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Background and purpose: Despite the superior dose conformity of proton therapy, the dose distribution is sensitive to daily anatomical changes, which can affect treatment accuracy. This study evaluated the dose recalculation accuracy of two synthetic computed tomography (sCT) generation algorithms in a commercial treatment planning system. Materials and methods: The evaluation was conducted for head-and-neck, thorax-and-abdomen, and pelvis sites treated with proton therapy. Thirty patients with two cone-beam computed tomography (CBCT) scans each were selected. The sCT images were generated from CBCT scans using two algorithms, Corrected CBCT (corrCBCT) and Virtual CT (vCT). Dose recalculations were performed based on these images for comparison with “ground truth” deformed CTs. Results: The choice of algorithm influenced dose recalculation accuracy, particularly in high dose regions. For head-and-neck cases, the corrCBCT method showed closer agreement with the “ground truth”, while for thorax-and-abdomen and pelvis cases, the vCT algorithm yielded better results (mean percentage dose discrepancy of 0.6 %, 1.3 % and 0.5 % for the three sites, respectively, in the high dose region). Head-and-neck and pelvis cases exhibited excellent agreement in high dose regions (2 %/2 mm gamma passing rate >98 %), while thorax-and-abdomen cases exhibited the largest differences, suggesting caution in sCT algorithm usage for this site. Significant systematic differences were observed in the clinical target volume and organ-at-risk doses in head-and-neck and pelvis cases, highlighting the importance of using the correct algorithm. Conclusions: This study provided treatment site-specific recommendations for sCT algorithm selection in proton therapy. The findings offered insights for proton beam centers implementing adaptive radiotherapy workflows.

Background and purpose: High-density dental fillings pose a non-negligible impact on head and neck cancer treatment. For proton therapy, stopping power ratio (SPR) prediction will be significantly impaired by the associated image artifacts. Dose perturbation is also inevitable, compromising the treatment plan quality. While plenty of work has been done on metal or amalgam fillings, none has touched on composite resin (CR) and glass ionomer cement (GIC) which have seen an increasing usage. Hence, this work aims to provide a detailed characterisation of SPR and dose perturbation in proton therapy caused by CR and GIC. Materials and methods: Four types of fillings were used: CR, Fuji Bulk (FB), Fuji II (FII) and Fuji IX (FIX). The latter three belong to GIC category. Measured SPR were compared with SPR predicted using single-energy computed tomography (SECT) and dual-energy computed tomography (DECT). Dose perturbation of proton beams with lower- and higher-energy levels was also quantified using Gafchromic films. Results: The measured SPR for CR, FB, FII and FIX were 1.68, 1.77, 1.77 and 1.76, respectively. Overall, DECT could predict SPR better than SECT. The lowest percentage error achieved by DECT was 19.7 %, demonstrating the challenge in estimating SPR, even for fillings with relatively lower densities. For both proton beam energies and all four fillings of about 4.5 mm thickness, the maximum dose perturbation was 3 %. Conclusion: This study showed that dose perturbation by CR and GIC was comparatively small. We have measured and recommended the SPR values for overriding the fillings in TPS.

