Automatic VMAT planning for post-operative prostate cancer cases using particle swarm optimization: A proof of concept study

ObjectiveTo investigate the potential of Particle Swarm Optimization (PSO) for fully automatic VMAT radiotherapy (RT) treatment planning.Material and MethodsIn PSO a solution space of planning constraints is searched for the best possible RT plan in an iterative, statistical method, optimizing a population of candidate solutions. To identify the best candidate solution and for final evaluation a plan quality score (PQS), based on dose volume histogram (DVH) parameters, was introduced.Automatic PSO-based RT planning was used for N = 10 postoperative prostate cancer cases, retrospectively taken from our clinical database, with a prescribed dose of EUD = 66 Gy in addition to two constraints for rectum and one for bladder. Resulting PSO-based plans were compared dosimetrically to manually generated VMAT plans.ResultsPSO successfully proposed treatment plans comparable to manually optimized ones in 9/10 cases. The median (range) PTV EUD was 65.4 Gy (64.7–66.0) for manual and 65.3 Gy (62.5–65.5) for PSO plans, respectively. However PSO plans achieved significantly lower doses in rectum D_2% 67.0 Gy (66.5–67.5) vs. 66.1 Gy (64.7–66.5, p = 0.016). All other evaluated parameters (PTV D_98% and D_2%, rectum V_40Gy and V_60Gy, bladder D_2% and V_60Gy) were comparable in both plans. Manual plans had lower PQS compared to PSO plans with −0.82 (−16.43–1.08) vs. 0.91 (−5.98–6.25).ConclusionPSO allows for fully automatic generation of VMAT plans with plan quality comparable to manually optimized plans. However, before clinical implementation further research is needed concerning further adaptation of PSO-specific parameters and the refinement of the PQS.     

Dosimetric comparison of RapidPlan and manually optimized plans in volumetric modulated arc therapy for prostate cancer

PurposeThis study evaluated whether RapidPlan based plans (RP plans) created by a single optimization, are usable in volumetric modulated arc therapy (VMAT) for patients with prostate cancer.MethodsWe used 51 previously administered VMAT plans to train a RP model. Thirty RP plans were created by a single optimization without planner intervention during optimization. Differences between RP plans and clinical manual optimization (CMO) plans created by an experienced planner for the same patients were analyzed (Wilcoxon tests) in terms of homogeneity index (HI), conformation number (CN), D_95%, and D_2% to planning target volume (PTV), mean dose, V_50Gy, V_70Gy, V_75Gy, and V_78Gy to rectum and bladder, monitor unit (MU), and multi-leaf collimator (MLC) sequence complexity.ResultsRP and CMO values for PTV D_95%, PTV D_2%, HI, and CN were significantly similar (p < 0.05 for all). RP mean dose, V_50Gy, and V_70Gy to rectum were superior or comparable to CMO values; RP V_75Gy and V_78Gy were higher than in CMO plans (p < 0.05). RP bladder dose-volume parameter values (except V_78Gy) were lower than in CMO plans (p < 0.05). MU values were RP: 730 ± 55 MU and CMO: 580 ± 37 MU (p < 0.05); and MLC sequence complexity scores were RP: 0.25 ± 0.02 and CMO: 0.35 ± 0.03 (p < 0.05).ConclusionsRP plans created by a single optimization were clinically acceptable in VMAT for patient with prostate cancer. Our simple model could reduce optimization time, independently of planner’s skill and knowledge.     

