Dose-volume Histogram Parameters Research Articles

Self-configuring nnU-Net for automatic delineation of the organs at risk and target in high-dose rate cervical brachytherapy, a low/middle-income country's experience.

The high-dose rate (HDR) brachytherapy treatment planning workflow for cervical cancer is a labor-intensive, time-consuming, and expertise-driven process. These issues are amplified in low/middle-income countries with large deficits in experienced healthcare professionals. Automation has the ability to substantially reduce bottlenecks in the planning process but often require a high level of expertise to develop. To implement the out of the box self-configuring nnU-Net package for the auto-segmentation of the organs at risk (OARs) and high-risk CTV (HR CTV) for Ring-Tandem (R-T) HDR cervical brachytherapy treatment planning. The computed tomography (CT) scans of 100 previously treated patients were used to train and test three different nnU-Net configurations (2D, 3DFR, and 3DCasc). The performance of the models was evaluated by calculating the Sørensen-dice similarity coefficient, Hausdorff distance (HD), 95th percentile Hausdorff distance, mean surface distance (MSD), and precision score for 20 test patients. The dosimetric accuracy between the manual and predicted contours was assessed by looking at the various dose volume histogram (DVH) parameters and volume differences. Three different radiation oncologists (ROs) scored the predicted bladder, rectum, and HR CTV contours generated by the best performing model. The manual contouring, prediction, and editing times were recorded. The mean DSC, HD, HD95, MSD and precision scores for our best performing model (3DFR) were 0.92/7.5mm/3.0mm/ 0.8mm/0.91 for the bladder, 0.84/13.8mm/5.3mm/1.4mm/0.84 for the rectum, and 0.81/8.5mm/6.0mm/2.2mm/0.80 for the HR CTV. Mean dose differences (D2cc/90% ) and volume differences were 0.08Gy/1.3 cm3 for the bladder, 0.02Gy/0.7 cm3 for the rectum, and 0.33Gy/1.5 cm3 for the HR CTV. On average, 65% of the generated contours were clinically acceptable, 33% requiring minor edits, 2% required major edits, and no contours were rejected. Average manual contouring time was 14.0 min, while the average prediction and editing times were 1.6 and 2.1 min, respectively. Our best performing model (3DFR) provided fast accurate auto generated OARs and HR CTV contours with a large clinical acceptance rate.

Comparison between supine and prone patient setup for lumbosacral spinal stereotactic body radiosurgery with CyberKnife.

The aim of this study is to analyze which tracking modality is more suitable for stereotactic body radiosurgery of lumbosacral spinal tumors by comparing prone and supine patient treatment setup. Eighteen patients with lumbosacral spinal tumors were selected. CT simulation was performed in the supine position (fixed with a vacuum cushion) and prone position (fixed with a thermoplastic mask and prone plate), respectively. The plans in the supine and prone positions were designed using the xsight spine tracking (XST) and xsight spine prone tracking (XSPT) modalities, respectively. The dose-volume histogram (DVH) parameters, namely, V100%, D95%, Dmean, conformity index (CI), and heterogeneity index (HI) in planning target volume (PTV), as well as Dmax, D0.1cc, D1cc, and D5cc in the cauda equina and bowel were recorded. The supine plans were simulation plans and were not used for treatment, which were only used to record the alignment errors. The spinal tracking correction errors (alignment error) and correlation errors of the synchrony respiratory model in the prone position were recorded during the treatment. After treatment, the simulation plan of the supine position was implemented and the spinal tracking correction errors were recorded. The parameters of correction error and DVH parameters for the two positions were analyzed using the paired t-test to compare the difference in positioning accuracy and dose distribution. In addition, the correlation errors of the synchrony respiratory model in the prone position were analyzed to evaluate the prediction accuracy of the synchrony model. For patient setup, the correction error of the supine position in interior/posterior was (0.18 ± 0.16) mm and the prone position was (0.31 ± 0.26) mm (P< 0.05). The correction error of the supine position in inferior/superior was (0.27 ± 0.24) mm, and the prone position was (0.5 ± 0.4) mm (P< 0.05). The average correlation errors of the synchrony model for left/right, inferior/superior, and anterior/posterior in the prone position were (0.21 ± 0.11) mm, (0.41 ± 0.38) mm, and (0.68 ± 0.42) mm, respectively. For the dose distribution, compared with prone plans, the average CI in supine plans was increased by 4.5% (P< 0.05). There was no significant difference in HI, PTV V100%, D95%, and Dmean between the prone and supine plans. Compared with supine plans, average D1cc and D5cc for the cauda equina was significantly decreased by 4.7 and 15.3% in the prone plan (P< 0.05). For the bowel, average Dmax, D0.1cc, D1cc, and D5cc were reduced by 8.0, 7.7, 5.2, and 26.6% in prone plans (P< 0.05) compared with supine plans. Compared with the supine setup, the prone setup combined with XSPT modality for the lumbosacral spinal stereotactic body radiosurgery can spare the bowel and cauda equina of the middle and low dose irradiation, and decrease the number of beams and monitor units.

