Contouring variation affects estimates of normal tissue complication probability for breast fibrosis after radiotherapy.

BACKGROUND: Normal tissue complication probability (NTCP) models can be useful to estimate the risk of fibrosis after breast-conserving surgery (BCS) and radiotherapy (RT) to the breast. However, they are subject to uncertainties. We present the impact of contouring variation on the prediction of fibrosis. MATERIALS AND METHODS: 280 breast cancer patients treated BCS-RT were included. Nine Clinical Target Volume (CTV) contours were created for each patient: i) CTV_crop (reference), cropped 5 mm from the skin and ii) CTV_skin, uncropped and including the skin, iii) segmenting the 95% isodose (Iso95%) and iv) 3 different auto-contouring atlases generating uncropped and cropped contours (Atlas_skin/Atlas_crop). To illustrate the impact of contour variation on NTCP estimates, we applied two equations predicting fibrosis grade ≥ 2 at 5 years, based on Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) models, respectively, to each contour. Differences were evaluated using repeated-measures ANOVA. For completeness, the association between observed fibrosis events and NTCP estimates was also evaluated using logistic regression. RESULTS: There were minimal differences between contours when the same contouring approach was followed (cropped and uncropped). CTV_skin and Atlas_skin contours had lower NTCP estimates (-3.92%, IQR 4.00, p < 0.05) compared to CTV_crop. No significant difference was observed for Atlas_crop and Iso95% contours compared to CTV_crop. For the whole cohort, NTCP estimates varied between 5.3% and 49.5% (LKB) or 2.2% and 49.6% (RS) depending on the choice of contours. NTCP estimates for individual patients varied by up to a factor of 4. Estimates from "skin" contours showed higher agreement with observed events. CONCLUSION: Contour variations can lead to significantly different NTCP estimates for breast fibrosis, highlighting the importance of standardising breast contours before developing and/or applying NTCP models.

Clinical and radiomics prediction of complete response in rectal cancer pre-chemoradiotherapy.

Background and purpose: Patients with rectal cancer could avoid major surgery if they achieve clinical complete response (cCR) post neoadjuvant treatment. Therefore, prediction of treatment outcomes before treatment has become necessary to select the best neo-adjuvant treatment option. This study investigates clinical and radiomics variables' ability to predict cCR in patients pre chemoradiotherapy. Materials and methods: Using the OnCoRe database, we recruited a matched cohort of 304 patients (152 with cCR; 152 without cCR) deriving training (N = 200) and validation (N = 104) sets. We collected pre-treatment MR (magnetic resonance) images, demographics and blood parameters (haemoglobin, neutrophil, lymphocyte, alkaline phosphate and albumin). We segmented the gross tumour volume on T2 Weighted MR Images and extracted 1430 stable radiomics features per patient. We used principal component analysis (PCA) and receiver operating characteristic area under the curve (ROC AUC) to reduce dimensionality and evaluate the models produced. Results: Using Logistic regression analysis, PCA-derived combined model (radiomics plus clinical variables) gave a ROC AUC of 0.76 (95% CI: 0.69-0.83) in the training set and 0.68 (95% CI 0.57-0.79) in the validation set. The clinical only model achieved an AUC of 0.73 (95% CI 0.66-0.80) and 0.62 (95% CI 0.51-0.74) in the training and validation set, respectively. The radiomics model had an AUC of 0.68 (95% CI 0.61-0.75) and 0.66 (95% CI 0.56-0.77) in the training and validation sets. Conclusion: The predictive characteristics of both clinical and radiomics variables for clinical complete response remain modest but radiomics predictability is improved with addition of clinical variables.

The geometric and dosimetric effect of algorithm choice on propagated contours from CT to cone beam CTs.

PURPOSE: Adaptive radiotherapy relies of rapid re-contouring, online more so than offline. Intra-patient contour propagation via non-rigid registration offers a solution but can be of limited accuracy. However, the dosimetric significance of the inaccuracies is unknown. Here we evaluate the dosimetric reliability of contours generated by different commercially-available software packages. METHOD: Planning CT contours for ten head and neck cancer patients were propagated via five commercial packages to five CBCT scans acquired throughout treatment. The treatment plan was recalculated on each of the CBCTs for each set of propagated contours, and DVH parameters extracted for the spinal cord, brainstem, parotids and larynx. The propagated contours were compared to two gold standard contours: contours manually outlined and a consensus STAPLE contours generated from the propagated contours. Geometrical similarity was evaluated using mean distance to agreement (mDTA), Hausdorff distance, centroid agreement and Dice similarity coefficient. Dosimetric reliability was assessed against clinical constraints and comparing via the intraclass correlation coefficient (ICC). RESULTS: All propagated contours were similar to the STAPLE (mDTA < 1.0 mm) whilst larger differences were seen for the manual contours (mDTA < 3.0 mm). The dosimetric comparison showed that the propagated contours gave excellent dose estimates for most organs. The spinal cord reliability was moderate (ICC > 0.66). CONCLUSIONS: Large differences in geometric metrics rarely had a statistically significant impact on DVH parameters for the OARs studied. For that reason, propagated contours on treatment CBCT images are suitable for estimating dose to the OARs.

