Adaptive Radiotherapy: Next-Generation Radiotherapy.

Radiotherapy, a crucial technique in cancer therapy, has traditionally relied on the premise of largely unchanging patient anatomy during the treatment course and encompassing uncertainties by target margins. This review introduces adaptive radiotherapy (ART), a notable innovation that addresses anatomy changes and optimizes the therapeutic ratio. ART utilizes advanced imaging techniques such as CT, MRI, and PET to modify the treatment plan based on observed anatomical changes and even biological changes during the course of treatment. The narrative review provides a comprehensive guide on ART for healthcare professionals and trainees in radiation oncology and anyone else interested in the topic. The incorporation of artificial intelligence in ART has played a crucial role in improving effectiveness, particularly in contour segmentation, treatment planning, and quality assurance. This has expedited the process to render online ART feasible, lowered the burden for radiation oncology practitioners, and enhanced the precision of dynamically personalized treatment. Current technical and clinical progress on ART is discussed in this review, highlighting the ongoing development of imaging technologies and AI and emphasizing their contribution to enhancing the applicability and effectiveness of ART.

Anatomical changes and dosimetric analysis of the neck region based on FBCT for nasopharyngeal carcinoma patients during radiotherapy.

BACKGROUND: The study aimed to investigate anatomical changes in the neck region and evaluate their impact on dose distribution in patients with nasopharyngeal carcinoma (NPC) undergoing intensity modulated radiation therapy (IMRT). Additionally, the study sought to determine the optimal time for replanning during the course of treatment. METHODS: Twenty patients diagnosed with NPC underwent IMRT, with weekly pretreatment kV fan beam computed tomography (FBCT) scans in the treatment room. Metastasized lymph nodes in the neck region and organs at risk (OARs) were redelineation using the images from the FBCT scans. Subsequently, the original treatment plan (PLAN0) was replicated to each FBCT scan to generate new plans labeled as PLAN 1-6. The dose-volume histograms (DVH) of the new plans and the original plan were compared. One-way repeated measure ANOVA was utilized to establish threshold(s) at various time points. The presence of such threshold(s) would signify significant change(s), suggesting the need for replanning. RESULTS: Progressive volume reductions were observed over time in the neck region, the gross target volume for metastatic lymph nodes (GTVnd), as well as the submandibular glands and parotids. Compared to PLAN0, the mean dose (Dmean) of GTVnd-L significantly increased in PLAN5, while the minimum dose covering 95% of the volume (D95%) of PGTVnd-L showed a significant decrease from PLAN3 to PLAN6. Similarly, the Dmean of GTVnd-R significantly increased from PLAN4 to PLAN6, whereas the D95% of PGTVnd-R exhibited a significant decrease during the same period. Furthermore, the dose of bilateral parotid glands, bilateral submandibular glands, brainstem and spinal cord was gradually increased in the middle and late period of treatment. CONCLUSION: Significant anatomical and dosimetric changes were noted in both the target volumes and OARs. Considering the thresholds identified, it is imperative to undertake replanning at approximately 20 fractions. This measure ensures the delivery of adequate doses to target volumes while mitigating the risk of overdosing on OARs.

Optimal timing of re-planning for head and neck adaptive radiotherapy.

BACKGROUND AND PURPOSE: Adaptive radiotherapy (ART) relies on re-planning to correct treatment variations, but the optimal timing of re-planning to account for dose changes in head and neck organs at risk (OARs) is still under investigation. We aimed to find out the optimal timing of re-planning in head and neck ART. MATERIALS AND METHODS: A total of 110 head and neck cancer patients were retrospectively enrolled. A semi auto-segmentation method was applied to obtain the weekly mean dose (Dmean) to OARs. The K-nearest-neighbour method was used for missing data imputation of weekly Dmean. A dose deviation map was built using the planning Dmean and weekly Dmean values and then used to simulate different ART scenarios consisting of 1 to 6 re-plannings. The difference between accumulated Dmean and planning Dmean before re-planning (ΔDmean_acc_noART) and after re-planning (ΔDmean_acc_ART) were evaluated and compared. RESULTS: Among all the OARs, supraglottic showed the largest ΔDmean_acc_noART (1.23 ± 3.13 Gy) and most cases of ΔDmean_acc_noART > 3 Gy (26 patients). The 3rd week is suggested in the optimal timing of re-planning for 10 OARs. For all the organs except arytenoid, 2 re-plannings were able to guarantee the ΔDmean_acc_ART below 3 Gy while the average |ΔDmean_acc_ART| was below 1 Gy. ART scenarios of 2_4, 3_4, 3_5 (week of re-planning separated with "_") were able to guarantee ΔDmean_acc_ART of 99 % of patients below 3 Gy simultaneously for 19 OARs. CONCLUSIONS: The optimal timing of re-planning was suggested for different organs at risk in head and neck adaptive radiotherapy. Generic scenarios of timing and frequency for re-planning can be applied to guarantee the increase of accumulated mean dose within 3 Gy simultaneously for multiple organs.

