Planning Target Volume Research Articles (Page 1)

Objective.To develop and evaluate a deep reinforcement learning (RL) framework for rapid and automatic machine parameter optimization of volumetric modulated arc therapy (VMAT) treatment plans for localized prostate cancer.Approach.A multi-task policy network combining convolution and long short-term memory was trained to sequentially predict the set of actions on the dose rate and multi-leaf collimator positions over the range of two arcs. The network uses as input the cumulative dose grid at the current gantry angle, contours of the planning target volume (PTV) and organs at risk, and the set of machine parameters at all preceding gantry angles. The method was evaluated on a set of 15 localized prostate cancer patients for a prescription dose of 60 Gy in 20 fractions. For each case, the final state dose distribution was compared against clinical plans. For seamless integration with the clinical workflow, the proposed model was integrated into a clinical treatment planning system (TPS), enabling dosimetric review and final plan adjustments.Main results.The RL framework produced deliverable dual-arc VMAT plans in an average of 20.7 ± 5.0 s over the test set. Dosimetric comparison to clinical plans showed no statistically significant differences for the mean rectum dose as well as for the bladder V6160 Gy, indicating that the RL model was as efficient in sparing these structures as human planners. While the approach showed limitations in terms of PTV coverage and maximum body dose, our proposed integration to TPS showed the RL plans could be automatically refined to clinical quality in an additional 83.8 ± 7.2 s.Significance.The accuracy and fast run time of the approach show the potential of the framework to significantly streamline VMAT treatment planning and enable adaptive radiation therapy.

Objective.Anatomy continuously deforms during radiation therapy. Although real-time volumetric imaging approaches are emerging, there is a lack of adaptive strategies that account for intrafraction deformations. The purpose of this study was to develop a multileaf collimator (MLC) tracking method that adapts to deformations and evaluate the performance for lung cancer with multiple lesions.Approach.Dose-optimised deformable MLC tracking was developed using a fast dose calculation to accumulate dose at each timestep. The accumulated planned doses were deformed to represent the desired dose distribution for the deformed anatomy and the MLC leaf positions were optimised to minimise the difference between the delivered and deformed planned dose. Dose-optimised deformable MLC tracking was evaluated using four lung cancer cases generated using the 4D XCAT digital phantom. Stereotactic ablative radiotherapy treatment plans were created using a planning target volume (PTV) margin expansion of 5 mm on the gross tumour volumes (GTV). Treatments were simulated using three patient-measured motions for each phantom. The doses accumulated using the fast dose calculation model with MLC tracking were compared to an internal target volume (ITV)-based approach.Main results.The volume of the PTVs were reduced by an average of 34% using dose-optimised deformable MLC tracking compared to the ITV-based approach. The mean differences and standard deviations from the planned doses were -0.5% ± 0.6% for the GTV D100%and -1.1% ± 0.6% for the PTV D98%when dose-optimised deformable MLC tracking was used, and -5.2% ± 8.8% for the ITV D100%and -13.8% ± 12.9% for the PTV D98%when no tracking was used.Significance.The study demonstrated a proof of concept for dose-optimised deformable MLC tracking to reduce dosimetric errors for deforming anatomy. The proposed method could enable the safe reduction of treatment margins for multiple independently moving targets in the lung compared to the standard of care.

