Multimodality imaging with CT, MR and FDG-PET for radiotherapy target volume delineation in oropharyngeal squamous cell carcinoma

Interobserver Variability in a Spanish Society of Radiation Oncology (SEOR) Head and Neck Course. Is Current Contouring Training Sufficient?

Purpose

External beam radiation therapy has grown significantly, incorporating advanced techniques like intensity modulation or stereotactic treatments, which enhance precision and accuracy. Nevertheless, variability in target volume delineation by radiation oncologists remains a challenge, influencing dose distribution. This study analyzes an online training course by the Spanish Society of Radiation Oncology, focusing on head and neck tumor contouring, to evaluate interobserver variability.

Material and Methods

Eight instructors provided clinical directives for 8 head and neck pathologies. Participants contoured structures using their own treatment planning systems, emphasizing gross tumor volume and high-, medium-, and low-risk clinical target volumes (CTV) contouring. Delineation variability was evaluated using the Dice similarity coefficient and volume relative change.

Results

The results reveal significant variability in contouring, with mean Dice similarity coefficient values ranging from 0.57 to 0.69. High-risk CTV demonstrated higher variability compared with medium-risk CTV. The presence of a gross tumor volume and supporting positron emission tomography/computed tomography or magnetic resonance imaging studies did not significantly improve the concordance. Parotid cases exhibited the greatest differences.

Conclusions

Despite the introduction of new automatic tools, this study points to the need for uniform contouring criteria. Training and standardization efforts are essential to enhance radiation therapy treatment consistency and quality.

Enhanced head and neck radiotherapy target definition through multidisciplinary delineation and peer review: A prospective single-center study

This study evaluates the benefit of weekly delineation and peer review by a multidisciplinary team (MDT) of radiation oncologists (ROs), radiologists (RXs), and nuclear medicine (NM) physicians in defining primary and lymph node tumor volumes (GTVp and GTVn) for head and neck cancer (HNC) radiotherapy. This study includes 30 consecutive HNC patients referred for definitive curative (chemo)-radiotherapy. Imaging data including head and neck MRI, [18F]-FDG-PET and CT scan were evaluated by the MDT. The RO identified the 'undeniable' tumor as GTVp_core and determined GTVp_max, representing the maximum tumoral volume. The MDT delineation (MDT-D) by RX and NM physicians outlined their respective primary GTVs (GTVp_RX and GTVp_NM). During the MDT meeting (MDT-M), these contours were discussed to reach a consensus on the final primary GTV (GTVp_final). In the comparative analysis of various GTVp delineations, we performed descriptive statistics and assessed two MDT-M factors: 1) the added value of MDT-M, which includes the section of GTVp_final outside GTVp_core but within GTVp_RX or GTVp_NM, and 2) the part of GTVp_final that deviates from GTVp_max, representing the area missed by the RO. For GTVn, discussions evaluated lymph node extent and malignancy, documenting findings and the frequency of disagreements. The average GTVp core and max volumes were 19.5 cc (range: 0.4-90.1) and 22.1 cc (range: 0.8-106.2), respectively. Compared to GTVp_core, MDT-D to GTVp_final added an average of 3.3 cc (range: 0-25.6) and spared an average of 1.3 cc (0-15.6). Compared to GTVp_max, MDT-D and -M added an average of 2.7 cc (range: 0-20.3) and removed 2.3 cc (0-21.3). The most frequent GTVn discussions included morphologically suspicious nodes not fixing on [18F]-FDG-PET and small [18F]-FDG-PET negative retropharyngeal lymph nodes. Multidisciplinary review of target contours in HNC is essential for accurate treatment planning, ensuring precise tumor and lymph node delineation, potentially improving local control and reducing toxicity.

Intermodality Variability in Gross Tumor Volume Delineation for Radiation Therapy Planning in Oropharyngeal Squamous Cell Carcinoma

Purpose

Multimodality imaging can enhance the precision of tumor delineation for intensity modulated radiation therapy planning. This study aimed to analyze intermodality variation for gross tumor volume (GTV) delineation in locally advanced oropharyngeal carcinomas (LAOCs).

Methods and Materials

We examined the pretreatment contrast-enhanced computed tomography (CECT), magnetic resonance imaging (MRI), and fluoro-deoxy-glucose-based positron emission tomography (FDG-PET) image data sets of 33 adult patients with primary LAOC. Automatic segmentation method was used to derive PET-based metabolic tumor volumes (MTVs) at 30%, 40%, 50%, 60%, and 70% of the primary tumor's maximum standardized uptake value (SUVmax). The geometric conformality or spatial overlap was assessed using the Dice similarity coefficient (DSC), which ranges from 0 to 1, indicating no overlap to complete overlap.

Results

The size of the tumor in the anteroposterior dimension of the GTV was found to be more on CT than MRI, with a mean difference of 0.29 cm (P value .015). Overall, PET-based MTV volumes were smaller than GTVs on CT and MR. Among various intensities on PET, MTV30 was the closest match with GTV-CT/MR. The mean difference for absolute tumor volumes (GTV-CT, GTV-MR, and MTV30) was not statistically significant; however, spatial overlap by DSC score was average, that is, <0.7. DSC was 0.65 ± 0.15 between GTV-CT and GTV-MR, 0.62 ± 0.15 between GTV-CT and MTV30, and 0.576 ± 0.16 between GTV-MR and MTV30 pairs, respectively. On qualitative analysis, overall tumor extension into adjacent muscles, parotid gland, retromolar trigone, and marrow infiltration of mandible was better appreciated on MRI.

Conclusions

Given the significant spatial variation, multimodality imaging can serve as an excellent complement for target volume delineation on CT scans during intensity modulated radiation therapy planning for LAOC by harnessing the improved soft tissue definition of MRI and the ability of PET to provide metabolic activity information.

The Use of MR-Guided Radiation Therapy for Head and Neck Cancer and Recommended Reporting Guidance

Although magnetic resonance imaging (MRI) has become standard diagnostic workup for head and neck malignancies and is currently recommended by most radiological societies for pharyngeal and oral carcinomas, its utilization in radiotherapy has been heterogeneous during the last decades. However, few would argue that implementing MRI for annotation of target volumes and organs at risk provides several advantages, so that implementation of the modality for this purpose is widely accepted. Today, the term MR-guidance has received a much broader meaning, including MRI for adaptive treatments, MR-gating and tracking during radiotherapy application, MR-features as biomarkers and finally MR-only workflows. First studies on treatment of head and neck cancer on commercially available dedicated hybrid-platforms (MR-linacs), with distinct common features but also differences amongst them, have also been recently reported, as well as "biological adaptation" based on evaluation of early treatment response via functional MRI-sequences such as diffusion weighted ones. Yet, all of these approaches towards head and neck treatment remain at their infancy, especially when compared to other radiotherapy indications. Moreover, the lack of standardization for reporting MR-guided radiotherapy is a major obstacle both to further progress in the field and to conduct and compare clinical trials. Goals of this article is to present and explain all different aspects of MR-guidance for radiotherapy of head and neck cancer, summarize evidence, as well as possible advantages and challenges of the method and finally provide a comprehensive reporting guidance for use in clinical routine and trials.

PET/MR Imaging in Head and Neck Cancer

Head and neck squamous cell carcinoma (HNSCC) can either be examined with hybrid PET/MR imaging systems or sequentially, using PET/CT and MR imaging. Regardless of the acquisition technique, the superiority of MR imaging compared to CT lies in its potential to interrogate tumor and surrounding tissues with different sequences, including perfusion and diffusion. For this reason, PET/MR imaging is preferable for the detection and assessment of locoregional residual/recurrent HNSCC after therapy. In addition, MR imaging interpretation is facilitated when combined with PET. Nevertheless, distant metastases and distant second primary tumors are detected equally well with PET/MR imaging and PET/CT.

Impact of MRI on target volume definition in head and neck cancer patients

BACKGROUND

Target volume definition for curative radiochemotherapy in head and neck cancer is crucial since the predominant recurrence pattern is local. Additional diagnostic imaging like MRI is increasingly used, yet it is usually hampered by different patient positioning compared to radiotherapy. In this study, we investigated the impact of diagnostic MRI in treatment position for target volume delineation.

