Dosimetric impact of contour editing on CT and MRI deep-learning autosegmentation for brain OARs.

PURPOSE: To establish the clinical applicability of deep-learning organ-at-risk autocontouring models (DL-AC) for brain radiotherapy. The dosimetric impact of contour editing, prior to model training, on performance was evaluated for both CT and MRI-based models. The correlation between geometric and dosimetric measures was also investigated to establish whether dosimetric assessment is required for clinical validation. METHOD: CT and MRI-based deep learning autosegmentation models were trained using edited and unedited clinical contours. Autosegmentations were dosimetrically compared to gold standard contours for a test cohort. D1%, D5%, D50%, and maximum dose were used as clinically relevant dosimetric measures. The statistical significance of dosimetric differences between the gold standard and autocontours was established using paired Student's t-tests. Clinically significant cases were identified via dosimetric headroom to the OAR tolerance. Pearson's Correlations were used to investigate the relationship between geometric measures and absolute percentage dose changes for each autosegmentation model. RESULTS: Except for the right orbit, when delineated using MRI models, the dosimetric statistical analysis revealed no superior model in terms of the dosimetric accuracy between the CT DL-AC models or between the MRI DL-AC for any investigated brain OARs. The number of patients where the clinical significance threshold was exceeded was higher for the optic chiasm D1% than other OARs, for all autosegmentation models. A weak correlation was consistently observed between the outcomes of dosimetric and geometric evaluations. CONCLUSIONS: Editing contours before training the DL-AC model had no significant impact on dosimetry. The geometric test metrics were inadequate to estimate the impact of contour inaccuracies on dose. Accordingly, dosimetric analysis is needed to evaluate the clinical applicability of DL-AC models in the brain.

The Special Delivery Unit: A Novel Concept for Mother-Baby Continuity for Major Fetal Abnormalities.

OBJECTIVE: This article describes the experience in the planning and development of a special delivery unit (SDU) at our free-standing children's hospital in Austin, Texas. STUDY DESIGN: Description of various aspects of the development of the SDU. In addition, telephone surveys were obtained from five other institutions regarding the planning and current status of their SDUs. RESULTS: Since the advent of the SDU at Children's Hospital of Philadelphia in 2008, several free-standing children's hospitals have opened similar units at their institutions. Developing an obstetrical unit in a children's hospital can be a daunting task on many fronts. The costs of providing 24-hour obstetrical, nursing, and anesthesiology coverage must be considered. Although most SDUs are associated with a fetal center and fetal surgery/interventions, some units function exclusively for the delivery of pregnancies complicated by major fetal conditions where the neonate will require immediate surgical care or other interventions. CONCLUSION: Research on the cost-effectiveness and the effect of SDUs on clinical outcome, teaching, and patient satisfaction is warranted. KEY POINTS: · Specialized delivery units are becoming more common at free-standing children's hospitals.. · The primary aim of the SDU is to maintain mother-baby continuity in cases of congenital anomalies.. · Developing an obstetrical unit at a pediatric hospital is a daunting task..

Intensity standardization of MRI prior to radiomic feature extraction for artificial intelligence research in glioma-a systematic review.

OBJECTIVES: Radiomics is a promising avenue in non-invasive characterisation of diffuse glioma. Clinical translation is hampered by lack of reproducibility across centres and difficulty in standardising image intensity in MRI datasets. The study aim was to perform a systematic review of different methods of MRI intensity standardisation prior to radiomic feature extraction. METHODS: MEDLINE, EMBASE, and SCOPUS were searched for articles meeting the following eligibility criteria: MRI radiomic studies where one method of intensity normalisation was compared with another or no normalisation, and original research concerning patients diagnosed with diffuse gliomas. Using PRISMA criteria, data were extracted from short-listed studies including number of patients, MRI sequences, validation status, radiomics software, method of segmentation, and intensity standardisation. QUADAS-2 was used for quality appraisal. RESULTS: After duplicate removal, 741 results were returned from database and reference searches and, from these, 12 papers were eligible. Due to a lack of common pre-processing and different analyses, a narrative synthesis was sought. Three different intensity standardisation techniques have been studied: histogram matching (5/12), limiting or rescaling signal intensity (8/12), and deep learning (1/12)-only two papers compared different methods. From these studies, histogram matching produced the more reliable features compared to other methods of altering MRI signal intensity. CONCLUSION: Multiple methods of intensity standardisation have been described in the literature without clear consensus. Further research that directly compares different methods of intensity standardisation on glioma MRI datasets is required. KEY POINTS: • Intensity standardisation is a key pre-processing step in the development of robust radiomic signatures to evaluate diffuse glioma. • A minority of studies compared the impact of two or more methods. • Further research is required to directly compare multiple methods of MRI intensity standardisation on glioma datasets.