Background and purposeThis study aimed to investigate the feasibility of safe-dose escalation to dominant intraprostatic lesions (DILs) and assess the clinical impact using dose-volume (DV) and biological metrics in photon and proton therapy. Biological parameters defined as late grade ≥ 2 gastrointestinal (GI) and genitourinary (GU) derived from planned (DP) and accumulated dose (DA) were utilized.Materials and methodsIn total, 10 patients with high-risk prostate cancer with multiparametric MRI-defined DILs were investigated. Each patient had two plans with a focal boost to the DILs using intensity-modulated proton therapy (IMPT) and volumetric-modulated arc therapy (VMAT). Plans were optimized to obtain DIL coverage while respecting the mandatory organ-at-risk constraints. For the planning evaluation, DV metrics, tumor control probability (TCP) for the DILs and whole prostate excluding the DILs (prostate-DILs), and normal tissue complication probability (NTCP) for the rectum and bladder were calculated. Wilcoxon signed-rank test was used for analyzing TCP and NTCP data.ResultsIMPT achieved a higher Dmean for the DILs compared to VMAT (IMPT: 68.1 GyRBE vs. VMAT: 66.6 Gy, p < 0.05). Intermediate–high rectal and bladder doses were lower for IMPT (p < 0.05), while the high-dose region (V60 Gy) remained comparable. IMPT-TCP for prostate-DIL were higher compared to VMAT (IMPT: 86%; α/β = 3, 94.3%; α/β = 1.5 vs. VMAT: 84.7%; α/β = 3, 93.9%; α/β = 1.5, p < 0.05). Likewise, IMPT obtained a moderately higher DIL TCP (IMPT: 97%; α/β = 3, 99.3%; α/β = 1.5 vs. VMAT: 95.9%; α/β = 3, 98.9%; α/β = 1.5, p < 0.05). Rectal DA-NTCP displayed the highest GI toxicity risk at 5.6%, and IMPT has a lower GI toxicity risk compared to VMAT-predicted Quantec-NTCP (p < 0.05). Bladder DP-NTCP projected a higher GU toxicity than DA-NTCP, with VMAT having the highest risk (p < 0.05).ConclusionDose escalation using IMPT is able to achieve a high TCP for the DILs, with the lowest rectal and bladder DV doses at the intermediate–high-dose range. The reduction in physical dose was translated into a lower NTCP (p < 0.05) for the bladder, although rectal toxicity remained equivalent.

Medical imaging is crucial for the detection and diagnosis of breast cancer. Artificial intelligence and computer vision have rapidly become popular in medical image analyses thanks to technological advancements. To improve the effectiveness and efficiency of medical diagnosis and treatment, significant efforts have been made in the literature on medical image processing, segmentation, volumetric analysis, and prediction. This paper is interested in the development of a prediction pipeline for breast cancer studies based on 3D computed tomography (CT) scans. Several algorithms were designed and integrated to classify the suitability of the CT slices. The selected slices from patients were then further processed in the pipeline. This was followed by data generalization and volume segmentation to reduce the computation complexity. The selected input data were fed into a 3D U-Net architecture in the pipeline for analysis and volumetric predictions of cancer tumors. Three types of U-Net models were designed and compared. The experimental results show that Model 1 of U-Net obtained the highest accuracy at 91.44% with the highest memory usage; Model 2 had the lowest memory usage with the lowest accuracy at 85.18%; and Model 3 achieved a balanced performance in accuracy and memory usage, which is a more suitable configuration for the developed pipeline.

Background and purpose: Significant dose deviations have been reported between planned (DP) and accumulated (DA) dose in prostate radiotherapy. This study aimed to develop multivariate analysis (MVA) models associating Grade 1 and 2 gastrointestinal (GI) toxicity with clinical and DP or DA dosimetric variables separately. Materials and methods: Dose volume (DV) metrics were compared between DA and DP for 150 high-risk prostate cancer patients. MV models were generated from significant clinical and dosimetric variables (p

Abstract A feasibility study was performed to determine if CT-based radiomics could play an augmentative role in predicting neoadjuvant rectal score (NAR), locoregional failure free survival (LRFFS), distant metastasis free survival (DMFS), disease free survival (DFS) and overall survival (OS) in locally advanced rectal cancer (LARC). The NAR score, which takes into account the pathological tumour and nodal stage as well as clinical tumour stage, is a validated surrogate endpoint used for early determination of treatment response whereby a low NAR score ( 16) has been correlated with poorer outcomes. CT images of 191 patients with LARC were used in this study. Primary tumour (GTV) and mesorectum (CTV) were contoured separately and radiomics features were extracted from both segments. Two NAR models (NAR > 16 and NAR 16 and NAR 16 and NAR