METHODS

We prospectively analyzed patients who were suitable and agreed to undergo an MRI in treatment position with immobilization devices prior to radiotherapy planning from 2017 to 2019. Target volume delineation for the primary tumor was first performed using all available information except for the MRI and subsequently with additional consideration of the co-registered MRI. The derived volumes were compared by subjective visual judgment and by quantitative mathematical methods.

RESULTS

Sixteen patients were included and underwent the planning CT, MRI and subsequent definitive radiochemotherapy. In 69% of the patients, there were visually relevant changes to the gross tumor volume (GTV) by use of the MRI. In 44%, the GTV_MRI would not have been covered completely by the planning target volume (PTV) of the CT-only contour. Yet, median Hausdorff und DSI values did not reflect these differences. The 3-year local control rate was 94%.

CONCLUSIONS

Adding a diagnostic MRI in RT treatment position is feasible and results in relevant changes in target volumes in the majority of patients.

Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics

Background

Radiomics can provide in-depth characterization of cancers for treatment outcome prediction. Conventional radiomics rely on extraction of image features within a pre-defined image region of interest (ROI) which are typically fed to a classification algorithm for prediction of a clinical endpoint. Deep learning radiomics allows for a simpler workflow where images can be used directly as input to a convolutional neural network (CNN) with or without a pre-defined ROI.

Purpose

The purpose of this study was to evaluate (i) conventional radiomics and (ii) deep learning radiomics for predicting overall survival (OS) and disease-free survival (DFS) for patients with head and neck squamous cell carcinoma (HNSCC) using pre-treatment 18F-fluorodeoxuglucose positron emission tomography (FDG PET) and computed tomography (CT) images.

Materials and methods

FDG PET/CT images and clinical data of patients with HNSCC treated with radio(chemo)therapy at Oslo University Hospital (OUS; n = 139) and Maastricht University Medical Center (MAASTRO; n = 99) were collected retrospectively. OUS data was used for model training and initial evaluation. MAASTRO data was used for external testing to assess cross-institutional generalizability. Models trained on clinical and/or conventional radiomics features, with or without feature selection, were compared to CNNs trained on PET/CT images without or with the gross tumor volume (GTV) included. Model performance was measured using accuracy, area under the receiver operating characteristic curve (AUC), Matthew's correlation coefficient (MCC), and the F1 score calculated for both classes separately.

Results

CNNs trained directly on images achieved the highest performance on external data for both endpoints. Adding both clinical and radiomics features to these image-based models increased performance further. Conventional radiomics including clinical data could achieve competitive performance. However, feature selection on clinical and radiomics data lead to overfitting and poor cross-institutional generalizability. CNNs without tumor and node contours achieved close to on-par performance with CNNs including contours.

Conclusion

High performance and cross-institutional generalizability can be achieved by combining clinical data, radiomics features and medical images together with deep learning models. However, deep learning models trained on images without contours can achieve competitive performance and could see potential use as an initial screening tool for high-risk patients.

Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability in FDG PET and CT images

Objective. Tumor segmentation is a fundamental step for radiotherapy treatment planning. To define an accurate segmentation of the primary tumor (GTVp) of oropharyngeal cancer patients (OPC) each image volume is explored slice-by-slice from different orientations on different image modalities. However, the manual fixed boundary of segmentation neglects the spatial uncertainty known to occur in tumor delineation. This study proposes a novel deep learning-based method that generates probability maps which capture the model uncertainty in the segmentation task.Approach. We included 138 OPC patients treated with (chemo)radiation in our institute. Sequences of 3 consecutive 2D slices of concatenated FDG-PET/CT images and GTVp contours were used as input. Our framework exploits inter and intra-slice context using attention mechanisms and bi-directional long short term memory (Bi-LSTM). Each slice resulted in three predictions that were averaged. A 3-fold cross validation was performed on sequences extracted from the axial, sagittal, and coronal plane. 3D volumes were reconstructed and single- and multi-view ensembling were performed to obtain final results. The output is a tumor probability map determined by averaging multiple predictions.Main Results. Model performance was assessed on 25 patients at different probability thresholds. Predictions were the closest to the GTVp at a threshold of 0.9 (mean surface DSC of 0.81, median HD₉₅of 3.906 mm).Significance. The promising results of the proposed method show that is it possible to offer the probability maps to radiation oncologists to guide them in a in a slice-by-slice adaptive GTVp segmentation.

Benefits of automated gross tumor volume segmentation in head and neck cancer using multi-modality information

PURPOSE

Gross tumor volume (GTV) delineation for head and neck cancer (HNC) radiation therapy planning is time consuming and prone to interobserver variability (IOV). The aim of this study was (1) to develop an automated GTV delineation approach of primary tumor (GTVp) and pathologic lymph nodes (GTVn) based on a 3D convolutional neural network (CNN) exploiting multi-modality imaging input as required in clinical practice, and (2) to validate its accuracy, efficiency and IOV compared to manual delineation in a clinical setting.

METHODS

Two datasets were retrospectively collected from 150 clinical cases. CNNs were trained for GTV delineation with consensus delineation as ground truth, with either single (CT) or co-registered multi-modal (CT + PET or CT + MRI) imaging data as input. For validation, GTVs were delineated on 20 new cases by two observers, once manually, once by correcting the delineations generated by the CNN.

RESULTS

Both multi-modality CNNs performed better than the single-modality CNN and were selected for clinical validation. Mean Dice Similarity Coefficient (DSC) for (GTVp, GTVn) respectively between automated and manual delineations was (69%, 79%) for CT + PET and (59%,71%) for CT + MRI. Mean DSC between automated and corrected delineations was (81%,89%) for CT + PET and (69%,77%) for CT + MRI. Mean DSC between observers was (76%,86%) for manual delineations and (95%,96%) for corrected delineations, indicating a significant decrease in IOV (p < 10^-5), while efficiency increased significantly (48%, p < 10^-5).

CONCLUSION

Multi-modality automated delineation of GTV of HNC was shown to be more efficient and consistent compared to manual delineation in a clinical setting and beneficial over a single-modality approach.

The Impact of Interactive MRI-Based Radiologist Review on Radiotherapy Target Volume Delineation in Head and Neck Cancer

BACKGROUND AND PURPOSE

Peer review of head and neck cancer radiation therapy target volumes by radiologists was introduced in our center to optimize target volume delineation. Our aim was to assess the impact of MR imaging-based radiologist peer review of head and neck radiation therapy gross tumor and nodal volumes, through qualitative and quantitative analysis.

MATERIALS AND METHODS

Cases undergoing radical radiation therapy with a coregistered MR imaging, between April 2019 and March 2020, were reviewed. The frequency and nature of volume changes were documented, with major changes classified as per the guidance of The Royal College of Radiologists. Volumetric alignment was assessed using the Dice similarity coefficient, Jaccard index, and Hausdorff distance.

RESULTS

Fifty cases were reviewed between April 2019 and March 2020. The median age was 59 years (range, 29-83 years), and 72% were men. Seventy-six percent of gross tumor volumes and 41.5% of gross nodal volumes were altered, with 54.8% of gross tumor volume and 66.6% of gross nodal volume alterations classified as "major." Undercontouring of soft-tissue involvement and unidentified lymph nodes were predominant reasons for change. Radiologist review significantly altered the size of both the gross tumor volume (P  = .034) and clinical target tumor volume (P  = .003), but not gross nodal volume or clinical target nodal volume. The median conformity and surface distance metrics were the following: gross tumor volume Dice similarity coefficient = 0.93 (range, 0.82-0.96), Jaccard index = 0.87 (range, 0.7-0.94), Hausdorff distance = 7.45 mm (range, 5.6-11.7 mm); and gross nodular tumor volume Dice similarity coefficient = 0.95 (0.91-0.97), Jaccard index = 0.91 (0.83-0.95), and Hausdorff distance = 20.7 mm (range, 12.6-41.6). Conformity improved on gross tumor volume-to-clinical target tumor volume expansion (Dice similarity coefficient = 0.93 versus 0.95, P  = .003).