Identification of modes of tumor regression in non-small cell lung cancer patients during radiotherapy.

PURPOSE: Observed gross tumor volume (GTV) shrinkage during radiotherapy (RT) raises the question of whether to adapt treatment to changes observed on the acquired images. In the literature, two modes of tumor regression have been described: elastic and non-elastic. These modes of tumor regression will affect the safety of treatment adaptation. This study applies a novel approach, using routine cone-beam computed tomography (CBCT) and deformable image registration to automatically distinguish between elastic and non-elastic tumor regression. METHODS: In this retrospective study, 150 locally advanced non-small cell lung cancer patients treated with 55 Gray of radiotherapy were included. First, the two modes of tumor regression were simulated. For each mode of tumor regression, one timepoint was simulated. Based on the results of simulated data, the approach used for analysis in real patients was developed. CBCTs were non-rigidly registered to the baseline CBCT using a cubic B-spline algorithm, NiftyReg. Next, the Jacobian determinants were computed from the deformation vector fields. To capture local volume changes, 10 Jacobian values were sampled perpendicular to the surface of the GTV, across the lung-tumor boundary. From the simulated data, we can distinguish elastic from non-elastic tumor regression by comparing the Jacobian values samples between 5 and 12.5 mm inside and 5 and 12.5 mm outside the planning GTV. Finally, morphometric results were compared between tumors of different histologies. RESULTS: Most patients (92.3%) in our cohort showed stable disease in the first week of treatment and non-elastic shrinkage in the later weeks of treatment. At week 2, 125 patients (88%) showed stable disease, three patients (2.1%) disease progression, and 11 patients (8%) regression. By treatment completion, 91 patients (64%) had stable disease, one patient (0.7%) progression and 46 patients (32%) regression. A slight difference in the mode of tumor change was observed between tumors of different histologies. CONCLUSION: Our novel approach shows that it may be possible to automatically quantify and identify global changes in lung cancer patients during RT, using routine CBCT images. Our results show that different regions of the tumor change in different ways. Therefore, careful consideration should be taken when adapting RT.

Early prediction of tumour-response to radiotherapy in NSCLC patients.

Objective. In this study we developed an automatic method to predict tumour volume and shape in weeks 3 and 4 of radiotherapy (RT), using cone-beam computed tomography (CBCT) scans acquired up to week 2, allowing identification of large tumour changes.Approach. 240 non-small cell lung cancer (NSCLC) patients, treated with 55 Gy in 20 fractions, were collected. CBCTs were rigidly registered to the planning CT. Intensity values were extracted in each voxel of the planning target volume across all CBCT images from days 1, 2, 3, 7 and 14. For each patient and in each voxel, four regression models were fitted to voxel intensity; applying linear, Gaussian, quadratic and cubic methods. These models predicted the intensity value for each voxel in weeks 3 and 4, and the tumour volume found by thresholding. Each model was evaluated by computing the root mean square error in pixel value and structural similarity index metric (SSIM) for all patients. Finally, the sensitivity and specificity to predict a 30% change in volume were calculated for each model.Main results. The linear, Gaussian, quadratic and cubic models achieved a comparable similarity score, the average SSIM for all patients was 0.94, 0.94, 0.90, 0.83 in week 3, respectively. At week 3, a sensitivity of 84%, 53%, 90% and 88%, and specificity of 99%, 100%, 91% and 42% were observed for the linear, Gaussian, quadratic and cubic models respectively. Overall, the linear model performed best at predicting those patients that will benefit from RT adaptation. The linear model identified 21% and 23% of patients in our cohort with more than 30% tumour volume reduction to benefit from treatment adaptation in weeks 3 and 4 respectively.Significance. We have shown that it is feasible to predict the shape and volume of NSCLC tumours from routine CBCTs and effectively identify patients who will respond to treatment early.

Identification of patterns of tumour change measured on CBCT images in NSCLC patients during radiotherapy.