Deep learning-based precise prediction and early detection of radiation-induced temporal lobe injury for nasopharyngeal carcinoma

Radiotherapy is the mainstay of treatment for nasopharyngeal carcinoma. Radiation-induced temporal lobe injury (TLI) can regress or resolve in the early phase, but it is irreversible at a later stage. However, no study has proposed a risk-based follow-up schedule for its early detection. Planning evaluation is difficult when dose-volume histogram (DVH) parameters are similar and optimization is terminated. This multicenter retrospective study included 6065 patients between 2014 and 2018. A 3D ResNet-based deep learning model was developed in training and validation cohorts and independently tested using concordance index in internal and external test cohorts. Accordingly, the patients were stratified into risk groups, and the model-predicted risks were used to develop risk-based follow-up schedules. The schedule was compared with the Radiation Therapy Oncology Group (RTOG) recommendation (every 3 months during the first 2 years and every 6 months in 3-5 years). Additionally, the model was used to evaluate plans with similar DVH parameters. Our model achieved concordance indexes of 0.831, 0.818, and 0.804, respectively, which outperformed conventional prediction models (all P<0.001). The temporal lobes in all the cohorts were stratified into three groups with discrepant TLI-free survival. Personalized follow-up schedules developed for each risk group could detect TLI 1.9 months earlier than the RTOG recommendation. According to a higher median predicted 3-year TLI-free survival (99.25% vs. 99.15%, P<0.001), the model identified a better plan than previous models. The deep learning model predicted TLI more precisely. The model-determined risk-based follow-up schedule detected the TLI earlier. The planning evaluation was refined because the model identified a better plan with a lower risk of TLI. The Sun Yat-sen University Clinical Research 5010 Program (2015020), Guangdong Basic and Applied Basic Research Foundation (2022A1515110356), Medical Scientific Research Foundation of Guangdong Province (A2022367), and Guangzhou Science and Technology Program (2023A04J1788).

Adjuvant Radiation Treatment of Breast Cancer After Mastectomy: Advanced Algorithms and Partial Bolus Improve the Dose Calculation Accuracy in the Case of Thin-Chest-Wall Irradiation

The aim of this study was to examine measured and calculated dose distributions in a thin-chest-wall phantom and estimate the variations in the dose-volume histogram (DVH) parameters used in plan evaluation for patient geometries with chest-wall thicknesses <15 mm with and without bolus implementation. Measurements were made using thermoluminescent dosimeters in a chest-wall phantom. The Monte Carlo method, anisotropic analytical algorithm, and Acuros XB Eclipse algorithms were used to calculate dose distributions for clinical plans. DVH parameters for clinical target volume tumor (CTVT) and planning target volume (PTV) and mean doses were evaluated for 15 patients with a chest-wall thickness of 8 to 15 mm with and without partial bolus and for 10 patients with a chest-wall thickness of 20 to 25 mm without bolus. Measurements showed that the dose at a depth of 2 to 12 mm at the beam entrance and laterally was within 90% of the dose at 8 mm depth. Monte Carlo and Acuros XB calculations were well aligned with the experimental data, whereas the anisotropic analytical algorithm underestimated the beam entrance and lateral doses. The DVH parameters for the patients with a thin chest wall were sensitive to calculation algorithm, resolution, body structure definition, and patient geometry. The parameters CTVTV95%, CTVTD98%, and PTVD98% were much lower than the tolerance criteria. Partial bolus improved the values for all algorithms and decreased the variations due to patient geometry. Dose calculations for patients with a chest-wall thickness of 20 to 25 mm resulted in sufficient target coverage and low dependence on patient geometry and calculation algorithm without the use of bolus. Dose calculations using advanced algorithms and resolution <2 mm are recommended for patients with a thin chest wall. Specific DVH criteria or the implementation of partial bolus was needed to facilitate plan development and evaluation for this patient group.

