Adaptive Radiotherapy for Anatomical Changes

Feasibility of Conebeam CT-based online adaptive radiotherapy for neoadjuvant treatment of rectal cancer

Background

Online adaptive radiotherapy has the potential to reduce toxicity for patients treated for rectal cancer because smaller planning target volumes (PTV) margins around the entire clinical target volume (CTV) are required. The aim of this study is to describe the first clinical experience of a Conebeam CT (CBCT)-based online adaptive workflow for rectal cancer, evaluating timing of different steps in the workflow, plan quality, target coverage and patient compliance.

Methods

Twelve consecutive patients eligible for 5 × 5 Gy pre-operative radiotherapy were treated on a ring-based linear accelerator with a multidisciplinary team present at the treatment machine for each fraction. The accelerator is operated using an integrated software platform for both treatment planning and delivery. In all directions for all CTVs a PTV margin of 5 mm was used, except for the cranial/caudal borders of the total CTV where a margin of 8 mm was applied. A reference plan was generated based on a single planning CT. After aligning the patient the online adaptive procedure started with acquisition of a CBCT. The planning CT scan was registered to the CBCT using deformable registration and a synthetic CT scan was generated. With the support of artificial intelligence, structure guided deformation and the synthetic CT scan contours were adapted by the system to match the anatomy on the CBCT. If necessary, these contours were adjusted before a new plan was generated. A second and third CBCT were acquired to validate the new plan with respect to CTV coverage just before and after treatment delivery, respectively. Treatment was delivered using volumetric modulated arc treatment (VMAT). All steps in this process were defined and timed.

Results

On average the timeslot needed at the treatment machine was 34 min. The process of acquiring a CBCT, evaluating and adjusting the contours, creating the new plan and verifying the CTV on the CBCT scan took on average 20 min. Including delivery and post treatment verification this was 26 min. Manual adjustments of the target volumes were necessary in 50% of fractions. Plan quality, target coverage and patient compliance were excellent.

Conclusions

First clinical experience with CBCT-based online adaptive radiotherapy shows it is feasible for rectal cancer.

Trial registration Medical Research Involving Human Subjects Act (WMO) does not apply to this study and was retrospectively approved by the Medical Ethics review Committee of the Academic Medical Center (W21_087 # 21.097; Amsterdam University Medical Centers, Location Academic Medical Center, Amsterdam, The Netherlands).

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01866-7.

Range probing as a quality control tool for CBCT-based synthetic CTs: In vivo application for head and neck cancer patients

Purpose

Cone‐beam CT (CBCT)‐based synthetic CTs (sCT) produced with a deep convolutional neural network (DCNN) show high image quality, suggesting their potential usability in adaptive proton therapy workflows. However, the nature of such workflows involving DCNNs prevents the user from having direct control over their output. Therefore, quality control (QC) tools that monitor the sCTs and detect failures or outliers in the generated images are needed.

This work evaluates the potential of using a range‐probing (RP)‐based QC tool to verify sCTs generated by a DCNN. Such a RP QC tool experimentally assesses the CT number accuracy in sCTs.

Methods

A RP QC dataset consisting of repeat CTs (rCT), CBCTs, and RP acquisitions of seven head and neck cancer patients was retrospectively assessed. CBCT‐based sCTs were generated using a DCNN. The CT number accuracy in the sCTs was evaluated by computing relative range errors between measured RP fields and RP field simulations based on rCT and sCT images.

Results

Mean relative range errors showed agreement between measured and simulated RP fields, ranging from −1.2% to 1.5% in rCTs, and from −0.7% to 2.7% in sCTs.

Conclusions

The agreement between measured and simulated RP fields suggests the suitability of sCTs for proton dose calculations. This outcome brings sCTs generated by DCNNs closer toward clinical implementation within adaptive proton therapy treatment workflows. The proposed RP QC tool allows for CT number accuracy assessment in sCTs and can provide means of in vivo range verification.

Delivered dose-effect analysis of radiation induced rib fractures after thoracic SBRT

BACKGROUND AND PURPOSE

Anatomical changes during the stereotactic body radiation therapy (SBRT) of early stage non-small cell lung cancer (NSCLC) may cause the delivered dose to deviate from the planned dose. We investigate if normal tissue complication probability (NTCP) models based on the delivered dose predict radiation-induced rib fractures better than models based on the planned dose.

MATERIAL AND METHODS

437 NSCLC patients treated to a median dose of 3x18 Gy were included. Delivered dose was estimated by accumulating EQD2-corrected fraction doses after being deformed with daily CBCT-to-planning CT deformable image registration. Dosimetric parameters D_x (dose to a relative volume x) were extracted for each rib included in the CBCTs field-of-view. An NTCP model was constructed for both planned and delivered dose, optimizing the parameters TD₅₀ (dose with 50% toxicity risk), m (steepness of the curve) and x, using maximum likelihood estimation. Best NTCP model was determined using Akaike weights (Aw). Differences between the models were tested for significance using the Vuong's test.

RESULTS

Median time to fracture of 110 fractured ribs was 22.5 months. The maximum rib dose, D₀, best predicted fractures for both planned and delivered dose. The average delivered D₀ was significantly lower than planned (p < 0.001). NTCP model based on the delivered D₀ was the best, with Aw = 0.95. The models were not significantly different.

CONCLUSION

Delivered maximum dose to the ribs was significantly lower than planned. The NTCP model based on delivered dose improved predictions of radiation-induced rib fractures but did not reach statistical significance.

Late Sequelae of Radiotherapy

BACKGROUND

Approximately half of all patients with tumors need radiotherapy. Long-term survivors may suffer from late sequelae of the treatment. The existing radiotherapeutic techniques are being refined so that radiation can be applied more precisely, with the goal of limiting the radiation exposure of normal tissue and reducing late sequelae.

METHODS

This review is based on the findings of a selective search in PubMed for publications on late sequelae of conventional percutaneous radiotherapy, January 2000 to May 2020. Late sequelae affecting the central nervous system, lungs, and heart and the development of second tumors are presented, and radiobiological mechanisms and the relevant technical and conceptual considerations are discussed.

RESULTS

The current standard of treatment involves the use of linear accelerators, intensity-modulated radiotherapy (IMRT), image-guided and respiratory-gated radiotherapy, and the integration of positron emission tomography combined with computed tomography (PET-CT) in radiation treatment planning. Cardiotoxicity has been reduced with regard to the risk of coronary heart disease after radiotherapy for Hodgkin's lymphoma (hazard ratio [HR] 0.44 [0.23; 0.85]). It was also found that the rate of radiation- induced pneumonitis dropped from 7.9% with conformal treatment to 3.5% with IMRT in a phase III lung cancer trial. It is hoped that neurocognitive functional impairment will be reduced by hippocampal avoidance in modern treatment planning: an initial phase III trial yielded a hazard ratio of 0.74 [0.58; 0.94]. It is estimated that 8% of second solid tumors in adults are induced by radiotherapy (3 additional tumors per 1000 patients at 10 years).

CONCLUSION

Special challenges for research in this field arise from the long latency of radiation sequelae and the need for largescale, well-documented patient collectives in order to discern dose-effect relationships, and take account of cofactors, when the overall number of events is small. It is hoped that further technical and conceptual advances will be made in the areas of adaptive radiotherapy, proton and heavy-ion therapy, and personalized therapy.

Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation

PURPOSE

Despite the widespread availability of in-treatment room cone beam computed tomography (CBCT) imaging, due to the lack of reliable segmentation methods, CBCT is only used for gross set up corrections in lung radiotherapies. Accurate and reliable auto-segmentation tools could potentiate volumetric response assessment and geometry-guided adaptive radiation therapies. Therefore, we developed a new deep learning CBCT lung tumor segmentation method.

METHODS

The key idea of our approach called cross-modality educed distillation (CMEDL) is to use magnetic resonance imaging (MRI) to guide a CBCT segmentation network training to extract more informative features during training. We accomplish this by training an end-to-end network comprised of unpaired domain adaptation (UDA) and cross-domain segmentation distillation networks (SDNs) using unpaired CBCT and MRI datasets. UDA approach uses CBCT and MRI that are not aligned and may arise from different sets of patients. The UDA network synthesizes pseudo MRI from CBCT images. The SDN consists of teacher MRI and student CBCT segmentation networks. Feature distillation regularizes the student network to extract CBCT features that match the statistical distribution of MRI features extracted by the teacher network and obtain better differentiation of tumor from background. The UDA network was implemented with a cycleGAN improved with contextual losses separately on Unet and dense fully convolutional segmentation networks (DenseFCN). Performance comparisons were done against CBCT only using 2D and 3D networks. We also compared against an alternative framework that used UDA with MR segmentation network, whereby segmentation was done on the synthesized pseudo MRI representation. All networks were trained with 216 weekly CBCTs and 82 T2-weighted turbo spin echo MRI acquired from different patient cohorts. Validation was done on 20 weekly CBCTs from patients not used in training. Independent testing was done on 38 weekly CBCTs from patients not used in training or validation. Segmentation accuracy was measured using surface Dice similarity coefficient (SDSC) and Hausdroff distance at 95th percentile (HD95) metrics.

RESULTS

The CMEDL approach significantly improved (p < 0.001) the accuracy of both Unet (SDSC of 0.83 ± 0.08; HD95 of 7.69 ± 7.86 mm) and DenseFCN (SDSC of 0.75 ± 0.13; HD95 of 11.42 ± 9.87 mm) over CBCT only 2DUnet (SDSC of 0.69 ± 0.11; HD95 of 21.70 ± 16.34 mm), 3D Unet (SDSC of 0.72 ± 0.20; HD95 15.01 ± 12.98 mm), and DenseFCN (SDSC of 0.66 ± 0.15; HD95 of 22.15 ± 17.19 mm) networks. The alternate framework using UDA with the MRI network was also more accurate than the CBCT only methods but less accurate the CMEDL approach.

CONCLUSIONS

Our results demonstrate feasibility of the introduced CMEDL approach to produce reasonably accurate lung cancer segmentation from CBCT images. Further validation on larger datasets is necessary for clinical translation.

Effect of treatment planning system parameters on beam modulation complexity for treatment plans with single-layer multi-leaf collimator and dual-layer stacked multi-leaf collimator

OBJECTIVE

High levels of beam modulation complexity (MC) and monitor units (MU) can compromise the plan deliverability of intensity-modulated radiotherapy treatments. Our study evaluates the effect of three treatment planning system (TPS) parameters on MC and MU using different multi-leaf collimator (MLC) architectures.

METHODS

192 volumetric modulated arc therapy plans were calculated using one virtual prostate phantom considering three main settings: (1) three TPS-parameters (Convergence; Aperture Shape Controller, ASC; and Dose Calculation Resolution, DCR) selected from Eclipse v15.6, (2) four levels of dose-sparing priority for organs at risk (OAR), and (3) two treatment units with same nominal conformity resolution and different MLC architectures (Halcyon-v2 dual-layer MLC, DL-MLC & TrueBeam single-layer MLC, SL-MLC). We use seven complexity metrics to evaluate the MC, including two new metrics for DL-MLC, assessed by their correlation with γ passing rate (GPR) analysis.

RESULTS

DL-MLC plans demonstrated lower dose-sparing values than SL-MLC plans (p<0.05). TPS-parameters did not change significantly the complexity metrics for either MLC architectures. However, for SL-MLC, significant variations of MU, target volume dose-homogeneity, and dose spillage were associated with ASC and DCR (p<0.05). MU were found to be correlated (highly or moderately) with all complexity metrics (p<0.05) for both MLC plans. Additionally, our new complexity metrics presented a moderate correlation with GPR (r<0.65). An important correlation was demonstrated between MC (plan deliverability) and dose-sparing priority level for DL-MLC.

CONCLUSIONS

TPS-parameters selected do not change MC for DL-MLC architecture, but they might have a potential use to control the MU, PTV homogeneity or dose spillage for SL-MLC. Our new DL-MLC complexity metrics presented important information to be considered in future pre-treatment quality assurance programs. Finally, the prominent dependence between plan deliverability and priority applied to OAR dose sparing for DL-MLC needs to be analyzed and considered as an additional predictor of GPRs in further studies.

ADVANCES IN KNOWLEDGE

Dose-sparing priority might influence in modulation complexity of DL-MLC.

Dosimetric influence of deformable image registration uncertainties on propagated structures for online daily adaptive proton therapy of lung cancer patients

PURPOSE

A major burden of introducing an online daily adaptive proton therapy (DAPT) workflow is the time and resources needed to correct the daily propagated contours. In this study, we evaluated the dosimetric impact of neglecting the online correction of the propagated contours in a DAPT workflow.

MATERIAL AND METHODS

For five NSCLC patients with nine repeated deep-inspiration breath-hold CTs, proton therapy plans were optimised on the planning CT to deliver 60 Gy-RBE in 30 fractions. All repeated CTs were registered with six different clinically used deformable image registration (DIR) algorithms to the corresponding planning CT. Structures were propagated rigidly and with each DIR algorithm and reference structures were contoured on each repeated CT. DAPT plans were optimised with the uncorrected, propagated structures (propagated DAPT doses) and on the reference structures (ideal DAPT doses), non-adapted doses were recalculated on all repeated CTs.

RESULTS

Due to anatomical changes occurring during the therapy, the clinical target volume (CTV) coverage of the non-adapted doses reduces on average by 9.7% (V95) compared to an ideal DAPT doses. For the propagated DAPT doses, the CTV coverage was always restored (average differences in the CTV V95 < 1% compared to the ideal DAPT doses). Hotspots were always reduced with any DAPT approach.

CONCLUSION

For the patients presented here, a benefit of online DAPT was shown, even if the daily optimisation is based on propagated structures with some residual uncertainties. However, a careful (offline) structure review is necessary and corrections can be included in an offline adaption.

Prognostic Significance of Interim Response Evaluation during Definitive Chemoradiotherapy for Locally Advanced Esophageal Squamous Cell Carcinoma

The study aimed to investigate the clinical significance of interim response evaluation during definitive chemoradiotherapy (dCRT) in predicting overall treatment response and survival of patients with locally advanced esophageal squamous cell carcinoma (LAESCC). We reviewed 194 consecutive patients treated with dCRT for biopsy-confirmed LAESCC. A total of 51 patients met the inclusion criteria. Interim response was assessed by defining a region of interest in initial and adaptive computed tomography (CT) images and subsequently examined against the overall treatment response assessed three months after dCRT, treatment failure pattern, overall survival (OS), and progression-free survival (PFS) estimates. Reductions in both the area and maximal diameter of the primary lesion (p < 0.001; p < 0.001, respectively) and those of the metastatic lymph nodes (LN) (p = 0.002; p < 0.001, respectively) in interim analysis were significantly higher among patients who achieved complete response (CR) than among those who did not. OS was significantly longer among patients who showed ≥30% interim reduction in the area and maximal diameter of the primary lesion and among those who showed such reduction in both the primary lesion and LN. PFS was significantly longer in the patients with ≥30% interim reduction in the area of the primary lesion. In addition, the proportion of cases with locoregional failure began decreasing at interim response of 20% or higher, while the proportion of cases with outfield failure followed the opposite pattern, increasing at interim response of 20% or higher. Among patients treated with dCRT for LAESCC, interim response assessed using adaptive CT images correlated with overall CR and OS rates. The evaluation of tumor burden reduction during dCRT may help predict patient prognosis.