Objective: This study aimed to assess the treatment methods and results linked to adjuvant three-dimensional conformal radiation therapy (3DCRT) compared to volumetric modulated arc therapy (VMAT) radiotherapy following bilateral lumpectomy for breast cancer. Methods: This prospective comparative study involved 22 female patients diagnosed with bilateral breast cancer, all of whom underwent bilateral lumpectomy. Treatment planning was performed using the MONACO version 5.1 treatment planning system (TPS), employing X-ray photon beams with energies of either 6 MV or 10 MV. Radiotherapy was delivered using the ELEKTA Agility linear accelerator. A total prescribed dose of 4005 cGy was administered over 15 fractions. Results: The study compares the performance of volumetric modulated arc therapy (VMAT) and three-dimensional conformal radiation therapy (3DCRT) in various parameters. VMAT shows better dose coverage for planning target volumes (PTVs) and regions of elevated radiation (V105% and V110%). However, 3DCRT shows slightly higher efficacy for the left planning target volume, particularly in the V90% metric. VMAT also shows superior performance in terms of homogeneity and conformity for the target volume. However, there is a significant difference in the left PTV, but no significant difference is found in the right PTV. The results suggest that 3D treatment planning is more effective in reducing the average dose to the heart and left and right lungs compared to VMAT. The discrepancy in average lung dosage between the left and right sides is less prominent in 3DCRT compared to VMAT. Conclusion: The study reveals that volumetric modulated arc therapy (VMAT) offers better dose coverage for planned target volumes in bilateral lumpectomy for breast cancer. At the same time, 3DCRT minimises radiation exposure to the left PTV. VMAT has better homogeneity and conformity indexes for acceptable goal volumes.

Background: Knowledge-based (KB) planning is a promising approach to model prior planning experience and optimize radiotherapy. To enable the sharing of models across institutions, their transferability must be evaluated. This study aimed to validate KB prediction models developed by a national consortium using data from another multi-institutional consortium in a different country. Methods: Ten right whole breast tangential field (RWB-TF) models were built within the national consortium. A cohort of 20 patients from the external consortium was used for testing. Transferability was defined when the ipsilateral (IPSI) lung first principal component (PC1) was within the 10th–90th percentile of the training set. Predicted dose–volume parameters were compared with clinical dose–volume histograms (cDVHs). Results: Planning target volume (PTV) coverage strategies were comparable between the two consortia, even though significant volume differences were observed for the PTV and contralateral breast (p = 0.002 and p = 0.02, respectively). For the IPSI lung, the standard deviation of predicted mean dose/V20 Gy was 1.13 Gy/2.9% in the external consortium versus 0.55 Gy/1.6% in the training consortium. Differences between the cDVH and the predicted IPSI lung mean dose and the volume receiving more than 20 Gy (V20 Gy) were <2 Gy and <5% in 88.7% and 92.3% of cases, respectively. PC1 values fell within the 10th–90th percentile for ≥90% of patients in 6/10 models and 65–85% for the remaining 4. Conclusions: This study demonstrates the feasibility of applying RWB-TF KB models beyond the consortium in which they were developed, supporting broader clinical implementation. This retrospective study was supported by AIRC (Associazione Italiana per la Ricerca sul Cancro) and registered on ClinicalTrials.gov (NCT06317948, 12 March 2024).

Background and Objective In stereotactic body radiotherapy (SBRT) for lung cancer, the choice of volumetric modulated arc therapy (VMAT) optimization strategy is critical for achieving optimal target dose coverage while minimizing exposure to normal tissues. This study aims to compare the dosimetric performance and plan complexity of two VMAT optimization strategies in Monaco: single-beam dual-arc (1B2A) versus dual-beam single-arc (2B1A). Methods A retrospective analysis was conducted on 50 lung cancer patients treated with SBRT (prescription dose: 50 Gy in 5 fractions). Two VMAT plans were re-optimized using the Monaco treatment planning system: the 1B2A plan (single-beam dual-arc, collimator angle 10°) and the 2B1A plan (dual-beam single-arc, collimator angles 10° and 350°). Dosimetric parameters, including target dose coverage, conformity index (CI), and gradient index (GI), were evaluated for the internal target volume (ITV) and planning target volume (PTV). Dose metrics for organs at risk (OARs) were also analyzed. Plan complexity was assessed based on monitor units (MU), number of control points, complexity index, and integral dose to normal tissues. Results Significant dosimetric differences were observed between the two strategies. When normalized to ensure the prescribed 50 Gy isodose line covers 95% of the PTV volume, the high-dose parameters (D1%, D50%, Dmean) of the ITV and PTV were significantly lower in the 1B2A group compared to the 2B1A group ( p < 0.001), indicating superior dose distribution with the 2B1A approach. Although the 1B2A plan exhibited marginally better CI, GI, and low-dose lung sparing (V5–V30), these differences were minimal and clinically insignicant. No substantial dierences were found in the dose sparing of other OARs, including the spinal cord, heart, and ribs. Additionally, the 1B2A plan required signicantly higher MU (+15.5%, p < 0.001) and had greater plan complexity (+9.47%, p < 0.001), suggesting lower treatment efficiency. Conclusions In peripheral lung cancer SBRT, the dual-beam single-arc (2B1A) strategy offers superior target dose distribution and treatment efficiency, making it a preferable optimization approach.