Applying the column generation method to the intensity modulated high dose rate brachytherapy inverse planning problem

Objective. Intensity modulated high dose rate brachytherapy (IMBT) is a rapidly developing application of brachytherapy where anisotropic dose distributions can be produced at each source dwell position. This technique is made possible by placing rotating metallic shields inside brachytherapy needles or catheters. By dynamically directing the radiation towards the tumours and away from the healthy tissues, a more conformal dose distribution can be obtained. The resulting treatment planning involves optimizing dwell position and shield angle (DPSA). The aim of this study was to investigate the column generation method for IMBT treatment plan optimization. Approach. A column generation optimization algorithm was developed to optimize the dwell times and shield angles. A retrospective study was performed on 10 prostate cases using RapidBrachyMCTPS. At every iteration, the plan was optimized with the chosen DPSA which would best improve the cost function that was added to the plan. The optimization process was stopped when the remaining DPSAs would not add value to the plan to limit the plan complexity. Main results. The average number of DPSAs and voxels were 2270 and 7997, respectively. The column generation approach yielded near-optimal treatment plans by using only 11% of available DPSAs on average in ten prostate cases. The coverage and organs at risk constraints passed in all ten cases. Significance. The column generation method produced high-quality deliverable prostate IMBT plans. The treatment plan quality reached a plateau, where adding more DPSAs had a minimal effect on dose volume histogram parameters. The iterative nature of the column generation method allows early termination of the treatment plan creation process as soon as the dosimetric indices from dose volume histogram satisfy the clinical requirements or if their values stabilize.

Scatter correction of 4D cone beam computed tomography to detect dosimetric effects due to anatomical changes in proton therapy for lung cancer.

The treatment of moving tumor entities is expected to have superior clinical outcomes, using image-guided adaptive intensity-modulated proton therapy (IMPT). For 21 lung cancer patients, IMPT dose calculations were performed on scatter-corrected 4D cone beam CTs (4DCBCTcor ) to evaluate their potential for triggering treatment adaptation. Additional dose calculations were performed on corresponding planning 4DCTs and day-of-treatment 4D virtual CTs (4DvCTs). A 4DCBCT correction workflow, previously validated on a phantom, generates 4DvCT (CT-to-CBCT deformable registration) and 4DCBCTcor images (projection-based correction using 4DvCT as a prior) with 10 phase bins, using day-of-treatment free-breathing CBCT projections and planning 4DCT images as input. Using a research planning system, robust IMPT plans administering eight fractions of 7.5 Gy were created on a free-breathing planning CT (pCT) contoured by a physician. The internal target volume (ITV) was overridden with muscle tissue. Robustness settings for range and setup uncertainties were 3% and 6 mm, and a Monte Carlo dose engine was used. On every phase of planning 4DCT, day-of-treatment 4DvCT, and 4DCBCTcor , the dose was recalculated. For evaluation, image analysis as well as dose analysis were performed using mean error (ME) and mean absolute error (MAE) analysis, dose-volume histogram (DVH) parameters, and 2%/2-mm gamma pass rate analysis. Action levels (1.6% ITV D98 and 90% gamma pass rate) based on our previous phantom validation study were set to determine which patients had a loss of dosimetriccoverage. Quality enhancements of 4DvCT and 4DCBCTcor over 4DCBCT were observed. ITV D98% and bronchi D2% had its largest agreement for 4DCBCTcor -4DvCT, and the largest gamma pass rates (>94%, median 98%) were found for 4DCBCTcor -4DvCT. Deviations were larger and gamma pass rates were smaller for 4DvCT-4DCT and 4DCBCTcor -4DCT. For five patients, deviations were larger than the action levels, suggesting substantial anatomical changes between pCT and CBCT projectionsacquisition. This retrospective study shows the feasibility of daily proton dose calculation on 4DCBCTcor for lung tumor patients. The applied method is of clinical interest as it generates up-to-date in-room images, accounting for breathing motion and anatomical changes. This information could be used to triggerreplanning.

Comparison of MR-guided radiotherapy accumulated doses for central lung tumors with non-adaptive and online adaptive proton therapy.

