Implementation Of Adaptive Radiotherapy Research Articles

Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer

ObjectivesAccurate segmentation of the clinical target volume (CTV) of CBCT images can observe the changes of CTV during patients' radiotherapy, and lay a foundation for the subsequent implementation of adaptive radiotherapy (ART). However, segmentation is challenging due to the poor quality of CBCT images and difficulty in obtaining target volumes. An uncertainty estimation- and attention-based semi-supervised model called residual convolutional block attention-uncertainty aware mean teacher (RCBA-UAMT) was proposed to delineate the CTV in cone-beam computed tomography (CBCT) images of breast cancer automatically.MethodsA total of 60 patients who undergone radiotherapy after breast-conserving surgery were enrolled in this study, which involved 60 planning CTs and 380 CBCTs. RCBA-UAMT was proposed by integrating residual and attention modules in the backbone network 3D UNet. The attention module can adjust channel and spatial weights of the extracted image features. The proposed design can train the model and segment CBCT images with a small amount of labeled data (5%, 10%, and 20%) and a large amount of unlabeled data. Four types of evaluation metrics, namely, dice similarity coefficient (DSC), Jaccard, average surface distance (ASD), and 95% Hausdorff distance (95HD), are used to assess the model segmentation performance quantitatively.ResultsThe proposed method achieved average DSC, Jaccard, 95HD, and ASD of 82%, 70%, 8.93, and 1.49 mm for CTV delineation on CBCT images of breast cancer, respectively. Compared with the three classical methods of mean teacher, uncertainty-aware mean-teacher and uncertainty rectified pyramid consistency, DSC and Jaccard increased by 7.89–9.33% and 14.75–16.67%, respectively, while 95HD and ASD decreased by 33.16–67.81% and 36.05–75.57%, respectively. The comparative experiment results of the labeled data with different proportions (5%, 10% and 20%) showed significant differences in the DSC, Jaccard, and 95HD evaluation indexes in the labeled data with 5% versus 10% and 5% versus 20%. Moreover, no significant differences were observed in the labeled data with 10% versus 20% among all evaluation indexes. Therefore, we can use only 10% labeled data to achieve the experimental objective.ConclusionsUsing the proposed RCBA-UAMT, the CTV of breast cancer CBCT images can be delineated reliably with a small amount of labeled data. These delineated images can be used to observe the changes in CTV and lay the foundation for the follow-up implementation of ART.

Adaptive radiotherapy for head and neck cancer: Pitfalls and possibilities from the radiation oncologist's point of view.

Patients with head and neck cancer (HNC) may experience substantial anatomical changes during the course of radiotherapy treatment. The implementation of adaptive radiotherapy (ART) proves effective in managing the consequent impact on the planned dose distribution. This narrative literature review comprehensively discusses the diverse strategies of ART in HNC and the documented dosimetric and clinical advantages associated with these approaches, while also addressing the current challenges for integration of ART into clinical practice. Although based on mainly non-randomized and retrospective trials, there is accumulating evidence that ART has the potential to reduce toxicity and improve quality of life and tumor control in HNC patients treated with RT. However, several questions remain regarding accurate patient selection, the ideal frequency and timing of replanning, and the appropriate way for image registration and dose calculation. Well-designed randomized prospective trials, with a predetermined protocol for both image registration and dose summation, are urgently needed to further investigate the dosimetric and clinical benefits of ART.

Empirical analysis of a plan-of-the-day strategy to approximate daily online reoptimization for prostate CBCT-guided adaptive radiotherapy.