Breast cancer dose distribution prediction based on deep joint learning.

Optimisation of Margin Adaptation for Respiratory Motion in Lung Cancer Stereotactic Body Radiation Therapy Using Virtual Four-dimensional Volumetric Modulated Arc Therapy System Radiotherapy.

Various methods have been developed to generate synthetic computed tomography (CT) images from magnetic resonance (MR) images, including segmentation-based approach with MR calculating attenuation (MRCAT) and deep learning (DL)-based approach. In this study, we aimed to validate the conventional radiotherapy (RT) planning process with MRCAT and DL-based synthetic CT images for five patients with cervical cancer. DL-based synthetic CT images of the five patients were inferred using a network trained with 40 pairs of CT and deformed, normalized T2-weighted MR scans; MRCAT images were obtained from mDixon sequences for the tested cases only. On the synthetic CT images, the contouring process for organs-at-risk (OARs) was automatically performed with minor adjustments, while two experienced radiation oncologists defined target volumes. Simultaneous integrated boost plans (2.2/2.0/1.8Gy with 25 fractions) were produced from a commercial treatment planning system (TPS) TomoTherapy. The plans with two synthetic CT images were compared with those based on genuine CT images for the five test cases. High geometric similarity was confirmed for the planning target volume (PTV), with average dice similarity coefficient (DSC) of 0.844 for the DL-based and 0.829 for the MRCAT images. The mean percentage difference in gross tumor volume (GTV) was 20.71 34.28% for DL-based synthetic CT and 30.31 46.20% for MRCAT images. By contrast, PTV, encompassing GTV, exhibited minimal changes with an average increase of 0.37 3.10% and 1.66 7.62%, respectively. MRCAT images and DL-based synthetic CT revealed significant differences, relative to true CT images, in the entire volume (p=0.03) of the bladder and in V20Gy and V30Gy of the resultant plans for the bladder (p=0.029 and 0.063), all plans generated on the synthetic CTs were clinically acceptable and met institutional for target coverage. MRCAT and DL-based synthetic CT images demonstrated clinical applicability, achieving plan quality similar to that of plans based on genuine planning CT images.

