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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

Inter-fraction organ variations cause deviations between planned and delivered doses in patients receiving radiotherapy for prostate cancer. This study compared planned (DP) vs accumulated doses (DA) obtained from daily cone-beam computed tomography (CBCT) scans in high-risk- prostate cancer with pelvic lymph nodes irradiation. An intensity-based deformable image registration algorithm used to estimate contours for DA was validated using geometrical agreement between radiation oncologist's and deformable image registration algorithm propagated contours. Spearman rank correlations (rs) between geometric measures and changes in organ volumes were evaluated for 20 cases. Dose-volume (DV) differences between DA and DP were compared (Wilcoxon rank test, p < 0.05). A novel region-of-interest (ROI) method was developed and mean doses were analyzed. Geometrical measures for the prostate and organ-at-risk contours were within clinically acceptable criteria. Inter-group mean (± SD) CBCT volumes for the rectum were larger compared to planning CT (pCT) (51.1 ± 11.3 cm3vs 46.6 ± 16.1 cm3), and were moderately correlated with variations in pCT volumes, rs = 0.663, p < 0.01. Mean rectum DV for DA was higher at V30-40 Gy and lower at V70-75 Gy, p < 0.05. Mean bladder CBCT volumes were smaller compared to pCT (198.8 ± 55 cm3vs 211.5 ± 89.1 cm3), and was moderately correlated with pCT volumes, rs = 0.789, p < 0.01. Bladder DA was higher at V30-65 Gy and lower at V70-75 Gy (p < 0.05). For the ROI method, rectum and bladder DA were lower at 5 to 10 mm (p < 0.01) as compared to DP, whilst bladder DA was higher than DP at 20 to 50 mm (p < 0.01). Generated DA demonstrated significant differences in organ-at-risk doses as compared to DP. A well-constructed workflow incorporating a ROI DV-extraction method has been validated in terms of efficiency and accuracy designed for seamless integration in the clinic to guide future plan adaptation.

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.

Correlative imaging and quantification of intracellular nanoparticles with the underlying ultrastructure is crucial for understanding cell-nanoparticle interactions in biological research. However, correlative nanoscale imaging of whole cells still remains a daunting challenge. Here, we report a straightforward nanoscopic approach for whole-cell correlative imaging, by simultaneous ionoluminescence and ultrastructure mapping implemented with a highly focused beam of alpha particles. We demonstrate that fluorescent nanodiamonds exhibit fast, ultrabright and stable emission upon excitation by alpha particles. Thus, by using fluorescent nanodiamonds as imaging probes, our approach enables quantification and correlative localization of single nanodiamonds within a whole cell at sub-30 nm resolution. As an application example, we show that our approach, together with Monte Carlo simulations and radiobiological experiments, can be employed to provide unique insights into the mechanisms of nanodiamond radiosensitization at the single whole-cell level. These findings may benefit clinical studies of radio-enhancement effects by nanoparticles in charged-particle cancer therapy., 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.

This paper aims to review on fetal dose in radiotherapy and extends and updates on a previous work

Spine stereotactic body radiation therapy (SBRT) uses high dose per fraction for palliative pain control. The treatment plans are often heavily modulated due to close proximity to spinal cord and this can lead to poor plan quality which are susceptible to dose delivery discrepancy. Therefore, we aim to assess the effectiveness of the monitor unit (MU) objective tool in Eclipse treatment planning systems in modulating the plan complexity to improve the plan quality in spine SBRT. Seven retrospective spine SBRT plans are re-optimized using the MU objective tool in Eclipse TPS v13.6 and were compared with the original plans. The dose metrics of the tumor PTV were compared using D