Background and purpose: Significant deviations between bladder dose planned (DP) and dose accumulated (DA) have been reported in patients receiving radiotherapy for prostate cancer. This study aimed to construct multivariate analysis (MVA) models to predict the risk of late genitourinary (GU) toxicity with clinical and DP or DA as dose-volume (DV) variables. Materials and methods: Bladder DA obtained from 150 patients were compared with DP. MVA models were built from significant clinical and DV variables (p 0.6. Prostate volume (OR: 0.87, p: 0.01) was significant in predicting Grade 2 GU toxicity with a high AUC of 0.81. Conclusions: Higher DA (V30-65 Gy) received by the bladder were not translated to higher late GU toxicity. DB-ROIs demonstrated higher predictive power than standard DV metrics in associating Grade ≥ 1 toxicity. Smaller prostate volumes have a minor protective effect on late Grade 2 GU toxicity.

ObjectiveThis work aims to use machine learning models to predict gamma passing rate of portal dosimetry quality assurance with log file derived features. This allows daily treatment monitoring for patients and reduce wear and tear on EPID detectors to save cost and prevent downtime.Methods578 VMAT trajectory log files selected from prostate, lung and spine SBRT were used in this work. Four machine learning models were explored to identify the best performing regression model for predicting gamma passing rate within each sub-site and the entire unstratified data. Predictors used in these models comprised of hand-crafted log file-derived features as well as modulation complexity score. Cross validation was used to evaluate the model performance in terms of R2 and RMSE.ResultUsing gamma passing rate of 1%/1mm criteria and entire dataset, LASSO regression has a R2 of 0.121 ± 0.005 and RMSE of 4.794 ± 0.013%, SVM regression has a R2 of 0.605 ± 0.036 and RMSE of 3.210 ± 0.145%, Random Forest regression has a R2 of 0.940 ± 0.019 and RMSE of 1.233 ± 0.197%. XGBoost regression has the best performance with a R2 and RMSE value of 0.981 ± 0.015 and 0.652 ± 0.276%, respectively.ConclusionLog file-derived features can predict gamma passing rate of portal dosimetry with an average error of less than 2% using the 1%/1mm criteria. This model can potentially be applied to predict the patient specific QA results for every treatment fraction.

Background and purposeNormal tissue complication probability (NTCP) parameters derived from traditional 3D plans may not be ideal in defining toxicity outcomes for modern radiotherapy techniques. This study aimed to derive parameters of the Lyman-Kutcher-Burman (LKB) NTCP model using prospectively scored clinical data for late gastrointestinal (GI) and genitourinary (GU) toxicities for high-risk prostate cancer patients treated using volumetric-modulated-arc-therapy (VMAT). Dose-volume-histograms (DVH) extracted from planned (DP) and accumulated dose (DA) were used.Material and methodsDP and DA obtained from the DVH of 150 prostate cancer patients with pelvic-lymph-nodes irradiation treated using VMAT were used to generate LKB-NTCP parameters using maximum likelihood estimations. Defined GI and GU toxicities were recorded up to 3-years post RT follow-up. Model performance was measured using Hosmer-Lemeshow goodness of fit test and the mean area under the receiver operating characteristics curve (AUC). Bootstrapping method was used for internal validation.ResultsFor mild-severe (Grade ≥1) GI toxicity, the model generated similar parameters based on DA and DP DVH data (DA-D50:71.6 Gy vs DP-D50:73.4; DA-m:0.17 vs DP-m:0.19 and DA/P-n 0.04). The 95% CI for DA-D50 was narrower and achieved an AUC of >0.6. For moderate-severe (Grade ≥2) GI toxicity, DA-D50 parameter was higher and had a narrower 95% CI (DA-D50:77.9 Gy, 95% CI:76.4-79.6 Gy vs DP-D50:74.6, 95% CI:69.1-85.4 Gy) with good model performance (AUC>0.7). For Grade ≥1 late GU toxicity, D50 and n parameters for DA and DP were similar (DA-D50: 58.8 Gy vs DP-D50: 59.5 Gy; DA-n: 0.21 vs DP-n: 0.19) with a low AUC of0.6.ConclusionsThe achieved NTCP parameters using modern RT techniques and accounting for organ motion differs from QUANTEC reported parameters. DA-D50 of 77.9 Gy for GI and DA/DP-D50 of 81.7-81.9 Gy for GU demonstrated good predictability in determining the risk of Grade ≥2 toxicities especially for GI derived D50 and are recommended to incorporate as part of the DV planning constraints to guide dose escalation strategies while minimising the risk of toxicity.