The benefit of MR-only radiotherapy treatment planning for anal and rectal cancers: A planning study.

INTRODUCTION: Limited evidence exists showing the benefit of magnetic resonance (MR)-only radiotherapy treatment planning for anal and rectal cancers. This study aims to assess the impact of MR-only planning on target volumes (TVs) and treatment plan doses to organs at risks (OARs) for anal and rectal cancers versus a computed tomography (CT)-only pathway. MATERIALS AND METHODS: Forty-six patients (29 rectum and 17 anus) undergoing preoperative or radical external beam radiotherapy received CT and T2 MR simulation. TV and OARs were delineated on CT and MR, and volumetric arc therapy treatment plans were optimized independently (53.2 Gy/28 fractions for anus, 45 Gy/25 fractions for rectum). Further treatment plans assessed gross tumor volume (GTV) dose escalation. Differences in TV volumes and OAR doses, in terms of Vx Gy (organ volume (%) receiving x dose (Gy)), were assessed. RESULTS: MR GTV and primary planning TV (PTV) volumes systematically reduced by 13 cc and 98 cc (anus) and 44 cc and 109 cc (rectum) respectively compared to CT volumes. Statistically significant OAR dose reductions versus CT were found for bladder and uterus (rectum) and bladder, penile bulb, and genitalia (anus). With GTV boosting, statistically significant dose reductions were found for sigmoid, small bowel, vagina, and penile bulb (rectum) and vagina (anus). CONCLUSION: Our findings provide evidence that the introduction of MR (whether through MR-only or CT-MR pathways) to radiotherapy treatment planning for anal and rectal cancers has the potential to improve treatments. MR-related OAR dose reductions may translate into less treatment-related toxicity for patients or greater ability to dose escalate.

4D-Precise: Learning-based 3D motion estimation and high temporal resolution 4DCT reconstruction from treatment 2D+t X-ray projections.

BACKGROUND AND OBJECTIVE: In radiotherapy treatment planning, respiration-induced motion introduces uncertainty that, if not appropriately considered, could result in dose delivery problems. 4D cone-beam computed tomography (4D-CBCT) has been developed to provide imaging guidance by reconstructing a pseudo-motion sequence of CBCT volumes through binning projection data into breathing phases. However, it suffers from artefacts and erroneously characterizes the averaged breathing motion. Furthermore, conventional 4D-CBCT can only be generated post-hoc using the full sequence of kV projections after the treatment is complete, limiting its utility. Hence, our purpose is to develop a deep-learning motion model for estimating 3D+t CT images from treatment kV projection series. METHODS: We propose an end-to-end learning-based 3D motion modelling and 4DCT reconstruction model named 4D-Precise, abbreviated from Probabilistic reconstruction of image sequences from CBCT kV projections. The model estimates voxel-wise motion fields and simultaneously reconstructs a 3DCT volume at any arbitrary time point of the input projections by transforming a reference CT volume. Developing a Torch-DRR module, it enables end-to-end training by computing Digitally Reconstructed Radiographs (DRRs) in PyTorch. During training, DRRs with matching projection angles to the input kVs are automatically extracted from reconstructed volumes and their structural dissimilarity to inputs is penalised. We introduced a novel loss function to regulate spatio-temporal motion field variations across the CT scan, leveraging planning 4DCT for prior motion distribution estimation. RESULTS: The model is trained patient-specifically using three kV scan series, each including over 1200 angular/temporal projections, and tested on three other scan series. Imaging data from five patients are analysed here. Also, the model is validated on a simulated paired 4DCT-DRR dataset created using the Surrogate Parametrised Respiratory Motion Modelling (SuPReMo). The results demonstrate that the reconstructed volumes by 4D-Precise closely resemble the ground-truth volumes in terms of Dice, volume similarity, mean contour distance, and Hausdorff distance, whereas 4D-Precise achieves smoother deformations and fewer negative Jacobian determinants compared to SuPReMo. CONCLUSIONS: Unlike conventional 4DCT reconstruction techniques that ignore breath inter-cycle motion variations, the proposed model computes both intra-cycle and inter-cycle motions. It represents motion over an extended timeframe, covering several minutes of kV scan series.