CONCLUSIONS

MR imaging-based radiologist review resulted in major changes to most radiotherapy target volumes and significant changes in volume size of both gross tumor volume and clinical target tumor volume, suggesting that this is a fundamental step in the radiotherapy workflow of patients with head and neck cancer.

Automatic gross tumor segmentation of canine head and neck cancer using deep learning and cross-species transfer learning

Background

Radiotherapy (RT) is increasingly being used on dogs with spontaneous head and neck cancer (HNC), which account for a large percentage of veterinary patients treated with RT. Accurate definition of the gross tumor volume (GTV) is a vital part of RT planning, ensuring adequate dose coverage of the tumor while limiting the radiation dose to surrounding tissues. Currently the GTV is contoured manually in medical images, which is a time-consuming and challenging task.

Purpose

The purpose of this study was to evaluate the applicability of deep learning-based automatic segmentation of the GTV in canine patients with HNC.

Materials and methods

Contrast-enhanced computed tomography (CT) images and corresponding manual GTV contours of 36 canine HNC patients and 197 human HNC patients were included. A 3D U-Net convolutional neural network (CNN) was trained to automatically segment the GTV in canine patients using two main approaches: (i) training models from scratch based solely on canine CT images, and (ii) using cross-species transfer learning where models were pretrained on CT images of human patients and then fine-tuned on CT images of canine patients. For the canine patients, automatic segmentations were assessed using the Dice similarity coefficient (Dice), the positive predictive value, the true positive rate, and surface distance metrics, calculated from a four-fold cross-validation strategy where each fold was used as a validation set and test set once in independent model runs.

Results

CNN models trained from scratch on canine data or by using transfer learning obtained mean test set Dice scores of 0.55 and 0.52, respectively, indicating acceptable auto-segmentations, similar to the mean Dice performances reported for CT-based automatic segmentation in human HNC studies. Automatic segmentation of nasal cavity tumors appeared particularly promising, resulting in mean test set Dice scores of 0.69 for both approaches.

Conclusion

In conclusion, deep learning-based automatic segmentation of the GTV using CNN models based on canine data only or a cross-species transfer learning approach shows promise for future application in RT of canine HNC patients.

Delineation uncertainties of tumour volumes on MRI of head and neck cancer patients

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Role of target delineation uncertainties in head and neck cancer patients.

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Knowing contouring variations for MRI allows better adaptation of MRLinac for H&N cancers.

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An interobserver variation for GTV among 8 observers was below 2 mm using MRI.

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Variability between observers might improve using other imaging modalities.

Background

During the last decade, radiotherapy using MR Linac has gone from research to clinical implementation for different cancer locations. For head and neck cancer (HNC), target delineation based only on MR images is not yet standard, and the utilisation of MRI instead of PET/CT in radiotherapy planning is not well established. We aimed to analyse the inter-observer variation (IOV) in delineating GTV (gross tumour volume) on MR images only for patients with HNC.

Material/methods

32 HNC patients from two independent departments were included. Four clinical oncologists from Denmark and four radiation oncologists from Australia had independently contoured primary tumour GTVs (GTV-T) and nodal GTVs (GTV-N) on T2-weighted MR images obtained at the time of treatment planning. Observers were provided with sets of images, delineation guidelines and patient synopsis. Simultaneous truth and performance level estimation (STAPLE) reference volumes were generated for each structure using all observer contours. The IOV was assessed using the DICE Similarity Coefficient (DSC) and mean absolute surface distance (MASD).

Results

32 GTV-Ts and 68 GTV-Ns were contoured per observer. The median MASD for GTV-Ts and GTV-Ns across all patients was 0.17 cm (range 0.08–0.39 cm) and 0.07 cm (range 0.04–0.33 cm), respectively. Median DSC relative to a STAPLE volume for GTV-Ts and GTV-Ns across all patients were 0.73 and 0.76, respectively. A significant correlation was seen between median DSCs and median volumes of GTV-Ts (Spearman correlation coefficient 0.76, p < 0.001) and of GTV-Ns (Spearman correlation coefficient 0.55, p < 0.001).

Conclusion

Contouring GTVs in patients with HNC on MRI showed that the median IOV for GTV-T and GTV-N was below 2 mm, based on observes from two separate radiation departments. However, there are still specific regions in tumours that are difficult to resolve as either malignant tissue or oedema that potentially could be improved by further training in MR-only delineation.

Exploring the impact of metabolic imaging in head and neck cancer treatment

Background

Target volume delineation is performed with anatomical imaging for head and neck cancer. Molecular imaging allows the recognition of specific tumor regions. Its inclusion in the pathway could lead to changes in delineation and resultant treatment plans.

Methods

PRISMA methodology was adhered to when selecting the articles for analysis and only full articles were quality assessed.

Results

Seventeen articles were included. Gross tumor volume (GTV) primary, GTV nodal, and other target volumes were evaluated. Positron emission tomography/computerized tomography (PET/CT) produced smaller primary GTVs, although not with diffusion‐weighted imaging‐magnetic resonance imaging (DWI‐MRI) or PET/MRI. The impact of these image modalities on GTV nodal did not display any consistency. Additionally, there was considerable heterogeneity in metrics comparing delineations. Four studies included appraised the dosimetric impact of the changes in target volume delineation.

Conclusion

Quantifying the impact of molecular imaging is difficult, due to heterogeneity in reporting metrics in molecular imaging modalities and a paucity of detail regarding delineation method and guideline adherence.

Target Definition in MR-Guided Adaptive Radiotherapy for Head and Neck Cancer

In recent years, MRI-guided radiotherapy (MRgRT) has taken an increasingly important position in image-guided radiotherapy (IGRT). Magnetic resonance imaging (MRI) offers superior soft tissue contrast in anatomical imaging compared to computed tomography (CT), but also provides functional and dynamic information with selected sequences. Due to these benefits, in current clinical practice, MRI is already used for target delineation and response assessment in patients with head and neck squamous cell carcinoma (HNSCC). Because of the close proximity of target areas and radiosensitive organs at risk (OARs) during HNSCC treatment, MRgRT could provide a more accurate treatment in which OARs receive less radiation dose. With the introduction of several new radiotherapy techniques (i.e., adaptive MRgRT, proton therapy, adaptive cone beam computed tomography (CBCT) RT, (daily) adaptive radiotherapy ensures radiation dose is accurately delivered to the target areas. With the integration of a daily adaptive workflow, interfraction changes have become visible, which allows regular and fast adaptation of target areas. In proton therapy, adaptation is even more important in order to obtain high quality dosimetry, due to its susceptibility for density differences in relation to the range uncertainty of the protons. The question is which adaptations during radiotherapy treatment are oncology safe and at the same time provide better sparing of OARs. For an optimal use of all these new tools there is an urgent need for an update of the target definitions in case of adaptive treatment for HNSCC. This review will provide current state of evidence regarding adaptive target definition using MR during radiotherapy for HNSCC. Additionally, future perspectives for adaptive MR-guided radiotherapy will be discussed.

Characterization of positioning uncertainties in PET-CT-MR trimodality solutions for radiotherapy

The purpose of this study was to evaluate the positioning uncertainties of two PET/CT‐MR imaging setups, C1 and C2. Because the PET/CT data were acquired on the same hybrid device with automatic image registration, experiments were conducted using CT‐MRI data. In C1, a transfer table was used, which allowed the patient to move from one imager to another while maintaining the same position. In C2, the patient stood up and was positioned in the same radiotherapy treatment position on each imager. The two setups provided a set of PET/CT and MR images. The accuracy of the registration software was evaluated on the CT‐MRI data of one patient using known translations and rotations of MRI data. The uncertainties on the two setups were estimated using a phantom and a cohort of 30 patients. The accuracy of the positioning uncertainties was evaluated using descriptive statistics and a t‐test to determine whether the mean shift significantly deviated from zero (p < 0.05) for each setup. The maximum registration errors were less than 0.97 mm and 0.6° for CT‐MRI registration. On the phantom, the mean total uncertainties were less than 2.74 mm and 1.68° for C1 and 1.53 mm and 0.33° for C2. For C1, the t‐test showed that the displacements along the z‐axis did not significantly deviate from zero (p = 0.093). For C2, significant deviations from zero were present for anterior‐posterior and superior‐inferior displacements. The mean total uncertainties were less than 4 mm and 0.42° for C1 and less than 1.39 mm and 0.27° for C2 in the patients. Furthermore, the t‐test showed significant deviations from zero for C1 on the anterior‐posterior and roll sides. For C2, there was a significant deviation from zero for the left‐right displacements.This study shows that transfer tables require careful evaluation before use in radiotherapy.