The authors demonstrate efficient excitation of nanodiamonds by a focused beam of helium ions, resulting in ionoluminescence. They use this for quantification and correlative localization of single particles within a whole cell at sub-30 nm resolution, and investigate nanodiamond radiosensitisation effects.

Abstract An automated dose accumulation and contour propagation workflow using daily cone beam computed tomography (CBCTs) images for prostate cases that require pelvic lymph nodes irradiation (PLNs) was developed. This workflow was constructed using MIM® software with the intention to provide accurate dose transformations for plans with two different isocentres, whereby two sequential treatment phases were prescribed. The pre‐processing steps for data extractions from treatment plans, CBCTs, determination of couch shift information and management of missing CBCTs are described. To ensure that the imported translational couch shifts were in the correct orientation and readable in MIM, phantom commissioning was performed. For dose transformation, rigid registration with corrected setup shifts and scaled fractional dose was performed for pCT to daily CBCTs, which were then deformed onto CBCT1. Fractional dose summation resulted in the final accumulated dose for the patient allowing differences in dosimetry between the planned and accumulated dose to be analysed. Contour propagations of the prostate, bladder and rectum were performed within the same workflow. Transformed contours were then deformed onto daily CBCTs to generate trending reports for analysis, including Dice Similarity Coefficient (DSC) and Mean Distance to Agreement (MDA). Results obtained from phantom commissioning (DSC = 0.96, MDA = 0.89 mm) and geometrical analysis of the propagated contours for twenty patients; prostate (DSC: 0.9 ± 0.0, MDA: 1.0 ± 0.3 mm), rectum (DSC: 0.8 ± 0.1, mm, MDA: 1.7 ± 0.6 mm) and bladder (DSC: 0.8 ± 0.1, MDA: 2.8 ± 1.0 mm) were within clinically accepted tolerances for both DSC (>0.8) and MDA (< 0.3 mm). The developed workflow is being performed on a larger patient cohort for predictive model building, with the goal of correlating observed toxicity with the actual accumulated dose received by the patient.

Purpose: Relative biological effectiveness (RBE) uncertainties have been a concern for treatment planning in proton therapy, particularly for treatment sites that are near organs at risk (OARs). In such a clinical situation, the utilization of variable RBE models is preferred over constant RBE model of 1.1. The problem, however, lies in the exact choice of RBE model, especially when current RBE models are plagued with a host of uncertainties. This paper aims to determine the influence of RBE models on treatment planning, specifically to improve the understanding of the influence of the RBE models with regard to the passing and failing of treatment plans. This can be achieved by studying the RBE-weighted dose uncertainties across RBE models for OARs in cases where the target volume overlaps the OARs. Multi-field optimization (MFO) and single-field optimization (SFO) plans were compared in order to recommend which technique was more effective in eliminating the variations between RBE models. Methods: Fifteen brain tumor patients were selected based on their profile where their target volume overlaps with both the brain stem and the optic chiasm. In this study, 6 RBE models were analyzed to determine the RBE-weighted dose uncertainties. Both MFO and SFO planning techniques were adopted for the treatment planning of each patient. RBE-weighted dose uncertainties in the OARs are calculated assuming (αβ)x of 3 Gy and 8 Gy. Statistical analysis was used to ascertain the differences in RBE-weighted dose uncertainties between MFO and SFO planning. Additionally, further investigation of the linear energy transfer (LET) distribution was conducted to determine the relationship between LET distribution and RBE-weighted dose uncertainties. Results: The results showed no strong indication on which planning technique would be the best for achieving treatment planning constraints. MFO and SFO showed significant differences (P