In image-guided radiotherapy (IGRT), four-dimensional cone-beam computed tomography (4D-CBCT) is critical for assessing tumor motion during a patient's breathing cycle prior to beam delivery. However, generating 4D-CBCT images with sufficient quality requires significantly more projection images than a standard 3D-CBCT scan, leading to extended scanning times and increased imaging dose to the patient. To introduce a novel spatiotemporal Gaussian neural representation framework to reconstruct high-temporal dynamic CBCT images from 1-minute acquisition, preserving motion dynamics and fine spatial details without relying on prior images or motion models. Our framework employs a differentiable 4D Gaussian representation initialized from average CBCT images. Gaussian points are characterized by position, covariance, rotation, and density, offering a compact and dynamic model for CBCT scenes. A Gaussian deformation network, incorporating a HexPlane encoder and multi-head decoder, predicts Gaussian deformations to minimize L1 and structural similarity index measure (SSIM) losses between rendered and measured projections. Adaptive Gaussian control refines the representation by pruning underutilized Gaussians and densifying points in high-gradient regions. The method was benchmarked on the AAPM SPARE challenge datasets and further validated with clinical CBCT scans from a Varian TrueBeam system. For the AAPM SPARE challenge datasets, the performance of the proposed method was evaluated using the root-mean-squared-error (RMSE) and the structural similarity index (SSIM) in the four regions of interest: Body, Lung, PTV, and Bone. The geometric accuracy was evaluated by calculating the registration error when aligning the tumor to the ground truth using the Elastix package, focusing on pixels within the planning target volume (PTV). To demonstrate our method's capability in high-temporal motion dynamic modeling using extremely undersampled projections, the clinical half-fan projections from a 1-minute Varian TrueBeam acquisition were sorted into 50 phases with approximately 18 projections per phase, significantly finer than the commonly used 10-phase binning. Compared to the AAPM SPARE challenge participant methods, our method achieved superior geometric accuracy in terms of PTV alignment error, and comparable RMSE and SSIM when no prior 4DCT or motion model is used for our reconstruction. For PTV alignment, our method achieved translational and rotational errors of 0.54mm (LR), 0.76mm (SI), 1.36mm (AP), 0.55° (rAP), and 0.93° (rSI), and 1.31° (rLR), respectively. For high temporal dynamic CBCT reconstruction, our method successfully reconstructed a 50-phase CBCT from a 1-minute Varian Truebeam half-fan scan, demonstrating effective streak artifact suppression, respiratory motion preservation, and fine detail restoration. Reconstruction on a single NVIDIA RTX A6000 GPU required approximately 30-80 min, depending on the number of Gaussian points used (ranging from 50 to 400K), to reconstruct CBCT from 680 projections acquired with a 30 × 40cm detector. Our code and reconstruction results can be found at: https://github.com/fuyabo/4DGS_for_4DCBCT/tree/main. The spatiotemporal Gaussian framework is a novel data-driven dynamic CBCT reconstruction technique that features excellent geometric accuracy in terms of PTV alignment and high-temporal motion modeling, indicating promise for tumor motion assessment and high-temporal respiratory motion modeling based on a 1-minute half-fan scan prior to beam delivery.

Clinical Evaluation of Stereotactic Ablative Radiotherapy for Oligometastases From Rare Primary Cancers.

This study aims to evaluate the impact of personalized 3D-printed headrest devices on setup errors and dosimetry in nasopharyngeal carcinoma radiotherapy. A total of 50 nasopharyngeal carcinoma patients (28 male, 22 female, aged 24-76 years, mean age 45 years) who received radiotherapy at our center between July 1, 2023, and August 10, 2024, were randomly divided into two groups. Group A: 3D-printed headrest and thermoplastic mask (Klarity Medical); Group B: Standard headrest and thermoplastic mask. All patients underwent a simulation CT (Philips IntelliSpace; 120kV/200 mAs, 3mm slices) in a supine position. The VMAT plan (70Gy/33 fx) was designed in Eclipse 15.6: Plan-P: Actual PLA CT values included; Plan-0: PLA CT values set to -1000 (control). Daily CBCT verification (Varian Halcyon) was performed to measure setup errors at the Clivus, C4, and C7 vertebral levels. The Van Herk formula was used to determine the appropriate planning target volume (PTV) margin. Dose measurements used PCI, HI, CR, Dmean, and Dmax to assess PTVs (GTVnx+nd, PTV1, PTV2) and OARs (brainstem, spinal cord, lens, optic nerve). A total of 340 CBCT images were collected from 50 patients, with 164 images from Group A and 176 from Group B. Setup errors in Group B were generally larger than those in Group A. Statistically significant differences were observed in the AP direction at the Clivus, C4, and C7 vertebral ROI registrations. Roll rotational errors showed statistically significant differences in ROI registration at C4 and C7 vertebral levels. The external radiation margins in all directions for Group A were smaller than those for Group B, with the largest difference observed at the C7 vertebral level. The external margins for the C7 ROI registration in the LR, SI, and AP directions were 2.91, 2.97, and 3.01mm, respectively. Compared with Plan-0, the Dmean and CR of the target volumes PTVnd+nx and PTV1 showed statistically significant differences (p<0.05). Differences in Dmean, CR, and PCI for PTV2 were also statistically significant (p<0.05). For the dosimetric evaluation of critical organs adjacent to the target volume, statistically significant differences were observed in the maximum doses to the brainstem, spinal cord, left lens, left optic nerve, and right optic nerve between the two planning approaches (p<0.05). However, no statistically significant differences were found in the mean doses to these organs at risk (OARs) (p>0.05). The application of 3D-printed immobilization technology significantly improves setup accuracy in nasopharyngeal carcinoma radiotherapy and reduces cervical spine displacement. The incorporation of 3D-printed materials exerts a measurable influence on target volume dose distribution and may notably increase the maximum dose delivered to adjacent critical organs.