Assessing the impact of magnetic resonance treatment simulation (MRSIM) on target volume delineation and dose to organs at risk for oropharyngeal radiotherapy

INTRODUCTION

Assessing the use of a radiation therapy (RT) planning MRI performed in the treatment position (pMRI) on target volume delineation and effect on organ at risk dose for oropharyngeal cancer patients planned with diagnostic MRI (dMRI) and CT scan.

METHODS

Diagnostic MRI scans were acquired for 26 patients in a neutral patient position using a 3T scanner (dMRI). Subsequent pMRI scans were acquired on the same scanner with a flat couch top and the patient in their immobilisation mask. Each series was rigidly registered to the patients planning CT scan and volumes were first completed with the CT/dMRI. The pMRI was then made available for volume modification. For the group with revised volumes, two IMRT plans were developed to demonstrate the impact of the modification. Image and registration quality was also evaluated.

RESULTS

The pMRI registration led to the modification of target volumes for 19 of 26 participants. The pMRI target volumes were larger in absolute volume resulting in reduced capacity for organ sparing. Predominantly, modifications occurred for the primary gross tumour volume (GTVp) with a mean Dice Similarity Coefficient (DSC) of 0.7 and the resulting high risk planning target volume, a mean DSC of 0.89. Both MRIs scored similarly for image quality, with the pMRI demonstrating improved registration quality and efficiency.

CONCLUSIONS

A pMRI provides improvement in registration efficiency, quality and a higher degree of oncologist confidence in target delineation. These results have led to a practice change within our department, where a pMRI is acquired for all eligible oropharyngeal cancer patients.

Comprehensive Quantitative Evaluation of Variability in MR-guided Delineation of Oropharyngeal Gross Tumor Volumes and High-risk Clinical Target Volumes: An R-IDEAL Stage 0 Prospective Study

PURPOSE

Tumor and target volume manual delineation remains a challenging task in head-and-neck cancer radiotherapy. The purpose of this study was to conduct a multi-institutional evaluation of manual delineations of gross tumor volume (GTV), high-risk clinical target volume (CTV), parotids, and submandibular glands on treatment simulation MR scans of oropharyngeal cancer (OPC) patients.

METHODS

Pre-treatment T1-weighted (T1w), T1-weighted with gadolinium contrast (T1w+C) and T2-weighted (T2w) MRI scans were retrospectively collected for 4 OPC patients under an IRB-approved protocol. The scans were provided to twenty-six radiation oncologists from seven international cancer centers who participated in this delineation study. In addition, patients' clinical history and physical examination findings, along with a medical photographic image and radiological results, were provided. The contours were compared using overlap/distance metrics using both STAPLE and pair-wise comparisons. Lastly, participants completed a brief questionnaire to assess participants' experience and CTV delineation institutional practices.

RESULTS

Large variability was measured between observers' delineations for GTVs and CTVs. The mean Dice Similarity Coefficient values across all physicians' delineations for GTVp, GTVn, CTVp, and CTVn were 0.77, 0.67, 0.77, and 0.69, respectively, for STAPLE comparison and 0.67, 0.60, 0.67, and 0.58, respectively, for pair-wise analysis. Normal tissue contours were defined more consistently when considering overlap/distance metrics. The median radiation oncology clinical experience was 7 years. The median experience delineating on MRI was 3.5 years. The GTV-to-CTV margin used was 10 mm for six of seven participant institutions. One institution used 8 mm and three participants (from three different institutions) used a margin of 5 mm.

CONCLUSION

The data from this study suggests that appropriate guidelines, contouring quality assurance sessions, and training are still needed for the adoption of MR-based treatment planning for head-and-neck cancers. Such efforts should play a critical role in reducing delineation variation and ensure standardization of target design across clinical practices.

Synthetic Computed Tomography Generation from 0.35T Magnetic Resonance Images for Magnetic Resonance-Only Radiation Therapy Planning Using Perceptual Loss Models

PURPOSE

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast, which makes it useful for delineating tumor and normal structures in radiation therapy planning, but MRI cannot readily provide electron density for dose calculation. Computed tomography (CT) is used but introduces registration uncertainty between MRI and CT. Previous studies have shown that synthetic CTs (sCTs) can be generated directly from MRI images with deep learning methods. However, mainly high-field MRI images have been validated. This study tested whether acceptable sCTs for MR-only radiation therapy planning can be synthesized using an integrated MR-guided linear accelerator at 0.35T, using MRI images and treatment plans in the liver region.

METHODS AND MATERIALS

Two models were investigated in this study: a convolutional neural network (Unet) with conventional mean square error (MSE) loss and a Unet using a secondary convolutional neural network for perceptual loss. A total of 37 cases were used in this study with 10-fold cross validation, and 37 treatment plans were generated and evaluated for target coverage and dose to organs at risk (OARs) in the MSE loss model, perceptual loss model, and original CT.

RESULTS

The sCTs predicted by the perceptual loss model had improved subjective visual quality compared with those predicted by the MSE loss model, but both were similar in mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). The MAE, PSNR, and NCC for the perceptual loss model were 35.64, 24.11, and 0.9539, respectively, and those for the MSE loss model were 35.67, 24.36, and 0.9566, respectively. No significant differences in target coverage and dose to OARs were found between the sCT predicted by the perceptual loss model or by the MSE model and the original CT image.

CONCLUSIONS

This study indicated that a Unet with both MSE loss and perceptual loss models can be used for generating sCT images from a 0.35T integrated MR linear accelerator.

Automated algorithm for calculation of setup corrections and planning target volume margins for offline image-guided radiotherapy protocols

Purpose

Each radiotherapy center should have a site‐specific planning target volume (PTV) margins and image‐guided (IG) radiotherapy (IGRT) correction protocols to compensate for the geometric errors that can occur during treatment. This study developed an automated algorithm for the calculation and evaluation of these parameters from cone beam computed tomography (CBCT)‐based IG‐intensity modulated radiotherapy (IG‐IMRT) treatment.

Methods and materials

A MATLAB algorithm was developed to extract the setup errors in three translational directions (x, y, and z) from the data logged by the CBCT system during treatment delivery. The algorithm also calculates the resulted population setup error and PTV margin based on the van Herk margin recipe and subsequently estimates their respective values for no action level (NAL) and extended no action level (eNAL) offline correction protocols. The algorithm was tested on 25 head and neck cancer (HNC) patients treated using IG‐IMRT.

Results

The algorithms calculated that the HNC patients require a PTV margin of 3.1, 2.7, and 3.2 mm in the x‐, y‐, and z‐direction, respectively, without IGRT. The margin can be reduced to 2.0, 2.2, and 3.0 mm in the x‐, y‐, and z‐direction, respectively, with NAL and 1.6, 1.7, and 2.2 mm in the x‐, y‐, and z‐direction, respectively, with eNAL protocol. The results obtained were verified to be the same with the margins calculated using an Excel spreadsheet. The algorithm calculates the weekly offline setup error correction values automatically and reduces the risk of input data error observed in the spreadsheet.

Conclusions

In conclusion, the algorithm provides an automated method for optimization and reduction of PTV margin using logged setup errors from CBCT‐based IGRT.

An Inter-observer Study to Determine Radiotherapy Planning Target Volumes for Recurrent Gynaecological Cancer Comparing Magnetic Resonance Imaging Only With Computed Tomography-Magnetic Resonance Imaging

AIMS

Target delineation uncertainty is arguably the largest source of geometric uncertainty in radiotherapy. Several factors can affect it, including the imaging modality used for delineation. It is accounted for by applying safety margins to the target to produce a planning target volume (PTV), to which treatments are designed. To determine the margin, the delineation uncertainty is measured as the delineation error, and then a margin recipe used. However, there is no published evidence of such analysis for recurrent gynaecological cancers (RGC). The aims of this study were first to quantify the delineation uncertainty for RGC gross tumour volumes (GTVs) and to calculate the associated PTV margins and then to quantify the difference in GTV, delineation uncertainty and PTV margin, between a computed tomography-magnetic resonance imaging (CT-MRI) and MRI workflow.