Simulation of ultrafast photodynamics of pyrrole with a multiconfigurational Ehrenfest method.

We report the first results of ab initio multiconfigurational Ehrenfest simulations of pyrrole photodynamics. We note that, in addition to the two intersections of 1(1)A2 and 1(1)B1 states with the ground state 1(1)A1, which are known to be responsible for N-H bond fission, another intersection between the 1(2)A2 and 1(2)B1 states of the resulting molecular radical becomes important after the departure of the H atom. This intersection, which is effectively between the two lowest electronic states of the pyrrolyl radical, may play a significant role in explaining the branching ratio between the two states observed experimentally. The exchange of population between the two states of pyrrolyl occurs on a longer scale than that of N-H bond fission.

Geometric evaluations of CT and MRI based deep learning segmentation for brain OARs in radiotherapy.

Objective.Deep-learning auto-contouring (DL-AC) promises standardisation of organ-at-risk (OAR) contouring, enhancing quality and improving efficiency in radiotherapy. No commercial models exist for OAR contouring based on brain magnetic resonance imaging (MRI). We trained and evaluated computed tomography (CT) and MRI OAR autosegmentation models in RayStation. To ascertain clinical usability, we investigated the geometric impact of contour editing before training on model quality.Approach.Retrospective glioma cases were randomly selected for training (n= 32, 47) and validation (n= 9, 10) for MRI and CT, respectively. Clinical contours were edited using international consensus (gold standard) based on MRI and CT. MRI models were trained (i) using the original clinical contours based on planning CT and rigidly registered T1-weighted gadolinium-enhanced MRI (MRIu), (ii) as (i), further edited based on CT anatomy, to meet international consensus guidelines (MRIeCT), and (iii) as (i), further edited based on MRI anatomy (MRIeMRI). CT models were trained using: (iv) original clinical contours (CTu) and (v) clinical contours edited based on CT anatomy (CTeCT). Auto-contours were geometrically compared to gold standard validation contours (CTeCT or MRIeMRI) using Dice Similarity Coefficient, sensitivity, and mean distance to agreement. Models' performances were compared using paired Student's t-testing.Main results.The edited autosegmentation models successfully generated more segmentations than the unedited models. Paired t-testing showed editing pituitary, orbits, optic nerves, lenses, and optic chiasm on MRI before training significantly improved at least one geometry metric. MRI-based DL-AC performed worse than CT-based in delineating the lacrimal gland, whereas the CT-based performed worse in delineating the optic chiasm. No significant differences were found between the CTeCT and CTu except for optic chiasm.Significance.T1w-MRI DL-AC could segment all brain OARs except the lacrimal glands, which cannot be easily visualized on T1w-MRI. Editing contours on MRI before model training improved geometric performance. MRI DL-AC in RT may improve consistency, quality and efficiency but requires careful editing of training contours.

Brain Re-Irradiation Robustly Accounting for Previously Delivered Dose.

