Interobserver delineation variation in lung tumour stereotactic body radiotherapy

Systematic review and meta-analysis of deep learning applications in computed tomography lung cancer segmentation

BACKGROUND

Accurate segmentation of lung tumors on chest computed tomography (CT) scans is crucial for effective diagnosis and treatment planning. Deep Learning (DL) has emerged as a promising tool in medical imaging, particularly for lung cancer segmentation. However, its efficacy across different clinical settings and tumor stages remains variable.

METHODS

We conducted a comprehensive search of PubMed, Embase, and Web of Science until November 7, 2023. We assessed the quality of these studies by using the Checklist for Artificial Intelligence in Medical Imaging and the Quality Assessment of Diagnostic Accuracy Studies-2 tools. This analysis included data from various clinical settings and stages of lung cancer. Key performance metrics, such as the Dice similarity coefficient, were pooled, and factors affecting algorithm performance, such as clinical setting, algorithm type, and image processing techniques, were examined.

RESULTS

Our analysis of 37 studies revealed a pooled Dice score of 79 % (95 % CI: 76 %-83 %), indicating moderate accuracy. Radiotherapy studies had a slightly lower score of 78 % (95 % CI: 74 %-82 %). A temporal increase was noted, with recent studies (post-2022) showing improvement from 75 % (95 % CI: 70 %-81 %). to 82 % (95 % CI: 81 %-84 %). Key factors affecting performance included algorithm type, resolution adjustment, and image cropping. QUADAS-2 assessments identified ambiguous risks in 78 % of studies due to data interval omissions and concerns about generalizability in 8 % due to nodule size exclusions., and CLAIM criteria highlighted areas for improvement, with an average score of 27.24 out of 42.

CONCLUSION

This meta-analysis demonstrates DL algorithms' promising but varied efficacy in lung cancer segmentation, particularly higher efficacy noted in early stages. The results highlight the critical need for continued development of tailored DL models to improve segmentation accuracy across diverse clinical settings, especially in advanced cancer stages with greater challenges. As recent studies demonstrate, ongoing advancements in algorithmic approaches are crucial for future applications.

Impact of bowel space contouring variability on radiation dose and volume assessments in treatment planning for gynaecologic cancers

INTRODUCTION

Correlations between radiation dose/volume measures and small bowel (SB) toxicity are inconsistent in the medical literature. We assessed the impact of inter-provider variation in bowel bag contouring technique on estimates of radiation dose received by the SB during pelvic radiotherapy.

METHODS

Ten radiation oncologists contoured rectum, bladder and bowel bags on treatment planning computed tomography (CT) scans of two patients receiving adjuvant radiation for endometrial cancer. A radiation plan was generated for each patient and used to determine the radiation dose/volume for each organ. Kappa statistics were applied to assess the inter-provider contouring agreement, and Levene test evaluated the homogeneity of variance for radiation dose/volume metrics, including the V45Gy (cm3 ).

RESULTS

The bowel bag showed greater variation in radiation dose/volume estimates compared to the bladder and rectum. The V45Gy ranged from 163 to 384 cm3 for data set A and 109 to 409 cm3 for dataset B. Kappa values were 0.82/0.83, 0.92/0.92 and 0.94/0.86 for the bowel bag, rectum, and bladder on data sets A/B, demonstrating lower inter-provider agreement for bowel bag compared with bladder and rectum.

CONCLUSION

Inter-provider contouring variability is more significant for the bowel bag than the rectum and bladder, with an associated greater variability in dose and volume estimates during radiation planning.

Deep learning model fusion improves lung tumor segmentation accuracy across variable training-to-test dataset ratios

This study aimed to investigate the robustness of a deep learning (DL) fusion model for low training-to-test ratio (TTR) datasets in the segmentation of gross tumor volumes (GTVs) in three-dimensional planning computed tomography (CT) images for lung cancer stereotactic body radiotherapy (SBRT). A total of 192 patients with lung cancer (solid tumor, 118; part-solid tumor, 53; ground-glass opacity, 21) who underwent SBRT were included in this study. Regions of interest in the GTVs were cropped based on GTV centroids from planning CT images. Three DL models, 3D U-Net, V-Net, and dense V-Net, were trained to segment the GTV regions. Nine fusion models were constructed with logical AND, logical OR, and voting of the two or three outputs of the three DL models. TTR was defined as the ratio of the number of cases in a training dataset to that in a test dataset. The Dice similarity coefficients (DSCs) and Hausdorff distance (HD) of the 12 models were assessed with TTRs of 1.00 (training data: validation data: test data = 40:20:40), 0.791 (35:20:45), 0.531 (31:10:59), 0.291 (20:10:70), and 0.116 (10:5:85). The voting fusion model achieved the highest DSCs of 0.829 to 0.798 for all TTRs among the 12 models, whereas the other models showed DSCs of 0.818 to 0.804 for a TTR of 1.00 and 0.788 to 0.742 for a TTR of 0.116, and an HD of 5.40 ± 3.00 to 6.07 ± 3.26 mm better than any single DL models. The findings suggest that the proposed voting fusion model is a robust approach for low TTR datasets in segmenting GTVs in planning CT images of lung cancer SBRT.