Stereotactic body radiation therapy (SBRT) of central lung tumors with photon or proton therapy has a risk of increased toxicity. Treatment planning studies comparing accumulated doses for state-of-the-art treatment techniques, such as MR-guided radiotherapy (MRgRT) and intensity modulated proton therapy (IMPT), are currently lacking. We conducted a comparison of accumulated doses for MRgRT, robustly optimized non-adaptive IMPT, and online adaptive IMPT for central lung tumors. A special focus was set on analyzing the accumulated doses to the bronchial tree, a parameter linked to high-grade toxicities. Data of 18 early-stage central lung tumor patients, treated at a 0.35T MR-linac in eight or five fractions, were analyzed. Three gated treatment scenarios were compared: (S1) online adaptive MRgRT, (S2) non-adaptive IMPT, and (S3) online adaptive IMPT. The treatment plans were recalculated or reoptimized on the daily imaging data acquired during MRgRT, and accumulated over all treatment fractions. Accumulated dose-volume histogram (DVH) parameters of the gross tumor volume (GTV), lung, heart, and organs-at-risk (OARs) within 2cm of the planning target volume (PTV) were extracted for each scenario and compared in Wilcoxon signed-rank tests between S1 & S2, and S1 & S3. The accumulated GTV D98% was above the prescribed dose for all patients and scenarios. Significant reductions (p < 0.05) of the mean ipsilateral lung dose (S2: -8%; S3: -23%) and mean heart dose (S2: -79%; S3: -83%) were observed for both proton scenarios compared to S1. The bronchial tree D0.1cc was significantly lower for S3 (S1: 48.1 Gy; S3: 39.2 Gy; p = 0.005), but not significantly different for S2 (S2: 45.0 Gy; p = 0.094), compared to S1. The D0.1cc for S2 and S3 compared to S1 was significantly (p < 0.05) smaller for OARs within 1-2cm of the PTV (S1: 30.2 Gy; S2: 24.6 Gy; S3: 23.1 Gy), but not significantlydifferent for OARs within 1cm of the PTV. A significant dose sparing potential of non-adaptive and online adaptive proton therapy compared to MRgRT for OARs in close, but not direct proximity of central lung tumors was identified. The near-maximum dose to the bronchial tree was not significantly different for MRgRT and non-adaptive IMPT. Online adaptive IMPT achieved significantly lower doses to the bronchial tree compared to MRgRT.

Improving normal tissue sparing using scripting in endometrial cancer radiation therapy planning.

The aim of this study was to improve the protection of organs at risk (OARs), decrease the total planning time and maintain sufficient target doses using scripting endometrial cancer external beam radiation therapy (EBRT) planning. Computed tomography (CT) data of 14 endometrial cancer patients were included in this study. Manual and automatic planning with scripting were performed for each CT. Scripts were created in the RayStation™ (RaySearch Laboratories AB, Stockholm, Sweden) planning system using a Python code. In scripting, seven additional contours were automatically created to reduce the OAR doses. The scripted and manual plans were compared to each other in terms of planning time, dose-volume histogram (DVH) parameters, and total monitor unit (MU) values. While the mean total planning time for manual planning was 368 ± 8s, it was only 55 ± 2s for the automatic planning with scripting (p < 0.001). The mean doses of OARs decreased with automatic planning (p < 0.001). In addition, the maximum doses (D2% and D1%) for bilateral femoral heads and the rectum were significantly reduced. It was observed that the total MU value increased from 1146 ± 126 (manual planning) to 1369 ± 95 (scripted planning). It is concluded that scripted planning has significant time and dosimetric advantages over manual planning for endometrial cancer EBRT planning.

Dosimetric benefit of a library of plans versus single-plan strategy for pre-operative gastric cancer radiotherapy

Background and purposeThe stomach experiences large volume and shape changes during pre-operative gastric radiotherapy. This study evaluates the dosimetric benefit for organs-at-risk (OARs) of a library of plans (LoP) compared to the traditional single-plan (SP) strategy. Materials and methodsTwelve patients who received SP CBCT-guided pre-operative gastric radiotherapy (45 Gy; 25 fractions) were included. Clinical target volume (CTV) consisted of CTVstomach (i.e., stomach + 10 mm uniform margin minus OARs) and CTVLN (i.e., regional lymph node stations). For LoP, five stomach volumes (approximately equidistant with fixed volumes) were created using a previously developed stomach deformation model (volume = 150–750 mL). Appropriate planning target volume (PTV) margins were calculated for CTVstomach (SP and LoP, separately) and CTVLN. Treatment plans were automatically generated/optimized and the best-fitting library plan was manually selected for each daily CBCT. OARs (i.e., liver, kidneys, heart, spleen, spinal canal) doses were accumulated and dose-volume histogram (DVH) parameters were evaluated. ResultsThe non-isotropic PTVstomach margins were significantly (p < 0.05) smaller for LoP than SP (median = 13.1 vs 19.8 mm). For each patient, the average PTV was smaller using a LoP (difference range 134–1151 mL). For all OARs except the kidneys, DVH parameters were significantly reduced using a LoP. Differences in mean dose (Dmean) for liver, heart and spleen ranged between −1.8 to 5.7 Gy. For LoP, a benefit of heart Dmean > 4 Gy and spleen Dmean > 2 Gy was found in 4 and 5 patients, respectively. ConclusionA LoP strategy for pre-operative gastric cancer reduced average PTV and reduced OAR dose compared to a SP strategy, thereby potentially reducing risks for radiation-induced toxicities.