Knowledge-based planning using pseudo-structures for volumetric modulated arc therapy (VMAT) of postoperative uterine cervical cancer: a multi-institutional study

BackgroundThe aim of this study was to investigate the performance of the RapidPlan (RP ) using models registered pseudostructures, and to determine how many structures are required for automatic optimization of volumetric modulated arc therapy (VMAT) for postoperative uterine cervical cancer.Materials and methodsPseudo-structures around the PTV were retrospectively contoured for patients who had completed treatment at five institutions. For 22 common patients, plans were generated with a single optimization for models with two (RP_2), four (RP_4), and five (RP_5) registered structures, and the dosimetric parameters of these models were compared with a clinical plan with several optimizations.ResultsMost dosimetric parameters showed no major differences between each RP model. In particular, the rectum D_max, V_50Gy, and V_40Gy with RP_2, RP_4, and RP_5 were not significantly different, and were lower than those of the clinical plan. The average proportions of plans achieving acceptable criteria for dosimetric parameters were close to 100% for all models. Using RP_2, the average time for the VMAT planning was reduced by 88 minutes compared with the clinical plan.ConclusionThe RapidPlan model with two registered pseudo-structures could generate clinically acceptable plans while saving time.     

Knowledge-based automatic optimization of adaptive early-regression-guided VMAT for rectal cancer

PurposeTo implement a knowledge-based (KB) optimization strategy to our adaptive (ART) early-regression guided boosting technique in neo-adjuvant radio-chemotherapy for rectal cancer.Material and methodsThe protocol consists of a first phase delivering 27.6 Gy to tumor/lymph-nodes (2.3 Gy/fr-PTV₁), followed by the ART phase concomitantly delivering 18.6 Gy (3.1 Gy/fr) and 13.8 Gy (2.3 Gy/fr) to the residual tumor (PTV_ART) and to PTV₁ respectively. PTV_ART is obtained by expanding the residual GTV, as visible on MRI at fraction 9. Forty plans were used to generate a KB-model for the first phase using the RapidPlan tool. Instead of building a new model, a robust strategy scaling the KB-model to the ART phase was applied. Both internal and external validation were performed for both phases: all automatic plans (RP) were compared in terms of OARs/PTVs parameters against the original plans (RA).ResultsThe resulting automatic plans were generally better than or equivalent to clinical plans. Of note, V_30Gy and V_40Gy were significantly improved in RP plans for bladder and bowel; gEUD analysis showed improvement for KB-modality for all OARs, up to 3 Gy for the bowel.ConclusionsThe KB-model generated for the first phase was robust and it was also efficiently adapted to the ART phase. The performance of automatically generated plans were slightly better than the corresponding manual plans for both phases.     

Assessment of daily dose accumulation for robustly optimized intensity modulated proton therapy treatment of prostate cancer

PurposeTo implement a daily CBCT based dose accumulation technique in order to assess ideal robust optimization (RO) parameters for IMPT treatment of prostate cancer.MethodsTen prostate cancer patients previously treated with VMAT and having daily CBCT were included. First, RO-IMPT plans were created with ± 3 mm and ± 5 mm patient setup and ± 3% proton range uncertainties, respectively. Second, the planning CT (pCT) was deformably registered to the CBCT to create a synthetic CT (sCT). Both daily and weekly sampling strategies were employed to determine optimal dose accumulation frequency. Doses were recalculated on sCTs for both ± 3 mm/±3% and ± 5 mm/±3% uncertainties and were accumulated back to the pCT. Accumulated doses generated from ± 3 mm/±3% and ± 5 mm/±3% RO-IMPT plans were evaluated using the clinical dose volume constraints for CTV, bladder, and rectum.ResultsDaily accumulated dose based on both ± 3mm/±3% and ±5 mm/±3% uncertainties for RO-IMPT plans resulted in satisfactory CTV coverage (RO-IMPT_3mm/3% CTV_V95 = 99.01 ± 0.87% vs. RO-IMPT_5mm/3% CTV_V95 = 99.81 ± 0.2%, P = 0.002). However, the accumulated dose based on ± 3 mm/3% RO-IMPT plans consistently provided greater OAR sparing than ±5 mm/±3% RO-IMPT plans (RO-IMPT_3mm/3% rectum_V65Gy = 2.93 ± 2.39% vs. RO-IMPT_5mm/3% rectum_V65Gy = 4.38 ± 3%, P < 0.01; RO-IMPT_3mm/3% bladder_V65Gy = 5.2 ± 7.12% vs. RO-IMPT_5mm/3% bladder_V65Gy = 7.12 ± 9.59%, P < 0.01). The gamma analysis showed high dosimetric agreement between weekly and daily accumulated dose distributions.ConclusionsThis study demonstrated that for RO-IMPT optimization, ±3mm/±3% uncertainty is sufficient to create plans that meet desired CTV coverage while achieving superior sparing to OARs when compared with ± 5 mm/±3% uncertainty. Furthermore, weekly dose accumulation can accurately estimate the overall dose delivered to prostate cancer patients.     