Impact of bowel space contouring variability on radiation dose and volume assessments in treatment planning for gynaecologic cancers

Introduction

Correlations between radiation dose/volume measures and small bowel (SB) toxicity are inconsistent in the medical literature. We assessed the impact of inter‐provider variation in bowel bag contouring technique on estimates of radiation dose received by the SB during pelvic radiotherapy.

Methods

Ten radiation oncologists contoured rectum, bladder and bowel bags on treatment planning computed tomography (CT) scans of two patients receiving adjuvant radiation for endometrial cancer. A radiation plan was generated for each patient and used to determine the radiation dose/volume for each organ. Kappa statistics were applied to assess the inter‐provider contouring agreement, and Levene test evaluated the homogeneity of variance for radiation dose/volume metrics, including the V45Gy (cm3).

Results

The bowel bag showed greater variation in radiation dose/volume estimates compared to the bladder and rectum. The V45Gy ranged from 163 to 384 cm3 for data set A and 109 to 409 cm3 for dataset B. Kappa values were 0.82/0.83, 0.92/0.92 and 0.94/0.86 for the bowel bag, rectum, and bladder on data sets A/B, demonstrating lower inter‐provider agreement for bowel bag compared with bladder and rectum.

Conclusion

Inter‐provider contouring variability is more significant for the bowel bag than the rectum and bladder, with an associated greater variability in dose and volume estimates during radiation planning.

Assessment of Optimizers and their Performance in Autosegmenting Lung Tumors

Purpose

Optimizers are widely utilized across various domains to enhance desired outcomes by either maximizing or minimizing objective functions. In the context of deep learning, they help to minimize the loss function and improve model's performance. This study aims to evaluate the accuracy of different optimizers employed for autosegmentation of non-small cell lung cancer (NSCLC) target volumes on thoracic computed tomography images utilized in oncology.

Materials and Methods

The study utilized 112 patients, comprising 92 patients from "The Cancer Imaging Archive" (TCIA) and 20 of our local clinical patients, to evaluate the efficacy of various optimizers. The gross tumor volume was selected as the foreground mask for training and testing the models. Of the 92 TCIA patients, 57 were used for training and validation, and the remaining 35 for testing using nnU-Net. The performance of the final model was further evaluated on the 20 local clinical patient datasets. Six different optimizers, namely AdaDelta, AdaGrad, Adam, NAdam, RMSprop, and stochastic gradient descent (SGD), were investigated. To assess the agreement between the predicted volume and the ground truth, several metrics including Dice similarity coefficient (DSC), Jaccard index, sensitivity, precision, Hausdorff distance (HD), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD) were utilized.

Results

The DSC values for AdaDelta, AdaGrad, Adam, NAdam, RMSprop, and SGD were 0.75, 0.84, 0.85, 0.84, 0.83, and 0.81, respectively, for the TCIA test data. However, when the model trained on TCIA datasets was applied to the clinical datasets, the DSC, HD, HD95, and ASSD metrics showed a statistically significant decrease in performance compared to the TCIA test datasets, indicating the presence of image and/or mask heterogeneity between the data sources.

Conclusion

The choice of optimizer in deep learning is a critical factor that can significantly impact the performance of autosegmentation models. However, it is worth noting that the behavior of optimizers may vary when applied to new clinical datasets, which can lead to changes in models' performance. Therefore, selecting the appropriate optimizer for a specific task is essential to ensure optimal performance and generalizability of the model to different datasets.

Clinical validation of a semi‐automated segmentation algorithm for target volume definition on planning CT and CBCT in stereotactic body radiotherapy ( SBRT ) for peripheral lung lesions

INTRODUCTION

Stereotactic body radiotherapy (SBRT) is an ablative method for lung malignancies. Here, the definition of the gross target volume (GTV) is subject to interobserver variation. In this study, we aimed to evaluate the interobserver variability during SBRT and its dosimetric impact, as well as to introduce a semi-automated delineation tool for both planning computer tomography (P-CT) and cone beam CT (CBCT) to help to standardise GTV delineation and adaptive volume-change registration.