(1) Background: The STRIDeR (Support Tool for Re-Irradiation Decisions guided by Radiobiology) planning pathway aims to facilitate anatomically appropriate and radiobiologically meaningful re-irradiation (reRT). This work evaluated the STRIDeR pathway for robustness compared to a more conservative manual pathway. (2) Methods: For ten high-grade glioma reRT patient cases, uncertainties were applied and cumulative doses re-summed. Geometric uncertainties of 3, 6 and 9 mm were applied to the background dose, and LQ model robustness was tested using α/ß variations (values 1, 2 and 5 Gy) and the linear quadratic linear (LQL) model δ variations (values 0.1 and 0.2). STRIDeR robust optimised plans, incorporating the geometric and α/ß uncertainties during optimisation, were also generated. (3) Results: The STRIDeR and manual pathways both achieved clinically acceptable plans in 8/10 cases but with statistically significant improvements in the PTV D98% (p < 0.01) for STRIDeR. Geometric and LQ robustness tests showed comparable robustness within both pathways. STRIDeR plans generated to incorporate uncertainties during optimisation resulted in a superior plan robustness with a minimal impact on PTV dose benefits. (4) Conclusions: Our results indicate that STRIDeR pathway plans achieved a similar robustness to manual pathways with improved PTV doses. Geometric and LQ model uncertainties can be incorporated into the STRIDeR pathway to facilitate robust optimisation.

DragNet: Learning-based deformable registration for realistic cardiac MR sequence generation from a single frame.

Deformable image registration (DIR) can be used to track cardiac motion. Conventional DIR algorithms aim to establish a dense and non-linear correspondence between independent pairs of images. They are, nevertheless, computationally intensive and do not consider temporal dependencies to regulate the estimated motion in a cardiac cycle. In this paper, leveraging deep learning methods, we formulate a novel hierarchical probabilistic model, termed DragNet, for fast and reliable spatio-temporal registration in cine cardiac magnetic resonance (CMR) images and for generating synthetic heart motion sequences. DragNet is a variational inference framework, which takes an image from the sequence in combination with the hidden states of a recurrent neural network (RNN) as inputs to an inference network per time step. As part of this framework, we condition the prior probability of the latent variables on the hidden states of the RNN utilised to capture temporal dependencies. We further condition the posterior of the motion field on a latent variable from hierarchy and features from the moving image. Subsequently, the RNN updates the hidden state variables based on the feature maps of the fixed image and the latent variables. Different from traditional methods, DragNet performs registration on unseen sequences in a forward pass, which significantly expedites the registration process. Besides, DragNet enables generating a large number of realistic synthetic image sequences given only one frame, where the corresponding deformations are also retrieved. The probabilistic framework allows for computing spatio-temporal uncertainties in the estimated motion fields. Our results show that DragNet performance is comparable with state-of-the-art methods in terms of registration accuracy, with the advantage of offering analytical pixel-wise motion uncertainty estimation across a cardiac cycle and being a motion generator. We will make our code publicly available.

Treatment plan optimisation for reirradiation.

BACKGROUND: The STRIDeR (Support Tool for Re-Irradiation Decisions guided by Radiobiology) project aims to create a clinically viable re-irradiation planning pathway within a commercial treatment planning system (TPS). Such a pathway should account for previously delivered dose, voxel-by-voxel, taking fractionation effects, tissue recovery and anatomical changes into account. This work presents the workflow and technical solutions in the STRIDeR pathway. METHODS: The pathway was implemented in RayStation (version 9B DTK) to allow an original dose distribution to be used as background dose to guide optimisation of re-irradiation plans. Organ at risk (OAR) planning objectives in equivalent dose in 2 Gy fractions (EQD2) were applied cumulatively across the original and re-irradiation treatments, with optimisation of the re-irradiation plan performed voxel-by-voxel in EQD2. Different approaches to image registration were employed to account for anatomical change. Data from 21 patients who received pelvic Stereotactic Ablative Radiotherapy (SABR) re-irradiation were used to illustrate the use of the STRIDeR workflow. STRIDeR plans were compared to those produced using a standard manual method. RESULTS: The STRIDeR pathway resulted in clinically acceptable plans in 20/21 cases. Compared to plans produced using the laborious manual method, less constraint relaxation was required or higher re-irradiation doses could be prescribed in 3/21. CONCLUSION: The STRIDeR pathway used background dose to guide radiobiologically meaningful, anatomically-appropriate re-irradiation treatment planning within a commercial TPS. This provides a standardised and transparent approach, offering more informed re-irradiation and improved cumulative OAR dose evaluation.

