Cardiac substructure delineation in radiation therapy - A state-of-the-art review.

Delineation of cardiac substructures is crucial for a better understanding of radiation-related cardiotoxicities and to facilitate accurate and precise cardiac dose calculation for developing and applying risk models. This review examines recent advancements in cardiac substructure delineation in the radiation therapy (RT) context, aiming to provide a comprehensive overview of the current level of knowledge, challenges and future directions in this evolving field. Imaging used for RT planning presents challenges in reliably visualising cardiac anatomy. Although cardiac atlases and contouring guidelines aid in standardisation and reduction of variability, significant uncertainties remain in defining cardiac anatomy. Coupled with the inherent complexity of the heart, this necessitates auto-contouring for consistent large-scale data analysis and improved efficiency in prospective applications. Auto-contouring models, developed primarily for breast and lung cancer RT, have demonstrated performance comparable to manual contouring, marking a significant milestone in the evolution of cardiac delineation practices. Nevertheless, several key concerns require further investigation. There is an unmet need for expanding cardiac auto-contouring models to encompass a broader range of cancer sites. A shift in focus is needed from ensuring accuracy to enhancing the robustness and accessibility of auto-contouring models. Addressing these challenges is paramount for the integration of cardiac substructure delineation and associated risk models into routine clinical practice, thereby improving the safety of RT for future cancer patients.

Potential dosimetric error in the adaptive workflow of a 1.5 T MR-Linac from patient movement relative to immobilisation systems.

In magnetic resonance- (MR-) based adaptive workflows for an MR-linac, the treatment plan is optimized and recalculated online using the daily MR images. The Unity MR-linac is supplied with a patient positioning device (ppd) using pelvic and abdomen thermoplastic masks attached to a board with high-density components. This study highlights the dosimetric effect of using this in such workflows when there are relative patient-ppd displacements, as these are not visualized on MR imaging and the treatment planning system assumes the patient is fixed relative to the ppd. The online adapted plans of two example rectum cancer patients treated at a Unity MR-linac were perturbed by introducing relative patient-ppd displacements, and the effect was evaluated on plan dosimetry. Forty-eight perturbed clinical adapted plans were recalculated, based on online MR-based synthetic computed tomography, and compared with the original plans, using dose-volume histogram parameters and gamma analysis. The target volume covered by the prescribed dose ( D pre ) and by at least 107% of D pre varied up to - 1.87% and + 3.67%, respectively for 0.5 cm displacements, and to - 3.18% and + 4.96% for 2 cm displacements; whilst 2%-2 mm gamma analysis showed a median value of 92.9%. The use of a patient positioning system with high-density components in a Unity MR-based online adaptive treatment workflow can introduce unrecognized errors in plan dosimetry and it is recommended not to use such a device for such treatments, without modifying the device and the workflow, followed by careful clinical evaluation, or alternatively to use other immobilization methods.

The rationale for a carbon ion radiation therapy facility in Australia.

Australia has taken a collaborative nationally networked approach to achieve particle therapy capability. This supports the under-construction proton therapy facility in Adelaide, other potential proton centres and an under-evaluation proposal for a hybrid carbon ion and proton centre in western Sydney. A wide-ranging overview is presented of the rationale for carbon ion radiation therapy, applying observations to the case for an Australian facility and to the clinical and research potential from such a national centre.

The Concise Guide to PHARMACOLOGY 2023/24: Transporters.