Validation of automated post-adjustments of HDR prostate brachytherapy treatment plans by quantitative measures and oncologist observer study

PURPOSEThe aim was to evaluate a postprocessing optimization algorithm's ability to improve the spatial properties of a clinical treatment plan while preserving the target coverage and the dose to the organs at risk. The goal was to obtain a more homogenous treatment plan, minimizing the need for manual adjustments after inverse treatment planning. MATERIALS AND METHODSThe study included 25 previously treated prostate cancer patients. The treatment plans were evaluated on dose-volume histogram parameters established clinical and quantitative measures of the high dose volumes. The volumes of the four largest hot spots were compared and complemented with a human observer study with visual grading by eight oncologists. Statistical analysis was done using ordinal logistic regression. Weighted kappa and Fleiss’ kappa were used to evaluate intra- and interobserver reliability. RESULTSThe quantitative analysis showed that there was no change in planning target volume (PTV) coverage and dose to the rectum. There were significant improvements for the adjusted treatment plan in: V150% and V200% for PTV, dose to urethra, conformal index, and dose nonhomogeneity ratio. The three largest hot spots for the adjusted treatment plan were significantly smaller compared to the clinical treatment plan. The observers preferred the adjusted treatment plan in 132 cases and the clinical in 83 cases. The observers preferred the adjusted treatment plan on homogeneity and organs at risk but preferred the clinical plan on PTV coverage. CONCLUSIONSQuantitative analysis showed that the postadjustment optimization tool could improve the spatial properties of the treatment plans while maintaining the target coverage.

Remote EPID-based dosimetric auditing using DVH patient dose analysis

Objective. The aim of this work was to develop and validate a method for remote dosimetric auditing that enables dose-volume histogram parameter comparisons of measured and planned dose in the patient CT volume. Approach. The method is derived by adapting and combining a remote electronic portal imaging (EPID) based auditing method (Virtual Epid based Standard Phantom Audit—VESPA) and a method to estimate 3D in-patient dose distributions from planar dosimetric measurements. The method was tested with a series of error-induced plans including monitor unit and multileaf collimator (MLC) positioning errors. A pilot audit study was conducted with eleven radiotherapy centres. IMRT plans from two clinical trials, a post-prostatectomy (RAVES trial) plan and a head and neck (HPV trial) plan were utilized. Clinically relevant DVH parameters for the planned dose and estimated measured dose were compared. Main results. The method was found to reproduce the induced dose errors within 0.5% and was sensitive to MLC positioning errors as small as 0.5 mm. For the RAVES plan audit all DVH results except one were within 3% and for the HPV plan audit all DVH results were within 3% except three with a maximum difference of 3.2%. Significance. The results from the audit method produce clinically meaningful DVH metrics for the audited plan and could enable an improved understanding of a centre’s radiotherapy quality.

The risk of valvular heart disease in the French Childhood Cancer Survivors’ Study: Contribution of dose-volume histogram parameters

Background and purposeValvular Heart Disease (VHD) is a known complication of childhood cancer after radiotherapy treatment. However, the dose-volume-effect relationships have not been fully explored. Materials and methodsWe obtained individual heart Dose Volume Histograms (DVH) for survivors of the French Childhood Cancer Survivors Study (FCCSS) who had received radiotherapy. We calculated the Mean Dose to the Heart (MHD) in Gy, as well as the heart DVH parameters (Vd Gy, which represents the percentage of heart volume receiving at least d Gy), fixing the thresholds to 0.1 Gy, 5 Gy, 20 Gy, and 40 Gy. We analyzed them furtherly in the subpopulation of the cohort that was treated with a dose lower than 5 Gy (V0.1Gy|V5Gy=0%), 20 Gy (V5Gy|V20Gy=0%), and 40 Gy (V20Gy|V40Gy=0%), respectively. We investigated their role in the occurrence of a VHD in this population-based observational cohort study using the Cox proportional hazard model, adjusting for age at cancer diagnosis and chemotherapy exposure. ResultsMedian follow-up was 30.6 years. Eighty-one patients out of the 7462 (1 %) with complete data experienced a severe VHD (grade ≥ 3). The risk of VHD increased along with the MHD, and it was associated with high doses to the heart (V40Gy < 50 %, hazard ratio (HR) = 7.96, 95 % CI: 4.26–14.88 and V20Gy|V40Gy=0% >50 %, HR = 5.03, 95 % CI: [2.35–10.76]). Doses 5–20 Gy to more than 50 % (V5Gy|V20Gy=0% >50 %) of the heart induced a marginally non-significant estimated risk. We also observed a remarkable risk increase with attained age. ConclusionsOur results provide new insight into the VHD risk that may impact current treatments and long-term follow-up of childhood cancer survivors.