Backgrounds: Respiratory gating is one of the motion management techniques that is used to deliver radiation dose to a tumor at a specific position under free breathing. However, due to the dynamic feedback process of this approach, regular equipment quality assurance (QA) and patient-specific QA checks need to be performed. This work proposes a new QA methodology using electronic portal imaging detector (EPID) to determine the target localization accuracy of phase gating. Methods: QA tools comprising 3D printed spherical tumor phantoms, programmable stages, and an EPID detector are characterized and assembled. Algorithms for predicting portal dose (PD) through moving phantoms are developed and verified using gamma analysis for two spherical tumor phantoms (2 cm and 4 cm), two different 6 MV volumetric modulated arc therapy plans, and two different gating windows (30%–70% and 40%–60%). Comparison between the two gating windows is then performed using the Wilcoxon signed-rank test. An optimizer routine, which is used to determine the optimal window, based on maximal gamma passing rate (GPR), was applied to an actual breathing curve and breathing plan. This was done to ascertain if our method yielded a similar result with the actual gating window. Results: High GPRs of more than 97% and 91% were observed when comparing the predicted PD with the measured PD in moving phantom at 2 mm/2% and1 mm/1% levels, respectively. Analysis of gamma heatmaps shows an excellent agreement with the tumor phantom. The GPR of 40%–60% PD was significantly lower than that of the 30%–70%PD at the 1 mm/1% level (p=0.0064). At the 2 mm/2% level, no significant differences were observed. The optimizer routine could accurately predict the center of the gating window to within a 10% range. Conclusion: We have successfully performed and verified a new method for QA with the use of a moving phantom with EPID for phase gating with real-time position management. Published version This work was supported by SingHealth Duke-NUS Academic Medicine and National Health Innovation Centre Singapore Joint MedTech grant (AM-NHIC/JMT006/2020) and Duke-NUS Oncology Aca-demic Clinical Programme Proton Research Fund(08/FY2021/EX(SL)/92-A146, 08/FY2020/EX(SL)/76-A152) grants.

Regional hyperthermia therapy (RHT) is a treatment that applies moderate heat to tumours in an attempt to potentiate the effects of oncological treatments and improve responses. Although it has been used for many years, the mechanisms of action are not fully understood. Heterogenous practices, poor quality assurance, conflicting clinical evidence and lack of familiarity have hindered its use. Despite this, several centres recognise its potential and have adopted it in their standard treatment protocols. In recent times, significant technical improvements have been made and there is an increasing pool of evidence that could revolutionise its use. Our narrative review aims to summarise the recently published prospective trial evidence and present the clinical effects of RHT when added to standard cancer treatments. In total, 31 studies with higher-quality evidence across various subsites are discussed herein. Although not all of these studies are level 1 evidence, benefits of moderate RHT in improving local tumour control, survival outcomes and quality of life scores were observed across the different cancer subsites with minimal increase in toxicities. This paper may serve as a reference when considering this technique for specific indications.

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).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.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 ICC0.9, mean DSC0.8 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, ICC0.9 and mean DSC0.8. Lastly, intra-observer reliability was excellent on both imaging modalities for the prostate centroid position, ICC0.9.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.

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

A new beam line facility dedicated to investigations into the radiobiology of biological single cells has been constructed at the Centre for Ion Beam Applications, National University of Singapore. This facility, which has a horizontal layout, has some novel features including post lens magnetic ion deflection and scanning and provision for a diamond exit window and confocal fluorescence microscope for high performance online investigations. The radiobiology facility uses a system of Oxford Microbeam compact magnetic quadrupole lenses enabling high demagnifications (up to 123 × 51) at the cell plane, and a relatively short lens to cell distances (22.5 cm). Beam optics and SRIM calculations have indicated a theoretical design performance of up to 450 nm for the proton spot size at the cell surface. To measure the spatial resolution of the proton beam at the cell position, we positioned a gold calibration grid inside the external cell chamber at a position normally occupied by cells. Preliminary results were obtained by scanning a 2 MeV focused beam over a grid and using a surface barrier detector to construct a STIM image of the grid we have measured an in-air spot size of sub-500 nm at the cell position. In this paper, the design of the new radiobiology beam line, preliminary results of resolution tests, as well as presenting first investigations into the damage caused by focused proton beam irradiation of human liver cancer cells by detecting DNA damage markers gamma-H2AX and 53BP1.