Investigating the impact of breast positioning control on physical treatment parameters in multi-catheter breast brachytherapy.

PURPOSE: To assess the effects of a workflow for reproducible patient and breast positioning on implant stability during high-dose-rate multi-catheter breast brachytherapy. METHODS: Thirty patients were treated with our new positioning control workflow. Implant stability was evaluated based on a comparison of planning-CTs to control-CTs acquired halfway through the treatment. To assess geometric stability, button-button distance variations as well as Euclidean dwell position deviations were evaluated. The latter were also quantified within various separated regions within the breast to investigate the location-dependency of implant alterations. Furthermore, dosimetric variations to target volume and organs at risk (ribs, skin) as well as isodose volume changes were analyzed. Results were compared to a previously treated cohort of 100 patients. RESULTS: With the introduced workflow, the patient fraction affected by button-button distance variations > 5 mm and by dwell position deviations > 7 mm were reduced from 37% to 10% and from 30% to 6.6%, respectively. Implant stability improved the most in the lateral to medial breast regions. Only small stability enhancements were observed regarding target volume dosimetry, but the stability of organ at risk exposure became substantially higher. D0.2ccm skin dose variations > 12.4% and D0.1ccm rib dose variations > 6.7% were reduced from 11% to 0% and from 16% to 3.3% of all patients, respectively. CONCLUSION: Breast positioning control improved geometric and dosimetric implant stability for individual patients, and thus enhanced physical plan validity in these cases.

Failure patterns of locoregional recurrence after reducing target volumes in patients with nasopharyngeal carcinoma receiving adaptive replanning during intensity-modulated radiotherapy: a single-center experience in China.

BACKGROUND: Previous researches have demonstrated that adaptive replanning during intensity-modulated radiation therapy (IMRT) could enhance the prognosis of patients with nasopharyngeal carcinoma (NPC). However, the delineation of replanning target volumes remains unclear. This study aimed to evaluate the feasibility of reducing target volumes through adaptive replanning during IMRT by analyzing long-term survival outcomes and failure patterns of locoregional recurrence in NPC. METHODS: This study enrolled consecutive NPC patients who received IMRT at our hospital between August 2011 and April 2018. Patients with initially diagnosed, histologically verified, non-metastatic nasopharyngeal cancer were eligible for participation in this study. The location and extent of locoregional recurrences were transferred to pretreatment planning computed tomography for dosimetry analysis. RESULTS: Among 274 patients, 100 (36.5%) received IMRT without replanning and 174 (63.5%) received IMRT with replanning. Five-year rates of locoregional recurrence-free survival (LRFS) were 90.1% (95%CI, 84.8% to 95.4%) and 80.8% (95%CI, 72.0% to 89.6%) for patients with and without replanning, P = 0.045. There were 17 locoregional recurrences in 15 patients among patients with replanning, of which 1 (5.9%) was out-field and 16 (94.1%) were in-field. Among patients without replanning, 19 patients developed locoregional recurrences, of which 1 (5.3%) was out-field, 2 (10.5%) were marginal, and 16 (84.2%) were in-field. CONCLUSIONS: In-field failure inside the high dose area was the most common locoregional recurrent pattern for non-metastatic NPC. Adapting the target volumes and modifying the radiation dose prescribed to the area of tumor reduction during IMRT was feasible and would not cause additional recurrence in the shrunken area.

A systematic review on clinical adaptive radiotherapy for head and neck cancer.

INTRODUCTION: Head and neck cancer (HNC) patients' anatomy may undergo significant changes during radiotherapy (RT). This potentially affects dose distribution and compromises conformity between planned and delivered dose. Adaptive radiotherapy (ART) is a promising technique to overcome this problem but requires a significant workload. This systematic review aims to estimate the clinical and dosimetric benefits of ART using prospective data. MATERIAL AND METHODS: A search on PubMed and Web of Science according to the PRISMA guidelines was made on Feb 6, 2023. Search string used was: 'adaptive radiotherapy head neck cancer'. English language filter was applied. All studies were screened for inclusion on title and abstract, and the full text was read and discussed in the research group in case of uncertainty. Inclusion criteria were a prospective ART strategy for HNC investigating clinical or dosimetric outcomes. RESULTS: A total of 1251 articles were identified of which 15 met inclusion criteria. All included studies were published between 2010 and 2023 with a substantial diversity in design, endpoints, and nomenclature. The number of patients treated with ART was small with a median of 20 patients per study (range 4 to 86), undergoing 1-2 replannings. Mean dose to the parotid glands was reduced by 0.4-7.1 Gy. Maximum dose to the spinal cord was reduced by 0.5-4.6 Gy. Only five studies reported clinical outcome and disease control was excellent. Data on toxicity were ambiguous with some studies indicating reduced acute toxicity and xerostomia, while others found reduced quality of life in patients treated with ART. CONCLUSION: The literature on clinical ART in HNC is limited. ART is associated with small reductions in doses to organs at risk, but the influence on toxicity and disease control is uncertain. There is a clear need for larger, prospective trials with a well-defined control group.