Bone marrow sparing RapidArc treatment in locally advanced rectal cancer – can it reduce haematological toxicity?

Abstract Context: Haematological toxicities are seen in rectal cancer patients receiving concurrent chemoradiotherapy (CRT) with capecitabine. Aims: To compare dose volume histogram (DVH) parameters and acute haematological toxicities using RapidArc with or without bone marrow constraints for rectal cancer patients receiving pelvic chemoradiation as part of curative treatment. Setting and designs: This is a prospective randomised controlled study including patients with rectal cancer initiated on chemoradiation. Patients were stratified into two arms, bone marrow sparing (BMS) arm and non-bone marrow sparing arm (NBMS). Materials and methods: DVH parameters and weekly toxicity data were collected. Grade 2 or more anaemia, leucopenia, neutropenia, or thrombocytopenia, any blood transfusions, colony-stimulating factor injection, platelet transfusions were considered as an event in acute haematological toxicity (HT). Statistical analysis: Independent t-test was used to compare quantitative parameters, and Mann–Whitney U-test was used for ordinal parameters between groups. Results: A total of 43 patients were enrolled. Bone marrow constraints were achieved without compromising the target coverage. There was a significant reduction in the bone marrow dose with BMS technique (p &lt; 0·05). A 16·7% reduction in the HT (33·3% versus 50%) and a 21·9% reduction in the grade 2 or more anaemia (19% versus 40·9%) were noted in the BMS arm when compared to NBMS arm, though not statistically significant. However, in the preoperative setting, a significant reduction in grade 2/more anaemia (7·1% versus 41·1%, p = 0·035) was noticed in the BMS arm. Conclusions: Pelvic BMS radiotherapy may benefit patients receiving chemoradiation for locally advanced carcinoma rectum as part of curative treatment.

Association Between Thyroid Radiation Dose and Hypothyroidism in Breast Cancer Patients Undergoing Volumetric Modulated Arc Therapy for Regional Nodal Irradiation.

To investigate the association between the thyroid dysfunction and thyroid radiation dose in regional nodal irradiation (RNI) using volumetric modulated arc therapy (VMAT) for breast cancer. We reviewed medical data of 67 patients with breast cancer who underwent curative surgery followed by adjuvant radiotherapy, including RNI using VMAT, between 2018 and 2021. All patients had normal thyroid functional test results, including thyroid stimulating hormone (TSH), T3, and free-T4. We defined subclinical hypothyroidism as increased TSH with or without decreased levels of free-T4 and T3 after the completion of VMAT. We calculated dose-volume histogram parameters (DVHPs), including the mean dose and relative thyroid volume receiving at least 10, 20, 30, and 40 Gy. The median follow-up time was 23.2 months. The 3-year locoregional failure-free survival, progression-free survival, and overall survival rates were 96.3%, 94.7%, and 96.2%, respectively. The mean thyroid dose was 21.4 Gy (range=11.5-29.4 Gy). Subclinical hypothyroidism was noted in 14 patients (20.9%) and the median time to the event was 4.1 months. Among the DVHPs, the relative volume receiving ≥20 Gy (V20Gy) was associated with subclinical hypothyroidism. The 2-year rates of subclinical hypothyroidism were 24.8% and 59.1% in patients with V20Gy ≤46.3% and >46.3%, respectively. A significant proportion of patients with breast cancer developed subclinical hypothyroidism after undergoing VMAT for RNI. Our findings highlight the importance of considering the thyroid as an organ at risk for VMAT planning, and suggest that V20Gy could be a useful dose-volume constraint.

Dosimetric impact of applying a model-based dose calculation algorithm for skin cancer brachytherapy (interventional radiotherapy).