e18063 Background: RN is a debilitating toxicity of head and neck RT, with incidences of 4% to 24%. Bev is a promising drug for managing RN, but there is currently no biomarker that could predict a priori if a patient would benefit from Bev. Here, we aimed to use radiomics to predict Bev response. However, current radiomics tools suffer from issues of interrater contouring variability and explanability of the radiomics models. We therefore investigated a voxel-based radiomics approach to characterize the spatial heterogeneity of RN, and define subregions that are associated with Bev response. Methods: 118 consecutive nasopharyngeal carcinoma (NPC) patients who were diagnosed with RN post-RT, and were treated with Bev between Jul 2012 and Mar 2019 at the Sun Yat-Sen Memorial Hospital were enrolled into the study. 77 patients with 101 brain lesions treated between Jul 2012 and Dec 2017 were assigned to the training set, and 41 patients with 51 brain lesions treated between Jan 2018 and Mar 2019 were used for validation. All patients received Bev prescribed at 5 mg/kg every fortnightly for up to 4 courses. We extracted voxel-based radiomics features from each segment drawn on T2 FLAIR MRI images, followed by a 3-step analysis (individual- and population-level clustering, before delta-radiomics) to obtain a clustered heat map of subregions within the RN lesion. Anatomical correlation to areas of edema, necrosis, and their interphase was performed. Delta-radiomics tracked the ∆ of each cluster to Bev. Features were then extracted from each radiomics cluster for model building. Results: 71 (70.3%) and 34 (66.7%) lesions had documented radiological responses to Bev in the training and validation sets, respectively. Responders (N = 105) and non-responders (N = 47) showed a 71.8% (IQR: 54.7-87.5) and 13.2% (IQR: 7.98-21.4) reduction in RN volumes, respectively. Five optimal clusters were determined using the shoulder method with ΔK metric. Spatial analyses revealed that 2 of the 5 clusters were associated with the edema areas, with 92.6% and 74.3% of the voxels located within this anatomic subregion. The two corresponding clusters were significantly associated with a response to Bev (Odds ratio [OR]: 11.12 (95% CI: 2.54-73.47), P = 0.004; OR: 1.63 [1.07-2.78], P = 0.042). Finally, we developed and validated a clinicoradiomics model based on features extracted from the 2 clusters that improved the prediction of Bev response, compared with a clinical-only model for both the training (AUC 0.852 vs 0.755) and validation (AUC 0.816 vs 0.691) cohorts. Conclusions: Our spatial radiomics approach revealed anatomic subregions that contain relevant radiomics features that could predict Bev efficacy in the treatment of RN in NPC patients. This method would improve the interpretability of radiomics, compared with current methods that lack spatial resolution.

BACKGROUND Radiomics and connectome analysis are distinct and non-invasive methods of deriving biologic information from MRI. Radiomics analyzes features intrinsic to the tumor, and connectomics incorporates data regarding the tumor and surrounding neural circuitry. In this study we used both techniques to predict glioma survival. METHODS We retrospectively identified 305 adult patients with histopathologically confirmed WHO grade II–IV gliomas who had presurgical, 3D, T1-weighted brain MRI. Available clinical variables included tumor lobe, hemisphere, multifocal nature grade, histology extent of surgical resection, patient age gender. For connectomics, we calculated nodal efficiencies, network size and degree for all pairs of 33 voxel cubes spanning the entire gray matter volume using similarity-based extraction and graph theory. Radiomic features were extracted using Pyradiomics and subjected to patient-level and population-level clustering (N=172). These clusters were then used to construct a multi-regional spatial interaction matrix for model building. Cox proportional hazards models were fit for clinical variables alone, connectomics alone, radiomics alone, connectomics+clinical and radiomics+clinical. We implemented 10-folds cross-validation and examined the mean area under the curve (AUC) across validation loops. RESULTS Median survival time was 134.2 months. The mean AUC for the clinical model was 0.79 +/- 0.01, the connectome model was 0.88 +/- 0.01, the combined connectome + clinical model was 0.93 +/- 0.01, the radiomic model was 0.64 +/- 0.05 and the radiomics+clinical model was 0.89+/-0.03. Radiomic analysis of the entire dataset as well as comparisons of radiomic+connectomics +/- clinical models are pending. CONCLUSIONS The combination of clinical variables and connectome analysis provided a more robust predictive model than other models. This suggests that connectome analysis incorporates valuable clinically-predictive information which can augment our capacity for prognostication of patients with diffuse glioma. These methods warrant further evaluation in larger prospective study of patients with diffuse glioma.