The implementation of MOSAIQ-based image-guided radiation therapy image matching within radiation therapy education

Image-guided radiation therapy (IGRT) technologies are routinely used by radiation therapists (RTs) in clinical departments. However, there is limited literature on the acquisition and assessment of IGRT image-matching competencies in undergraduate educational environments. This commentary paper aims to share the authors' experiences in the development of teaching IGRT and image-matching concepts in an undergraduate radiation therapy programme. It outlines how MOSAIQ oncology information systems (OIS) have enabled the university to embed hands-on IGRT image matching on a range of clinical cases. The hands-on exposure to case-based planar and volumetric kilovoltage (kV) image matching has resulted in improved teaching and better preparation of students for clinical IGRT encounters. Students are likely to benefit from critical image assessment and decision-making as well as the improved engagement in teaching and learning.

Using Spatial Probability Maps to Highlight Potential Inaccuracies in Deep Learning-Based Contours: Facilitating Online Adaptive Radiation Therapy

PURPOSE

Contouring organs at risk remains a largely manual task, which is time consuming and prone to variation. Deep learning-based delineation (DLD) shows promise both in terms of quality and speed, but it does not yet perform perfectly. Because of that, manual checking of DLD is still recommended. There are currently no commercial tools to focus attention on the areas of greatest uncertainty within a DLD contour. Therefore, we explore the use of spatial probability maps (SPMs) to help efficiency and reproducibility of DLD checking and correction, using the salivary glands as the paradigm.

METHODS AND MATERIALS

A 3-dimensional fully convolutional network was trained with 315/264 parotid/submandibular glands. Subsequently, SPMs were created using Monte Carlo dropout (MCD). The method was boosted by placing a Gaussian distribution (GD) over the model's parameters during sampling (MCD + GD). MCD and MCD + GD were quantitatively compared and the SPMs were visually inspected.

RESULTS

The addition of the GD appears to increase the method's ability to detect uncertainty. In general, this technique demonstrated uncertainty in areas that (1) have lower contrast, (2) are less consistently contoured by clinicians, and (3) deviate from the anatomic norm.

CONCLUSIONS

We believe the integration of uncertainty information into contours made using DLD is an important step in highlighting where a contour may be less reliable. We have shown how SPMs are one way to achieve this and how they may be integrated into the online adaptive radiation therapy workflow.

Cone Beam CT-Based Daily Adaptive Planning or Defined-Filling Protocol for Neoadjuvant Gastric Cancer Radiation Therapy: A Comparison

Purpose

This study aimed to investigate, in the setting of neoadjuvant gastric irradiation with integrated boost, whether cone beam computed tomography (CBCT)-based adaptive radiation therapy compared with a defined-filling protocol would be beneficial in terms of feasibility and achieving daily reproducible dose volume indexes of the planning target volume (PTV) and organs at risk (OARs) and workflow.

Methods and materials

Planning computed tomography (PCT) and 25 CBCT scans of a previously treated patient were used, and neoadjuvant therapy of gastric carcinoma was simulated offline. PTVs and OARs were defined per the TOPGEAR protocol (PTV: 45 Gy/1.8 Gy), and an integrated boost (gross tumor volume [GTV]: 50.4 Gy/2.016 Gy) was added. The patient followed a filling regimen consisting of 12-hour fasting followed by 200 mL of water intake (2 glasses of water) immediately before irradiation. OARs and PTVs were newly contoured on each CBCT. Nonrigid registration of PCT and CBCT scans was performed. Nonadapted plans were recalculated on each CBCT (R-CBCT). Furthermore, an adapted plan was created for the new anatomy (A-CBCT). Dose parameters and comparison of R-CBCT and A-CBCT for the kidneys, liver, and heart were analyzed using a paired t test.

Results

A total of 200 plans for R-CBCT and A-CBCT were obtained. Mean gastric volumes were 277.32 cm³ (±54.40 cm³) in CBCT scans and 519.2 cm³ in PCT. Mean doses to the PTV did not differ meaningfully within the CBCT scans, with an average of 1.54%. The D₉₅ improved in GTV coverage by 5.26% compared with the R-CBCT plan. Mean heart, liver, and right kidney doses were reduced with the A-CBCT plan by 35.74%, 10.71% and 29.47%, respectively. The R- and A-CBCT comparison for GTV and OARs was significantly different in all cases (P < .0001).

Conclusions

Adaptive radiation therapy through deformable registration represents an important tool in neoadjuvant gastric irradiation, encompassing daily variability and organ motion, compared with the defined-filling protocol while improving OAR sparing.

Patterns of practice for adaptive and real-time radiation therapy (POP-ART RT) part I: Intra-fraction breathing motion management

PURPOSE

The POP-ART RT study aims to determine to what extent and how intra-fractional real-time respiratory motion management (RRMM) and plan adaptation for inter-fractional anatomical changes (ART), are used in clinical practice and to understand barriers to implementation. Here we report on part I: RRMM.

MATERIAL AND METHODS

A questionnaire was distributed worldwide to assess current clinical practice, wishes for expansion or new implementation and barriers to implementation. RRMM was defined as inspiration/expiration gating in free-breathing or breath-hold, or tracking where the target and the beam are continuously realigned.

RESULTS

The questionnaire was completed by 200 centres from 41 countries. RRMM was used by 68% of respondents ('users') for a median (range) of 2 (1-6) tumour sites. Eighty-one percent of users applied inspiration breath-hold in at least one tumour site (breast: 96%). External marker was used to guide RRMM by 61% of users. KV/MV imaging was frequently used for liver and pancreas (with fiducials) and for lung (with or without fiducials). Tracking was mainly performed on robotic linacs with hybrid internal-external monitoring. For breast and lung, approximately 75% of respondents used or wished to implement RRMM, which was lower for liver (44%) and pancreas (27%). Seventy-one percent of respondents wished to implement RRMM for a new tumour site. Main barriers were human/financial resources and capacity on the machine.

CONCLUSION

Sixty-eight percent of respondents used RRMM and 71% wished to implement RRMM for a new tumour site. The main barriers to implementation were human/financial resources and capacity on treatment machines.

Synthetic CT in assessment of anatomical and dosimetric variations in radiotherapy - procedure validation

Introduction: One of many procedures to control the quality of radiotherapy is daily imaging of the patient’s anatomy. The CBCT (Cone Beam Computed Tomography) plays an important role in patient positioning, and dose delivery monitoring. Nowadays, CBCT is a baseline for the calculation of fraction and total dose. Thus, it provides the potential for more comprehensive monitoring of the delivered dose and adaptive radiotherapy. However, due to the poor quality and the presence of numerous artifacts, the replacement of the CBCT image with the corrected one is desired for dose calculation. The aim of the study was to validate a method for generating a synthetic CT image based on deformable image registration.