Adaptive radiotherapy (ART) can improve the dose delivered to the patient in the presence of anatomic variations. However, the required time, effort, and clinical resources are intensive. This work analyzed a plan-of-the-day (POD) approach on clinical patients treated with online ART to explore implementations that balance dosimetric benefit and clinical resource cost. Eight patients treated to the prostate and proximal seminal vesicles with 26 fractions of CBCT-guided, daily online ART were retrospectively analyzed. With a plan library composed of daily adaptive plans from the initial week of treatment and the original plan, the effect of a POD approach starting the following week was investigated by simulating use of these previously generated plans under 3- and 6-degree-of-freedom patient alignment. The plan selected for each treatment was that from the library that maximized the Dice similarity coefficient of the clinical target volume with that of the current treatment fraction. The resulting distribution of several target coverage and organ-at-risk dose metrics are described relative to those achieved with the daily online reoptimized adaptive technique. The values of target coverage and organ-at-risk dose metrics varied across patients and metrics. The POD schemas closely approximated the reference values from a fully reoptimized adaptive plan yet required less than 20% of the reoptimization effort. The POD schemas also had a much greater effect on target coverage metrics than 6-degree-of-freedom registration did. Organ-at-risk dose metrics also varied considerably across patients but did not exhibit a consistent dependence on the particular schema. POD schemas were able to achieve the vast majority of the dosimetric benefit of daily online ART with a small fraction of the online reoptimization effort. Strategies like this might allow for more practical and strategic implementation of ART so as to benefit a greater number of patients.

Different benefits of adaptive radiotherapy for different histologies of NSCLC

Background Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are the most frequent histological subtypes of non-small cell lung cancer (NSCLC). The aim of this study was to investigate how patients with AC and SCC benefit from image-guided adaptive radiotherapy (ART) with tumour match. Material and methods Consecutive patients diagnosed with AC or SCC of the lung treated with definitive chemo-radiotherapy before and after the implementation of ART and tumour match were retrospectively included for analyses. Data collection included baseline patient and treatment characteristics in addition to clinical data on radiation pneumonitis (RP), failure, and survival. Patients were divided into four categories based on their histology and treatment before (n = 173 [89 AC and 84 SCC]) and after implementation of ART (n = 240 [141 AC and 99 SCC]). Results Median follow-up was 5.7 years for AC and 6.3 years for SCC. Mean lung dose decreased for both histologies with ART, whereas mean heart dose only decreased for patients with AC. Incidences of grade 3 and 5 RP decreased for both histologies with ART. Loco-regional failure (LRF) rates decreased significantly for patients with SCC after ART (p = .04), no significant difference was observed for AC. Overall survival (OS) increased significantly for SCC after ART (p < .01): the 2-year OS increased from 31.0% (95% confidence interval [CI] [22.5–42.6]) to 54.5% (95% CI [45.6–65.3]). No significant effect on OS was observed for patients with AC. Conclusion ART and tumour match in the radiotherapeutic treatment of patients with locally advanced NSCLC primarily led to decreased LRF and improved OS for patients with SCC.

Detecting and quantifying spatial misalignment between longitudinal kilovoltage computed tomography (kVCT) scans of the head and neck by using convolutional neural networks (CNNs).

Adaptive radiotherapy (ART) aims to address anatomical modifications appearing during the treatment of patients by modifying the planning treatment according to the daily positioning image. Clinical implementation of ART relies on the quality of the deformable image registration (DIR) algorithms included in the ART workflow. To translate ART into clinical practice, automatic DIR assessment is needed. This article aims to estimate spatial misalignment between two head and neck kilovoltage computed tomography (kVCT) images by using two convolutional neural networks (CNNs). The first CNN quantifies misalignments between 0 mm and 15 mm and the second CNN detects and classifies misalignments into two classes (poor alignment and good alignment). Both networks take pairs of patches of 33x33x33 mm3 as inputs and use only the image intensity information. The training dataset was built by deforming kVCT images with basis splines (B-splines) to simulate DIR error maps. The test dataset was built using 2500 landmarks, consisting of hard and soft landmark tissues annotated by 6 clinicians at 10 locations. The quantification CNN reaches a mean error of 1.26 mm (± 1.75 mm) on the landmark set which, depending on the location, has annotation errors between 1 mm and 2 mm. The errors obtained for the quantification network fit the computed interoperator error. The classification network achieves an overall accuracy of 79.32%, and although the classification network overdetects poor alignments, it performs well (i.e., it achieves a rate of 90.4%) in detecting poor alignments when given one. The performances of the networks indicate the feasibility of using CNNs for an agnostic and generic approach to misalignment quantification and detection.