Background: Lactate dehydrogenase (LDH) is a known prognostic biomarker for the endemic variant of nasopharyngeal carcinoma (NPC). Here, we investigate whether serial changes in LDH level between chemotherapy (CT) cycles are associated with tumour response to CT. Methods: Patients with biopsy-proven, recurrent or treatment-naïve metastatic NPC (mNPC) were recruited. All patients had received at least two cycles of platinum-based doublet or triplet CT, with serial assessment of LDH prior to every cycle of chemotherapy (CT1–6). Patients harbouring conditions that affect LDH levels (IU/L) were excluded. Tumour response was assessed after every two cycles of CT by RECIST v1.1. Results: A total of 158 patients were analysed, including 77 with recurrent and 81 with treatment-naïve mNPC. High pre-CT LDH was associated with an inferior overall survival [hazard ratio 1.93 for ⩾240 versus

Background and purpose: This study aimed to investigate the feasibility of safe-dose escalation to dominant intraprostatic lesions (DILs) and assess the clinical impact using dose-volume (DV) and biological metrics in photon and proton therapy. Biological parameters defined as late grade ≥ 2 gastrointestinal (GI) and genitourinary (GU) derived from planned (DP) and accumulated dose (DA) were utilized. Materials and methods: In total, 10 patients with high-risk prostate cancer with multiparametric MRI-defined DILs were investigated. Each patient had two plans with a focal boost to the DILs using intensity-modulated proton therapy (IMPT) and volumetric-modulated arc therapy (VMAT). Plans were optimized to obtain DIL coverage while respecting the mandatory organ-at-risk constraints. For the planning evaluation, DV metrics, tumor control probability (TCP) for the DILs and whole prostate excluding the DILs (prostate-DILs), and normal tissue complication probability (NTCP) for the rectum and bladder were calculated. Wilcoxon signed-rank test was used for analyzing TCP and NTCP data. Results: IMPT achieved a higher Dmean for the DILs compared to VMAT (IMPT: 68.1 GyRBE vs. VMAT: 66.6 Gy, p < 0.05). Intermediate-high rectal and bladder doses were lower for IMPT (p < 0.05), while the high-dose region (V60 Gy) remained comparable. IMPT-TCP for prostate-DIL were higher compared to VMAT (IMPT: 86%; α/β = 3, 94.3%; α/β = 1.5 vs. VMAT: 84.7%; α/β = 3, 93.9%; α/β = 1.5, p < 0.05). Likewise, IMPT obtained a moderately higher DIL TCP (IMPT: 97%; α/β = 3, 99.3%; α/β = 1.5 vs. VMAT: 95.9%; α/β = 3, 98.9%; α/β = 1.5, p < 0.05). Rectal DA-NTCP displayed the highest GI toxicity risk at 5.6%, and IMPT has a lower GI toxicity risk compared to VMAT-predicted Quantec-NTCP (p < 0.05). Bladder DP-NTCP projected a higher GU toxicity than DA-NTCP, with VMAT having the highest risk (p < 0.05). Conclusion: Dose escalation using IMPT is able to achieve a high TCP for the DILs, with the lowest rectal and bladder DV doses at the intermediate-high-dose range. The reduction in physical dose was translated into a lower NTCP (p < 0.05) for the bladder, although rectal toxicity remained equivalent. [ABSTRACT FROM AUTHOR]