Material and methods: A Head & Torso Freepoint phantom, model 002H9K (Computerized Imaging Reference Systems, Norfolk, USA) with inserts was imaged with CT (Computed Tomography). Then, contouring and treatment plan were created in Eclipse (Varian Medical Systems, Palo Alto, CA, USA) treatment planning system. The phantom was scanned again with the CBCT. The planning CT was registered and deformed to the CBCT, resulting in a synthetic CT in Velocity software (Varian Medical Systems, Palo Alto, CA, USA). The dose distribution was recalculated based on the created CT image.

Results: Differences in structure volumes and dose statistics calculated both on CT and synthetic CT were evaluated. Discrepancies between the original and delivered plan from 0.0 to 2.5% were obtained. Dose comparison was performed on the DVH (Dose-Volume Histogram) for all delineated inserts.

Conclusions: Our findings suggest the potential utility of deformable registration and synthetic CT for providing dose reconstruction. This study reports on the limitation of the procedure related to the limited length of the CBCT volume and deformable fusion inaccuracies.

Patterns of practice for adaptive and real-time radiation therapy (POP-ART RT) part II: Offline and online plan adaption for interfractional changes

PURPOSE

The POP-ART RT study aims to determine to what extent and how intrafractional real-time respiratory motion management (RRMM), and plan adaptation for interfractional anatomical changes (ART) are used in clinical practice and to understand barriers to implementation. Here we report on part II: ART using more than one plan per target per treatment course.

MATERIALS AND METHODS

A questionnaire on the current practice of ART, wishes for expansion or implementation, and barriers to implementation was distributed worldwide. Four types of ART were discriminated: daily online replanning, online plan library, protocolled offline replanning (all three based on a protocol), and ad-hoc offline replanning.

RESULTS

The questionnaire was completed by 177 centres from 40 countries. ART was used by 61% of respondents (31% with protocol) for a median (range) of 3 (1-8) tumour sites. CBCT/MVCT was the main imaging modality except for online daily replanning (11 users) where 10 users used MR. Two thirds of respondents wished to implement ART for a new tumour site; 40% of these had plans to do it in the next 2 years. Human/material resources and technical limitations were the main barriers to further use and implementation.

CONCLUSIONS

ART was used for a broad range of tumour sites, mainly with ad-hoc offline replanning and for a median of 3 tumour sites. There was a large interest in implementing ART for more tumour sites, mainly limited by human/material resources and technical limitations. Daily online replanning was primarily performed on MR-linacs.

Deforming to Best Practice: Key considerations for deformable image registration in radiotherapy

Image registration is a process that underlies many new techniques in radiation oncology - from multimodal imaging and contour propagation in treatment planning to dose accumulation throughout treatment. Deformable image registration (DIR) is a subset of image registration subject to high levels of complexity in process and validation. A need for local guidance to assist in high-quality utilisation and best practice was identified within the Australian community, leading to collaborative activity and workshops. This report communicates the current limitations and best practice advice from early adopters to help guide those implementing DIR in the clinic at this early stage. They are based on the state of image registration applications in radiotherapy in Australia and New Zealand (ANZ), and consensus discussions made at the 'Deforming to Best Practice' workshops in 2018. The current status of clinical application use cases is presented, including multimodal imaging, automatic segmentation, adaptive radiotherapy, retreatment, dose accumulation and response assessment, along with uptake, accuracy and limitations. Key areas of concern and preliminary suggestions for commissioning, quality assurance, education and training, and the use of automation are also reported. Many questions remain, and the radiotherapy community will benefit from continued research in this area. However, DIR is available to clinics and this report is intended to aid departments using or about to use DIR tools now.

Evaluation of a cycle-generative adversarial network-based cone-beam CT to synthetic CT conversion algorithm for adaptive radiation therapy

PURPOSE

Image-guided radiation therapy could benefit from implementing adaptive radiation therapy (ART) techniques. A cycle-generative adversarial network (cycle-GAN)-based cone-beam computed tomography (CBCT)-to-synthetic CT (sCT) conversion algorithm was evaluated regarding image quality, image segmentation and dosimetric accuracy for head and neck (H&N), thoracic and pelvic body regions.

METHODS

Using a cycle-GAN, three body site-specific models were priorly trained with independent paired CT and CBCT datasets of a kV imaging system (XVI, Elekta). sCT were generated based on first-fraction CBCT for 15 patients of each body region. Mean errors (ME) and mean absolute errors (MAE) were analyzed for the sCT. On the sCT, manually delineated structures were compared to deformed structures from the planning CT (pCT) and evaluated with standard segmentation metrics. Treatment plans were recalculated on sCT. A comparison of clinically relevant dose-volume parameters (D₉₈, D₅₀ and D₂ of the target volume) and 3D-gamma (3%/3mm) analysis were performed.

RESULTS

The mean ME and MAE were 1.4, 29.6, 5.4 Hounsfield units (HU) and 77.2, 94.2, 41.8 HU for H&N, thoracic and pelvic region, respectively. Dice similarity coefficients varied between 66.7 ± 8.3% (seminal vesicles) and 94.9 ± 2.0% (lungs). Maximum mean surface distances were 6.3 mm (heart), followed by 3.5 mm (brainstem). The mean dosimetric differences of the target volumes did not exceed 1.7%. Mean 3D gamma pass rates greater than 97.8% were achieved in all cases.

CONCLUSIONS

The presented method generates sCT images with a quality close to pCT and yielded clinically acceptable dosimetric deviations. Thus, an important prerequisite towards clinical implementation of CBCT-based ART is fulfilled.

Radiotherapy for Head and Neck Cancer: Evaluation of Triggered Adaptive Replanning in Routine Practice

Purpose and Objective

A proportion of patients receiving radiotherapy for head and neck squamous cell carcinoma (HNSCC) require ad hoc treatment re-planning. The aim of this retrospective study is to analyze the patients who required ad hoc re-planning and to identify factors, which may predict need for re-planning.

Materials and Methods

A single center evaluation of all patients receiving radical or adjuvant (chemo)radiotherapy (CRT) for HNSCC between January and December 2016 was undertaken. Patients who underwent ad hoc re-planning during the treatment were identified in electronic records. Reasons for re-planning were categorized as: weight loss, tumor shrinkage, changes in patient position and immobilization-related factors. Potential trigger factors for adaptive radiotherapy such as patient characteristics, primary tumor site, stage, concomitant chemotherapy, weight loss ratios, radical/adjuvant treatment, and nutritional interventions were investigated.

Results

31/290 (10.6%) HNSCC patients who underwent radical/adjuvant radiotherapy required re-planning. The adaptive radiotherapy (ART) was performed at a mean fraction of 15. The most common documented reasons for re-planning were tumor shrinkage (35.5%) and weight loss (35.5%). Among the patient/tumor/treatment factors, nasopharyngeal primary site (p = 0.013) and use of concurrent chemotherapy with radiotherapy (p = 0.034) were found to be significantly correlated with the need for re-planning.

Conclusion

Effective on-treatment verification schedules and close follow up of patients especially with NPC primary and/or treated with concurrent chemoradiotherapy are crucial to identify patients requiring ART. We suggest an individualized triggered approach to ART rather than scheduled strategies as it is likely to be more feasible in terms of utilization of workload and resources.

Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers

Importance

Personalized radiotherapy planning depends on high-quality delineation of target tumors and surrounding organs at risk (OARs). This process puts additional time burdens on oncologists and introduces variability among both experts and institutions.

