Feature selection methodology for longitudinal cone-beam CT radiomics

<p><b>Background:</b> Cone-beam CT (CBCT) scans are typically acquired daily for positioning verification of non-small cell lung cancer (NSCLC) patients. Quantitative information, derived using radiomics, can potentially contribute to (early) treatment adaptation. The aims of this study were to (1) describe and investigate a methodology for feature selection of a longitudinal radiomics approach (2) investigate which time-point during treatment is potentially useful for early treatment response assessment.</p> <p><b>Material and methods:</b> For 90 NSCLC patients CBCT scans of the first two fractions of treatment (considered as ‘test-retest’ scans) were analyzed, as well as weekly CBCT images. One hundred and sixteen radiomic features were extracted from the GTV of all scans and subsequently absolute and relative differences were calculated between weekly CBCT images and the CBCT of the first fraction. Test-retest scans were used to determine the smallest detectable change (C = 1.96 * SD) allowing for feature selection by choosing a minimum number of patients for which a feature should change more than ‘C’ to be considered as relevant. Analysis of which features change at which moment during treatment was used to investigate which time-point is potentially relevant to extract longitudinal radiomics information for early treatment response assessment.</p> <p><b>Results:</b> A total of six absolute delta features changed for at least ten patients at week 2 of treatment and increased to 61 at week 3, 79 at week 4 and 85 at week 5. There was 93% overlap between features selected at week 3 and the other weeks.</p> <p><b>Conclusions:</b> This study describes a feature selection methodology for longitudinal radiomics that is able to select reproducible delta radiomics features that are informative due to their change during treatment, which can potentially be used for treatment decisions concerning adaptive radiotherapy. Nonetheless, the prognostic value of the selected delta radiomic features should be investigated in future studies.</p

Defining the hypoxic target volume based on positron emission tomography for image guided radiotherapy – the influence of the choice of the reference region and conversion function

<p><b>Background:</b> Hypoxia imaged by positron emission tomography (PET) is a potential target for optimization in radiotherapy. However, the implementation of this approach with respect to the conversion of intensities in the images into oxygenation and radiosensitivity maps is not straightforward. This study investigated the feasibility of applying two conversion approaches previously derived for ¹⁸F-labeled fluoromisonidazole (¹⁸F-FMISO)-PET images for the hypoxia tracer ¹⁸F-flortanidazole (¹⁸F-HX4).</p> <p><b>Material and methods:</b> Ten non-small-cell lung cancer patients imaged with ¹⁸F-HX4 before the start of radiotherapy were considered in this study. PET image uptake was normalized to a well-oxygenated reference region and subsequently linear and non-linear conversions were used to determine tissue oxygenations maps. These were subsequently used to delineate hypoxic volumes based partial oxygen pressure (pO₂) thresholds. The results were compared to hypoxic volumes segmented using a tissue-to-background ratio of 1.4 for ¹⁸F-HX4 uptake.</p> <p><b>Results:</b> While the linear conversion function was not found to result in realistic oxygenation maps, the non-linear function resulted in reasonably sized sub-volumes in good agreement with uptake-based segmented volumes for a limited range of pO₂ thresholds. However, the pO₂ values corresponding to this range were significantly higher than what is normally considered as hypoxia. The similarity in size, shape, and relative location between uptake-based sub-volumes and volumes based on the conversion to pO₂ suggests that the relationship between uptake and pO₂ is similar for ¹⁸F-FMISO and ¹⁸F-HX4, but that the model parameters need to be adjusted for the latter.</p> <p><b>Conclusions:</b> A non-linear conversion function between uptake and oxygen partial pressure for ¹⁸F-FMISO-PET could be applied to ¹⁸F-HX4 images to delineate hypoxic sub-volumes of similar size, shape, and relative location as based directly on the uptake. In order to apply the model for e.g., dose-painting, new parameters need to be derived for the accurate calculation of dose-modifying factors for this tracer.</p

Radiomics.PET.features

PET features used in model development: imaging features computed for tumor (Tumor) and merging structure including involved lymph nodes (Nodes). Survival information (in years) is available for all patients under analysis (status: 1 - deceased, 0 - alive)

PET.Nodes.Features

This file comprises the data used in the sub-analysis as described in the "Supporting Information - Feature Selection" file

Distribution of features included in the Cox regression model for FDG-PET-CT-based features extracted from pre-radiotherapy scans of NSCLC patients.

<p>Distribution of features included in the Cox regression model for FDG-PET-CT-based features extracted from pre-radiotherapy scans of NSCLC patients.</p

Pearson correlation plot for metabolic descriptors and volume of primary tumor and metastatic lymph nodes in the development dataset.