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., An automated dose accumulation and contour propagation workflow using daily cone beam computed tomography images for prostate cancer was developed.

Correlative imaging and quantification of intracellular nanoparticles with the underlying ultrastructure is crucial for understanding cell-nanoparticle interactions in biological research. However, correlative nanoscale imaging of whole cells still remains a daunting challenge. Here, we report a straightforward nanoscopic approach for whole-cell correlative imaging, by simultaneous ionoluminescence and ultrastructure mapping implemented with a highly focused beam of alpha particles. We demonstrate that fluorescent nanodiamonds exhibit fast, ultrabright and stable emission upon excitation by alpha particles. Thus, by using fluorescent nanodiamonds as imaging probes, our approach enables quantification and correlative localization of single nanodiamonds within a whole cell at sub-40 nm resolution. As an application example, we show that our approach, together with Monte Carlo simulations, can be employed to provide unique insights into the mechanisms of nanodiamond radiosensitization at the single whole-cell level. These findings may benefit clinical studies of radio-enhancement effects by nanoparticles in charged-particle cancer therapy.

There is an increasing number of radiobiological experiments being conducted with low energy protons (less than 5 MeV) for radiobiological studies due to availability of sub-millimetre focused beam. However, low energy proton has broad microdosimetric spectra which can introduce dosimetric uncertainty. In this work, we quantify the impact of this dosimetric uncertainties on the cell survival curve and how it affects the estimation of the alpha and beta parameters in the LQ formalism. Monte Carlo simulation is used to generate the microdosimetric spectra in a micrometer-sized water sphere under proton irradiation. This is modelled using radiobiological experiment set-up at the Centre of Ion Beam Application (CIBA) in National University of Singapore. Our results show that the microdosimetric spectra can introduce both systematic and random shifts in dose and cell survival; this effect is most pronounced with low energy protons. The alpha and beta uncertainties can be up to 10% and above 30%, respectively for low energy protons passing through thin cell target (about 10 microns). These uncertainties are non-negligible and show that care must be taken in using the cell survival curve and its derived parameters for radiobiological models.

Objective: Dose-averaged linear energy transfer (LETD) is one of the factors which determines relative biological effectiveness (RBE) for treatment planning in proton therapy. It is usually determined from Monte Carlo (MC) simulation. However, no standard simulation protocols were established for sampling of LETD. Simulation parameters like maximum step length and range cut will affect secondary electrons production and have an impact on the accuracy of dose distribution and LETD. We aim to show how different combinations of step length and range cut in GEANT4 will affect the result in sampling of LETD using different MC scoring methods. Methods: In this work, different step length and range cut value in a clinically relevant voxel geometry were used for comparison. Different LETD scoring methods were established and the concept of covariance between energy deposition per step and step length is used to explain the differences between them. Results: We recommend a maximum step length of 0.05 mm and a range cut of 0.01 mm in MC simulation as this yields the most consistent LETD value across different scoring methods. Different LETD scoring methods are also compared and variation up to 200% can be observed at the plateau of 80 MeV proton beam. Scoring Method one has one of the lowest percentage differences compared across all simulation parameters. Conclusion: We have determined a set of maximum step length and range cut parameters to be used for LETD scoring in a 1 mm voxelized geometry. LETD scoring method should also be clearly defined and standardized to facilitate cross-institutional studies. Advances in knowledge: Establishing a standard simulation protocol for sampling LETD would reduce the discrepancy when comparing data across different centres, and this can improve the calculation for RBE.