Collision-induced dissociation of halide ion-arginine complexes: evidence for anion-induced zwitterion formation in gas-phase arginine.

We report the first low-energy collisional-induced dissociation studies of the X(-)·arginine (X(-) = F(-), Cl(-), Br(-), I(-), NO(3)(-), ClO(3)(-)) series of clusters to investigate the novel phenomenom of anion-induced zwitterion formation in a gas-phase amino acid. Fragmentation of the small halide ion clusters (F(-)·arginine and Cl(-)·arginine) is dominated by deprotonation of the arginine, whereas the major fragmentation channel for the largest ion clusters (I(-)·arginine and ClO(3)(-)·arginine) corresponds to simple cluster fission into the ion and neutral molecule. However, the fragmentation profiles of Br(-)·arginine and NO(3)(-)·arginine, display distinctive features that are consistent with the presence of the zwitterionic form of the amino acid in these clusters. The various dissociation pathways have been studied as a function of % collision energy and are discussed in comparison to the fragmentation profiles of protonated and deprotonated arginine. Electronic structure calculations are presented for Br(-)·arginine to support the presence of the zwitterionic amino acid in this complex. The results obtained in this work provide important information on the low-energy potential energy surfaces of these anion-amino acid clusters and reveal the presence of several overlapping surfaces in the low-energy region for the Br(-)·arginine and NO(3)(-)·arginine systems.

Harmonisation of scanner-dependent contrast variations in magnetic resonance imaging for radiation oncology, using style-blind auto-encoders.

Background and purpose: Magnetic Resonance Imaging (MRI) exhibits scanner dependent contrast, which limits generalisability of radiomics and machine-learning for radiation oncology. Current deep-learning harmonisation requires paired data, retraining for new scanners and often suffers from geometry-shift which alters anatomical information. The aim of this study was to investigate style-blind auto-encoders for MRI harmonisation to accommodate unpaired training data, avoid geometry-shift and harmonise data from previously unseen scanners. Materials and methods: A style-blind auto-encoder, using adversarial classification on the latent-space, was designed for MRI harmonisation. The public CC359 T1-w MRI brain dataset includes six scanners (three manufacturers, two field strengths), of which five were used for training. MRI from all six (including one unseen) scanner were harmonised to common contrast. Harmonisation extent was quantified via Kolmogorov-Smirnov testing of residual scanner dependence of 3D radiomic features, and compared to WhiteStripe normalisation. Anatomical content preservation was measured through change in structural similarity index on contrast-cycling (δSSIM). Results: The percentage of radiomics features showing statistically significant scanner-dependence was reduced from 41% (WhiteStripe) to 16% for white matter and from 39% to 27% for grey matter. δSSIM < 0.0025 on harmonisation and de-harmonisation indicated excellent anatomical content preservation. Conclusions: Our method harmonised MRI contrast effectively, preserved critical anatomical details at high fidelity, trained on unpaired data and allowed zero-shot harmonisation. Robust and clinically translatable harmonisation of MRI will enable generalisable radiomic and deep-learning models for a range of applications, including radiation oncology treatment stratification, planning and response monitoring.

Dose-Response Analysis Describes Particularly Rapid Repopulation of Non-Small Cell Lung Cancer during Concurrent Chemoradiotherapy.