MATERIALS AND METHODS

Seven clinicians delineated the GTV for 20 RGC tumours on co-registered CT and MRI datasets (CT-MRI) and on MRI alone. The delineation error, the standard deviation of distances from each clinician's outline to a reference, was measured and the required PTV margin determined. Differences between using CT-MRI and MRI alone were assessed.

RESULTS

The overall delineation error and the resulting margin were 3.1 mm and 8.5 mm, respectively, for CT-MRI, reducing to 2.5 mm and 7.1 mm, respectively, for MRI alone. Delineation errors and therefore the theoretical margins, varied widely between patients. MRI tumour volumes were on average 15% smaller than CT-MRI tumour volumes.

DISCUSSION

This study is the first to quantify delineation error for RGC tumours and to calculate the corresponding PTV margin. The determined margins were larger than those reported in the literature for similar patients, bringing into question both current margins and margin calculation methods. The wide variation in delineation error between these patients suggests that applying a single population-based margin may result in PTVs that are suboptimal for many. Finally, the reduced tumour volumes and safety margins suggest that patients with RGC may benefit from an MRI-only treatment workflow.

A comparison of methods for fully automatic segmentation of tumors and involved nodes in PET/CT of head and neck cancers

Target volume delineation is a vital but time-consuming and challenging part of radiotherapy, where the goal is to deliver sufficient dose to the target while reducing risks of side effects. For head and neck cancer (HNC) this is complicated by the complex anatomy of the head and neck region and the proximity of target volumes to organs at risk. The purpose of this study was to compare and evaluate conventional PET thresholding methods, six classical machine learning algorithms and a 2D U-Net convolutional neural network (CNN) for automatic gross tumor volume (GTV) segmentation of HNC in PET/CT images. For the latter two approaches the impact of single versus multimodality input on segmentation quality was also assessed. 197 patients were included in the study. The cohort was split into training and test sets (157 and 40 patients, respectively). Five-fold cross-validation was used on the training set for model comparison and selection. Manual GTV delineations represented the ground truth. Tresholding, classical machine learning and CNN segmentation models were ranked separately according to the cross-validation Sørensen-Dice similarity coefficient (Dice). PET thresholding gave a maximum mean Dice of 0.62, whereas classical machine learning resulted in maximum mean Dice scores of 0.24 (CT) and 0.66 (PET; PET/CT). CNN models obtained maximum mean Dice scores of 0.66 (CT), 0.68 (PET) and 0.74 (PET/CT). The difference in cross-validation Dice between multimodality PET/CT and single modality CNN models was significant (p ≤ 0.0001). The top-ranked PET/CT-based CNN model outperformed the best-performing thresholding and classical machine learning models, giving significantly better segmentations in terms of cross-validation and test set Dice, true positive rate, positive predictive value and surface distance-based metrics (p ≤ 0.0001). Thus, deep learning based on multimodality PET/CT input resulted in superior target coverage and less inclusion of surrounding normal tissue.

Deep learning-based auto-delineation of gross tumour volumes and involved nodes in PET/CT images of head and neck cancer patients

PURPOSE

Identification and delineation of the gross tumour and malignant nodal volume (GTV) in medical images are vital in radiotherapy. We assessed the applicability of convolutional neural networks (CNNs) for fully automatic delineation of the GTV from FDG-PET/CT images of patients with head and neck cancer (HNC). CNN models were compared to manual GTV delineations made by experienced specialists. New structure-based performance metrics were introduced to enable in-depth assessment of auto-delineation of multiple malignant structures in individual patients.

METHODS

U-Net CNN models were trained and evaluated on images and manual GTV delineations from 197 HNC patients. The dataset was split into training, validation and test cohorts (n= 142, n = 15 and n = 40, respectively). The Dice score, surface distance metrics and the new structure-based metrics were used for model evaluation. Additionally, auto-delineations were manually assessed by an oncologist for 15 randomly selected patients in the test cohort.

RESULTS

The mean Dice scores of the auto-delineations were 55%, 69% and 71% for the CT-based, PET-based and PET/CT-based CNN models, respectively. The PET signal was essential for delineating all structures. Models based on PET/CT images identified 86% of the true GTV structures, whereas models built solely on CT images identified only 55% of the true structures. The oncologist reported very high-quality auto-delineations for 14 out of the 15 randomly selected patients.

CONCLUSIONS

CNNs provided high-quality auto-delineations for HNC using multimodality PET/CT. The introduced structure-wise evaluation metrics provided valuable information on CNN model strengths and weaknesses for multi-structure auto-delineation.

Trimodality PET/CT/MRI and Radiotherapy: A Mini-Review

Computed tomography (CT) has revolutionized external radiotherapy by making it possible to visualize and segment the tumors and the organs at risk in a three-dimensional way. However, if CT is a now a standard, it presents some limitations, notably concerning tumor characterization and delineation. Its association with functional and anatomical images, that are positron emission tomography (PET) and magnetic resonance imaging (MRI), surpasses its limits. This association can be in the form of a trimodality PET/CT/MRI. The objective of this mini-review is to describe the process of performing this PET/CT/MRI trimodality for radiotherapy and its potential clinical applications. Trimodality can be performed in two ways, either a PET/MRI fused to a planning CT (possibly with a pseudo-CT generated from the MRI for the planning), or a PET/CT fused to an MRI and then registered to a planning CT (possibly the CT of PET/CT if calibrated for radiotherapy). These examinations should be performed in the treatment position, and in the second case, a patient transfer system can be used between the PET/CT and MRI to limit movement. If trimodality requires adapted equipment, notably compatible MRI equipment with high-performance dedicated coils, it allows the advantages of the three techniques to be combined with a synergistic effect while limiting their disadvantages when carried out separately. Trimodality is already possible in clinical routine and can have a high clinical impact and good inter-observer agreement, notably for head and neck cancers, brain tumor, prostate cancer, cervical cancer.

A Formula to Calculate the Threshold for Radiotherapy Targets on PET Images: Simulation Study

Background

Positron emission tomography (PET) images are being applied for defining radiotherapy targets. However, a recognized method for defining radiotherapy targets is lacking. We investigate the threshold to outline the radiotherapy target of a tumor on PET images and its influencing factors, and then expressed it by formula.

Methods

PET imaging for spherical tumors with a different tumor diameter (D), under different system resolutions [full width at half maximum (FWHM)], in different backgrounds with different pixel sizes, was simulated. PET images were analyzed to determine the relationship between the threshold and the factors mentioned above. Finally, the simulation results were verified by phantom experiments.

Results

The threshold decreased sharply with D for D < 2 FWHM, reached the minimum of 31% at D = 2 FWHM and then increased slowly, and it tended to constant for D > 8 FWHM. The threshold decreased with FWHM for FWHM < D/2, reached a minimum at FWHM = D/2, and then increased. The threshold increased with pixel size for D ≤ FWHM and decreased for D > FWHM. The threshold was independent of the background. The relationship between the threshold and its influencing factors was expressed as a formula. The results of the phantom verification indicated that the error of the target volume delineation that was calculated by the formula was less than 9%.

Conclusions

The threshold changes with tumor size, resolution of the PET system and pixel size according to certain rules. The formula to calculate the threshold could provide a method to estimate threshold to outline the radiotherapy target (tumor).