Virtual Tangential-fields Arc Therapy (ViTAT) for whole breast irradiation: Technique optimization and validation

PurposeTo test the performances of a volumetric arc technique named ViTAT (Virtual Tangential-fields Arc Therapy) mimicking tangential field irradiation for whole breast radiotherapy.MethodsViTAT plans consisted in 4 arcs whose starting/ending position were established based on gantry angle distribution of clinical plans for right and left-breast. The arcs were completely blocked excluding the first and last 20°. Different virtual bolus densities and thicknesses were preliminarily evaluated to obtain the best plan performances. For 40 patients with tumor laterality equally divided between right and left sides, ViTAT plans were optimized considering the clinical DVHs for OARs (resulting from tangential field manual planning) to constrain them: ViTAT plans were compared with the clinical tangential-fields in terms of DVH parameters for both PTV and OARs.ResultsDistal angle values were suggested in the ranges [220°,240°] for the right-breast and [115°,135°] for the left-breast cases; medial angles were [60°,40°] for the right side and [295°,315°] for the left side, limiting the risk of collision. The optimal virtual bolus had −500 HU density and 1.5 cm thickness. ViTAT plans generated dose distributions very similar to the tangential-field plans, with significantly improved PTV homogeneity. The mean doses of ipsilateral OARs were comparable between the two techniques with minor increase of the low-dose spread in the range 2–15 Gy (few % volume); contralateral OARs were slightly better spared with ViTAT.ConclusionViTAT dose distributions were similar to tangential-fields. ViTAT should allow automatic plan optimization by developing knowledge-based DVH prediction models of patients treated with tangential-fields.     

Novel knowledge-based treatment planning model for hypofractionated radiotherapy of prostate cancer patients

PurposeTo demonstrate the strength of an innovative knowledge-based model-building method for radiotherapy planning using hypofractionated, multi-target prostate patients.Material and methodsAn initial RapidPlan model was trained using 48 patients who received 60 Gy to prostate (PTV60) and 44 Gy to pelvic nodes (PTV44) in 20 fractions. To improve the model's goodness-of-fit, an intermediate model was generated using the dose-volume histograms of best-spared organs-at-risk (OARs) of the initial model. Using the intermediate model and manual tweaking, all 48 cases were re-planned. The final model, trained using these re-plans, was validated on 50 additional patients. The validated final model was used to determine any planning advantage of using three arcs instead of two on 16 VMAT cases and tested on 25 additional cases to determine efficacy for single-PTV (PTV60-only) treatment planning.ResultsFor model validation, PTV V_95% of 99.9% was obtained by both clinical and knowledge-based planning. D_1% was lower for model plans: by 1.23 Gy (PTV60, CI = [1.00, 1.45]), and by 2.44 Gy (PTV44, CI = [1.72, 3.16]). OAR sparing was superior for knowledge-based planning: ΔD_mean = 3.70 Gy (bladder, CI = [2.83, 4.57]), and 3.22 Gy (rectum, CI = [2.48, 3.95]); ΔD_2% = 1.17 Gy (bowel bag, CI = [0.64, 1.69]), and 4.78 Gy (femoral heads, CI = [3.90, 5.66]). Using three arcs instead of two, improvements in OAR sparing and PTV coverage were statistically significant, but of magnitudes < 1 Gy. The model failed at reliable DVH predictions for single PTV plans.ConclusionsOur knowledge-based model delivers efficient, consistent plans with excellent PTV coverage and improved OAR sparing compared to clinical plans.     