Objective

To explore clinically acceptable autocontouring solutions that can be integrated into existing workflows and used in different domains of radiotherapy.

Design, Setting, and Participants

This quality improvement study used a multicenter imaging data set comprising 519 pelvic and 242 head and neck computed tomography (CT) scans from 8 distinct clinical sites and patients diagnosed either with prostate or head and neck cancer. The scans were acquired as part of treatment dose planning from patients who received intensity-modulated radiation therapy between October 2013 and February 2020. Fifteen different OARs were manually annotated by expert readers and radiation oncologists. The models were trained on a subset of the data set to automatically delineate OARs and evaluated on both internal and external data sets. Data analysis was conducted October 2019 to September 2020.

Main Outcomes and Measures

The autocontouring solution was evaluated on external data sets, and its accuracy was quantified with volumetric agreement and surface distance measures. Models were benchmarked against expert annotations in an interobserver variability (IOV) study. Clinical utility was evaluated by measuring time spent on manual corrections and annotations from scratch.

Results

A total of 519 participants' (519 [100%] men; 390 [75%] aged 62-75 years) pelvic CT images and 242 participants' (184 [76%] men; 194 [80%] aged 50-73 years) head and neck CT images were included. The models achieved levels of clinical accuracy within the bounds of expert IOV for 13 of 15 structures (eg, left femur, κ = 0.982; brainstem, κ = 0.806) and performed consistently well across both external and internal data sets (eg, mean [SD] Dice score for left femur, internal vs external data sets: 98.52% [0.50] vs 98.04% [1.02]; P = .04). The correction time of autogenerated contours on 10 head and neck and 10 prostate scans was measured as a mean of 4.98 (95% CI, 4.44-5.52) min/scan and 3.40 (95% CI, 1.60-5.20) min/scan, respectively, to ensure clinically accepted accuracy. Manual segmentation of the head and neck took a mean 86.75 (95% CI, 75.21-92.29) min/scan for an expert reader and 73.25 (95% CI, 68.68-77.82) min/scan for a radiation oncologist. The autogenerated contours represented a 93% reduction in time.

Conclusions and Relevance

In this study, the models achieved levels of clinical accuracy within expert IOV while reducing manual contouring time and performing consistently well across previously unseen heterogeneous data sets. With the availability of open-source libraries and reliable performance, this creates significant opportunities for the transformation of radiation treatment planning.

Quantitative analysis of MRI-guided radiotherapy treatment process time for tumor real-time gating efficiency

PURPOSE

Magnetic Resonance-guided radiotherapy (MRgRT) systems allow continuous monitoring of therapy volumes during treatment delivery and personalized respiratory gating approaches. Treatment length may therefore be significantly affected by patient's compliance and breathing control. We quantitatively analyzed treatment process time efficiency (TE ) using data obtained from real-world patient treatment logs to optimize MRgRT delivery settings.

METHODS

Data corresponding to the first 100 patients treated with a low T hybrid MRI-Linac system, both in free breathing (FB) and in breath hold inspiration (BHI) were collected. TE has been computed as the percentage difference of the actual single fraction's total treatment time and the predicted treatment process time, as computed by the TPS during plan optimization. Differences between the scheduled and actual treatment room occupancy time were also evaluated. Finally, possible correlations with planning, delivery and clinical parameters with TE were also investigated.

RESULTS

Nine hundred and nineteen treatment fractions were evaluated. TE difference between BHI and FB patients' groups was statistically significant and the mean TE were 42.4%, and -0.5% respectively. No correlation was found with TE for BHI and FB groups. Planning, delivering and clinical parameters classified BHI and FB groups, but no correlation with TE was found.

CONCLUSION

The use of BHI gating technique can increase the treatment process time significantly. BHI technique could be not always an adequate delivery technique to optimize the treatment process time. Further gating techniques should be considered to improve the use of MRgRT.

Comparison of CBCT conversion methods for dose calculation in the head and neck region

The purpose of this study was to compare different methods of CBCT conversion respect to dose calculation accuracy. Twelve head and neck cancer patients treated with VMAT using simultaneous integrated boost technique were selected for the study. For each patient a planning CT (pCT), a control. CT acquired in the fourth week of treatment and a CBCT scan acquired on the closest day with the control CT were used. In order to re-calculate dose directly on CBCT image sets, a population based approach (CBCTPop) and a Histogram Matching (HM) approach based on rigid (CBCTHM-R) and deformable registration (CBCTHM-D) were used. Additionally, virtual CTs (vCTs) were generated using two deformable image registration algorithms (CTELX and CTANC) of the planning CT to the CBCT by using two different deformable image registration (DIR) algorithms. The corresponding control CTs were selected as ground truth and dose distributions on CBCT were analyzed using 3D global gamma index analysis applying a threshold of 10% with respect to the prescribed dose. Using the 2%/2mm gamma criterion, the results were 89.9%(±8.3%), 94.1%(±5.0%), 94.3%(±5.7%), 96.1%(±3.9%), 93.4%(±6.3%) for the CBCTPop, CBCTHM-R, CBCTHM-D, CTELX and CTANC, respectively. On average, the HM and DIR techniques showed a higher accuracy compared to the population based approach, but Kruskal-Wallis test did not show significant difference among the investigated dose calculation techniques assuming p<0.05. More sophisticated CBCT dose calculation methods seem to improve the dose calculation accuracy, but statistical significance remains to be demonstrated.

An international survey on the clinical use of rigid and deformable image registration in radiotherapy

OBJECTIVES

Rigid image registration (RIR) and deformable image registration (DIR) are widely used in radiotherapy. This project aims to capture current international approaches to image registration.

METHODS

A survey was designed to identify variations in use, resources, implementation, and decision-making criteria for clinical image registration. This was distributed to radiotherapy centers internationally in 2018.

RESULTS

There were 57 responses internationally, from the Americas (46%), Australia/New Zealand (32%), Europe (12%), and Asia (10%). Rigid image registration and DIR were used clinically for computed tomography (CT)-CT registration (96% and 51%, respectively), followed by CT-PET (81% and 47%), CT-CBCT (84% and 19%), CT-MR (93% and 19%), MR-MR (49% and 5%), and CT-US (9% and 0%). Respondent centers performed DIR using dedicated software (75%) and treatment planning systems (29%), with 84% having some form of DIR software. Centers have clinically implemented DIR for atlas-based segmentation (47%), multi-modality treatment planning (65%), and dose deformation (63%). The clinical use of DIR for multi-modality treatment planning and accounting for retreatments was considered to have the highest benefit-to-risk ratio (69% and 67%, respectively).

CONCLUSIONS

This survey data provides useful insights on where, when, and how image registration has been implemented in radiotherapy centers around the world. DIR is mainly in clinical use for CT-CT (51%) and CT-PET (47%) for the head and neck (43-57% over all use cases) region. The highest benefit-risk ratio for clinical use of DIR was for multi-modality treatment planning and accounting for retreatments, which also had higher clinical use than for adaptive radiotherapy and atlas-based segmentation.