(1) Purpose: We analysed overall survival (OS) rates following radiotherapy (RT) and chemo-RT of locally-advanced non-small cell lung cancer (LA-NSCLC) to investigate whether tumour repopulation varies with treatment-type, and to further characterise the low α/ß ratio found in a previous study. (2) Materials and methods: Our dataset comprised 2-year OS rates for 4866 NSCLC patients (90.5% stage IIIA/B) belonging to 51 cohorts treated with definitive RT, sequential chemo-RT (sCRT) or concurrent chemo-RT (cCRT) given in doses-per-fraction ≤3 Gy over 16-60 days. Progressively more detailed dose-response models were fitted, beginning with a probit model, adding chemotherapy effects and survival-limiting toxicity, and allowing tumour repopulation and α/ß to vary with treatment-type and stage. Models were fitted using the maximum-likelihood technique, then assessed via the Akaike information criterion and cross-validation. (3) Results: The most detailed model performed best, with repopulation offsetting 1.47 Gy/day (95% confidence interval, CI: 0.36, 2.57 Gy/day) for cCRT but only 0.30 Gy/day (95% CI: 0.18, 0.47 Gy/day) for RT/sCRT. The overall fitted tumour α/ß ratio was 3.0 Gy (95% CI: 1.6, 5.6 Gy). (4) Conclusion: The fitted repopulation rates indicate that cCRT schedule durations should be shortened to the minimum in which prescribed doses can be tolerated. The low α/ß ratio suggests hypofractionation should be efficacious.

Conformer-independent ureidoimidazole motifs--tools to probe conformational and tautomeric effects on the molecular recognition of triply hydrogen-bonded heterodimers.

Linear arrays of hydrogen bonds are useful for the reversible assembly of "stimuli-responsive" supramolecular materials. There is thus an ongoing requirement for easy-to-synthesise motifs that are capable of presenting hydrogen-bonding functionality in a predictable manner, such that high-affinity and high-fidelity recognition occurs. The design of linear arrays is made challenging as a consequence of their ability to adopt multiple conformational and tautomeric configurations; with each additional hydrogen-bonding heteroatom added to an array, the available tautomeric and conformational space increases and it can be difficult to anticipate where unproductive conformers/tautomers will arise. This paper describes a detailed study on the complementary ureidoimidazole donor-donor-acceptor (DDA) array (1) and amidoisocytosine donor-acceptor-acceptor (DAA) array (2). A specific feature of 1 is that two degenerate, intramolecular hydrogen-bonded conformations are postulated, both of which present a DDA array that is complementary to appropriate DAA partners. 1D and 2D (1)H NMR spectroscopy, isothermal titration calorimetry, and ab initio structure calculations confirm 1 interacts with 2 (K(a) ≈ 33,000 M(-1) in CDCl(3)) in a conformer-independent fashion driven by enthalpy. Comparison of the binding behaviour of 1 with hexylamidocytosine (4) and amidonaphthyridine (5) provides insight on the role that intramolecular hydrogen-bonding plays in mediating affinity towards DAA partners.

Evidence for hydrogen bond network formation in microsolvated clusters of Pt(CN)4(2-): collision induced dissociation studies of Pt(CN)4(2-)·(H2O)(n) n = 1-4, and Pt(CN)4(2-)·(MeCN)(m) m = 1, 2 cluster ions.

Low-energy collision induced dissociation has been used to investigate the structure and stability of microsolvated clusters of the prototypical, aprotic multiply charged anion, Pt(CN)(4)(2-), i.e. Pt(CN)(4)(2-)·(H(2)O)(n) n = 1-4, Pt(CN)(4)(2-)·(MeCN)(m) m =1, 2, and Pt(CN)(4)(2-)·(H(2)O)(3)·MeCN. For all of the systems studied, the lowest energy fragmentation pathway was found to correspond to decay of the cluster with loss of the entire solvent ensemble. No sequential solvent evaporation was observed. These observations suggest that the Pt(CN)(4)(2-) solvent clusters studied here form hydrogen-bonded "surface solvated" structures. Electronic structure calculations are presented to support the experimental results. In addition, the detailed fragmentation patterns observed are interpreted with reference to the differential solvation of the ionic fragmentation and electron detachment potential energy surfaces of the core Pt(CN)(4)(2-) dianion. The results described represent some of the first experiments to probe the microsolvation of this important class of multiply charged anions.

Exploring nuclear motion through conical intersections in the UV photodissociation of phenols and thiophenol.