In this study, we propose a novel approach to investigate changes in the visible tumour and surrounding tissues with the aim of identifying patterns of tumour change during radiotherapy (RT) without segmentation on the follow-up images. On-treatment cone-beam computed tomography (CBCT) images of 240 non-small cell lung cancer (NSCLC) patients who received 55 Gy of RT were included. CBCTs were automatically aligned onto planning computed tomography (planning CT) scan using a two-step rigid registration process. To explore density changes across the lung-tumour boundary, eight shells confined to the shape of the gross tumour volume (GTV) were created. The shells extended 6 mm inside and outside of the GTV border, and each shell is 1.5 mm thick. After applying intensity correction on CBCTs, the mean intensity was extracted from each shell across all CBCTs. Thereafter, linear fits were created, indicating density change over time in each shell during treatment. The slopes of all eight shells were clustered to explore patterns in the slopes that show how tumours change. Seven clusters were obtained, 97% of the patients were clustered into three groups. After visual inspection, we found that these clusters represented patients with little or no density change, progression and regression. For the three groups, the survival curves were not significantly different between the groups, p-value = 0.51. However, the results show that definite patterns of tumour change exist, suggesting that it may be possible to identify patterns of tumour changes from on-treatment CBCT images.

Cardiac sub-volume targeting demonstrates regional radiosensitivity in the mouse heart.

BACKGROUND AND PURPOSE: Radiation-induced cardiac toxicity (RICT) remains one of the most critical dose limiting constraints in radiotherapy. Recent studies have shown higher doses to the base of the heart are associated with worse overall survival in lung cancer patients receiving radiotherapy. This work aimed to investigate the impact of sub-volume heart irradiation in a mouse model using small animal image-guided radiotherapy. MATERIALS AND METHODS: C57BL/6 mice were irradiated with a single fraction of 16 Gy to the base, middle or apex of the heart using a small animal radiotherapy research platform. Cone beam CT and echocardiography were performed at baseline and at 10 week intervals until 50 weeks post-treatment. Structural and functional parameters were correlated with mean heart dose (MHD) and volume of heart receiving 5 Gy (V5). RESULTS: All irradiated mice showed a time dependent increase in left ventricle wall thickness in diastole of ~0.2 mm detected at 10 weeks post-treatment, with the most significant and persistent changes occurring in the heart base-irradiated animals. Similarly, statistically different functional effects (p < 0.01) were observed in base-irradiated animals which showed the most significant decreases compared to controls. The observed functional changes did not correlate with MHD and V5 (R2 < 0.1), indicating that whole heart dosimetry parameters do not predict physiological changes resulting from cardiac sub-volume irradiation. CONCLUSIONS: This is the first report demonstrating the structural and functional consequences of sub-volume targeting in the mouse heart and reverse translates clinical observations indicating the heart base as a critical radiosensitive region.

An individualized strategy to estimate the effect of deformable registration uncertainty on accumulated dose in the upper abdomen.

In the abdomen, it is challenging to assess the accuracy of deformable image registration (DIR) for individual patients, due to the lack of clear anatomical landmarks, which can hamper clinical applications that require high accuracy DIR, such as adaptive radiotherapy. In this study, we propose and evaluate a methodology for estimating the impact of uncertainties in DIR on calculated accumulated dose in the upper abdomen, in order to aid decision making in adaptive treatment approaches. Sixteen liver metastasis patients treated with SBRT were evaluated. Each patient had one planning and three daily treatment CT-scans. Each daily CT scan was deformably registered 132 times to the planning CT-scan, using a wide range of parameter settings for the registration algorithm. A subset of 'realistic' registrations was then objectively selected based on distances between mapped and target contours. The underlying 3D transformations of these registrations were used to assess the corresponding uncertainties in voxel positions, and delivered dose, with a focus on accumulated maximum doses in the hollow OARs, i.e. esophagus, stomach, and duodenum. The number of realistic registrations varied from 5 to 109, depending on the patient, emphasizing the need for individualized registration parameters. Considering for all patients the realistic registrations, the 99th percentile of the voxel position uncertainties was 5.6 ± 3.3 mm. This translated into a variation (difference between 1st and 99th percentile) in accumulated D max in hollow OARs of up to 3.3 Gy. For one patient a violation of the accumulated stomach dose outside the uncertainty band was detected. The observed variation in accumulated doses in the OARs related to registration uncertainty, emphasizes the need to investigate the impact of this uncertainty for any DIR algorithm prior to clinical use for dose accumulation. The proposed method for assessing on an individual patient basis the impact of uncertainties in DIR on accumulated dose is in principle applicable for all DIR algorithms allowing variation in registration parameters.