<p>Pearson correlation plot for metabolic descriptors and volume of primary tumor and metastatic lymph nodes in the development dataset.</p

Three-dimensional dose evaluation in breast cancer patients to define decision criteria for adaptive radiotherapy

<p><b>Background:</b> Dose-guided adaptive radiation therapy (DGART) is the systematic evaluation and adaptation of the dose delivery during treatment for an individual patient. The aim of this study is to define quantitative action levels for DGART by evaluating changes in 3D dose metrics in breast cancer and correlate them with clinical expert evaluation.</p> <p><b>Material and methods:</b> Twenty-three breast cancer treatment plans were evaluated, that were clinically adapted based on institutional IGRT guidelines. Reasons for adaptation were variation in seroma, hematoma, edema, positioning or problems using voluntary deep inspiration breath hold. Sixteen patients received a uniform dose to the breast (clinical target volume 1; CTV1). Six patients were treated with a simultaneous integrated boost to CTV2. The original plan was copied to the CT during treatment (re-CT) or to the stitched cone-beam CT (CBCT). Clinical expert evaluation of the re-calculated dose distribution and extraction of dose-volume histogram (DVH) parameters were performed. The extreme scenarios were evaluated, assuming all treatment fractions were given to the original planning CT (pCT), re-CT or CBCT. Reported results are mean ± SD.</p> <p><b>Results:</b> DVH results showed a mean dose (Dmean) difference between pCT and re-CT of -0.4 ± 1.4% (CTV1) and −1.4 ± 2.1% (CTV2). The difference in V95% was −2.6 ± 4.4% (CTV1) and −9.8 ± 8.3% (CTV2). Clinical evaluation and DVH evaluation resulted in a recommended adaptation in 17/23 or 16/23 plans, respectively. Applying thresholds on the DVH parameters: D_mean CTV, V95% CTV, D_max, mean lung dose, volume exceeding 107% (uniform dose) or 90% (SIB) of the prescribed dose enabled the identification of patients with an assumed clinically relevant dose difference, with a sensitivity of 0.89 and specificity of 1.0. Re-calculation on CBCT imaging identified the same plans for adaptation as re-CT imaging.</p> <p><b>Conclusions:</b> Clinical expert evaluation can be related to quantitative DVH parameters on re-CT or CBCT imaging to select patients for DGART.</p

Diagram of the workflow followed in the multivariable model development phase.

<p>After a test-retest and inter-observer study, 77 features remained for further analysis, based on a cut-off of 0.85 for the ICC analysis. Further identification of comparable features extracted from the structure merging all metastatic lymph nodes (LN_merged) to the largest (LN_volume) or most active node (LN_max), by means of an intraclass correlation (ICC) over 0.85 and ±10% limits of agreement (LoA) between measurements, was performed (further details in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192859#pone.0192859.s001" target="_blank">S1 File</a>. Feature pre selection). In summary, 77 features of the primary tumor and 16 from the metastatic lymph nodes were entered in the model development phase.</p

Distribution of common PET descriptors (maximum, peak and mean) and volume of the primary tumor and LNs.

<p>Distribution of common PET descriptors (maximum, peak and mean) and volume of the primary tumor and LNs.</p

Log-linear and proportional hazards assumptions verification.

<p>Graphically, log-linearity was verified by fitting a penalised smoothing spline on the univariable effect of each variable included in models (left graph), while proportional hazards were analysed by plotting Schoenfeld residuals versus log (time) (right graph). These included variables for LN, the (A) volume, (B) GLRLM grey level non-uniformity, (C) GLRLM short run high grey level emphasis, (D) GLCM entropy, (E) surface to volume ratio, and (F) uniformity, and (G) GLRLM short run emphasis of tumour. All variables were log (linear), except LN volume (A left), for which a logarithmic transformation was performed (A middle). All variables satisfied the proportional hazards assumption. Automatic feature selection for model 1 (based solely on primary tumor imaging features) converged to a single metric of the GLRLM group—short run emphasis, with a C-index of 0.53 (95% confidence interval [CI] = 0.49–0.58) and an external validation of 0.54. Model 2 (based on imaging features from LN) included total volume and the surface to volume ratio (shape), histogram uniformity (first order statistics), grey level non-uniformity and short run high grey level emphasis (GLRLM of the textural group), reaching a C-index of 0.62 (95% CI = 0.57–0.66) with an external validation of 0.56. Important to note that LN volume is an independent prognostic metric, with an univariable performance of 0.60 (95% CI = 0.51–0.68). Finally, model 3 selected the same feature as model 1 and four features from the LN, replacing short run high grey level emphasis–GLRLM, by entropy–GLCM, and reached a performance of 0.62 (95% CI = 0.58–0.67), and 0.59 in the external cohort. No metrics from the IVH sub-category were selected from any of the analyzed structures for the derived models. Based on an AIC test, model 3 (1854.5) was shown to be a better fit than model 2 (1857.4), which itself was already a more precise fit compared to model 1 (1876.4). In summary, the addition of nodal imaging information resulted in a better model fit, compared to a model based exclusively on features derived from the primary tumor.</p