Adaptive volumetric modulated arc treatment planning for esophageal cancers using cone beam computed tomography

PurposeTo assess the potential of cone beam CT (CBCT) derived adaptive RapidArc treatment for esophageal cancers in reducing the dose to organs at risk (OAR).Methods and materialsTen patients with esophageal cancer were CT scanned in free breathing pattern. The PTV is generated by adding a 3D margin of 1 cm to the CTV as per ICRU 62 recommendations. The double arc RapidArc plan (Clin_RA) was generated for the PTV. Patients were setup using kV orthogonal images and kV-CBCT scan was acquired daily during first week of therapy, then weekly. These images were exported to the Eclipse TPS. The adaptive CTV which includes tumor and involved nodes was delineated in each CBCT image set for the length of the PTV. The composite CTV from first week CBCT was generated using Boolean union operator and 5 mm margin was added circumferentially to generate adaptive PTV (PTV1). Adaptive RapidArc plan (Adap_RA) was generated. NTCP and DVH of the OARs of the two plans were compared. Similarly, PTV2 was generated from weekly CBCT. PTV2 was evaluated for the coverage of 95% isodose of Adap_RA plan.ResultsThe PTV1 and PTV2 volumes covered by 95% isodose in adaptive plans were 93.51 ± 1.17% and 94.59 ± 1.43% respectively. The lung V_10Gy, V_20Gy and mean dose in Adap_RA plan was reduced by 17.43% (p = 0.0012), 34.64% (p = 0.0019) and 16.50% (p = 0.0002) respectively compared to Clin_RA. The Adap_RA plan reduces the heart D_35% and mean dose by 17.35% (p = 0.0011) and 17.16% (p = 0.0012). No significant reduction in spinal cord and liver doses were observed. NTCP for the lung (0.42% vs. 0.08%) and heart (1.39% vs. 0.090%) was reduced significantly in adaptive plans.ConclusionThe adaptive re-planning strategy based on the first week CBCT dataset significantly reduces the doses and NTCP to OARs.     

Clinical verification of treatment planning dose calculation in lung SBRT with GATE Monte Carlo simulation code

PurposeThis study aims to use GATE/Geant4 simulation code to evaluate the performance of dose calculations with Anisotropic Analytical Algorithm (AAA) in the context of lung SBRT for complex treatments considering images of patients.MethodsFour cases of non-small cell lung cancer treated with SBRT were selected for this study. Irradiation plans were created with AAA and recalculated end to end using Monte Carlo (MC) method maintaining field configurations identical to the original plans. Each treatment plan was evaluated in terms of PTV and organs at risk (OARs) using dose-volume histograms (DVH). Dosimetric parameters obtained from DVHs were used to compare AAA and MC.ResultsThe comparison between the AAA and MC DVH using gamma analysis with the passing criteria of 3%/3% showed an average passing rate of more than 90% for the PTV structure and 97% for the OARs. Tightening the criteria to 2%/2% showed a reduction in the average passing rate of the PTV to 86%. The agreement between the AAA and MC dose calculations for PTV dosimetric parameters (V₁₀₀; V₉₀; Homogeneity index; maximum, minimum and mean dose; CI_Paddick and D_2cm) was within 18.4%. For OARs, the biggest differences were observed in the spinal cord and the great vessels.ConclusionsIn general, we did not find significant differences between AAA and MC. The results indicate that AAA could be used in complex SBRT cases that involve a larger number of small treatment fields in the presence of tissue heterogeneities.     