METHODS

The interobserver variation of GTV manual contours from five physicians was analysed in 15 patients after lung SBRT on free breathing (FB) P-CT (n = 15) and CBCT (n = 90) before and after each fraction. The dosimetric impact from interobserver variations of GTV based on the original treatment plan was analysed. Next, the accuracy of an in-house easy-to-use semi-automated-segmentation algorithm for pulmonary lesions was compared with gold standard contours in FB P-CT and CBCT, as well as 4D P-CT of additional 10 patients.

RESULTS

The interobserver variability in manual contours resulted in violations of dose coverage of the planning target volume (PTV), which, in turn, resulted in compromised tumour control probability in contours from four physicians. The validation of the semi-automated delineation algorithm using thorax phantom led to a highly reliable accuracy in defining GTVs. Comparing the unsupervised auto-contours with the gold standard delineation revealed high equal high concordance for FB P-CT, 4D P-CT and CBCT, with a DSC of 0.83, 0.76 and 0.8, respectively. The supervised use of the semi-automated delineation tool improved its accuracy, with DSCs of 0.86, 0.86 and 0.8 for FB P-CT, 4D P-CT and CBCT, respectively. The use of the algorithm was associated with a significantly shorter working time. The semi-automated delineation tool can accurately register volume changes in CBCTs.

CONCLUSION

The segmentation algorithm provides a reliable, standardised and time-saving alternative for manual delineation in lung SBRT in P-CT and CBCT.

Challenges in the target volume definition of lung cancer radiotherapy

Radiotherapy, with or without systemic treatment has an important role in the management of lung cancer. In order to deliver the treatment accurately, the clinician must precisely outline the gross tumour volume (GTV), mostly on computed tomography (CT) images. However, due to the limited contrast between tumour and non-malignant changes in the lung tissue, it can be difficult to distinguish the tumour boundaries on CT images leading to large interobserver variation and differences in interpretation. Therefore the definition of the GTV has often been described as the weakest link in radiotherapy with its inaccuracy potentially leading to missing the tumour or unnecessarily irradiating normal tissue. In this article, we review the various techniques that can be used to reduce delineation uncertainties in lung cancer.

Automated approach for segmenting gross tumor volumes for lung cancer stereotactic body radiation therapy using CT-based dense V-networks

The aim of this study was to develop an automated segmentation approach for small gross tumor volumes (GTVs) in 3D planning computed tomography (CT) images using dense V-networks (DVNs) that offer more advantages in segmenting smaller structures than conventional V-networks. Regions of interest (ROI) with dimensions of 50 × 50 × 6-72 pixels in the planning CT images were cropped based on the GTV centroids when applying stereotactic body radiotherapy (SBRT) to patients. Segmentation accuracy of GTV contours for 192 lung cancer patients [with the following tumor types: 118 solid, 53 part-solid types and 21 pure ground-glass opacity (pure GGO)], who underwent SBRT, were evaluated based on a 10-fold cross-validation test using Dice's similarity coefficient (DSC) and Hausdorff distance (HD). For each case, 11 segmented GTVs consisting of three single outputs, four logical AND outputs, and four logical OR outputs from combinations of two or three outputs from DVNs were obtained by three runs with different initial weights. The AND output (combination of three outputs) achieved the highest values of average 3D-DSC (0.832 ± 0.074) and HD (4.57 ± 2.44 mm). The average 3D DSCs from the AND output for solid, part-solid and pure GGO types were 0.838 ± 0.074, 0.822 ± 0.078 and 0.819 ± 0.059, respectively. This study suggests that the proposed approach could be useful in segmenting GTVs for planning lung cancer SBRT.

Evaluation of Varian's SmartAdapt for clinical use in radiation therapy for patients with thoracic lesions

INTRODUCTION

Deformable image registration (DIR) is a required tool in any adaptive radiotherapy program to help account for anatomical changes that occur during a multifraction treatment. SmartAdapt is a DIR tool from Varian incorporated within the eclipse treatment planning system, that can be used for contour propagation and transfer of PET, MRI, or computed tomography (CT) data. The purpose of this work is to evaluate the registration and contour propagation accuracy of SmartAdapt for thoracic CT studies using the guidelines from AAPM TG 132.

METHODS

To evaluate the registration accuracy of SmartAdapt the mean target registration error (TRE) was measured for ten landmarked 4DCT images from the https://www.dir-labs.com/ which included 300 landmarks matching the inspiration and expiration phase images. To further characterize the registration accuracy, the magnitude of deformation for each 4DCT was measured and compared against the mean TRE for each study. Contour propagation accuracy was evaluated using 22 randomly selected lung cancer cases from our center where there was either a replan, or the patient was treated for a new lesion within the lung. Contours evaluated included the right and left lung, esophagus, spinal canal, heart and the GTV and the results were quantified using the DICE similarity coefficient.