High-resolution time-of-flight measurements of H atom products from photolysis of phenol, 4-methylphenol, 4-fluorophenol, and thiophenol, at many UV wavelengths (lambda(phot)), have allowed systematic study of the influence of ring substituents and the heteroatom on the fragmentation dynamics. All dissociate by X-H (X = O, S) bond fission after excitation at their respective S(1)((1)pipi*)-S(0) origins and at all shorter wavelengths. The achieved kinetic energy resolution reveals population of selected vibrational levels of the various phenoxyl and thiophenoxyl coproducts, providing uniquely detailed insights into the fragmentation dynamics. Dissociation in all cases is deduced to involve nuclear motion on the (1)pisigma* potential energy surface (PES). The route to accessing this PES, and the subsequent dynamics, is seen to be very sensitive to lambda(phot) and substitution of the heteroatom. In the case of the phenols, dissociation after excitation at long lambda(phot) is rationalized in terms of radiationless transfer from S(1) to S(0) levels carrying sufficient O-H stretch vibrational energy to allow coupling via the conical intersection between the S(0) and (1)pisigma* PESs at longer O-H bond lengths. In contrast, H + C(6)H(5)O(X(2)B(1)) products formed after excitation at short lambda(phot) exhibit anisotropic recoil-velocity distributions, consistent with prompt dissociation induced by coupling between the photoprepared (1)pipi* excited state and the (1)pisigma* PES. The fragmentation dynamics of thiophenol at all lambda(phot) matches the latter behavior more closely, reflecting the different relative dispositions of the (1)pipi* and (1)pisigma* PESs. Additional insights are provided by the observed branching into the ground (X(2)B(1)) and first excited ((2)B(2)) states of the resulting C(6)H(5)S radicals.

Volumetric and dosimetric impact of post-surgical MRI-guided radiotherapy for glioblastoma: A pilot study.

Objectives: Glioblastoma (GBM) radiotherapy (RT) target delineation requires MRI, ideally concurrent with CT simulation (pre-RT MRI). Due to limited MRI availability, <72 h post-surgery MRI is commonly used instead. Whilst previous investigations assessed volumetric differences between post-surgical and pre-RT delineations, dosimetric impact remains unknown. We quantify volumetric and dosimetric impact of using post-surgical MRI for GBM target delineation. Methods: Gross tumour volumes (GTVs) for five GBM patients receiving chemo-RT with post-surgical and pre-RT MRIs were delineated by three independent observers. Planning target volumes (PTVs) and RT plans were generated for each GTV. Volumetric and dosimetric differences were assessed through: absolute volumes, volume-distance histograms and dose-volume histogram statistics. Results: Post-surgical MRI delineations had significantly (p < 0.05) larger GTV and PTV volumes (median 16.7 and 64.4 cm3, respectively). Post-surgical RT plans, applied to pre-RT delineations, had significantly decreased (p < 0.01) median PTV doses (ΔD99% = -8.1 Gy and ΔD95% = -2.0 Gy). Median organ-at-risk (OAR) dose increases (brainstem ΔD5% =+0.8, normal brain mean dose =+2.9 and normal brain ΔD10% = 5.3 Gy) were observed. Conclusion: Post-surgical MRI delineation significantly impacted RT planning, with larger normal-appearing tissue volumes irradiated and increased OAR doses, despite a reduced coverage of the pre-RT defined target. Advances in knowledge: We believe this is the first investigation assessing the dosimetric impact of using post-surgical MRI for GBM target delineation. It highlights the potential of significantly degraded RT plans, showing the clinical need for dedicated MRI for GBM RT.

Patient position verification in magnetic-resonance imaging only radiotherapy of anal and rectal cancers.