Imaging for Target Delineation in Head and Neck Cancer Radiotherapy

The definition of tumor involved volumes in patients with head and neck cancer poses great challenges with the increasing use of highly conformal radiotherapy techniques eg, volumetric modulated arc therapy and intensity modulated proton therapy. The risk of underdosing the tumor might increase unless great care is taken in the process. The information gained from imaging is increasing with both PET and MRI becoming readily available for the definition of targets. The information gained from these techniques is indeed multidimensional as one often acquire data on eg, metabolism, diffusion, and hypoxia together with anatomical and structural information. Nevertheless, much work remains to fully exploit the available information on a patient-specific level. Multimodality target definition in radiotherapy is a chain of processes that must be individually scrutinized, optimized and quality assured. Any uncertainties or errors in image acquisition, reconstruction, interpretation, and delineation are systematic errors and hence will potentially have a detrimental effect on the entire radiotherapy treatment and hence; the chance of cure or the risk of unnecessary side effects. Common guidelines and procedures create a common minimum standard and ground for evaluation and development. In Denmark, the treatment of head and neck cancer is organized within the multidisciplinary Danish Head and Neck Cancer Group (DAHANCA). The radiotherapy quality assurance group of DAHANCA organized a workshop in January 2020 with participants from oncology, radiology, and nuclear medicine from all centers in Denmark, treating patients with head and neck cancer. The participants agreed on a national guideline on imaging for target delineation in head and neck cancer radiotherapy, which has been approved by the DAHANCA group. The guidelines are available in the Supplementary. The use of multimodality imaging is being recommended for the planning of all radical treatments with a macroscopic tumor. 2-[¹⁸F]FDG-PET/CT should be available, preferable in the treatment position. The recommended MRI sequences are T1, T2 with and without fat suppression, and T1 with contrast enhancement, preferable in the treatment position. The interpretation of clinical information, including thorough physical examination as well as imaging, should be done in a multidisciplinary setting with an oncologist, radiologist, and nuclear medicine specialist.

Gross tumour volume comparison in oropharynx carcinomas using different intelligent imaging software. A retrospective analysis

Purpose

To compare gross tumour volume (GTV) in oropharynx carcinomas using different intelligent imaging software and to evaluate which method is more reliable for tumour volume definition in comparison with 3D ProSoma software.

Material and methods

We retrospectively studied 32 patients with histopathologically confirmed oropharynx carcinomas on dual-source computed tomography (CT) (all patients underwent multislice CT examination after applying 75 ml iodinated non-ionic contrast media). One radiologist calculated the tumour volume – manually measuring tumour length (L), width (W), and height (H) – and then calculated the tumour volume using the formula 0.5236 × L × W × H. The other radiologist used the syngo.CT-Liver-Analysis software to calculate the tumour volumes. Both volume measuring methods were compared with the 3D ProSoma software, which is used by radiotherapists to calculate tumour volumes. Graphpad Prism software was used for statistical data.

Results

syngo.CT-Liver-Analysis software for gross tumour volume determination has greater reliability than the standard manual method with Syngo Plaza in comparison with the 3D ProSoma software.

Conclusions

syngo.CT-Liver-Analysis software is a reliable tool for GTV calculation, with a high correlation score, like that of radiotherapeutic 3D ProSoma software.

Comparison of tumor delineation using dual energy computed tomography versus magnetic resonance imaging in head and neck cancer re-irradiation cases

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GTVs on the 60 kV and 140 kV from DECT, and the T1c and T2 from MRI were compared.

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Delineation was the most consistent using T1c (no interobserver difference in DSC).

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T1c MRI provided higher interobserver agreement for skull base tumors.

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60 kV DECT provided higher interobserver agreement for non-skull base tumors.

In treatment planning, multiple imaging modalities can be employed to improve the accuracy of tumor delineation but this can be costly. This study aimed to compare the interobserver consistency of using dual energy computed tomography (DECT) versus magnetic resonance imaging (MRI) for delineating tumors in the head and neck cancer (HNC) re-irradiation scenario. Twenty-three patients with recurrent HNC and had planning DECT and MRI were identified. Contoured tumor volumes by seven radiation oncologists were compared. Overall, T1c MRI performed the best with median DSC of 0.58 (0–0.91) for T1c. T1c MRI provided higher interobserver agreement for skull base sites and 60 kV DECT provided higher interobserver agreement for non-skull base sites.

[Target volume concepts in radiotherapy and their implications for imaging]

CLINICAL ISSUE

Successful radiotherapy requires precise localization of the tumor and requires high-quality imaging for developing a treatment plan.

STANDARD TREATMENT

Irradiation of the tumor region, including a safety margin.

TREATMENT INNOVATIONS

The target volume consists of the gross tumor volume (GTV) containing visible parts of the tumor, the clinical target volume (CTV) covering the GTV plus invisible tumor extensions, and the planning target volume (PTV) to account for uncertainties. The non-GTV parts of the CTV are based on historical patient data. The PTV margins are based on a calculation of possible uncertainties during planning, setup, or treatment. Normal tissue deserves the identical care in contouring, since its tolerance may limit the tumor dose, taking into account the contours of organs at risk. Serial risk organs benefit from defining a planning organ of risk volume (PRV) to better limit the dose delivered to them.

DIAGNOSTIC WORK-UP

The better the imaging, the more reliable the definition of the GTV and treatment success will be. Multiple imaging sequences are desirable to support the delineation of the tumor. They may result in different CTVs that, depending on their tumor burden, may require different doses.

PERFORMANCE

The definition of standardized target volumes according to the ICRU reports 50, 62, and 83 forms the basis for an individualized radiation treatment planning according to unified criteria on a high-quality level.

ACHIEVEMENTS

Radio-oncology is by nature interdisciplinary, the diagnostic radiologist being an indispensable team partner. A regular dialogue between the disciplines is pivotal for target volume definition and treatment success.

PRACTICAL RECOMMENDATIONS

Imaging for target volume definition requires highest quality imaging, the use of functional imaging methods and close cooperation with a diagnostic radiologist experienced in this field.

Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer

The analysis of multi-modality positron emission tomography and computed tomography (PET-CT) images for computer aided diagnosis applications (e.g., detection and segmentation) requires combining the sensitivity of PET to detect abnormal regions with anatomical localization from CT. Current methods for PET-CT image analysis either process the modalities separately or fuse information from each modality based on knowledge about the image analysis task. These methods generally do not consider the spatially varying visual characteristics that encode different information across the different modalities, which have different priorities at different locations. For example, a high abnormal PET uptake in the lungs is more meaningful for tumor detection than physiological PET uptake in the heart. Our aim is to improve fusion of the complementary information in multi-modality PET-CT with a new supervised convolutional neural network (CNN) that learns to fuse complementary information for multi-modality medical image analysis. Our CNN first encodes modality-specific features and then uses them to derive a spatially varying fusion map that quantifies the relative importance of each modality's features across different spatial locations. These fusion maps are then multiplied with the modality-specific feature maps to obtain a representation of the complementary multi-modality information at different locations, which can then be used for image analysis. We evaluated the ability of our CNN to detect and segment multiple regions (lungs, mediastinum, tumors) with different fusion requirements using a dataset of PET-CT images of lung cancer. We compared our method to baseline techniques for multi-modality image fusion (fused inputs (FS), multi-branch (MB) techniques, and multichannel (MC) techniques) and segmentation. Our findings show that our CNN had a significantly higher foreground detection accuracy (99.29%, p < 0:05) than the fusion baselines (FS: 99.00%, MB: 99.08%, TC: 98.92%) and a significantly higher Dice score (63.85%) than recent PET-CT tumor segmentation methods.

Evaluating diffusion-weighted magnetic resonance imaging for target volume delineation in head and neck radiotherapy

INTRODUCTION

Inter-observer variability (IOV) in target volume delineation is a source of error in head and neck radiotherapy. Diffusion-weighted imaging (DWI) has been shown to be useful in detecting recurrent head and neck cancer. This study aims to determine whether DWI improves target volume delineation and IOV.

METHODS

Four radiation oncologists delineated the gross tumour volume (GTV) for ten head and neck cancer patients. Delineation was performed on CT alone as well as fused image sets which incorporated fluorodeoxyglucose (FDG)-positron emission tomography (PET) and magnetic resonance imaging (MRI) in the form of CT/PET, CT/PET/T2W and CT/PET/T2W/DWI image sets. Analysis of the variability of contour volumes was completed by comparison to the simultaneous truth and performance level estimation (STAPLE) volumes. The DICE Similarity Coefficient (DSC) and other IOV metrics for each observer's contour were compared to the STAPLE for each patient and image dataset. A DWI usability scoresheet for delineation was completed.