[Quantitative evaluation of radiotherapy plan in precise external beam radiotherapy process management for cervical cancer].

OBJECTIVE: To identify the problems in clinical radiotherapy planning for cervical cancer through quantitative evaluation of the radiotherapy plans to improve the quality of the plans and the radiotherapy process. METHODS: We selected the clinically approved and administered radiotherapy plans for 227 cervical cancer patients undergoing external radiotherapy at Sun Yat-sen University Cancer Center from May, 2019 to January, 2022. These plans were transferred from the treatment planning system to the Plan IQTM workstation. The plan quality metrics were determined based on the guidelines of ICRU83 report, the GEC-ESTRO Working Group, and the clinical requirements of our center and were approved by a senior clinician. The problems in the radiotherapy plans were summarized and documented, and those with low scores were re-planned and the differences were analyzed. RESULTS: We identified several problems in the 277 plans by quantitative evaluation. Inappropriate target volume selection (with scores < 60) in terms of GTV, PGTV (CI) and PGTV (V66 Gy) was found in 10.6%, 65.2%, and 1% of the plans, respectively; and the PGTV (CI), GTV, and PCTV (D98%, HI) had a score of 0 in 0.4%, 10.1%, 0.4%, 0.4% of the plans, respectively. The problems in the organs at risk (OARs) involved mainly the intestines (the rectum, small intestine, and colon), found in 20.7% of the plans, and in occasional cases, the rectum, small intestine, colon, kidney, and the femoral head had a score of 0. Senior planners showed significantly better performance than junior planners in PGTV (V60 Gy, D98%), PCTV (CI), and CTV (D98%) (P≤0.046) especially in terms of spinal cord and small intestine protection (P≤0.034). The bowel (the rectum, small intestine and colon) dose was significantly lower in the prone plans than supine plans (P < 0.05), and targets coverage all met clinical requirements. Twenty radiotherapy plans with low scores were selected for re-planning. The re-planned plans had significantly higher GTV (Dmin) and PTV (V45 Gy, D98%) (P < 0.05) with significantly reduced doses of the small intestines (V40 Gy vs V30 Gy), the colon (V40 Gy vs V30 Gy), and the bladder (D35%) (P < 0.05). CONCLUSION: Quantitative evaluation of the radiotherapy plans can not only improve the quality of radiotherapy plan, but also facilitate risk management of the radiotherapy process.

A hybrid method of correcting CBCT for proton range estimation with deep learning and deformable image registration.

Objective. This study aimed to develop a novel method for generating synthetic CT (sCT) from cone-beam CT (CBCT) of the abdomen/pelvis with bowel gas pockets to facilitate estimation of proton ranges.Approach. CBCT, the same-day repeat CT, and the planning CT (pCT) of 81 pediatric patients were used for training (n= 60), validation (n= 6), and testing (n= 15) of the method. The proposed method hybridizes unsupervised deep learning (CycleGAN) and deformable image registration (DIR) of the pCT to CBCT. The CycleGAN and DIR are respectively applied to generate the geometry-weighted (high spatial-frequency) and intensity-weighted (low spatial-frequency) components of the sCT, thereby each process deals with only the component weighted toward its strength. The resultant sCT is further improved in bowel gas regions and other tissues by iteratively feeding back the sCT to adjust incorrect DIR and by increasing the contribution of the deformed pCT in regions of accurate DIR.Main results. The hybrid sCT was more accurate than deformed pCT and CycleGAN-only sCT as indicated by the smaller mean absolute error in CT numbers (28.7 ± 7.1 HU versus 38.8 ± 19.9 HU/53.2 ± 5.5 HU;P≤ 0.012) and higher Dice similarity of the internal gas regions (0.722 ± 0.088 versus 0.180 ± 0.098/0.659 ± 0.129;P≤ 0.002). Accordingly, the hybrid method resulted in more accurate proton range for the beams intersecting gas pockets (11 fields in 6 patients) than the individual methods (the 90th percentile error in 80% distal fall-off, 1.8 ± 0.6 mm versus 6.5 ± 7.8 mm/3.7 ± 1.5 mm;P≤ 0.013). The gamma passing rates also showed a significant dosimetric advantage by the hybrid method (99.7 ± 0.8% versus 98.4 ± 3.1%/98.3 ± 1.8%;P≤ 0.007).Significance. The hybrid method significantly improved the accuracy of sCT and showed promises in CBCT-based proton range verification and adaptive replanning of abdominal/pelvic proton therapy even when gas pockets are present in the beam path.