BACKGROUND AND PURPOSE: Magnetic resonance (MR)-only treatment pathways require either the MR-simulation or synthetic-computed tomography (sCT) as an alternative reference image for cone beam computed tomography (CBCT) patient position verification. This study assessed whether using T2 MR or sCT as CBCT reference images introduces systematic registration errors as compared to CT for anal and rectal cancers. MATERIALS AND METHODS: A total of 32 patients (18 rectum,14 anus) received pre-treatment CT- and T2 MR- simulation. Routine treatment CBCTs were acquired. sCTs were generated using a validated research model. The local clinical registration protocol, using a grey-scale registration algorithm, was performed for 216 CBCTs using CT, MR and sCT as the reference image. Linear mixed effects modelling identified systematic differences between modalities. RESULTS: Systematic translation and rotation differences to CT for MR were -0.3 to + 0.3 mm and -0.1 to 0.4° for anal cancers and -0.4 to 0.0 mm and 0.0 to 0.1° for rectal cancers, and for sCT were -0.4 to + 0.8 mm, -0.1 to 0.2° for anal cancers and -0.6 to + 0.2 mm, -0.1 to + 0.1° for rectal cancers. CONCLUSIONS: T2 MR or sCT can successfully be used as reference images for anal and rectal cancer CBCT position verification with systematic differences to CT <±1 mm and <±0.5°. Clinical enabling of alternative modalities as reference images by vendors is required to reduce challenges associated with their use.

Multicentre, deep learning, synthetic-CT generation for ano-rectal MR-only radiotherapy treatment planning.

BACKGROUND AND PURPOSE: Comprehensive dosimetric analysis is required prior to the clinical implementation of pelvic MR-only sites, other than prostate, due to the limited number of site specific synthetic-CT (sCT) dosimetric assessments in the literature. This study aims to provide a comprehensive assessment of a deep learning-based, conditional generative adversarial network (cGAN) model for a large ano-rectal cancer cohort. The following challenges were investigated; T2-SPACE MR sequences, patient data from multiple centres and the impact of sex and cancer site on sCT quality. METHOD: RT treatment position CT and T2-SPACE MR scans, from two centres, were collected for 90 ano-rectal patients. A cGAN model trained using a focal loss function, was trained and tested on 46 and 44 CT-MR ano-rectal datasets, paired using deformable registration, respectively. VMAT plans were created on CT and recalculated on sCT. Dose differences and gamma indices assessed sCT dosimetric accuracy. A linear mixed effect (LME) model assessed the impact of centre, sex and cancer site. RESULTS: A mean PTV D95% dose difference of 0.1% (range: -0.5% to 0.7%) was found between CT and sCT. All gamma index (1%/1 mm threshold) measurements were >99.0%. The LME model found the impact of modality, cancer site, sex and centre was clinically insignificant (effect ranges: -0.4% and 0.3%). The mean dose difference for all OAR constraints was 0.1%. CONCLUSION: Focal loss cGAN models using T2-SPACE MR sequences from multiple centres can produce generalisable, dosimetrically accurate sCTs for ano-rectal cancers.

Pi sigma* excited states in molecular photochemistry.

The last few years have seen a surge in interest (both theoretical and experimental) in the photochemistry of heteroaromatic molecules (e.g. azoles, phenols), which has served to highlight the importance of dissociative excited states formed by electron promotion to sigma* molecular orbitals. Such excited states--which, for brevity, are termed pi sigma* states in this Perspective article--may be populated by direct photo-excitation (though the transition cross-sections are intrinsically small), or indirectly, by non-adiabatic coupling from an optically 'bright' excited state (e.g. an excited state resulting from pi* <--pi excitation). The analogous pi sigma* excited states in prototypical hydride molecules like H(2)O and NH(3) have long been recognised. They have served as test-beds for developing concepts like Rydbergisation, conical intersections (CIs) between potential energy surfaces, and for investigating the ways in which non-adiabatic couplings at such CIs influence the eventual photofragmentation dynamics. This Perspective article seeks to highlight the continuity of behaviour revealed by the earlier small molecule studies and by the more recent studies of heteroaromatic systems, and to illustrate the photochemical importance of pi sigma* excited states in many broad families of molecules. Furthermore, the dynamical influence of such excited states is not restricted to closed shell species; the Article concludes with a brief consideration of the consequences of populating sigma* orbitals in free radical species, in molecular cations, and in dissociative electron attachment processes.