The Concise Guide to PHARMACOLOGY 2023/24 is the sixth in this series of biennial publications. The Concise Guide provides concise overviews, mostly in tabular format, of the key properties of approximately 1800 drug targets, and over 6000 interactions with about 3900 ligands. There is an emphasis on selective pharmacology (where available), plus links to the open access knowledgebase source of drug targets and their ligands (https://www.guidetopharmacology.org/), which provides more detailed views of target and ligand properties. Although the Concise Guide constitutes almost 500 pages, the material presented is substantially reduced compared to information and links presented on the website. It provides a permanent, citable, point-in-time record that will survive database updates. The full contents of this section can be found at http://onlinelibrary.wiley.com/doi/10.1111/bph.16182. Transporters are one of the six major pharmacological targets into which the Guide is divided, with the others being: G protein-coupled receptors, ion channels, nuclear hormone receptors, catalytic receptors and enzymes. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The landscape format of the Concise Guide is designed to facilitate comparison of related targets from material contemporary to mid-2023, and supersedes data presented in the 2021/22, 2019/20, 2017/18, 2015/16 and 2013/14 Concise Guides and previous Guides to Receptors and Channels. It is produced in close conjunction with the Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology (NC-IUPHAR), therefore, providing official IUPHAR classification and nomenclature for human drug targets, where appropriate.

Co-option of a conserved host glutamine transporter facilitates aphid/Buchnera metabolic integration.

Organisms across the tree of life colonize novel environments by partnering with bacterial symbionts. These symbioses are characterized by intimate integration of host/endosymbiont biology at multiple levels, including metabolically. Metabolic integration is particularly important for sap-feeding insects and their symbionts, which supplement nutritionally unbalanced host diets. Many studies reveal parallel evolution of host/endosymbiont metabolic complementarity in amino acid biosynthesis, raising questions about how amino acid metabolism is regulated, how regulatory mechanisms evolve, and the extent to which similar mechanisms evolve in different systems. In the aphid/Buchnera symbiosis, the transporter ApGLNT1 (Acyrthosiphon pisum glutamine transporter 1) supplies glutamine, an amino donor in transamination reactions, to bacteriocytes (where Buchnera reside) and is competitively inhibited by Buchnera-supplied arginine-consistent with a role regulating amino acid metabolism given host demand for Buchnera-produced amino acids. We examined how ApGLNT1 evolved a regulatory role by functionally characterizing orthologs in insects with and without endosymbionts. ApGLNT1 orthologs are functionally similar, and orthology searches coupled with homology modeling revealed that GLNT1 is ancient and structurally conserved across insects. Our results indicate that the ApGLNT1 symbiotic regulatory role is derived from its ancestral role and, in aphids, is likely facilitated by loss of arginine biosynthesis through the urea cycle. Given consistent loss of host arginine biosynthesis and retention of endosymbiont arginine supply, we hypothesize that GLNT1 is a general mechanism regulating amino acid metabolism in sap-feeding insects. This work fills a gap, highlighting the broad importance of co-option of ancestral proteins to novel contexts in the evolution of host/symbiont systems.

An international approach to estimating the indications and number of eligible patients for carbon ion radiation therapy (CIRT) in Australia.

BACKGROUND AND PURPOSE: To establish the treatment indications and potential patient numbers for carbon ion radiation therapy (CIRT) at the proposed national carbon ion (and proton) therapy facility in the Westmead precinct, New South Wales (NSW), Australia. METHODS: An expert panel was convened, including representatives of four operational and two proposed international carbon ion facilities, as well as NSW-based CIRT stakeholders. They met virtually to consider CIRT available evidence and experience. Information regarding Japanese CIRT was provided pre- and post- the virtual meeting. Published information for South Korea was included in discussions. RESULTS: There was jurisdictional variation in the tumours treated by CIRT due to differing incidences of some tumours, referral patterns, differences in decisions regarding which tumours to prioritise, CIRT resources available and funding arrangements. The greatest level of consensus was reached that CIRT in Australia can be justified currently for patients with adenoid cystic carcinomas and mucosal melanomas of the head and neck, hepatocellular cancer and liver metastases, base of skull meningiomas, chordomas and chondrosarcomas. Almost 1400 Australian patients annually meet the consensus-derived indications now. CONCLUSION: A conservative estimate is that 1% of cancer patients in Australia (or 2% of patients recommended for radiation therapy) may preferentially benefit from CIRT for initial therapy of radiation resistant tumours, or to boost persistently active disease after other therapies, or for re-irradiation of recurrent disease. On this basis, one national carbon ion facility with up to four treatment rooms is justified for Australian patients.