Three-dimensional assessment of interfractional cervical and uterine motions using daily magnetic resonance images to determine margins and timing of replanning.

PURPOSE: This study was conducted to determine the margins and timing of replanning by assessing the daily interfractional cervical and uterine motions using magnetic resonance (MR) images. METHODS: Eleven patients with cervical cancer, who underwent intensity-modulated radiotherapy (IMRT) in 23-25 fractions, were considered in this study. The daily and reference MR images were converted into three-dimensional (3D) shape models. Patient-specific anisotropic margins were calculated from the proximal 95% of vertices located outside the surface of the reference model. Population-based margins were defined as the 90th percentile values of the patient-specific margins. The expanded volume of interest (expVOI) for the cervix and uterus was generated by expanding the reference model based on the population-based margin to calculate the coverage for daily deformable mesh models. For comparison, expVOIconv was generated using conventional margins: right (R), left (L), anterior (A), posterior (P), superior (S), and inferior (I) were (5, 5, 15, 15, 10, 10) and (10, 10, 20, 20, 15, 15) mm for the cervix and uterus, respectively. Subsequently, a replanning scenario was developed based on the cervical volume change. ExpVOIini and expVOIreplan were generated before and after replanning, respectively. RESULTS: Population-based margins were (R, L, A, P, S, I) of (7, 7, 11, 6, 11, 8) and (14, 13, 27, 19, 15, 21) mm for the cervix and uterus, respectively. The timing of replanning was found to be the 16th fraction, and the volume of expVOIreplan decreased by >30% compared to that of expVOIini . However, margins cannot be reduced to ensure equivalent coverage after replanning. CONCLUSION: We determined the margins and timing of replanning through detailed daily analysis. The margins of the cervix were smaller than conventional margins in some directions, while the margins of the uterus were larger in almost all directions. A margin equivalent to that at the initial planning was required for replanning.

Deep learning-based prediction of deliverable adaptive plans for MR-guided adaptive radiotherapy: A feasibility study.

Purpose: Fast and automated plan generation is desirable in radiation therapy (RT), in particular, for MR-guided online adaptive RT (MRgOART) or real-time (intrafractional) adaptive RT (MRgRART), to reduce replanning time. The purpose of this study is to investigate the feasibility of using deep learning to quickly predict deliverable adaptive plans based on a target dose distribution for MRgOART/MRgRART. Methods: A conditional generative adversarial network (cGAN) was trained to predict the MLC leaf sequence corresponding to a target dose distribution based on reference plan created prior to MRgOART using a 1.5T MR-Linac. The training dataset included 50 ground truth dose distributions and corresponding beam parameters (aperture shapes and weights) created during MRgOART for 10 pancreatic cancer patients (each with five fractions). The model input was the dose distribution from each individual beam and the output was the predicted corresponding field segments with specific shape and weight. Patient-based leave-one-out-cross-validation was employed and for each model trained, four (44 training beams) out of five fractionated plans of the left-out patient were set aside for testing purposes. We deliberately kept a single fractionated plan in the training dataset so that the model could learn to replan the patient based on a prior plan. The model performance was evaluated by calculating the gamma passing rate of the ground truth dose vs. the dose from the predicted adaptive plan and calculating max and mean dose metrics. Results: The average gamma passing rate (95%, 3mm/3%) among 10 test cases was 88%. In general, we observed 95% of the prescription dose to PTV achieved with an average 7.6% increase of max and mean dose, respectively, to OARs for predicted replans. Complete adaptive plans were predicted in ≤20 s using a GTX 1660TI GPU. Conclusion: We have proposed and demonstrated a deep learning method to generate adaptive plans automatically and rapidly for MRgOART. With further developments using large datasets and the inclusion of patient contours, the method may be implemented to accelerate MRgOART process or even to facilitate MRgRART.

Technical note: First implementation of a one-stop solution of radiotherapy with full-workflow automation based on CT-linac combination.