Radiotherapy (RT) is a primary treatment modality that is used in 50% of cancer patients. Clinically, tumor radioresistance poses a challenge to achieve complete anti-tumor response following conventional photon-RT. Diverse mechanisms have been proposed to underpin radioresistance. However, targeting these aberrant pathways to overcome tumor radioresistance remains a clinical challenge. Here, we investigate if proton beam therapy (PBT) invokes differential cellular responses compared to photon-RT in a broad panel of radioresistant (RR) cancer cell lines. We generated isogenic RR human cancer cell lines: 22Rv1 prostate cancer, FaDu hypopharyngeal cancer and C666-1 nasopharyngeal cancer, by exposure of respective wildtype (wt) to 90 Gy photon-RT (2 Gy x 45 fr). RR was confirmed by clonogenic survival, with surviving fraction ratio (SFRR/SFwt) at 1-4 Gy (range: 1.2-1.8). Whole exome sequencing (100x; Illumina NovaSeq) was performed to profile RR-associated mutational drivers. We performed transcriptomic and proteome profiling of cellular response such as non-homologous end-joining (NHEJ) and homologous recombination (HR) repair at 1-24 h after 4 Gy PBT (2.5 MeV, 2 Gy/min) and photon-RT (0.66 MeV, 0.716 Gy/min). At baseline, we found common mutations and differential activation of several pro-survival (Akt, mTOR), epithelial-mesenchymal transition (EMT) (PDGF, TGFb) and DNA repair pathways (ATM, BRCA1, NHEJ- and HR-associated) genes in RR- relative to wt-22Rv1. Post-photon-RT, we observed residual persistent DNA repair in wt-cell lines up to 48 h. In contrast, repair in RR-cell lines was more proficient, as shown by recovery of γH2AX, ATM-Chk2 within 6 h, with significant NHEJ activation (DNA-PKcs). Next, we observed that RR-cell lines were more sensitive to PBT than photon-RT (mean SF4Gy PBT/SF4Gy photon-RT = 0.61), with delayed DNA repair (γH2AX) observed up to 48 h, which may be attributed to the difficulty in repairing PBT-induced DNA damage. This phenomenon, seems to be primarily orchestrated by decreased NHEJ activation combined with diminished cell cycle checkpoint arrest (p21) and anti-apoptotic (bcl-2) signaling. Radiosensitization by PBT was however not observed in wt (mean SF4Gy proton/SF4Gy photon = 1.14), corroborated by gene and protein expression of DDR-related pathways. We observed dysregulation of multiple pathways relating to EMT and DNA repair in our panel of RR-cancer cell lines, compared to the wt-counterparts. Additionally, we show the potential for PBT to overcome radioresistance in these models by targeting the DNA repair machinery. Due to the high cost of PBT, precise patient stratification is needed to ensure that PBT is applied only to patients who benefit significantly from this procedure over photon-RT. Our preliminary data represents a proof-of-concept for feasibility of patient stratification to PBT. Citation Format: Pek Lim Chu, Eugenia L.L. Yeo, Dennis J.J. Poon, Ce-belle Chen, Minqin Ren, Saumitra Vajandar, Dewi Susanti, Hong Qi Tan, Sung Yong Park, Thomas Osipowicz, Kwok Wai Lo, Stanley K. Liu, Khee Chee Soo, Melvin Lk Chua. Proton irradiation sensitizes cancer cell lines with acquired radioresistance by exploiting differentially dysregulated DNA damage response (DDR) pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3062.

The Xikang Group represents the main stratigraphic sequence of the Songpan–Ganzi complex (SGC), a flysch basin with a wide distribution and monotonous lithology. Strong tectonic deformation and accumulation of folds have led to destruction of the original sedimentary sequence and sedimentary structure of the strata, such that there exists no reasonable sedimentary model that can explain the massive flysch. Herein, we investigated in detail the sedimentary structures, paleocurrent direction, particle sizes, major and trace elements, and magmatic zircon and detrital zircon U–Pb ages of the typical sections of the Xikang Group in the eastern SGC. Our analysis led to the following findings: 1) The U–Pb zircon ages from the Galazi (215.6 ± 1.1 Ma), Sudi (214.5 ± 1.5 Ma and 221.1 ± 1.5 Ma), and Gulu (221.3 ± 1.2 Ma) plutons, which intrude the Xikang Group, and detrital zircon samples from the uppermost part of the Xinduqiao Fm. indicated the youngest mean age (YC1σ[2+]) of 222.4 ± 2.5 Ma. This in turn indicates a pre-221 Ma depositional age for the Xikang Group. 2) The provenance was derived from the North China Block and Qiangtang Terrane, Qinling–Dabie orogenic belt, South China Block, and East Kunlun–Qaidam, and the construction of the flysch in the SGC belongs to the multi-source or line source provided by the peripheral orogenic belt. 3) Trace elements suggested that the Xikang Group may have been deposited in a sedimentary basin associated with a continental island arc. 4) The sedimentary environment in this area belonged to the distal fan of a submarine, and the new sedimentation age and sedimentation rate indicate that the Carnian pluvial event may have been the trigger mechanism. 5) The formation of the flysch deposition in SGC related to the Paleo-Tethys Ocean underwent a scissor-like closure from east to west.