Online Adaptive Radiotherapy and Opportunity Cost

<h3>Purpose/Objective(s)</h3> Changes in patient anatomy and tumor geometry pose a challenge to ensuring consistent target coverage and organs-at-risk (OAR) sparing; online adaptive radiotherapy (ART) has been used to account for these inter-fractional changes by facilitating replanning prior to each treatment. This project explores the opportunity cost of CT-based online ART by evaluating the time and human resource requirements for adaptive versus conventional image-guided radiation therapy (IGRT). This study seeks to quantify time requirements for ART and, using time driven activity-based costing (TDABC), determine the cost of this time to determine if the dosimetric benefit is worthwhile. <h3>Materials/Methods</h3> Online ART for pelvic disease sites was evaluated using Ethos based planning technology. Time, which at our facility is best represented by the duration between two cone beam CT (CBCT) scans taken for each adaptive fraction, was used as a broadly applicable and transferable metric, representing the additional time required for adaptive treatment on top of standard IGRT. Dosimetric impact was also considered. All parties (dosimetrist, therapists, physicist, physician) need to be present for the duration of treatment planning given the rapid response time required. Using recently validated TDABC at this facility, the per fraction cost of ART was determined, reflecting the added cost of ART on top of IGRT. <h3>Results</h3> Preliminary data suggests a time difference of approximately 7 versus 24 minutes for daily IGRT and ART, respectively. Each adaptive fraction requires more staffing for the duration of this time delaying completion of other tasks and functionally increasing resource utilization. <h3>Conclusion</h3> While online ART decreases the uncertainty of anatomical shifts, the added human resources required per fraction complicate the workflow of daily treatment and increase expense. This has an even greater impact on the implementation of ART in the context of the alternative payment model. Moreover, online ART removes physicians from clinical time. This opportunity cost is likely to be similar across different disease sites, unlike toxicity benefits which may vary more depending on anatomic location and require further longitudinal studies. Regardless, the implementation of progressively complex radiotherapy technologies, like ART, requires consideration of the time and human resource requirements and subsequent opportunity cost.

Clinical implementation of kVCT-guided tomotherapy with ClearRT.

A helical fan-beam kilovoltage computed tomography (kVCT) was recently introduced into Tomotherapy units. This study aims to share the initial experience of kVCT in clinical workflow, compare its performance with that of the existing megavoltage computed tomography (MVCT), and explore its potential in adaptive planning. We retrospectively enrolled 23 patients who underwent both MVCT and kVCT scans. The clinical performance data regarding image acquisition time, nominal dose length product (DLP), registration time and registration corrections were extracted and compared. Image quality was scored by six experienced radiation therapists and quantified based on phantom measurements. CT number stability and the implementation of adaptive radiotherapy were dosimetrically evaluated by performing the dose recalculation on kVCT. Compared to MVCT, kVCT significantly reduced DLP (except the highest kVp protocol), image acquisition and registration time. KVCT obtained higher scores than MVCT on all criteria except artifacts. Phantom measurements also revealed a better image performance characterization of kVCT except for image uniformity. The CT number variation could lead to a dose difference of 0.5% for D95% of target and Dmean of organ-at-risk. For the treatment planning with kVCT, a systematic dose difference (> 1%) in PTV dose metrics was observed at regions with large longitudinal density discontinuities compared to the reference plans. The new kVCT imaging provides enhanced soft-tissue visualization. The improved efficiency with kVCT-guided treatment will allow more patients to be treated each day. In most cases, the dose calculation accuracy of kVCT images is acceptable except for regions with severe artifacts.