BACKGROUND: Radiotherapy initiation is a laborious and time-consuming process that involves multiple steps and units. Workflow automation is in demand to improve the work efficiency and patient experience. PURPOSE: The purposes of this study are to describe the technical characteristics and clinical performance of an AI-powered one-stop radiotherapy workflow for initial treatment based on CT-linac combination, and provide insight into the behavior of full-workflow automation in radiotherapy. METHODS: Based on a CT-integrated linear accelerator and AI model implementation, the so-called "All-in-One" workflow incorporates routine procedures from simulation, autosegmentation, autoplanning, image guidance, beam delivery, and in vivo quality assurance (QA) into one scheme, while the patient is on the treatment couch. Clinical outcomes of the new workflow were evaluated for 10 enrolled patients with rectal cancer. RESULTS: For the enrolled patients, manual modifications of the autosegmented target volumes were necessary. The Dice similarity coefficient and 95% Hausdorff distance before and after the modifications were 0.892 ± 0.061 and 18.2 ± 13.0 mm, respectively. The autosegmented normal tissues and automatic plans were clinically acceptable without any modifications or reoptimization. The pretreatment IGRT corrections were within 2 mm in all directions, and the EPID-based in vivo QA showed γ passing rate of above 97% (3%/3 mm/10% threshold) at all the checkpoints, better than the results of rectal patients who followed a routine workflow. The duration of the whole process was 23.2 ± 3.5 minutes for the enrolled patients, depending mostly on the time required for manual modification and plan evaluation. CONCLUSION: The All-in-One workflow enables full-process automation of radiotherapy via seamless procedure integration. Compared to the routine workflow, the one-stop solution shortens the time scale it takes to ready the first treatment from days to minutes, significantly improving the patient experience and the workflow efficiency, and it also shows potential to facilitate clinical application of online adaptive replanning.

Auto-detection of necessity for MRI-guided online adaptive replanning using a machine learning classifier.

PURPOSE: MRI-guided adaptive radiation therapy (MRgART), particularly daily online adaptive replanning (OLAR) can substantially improve radiation therapy delivery, however, it can be labor-intensive and time-consuming. Currently, the decision to perform OLAR for a treatment fraction is determined subjectively. In this work, we develop a machine learning algorithm based on structural similarity index measure (SSIM) and change in entropy to quickly and objectively determine whether OLAR is necessary for a daily MRI set. METHODS: A total of 109 daily MRI sets acquired on a 1.5T MR-Linac during MRgART for 22 pancreatic cancer patients each treated with five fractions were retrospectively analyzed. For each daily MRI set, OLAR and reposition (No-OLAR) plans were created and the superior plan with the daily fraction determined per clinical dose-volume criteria. SSIM and entropy maps were extracted from each daily MRI set, with respect to its reference (e.g., dry-run) MRI in the region enclosed by 50-100% isodose surfaces. A total of six common features were extracted from SSIM maps. Pearson's rank correlation coefficient was utilized to rule out redundant SSIM features. A t-test was used to determine significant SSIM features which were combined with the change in entropy to develop anensemble machine classifier with fivefold cross validation. The performance of the classifier was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS: A machine learning classifier model using two SSIM features (mean and full width at half maximum) and change in entropy was determined to be able to significantly discriminate between No-OLAR and OLAR groups. The obtained machine learning ensemble classifier can predict OLAR necessity with a cross validated AUC of 0.93. Misclassification was found primarily for No-OLAR cases with dosimetric plan quality closely comparable to the corresponding OLAR plans, thus, are not a major practical concern. CONCLUSION: A machine learning classifier based on simple first-order image features, that is, SSIM features and change in entropy, was developed to determine when OLAR is necessary for a daily MRI set with practical acceptable prediction accuracy. This classifier may be implemented in the MRgART process to automatically and objectively determine if OLAR is required following daily MRI.

Quantitative evaluation of radiotherapy plan in precise external beam radiotherapy process management for cervical cancer / 南方医科大学学报