RESULTS

The CT/PET/T2W/DWI mean GTV volume of 13.37 (10.35-16.39)cm³ was shown to be different to the mean GTV of 10.92 (8.32-13.51)cm³ when using CT alone (P < 0.001). The GTV DSC amongst observers for CT alone was 0.72 (0.65-0.79), CT/PET was 0.73 (0.67-0.80), CT/PET/T2W was 0.71 (0.64-0.77) and CT/PET/T2W/DWI was 0.69 (0.61-0.75).

CONCLUSION

Mean GTVs with the addition of DWI had slightly larger volumes compared to standard CT and CT/PET volumes. DWI may add supplemental visual information for GTV delineation while having a small impact on IOV, therefore potentially improving target volume delineation.

Assessment of the Impact of Deformable Registration of Diagnostic MRI to Planning CT on GTV Delineation for Radiotherapy for Oropharyngeal Carcinoma in Routine Clinical Practice

Background: Aim of study was to assess impact of deformable registration of diagnostic MRI to planning CT upon gross tumour volume (GTV) delineation of oropharyngeal carcinoma in routine practice. Methods: 22 consecutive patients with oropharyngeal squamous cell carcinoma treated with definitive (chemo)radiotherapy between 2015 and 2016, for whom primary GTV delineation had been performed by a single radiation oncologist using deformable registration of diagnostic MRI to planning CT, were identified. Separate GTVs were delineated as part of routine clinical practice (all diagnostic imaging available side-by-side for each delineation) using: CT (GTV_CT), MRI (GTV_MR), and CT and MRI (GTV_CTMR). Volumetric and positional metric analyses were undertaken using contour comparison metrics (Dice conformity index, centre of gravity distance, mean distance to conformity). Results: Median GTV volumes were 13.7 cm³ (range 3.5–41.7), 15.9 cm³ (range 1.6–38.3), 19.9 cm³ (range 5.5–44.5) for GTV_CT, GTV_MR and GTV_CTMR respectively. There was no significant difference in GTV_CT and GTV_MR volumes; GTV_CTMR was found to be significantly larger than both GTV_MR and GTV_CT. Based on positional metrics, GTV_CT and GTV_MR were the least similar (mean Dice similarity coefficient (DSC) 0.71, 0.84, 0.82 for GTV_CT–GTV_MR, GTV_CTMR–GTV_CT and GTV_CTMR–GTV_MR respectively). Conclusions: These data suggest a complementary role of MRI to CT to reduce the risk of geographical misses, although they highlight the potential for larger target volumes and hence toxicity.

Tumor volume as a prognostic marker in p16-positive and p16-negative oropharyngeal cancer patients treated with definitive intensity-modulated radiotherapy

PURPOSE

To investigate the impact of primary gross tumor volume (pGTV) and nodal gross tumor volume (nGTV) in oropharyngeal squamous cell carcinoma (OPSCC) and the difference in their role between human papillomavirus (HPV)-positive and HPV-negative patients.

METHODS

The patient cohort consists of 91 OPSCC patients treated with definitive radiochemotherapy or radiotherapy using intensity-modulated radiotherapy (IMRT). All patients had a minimum follow-up of 31 months. Volume measurements were made from computer tomography (CT) scans and HPV status was assessed by p16 immunohistochemistry. The end points were as follows: overall survival (OS), disease-free survival (DFS) and locoregional control (LRC).

RESULTS

pGTV was a significant independent prognostic factor for overall survival (OS; p = 0.020) in p16-negative patients. nGTV of p16-negative tumors had significant prognostic value in all end points in multivariate analyses. High-stage (III-IVc) p16-negative tumors were only associated with significantly poorer OS (p = 0.046) but not with poorer LRC or DFS when compared with the low-stage (I-II) tumors. nGTV of p16-positive tumors was an independent prognostic factor for DFS (p = 0.005) and LRC (p = 0.007) in multivariate analyses.

CONCLUSION

pGTV may serve as an independent prognostic factor in p16-negative patients and nGTV may serve as an independent prognostic factor both in p16-positive and p16-negative patients treated with radiochemotherapy or radiotherapy using IMRT. Tumor volume may have an impact on selecting patients for de-escalation protocols in the future, both in p16-positive and p16-negative patients.

Practice patterns for the radical treatment of nasopharyngeal cancer by head and neck oncologists in the United Kingdom

OBJECTIVE

Advances in radiation delivery, imaging techniques, and chemotherapy have significantly improved treatment options for non-metastatic nasopharyngeal cancers (NPC). However, their impact on the practice in the United Kingdom (UK), where this tumour is rare, is unknown. This study examined the current attitudes of UK head and neck oncologists to the treatment of NPC.

METHODS

UK head and neck oncologists representing 19/23 cancer networks were sent an invitation email with a personalised link to a web-based survey designed to identify the influence of tumour and nodal staging on current NPC management practices.

RESULTS

26/42 (61%) of clinicians responded. Induction chemotherapy followed by concomitant chemoradiation was the treatment of choice for Stage III (69%) and IVa/b (96%), with cisplatin and 5-fluorouracil combination being the most commonly used induction chemotherapy regimen (88%). 16 centres (61%) used a geometric approach, adding variable margins of 0-10 mm to the gross tumour volume to define their therapeutic dose clinical target volume. 54% of respondents used 3 radiotherapy (RT) prescription doses to treat NPC. Retropharyngeal nodal region irradiation policy was inconsistent, with nearly one-quarter treating the entire group to a radical dose.

CONCLUSION

Significant heterogeneity currently exists in the RT practice of NPC in the UK. A consensus regarding the optimal curative, function-sparing treatment paradigm for NPC is necessary to ensure cancer survivors have satisfactory long-term health-related quality of life. Advances in knowledge: This is the first study to highlight the significant variation in RT practice of NPC in the UK.

Using primary tumor volumetry to predict treatment outcome for patients with oropharyngeal cancer who were treated with definitive chemoradiotherapy

AIM

This study aimed to determine predictive values for volumetric measures in patients with oropharyngeal cancer who received definitive chemoradiotherapy (CCRT).

METHODS

Contrast-enhanced computed tomography (CT) scans were obtained before radiotherapy (RT) (I), after delivering a median RT of 50.6 Gy (R) and three months after RT (F). Primary site gross tumor volumes (GTV) were assessed using these scans (GTVI , GTVR and GTVF ). The percentage volume change between GTVI and GTVR (GTV change) was calculated. Volumetric analyses of primary site local control (LC) and progression-free survival (PFS) were performed.

RESULTS

In total, 35 patients were evaluated, with a median 31 months of follow-up. The 2-year LC rates (LCRs) were 95.0% for patients with GTVI <23 cc, and 42.9% for those with GTVI ≥23 cc (P = 0.001); the 2-year PFS rates were 85.9% and 21.9% (P = 0.002), respectively. Using GTVR classifications <11 cc or ≥11 cc, log-rank tests demonstrated differences in 2-year LCR (95.2% vs 33.3%, P < 0.001) and 2-year PFS (86.5% vs 0%, P < 0.001). There was no local progression in patients with GTV change ≥75%, and GTV change predicted poor PFS (P = 0.026). On multivariate analysis, GTVR ≥11 cc was a significant predictor of poor LCR (hazard ratio [HR] = 26, P = 0.009) and PFS (HR = 8.33, P = 0.046).

CONCLUSION

For patients with oropharyngeal cancer treated with definitive CCRT, GTVI , GTVR and GTV changes predicted LC and PFS; GTVR was the most significant predictor of LC and PFS. RT intensification should be considered for patients with larger remaining tumors after CCRT.