Minimum data elements for the Australian Particle Therapy Clinical Quality Registry.

INTRODUCTION: Construction of the first Australian particle therapy (PT) centre is underway. Establishment of a national registry, to be known as the Australian Particle Therapy Clinical Quality Registry (ASPIRE), has been identified as a mandatory requirement for PT treatment to be reimbursed by the Australian Medicare Benefits Schedule. This study aimed to determine a consensus set of Minimum Data Elements (MDEs) for ASPIRE. METHODS: A modified Delphi and expert consensus process was completed. Stage 1 compiled currently operational English-language international PT registries. Stage 2 listed the MDEs included in each of these four registries. Those included in three or four registries were automatically included as a potential MDE for ASPIRE. Stage 3 interrogated the remaining data items, and involved three rounds - an online survey to a panel of experts, followed by a live poll session of PT-interested participants, and finally a virtual discussion forum of the original expert panel. RESULTS: One hundred and twenty-three different MDEs were identified across the four international registries. The multi-staged Delphi and expert consensus process resulted in a total of 27 essential MDEs for ASPIRE; 14 patient factors, four tumour factors and nine treatment factors. CONCLUSIONS: The MDEs provide the core mandatory data items for the national PT registry. Registry data collection for PT is paramount in the ongoing global effort to accumulate more robust clinical evidence regarding PT patient and tumour outcomes, quantifying the magnitude of clinical benefit and justifying the relatively higher costs of PT investment.

Improving plan quality in cervical brachytherapy using a simple knowledge-based prediction tool for OAR dose (D2cm3).

PURPOSE: Toxicity from cervical brachytherapy has been demonstrated to correlate with the D2cm3 of the bladder, rectum, and bowel. This suggests a simplified version of knowledge-based planning investigating the relationship of the overlap distance for 2cm3 and the D2cm3 from planning may be possible. This work demonstrates the feasibility of simple knowledge-based planning to predict the D2cm3, detect suboptimal plans, and improve plan quality. METHODS AND MATERIALS: The overlap volume histogram (OVH) method was used to determine the distance for 2cm3 of overlap between the OAR and CTV_HR. Linear plots modeled the OAR D2cm3 and 2cm3 overlap distance. Two datasets of 20 patients (plans from 43 insertions in each dataset) were used to create two independent models, and the performance of each model was compared using cross-validation. Doses were scaled to ensure consistent CTV_HR D90 values. The predicted D2cm3 is entered as the maximum constraint in the inverse planning algorithm. RESULTS: Mean bladder D2cm3 decreased by 2.9% for the models from each dataset, mean rectal D2cm3 decreased 14.9% for the model from dataset 1 and 6.0% for the model from dataset 2, mean sigmoid D2cm3 decreased 10.7% for the model from dataset 1 and 6.1% for the model from dataset 2, mean bowel D2cm3 decreased 4.1% for the model from dataset 1 but no statistically significant difference was observed for the model from dataset 2. CONCLUSIONS: A simplified knowledge-based planning method was used to predict D2cm3 and was able to automate optimization of brachytherapy plans for locally advanced cervical cancer.

Clinical validation of MR imaging time reduction for substitute/synthetic CT generation for prostate MRI-only treatment planning.