Performance evaluation of image reconstruction algorithms for a megavoltage computed tomography system on a helical tomotherapy unit

Objective. To evaluate the impact of image reconstruction algorithm selection, as well as imaging mode and the reconstruction interval, on image quality metrics for megavoltage computed tomography (MVCT) image acquisition for use in image-guided (IGRT) and adaptive radiotherapy (ART) on a next-generation helical tomotherapy system. Approach. A CT image quality phantom was scanned across all available acquisition modes for filtered back projection (FBP) and both iterative reconstruction (IR) algorithms available on the system. Image quality metrics including noise, uniformity, contrast, spatial resolution, and mean CT number were compared. Analysis of DICOM data was performed using ImageJ software and Python code. ANOVA single factor and Tukey’s honestly significant difference post-hoc tests were utilized for statistical analysis. Main Results. Application of both IR algorithms noticeably improved noise and image contrast when compared to the FBP algorithm available on all previous-generation helical tomotherapy systems. Use of the FBP algorithm improved image uniformity and spatial resolution in the axial plane, though values for the IR algorithms were well within tolerances recommended for IGRT and/or MVCT-based ART implementation by the American Association of Physicists in Medicine (AAPM). Additionally, longitudinal resolution showed little dependence on the reconstruction algorithm, while a negligible variation in mean CT number was observed regardless of the reconstruction algorithm or acquisition parameters. Statistical analysis confirmed the significance of these results. Significance. An overall improvement in image quality for metrics most important to IGRT and ART—mainly image noise and contrast—was evident in the application of IR when compared to FBP. Furthermore, since other imaging parameters remain identical regardless of the reconstruction algorithm, this improved image quality does not come at the expense of additional patient dose or an increased scan acquisition time for otherwise identical parameters. These improvements are expected to enhance fidelity in IGRT and ART implementation.

Technical Note: First report on an in vivo range probing quality control procedure for scanned proton beam therapy in head and neck cancer patients.

The capability of proton therapy to provide highly conformal dose distributions is impaired by range uncertainties. The aim of this work is to apply range probing (RP), a form of a proton radiography-based quality control (QC) procedure for range accuracy assessment in head and neck cancer (HNC) patients in a clinical setting. This study included seven HNC patients. RP acquisition was performed using a multi-layer ionization chamber (MLIC). Per patient, two RP frames were acquired within the first two weeks of treatment, on days when a repeated CT scan was obtained. Per RP frame, integral depth dose (IDD) curves of 81 spots around the treatment isocenter were acquired. Range errors are determined as a discrepancy between calculated IDDs in the treatment planning system and measured residual ranges by the MLIC. Range errors are presented relative to the water equivalent path length of individual proton spots. In addition to reporting results for complete measurement frames, an analysis, excluding range error contributions due to anatomical changes, is presented. Discrepancies between measured and calculated ranges are smaller when performing RP calculations on the day-specific patient anatomy rather than the planning CT. The patient-specific range evaluation shows an agreement between calculated and measured ranges for spots in anatomically consistent areas within 3% (1.5 standard deviation). The results of an RP-based QC procedure implemented in the clinical practice for HNC patients have been demonstrated. The agreement of measured and simulated proton ranges confirms the 3% uncertainty margin for robust optimization. Anatomical variations show a predominant effect on range accuracy, motivating efforts towards the implementation of adaptive radiotherapy.

UK adaptive radiotherapy practices for head and neck cancer patients.