BackgroundIn magnetic resonance imaging (MRI)‐guided online adaptive radiotherapy, MRI lacks tissue attenuation information necessary for accurate dose calculations. Although deep learning (DL)‐based synthetic computed tomography (CT) generation models have been developed to obtain CT density information from MRI, they usually do not meet the requirement of real‐time plan adaptation.PurposeWe propose a DL‐based photon dose calculation method directly on 0.35 T MRI to skip synthetic CT generation and show its feasibility for prostate patient cases.MethodsThe 0.35 T planning MRI and deformed planning CT (registered to the planning MRI) of 34 prostate cancer patients treated with a 0.35 T magnetic resonance‐linear accelerator (MR‐Linac) were collected. The air cavities (ACs) in the abdominopelvic area of the deformed CT images were corrected based on manual AC contouring on the MRI. Monte Carlo (MC) dose simulations under a 0.35 T magnetic field were performed on the corrected CT images. All photon beams were simulated using a uniform field size of 1cm×1cm. 10 800 beams were simulated with 5×106 initial photons for training (20 patients) and 2160 beams with 5×107 photons for validation (4 patients). For testing, 1080 beams shooting through the planning target volume (PTV) in 10 patients and five optimized nine‐field intensity‐modulated plans were simulated with 5×107 photons. 3D MRI cuboids covering the photon beams were input into a Unet model to predict AC segmentation, and 3D MRI and predicted AC cuboids were input into a long short‐term memory (LSTM) model for beam's eye view (BEV) processing to predict dose. The gamma passing rate γpr (2%/2mm, D>10%Dmax), beam dose profiles of single beams and dose volume histogram (DVH) of intensity‐modulated plans were evaluated.ResultsThe test results for all photon beams from the proposed models demonstrated a mean γpr above 99.50%. The five treatment plans recalculated by the DL model each achieved γpr values exceeding 99.80%. Additionally, the model's inference time was approximately 12 ms per photon beam.ConclusionsThe proposed method showed that DL‐based dose calculation directly on MRI is feasible for prostate cases, which has the potential to simplify the procedure for MRI‐only workflows and can be beneficial for real‐time plan adaptation.

Background: Although whole-brain radiotherapy (WBRT) has been the standard treatment for brain metastases, its cognitive side effects have led to the rise of stereotactic radiosurgery (SRS), which offers better local control with less neurocognitive decline. Aims and Objectives: This study aimed to determine toxicity using common terminology criteria for adverse events (CTCAE) grading and assess local control at the metastatic site and distant intracranial control using the response assessment in neuro-oncology (RANO) criteria for hippocampal avoidance WBRT (HA-WBRT) with a simultaneous integrated boost (SIB). Materials and Methods: This cross-sectional study included 22 patients from Tirunelveli Medical College between February 2022 and June 2024. Data were collected through direct patient interviews, and baseline quality of life (QoL) was assessed using the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life questionnaire (QLQ)-C15-PAL. Each patient underwent a planning computed tomography (CT) scan with thermoplastic mask immobilization. Gadolinium-enhanced T1-weighted magnetic resonance imaging (MRI) was co-registered with CT to delineate target volumes and organs at risk. The hippocampus was contoured per standard guidelines. The planning target volume for WBRT (PTV-WBRT) included the whole brain with a 3 mm margin, excluding the PTV-metastases and HA region. Toxicity (CTCAE) and QoL (EORTC) were evaluated at 3 months, and local tumor control was assessed at 6 months using RANO criteria and MRI assay. Results: Local tumor control was achieved in 77.27% of cases. Local control was not significantly associated with age, gender, primary tumor site, or number of metastases, but was significantly lower in patients with associated extracranial metastases (P=0.0005). Patients with local control had significantly better QoL scores at baseline, 3-, and 6-month post-treatment (P&lt;0.05). Radiologic tumor response assessed by RANO criteria at 6 months showed a strong correlation with local control (P=0.0005). Similarly, higher toxicity grades were significantly associated with a lack of local control at both 3 and 6 months (P=0.0002 and P=0.002, respectively). Conclusion: HA-WBRT with a SIB achieved effective local tumor control and improved intracranial progression-free survival in patients with single and limited brain metastases. This approach showed minimal toxicity and was associated with significantly better QoL outcomes.