RESULTS

The mean TRE from all ten cases was 1.89 mm, the maximum mean TRE per case was 3.8 mm from case #8, which also had the most landmark pairs with displacements >2 cm. For contour propagation accuracy, the DICE coefficient results for left lung, right lung, heart, esophagus, and spinal canal were 0.93, 0.94, 0.90, 0.61, and 0.82 respectively.

CONCLUSION

The results from our study demonstrate that for thoracic images SmartAdapt in most cases will be accurate to below 2 mm in registration error unless there is deformation greater than 2 cm.

Automated gross tumor volume contour generation for large-scale analysis of early-stage lung cancer patients planned with 4D-CT

PURPOSE

Patients with early-stage lung cancer undergoing stereotactic ablative radiotherapy receive four-dimensional computed tomography (4D-CT) for treatment planning. Often, an internal gross target volume (iGTV), which approximates the motion envelope of a tumor over the breathing cycle, is delineated without defining a gross tumor volume (GTV). However, the GTV volume and shape are important parameters for prognostic and dose modelling, and there is interest in radiomic features extracted from the GTV and surrounding tissue. We demonstrate and validate a method to generate the GTV from an iGTV contour to aid retrospective analysis on routine data.

METHOD

It is possible to reconstruct the geometry of a tumor with knowledge of tumor motion and the motion envelope formed during respiration. To demonstrate this, the tumor motion path was estimated with local rigid registration, and the iGTV positioned incrementally at stations along the reverse path. It is shown that the tumor volume is the largest set common to the intersection of the iGTV at these positions, hence can be derived. This was implemented for 521 lung lesions on 4D-CT. Eleven patients with a GTV delineation performed by a radiation oncologist on a reference phase (50%) were used for validation. The generated GTV was compared to that delineated by the expert using distance-to-agreement (DTA), volume, and distance between centres of mass. An overall success rate was determined by detecting registration inaccuracy and performing a quality check on the routine iGTV. For successfully generated contours, GTV volume was compared to iGTV volume in a prognostic model for overall survival.

RESULTS

For the validation dataset, DTA mean (0.79 - 1.55 mm) and standard deviation (0.68 - 1.51 mm) were comparable to expected observer variation. Difference in volume was < 5 cm³ , and average difference in position was 1.21 mm. Deviations in shape and position were mainly caused by observer differences in iGTV and GTV interpretation as opposed to algorithm performance. For the complete dataset, an acceptable contour was generated for 94% of patients using statistical and visual assessment to detect failures. Generated GTV volumes improved prognostic model performance over iGTV volumes.

CONCLUSION

A method to generate a GTV from an iGTV and 4D-CT dataset was developed. This method facilitates data analysis of patients with early-stage lung cancer treated in the routine setting, that is, data mining, prognostic modeling, and radiomics. Generation failure detection removes the need for visual assessment of all contours, reducing a time-consuming aspect of big-data analysis. Favorable prognostic performance of generated GTV volumes over iGTV ones demonstrates opportunities to use this methodology for future study.

The application of artificial intelligence and radiomics in lung cancer

Lung cancer is one of the most leading causes of death throughout the world, and there is an urgent requirement for the precision medical management of it. Artificial intelligence (AI) consisting of numerous advanced techniques has been widely applied in the field of medical care. Meanwhile, radiomics based on traditional machine learning also does a great job in mining information through medical images. With the integration of AI and radiomics, great progress has been made in the early diagnosis, specific characterization, and prognosis of lung cancer, which has aroused attention all over the world. In this study, we give a brief review of the current application of AI and radiomics for precision medical management in lung cancer.

"Après Mois, Le Déluge": Preparing for the Coming Data Flood in the MRI-Guided Radiotherapy Era

Magnetic resonance imaging provides a sea of quantitative and semi-quantitative data. While radiation oncologists already navigate a pool of clinical (semantic) and imaging data, the tide will swell with the advent of hybrid MRI/linear accelerator devices and increasing interest in MRI-guided radiotherapy (MRIgRT), including adaptive MRIgRT. The variety of MR sequences (of greater complexity than the single parameter Hounsfield unit of CT scanning routinely used in radiotherapy), the workflow of adaptive fractionation, and the sheer quantity of daily images acquired are challenges for scaling this technology. Biomedical informatics, which is the science of information in biomedicine, can provide helpful insights for this looming transition. Funneling MRIgRT data into clinically meaningful information streams requires committing to the flow of inter-institutional data accessibility and interoperability initiatives, standardizing MRIgRT dosimetry methods, streamlining MR linear accelerator workflow, and standardizing MRI acquisition and post-processing. This review will attempt to conceptually ford these topics using clinical informatics approaches as a theoretical bridge.