Quality assurance-based optimization (QAO): Towards improving patient-specific quality assurance in volumetric modulated arc therapy plans using machine learning

IntroductionPrevious literature has shown general trade-offs between plan complexity and resulting quality assurance (QA) outcomes. However, existing solutions for controlling this trade-off do not guarantee corresponding improvements in deliverability. Therefore, this work explored the feasibility of an optimization framework for directly maximizing predicted QA outcomes of plans without compromising the dosimetric quality of plans designed with an established knowledge-based planning (KBP) technique.Materials and MethodsA support vector machine (SVM) was developed – using a database of 500 previous VMAT plans – to predict gamma passing rates (GPRs; 3%/3mm percent dose-difference/distance-to-agreement with local normalization) based on selected complexity features. A heuristic, QA-based optimization (QAO) framework was devised by utilizing the SVM model to iteratively modify mechanical treatment features most commonly associated with suboptimal GPRs. Specifically, leaf gaps (LGs) <50 mm were widened by random amounts, which impacts all aperture-based complexity features. 13 prostate KBP-guided VMAT plans were optimized via QAO using user-specified maximum LG displacements before corresponding changes in predicted GPRs and dose were assessed.ResultsPredicted GPRs increased by an average of 1.14 ± 1.25% (p = 0.006) with QAO using a 3 mm maximum random LG displacement. There were small differences in dose, resulting in similarly small changes in tumor control probability (maximum increase = 0.05%) and normal tissue complication probabilities in the bladder, rectum, and femoral heads (maximum decrease = 0.2% in the rectum).ConclusionThis study explored the feasibility of QAO and warrants future investigations of further incorporating QA endpoints into plan optimization.     

Equivalent uniform dose concept evaluated by theoretical dose volume histograms for thoracic irradiation

Background and PurposeThe goal of our study was to quantify the limits of the EUD models for use in score functions in inverse planning software, and for clinical application.Materials and MethodsWe focused on oesophagus cancer irradiation. Our evaluation was based on theoretical dose volume histograms (DVH), and we analyzed them using volumetric and linear quadratic EUD models, average and maximum dose concepts, the linear quadratic model and the differential area between each DVH.ResultsWe evaluated our models using theoretical and more complex DVHs for the above regions of interest. We studied three types of DVH for the target volume: the first followed the ICRU dose homogeneity recommendations; the second was built out of the first requirements and the same average dose was built in for all cases; the third was truncated by a small dose hole. We also built theoretical DVHs for the organs at risk, in order to evaluate the limits of, and the ways to use both EUD(1) and EUD/LQ models, comparing them to the traditional ways of scoring a treatment plan. For each volume of interest we built theoretical treatment plans with differences in the fractionation.ConclusionWe concluded that both volumetric and linear quadratic EUDs should be used. Volumetric EUD(1) takes into account neither hot–cold spot compensation nor the differences in fractionation, but it is more sensitive to the increase of the irradiated volume. With linear quadratic EUD/LQ, a volumetric analysis of fractionation variation effort can be performed.     

Characterization of knowledge-based volumetric modulated arc therapy plans created by three different institutions’ models for prostate cancer

BackgroundThe aim of this study was to clarify factors predicting the performance of knowledge-based planning (KBP) models in volume modulated arc therapy for prostate cancer in terms of sparing the organ at risk (OAR).Materials and methodsIn three institutions, each KBP model was trained by more than 20 library plans (LP) per model. To validate the characterization of each KBP model, 45 validation plans (VP) were calculated by the KBP system. The ratios of overlap between the OAR volume and the planning target volume (PTV) to the whole organ volume (V_overlap/V_whole) were analyzed for each LP and VP. Regression lines between dose–volume parameters (V₉₀, V₇₅, and V₅₀) and V_overlap/V_whole were evaluated. The mean OAR dose, V₉₀, V₇₅, and V₅₀ of LP did not necessarily match those of VP.ResultsIn both the rectum and bladder, the dose–volume parameters for VP were strongly correlated with V_overlap/V_whole at institutes A, B, and C (R > 0.74, 0.85, and 0.56, respectively). Except in the rectum at institute B, the slopes of the regression lines for LP corresponded to those for VP. For dose–volume parameters for the rectum, the ratios of slopes of the regression lines in VP to those in LP ranged 0.51–1.26. In the bladder, most ratios were less than 1.0 (mean: 0.77).ConclusionFor each OAR, each model made distinct dosimetric characterizations in terms of V_overlap/V_whole. The relationship between dose–volume parameters and V_overlap/V_whole of OARs in LP predicts the KBP models’ performance sparing OARs.     