This study evaluated and compared dynamic conformal arc therapy (DCAT) and volumetric-modulated arc therapy (VMAT) in stereotactic body radiation therapy (SBRT) for peripheral lung tumors, focusing on dosimetric outcomes, treatment delivery efficiency, and plan robustness against multileaf collimator (MLC) positional errors. Thirty patients with peripheral lung tumors who underwent SBRT during the study period were included. All treatment plans were generated using the Monaco treatment planning system and delivered via a Versa HD linear accelerator. Dosimetric parameters - including target coverage, organ-at-risk (OAR) sparing, and plan complexity - were evaluated based on Radiation Therapy Oncology Group (RTOG) guidelines. The effect of MLC leaf positional errors on the dose distribution was systematically simulated and analyzed using a generalized equivalent uniform dose (gEUD). DCAT demonstrated significantly lower D98%, D50%, Dmax, and homogeneity index, along with a higher conformity index than VMAT; however, all were within clinically acceptable ranges. The DCAT plans exhibited significantly lower plan complexity, fewer monitor units, and shorter delivery times compared to VMAT. Additionally, DCAT showed superior robustness to systematic MLC leaf opening and closing errors, with smaller dose deviations in both the planning target volume and OARs. No significant differences were observed between DCAT and VMAT in shift and random MLC errors. DCAT demonstrated clinically acceptable dosimetric results comparable to those of VMAT for lung tumor SBRT, while offering superior treatment efficiency and robustness to MLC positional errors. These findings suggested that DCAT is an effective alternative to VMAT in clinical practice.

Objective.Anatomical changes in target volumes and surrounding organs-at-risk (OARs) commonly occur during radiation therapy (RT). Relying solely on the initial treatment plan can lead to suboptimal dose delivery and increased risk to healthy tissues. This study investigates a fluence map (FM) prediction-based method (FM_PD) for rapid plan adaptation. It enables online adaptive RT (OART) to better account for structural changes throughout treatment and assess its potential for improved normal tissue sparing.Approach.The planning target volumes (PTVs) and corresponding dose distribution were converted into 2D projection matrices during training. A 2D Dense-U-Net model incorporating a PTV-specific loss function (PTV_loss) was trained on a dataset of 93 intensity-modulated RT plans for hypopharyngeal carcinoma. Nine re-planning scenarios (time intervals: 32-47 days) were used for testing to simulate an OART setting. Predicted FMs were applied to the daily CTs to calculate updated dose distributions. These doses were compared to the original (non-adapted) plans to evaluate the dosimetric impact on OARs.Main results.FM_PD significantly reduced the dose to normal tissues while maintaining tumor coverage. The D2of the PTV decreased by 1.13 ± 5.85%, moreover, substantial dose decreases were observed in critical structures: Dmaxto the lens, optic nerves, and brainstem decreased by 18.67 ± 19.04%, 19.17 ± 19.57%, and 14.54%, respectively. The total body Dmeandecreased by 25.65 ± 15.44%. In cases where the PTV was adjacent to lung tissue, the Dmeandropped significantly by 46.40 ± 36.89%.Significance.FM_PD offers a rapid and effective approach for adapting RT plans in response to anatomical changes, significantly reducing doses to healthy tissues. Compared to maintaining the initial plan, FM_PD is a recommended strategy for cross-fraction adaptation scenarios in clinical OART practice.