Brachytherapy (BT, interventional radiotherapy) is a well-established radiotherapy technique capable of delivering high doses to tumors while sparing organs at risk (OARs). Currently, the clinically accepted dose calculation algorithm used is TG-43. In the TG-186 report, new model-based dose calculation algorithms (MBDCA), such as Elekta's advanced collapsed cone engine (ACE), have been introduced, although their clinical application is yet to be fully realized. This study aimed to investigate two aspects of TG-186: firstly, a comparison of dose distributions calculated with TG-43 and TG-186 for skin tumors; and secondly, an exploration of the impact of using a water bolus on the coverage of clinical target volume (CTV) and OARs. Ten treatment plans for high-dose-rate IRT were developed. All plans were initially calculated using the TG-43 algorithm, and were subsequently re-calculated with TG-186. In addition, one of the treatment plans was assessed with both TG-43 and TG-186, using 10 different water bolus thicknesses ranging from 0 to 5 cm. To assess dose variations, the following dose-volume histogram (DVH) parameters were compared: D2cc and D0.01cc for OARs, and V150, V100, V95 and V90 for CTV coverage. The average dosimetric results for CTV and OARs, as calculated by both algorithms, revealed statistically significant lower values for TG-186 when compared with TG-43. The presence of a bolus was observed to enhance CTV coverage for the TG-186 algorithm, with a bolus thickness of 2 cm being the point at which ACE calculations matched those of TG-43. This study identified significant differences in dosimetric parameters for skin tumors when comparing the TG-43 and TG-186 algorithms. Moreover, it was demonstrated that the inclusion of a water bolus increased CTV coverage in TG-186 calculations.

Synthetic computed tomography generation for abdominal adaptive radiotherapy using low-field magnetic resonance imaging.

Magnetic Resonance guided Radiotherapy (MRgRT) still needs the acquisition of Computed Tomography (CT) images and co-registration between CT and Magnetic Resonance Imaging (MRI). The generation of synthetic CT (sCT) images from the MR data can overcome this limitation. In this study we aim to propose a Deep Learning (DL) based approach for sCT image generation for abdominal Radiotherapy using low field MR images. CT and MR images were collected from 76 patients treated on abdominal sites. U-Net and conditional Generative Adversarial Network (cGAN) architectures were used to generate sCT images. Additionally, sCT images composed of only six bulk densities were generated with the aim of having a Simplified sCT.Radiotherapy plans calculated using the generated images were compared to the original plan in terms of gamma pass rate and Dose Volume Histogram (DVH) parameters. sCT images were generated in 2s and 2.5s with U-Net and cGAN architectures respectively.Gamma pass rates for 2%/2mm and 3%/3mm criteria were 91% and 95% respectively. Dose differences within 1% for DVH parameters on the target volume and organs at risk were obtained. U-Net and cGAN architectures are able to generate abdominal sCT images fast and accurately from low field MRI.

Initial tumor volume as an important predictor for indication of intra-cavitary brachytherapy, intra-cavitary/interstitial brachytherapy, and multi-catheter sole interstitial brachytherapy in cervical cancer patients treated with chemoradiotherapy.

Advances in three-dimensional image-guided brachytherapy technique allow for using intra-cavitary and interstitial brachytherapy (ICIS-BT), and sole interstitial brachytherapy (ISBT) in addition to conventional intra-cavitary brachytherapy (ICBT). However, no consensus has been reached regarding the choice of these techniques. The aim of this study was to propose the size criteria for indication of interstitial techniques. We examined initial gross tumor volume (GTV) at presentation and at each brachytherapy session. Also, dose volume histogram parameters for each modality were compared in 112 patients with cervical cancer treated with brachytherapy (ICBT, 54; ICIS-BT, 11; and ISBT, 47). The average GTV at diagnosis was 80.9 cm3 (range, 4.4-343.2 cm3), which shrank to 20.6 cm3 (25.5% of initial volume, range, 0.0-124.8 cm3) at initial brachytherapy. GTV > 30 cm3 at brachytherapy and high-risk clinical target volume > 40 cm3 were good threshold values for indication of interstitial technique, and tumors with initial GTV > 150 cm3 could be candidates for ISBT. An ISBT dose of 89.10 Gy can be prescribed in equivalent dose in 2 Gy fractions (range, 65.5-107.6 Gy), which was higher than those of ICIS (73.94 Gy, range, 71.44-82.50 Gy) and ICBT (72.83 Gy, range, 62.50-82.27 Gy) (p < 0.0001). Initial tumor volume is an important predictor for indication of ICBT and ICIS-BT. ISBT or at least an interstitial technique is recommended for initial GTV > 150 cm3.