Objective:To provide evidence on the extent and manner in which adaptive practices have been employed in the UK and identify the main barriers for the clinical implementation of adaptive radiotherapy (ART) in head and neck (HN) cancer cases.Methods:In December 2019, a Supplementary Material 1, of 23 questions, was sent to all UK radiotherapy centres (67). This covered general information to current ART practices and perceived barriers to implementation.Results:31 centres responded (46%). 56% responding centres employed ART for between 10 and 20 patients/annum. 96% of respondents were using CBCT either alone or with other modalities for assessing “weight loss” and “shell gap,” which were the main reasons for ART. Adaptation usually occurs at week three or four during the radiotherapy treatment. 25 responding centres used an online image-guided radiotherapy (IGRT) approach and 20 used an offline ad hoc ART approach, either with or without protocol level. Nearly 70% of respondents required 2 to 3 days to create an adaptive plan and 95% used 3–5 mm adaptive planning target volume margins. All centres performed pre-treatment QA. “Limited staff resources” and “lack of clinical relevance” were identified as the two main barriers for ART implementation.Conclusion:There is no consensus in adaptive practice for HN cancer patients across the UK. For those centres not employing ART, similar clinical implementation barriers were identified.Advances in knowledge:An insight into contemporary UK practices of ART for HN cancer patients indicating national guidance for ART implementation for HN cancer patients may be required

Adaptive Radiotherapy in Locally Advanced Lung Cancers – Real World Scenario from a Tertiary Cancer Center

Concurrent chemo-radiation (CTRT) for locally advanced lung cancer (LALC) leads to dynamic changes in tumor volume and location especially in patients with large tumor or atelectasis. Using adaptive radiotherapy (ART) prevents under dosage of the tumor and over dosage of the organs at risk (OAR). One hundred forty patients of LALC from Jan 2017 to Dec 2019 were treated with concurrent CTRT. A four-dimensional computed tomography (4D CT) scan was acquired and contouring of gross target volume (GTV) was guided by PET-scan fusion. All patients were planned to receive 60Gy/30 daily fractions with volumetric modulated arc therapy. Cone-beam CT (CBCT) for set-up verification was performed at least twice weekly. Change in anatomy and tumor response was determined on CBCT during weekly radiotherapy audit. Patients suitable for ART were re-simulated and re-planned. GTV now consisted of residual tumor and 5mm isotropic clinical target volume (CTV) was generated and manually extended to include areas suspicious for residual disease. Dose delivered prior to ART and time needed to execute ART (calculated from the day of re-simulation to the day of starting ART) were documented. The volumetric and dosimetric changes were analyzed using paired t-test between the 2 plans for full prescription dose. We also evaluated local control for this cohort. Out of 140 patients, 35 (25%) required re-planning for ART. Median age was 61 years (range 50 - 65). Twenty-six patients (75%) had upper lobe tumors. Fifteen (43%) patients had squamous carcinoma, 13 (37%) had adenocarcinoma, and 7 (20%) had small cell carcinoma. Requirement for ART was anticipated in 10/35 (28%) patients prior to CTRT. The triggers for ART were resolution of atelectasis in 7 (20%) patients, tumor response in 26 (74%) and progression in 2 (6%) patients. A median of 6 CBCTs prior to ART were acquired. A median of 26Gy (IQR 14-36Gy) was delivered prior to ART. While it required 5 days (IQR:3.5-7.5) for executing ART. There was a reduction in mean planning target volume of 23% (544cc - 418 cc; p = 0.003), mean GTV primary of 47% (153cc - 80.8cc; p = 0.024), and GTV nodes of 33.7% (8 cc - 5.3 cc; p = 0.033). The V20 of total lung minus PTV and the mean lung dose increased by 3.7% (18.8% - 19.5%; p = 0.158) and 4.5% (10.9Gy - 11.4Gy; p = 0.274) respectively whereas there was a reduction in heart V20 and Spinal cord Dmax by 18.3% (9.7 Gy to 8.2 Gy; p = 0.005) and 6.7% (36.6 Gy to 34.3 Gy; p = 0.100) respectively. At a median follow up of 14.7 months, the loco regional control rate was 70% and no patient experienced ³ grade 3 toxicity. In our practice approximately 25% patients of locally advanced lung cancer required ART, with tumor response and resolution of atelectasis being the most common triggers. Anticipation of clinically significant change, frequent use of on-board imaging and implementation of ART could prevent under dosage of the tumor and over dosage of OAR.