Radiotherapy treatment planning based only on magnetic resonance imaging (MRI) has become clinically achievable. Though computed tomography (CT) is the gold standard for radiotherapy imaging, directly providing the electron density values needed for planning calculations, MRI has superior soft tissue visualisation to guide treatment planning decisions and optimisation. MRI-only planning removes the need for the CT scan, but requires generation of a substitute/synthetic/pseudo CT (sCT) for electron density information. Shortening the MRI imaging time would improve patient comfort and reduce the likelihood of motion artefacts. A volunteer study was previously carried out to investigate and optimise faster MRI sequences for a hybrid atlas-voxel conversion to sCT for prostate treatment planning. The aim of this follow-on study was to clinically validate the performance of the new optimised sequence for sCT generation in a treated MRI-only prostate patient cohort. 10 patients undergoing MRI-only treatment were scanned on a Siemens Skyra 3T MRI as part of the MRI-only sub-study of the NINJA clinical trial (ACTRN12618001806257). Two sequences were used, the standard 3D T2-weighted SPACE sequence used for sCT conversion which has been previously validated against CT, and a modified fast SPACE sequence, selected based on the volunteer study. Both were used to generate sCT scans. These were then compared to evaluate the fast sequence conversion for anatomical and dosimetric accuracy against the clinically approved treatment plans. The average Mean Absolute Error (MAE) for the body was 14.98 ± 2.35 HU, and for bone was 40.77 ± 5.51 HU. The external volume contour comparison produced a Dice Similarity Coefficient (DSC) of at least 0.976, and an average of 0.985 ± 0.004, and the bony anatomy contour comparison a DSC of at least 0.907, and an average of 0.950 ± 0.018. The fast SPACE sCT agreed with the gold standard sCT within an isocentre dose of -0.28% ± 0.16% and an average gamma pass rate of 99.66% ± 0.41% for a 1%/1 mm gamma tolerance. In this clinical validation study, the fast sequence, which reduced the required imaging time by approximately a factor of 4, produced an sCT with similar clinical dosimetric results compared to the standard sCT, demonstrating its potential for clinical use for treatment planning.

Open-source, fully-automated hybrid cardiac substructure segmentation: development and optimisation.

Radiotherapy for thoracic and breast tumours is associated with a range of cardiotoxicities. Emerging evidence suggests cardiac substructure doses may be more predictive of specific outcomes, however, quantitative data necessary to develop clinical planning constraints is lacking. Retrospective analysis of patient data is required, which relies on accurate segmentation of cardiac substructures. In this study, a novel model was designed to deliver reliable, accurate, and anatomically consistent segmentation of 18 cardiac substructures on computed tomography (CT) scans. Thirty manually contoured CT scans were included. The proposed multi-stage method leverages deep learning (DL), multi-atlas mapping, and geometric modelling to automatically segment the whole heart, cardiac chambers, great vessels, heart valves, coronary arteries, and conduction nodes. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD), and volume ratio. Performance was reliable, with no errors observed and acceptable variation in accuracy between cases, including in challenging cases with imaging artefacts and atypical patient anatomy. The median DSC range was 0.81-0.93 for whole heart and cardiac chambers, 0.43-0.76 for great vessels and conduction nodes, and 0.22-0.53 for heart valves. For all structures the median MDA was below 6 mm, median HD ranged 7.7-19.7 mm, and median volume ratio was close to one (0.95-1.49) for all structures except the left main coronary artery (2.07). The fully automatic algorithm takes between 9 and 23 min per case. The proposed fully-automatic method accurately delineates cardiac substructures on radiotherapy planning CT scans. Robust and anatomically consistent segmentations, particularly for smaller structures, represents a major advantage of the proposed segmentation approach. The open-source software will facilitate more precise evaluation of cardiac doses and risks from available clinical datasets.

Larynx cancer survival model developed through open-source federated learning.

INTRODUCTION: Federated learning has the potential to perfrom analysis on decentralised data; however, there are some obstacles to survival analyses as there is a risk of data leakage. This study demonstrates how to perform a stratified Cox regression survival analysis specifically designed to avoid data leakage using federated learning on larynx cancer patients from centres in three different countries. METHODS: Data were obtained from 1821 larynx cancer patients treated with radiotherapy in three centres. Tumour volume was available for all 786 of the included patients. Parameter selection among eleven clinical and radiotherapy parameters were performed using best subset selection and cross-validation through the federated learning system, AusCAT. After parameter selection, ß regression coefficients were estimated using bootstrap. Calibration plots were generated at 2 and 5-years survival, and inner and outer risk groups' Kaplan-Meier curves were compared to the Cox model prediction. RESULTS: The best performing Cox model included log(GTV), performance status, age, smoking, haemoglobin and N-classification; however, the simplest model with similar statistical prediction power included log(GTV) and performance status only. The Harrell C-indices for the simplest model were for Odense, Christie and Liverpool 0.75[0.71-0.78], 0.65[0.59-0.71], and 0.69[0.59-0.77], respectively. The values are slightly higher for the full model with C-index 0.77[0.74-0.80], 0.67[0.62-0.73] and 0.71[0.61-0.80], respectively. Smoking during treatment has the same hazard as a ten-years older nonsmoking patient. CONCLUSION: Without any patient-specific data leaving the hospitals, a stratified Cox regression model based on data from centres in three countries was developed without data leakage risks. The overall survival model is primarily driven by tumour volume and performance status.