Advanced dose calculation algorithms in lung cancer radiotherapy: Implications for SBRT and locally advanced disease in deep inspiration breath hold

PURPOSE

Evaluating performance of modern dose calculation algorithms in SBRT and locally advanced lung cancer radiotherapy in free breathing (FB) and deep inspiration breath hold (DIBH).

METHODS

For 17 patients with early stage and 17 with locally advanced lung cancer, a plan in FB and in DIBH were generated with Anisotropic Analytical Algorithm (AAA). Plans for early stage were 3D-conformal SBRT, 45 Gy in 3 fractions, prescribed to 95% isodose covering 95% of PTV and aiming for 140% dose centrally in the tumour. Locally advanced plans were volumetric modulated arc therapy, 66 Gy in 33 fractions, prescribed to mean PTV dose. Calculation grid size was 1 mm for SBRT and 2.5 mm for locally advanced plans. All plans were recalculated with AcurosXB with same MU as in AAA, for comparison on target coverage and dose to risk organs.

RESULTS

Lung volume increased in DIBH, resulting in decreased lung density (6% for early and 13% for locally-advanced group). In SBRT, AAA overestimated mean and near-minimum PTV dose (p-values < 0.01) compared to AcurosXB, with largest impact in DIBH (differences of up to 11 Gy). These clinically relevant differences may be a combination of small targets and large dose gradients within the PTV. In locally advanced group, AAA overestimated mean GTV, CTV and PTV doses by median less than 0.8 Gy and near-minimum doses by median 0.4-2.7 Gy. No clinically meaningful difference was observed for lung and heart dose metrics between the algorithms, for both FB and DIBH.

CONCLUSIONS

AAA overestimated target coverage compared to AcurosXB, especially in DIBH for SBRT.

The impact of technology on the changing practice of lung SBRT

Stereotactic body radiotherapy (SBRT) for lung tumours has been gaining wide acceptance in lung cancer. Here, we review the technological evolution of SBRT delivery in lung cancer, from the first treatments using the stereotactic body frame in the 1990's to modern developments in image guidance and motion management. Finally, we discuss the impact of current technological approaches on the requirements for quality assurance as well as future technological developments.

Variability of Gross Tumor Volume Delineation for Stereotactic Body Radiotherapy of the Lung With Tri-60Co Magnetic Resonance Image-Guided Radiotherapy System (ViewRay): A Comparative Study With Magnetic Resonance- and Computed Tomography-Based Target Delineation

Introduction:

To evaluate the intra-/interobserver variability of gross target volumes between delineation based on magnetic resonance imaging and computed tomography in patients simulated for stereotactic body radiotherapy for primary lung cancer and lung metastasis.

Materials and Methods:

Twenty-five patients (27 lesions) who underwent computed tomography and magnetic resonance simulation with the MR-⁶⁰Co system (ViewRay) were included in the study. Gross target volumes were delineated on the magnetic resonance imaging (GTV_MR) and computed tomography (GTV_CT) images by 2 radiation oncologists (RO1 and RO2). Volumes of all contours were measured. Levels of intraobserver (GTV_MR_RO vs GTV_CT_RO) and interobserver (GTV_MR_RO1 vs GTV_MR_RO2; GTV_CT_RO1 vs GTV_CT_RO2) agreement were evaluated using the generalized κ statistics and the paired t test.

Results:

No significant volumetric difference was observed between all 4 comparisons (GTV_MR_RO1 vs GTV_CT_RO1, GTV_MR_RO2 vs GTV_CT_RO2, GTV_MR_RO1 vs GTV_MR_RO2, and GTV_CT_RO1 vs GTV_CT_RO2; P > .05), with mean volumes of GTVs ranging 5 to 6 cm³. The levels of agreement between those 4 comparisons were all substantial with mean κ values of 0.64, 0.66, 0.74, and 0.63, respectively. However, the interobserver agreement level was significantly higher for GTV_CT compared to GTV_MR (P <.001). The mean κ values significantly increased in all 4 comparisons for tumors >5 cm³ compared to tumors ≤5 cm³ (all P < .05).

Conclusion:

No significant differences in volumes between magnetic resonance- and computed tomograpghy-based Gross target volumes were found among 2 ROs. Magnetic resonance-based GTV delineation for lung stereotactic body radiotherapy also demonstrated acceptable interobserver agreement. Tumors >5 cm³ show higher intra-/interobserver agreement compared to tumors <5 cm³. More experience should be accumulated to reduce variability in magnetic resonance-based Gross target volumes delineation in lung stereotactic body radiotherapy.