Effects of lung tissue characterization in radiotherapy of breast cancer under deep inspiration breath hold when using Monte Carlo dosimetry

PurposeTo investigate the sensitivity of Monte Carlo (MC) calculated lung dose distributions to lung tissue characterization in external beam radiotherapy of breast cancer under Deep Inspiration Breath Hold (DIBH).MethodsEGSnrc based MC software was employed. Mean lung densities for one hundred patients were analysed. CT number frequency and clinical dose distributions were calculated for 15 patients with mean lung density below 0.14 g/cm³. Lung volume with a pre-defined CT numbers was also considered. Lung tissue was characterized by applying different CT calibrations in the low-density region and air-lung tissue thresholds. Dose impact was estimated by Dose Volume Histogram (DVH) parameters.ResultsMean lung densities below 0.14 g/cm³ were found in 10% of the patients. CT numbers below −960 HU dominated the CT frequency distributions with a high rate of CT numbers at −990 HU. Mass density conversion approach influenced the DVH shape. V_4Gy and V_8Gy varied by 7% and 5% for the selected patients and by 9% and 3.5% for the pre-defined lung volume. V_16Gy and V_20Gy, were within 2.5%. Regions above 20 Gy were affected. Variations in air- lung tissue differentiation resulted in DVH parameters within 1%. Threshold at −990 HU was confirmed by the CT number frequency distributions.ConclusionsLung dose distributions were more sensitive to variations in the CT calibration curve below lung (inhale) density than to air-lung tissue differentiation. Low dose regions were mostly affected. The dosimetry effects were found to be potentially important to 10% of the patients treated under DIBH.     

Impact of acuros XB algorithm in deep-inspiration breath-hold (DIBH) respiratory techniques used for the treatment of left breast cancer

AimTo investigate the impact of Acuros XB (AXB) algorithm in the deep-inspiration breath-hold (DIBH) technique used for treatment of left sided breast cancer.BackgroundAXB may estimate better lung toxicities and treatment outcome in DIBH.Materials and MethodsTreatment plans were computed using the field-in-field technique for a 6 MV beam in two respiratory phases - free breathing (FB) and DIBH. The AXB-calculations were performed under identical beam setup and the same numbers of monitor units as used for AAA-calculation.ResultsMean Hounsfield units (HU), mass density (g/cc) and relative electron density were -782.1 ± 24.8 and -883.5 ± 24.9; 0.196 ± 0.025 and 0.083 ± 0.032; 0.218 ± 0.025 and 0.117 ± 0.025 for the lung in the FB and DIBH respiratory phase, respectively. For a similar target coverage (p > 0.05) in the DIBH respiratory phase between the AXB and AAA algorithm, there was a slight increase in organ at risk (OAR) dose for AXB in comparison to AAA, except for mean dose to the ipsilateral lung. AAA predicts higher mean dose to the ipsilateral lung and lesser V_20Gy for the ipsilateral and common lung in comparison to AXB. The differences in mean dose to the ipsilateral lung were 0.87 ± 2.66 % (p > 0.05) in FB, and 1.01 ± 1.07% (p < 0.05) in DIBH, in V_20Gy the differences were 1.76 ± 0.83% and 1.71 ± 0.82% in FB (p < 0.05), 3.34 ± 1.15 % and 3.24 ± 1.17 % in DIBH (p < 0.05), for the ipsilateral and common lung, respectively.ConclusionFor a similar target volume coverage, there were important differences between the AXB and AAA algorithm for low-density inhomogeneity medium present in the DIBH respiratory phase for left sided breast cancer patients. DIBH treatment in conjunction with AXB may result in better estimation of lung toxicities and treatment outcome.     