Background and purposeThe study aims to evaluate dosimetric properties of hypofractionated treatment plans integrating focal boost, using registered whole-mount histopathology (WMHP) as reference standard.MethodsFifteen men from the PAMP trial (EudraCT: 2015-005046-55) were included. Participants had ≥ 1 ISUP Grade group ≥ 4 lesion and underwent [68Ga]prostate-specific membrane antigen (PSMA) positron emission tomography/multiparametric magnetic resonance imaging (PET/mpMRI) and [11C]Acetate-PET/computed tomography before radical prostatectomy. Four radiation oncologists delineated gross tumor volumes (GTVs) on PSMA-PET/mpMRI. Sixty treatment plans were optimized, one per GTV and patient. Prostate planning target volumes were prescribed 42.7 Gy in seven fractions, with a simultaneous GTV boost up to 49.0 Gy, prioritizing organs at risk (OARs). Digital WMHP provided Gleason grading and was co-registered with in-vivo imaging. Target coverage for GTVs and voxels sharing Gleason patterns (GPs) was assessed via dose-volume histogram (DVH) analysis. Interobserver agreement in GTV-delineations was quantified with Fleiss’ kappa.ResultsThe median GTV dose per plan (D50) ranged from 48.3 to 49.1 Gy. For voxels with the highest GP, D50 was 42.9–49.2 Gy, exceeding 47.2 Gy in all except one plan. In lowest pattern voxels, D50 was 42.5–49.3 Gy, and below 43.4 Gy in over half the plans. Significant positive correlations between Fleiss’ kappa and DVH parameters appeared only for GP 5 regions, specifically for Fleiss’ kappa and D50 for two observers and the average D50 across observers.InterpretationThe histologically confirmed tumor was only partially boosted. Regions with more aggressive disease received better coverage. These findings provide a rational for prioritizing OARs in treatment planning.

Systematic and random errors in radiation dose delivery necessitate the use of planning target volume (PTV) margins to ensure adequate clinical target volume (CTV) treatment. Advances in magnetic resonance-guided radiation therapy (MRgRT) have enabled improved imaging with possible margin reduction; however, the optimal PTV margins remain uncertain. This study aimed to evaluate the adaptive radiotherapy component of intra-fractional prostate movement in MRgRT for prostate cancer (PCa) patients and determine appropriate PTV margins. This study retrospectively analyzed 18 PCa patients treated using a 1.5 T MR-Linac. The initial fusion MR and verification MR scans were registered offline to assess prostate displacement between the two scans in the anterior-posterior (AP), left-right (LR) and superior-inferior (SI) directions. Random and systematic errors were calculated, and the PTV margins were determined using the Van Herk formula. The average time between MR scans was 22 min (range 9-54 min) compared to an average beam-on time of 6 min (range 2-11 min). Mean and standard deviation of translational displacement was 1.2 ± 0.9 mm in the AP, 0.6 ± 0.5 mm in the LR, and 1.1 ± 0.8 mm in the SI directions. The calculated PTV margin was 3.2 mm in AP, 1.7 mm in LR, and 3.2 mm in SI directions. There was an observed trend of increased prostate motion with increased treatment duration. MRgRT facilitates PTV margin reduction for PCa; however, our findings suggest that increased on-couch time may be associated with greater prostate motion. Future studies with larger patient cohorts and real-time motion monitoring are recommended to optimise margin strategies.