Potential advantages of gEUD optimisation as compared with conventional physical optimisation for stereotactic treatment planning

Abstract Introduction: A small number of studies have confirmed the advantage of generalised equivalent uniform dose (gEUD) optimisation for some standard clinical scenarios; however, its performance with complicated stereotactic treatments is yet to be explored. Therefore, this study compared two planning optimisation methods, gEUD and Physical dose, in stereotactic treatments for several complex anatomical locations. Methods: Thirty patients were selected, ten each for sites of brain, lung and spine. Two stereotactic plans were generated for each case using the gEUD objective and Physical objective cost functions. Within each of the three sites, dosimetric indices for conformity, gradient and homogeneity, along with parameters of monitor units and dose–volume histograms (DVHs), were compared for statistical significance. Additionally, patient-specific quality assurance was conducted using portal dosimetry, and the gamma passing rate between the two plans was evaluated. Results: Optimisation was better with a gEUD objective as compared with Physical objective, notably sparing critical organs. Overall, the differences in mean values for six critical organs at risk favoured gEUD-based over Physical-based plans (all six 2-tailed p-values were &lt; 0·0002). Furthermore, all differences in mean values for DVH parameters favoured gEUD-based plans: GTVmean, GTVmax, PTVD100V, homogeneity index, gradient index and monitor unit (treatment time) (each 2-tailed p &lt; 0·05). Conclusions: gEUD optimisation in stereotactic treatment plans has a clear and general statistical advantage over Physical dose optimisation.

Normal Tissue Integral Dose as a Result of Prostate Radiation Therapy: A Quantitative Comparison Between High-Dose-Rate Brachytherapy and Modern External Beam Radiation Therapy Techniques

Quantification of integral radiation dose delivered during treatment for prostate cancer is lacking. We performed a comparative quantification of dose to nontarget body tissues delivered via 4 common radiation techniques: conventional volumetric modulated arc therapy, stereotactic body radiation therapy, pencil-beam scanning proton therapy, and high-dose-rate brachytherapy. Plans for each radiation technique were generated for 10 patients with typical anatomy. For brachytherapy plans, virtual needles were placed to achieve standard dosimetry. Standard planning target volume margins or robustness margins were applied as appropriate. A "normal tissue" structure (entire computed tomography simulation volume minus planning target volume) was generated for integral dose computation. Dose-volume histogram parameters for targets and normal structures were tabulated. Normal tissue integral dose was calculated by multiplying normal tissue volume by mean dose. Normal tissue integral dose was lowest for brachytherapy. Pencil-beam scanning protons, stereotactic body radiation therapy, and brachytherapy resulted in 17%, 57%, and 91% absolute reductions compared with standard volumetric modulated arc therapy, respectively. Mean nontarget tissues receiving 25%, 50%, and 75% of the prescription dose were reduced by 85%, 76%, and 83% for brachytherapy relative to volumetric modulated arc therapy, by 79%, 64%, and 74% relative to stereotactic body radiation therapy, and 73%, 60%, and 81% relative to proton therapy. All reductions observed using brachytherapy were statistically significant. High-dose-rate brachytherapy is an effective technique for reducing dose to nontarget body tissues relative to volumetric modulated arc therapy, stereotactic body radiation therapy, and pencil-beam scanning proton therapy.

Comparison of Machine-Learning and Deep-Learning Methods for the Prediction of Osteoradionecrosis Resulting From Head and Neck Cancer Radiation Therapy

Deep-learning (DL) techniques have been successful in disease-prediction tasks and could improve the prediction of mandible osteoradionecrosis (ORN) resulting from head and neck cancer (HNC) radiation therapy. In this study, we retrospectively compared the performance of DL algorithms and traditional machine-learning (ML) techniques to predict mandible ORN binary outcome in an extensive cohort of patients with HNC. Patients who received HNC radiation therapy at the University of Texas MD Anderson Cancer Center from 2005 to 2015 were identified for the ML (n=1259) and DL (n=1236) studies. The subjects were followed for ORN development for at least 12 months, with 173 developing ORN and 1086 having no evidence of ORN. The ML models used dose-volume histogram parameters to predict ORN development. These models included logistic regression, random forest, support vector machine, and a random classifier reference. The DL models were based on ResNet, DenseNet, and autoencoder-based architectures. The DL models used each participant's dose cropped to the mandible. The effect of increasing the amount of available training data on the DL models' prediction performance was evaluated by training the DL models using increasing ratios of the original training data. The F1 score for the logistic regression model, the best-performing ML model, was 0.3. The best-performing ResNet, DenseNet, and autoencoder-based models had F1 scores of 0.07, 0.14, and 0.23, respectively, whereas the random classifier's F1 score was 0.17. No performance increase was apparent when we increased the amount of training data available for DL model training. The ML models had superior performance to their DL counterparts. The lack of improvement in DL performance with increased training data suggests that either more data are needed for appropriate DL model construction or that the image features used in DL models are not suitable for this task.