Repeatability of Radiomic Features in Non-Small-Cell Lung Cancer [(18)F]FDG-PET/CT Studies: Impact of Reconstruction and Delineation

Purpose

To assess (1) the repeatability and (2) the impact of reconstruction methods and delineation on the repeatability of 105 radiomic features in non-small-cell lung cancer (NSCLC) 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) positron emission tomorgraphy/computed tomography (PET/CT) studies.

Procedures

Eleven NSCLC patients received two baseline whole-body PET/CT scans. Each scan was reconstructed twice, once using the point spread function (PSF) and once complying with the European Association for Nuclear Medicine (EANM) guidelines for tumor PET imaging. Volumes of interest (n = 19) were delineated twice, once on PET and once on CT images.

Results

Sixty-three features showed an intraclass correlation coefficient ≥ 0.90 independent of delineation or reconstruction. More features were sensitive to a change in delineation than to a change in reconstruction (25 and 3 features, respectively).

Conclusions

The majority of features in NSCLC [¹⁸F]FDG-PET/CT studies show a high level of repeatability that is similar or better compared to simple standardized uptake value measures.

Electronic supplementary material

The online version of this article (doi:10.1007/s11307-016-0940-2) contains supplementary material, which is available to authorized users.

Impact of pixel-based machine-learning techniques on automated frameworks for delineation of gross tumor volume regions for stereotactic body radiation therapy

The aim of this study was to investigate the impact of pixel-based machine learning (ML) techniques, i.e., fuzzy-c-means clustering method (FCM), and the artificial neural network (ANN) and support vector machine (SVM), on an automated framework for delineation of gross tumor volume (GTV) regions of lung cancer for stereotactic body radiation therapy. The morphological and metabolic features for GTV regions, which were determined based on the knowledge of radiation oncologists, were fed on a pixel-by-pixel basis into the respective FCM, ANN, and SVM ML techniques. Then, the ML techniques were incorporated into the automated delineation framework of GTVs followed by an optimum contour selection (OCS) method, which we proposed in a previous study. The three-ML-based frameworks were evaluated for 16 lung cancer cases (six solid, four ground glass opacity (GGO), six part-solid GGO) with the datasets of planning computed tomography (CT) and ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT images using the three-dimensional Dice similarity coefficient (DSC). DSC denotes the degree of region similarity between the GTVs contoured by radiation oncologists and those estimated using the automated framework. The FCM-based framework achieved the highest DSCs of 0.79±0.06, whereas DSCs of the ANN-based and SVM-based frameworks were 0.76±0.14 and 0.73±0.14, respectively. The FCM-based framework provided the highest segmentation accuracy and precision without a learning process (lowest calculation cost). Therefore, the FCM-based framework can be useful for delineation of tumor regions in practical treatment planning.

Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132

Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is important to understand and communicate the uncertainty associated with the software in general and the result of a specific registration. Unfortunately, there is no standard mathematical formalism to perform this for real-world situations where noise, distortion, and complex anatomical variations can occur. Validation of the software systems performance is also complicated by the lack of documentation available from commercial systems leading to use of these systems in undesirable 'black-box' fashion. In view of this situation and the central role that image registration and fusion play in treatment planning and delivery, the Therapy Physics Committee of the American Association of Physicists in Medicine commissioned Task Group 132 to review current approaches and solutions for image registration (both rigid and deformable) in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes.

The role of dosimetry audit in lung SBRT multi-centre clinical trials

Stereotactic Body Radiotherapy (SBRT) in the lung is a challenging technique which requires high quality clinical trials to answer the un-resolved clinical questions. Quality assurance of these clinical trials not only ensures the safety of the treatment of the participating patients but also minimises the variation in treatment, thus allowing the lowest number of patient treatments to answer the trial question. This review addresses the role of dosimetry audits in the quality assurance process and considers what can be done to ensure the highest accuracy of dose calculation and delivery and it's assessment in multi-centre trials.

Uncertainties in volume delineation in radiation oncology: A systematic review and recommendations for future studies

BACKGROUND AND PURPOSE

Volume delineation is a well-recognised potential source of error in radiotherapy. Whilst it is important to quantify the degree of interobserver variability (IOV) in volume delineation, the resulting impact on dosimetry and clinical outcomes is a more relevant endpoint. We performed a literature review of studies evaluating IOV in target volume and organ-at-risk (OAR) delineation in order to analyse these with respect to the metrics used, reporting of dosimetric consequences, and use of statistical tests.

METHODS AND MATERIALS

Medline and Pubmed databases were queried for relevant articles using keywords. We included studies published in English between 2000 and 2014 with more than two observers.