Objectives: This study aims to perform a dosimetric and radiobiological comparison between Three-Dimensional Conformal Radiotherapy (3DCRT) and Intensity-Modulated Radiotherapy (IMRT) for a hypofractionated regimen. Methods: A retrospective analysis was conducted on fifty patients with left-sided, node-negative breast cancer after breast-conserving surgery. Treatment plans were generated for each patient using both 3DCRT (field-in-field technique) and IMRT (inverse-planned with 5 fields) techniques for a prescription dose of 40.05 Gy in 15 fractions. Plans were evaluated based on dosimetric parameters for planning target volume (PTV) coverage (Dmin, Dmean, V95%, V105%), conformity (CI), homogeneity (HI), and doses to OARs (heart, lungs, spinal cord). Radiobiological evaluation included calculating Tumor Control Probability (TCP) and Normal Tissue Complication Probability (NTCP). Results: IMRT demonstrated significantly superior PTV coverage, with higher Dmin (27.55 vs. 16.54 Gy, p&lt;0.0001), V95% (99.81% vs. 87.55%, p&lt;0.0001), and ideal conformity (CI=1.00 vs. 0.87, p&lt;0.0001). However, IMRT resulted in a larger volume receiving 105% of the dose (V105%=44.68% vs. 11.70%, p&lt;0.0001). For OARs, IMRT reduced the mean heart dose (3.40 vs. 2.51 Gy, p&lt;0.0001) and ipsilateral lung V20Gy (17.42% vs. 18.21%, p=0.31), but increased low-dose exposure (e.g., heart V10Gy and lung V5Gy). IMRT demonstrated a superior TCP (96.2% compared to 93.1%, p&lt;0.0001) and IMRT demonstrated a superior TCP (96.2% compared to 93.1%, p&lt;0.0001) and markedly reduced NTCP for radiation pneumonitis (2.1% versus 19.2%, p&lt;0.0001) and cardiac problems (0.10% versus 0.20%, p&lt;0.0001). NTCP for radiation pneumonitis (2.1% versus 19.2%, p&lt;0.0001) and cardiac problems (0.10% versus 0.20%, p&lt;0.0001). IMRT required substantially more monitor units and longer beam-on time. Conclusion: IMRT offers a dosimetrically and radiobiologically enhanced plan for hypofractionated radiation treatment of left-sided breast cancer, ensuring superior target coverage and a marked decrease in the anticipated risk of pulmonary and cardiac problems. Nonetheless, this strategy incurs the expense of heightened low-dose exposure to adjacent tissues and increased complexity in treatment administration. The selected approach must be tailored according to patient anatomy and specific risk profiles.

BackgroundRadiotherapy techniques have advanced significantly over the past few decades. Whole‐brain radiotherapy combined with a simultaneous integrated boost (WBRT+SIB) is increasingly used to treat limited brain metastases.PurposeTo retrospectively compare helical tomotherapy and coplanar volumetric modulated arc therapy (VMAT) for WBRT with a SIB‐WBRT in patients with multiple brain metastases. Additionally, it emphasizes the importance of selecting appropriate evaluation indices when comparing SIB plans.Materials and methodsFifteen patients with 2‐ 3 metastatic lesions were retrospectively analyzed in this study. Treatment planning was performed using TomoHD and eclipse planning systems for tomotherapy and VMAT, respectively. Dose–volume histograms were used to assess the doses delivered to the target volumes and organs at risk (OARs). Quantitative metrics, including the homogeneity index (HI), conformity index (CI), and plan quality index (PQI), were used for the evaluation.ResultsTomotherapy yielded significantly higher D98% values for both the planning target volume (PTV) WB and PTV_met compared with VMAT (p < 0.05). It also provided lower Dmax and Dmean values for the lenses and eyes (p < 0.001 and p < 0.02, respectively). Tomotherapy was superior in terms of PTV whole‐brain CI and PTV_met HI and CI (p < 0.05). However, no significant difference was observed in the PQI values between the techniques (p > 0.05).ConclusionBoth tomotherapy and VMAT achieved acceptable target volumes and OAR doses in SIB applications. Tomotherapy showed advantages in terms of dose conformity and critical organ sparing. Moreover, this study highlights the impact of selecting appropriate evaluation indices on interpreting plan quality, particularly for complex treatment approaches such as SIB.

Ratio of PTV to normal lung volumes as a dosimetric predictor for lung metrics in peripheral lung SBRT.