Reshaping the Binding Pocket of the Neurotransmitter:Solute Symporter (NSS) Family Transporter SLC6A14 (ATB0,+) Selectively Reduces Access for Cationic Amino Acids and Derivatives.

SLC6A14 (ATB0,+) is unique among SLC proteins in its ability to transport 18 of the 20 proteinogenic (dipolar and cationic) amino acids and naturally occurring and synthetic analogues (including anti-viral prodrugs and nitric oxide synthase (NOS) inhibitors). SLC6A14 mediates amino acid uptake in multiple cell types where increased expression is associated with pathophysiological conditions including some cancers. Here, we investigated how a key position within the core LeuT-fold structure of SLC6A14 influences substrate specificity. Homology modelling and sequence analysis identified the transmembrane domain 3 residue V128 as equivalent to a position known to influence substrate specificity in distantly related SLC36 and SLC38 amino acid transporters. SLC6A14, with and without V128 mutations, was heterologously expressed and function determined by radiotracer solute uptake and electrophysiological measurement of transporter-associated current. Substituting the amino acid residue occupying the SLC6A14 128 position modified the binding pocket environment and selectively disrupted transport of cationic (but not dipolar) amino acids and related NOS inhibitors. By understanding the molecular basis of amino acid transporter substrate specificity we can improve knowledge of how this multi-functional transporter can be targeted and how the LeuT-fold facilitates such diversity in function among the SLC6 family and other SLC amino acid transporters.

Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer.

INTRODUCTION: Emerging evidence suggests that data-driven support tools have found their way into clinical decision-making in a number of areas, including cancer care. Improving them and widening their scope of availability in various differing clinical scenarios, including for prognostic models derived from retrospective data, requires co-ordinated data sharing between clinical centres, secondary analyses of large multi-institutional clinical trial data, or distributed (federated) learning infrastructures. A systematic approach to utilizing routinely collected data across cancer care clinics remains a significant challenge due to privacy, administrative and political barriers. METHODS: An information technology infrastructure and web service software was developed and implemented which uses machine learning to construct clinical decision support systems in a privacy-preserving manner across datasets geographically distributed in different hospitals. The infrastructure was deployed in a network of Australian hospitals. A harmonized, international ontology-linked, set of lung cancer databases were built with the routine clinical and imaging data at each centre. The infrastructure was demonstrated with the development of logistic regression models to predict major cardiovascular events following radiation therapy. RESULTS: The infrastructure implemented forms the basis of the Australian computer-assisted theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning. Four radiation oncology departments (across seven hospitals) in New South Wales (NSW) participated in this demonstration study. Infrastructure was deployed at each centre and used to develop a model predicting for cardiovascular admission within a year of receiving curative radiotherapy for non-small cell lung cancer. A total of 10,417 lung cancer patients were identified with 802 being eligible for the model. Twenty features were chosen for analysis from the clinical record and linked registries. After selection, 8 features were included and a logistic regression model achieved an area under the receiver operating characteristic (AUROC) curve of 0.70 and C-index of 0.65 on out-of-sample data. CONCLUSION: The infrastructure developed was demonstrated to be usable in practice between clinical centres to harmonize routinely collected oncology data and develop models with federated learning. It provides a promising approach to enable further research studies in radiation oncology using real world clinical data.