Radiotherapy Treatment plannINg study Guidelines (RATING): A framework for setting up and reporting on scientific treatment planning studies

Radiotherapy treatment planning studies contribute significantly  to advances and improvements in radiation treatment of cancer patients. They are a pivotal step to support and facilitate the introduction of novel techniques into clinical practice, or as a first step before clinical trials can be carried out. There have been numerous examples published in the literature that demonstrated the feasibility of such techniques as IMRT, VMAT, IMPT, or that compared different treatment methods (e.g. non-coplanar vs coplanar treatment), or investigated planning approaches (e.g. automated planning). However, for a planning study to generate trustworthy new knowledge and give confidence in applying its findings, then its design, execution and reporting all need to meet high scientific standards. This paper provides a 'quality framework' of recommendations and guidelines that can contribute to the quality of planning studies and resulting publications. Throughout the text, questions are posed and, if applicable to a specific study and if met, they can be answered positively in the provided 'RATING' score sheet. A normalised weighted-sum score can then be calculated from the answers as a quality indicator. The score sheet can also be used to suggest how the quality might be improved, e.g. by focussing on questions with high weight, or by encouraging consideration of aspects given insufficient attention. Whilst the overall aim of this framework and scoring system is to improve the scientific quality of treatment planning studies and papers, it might also be used by reviewers and journal editors to help to evaluate scientific manuscripts reporting planning studies.

Factors influencing the use of adaptive radiation therapy in vulvar carcinoma

AIM

We aim to evaluate the variables affecting the frequency of adaptive radiotherapy (ART) in vulvar cancer.

BACKGROUND

ART may be needed throughout a definitive RT course for vulvar carcinoma due to changes in patient's anatomy and tumor response.

MATERIALS AND METHODS

Charts of patients charts who had been treated with definitive concurrent chemo-radiotherapy for vulvar carcinoma, between January 2015 and December 2019 were inquired. Radiation therapy was delivered using intensity modulated radiotherapy (IMRT) with daily image-guided radiotherapy (IGRT). ART was defined as re-simulation and re-planning based on deformation in the irradiated volume by more than 1 cm. Univariate analysis was conducted to study the impact of patient's demographics as well as tumor characteristics on the frequency of ART.

RESULTS

22 patients were eligible for analysis. Median age at diagnosis was 55 years (range 43-82). Radiotherapy dose was 60-66 Gy over 30-35 fractions (fx). Median primary tumor volume was 30cc (9-140). Median Body Mass Index (BMI) was 32 (range 21-40). Thirteen out of 22 patients (59%) required ART, with median timing at 25 fx (19-31). On univariate analysis, larger primary tumor volume (> = 30cc) was associated significantly with increased frequency of ART (p value = 0.0005). There was no significant impact of ART on the frequency with respect to patient's age, BMI, tumor stage, grade and location.

CONCLUSION

Changes in radiation target volume are common among vulvar carcinoma patients who are treated with definitive radiotherapy, especially large primary tumors. This review highlights the importance of ART for patients with vulvar carcinoma treated with definitive radiotherapy.

Online adaptive radiotherapy compared to plan selection for rectal cancer: quantifying the benefit

Background

To compare online adaptive radiation therapy (ART) to a clinically implemented plan selection strategy (PS) with respect to dose to the organs at risk (OAR) for rectal cancer.

Methods

The first 20 patients treated with PS between May–September 2016 were included. This resulted in 10 short (SCRT) and 10 long (LCRT) course radiotherapy treatment schedules with a total of 300 Conebeam CT scans (CBCT). New dual arc VMAT plans were generated using auto-planning for both the online ART and PS strategy.

For each fraction bowel bag, bladder and mesorectum were delineated on daily Conebeam CTs. The dose distribution planned was used to calculate daily DVHs. Coverage of the CTV was calculated, as defined by the dose received by 99% of the CTV volume (D99%). The volume of normal tissue irradiated with 95% of the prescribed fraction dose was calculated by calculating the volume receiving 95% of the prescribed fraction or more dose minus the volume of the CTV. For each fraction the difference between the plan selection and online adaptive strategy of each DVH parameter was calculated, as well as the average difference per patient.

Results

Target coverage remained the same for online ART. The median volume of the normal tissue irradiated with 95% of the prescribed dose dropped from 642 cm3 (PS) to 237 cm3 (online-ART)(p < 0.001). Online ART reduced dose to the OARs for all tested dose levels for SCRT and LCRT (p < 0.001). For V15Gy of the bowel bag the median difference over all fractions of all patients was − 126 cm³ in LCRT, while the average difference per patient ranged from − 206 cm³ to − 40 cm³. For SCRT the median difference was − 62 cm³, while the range of the average difference per patient was − 105 cm3 to − 51 cm³.

For V15Gy of the bladder the median difference over all fractions of all patients was 26% in LCRT, while the average difference per patient ranged from − 34 to 12%. For SCRT the median difference of V95% was − 8%, while the range of the average difference per patient was − 29 to 0%.

Conclusions

Online ART for rectal cancer reduces dose the OARs significantly compared to a clinically implemented plan selection strategy, without compromising target coverage.

Trial registration

Medical Research Involving Human Subjects Act (WMO) does not apply to this study and was retrospectively approved by the Medical Ethics review Committee of the Academic Medical Center (W19_357 # 19.420; Amsterdam University Medical Centers, Location Academic Medical Center, Amsterdam, The Netherlands).

Tumor volume regression during neoadjuvant chemoradiotherapy for esophageal cancer: a prospective study with weekly MRI

Background: Neoadjuvant chemoradiotherapy (nCRT) for esophageal cancer causes tumor regression during treatment. Tumor regression can induce changes in the thoracic anatomy, with smaller target volumes and displacement of organs at risk (OARs) surrounding the tumor as a result. Adaptation of the radiotherapy treatment plan according to volumetric changes during treatment might reduce radiation dose to the OARs, while maintaining adequate target coverage. Data on the magnitude of the volumetric changes and its impact on the thoracic anatomy is scarce. The aim of this study was to assess the volumetric changes in the primary tumor during nCRT for esophageal cancer based on weekly MRI scans.Material and methods: In this prospective study, patients with adeno- or squamous cell carcinoma of the esophagus treated with neoajduvant chemoradiotherapy according to the CROSS regimen (carboplatin + paclitaxel + 23 × 1.8 Gy) were included. Of each patient, six sequential MRI scans were acquired: one prior to nCRT, and five in each subsequent week during nCRT. Tumor volumes were delineated on the transversal T2 weighted images by two radiation oncologists. Volumetric changes were analyzed using linear mixed effects models.Results: A total of 170 MRI scans from 29 individual patients were included. The mean (± standard deviation (SD)) tumor volume at baseline was 45 cm³ (± 23). Tumor volume regression started after the first week of nCRT with a significant decrease in tumor volumes every subsequent week. A decrease to 42 cm³ (91% of initial volume), 38 cm³ (81%), 35 cm³ (77%), and 32 cm³ (72%) was observed in the second, third, fourth and fifth week of nCRT, respectively.Conclusion: Based on weekly MRI scanning during nCRT for esophageal cancer, a considerable decrease in tumor volume was observed during treatment. Volume regression and consequential anatomical changes suggest the possible benefit of adaptive radiotherapy.