Background and purpose: Normal tissue complication probability (NTCP) parameters derived from traditional 3D plans may not be ideal in defining toxicity outcomes for modern radiotherapy techniques. This study aimed to derive parameters of the Lyman-Kutcher-Burman (LKB) NTCP model using prospectively scored clinical data for late gastrointestinal (GI) and genitourinary (GU) toxicities for high-risk prostate cancer patients treated using volumetricmodulated-arc-therapy (VMAT). Dose-volume-histograms (DVH) extracted from planned (DP) and accumulated dose (DA) were used. Material and methods: DP and DA obtained from the DVH of 150 prostate cancer patients with pelvic-lymph-nodes irradiation treated using VMAT were used to generate LKB-NTCP parameters using maximum likelihood estimations. Defined GI and GU toxicities were recorded up to 3-years post RT follow-up. Model performance was measured using Hosmer-Lemeshow goodness of fit test and the mean area under the receiver operating characteristics curve (AUC). Bootstrapping method was used for internal validation. Results: For mild-severe (Grade =1) GI toxicity, the model generated similar parameters based on DA and DP DVH data (DA-D50:71.6 Gy vs DP-D50:73.4; DAm: 0.17 vs DP-m:0.19 and DA/P-n 0.04). The 95% CI for DA-D50 was narrower and achieved an AUC of >0.6. For moderate-severe (Grade =2) GI toxicity, DAD50 parameter was higher and had a narrower 95% CI (DA-D50:77.9 Gy, 95% CI:76.4-79.6 Gy vs DP-D50:74.6, 95% CI:69.1-85.4 Gy) with good model performance (AUC>0.7). For Grade =1 late GU toxicity, D50 and n parameters for DA and DP were similar (DA-D50: 58.8 Gy vs DP-D50: 59.5 Gy; DA-n: 0.21 vs DP-n: 0.19) with a low AUC of<0.6. For Grade =2 late GU toxicity, similar NTCP parameters were attained from DA and DP DVH Data (DA-D50:81.7 Gy vs DPD50: 81.9 Gy; DA-n:0.12 vs DP-n:0.14) with an acceptable AUCs of >0.6. Conclusions: The achieved NTCP parameters using modern RT techniques and accounting for organmotion differs from QUANTEC reported parameters. DA-D50 of 77.9 Gy for GI and DA/DP-D50 of 81.7-81.9 Gy for GU demonstrated good predictability in determining the risk of Grade =2 toxicities especially for GI derived D50 and are recommended to incorporate as part of the DV planning constraints to guide dose escalation strategies while minimising the risk of toxicity. [ABSTRACT FROM AUTHOR]

Background: This study aims to explore the role of four-dimensional (4D) transperineal ultrasound (TPUS) in the contouring of prostate gland with planning computed tomography (CT) images, in the absence of magnetic resonance imaging (MRI). Materials and methods: Five radiation oncologists (ROs) performed two rounds of prostate gland contouring (single-blinded) on CT-alone and CT/TPUS datasets obtained from 10 patients who underwent TPUS-guided external beam radiotherapy. Parameters include prostate volume, DICE similarity coefficient (DSC) and centroid position. Wilcoxon signed-rank test assessed the significance of inter-modality differences, and the intraclass correlation coefficient (ICC) reflected inter- and intra-observer reliability of parameters. Results: Inter-modality analysis revealed high agreement (based on DSC and centroid position) of prostate gland contours between CT-alone and CT/TPUS. Statistical significant difference was observed in the superior-inferior direction of the prostate centroid position (p = 0.011). All modalities yielded excellent inter-observer reliability of delineated prostate volume with ICC > 0.9, mean DSC > 0.8 and centroid position: CT-alone (ICC = 1.000) and CT/TPUS (ICC = 0.999) left-right (L/R); CT-alone (ICC = 0.999) and CT/TPUS (ICC = 0.998) anterior-posterior (A/P); CT-alone (ICC = 0.999) and CT/TPUS (ICC = 1.000) superior-inferior (S/I). Similarly, all modalities yielded excellent intra-observer reliability of delineated prostate volume, ICC > 0.9 and mean DSC > 0.8. Lastly, intra-observer reliability was excellent on both imaging modalities for the prostate centroid position, ICC > 0.9. Conclusion: TPUS does not add significantly to the amount of anatomical information provided by CT images. However, TPUS can supplement planning CT to achieve a higher positional accuracy in the S/I direction if access to CT/MRI fusion is limited. [ABSTRACT FROM AUTHOR]