RESULTS

119 studies were identified covering all major tumour sites. CTV (n=47) and GTV (n=38) were most commonly contoured. Median number of participants and data sets were 7 (3-50) and 9 (1-132) respectively. There was considerable heterogeneity in the use of metrics and methods of analysis. Statistical analysis of results was reported in 68% (n=81) and dosimetric consequences in 21% (n=25) of studies.

CONCLUSION

There is a lack of consistency in conducting and reporting analyses from IOV studies. We suggest a framework to use for future studies evaluating IOV.

The first patient treatment of electromagnetic-guided real time adaptive radiotherapy using MLC tracking for lung SABR

BACKGROUND AND PURPOSE

Real time adaptive radiotherapy that enables smaller irradiated volumes may reduce pulmonary toxicity. We report on the first patient treatment of electromagnetic-guided real time adaptive radiotherapy delivered with MLC tracking for lung stereotactic ablative body radiotherapy.

MATERIALS AND METHODS

A clinical trial was developed to investigate the safety and feasibility of MLC tracking in lung. The first patient was an 80-year old man with a single left lower lobe lung metastasis to be treated with SABR to 48Gy in 4 fractions. In-house software was integrated with a standard linear accelerator to adapt the treatment beam shape and position based on electromagnetic transponders implanted in the lung. MLC tracking plans were compared against standard ITV-based treatment planning. MLC tracking plan delivery was reconstructed in the patient to confirm safe delivery.

RESULTS

Real time adaptive radiotherapy delivered with MLC tracking compared to standard ITV-based planning reduced the PTV by 41% (18.7-11cm³) and the mean lung dose by 30% (202-140cGy), V20 by 35% (2.6-1.5%) and V5 by 9% (8.9-8%).

CONCLUSION

An emerging technology, MLC tracking, has been translated into the clinic and used to treat lung SABR patients for the first time. This milestone represents an important first step for clinical real-time adaptive radiotherapy that could reduce pulmonary toxicity in lung radiotherapy.

A comparison between radiation therapists and medical specialists in the use of kilovoltage cone-beam computed tomography scans for potential lung cancer radiotherapy target verification and adaptation

Target volume matching using cone-beam computed tomography (CBCT) is the preferred treatment verification method for lung cancer in many centers. However, radiation therapists (RTs) are trained in bony matching and not soft tissue matching. The purpose of this study was to determine whether RTs were equivalent to radiation oncologists (ROs) and radiologists (RDs) in alignment of the treatment CBCT with the gross tumor volume (GTV) defined at planning and in delineating the GTV on the treatment CBCT, as may be necessary for adaptive radiotherapy. In this study, 10 RTs, 1 RO, and 1 RD performed a manual tumor alignment and correction of the planning GTV to a treatment CBCT to generate an isocenter correction distance for 15 patient data sets. Participants also contoured the GTV on the same data sets. The isocenter correction distance and the contoured GTVs from the RTs were compared with the RD and RO. The mean difference in isocenter correction distances was 0.40cm between the RO and RD, 0.51cm between the RTs, and RO and 0.42cm between the RTs and RD. The 95% CIs were smaller than the equivalence limit of 0.5cm, indicating that the RTs were equivalent to the RO and RD. For GTV delineation comparisons, the RTs were not found to be equivalent to the RD or RO. The alignment of the planning defined GTV and treatment CBCT using soft tissue matching by the RTs has been shown to be equivalent to those by the RO and RD. However, tumor delineation by the RTs on the treatment CBCT was not equivalent to that of the RO and RD. Thus, it may be appropriate for RTs to undertake soft tissue alignment based on CBCT; however, further investigation may be necessary before RTs undertake delineation for adaptive radiotherapy purposes.

Image-guided radiotherapy and motion management in lung cancer

In this review, image guidance and motion management in radiotherapy for lung cancer is discussed. Motion characteristics of lung tumours and image guidance techniques to obtain motion information are elaborated. Possibilities for management of image guidance and motion in the various steps of the treatment chain are explained, including imaging techniques and beam delivery techniques. Clinical studies using different motion management techniques are reviewed, and finally future directions for image guidance and motion management are outlined.

PET/CT-guided treatment planning for paediatric cancer patients: a simulation study of proton and conventional photon therapy

OBJECTIVE

To investigate the impact of including fluorine-18 fludeoxyglucose ((18)F-FDG) positron emission tomography (PET) scanning in the planning of paediatric radiotherapy (RT).

METHODS

Target volumes were first delineated without and subsequently re-delineated with access to (18)F-FDG PET scan information, on duplicate CT sets. RT plans were generated for three-dimensional conformal photon RT (3DCRT) and intensity-modulated proton therapy (IMPT). The results were evaluated by comparison of target volumes, target dose coverage parameters, normal tissue complication probability (NTCP) and estimated risk of secondary cancer (SC).