Interobserver reliability of computed tomographic contouring of canine tonsils in radiation therapy treatment planning

In radiation therapy (RT) treatment planning for canine head and neck cancer, the tonsils may be included as part of the treated volume. Delineation of tonsils on computed tomography (CT) scans is difficult. Error or uncertainty in the volume and location of contoured structures may result in treatment failure. The purpose of this prospective, observer agreement study was to assess the interobserver agreement of tonsillar contouring by two groups of trained observers. Thirty dogs undergoing pre- and post-contrast CT studies of the head were included. After the pre- and postcontrast CT scans, the tonsils were identified via direct visualization, barium paste was applied bilaterally to the visible tonsils, and a third CT scan was acquired. Data from each of the three CT scans were registered in an RT treatment planning system. Two groups of observers (one veterinary radiologist and one veterinary radiation oncologist in each group) contoured bilateral tonsils by consensus, obtaining three sets of contours. Tonsil volume and location data were obtained from both groups. The contour volumes and locations were compared between groups using mixed (fixed and random effect) linear models. There was no significant difference between each group's contours in terms of three-dimensional coordinates. However there was a significant difference between each group's contours in terms of the tonsillar volume (P < 0.0001). Pre- and postcontrast CT can be used to identify the location of canine tonsils with reasonable agreement between trained observers. Discrepancy in tonsillar volume between groups of trained observers may affect RT treatment outcome.

Automated, reference-free local error assessment of multimodal deformable image registration for radiotherapy in the head and neck

BACKGROUND

Head and neck MR-CT deformable image registration (DIR) for radiotherapy planning is hindered by the lack of both ground-truth and per-patient accuracy assessment methods. This study assesses novel post-registration reference-free error assessment algorithms, based on local rigid re-registration of native and pseudomodality images.

METHODS

Head and neck MR obtained in and out of the treatment position underwent DIR to planning CT. Block-wise mutual information (b-MI) and pseudomodality mutual information (b-pmMI) algorithms were validated against applied rotations and translations. Inherent registration error detection was compared across 14 patient datasets.

RESULTS

Using radiotherapy position MR-CT DIR, quantitative comparison of applied rotations and translations revealed that errors between 1 and 4 mm were accurately determined by both algorithms. Using diagnostic position MR-CT DIR, translations of up to 5 mm were accurately detected within the gross tumour volume by both methods. In 14 patient datasets, b-MI and b-pmMI detected similar errors with improved stability in regions of low contrast or CT artefact and a 10-fold speedup for b-pmMI.

CONCLUSIONS

b-MI and b-pmMI algorithms have been validated as providing accurate reference-free quantitative assessment of DIR accuracy on a per-patient basis. b-pmMI is faster and more robust in the presence of modality-specific information.

The need for multidisciplinarity in specialist training to optimize future patient care

Harmonious interactions between radiation, medical, interventional and surgical oncologists, as well as other members of multidisciplinary teams, are essential for the optimization of patient care in oncology. This multidisciplinary approach is particularly important in the current landscape, in which standard-of-care approaches to cancer treatment are evolving towards highly targeted treatments, precise image guidance and personalized cancer therapy. Herein, we highlight the importance of multidisciplinarity and interdisciplinarity at all levels of clinical oncology training. Potential deficits in the current career development pathways and suggested strategies to broaden clinical training and research are presented, with specific emphasis on the merits of trainee involvement in functional multidisciplinary teams. Finally, the importance of training in multidisciplinary research is discussed, with the expectation that this awareness will yield the most fertile ground for future discoveries. Our key message is for cancer professionals to fulfil their duty in ensuring that trainees appreciate the importance of multidisciplinary research and practice.

Considerable interobserver variation in delineation of pancreatic cancer on 3DCT and 4DCT: a multi-institutional study

Background

The delineation of pancreatic tumors on CT is challenging. In this study, we quantified the interobserver variation for pancreatic tumor delineation on 3DCT as well as on 4DCT.

Methods

Eight observers (radiation oncologists) from six institutions delineated pancreatic tumors of four patients with (borderline) resectable pancreatic cancer. The study consisted of two stages. In the 3DCT-stage, the gross tumor volume (GTV) was delineated on a contrast-enhanced scan. In the 4DCT-stage, the internal GTV (iGTV) was delineated, accounting for the respiratory motion. We calculated the volumes of the (i)GTV, the overlap of the delineated volumes (expressed as generalized conformity index: CI_gen), the local observer variation (local standard deviation: SD) and the overall observer variation (overall SD). We compared these results between GTVs and iGTVs. Additionally, observers were asked to fill out a questionnaire concerning the difficulty of the delineation and their experience in delineating pancreatic tumors.

Results

The ratios of the largest to the smallest delineated GTV and iGTV within the same patient were 6.8 and 16.5, respectively. As the iGTV incorporates the GTV during all respiratory phases, the mean volumes of the iGTV (40.07 cm³) were larger than those of the GTV (29.91 cm³). For all patients, CI_gen was larger for the iGTV than for the GTV. The mean overall observer variation (root-mean-square of all local SDs over four patients) was 0.63 cm and 0.80 cm for GTV and iGTV, respectively. The largest local observer variations were seen close to biliary stents and suspicious pathological enlarged lymph nodes, as some observers included them and some did not. This variation was more pronounced for the iGTV than for the GTV. The observers rated the 3DCT-stage and 4DCT-stage equally difficult and treated on average three to four pancreatic cancer patients per year.

Conclusions

A considerable interobserver variation in delineation of pancreatic tumors was observed. This variation was larger for 4D than for 3D delineation. The largest local observer variation was found around biliary stents and suspicious pathological enlarged lymph nodes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13014-017-0777-0) contains supplementary material, which is available to authorized users.

Modality-specific target definition for laryngeal and hypopharyngeal cancer on FDG-PET, CT and MRI

BACKGROUND AND PURPOSE

The goal of this study was to improve target definition by deriving modality-specific margins for clinical target volumes (CTV) for laryngeal and hypopharyngeal cancer on CT, MRI and 18-FDG-PET.

MATERIAL AND METHODS

Twenty-five patients with T3/T4 laryngeal/hypopharyngeal cancer underwent CT, MRI and 18-FDG-PET scans before laryngectomy. HE-sections were obtained from the surgical specimen and tumor was delineated (tumor_HE). The GTVs on CT and MRI were delineated in consensus. PET-based GTVs were automatically segmented. The three-dimensionally reconstructed specimen was registered to the various images. Modality-specific CTV margins were derived and added to the GTVs to achieve adequate tumor coverage. The resulting CTVs were compared with each other, to tumor_HE, and to CTV_CT10 constructed on CT with the clinical margin of 10mm.

RESULTS

CTV margins of 4.3mm (CT), 6.1mm (MRI) and 5.2mm (PET) were needed to achieve adequate tumor coverage. The median volumes of the resulting modality-specific CTVs were 44ml (CT), 48ml (MRI) and 39ml (PET), while the CTV_10mm was 80ml.

CONCLUSION

For laryngohypopharyngeal tumors, 45-52% target volume reduction compared with CTV_10mm is achievable when modality-specific CTV margins are used. PET-based CTVs were significantly smaller compared to CT- and MRI-based CTVs.

Analysis of GTV reduction during radiotherapy for oropharyngeal cancer: Implications for adaptive radiotherapy

BACKGROUND AND PURPOSE

Adaptive field size reduction based on gross tumor volume (GTV) shrinkage imposes risk on coverage. Fiducial markers were used as surrogate for behavior of tissue surrounding the GTV edge to assess this risk by evaluating if GTVs during treatment are dissolving or actually shrinking.

MATERIALS AND METHODS

Eight patients with oropharyngeal tumors treated with chemo-radiation were included. Before treatment, fiducial markers (0.035×0.2cm², n=40) were implanted at the edge of the primary tumor. All patients underwent planning-CT, daily cone beam CT (CBCT) and MRIs (pre-treatment, weeks 3 and 6). Marker displacement on CBCT was compared to local GTV surface displacement on MRIs. Additionally, marker displacement relative to the GTV surfaces during treatment was measured.

RESULTS

GTV surface displacement derived from MRI was larger than derived from fiducial markers (average difference: 0.1cm in week 3). During treatment, the distance between markers and GTV surface on MRI in week 3 increased in 33%>0.3cm and in 10%>0.5cm. The MRI-GTV shrank faster than the surrounding tissue represented by the markers, i.e. adapting to GTV shrinkage may cause under-dosage of microscopic disease.

CONCLUSIONS

We showed that adapting to primary tumor GTV shrinkage on MRI mid-treatment is potentially not safe since at least part of the GTV is likely to be dissolving. Adjustment to clear anatomical boundaries, however, may be done safely.