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 CTn: LDHCTn–1) were associated with tumour response [partial response versus progressive disease: median value across CT1–6 = 168–190 versus 222–398 (absolute); 0.738–0.988 versus 1.039–1.406 (ratio)], albeit LDH ratio had a tighter variance between patients. Finally, we showed that an LDH ratio cut-off of 1.0 at CT1, CT3 and CT5 was predictive of progressive disease at CT2, CT4, CT6 [area under the curve of 0.73 (0.65–0.80)]. Conclusion: Herein, we characterised the longitudinal variation of LDH in response to CT in mNPC. Our findings suggest the potential utility of interval LDH ratio to predict subsequent tumour response to CT.

In anticipation of the new Proton Therapy Centre in Singapore at 2020, the medical physicists and physicists in NCCS and NUS have come together to do several preparation/preliminary works. Of which, one of the most important programme is to develop the Monte Carlo simulation capability using GEANT4 toolkit. We simulated the entire beam delivery and nozzle based on specifications given by Hitachi. The results of the Energy-Range curves in water phantom together with spot size in air agrees well with measurement data provided by Hitachi. We will show the complete methodology of simulation leading up to the comparison with measurement data. There were 3 main aspects we focused on—(1) The choice of physics models, (2) The determination of initial proton phase space based on Twiss parameters from beam profile measurement and (3) The choice of cut-off energy and step size. This simulation is important for the commissioning of the Proton Therapy System and the methodology presented will be helpful for other upcoming centers who are interested in doing the same.

Current Treatment Planning System uses advanced optimization algorithm with Dose Calculation Engine to optimize the dose delivered to the tumor to maximize the tumor-killing potential while limiting the side-effects to the normal tissues. Working in parallel, there are various radiobiology simulation programs being developed by different research groups to quantify biological endpoints such as Double Strand Breaks (DBSs) yield and Chromosome Aberration (CA) induced by different radiation types. In this paper, we conduct a novel preliminary work to combine these two areas to determine DSB yields in a 3D CT geometry. Our preliminary result shows that the position of the Bragg Peak determined from dose deposition differs from the position of maximal DSB yields. The difference can range from 0 to 1 mm, hence showing that dose is not entirely indicative of biological damage in tissue.

It is known that high energy protons in proton therapy generate secondary particles. Of which, secondary neutrons are a main concern as they deposit out-of-field doses and can have long-term health effects on cancer patients. In this paper, the energy, 3-D spatial and angular distribution of the production yield of neutrons is scored along the proton beam path in different types of tissue medium. The degree of biological damage is then quantified through factoring in the relative biological effectiveness of neutrons. This systematic study involved simulating 70, 150 and 200 MeV proton beam transport in various tissue compositions with the GEANT4 code. System specifications of the Hitachi proton therapy system were used in this study. Simulation results showed that the neutrons are forward facing and are generally emitted at a preferential angle. With considerations on the RBE variation with neutron’s energy, the spatial and angular distribution of the production of lethal neutrons were identified along the proton track. Non-trivial relations between biological damage in different tissue medium were observed. Such comprehensive simulation studies have not been reported and this input information can be useful for treatment planning in reducing out-of-field neutron dose in sensitive organs.

We present a simple and universal approach to calculate the total ionization cross section (TICS) for electron impact ionization in DNA bases and other biomaterials in the condensed phase. Evaluating the electron impact TICS plays a vital role in ion-beam radiobiology simulation at the cellular level, as secondary electrons are the main cause of DNA damage in particle cancer therapy. Our method is based on extending the dielectric formalism. The calculated results agree well with experimental data and show a good comparison with other theoretical calculations. This method only requires information of the chemical composition and density and an estimate of the mean binding energy to produce reasonably accurate TICS of complex biomolecules. Because of its simplicity and great predictive effectiveness, this method could be helpful in situations where the experimental TICS data are absent or scarce, such as in particle cancer therapy.