OBJECTIVE@#To identify the problems in clinical radiotherapy planning for cervical cancer through quantitative evaluation of the radiotherapy plans to improve the quality of the plans and the radiotherapy process.@*METHODS@#We selected the clinically approved and administered radiotherapy plans for 227 cervical cancer patients undergoing external radiotherapy at Sun Yat-sen University Cancer Center from May, 2019 to January, 2022. These plans were transferred from the treatment planning system to the Plan IQTM workstation. The plan quality metrics were determined based on the guidelines of ICRU83 report, the GEC-ESTRO Working Group, and the clinical requirements of our center and were approved by a senior clinician. The problems in the radiotherapy plans were summarized and documented, and those with low scores were re-planned and the differences were analyzed.@*RESULTS@#We identified several problems in the 277 plans by quantitative evaluation. Inappropriate target volume selection (with scores < 60) in terms of GTV, PGTV (CI) and PGTV (V66 Gy) was found in 10.6%, 65.2%, and 1% of the plans, respectively; and the PGTV (CI), GTV, and PCTV (D98%, HI) had a score of 0 in 0.4%, 10.1%, 0.4%, 0.4% of the plans, respectively. The problems in the organs at risk (OARs) involved mainly the intestines (the rectum, small intestine, and colon), found in 20.7% of the plans, and in occasional cases, the rectum, small intestine, colon, kidney, and the femoral head had a score of 0. Senior planners showed significantly better performance than junior planners in PGTV (V60 Gy, D98%), PCTV (CI), and CTV (D98%) (P≤0.046) especially in terms of spinal cord and small intestine protection (P≤0.034). The bowel (the rectum, small intestine and colon) dose was significantly lower in the prone plans than supine plans (P < 0.05), and targets coverage all met clinical requirements. Twenty radiotherapy plans with low scores were selected for re-planning. The re-planned plans had significantly higher GTV (Dmin) and PTV (V45 Gy, D98%) (P < 0.05) with significantly reduced doses of the small intestines (V40 Gy vs V30 Gy), the colon (V40 Gy vs V30 Gy), and the bladder (D35%) (P < 0.05).@*CONCLUSION@#Quantitative evaluation of the radiotherapy plans can not only improve the quality of radiotherapy plan, but also facilitate risk management of the radiotherapy process.

Guidance, Navigation and Control for Autonomous Quadrotor Flight in an Agricultural Field: The Case of Vineyards.

In this paper, we present a complete and efficient solution of guidance, navigation and control for a quadrotor platform to accomplish 3D coverage flight missions in mapped vineyard terrains. Firstly, an occupancy grid map of the terrain is used to generate a safe guiding coverage path using an Iterative Structured Orientation planning algorithm. Secondly, way-points are extracted from the generated path and added to them trajectory's velocities and accelerations constraints. The constrained way-points are fed into a Linear Quadratic Regulator algorithm so as to generate global minimum snap optimal trajectory while satisfying both the pointing and the corridor constraints. Then, when facing unexpected obstacles, the quadrotor tends to re-plan its path in real-time locally using an Improved Artificial Potential Field algorithm. Finally, a geometric trajectory tracking controller is developed on the Special Euclidean group SE(3). The aim of this controller is to track the generated trajectory while pointing towards predetermined direction using the vector measurements provided by the inertial unit. The performance of the proposed method is demonstrated through several simulation results. In particular, safe guiding paths are achieved. Obstacle-free optimal trajectories that satisfy the way-point position, the pointing direction, and the corridor constraints, are successfully generated with optimized platform snap. Besides, the implemented geometric controller can achieve higher trajectory tracking accuracy with an absolute value of the maximum error in the order of 10-3 m.

Predicting necessity of daily online adaptive replanning based on wavelet image features for MRI guided adaptive radiation therapy.

PURPOSE: Online adaptive replanning (OLAR) is generally labor-intensive and time-consuming during MRI-guided adaptive radiation therapy (MRgART). This work aims to develop a method to determine OLAR necessity during MRgART. METHODS: A machine learning classifier was developed to predict OLAR necessity based on wavelet multiscale texture features extracted from daily MRIs and was trained and tested with data from 119 daily MRI datasets acquired during MRgART for 24 pancreatic cancer patients treated on a 1.5 T MR-Linac. Spearman correlations, interclass correlation (ICC), coefficient of variance (COV), t-test (p < 0.05), self-organized map (SOM) and maximum stable extremal region (MSER) algorithm were used to determine candidate features, which were used to build the prediction models using Bayesian classifiers. The model performance was judged using the AUC of the ROC curve. RESULTS: Spearman correlation identified 123 features that were not redundant (r < 0.9). Of them 82 showed high ICC for repositioning > 0.6, 67 had a COV greater than 9% for OLAR. Among the 38 features passed the t-test, 25 passed the SOM and 12 passed the MSER. These final 12 features were used to build the classifier model. The combination of 2-3 features at a time was used to build the classifier models. The best performing model was a 3-feature combination, which can predict OLAR necessity with a CV-AUC of 0.98. CONCLUSIONS: A machine learning classifier model based on the wavelet features extracted from daily MRI for pancreatic cancer was developed to automatically and objectively determine if OLAR is necessary for a treatment fraction avoiding unnecessary effort during MRgART.

Analysis of the Rate of Re-planning in Spot-Scanning Proton Therapy.

