP radiomic features, that described tumor shape and heterogeneity, were (CI range: 0.638–0.676). Conventional features from FB images were not associated with DM, however, AIP conventional features were (CI range: 0.643–0.658). Radiomic and conventional multivariate models were compared between FB and AIP images using cross validation. The differences between the models were assessed using a permutation test. AIP radiomic multivariate models (median CI = 0.667) outperformed all other models (median CI range: 0.601–0.630) in predicting DM. None of the imaging features were prognostic of LRR. Therefore, image type impacts the performance of radiomic models in their association with disease recurrence. AIP images contained more information than FB images that were associated with disease recurrence in early stage NSCLC patients treated with SBRT, which suggests that AIP images may potentially be more optimal for the development of an imaging biomarker.

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Section: Discussionmentioning

confidence: 97%

Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT

et al. 2017

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“…81 Other works focused on prediction of lung recurrence. For example, from PET images, it was found that heterogeneity measures, such as entropy, can predict diseasespecific survival, 82,83 whereas CT images were used for the assessment of pathologic response, 84 overall survival and distant metastases, 85 finding that texture analysis can outperform conventional indices (as tumour volume and diameter).…”

Section: Application Of Texture Analysis In Radiotherapymentioning

confidence: 99%

Texture analysis of medical images for radiotherapy applications

Scalco¹,

Rizzo²

2017

The British Journal of Radiology

125

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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“…Machine learning has recently been introduced in radiation oncology as a prediction technology based on existing data . In pathognomy, several studies have addressed the prediction of clinical outcome of stereotactic body radiation therapy for lung cancer using radiomic features based on CT images . In medical physics, Carlson et al reported the prediction of multi‐leaf collimator positional errors using machine learning .…”

Section: Introductionmentioning

confidence: 99%

Prediction of dosimetric accuracy for VMAT plans using plan complexity parameters via machine learning

et al. 2019

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Purpose The dosimetric accuracies of volumetric modulated arc therapy (VMAT) plans were predicted using plan complexity parameters via machine learning. Methods The dataset consisted of 600 cases of clinical VMAT plans from a single institution. The predictor variables (n = 28) for each plan included complexity parameters, machine type, and photon beam energy. Dosimetric measurements were performed using a helical diode array (ArcCHECK), and the dosimetric accuracy of the passing rates for a 5% dose difference (DD5%) and gamma index of 3%/3 mm (γ3%/3 mm) were predicted using three machine learning models: regression tree analysis (RTA), multiple regression analysis (MRA), and neural networks (NNs). First, the prediction models were applied to 500 cases of the VMAT plans. Then, the dosimetric accuracy was predicted using each model for the remaining 100 cases (evaluation dataset). The error between the predicted and measured passing rates was evaluated. Results For the 600 cases, the mean ± standard deviation of the measured passing rates was 92.3% ± 9.1% and 96.8% ± 3.1% for DD5% and γ3%/3 mm, respectively. For the evaluation dataset, the mean ± standard deviation of the prediction errors for DD5% and γ3%/3 mm was 0.5% ± 3.0% and 0.6% ± 2.4% for RTA, 0.0% ± 2.9% and 0.5% ± 2.4% for MRA, and –0.2% ± 2.7% and –0.2% ± 2.1% for NN, respectively. Conclusions NNs performed slightly better than RTA and MRA in terms of prediction error. These findings may contribute to increasing the efficiency of patient‐specific quality‐assurance procedures.

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CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer

Cited by 173 publications

References 42 publications

Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT

Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT

Texture analysis of medical images for radiotherapy applications

Prediction of dosimetric accuracy for VMAT plans using plan complexity parameters via machine learning

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