Tracking,gating, free-breathing,which technique to use for lung stereotactic treatments? A dosimetric comparison

BackgroundThe management of breath-induced tumor motion is a major challenge for lung stereotactic body radiation therapy (SBRT). Three techniques are currently available for these treatments: tracking (T), gating (G) and free-breathing (FB).AimTo evaluate the dosimetric differences between these three treatment techniques for lung SBRT.Materials and methodsPretreatment 4DCT data were acquired for 10 patients and sorted into 10 phases of a breathing cycle, such as 0% and 50% phases defined respectively as the inhalation and exhalation maximum. GTV_ph, PTV_ph (=GTV_ph + 3 mm) and the ipsilateral lung were contoured on each phase.For the tracking technique, 9 fixed fields were adjusted to each PTV_ph for the 10 phases. The gating technique was studied with 3 exhalation phases (40%, 50% and 60%). For the free-breathing technique, ITV_FB was created from a sum of all GTV_ph and a 3 mm margin was added to define a PTV_FB. Fields were adjusted to PTV_FB and dose distributions were calculated on the average intensity projection (AIP) CT. Then, the beam arrangement with the same monitor units was planned on each CT phase.The 3 modalities were evaluated using DVHs of each GTV_ph, the homogeneity index and the volume of the ipsilateral lung receiving 20 Gy (V_20Gy).ResultsThe FB system improved the target coverage by increasing D_mean (75.87_(T)–76.08_(G)–77.49_(FB)Gy). Target coverage was slightly more homogeneous, too (HI: 0.17_{(T and G)}–0.15_(FB)). But the lung was better protected with the tracking system (V_20Gy: 3.82_(T)–4.96_(G)–6.34_(FB)%).ConclusionsEvery technique provides plans with a good target coverage and lung protection. While irradiation with free-breathing increases doses to GTV, irradiation with the tracking technique spares better the lung but can dramatically increase the treatment complexity.     

Sentinel-2 Satellite Imagery Application to Monitor Soil Salinity and Calcium Carbonate Contents in Agricultural Fields

The estuary tides affect groundwater dynamics; these areas are susceptible to waterlogging and salinity issues. A study was conducted on two fields with a total area of 60 hectares under a center pivot irrigation system that works with solar energy and belong to a commercial farm located in Northern Sudan. To monitor soil salinity and calcium carbonate in the area and stop future degradation of soil resources, easy, non-intrusive, and practical procedures are required. The objective of this study was to use remote sensing-determined Sentinel-2 satellite imagery using various soil indices to develop prediction models for the estimation of soil electrical conductivity (EC) and soil calcium carbonate (CaCO₃). Geo-referenced soil samples were collected from 72 locations and analyzed in the laboratory for soil EC and CaCO₃. The electrical conductivity of the soil saturation paste extract was represented by average values in soil dataset samples from two fields collected from the topsoil layer (0 to 15 cm) characteristic of the local salinity gradient. The various soil indices, used in this study, were calculated from the Sentinel-2 satellite imagery. The prediction was determined using the root mean square error (RMSE) and cross validation was done using coefficient of determination. The results of regression analysis showed linear relationships with significant correlation between the EC analyzed in laboratory and the salinity index-2 “SI2” (Model-1: R² = 0.59, p = 0.00019 and root mean square error (RMSE = 1.32%) and the bare soil index “BSI” (Model-2: R² = 0.63, p = 0.00012 and RMSE = 6.42%). Model-1 demonstrated the best model for predicting soil EC, and validation R² and RMSE values of 0.48% and 1.32%, respectively. The regression analysis results for soil CaCO₃ determination showed linear relationships with data obtained in laboratory and the bare soil index “BSI” (Model- 3: R² = 0. 45, p = 0.00021 and RMSE = 1.29%) and the bare soil index “BSI” & Normalized difference salinity index “NDSI” (Model-4: R² = 0.53, p = 0.00015 and RMSE = 1.55%). The validation confirmed the Model-3 results for prediction of soil CaCO₃ with R² and RMSE values of 0.478% and 1.29%, respectively. Future soil monitoring programs might consider the use of remote sensing data for assessing soil salinity and CaCO₃ using soil indices results generated from satellite image (i.e., Sentinel-2).     