RESULTS

Considerable deviations between CT- and PET/CT-guided target volumes were seen in 3 out of the 11 patients studied. However, averaging over the whole cohort, CT or PET/CT guidance introduced no significant difference in the shape or size of the target volumes, target dose coverage, irradiated volumes, estimated NTCP or SC risk, neither for IMPT nor 3DCRT.

CONCLUSION

Our results imply that the inclusion of PET/CT scans in the RT planning process could have considerable impact for individual patients. There were no general trends of increasing or decreasing irradiated volumes, suggesting that the long-term morbidity of RT in childhood would on average remain largely unaffected.

ADVANCES IN KNOWLEDGE

(18)F-FDG PET-based RT planning does not systematically change NTCP or SC risk for paediatric cancer patients compared with CT only. 3 out of 11 patients had a distinct change of target volumes when PET-guided planning was introduced. Dice and mismatch metrics are not sufficient to assess the consequences of target volume differences in the context of RT.

Dynamic contrast enhanced CT aiding gross tumor volume delineation of liver tumors: an interobserver variability study

PURPOSE

To evaluate the application of perfusion CT for gross tumor volume (GTV) delineation for radiotherapy of intrahepatic tumors.

MATERIALS AND METHODS

15 radiotherapy patients with confirmed liver tumors underwent contrast enhanced 4D-CT (Philips Brilliance Big-bore) as well as dynamic contrast enhanced (DCE) CT (GE 750HD). Perfusion maps were generated with CT perfusion v5 from GE. Five observers delineated GTVs of all intrahepatic foci on the 4D-CT, time-averaged DCE-CT and perfusion CT for every patient. STAPLE consensus contours were generated. Dice's coefficients were compared between GTVs generated by observers on each image set and the corresponding consensus GTVs. Comparisons were also performed with patients stratified by hepatocellular carcinoma (HCC) metastatic tumors, and by tumor volume.

RESULTS

Overall, mean Dice's coefficients were 0.81±0.14, 0.84±0.10, and 0.81±0.14 for 4D-CT, DCECT and perfusion. DCE-CT performed significantly better than 4D-CT and perfusion (p=0.005 and p=0.01 respectively). For patients with HCC, DCE-CT reduced interobserver variability significantly compared to 4D-CT (Dice's coefficients 0.87 vs. 0.84, p<0.05). For patients with metastatic disease time-averaged DCE-CT images decreased variability compared to 4D-CT (Dice's coefficient 0.81 vs. 0.76, p<0.05), especially true for tumors<100cc. The smaller tumors results are important to be included here.

CONCLUSIONS

DCE-CT imaging of liver perfusion reduced interobserver variability in GTV delineation for both HCC and metastatic liver tumors.

Evaluation of methods for selecting the midventilation bin in 4DCT scans of lung cancer patients

BACKGROUND

In lung cancer radiotherapy, planning on the midventilation (MidV) bin of a four-dimensional (4D) CT scan can reduce the systematic errors introduced by respiratory tumour motion compared to conventional CT. In this study four different methods for MidV bin selection are evaluated.

MATERIAL AND METHODS

The study is based on 4DCT scans of 19 patients with a total of 23 peripheral lung tumours having peak-to-peak displacement ≥ 5 mm in at least one of the left-right (LR), anterior-posterior (AP) or cranio-caudal (CC) directions. For each tumour, the MidV bin was selected based on: 1) visual evaluation of tumour displacement; 2) rigid registration of tumour position; 3) diaphragm displacement in the CC direction; and 4) carina displacement in the CC direction. Determination of the MidV bin based on the displacement of the manually delineated gross tumour volume (GTV) was used as a reference method. The accuracy of each method was evaluated by the distance between GTV position in the selected MidV bin and the time-weighted mean position of GTV throughout the bins (i.e. the geometric MidV error).

RESULTS

Median (range) geometric MidV error was 1.4 (0.4-5.4) mm, 1.4 (0.4-5.4) mm, 1.9 (0.5-6.9) mm, 2.0 (0.5-12.3) mm and 1.1 (0.4-5.4) mm for the visual, rigid registration, diaphragm, carina, and reference method. Median (range) absolute difference between geometric MidV error for the evaluated methods and the reference method was 0.0 (0.0-1.2) mm, 0.0 (0.0-1.7) mm, 0.7 (0.0-3.9) mm and 1.0 (0.0-6.9) mm for the visual, rigid registration, diaphragm and carina method.

CONCLUSION

The visual and semi-automatic rigid registration methods were equivalent in accuracy for selecting the MidV bin of a 4DCT scan. The methods based on diaphragm and carina displacement cannot be recommended without modifications.