Purpose: Finite proton range affords improved dose conformality of radiation therapy when patient regions-of-interest geometries are well characterized. Substantial changes in patient anatomy necessitate re-planning (RP) to maintain effective, safe treatment. Regularly planned verification scanning (VS) is performed to ensure consistent treatment quality. Substantial resources, however, are required to conduct an effective proton plan verification program, which includes but is not limited to, additional computed tomography (CT) scanner time and dedicated personnel: radiation therapists, medical physicists, physicians, and medical dosimetrists. Materials and Methods: Verification scans (VSs) and re-plans (RPs) of 711 patients treated with proton therapy between June 2015 and June 2018 were studied. All treatment RP was performed with the intent to maintain original plan integrity and coverage. The treatments were classified by anatomic site: brain, craniospinal, bone, spine, head and neck (H&N), lung or chest, breast, prostate, rectum, anus, pelvis, esophagus, liver, abdomen, and extremity. Within each group, the dates of initial simulation scan, number of VSs, number of fractions completed at the time of VS, and the frequency of RP were collected. Data were analyzed in terms of rate of RP and individual likelihood of RP. Results: A total of 2196 VSs and 201 RPs were performed across all treatment sites. H&N and lung or chest disease sites represented the largest proportion of plan modifications in terms of rate of re-plan (RoR: 54% and 58%, respectively) and individual likelihood of RP on a per patient basis (likelihood of RP [RP%]: 46% and 39%, respectively). These sites required RP beyond 4 weeks of treatment, suggesting continued benefit for frequent, periodic VS. Disease sites in the lower pelvis demonstrated a low yield for RP per VS (0.01-0.02), suggesting that decreasing VS frequency, particularly late in treatment, may be reasonable. Conclusions: A large degree of variation in RoR and individual RP% was observed between anatomic treatment sites. The present retrospective analysis provides data to help develop anatomic site-based VS protocols.

A prediction model for dosimetric-based lung adaptive radiotherapy.

PURPOSE: Anatomical changes occurred during the treatment course of radiation therapy for lung cancer patients may introduce clinically unacceptable dosimetric deviations from the planned dose. Adaptive radiotherapy (ART) can compensate these dosimetric deviations in subsequent treatments via plan adaption. Determining whether and when to trigger plan adaption during the treatment course is essential to the effectiveness and efficiency of ART. In this study, we aimed to develop a prediction model as an auxiliary decision-making tool for lung ART to identify the patients with intrathoracic anatomical changes that would potentially benefit from the plan adaptions during the treatment course. METHODS: Seventy-one pairs of weekly cone-beam computer tomography (CBCT) and planning CT (pCT) from 17 advanced non-small cell lung cancer patients were enrolled in this study. To assess the dosimetric impacts brought by anatomical changes observed on each CBCT, dose distribution of the original treatment plan on the CBCT anatomy was calculated on a virtual CT generated by deforming the corresponding pCT to the CBCT and compared to that of the original plan. A replan was deemed needed for the CBCT anatomy once the recalculated dose distribution violated our dosimetric-based trigger criteria. A three-dimensional region of significant anatomical changes (region of interest, ROI) between each CBCT and the corresponding pCT was identified, and 16 morphological features of the ROI were extracted. Additionally, eight features from the overlapped volume histograms (OVHs) of patient anatomy were extracted for each patient to characterize the patient-specific anatomy. Based on the 24 extracted features and the evaluated replanning needs of the pCT-CBCT pairs, a nonlinear supporting vector machine was used to build a prediction model to identify the anatomical changes on CBCTs that would trigger plan adaptions. The most relevant features were selected using the sequential backward selection (SBS) algorithm and a shuffling-and-splitting validation scheme was used for model evaluation. RESULTS: Fifty-five CBCT-pCT pairs were identified of having an ROI, among which 21 CBCT anatomies required plan adaptions. For these 21 positive cases, statistically significant improvements in the sparing of lung, esophagus and spinal cord were achieved by plan adaptions. A high model performance of 0.929 AUC (area under curve) and 0.851 accuracy was achieved with six selected features, including five ROI shape features and one OVH feature. Without involving the OVH features in the feature selection process, the mean AUC and accuracy of the model significantly decreased to 0.826 and 0.779, respectively. Further investigation showed that poor prediction performance with AUC of 0.76 was achieved by the univariate model in solving this binary classification task. CONCLUSION: We built a prediction model based on the features of patient anatomy and the anatomical changes captured by on-treatment CBCT imaging to trigger plan adaption for lung cancer patients. This model effectively associated the anatomical changes with the dosimetric impacts for lung ART. This model can be a promising tool to assist the clinicians in making decisions for plan adaptions during the treatment courses.

Impact of Using Unedited CT-Based DIR-Propagated Autocontours on Online ART for Pancreatic SBRT.