Adaptive radiotherapy for head and neck cancer reduces the requirement for rescans during treatment due to spinal cord dose

BackgroundPatients treated with radiotherapy for head and neck (H&N) cancer often experience anatomical changes. The potential compromises to Planning Target Volume (PTV) coverage or Organ at Risk (OAR) sparing has prompted the use of adaptive radiotherapy (ART) for these patients. However, implementation of ART is time and resource intensive. This study seeks to define a clinical trigger for H&N re-plans based on spinal cord safety using kV Cone-Beam Computed Tomography (CBCT) verification imaging, in order to best balance clinical benefit with additional workload.MethodsThirty-one H&N patients treated with Volumetric Modulated Arc Therapy (VMAT) who had a rescan CT (rCT) during treatment were included in this study. Contour volume changes between the planning CT (pCT) and rCT were determined. The original treatment plan was calculated on the pCT, CBCT prior to the rCT, pCT deformed to the anatomy of the CBCT (dCT), and rCT (considered the gold standard). The dose to 0.1 cc (D0.1cc) spinal cord was evaluated from the Dose Volume Histograms (DVHs).ResultsThe median dose increase to D0.1cc between the pCT and rCT was 0.7 Gy (inter-quartile range 0.2–1.9 Gy, p < 0.05). No correlation was found between contour volume changes and the spinal cord dose increase. Three patients exhibited an increase of 7.0–7.2 Gy to D0.1cc, resulting in a re-plan; these patients were correctly identified using calculations on the CBCT/dCT.ConclusionsAn adaptive re-plan can be triggered using spinal cord doses calculated on the CBCT/dCT. Implementing this trigger can reduce patient appointments and radiation dose by eliminating up to 90% of additional un-necessary CT scans, reducing the workload for radiographers, physicists, dosimetrists, and clinicians.

Pretreatment Prediction of Adaptive Radiation Therapy Eligibility Using MRI-Based Radiomics for Advanced Nasopharyngeal Carcinoma Patients.

Background and purpose: Adaptive radiotherapy (ART) can compensate for the dosimetric impacts induced by anatomic and geometric variations in patients with nasopharyngeal carcinoma (NPC); Yet, the need for ART can only be assessed during the radiation treatment and the implementation of ART is resource intensive. Therefore, we aimed to determine tumoral biomarkers using pre-treatment MR images for predicting ART eligibility in NPC patients prior to the start of treatment.Methods: Seventy patients with biopsy-proven NPC (Stage II-IVB) in 2015 were enrolled into this retrospective study. Pre-treatment contrast-enhanced T1-w (CET1-w), T2-w MR images were processed and filtered using Laplacian of Gaussian (LoG) filter before radiomic features extraction. A total of 479 radiomics features, including the first-order (n = 90), shape (n = 14), and texture features (n = 375), were initially extracted from Gross-Tumor-Volume of primary tumor (GTVnp) using CET1-w, T2-w MR images. Patients were randomly divided into a training set (n = 51) and testing set (n = 19). The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for radiomic model construction in training set to select the most predictive features to predict patients who were replanned and assessed in the testing set. A double cross-validation approach of 100 resampled iterations with 3-fold nested cross-validation was employed in LASSO during model construction. The predictive performance of each model was evaluated using the area under the receiver operator characteristic (ROC) curve (AUC).Results: In the present cohort, 13 of 70 patients (18.6%) underwent ART. Average AUCs in training and testing sets were 0.962 (95%CI: 0.961–0.963) and 0.852 (95%CI: 0.847–0.857) with 8 selected features for CET1-w model; 0.895 (95%CI: 0.893–0.896) and 0.750 (95%CI: 0.745–0.755) with 6 selected features for T2-w model; and 0.984 (95%CI: 0.983–0.984) and 0.930 (95%CI: 0.928–0.933) with 6 selected features for joint T1-T2 model, respectively. In general, the joint T1-T2 model outperformed either CET1-w or T2-w model alone.Conclusions: Our study successfully showed promising capability of MRI-based radiomics features for pre-treatment identification of ART eligibility in NPC patients.