A review of Image Guided Radiation Therapy in head and neck cancer from 2009-201 - Best Practice Recommendations for RTTs in the Clinic

Radiation therapy (RT) is beneficial in Head and Neck Cancer (HNC) in both the definitive and adjuvant setting. Highly complex and conformal planning techniques are becoming standard practice in delivering increased doses in HNC. A sharp falloff in dose outside the high dose area is characteristic of highly complex techniques and geometric uncertainties must be minimised to prevent under dosage of the target volume and possible over dosage of surrounding critical structures. CTV-PTV margins are employed to account for geometric uncertainties such as set up errors and both interfraction and intrafraction motion. Robust immobilisation and Image Guided Radiation Therapy (IGRT) is also essential in this group of patients to minimise discrepancies in patient position during the treatment course. IGRT has evolved with increased 2-Dimensional (2D) and 3-Dimensional (3D) IGRT modalities available for geometric verification. 2D and 3D IGRT modalities are both beneficial in geometric verification while 3D imaging is a valuable tool in assessing volumetric changes that may have dosimetric consequences for this group of patients. IGRT if executed effectively and efficiently provides clinicians with confidence to reduce CTV-PTV margins thus limiting treatment related toxicities in patients. Accumulated exposure dose from IGRT vary considerably and may be incorporated into the treatment plan to avoid excess dose. However, there are considerable variations in the application of IGRT in RT practice. This paper aims to summarise the advances in IGRT in HNC treatment and provide clinics with recommendations for an IGRT strategy for HNC in the clinic.

Pelvic multi-organ segmentation on cone-beam CT for prostate adaptive radiotherapy

BACKGROUND AND PURPOSE

The purpose of this study is to develop a deep learning-based approach to simultaneously segment five pelvic organs including prostate, bladder, rectum, left and right femoral heads on cone-beam CT (CBCT), as required elements for prostate adaptive radiotherapy planning.

MATERIALS AND METHODS

We propose to utilize both CBCT and CBCT-based synthetic MRI (sMRI) for the segmentation of soft tissue and bony structures, as they provide complementary information for pelvic organ segmentation. CBCT images have superior bony structure contrast and sMRIs have superior soft tissue contrast. Prior to segmentation, sMRI was generated using a cycle-consistent adversarial networks (CycleGAN), which was trained using paired CBCT-MR images. To combine the advantages of both CBCT and sMRI, we developed a cross-modality attention pyramid network with late feature fusion. Our method processes CBCT and sMRI inputs separately to extract CBCT-specific and sMRI-specific features prior to combining them in a late-fusion network for final segmentation. The network was trained and tested using 100 patients' datasets, with each dataset including the CBCT and manual physician contours. For comparison, we trained another two networks with different network inputs and architectures. The segmentation results were compared to manual contours for evaluations.

RESULTS

For the proposed method, dice similarity coefficients and mean surface distances between the segmentation results and the ground truth were 0.96 ± 0.03, 0.65 ± 0.67 mm; 0.91 ± 0.08, 0.93 ± 0.96 mm; 0.93 ± 0.04, 0.72 ± 0.61 mm; 0.95 ± 0.05, 1.05 ± 1.40 mm; and 0.95 ± 0.05, 1.08 ± 1.48 mm for bladder, prostate, rectum, left and right femoral heads, respectively. As compared to the other two competing methods, our method has shown superior performance in terms of the segmentation accuracy.

CONCLUSION

We developed a deep learning-based segmentation method to rapidly and accurately segment five pelvic organs simultaneously from daily CBCTs. The proposed method could be used in the clinic to support rapid target and organs-at-risk contouring for prostate adaptive radiation therapy.

Comparison of CBCT based synthetic CT methods suitable for proton dose calculations in adaptive proton therapy

In-room imaging is a prerequisite for adaptive proton therapy. The use of onboard cone-beam computed tomography (CBCT) imaging, which is routinely acquired for patient position verification, can enable daily dose reconstructions and plan adaptation decisions. Image quality deficiencies though, hamper dose calculation accuracy and make corrections of CBCTs a necessity. This study compared three methods to correct CBCTs and create synthetic CTs that are suitable for proton dose calculations. CBCTs, planning CTs and repeated CTs (rCT) from 33 H&N cancer patients were used to compare a deep convolutional neural network (DCNN), deformable image registration (DIR) and an analytical image-based correction method (AIC) for synthetic CT (sCT) generation. Image quality of sCTs was evaluated by comparison with a same-day rCT, using mean absolute error (MAE), mean error (ME), Dice similarity coefficient (DSC), structural non-uniformity (SNU) and signal/contrast-to-noise ratios (SNR/CNR) as metrics. Dosimetric accuracy was investigated in an intracranial setting by performing gamma analysis and calculating range shifts. Neural network-based sCTs resulted in the lowest MAE and ME (37/2 HU) and the highest DSC (0.96). While DIR and AIC generated images with a MAE of 44/77 HU, a ME of -8/1 HU and a DSC of 0.94/0.90. Gamma and range shift analysis showed almost no dosimetric difference between DCNN and DIR based sCTs. The lower image quality of AIC based sCTs affected dosimetric accuracy and resulted in lower pass ratios and higher range shifts. Patient-specific differences highlighted the advantages and disadvantages of each method. For the set of patients, the DCNN created synthetic CTs with the highest image quality. Accurate proton dose calculations were achieved by both DCNN and DIR based sCTs. The AIC method resulted in lower image quality and dose calculation accuracy was reduced compared to the other methods.

Quantification of accumulated dose and associated anatomical changes of esophagus using weekly Magnetic Resonance Imaging acquired during radiotherapy of locally advanced lung cancer

•

MRI is suited for tracking volumetric changes/accumulating doses in the esophagus.

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Introduced medial axis of esophagus to calculate inter-fraction positional uncertainty.

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Planned and accumulated esophagus dose-volume parameter differences are significant.

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Longitudinal expansion of esophagus may link to acute esophagitis.

Background and purpose

Minimizing acute esophagitis (AE) in locally advanced non-small cell lung cancer (LA-NSCLC) is critical given the proximity between the esophagus and the tumor. In this pilot study, we developed a clinical platform for quantification of accumulated doses and volumetric changes of esophagus via weekly Magnetic Resonance Imaging (MRI) for adaptive radiotherapy (RT).

Material and methods

Eleven patients treated via intensity-modulated RT to 60–70 Gy in 2–3 Gy-fractions with concurrent chemotherapy underwent weekly MRIs. Eight patients developed AE grade 2 (AE2), 3–6 weeks after RT started. First, weekly MRI esophagus contours were rigidly propagated to planning CT and the distances between the medial esophageal axes were calculated as positional uncertainties. Then, the weekly MRI were deformably registered to the planning CT and the total dose delivered to esophagus was accumulated. Weekly Maximum Esophagus Expansion (MEex) was calculated using the Jacobian map. Eventually, esophageal dose parameters (Mean Esophagus Dose (MED), V_90% and D_5cc) between the planned and accumulated dose were compared.

Results

Positional esophagus uncertainties were 6.8 ± 1.8 mm across patients. For the entire cohort at the end of RT: the median accumulated MED was significantly higher than the planned dose (24 Gy vs. 21 Gy p = 0.006). The median V_90% and D_5cc were 12.5 cm³ vs. 11.5 cm³ (p = 0.05) and 61 Gy vs. 60 Gy (p = 0.01), for accumulated and planned dose, respectively. The median MEex was 24% and was significantly associated with AE2 (p = 0.008).

Conclusions

MRI is well suited for tracking esophagus volumetric changes and accumulating doses. Longitudinal esophagus expansion could reflect radiation-induced inflammation that may link to AE.

