Adaptive Radiotherapy for Anatomical Changes

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.

•

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

•

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

•

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.