Dosimetric impact of Acuros XB on cervix radiotherapy using RapidArc technique: a dosimetric study

BackgroundAcuros XB (AXB) may predict better rectal toxicities and treatment outcomes in cervix carcinoma. The aim of the study was to quantify the potential impact of AXB computations on the cervix radiotherapy using the RapidArc (RA ) technique as compared to anisotropic analytical algorithm (AA) computations.Materials and methodsA cohort of 30 patients previously cared for cervix carcinoma (stages II–IIIB) was selected for the present analysis. The RA plans were computed using AA and AXB dose computation engines under identical beam setup and MLC pattern.ResultsThere was no significant (p > 0.05) difference in D_95% and D_98% to the planning target volume (PTV); moreover, a significant (p < 0.05) rise was noticed for mean dose to the PTV (0.26%), D_50% (0.26%), D_2% (0.80%) and V_110% (44.24%) for AXB computation as compared to AA computations. Further, AXB estimated a significantly (p < 0.05) lower value for maximum and minimum dose to the PTV. Additionally, there was a significant (p < 0.05) reduction observed in mean dose to organs at risk (OARs) for AXB computation as compared to AA, though the reduction in mean dose was non-significant (p > 0.05) for the rectum. The maximum difference observed was 4.78% for the rectum V_50Gy, 1.72%, 1.15% in mean dose and 2.22%, 1.48% in D_2% of the left femur and right femur, respectively, between AA and AXB dose estimations.ConclusionFor similar target coverage, there were significant differences observed between the AAA and AXB computations. AA underestimates the V_50Gy of the rectum and overestimates the mean dose and D_2% for femoral heads as compared to AXB. Therefore, the use of AXB in the case of cervix carcinoma may predict better rectal toxicities and treatment outcomes in cervix carcinoma using the RA technique.     

An automated Monte Carlo QC system for volumetric modulated arc therapy: Possibilities and challenges

PurposeTo develop and implement an automated Monte Carlo (MC) system for patient specific VMAT quality control in a patient geometry that generates treatment planning system (TPS) compliant DICOM objects and includes a module for 3D analysis of dose deviations. Also, the aims were to recommend diagnose specific tolerance criteria and an evaluation procedure.MethodsThe EGSnrc code package formed the basis for development of the MC system. The workflow consists of a number of modules connected to a TPS by means of manual DICOM exports and imports which were executed sequentially without user interaction. DVH comparison was performed in the TPS. In addition, MC- and TPS dose distributions were analysed by applying the normalized dose difference (NDD) formalism. NDD failure maps and a pass rate for a certain threshold were obtained. 170 clinical plans (prostate, thorax, head-and-neck and gynecological) were selected for analysis.ResultsAgreement within 1.5% was found between clinical- and MC data for the mean dose to the target volumes and within 3% for parameters more sensitive to the shape of the DVH e.g. D_98% PTV. Regarding the NDD analysis, tolerance criteria 2%/3 mm were established for prostate plans and 3%/3 mm for the rest of the cases.ConclusionsAn automated MC system was developed and implemented. Evaluation procedure is recommended with NDD-analysis as a first step. For pass rate < 95%, the evaluation continues with comparison of DVH parameters. For deviations larger than 2%, a visual inspection of the clinical- and MC dose distributions is performed.