Objective: Radiomics pipelines have been developed to extract novel information from radiological images, which may help in phenotypic profiling of tumours that would correlate to prognosis. Here, we compared two publicly available pipelines for radiomics analyses on head and neck CT and MRI in nasopharynx cancer (NPC). Methods and materials: 100 biopsy-proven NPC cases stratified by T- and N-categories were enrolled in this study. Two radiomics pipeline, Moddicom (v. 0.51) and Pyradiomics (v. 2.1.2) were used to extract radiomics features of CT and MRI. Segmentation of primary gross tumour volume was performed using Velocity v. 4.0 by consensus agreement between three radiation oncologists. Intraclass correlation between common features of the two pipelines was analysed by Spearman’s rank correlation. Unsupervised hierarchical clustering was used to determine association between radiomics features and clinical parameters. Results: We observed a high proportion of correlated features in the CT data set, but not for MRI; 76.1% (51 of 67 common between Moddicom and Pyradiomics) of CT features and 28.6% (20 of 70 common) of MRI features were significantly correlated. Of these, 100% were shape-related for both CT and MRI, 100 and 23.5% were first-order-related, 61.9 and 19.0% were texture-related, respectively. This interpipeline heterogeneity affected the downstream clustering with known prognostic clinical parameters of cTN-status and GTVp. Nonetheless, shape features were the most reproducible predictors of clinical parameters among the different radiomics modules. Conclusion: Here, we highlighted significant heterogeneity between two publicly available radiomics pipelines that could affect the downstream association with prognostic clinical factors in NPC Advances in knowledge: The present study emphasized the broader importance of selecting stable radiomics features for disease phenotyping, and it is necessary prior to any investigation of multicentre imaging datasets to validate the stability of CT-related radiomics features for clinical prognostication.

Biological uncertainty remains one of the main sources of uncertainties in proton therapy, and is encapsulated in a scalar quantity known as relative biological effective (RBE). It is currently recognised that a constant RBE of 1.1 is not consistent with radiobiological experiment and may lead to sub-optimal exploitation of the benefits of proton therapy. To overcome this problem, several RBE models have been developed, and in most of these models, there is a dependence of RBE on dose-averaged linear energy transfer (LET), [Formula: see text]. In this work, we show that the [Formula: see text] estimation in these models during the data-fitting (or parameter estimation) phase could be subjected to a huge uncertainty due to not taking into account cellular materials during simulation, and this uncertainty can propagate down to the resulting RBE models. The dosimetric impact of this [Formula: see text] uncertainty is then evaluated on a simple clinical spread out Bragg peak (SOBP) and a prostate example. Our simulation shows that [Formula: see text] uncertainty due to the use of water as cellular material is non-negligible under low [Formula: see text] and low dose (2 Gy), and can be neglected otherwise. Thus, this study indicates that further dose and range margins may be required for low [Formula: see text] target under low dose. This is due to greater uncertainties in RBE model associated with incomplete knowledge of cellular composition for [Formula: see text] computation.

The combination of an optical microscope and a luminescent probe plays a pivotal role in biological imaging because it allows for probing subcellular structures. However, the optical resolutions are largely constrained by Abbe's diffraction limit, and the common dye probes often suffer from photobleaching. Here we present a new method for subwavelength imaging by combining lanthanide-doped upconversion nanocrystals with the ionoluminescence imaging technique. We experimentally observed that the ion beam can be used as a new form of excitation source to induce photon upconversion in lanthanide-doped nanocrystals. This approach enables luminescence imaging and simultaneous mapping of cellular structures with a spatial resolution of sub-30 nm., Combining high-resolution microscopic techniques with luminescent probes is important for biological imaging. Here, Mi et al. demonstrate subwavelength imaging by combining lanthanide-doped upconversion nanocrystals with ionoluminescence, revealing cellular structure and particle spatial distribution at high resolution.