Recommendations of Megavoltage Computed Tomography Settings for the Implementation of Adaptive Radiotherapy on Helical Tomotherapy Units

Recommendations of Megavoltage Computed Tomography Settings for the Implementation of Adaptive Radiotherapy on Helical Tomotherapy Units

SP-0705 Practicalities and Not Technical Uncertainties Limit the Clinical Implementation of Adaptive Radiotherapy

SP-0705 Practicalities and Not Technical Uncertainties Limit the Clinical Implementation of Adaptive Radiotherapy

SP-0547 Role of the RTT in the clinical implementation of adaptive radiotherapy

SP-0547 Role of the RTT in the clinical implementation of adaptive radiotherapy

SP-0214: Clinical implementation of adaptive radiotherapy

SP-0214: Clinical implementation of adaptive radiotherapy

SP-038: Clinical implementation of adaptive radiotherapy: challenges ahead

SP-038: Clinical implementation of adaptive radiotherapy: challenges ahead

Adaptive Radiotherapy for Head and Neck Cancer.

To investigate the effects of adaptive radiotherapy on dosimetric, clinical, and toxicity outcomes for patients with head and neck cancer undergoing chemoradiotherapy with intensity-modulated radiotherapy. Fifty-one patients with advanced head and neck cancer underwent definitive chemoradiotherapy with the original plan optimized to deliver 70.2 Gy. All patients were resimulated at a median dose of 37.8 Gy (range, 27.0-48.6 Gy) due to changes in tumor volume and/or patient weight loss (>15% from baseline). Thirty-four patients underwent adaptive replanning for their boost planning (21.6 Gy). The dosimetric effects of the adaptive plan were compared to the original plan and the original plan copied on rescan computed tomography. Acute and late toxicities and tumor local control were assessed. Gross tumor volume reduction rate was calculated. With adaptive replanning, the maximum dose to the spinal cord, brain stem, mean ipsilateral, and contralateral parotid had a median reduction of -4.5%, -3.0%, -6.2%, and -2.5%, respectively (median of 34 patients). Median gross tumor volume and boost planning target volume coverage improved by 0.8% and 0.5%, respectively. With a median follow-up time of 17.6 months, median disease-free survival and overall survival was 14.8 and 21.1 months, respectively. Median tumor volume reduction rate was 35.2%. For patients with tumor volume reduction rate ≤35.2%, median disease-free survival was 8.7 months, whereas it was 16.9 months for tumor volume reduction rate >35.2%. Four patients had residual disease after chemoradiotherapy, whereas 64.7% (20 of 34) of patients achieved locoregional control. Implementation of adaptive radiotherapy in head and neck cancer offers benefits including improvement in tumor coverage and decrease in dose to organs at risk. The tumor volume reduction rate during treatment was significantly correlated with disease-free survival and overall survival.

Radiothérapie adaptative en routine ? État de l’art : point de vue du physicien médical

The development of both image-guided and intensity-modulated radiotherapy has underlined the question of treatment adaptation to anatomical and/or biological changes occurring during radiotherapy course and modifying delivered dose to the patient. Adaptive radiotherapy has been introduced when several plans are used to treat a patient during radiotherapy. Adaptation may be performed online, offline or in a hybrid way. New images of the patient are needed for adaptive radiotherapy to perform many processes: image registration, segmentation and evaluation of cumulative dose. Deformable image registration methods are generally used to image registration and contours propagation. Fraction and cumulative dose evaluations use deformable image registration methods or more complex methods based on Monte-Carlo calculation. These methods have uncertainties and have to be evaluated. However, evaluation and validation tools are still being developed. The physicist's mission is to ensure that every new technology, such as adaptive radiotherapy, is deployed with highest safety, by technical validation and by implementing a specific quality assurance program. Adaptive radiotherapy implementation still raises many questions, so its potential clinical application requires great caution and should be carefully explored in prospective clinical trials.