Purpose: To determine the dosimetric impact of using unedited autocontours in daily plan adaptation of patients with locally advanced pancreatic cancer (LAPC) treated with stereotactic body radiotherapy using tumor tracking. Materials and Methods: The study included 98 daily CT scans of 35 LAPC patients. All scans were manually contoured (MAN), and included the PTV and main organs-at-risk (OAR): stomach, duodenum and bowel. Precision and MIM deformable image registration (DIR) methods followed by contour propagation were used to generate autocontour sets on the daily CT scans. Autocontours remained unedited, and were compared to MAN on the whole organs and at 3, 1 and 0.5 cm from the PTV. Manual and autocontoured OAR were used to generate daily plans using the VOLO™ optimizer, and were compared to non-adapted plans. Resulting planned doses were compared based on PTV coverage and OAR dose-constraints. Results: Overall, both algorithms reported a high agreement between unclipped MAN and autocontours, but showed worse results when being evaluated on the clipped structures at 1 cm and 0.5 cm from the PTV. Replanning with unedited autocontours resulted in better OAR sparing than non-adapted plans for 95% and 84% plans optimized using Precision and MIM autocontours, respectively, and obeyed OAR constraints in 64% and 56% of replans. Conclusion: For the majority of fractions, manual correction of autocontours could be avoided or be limited to the region closest to the PTV. This practice could further reduce the overall timings of adaptive radiotherapy workflows for patients with LAPC.

Adaptive Radiotherapy in Head and Neck Cancer Using Volumetric Modulated Arc Therapy.

A dosimetric study was performed to show the importance of adaptive radiotherapy (ART) for head and neck cancer (HNC) patients using volumetric modulated arc therapy (VMAT). A total of 13 patients with HNC who required replanning during radiotherapy were included in this study. All plans succeeded to achieve the set objectives regarding target volume coverage and organ sparing. All target volumes presented a significant decrease with an average of 76.44 cm3 (p = 0.007) for PTVlow risk, 102.81 cm3 (p = 0.021) for PTVintermediate risk, and 47.10 cm3 (p = 0.003) for PTVhigh risk. Additionally, a positive correlation was found between PTV shrinkage and the number of fractions completed before replanning. Significant volume decrease was also observed for the parotid glands. The ipsilateral parotid decreased in volume by a mean of 3.75 cm3 (14.43%) (p = 0.067), while the contralateral decreased by 4.23 cm3 (13.23%) (p = 0.033). For all analyzed organs, a reduction in the final dose received after replanning was found. Our study showed that ART via rescanning, recontouring, and replanning using VMAT is essential whenever anatomical and positional variations occur. Furthermore, comparison with the literature has confirmed that ART using VMAT offers similar results to ART with intensity modulated radiotherapy.

Criteria for Verification and Replanning Based on the Adaptive Radiotherapy Protocol "Best for Adaptive Radiotherapy" in Head and Neck Cancer.

No clear criteria have yet been established to guide decision-making for patient selection and the optimal timing of adaptive radiotherapy (ART) based on image-guided radiotherapy (IGRT). We have developed a novel protocol­the Best for Adaptive Radiotherapy (B-ART) protocol­to guide patient selection for ART. The aim of the present study is to describe this protocol, to evaluate its validity in patients with head and neck (HN) cancer, and to identify the anatomical and clinical predictors of the need for replanning. We retrospectively evaluated 82 patients with HN cancer who underwent helical tomotherapy (HT) and subsequently required replanning due to soft tissue changes upon daily MVCT. Under the proposed criteria, patients with anatomical changes >3 mm on three to four consecutive scans are candidates for ART. We compared the volumes on the initial CT scan (iCT) and the replanning CT (rCT) scan for the clinical target volumes (CTV1, referring to primary tumor or tumor bed and CTV2, metastatic lymph nodes) and for the parotid glands (PG) and body contour (B-body). The patients were stratified by primary tumor localization, clinical stage, and treatment scheme. The main reasons for replanning were: (1) a planning target volume (PTV) outside the body contour (n = 70; 85.4%), (2) PG shrinkage (n = 69; 84.1%), (3) B-body deviations (n = 69; 84.1%), and (4) setup deviations (n = 40; 48.8%). The replanning decision was made, on average, during the fourth week of treatment (n = 47; 57.3%). The mean reductions in the size of the right and left PG volumes were 6.31 cc (20.9%) and 5.98 cc (20.5%), respectively (p < 0.001). The reduction in PG volume was ≥30% in 30 patients (36.6%). The volume reduction in all of the anatomical structures was statistically significant. Four variables­advanced stage disease (T3−T4), chemoradiation, increased weight loss, and oropharyngeal localization­were significantly associated with the need for ART. The B-ART protocol provides clear criteria to eliminate random errors, and to allow for an early response to relevant changes in target volumes.