A Fast Online Replanning Algorithm Based on Intensity Field Projection for Adaptive Radiotherapy

Purpose: The purpose of this work was to propose an online replanning algorithm, named intensity field projection (IFP), that directly adjusts intensity distributions for each beam based on the deformation of structures. IFP can be implemented within a reasonably acceptable time frame. Methods and Materials: The online replanning method is based on the gradient-based free form deformation (GFFD) algorithm, which we have previously proposed. The method involves the following steps: The planning computed tomography (CT) and cone-beam CT image are registered to generate a three-dimensional (3-D) deformation field. According to the 3-D deformation field, the registered image and a new delineation are generated. The two-dimensional (2-D) deformation field of ray intensity in each beam direction is determined based on the 3-D deformation field in the region of interest. The 2-D ray intensity distribution in the corresponding beam direction is deformed to generate a new 2-D ray intensity distribution. According to the new 2-D ray intensity distribution, corresponding multi-leaf collimator (MLC), and jaw motion data are generated. The feasibility and advantages of our method have been demonstrated in 20 lung cancer intensity modulated radiation therapy (IMRT) cases. Results: Substantial underdosing in the CTV is seen in the original and the repositioning plans. The average prescription dose coverage (V100%) and D95 for CTV were 100% and 60.3 Gy for the IFP plans compared to 82.6% (P < 0.01) and 44.0 Gy (P < 0.01) for original plans, 86.7% (P < 0.01), and 58.5 Gy (P < 0.01) for repositioning plans. On average, the mean total lung doses were 12.2 Gy for the IFP plan compared to the 12.4 Gy (P < 0.01) and 12.6 Gy (P < 0.01) for the original and the repositioning plans. The entire process of IFP can be completed within 3 min. Conclusions: We proposed an online replanning strategy for automatically correcting interfractional anatomy variations. The preliminary results indicate that the IFP method substantially increased planning speed for online adaptive replanning.

Technology-driven research for radiotherapy innovation

Technology has a pivotal role in the continuous development of radiotherapy. The long road toward modern ‘high‐tech’ radiation oncology has been studded with discoveries and technological innovations that resulted from the interaction of various disciplines. In the last decades, a dramatic technology‐driven revolution has hugely improved the capability of accurately and safely delivering complex‐shaped dose distributions. This has contributed to many clinical improvements, such as the successful management of lung cancer and oligometastatic disease through stereotactic body radiotherapy. Technology‐driven research is an active and lively field with promising potential in several domains, including image guidance, adaptive radiotherapy, integration of artificial intelligence, heavy‐particle therapy, and ‘flash’ ultra‐high dose‐rate radiotherapy. The evolution toward personalized Oncology will deeply influence technology‐driven research, aiming to integrate predictive models and omics analyses into fast and efficient solutions to deliver the best treatment for each single patient. Personalized radiation oncology will need affordable technological solutions for middle‐/low‐income countries, as these are expected to experience the highest increase of cancer incidence and mortality. Moreover, technology solutions for automation of commissioning, quality assurance, safety tests, image segmentation, and plan optimization will be required. Although a large fraction of cancer patients receive radiotherapy, this is certainly not reflected in the worldwide budget for radiotherapy research. Differently from the pharmaceutical companies‐driven research, resources for research in radiotherapy are highly limited to equipment vendors, who can, in turn, initiate a limited number of collaborations with academic research centers. Thus, enhancement of investments in technology‐driven radiotherapy research via public funds, national governments, and the European Union would have a crucial societal impact. It would allow for radiotherapy to further strengthen its role as a highly effective and cost‐efficient cancer treatment modality, and it could facilitate a rapid and equalitarian large‐scale transfer of technology to clinic, with direct impact on patient care.

Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century

Advances in computing hardware and software platforms have led to the recent resurgence in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for automating complex tasks or providing superior predictive analytics. AI applications are currently spanning many diverse fields from economics to entertainment, to manufacturing, as well as medicine. Since modern AI's inception decades ago, practitioners in radiological sciences have been pioneering its development and implementation in medicine, particularly in areas related to diagnostic imaging and therapy. In this anniversary article, we embark on a journey to reflect on the learned lessons from past AI's chequered history. We further summarize the current status of AI in radiological sciences, highlighting, with examples, its impressive achievements and effect on re-shaping the practice of medical imaging and radiotherapy in the areas of computer-aided detection, diagnosis, prognosis, and decision support. Moving beyond the commercial hype of AI into reality, we discuss the current challenges to overcome, for AI to achieve its promised hope of providing better precision healthcare for each patient while reducing cost burden on their families and the society at large.

Template-based automation of treatment planning in advanced radiotherapy: a comprehensive dosimetric and clinical evaluation

Despite the recent advanced developments in radiation therapy planning, treatment planning for head-neck and pelvic cancers remains challenging due to large concave target volumes, multiple dose prescriptions and numerous organs at risk close to targets. Inter-institutional studies highlighted that plan quality strongly depends on planner experience and skills. Automated optimization of planning procedure may improve plan quality and best practice. We performed a comprehensive dosimetric and clinical evaluation of the Pinnacle3 AutoPlanning engine, comparing automatically generated plans (AP) with the historically clinically accepted manually-generated ones (MP). Thirty-six patients (12 for each of the following anatomical sites: head-neck, high-risk prostate and endometrial cancer) were re-planned with the AutoPlanning engine. Planning and optimization workflow was developed to automatically generate "dual-arc" VMAT plans with simultaneously integrated boost. Various dose and dose-volume parameters were used to build three metrics able to supply a global Plan Quality Index evaluation in terms of dose conformity indexes, targets coverage and sparing of critical organs. All plans were scored in a blinded clinical evaluation by two senior radiation oncologists. Dose accuracy was validated using the PTW Octavius-4D phantom together with the 1500 2D-array. Autoplanning was able to produce high-quality clinically acceptable plans in all cases. The main benefit of Autoplanning strategy was the improvement of overall treatment quality due to significant increased dose conformity and reduction of integral dose by 6-10%, keeping similar targets coverage. Overall planning time was reduced to 60-80 minutes, about a third of time needed for manual planning. In 94% of clinical evaluations, the AP plans scored equal or better to MP plans. Despite the increased fluence modulation, dose measurements reported an optimal agreement with dose calculations with a γ-pass-rate greater than 95% for 3%(global)-2 mm criteria. Autoplanning engine is an effective device enabling the generation of VMAT high quality treatment plans according to institutional specific planning protocols.

Technological evolution of radiation treatment: Implications for clinical applications

The contemporary approach to the management of a cancer patient requires an "ab initio" involvement of different medical domains in order to correctly design an individual patient's pathway toward cure. With new therapeutic tools in every medical field developing faster than ever before the patient care outcomes can be achieved if all surgical, drug, and radiation options are considered in the design of the appropriate therapeutic strategy for a given patient. Radiation therapy (RT) is a clinical discipline in which experts from different fields continuously interact in order to manage the multistep process of the radiation treatment. RT is found to be an appropriate intervention for diverse indications in about 50% of cancer patients during the course of their disease. Technologies are essential in dealing with the complexity of RT treatments and for driving the increasingly sophisticated RT approaches becoming available for the treatment of Cancer. High conformal techniques, namely intensity modulated or volumetric modulated arc techniques, ablative techniques (Stereotactic Radiotherapy and Stereotactic Radiosurgery), particle therapy (proton or carbon ion therapy) allow for success in treating irregularly shaped or critically located targets and for the sharpness of the dose fall-off outside the target. The advanced on-board imaging, including real-time position management systems, makes possible image-guided radiation treatment that results in substantial margin reduction and, in select cases, implementation of an adaptive approach. The therapeutic gains of modern RT are also due in part to the enhanced anticancer activity obtained by coadministering RT with chemotherapy, targeted molecules, and currently immune checkpoints inhibitors. These main clinically relevant steps forward in Radiation Oncology represent a change of gear in the field that may have a profound impact on the management of cancer patients.