On the link between the reaction force constant and conceptual DFT.

CONTEXT: The reaction force constant ( κ ), introduced by Professor Alejandro Toro-Labbé, plays a pivotal role in characterizing the reaction pathway by assessing the curvature of the potential energy profile along the intrinsic reaction coordinate. This study establishes a novel link between κ and the reactivity descriptors of conceptual density functional theory (c-DFT). Specifically, we derive expressions that relate the reaction force constant to nuclear softness and variations in chemical potential. Our findings indicate that regions of the reaction pathway where κ is negative match with significant electronic structure rearrangements, while positive κ regions match mostly with geometric rearrangements. This correlation between κ and c-DFT reactivity descriptors enhances our understanding of the underlying forces driving chemical reactions and offers new perspectives for analyzing reaction mechanisms. METHODS: The internal reaction path for the proton transfer in SNOH, chemical potential, and nuclear softness were computed using DFT with B3LYP exchange-correlation functional and 6-311++G(d,2p) basis set.

Autocontouring of primary lung lesions and nodal disease for radiotherapy based only on computed tomography images.

Background and purpose: In many clinics, positron-emission tomography is unavailable and clinician time extremely limited. Here we describe a deep-learning model for autocontouring gross disease for patients undergoing palliative radiotherapy for primary lung lesions and/or hilar/mediastinal nodal disease, based only on computed tomography (CT) images. Materials and methods: An autocontouring model (nnU-Net) was trained to contour gross disease in 379 cases (352 training, 27 test); 11 further test cases from an external centre were also included. Anchor-point-based post-processing was applied to remove extraneous autocontoured regions. The autocontours were evaluated quantitatively in terms of volume similarity (Dice similarity coefficient [DSC], surface Dice coefficient, 95th percentile Hausdorff distance [HD95], and mean surface distance), and scored for usability by two consultant oncologists. The magnitude of treatment margin needed to account for geometric discrepancies was also assessed. Results: The anchor point process successfully removed all erroneous regions from the autocontoured disease, and identified two cases to be excluded from further analysis due to 'missed' disease. The average DSC and HD95 were 0.8 ± 0.1 and 10.5 ± 7.3 mm, respectively. A 10-mm uniform margin-distance applied to the autocontoured region was found to yield "full coverage" (sensitivity > 0.99) of the clinical contour for 64 % of cases. Ninety-seven percent of evaluated autocontours were scored by both clinicians as requiring no or minor edits. Conclusions: Our autocontouring model was shown to produce clinically usable disease outlines, based on CT alone, for approximately two-thirds of patients undergoing lung radiotherapy. Further work is necessary to improve this before clinical implementation.

Fully automated radiotherapy treatment planning: A scan to plan challenge.

BACKGROUND AND PURPOSE: Over the past decade, tools for automation of various sub-tasks in radiotherapy planning have been introduced, such as auto-contouring and auto-planning. The purpose of this study was to benchmark what degree of automation is possible. MATERIALS AND METHODS: A challenge to perform automated treatment planning for prostate and prostate bed radiotherapy was set up. Participants were provided with simulation CTs and a treatment prescription and were asked to use automated tools to produce a deliverable radiotherapy treatment plan with as little human intervention as possible. Plans were scored for their adherence to the protocol when assessed using consensus expert contours. RESULTS: Thirteen entries were received. The top submission adhered to 81.8% of the minimum objectives across all cases using the consensus contour, meeting all objectives in one of the ten cases. The same system met 89.5% of objectives when assessed with their own auto-contours, meeting all objectives in four of the ten cases. The majority of systems used in the challenge had regulatory clearance (Auto-contouring: 82.5%, Auto-planning: 77%). Despite the 'hard' rule that participants should not check or edit contours or plans, 69% reported looking at their results before submission. CONCLUSIONS: Automation of the full planning workflow from simulation CT to deliverable treatment plan is possible for prostate and prostate bed radiotherapy. While many generated plans were found to require none or minor adjustment to be regarded as clinically acceptable, the result indicated there is still a lack of trust in such systems preventing full automation.

Is full-automation in radiotherapy treatment planning ready for take off?

Radiotherapy treatment planning is undergoing a transformation with the increasing integration of automation. This transition draws parallels with the aviation industry, which has a long-standing history of addressing challenges and opportunities introduced by automated systems. Both fields witness a shift from manual operations to systems capable of operating independently, raising questions about the risks and evolving role of humans within automated workflows. In response to this shift, a working group assembled during the ESTRO Physics Workshop 2023, reflected on parallels to draw lessons for radiotherapy. A taxonomy is proposed, leveraging insights from aviation, that outlines the observed levels of automation within the context of radiotherapy and their corresponding implications for human involvement. Among the common identified risks associated with automation integration are complacency, overreliance, attention tunneling, data overload, a lack of transparency and training. These risks require mitigation strategies. Such strategies include ensuring role complementarity, introducing checklists and safety requirements for human-automation interaction and using automation for cognitive unload and workflow management. Focusing on already automated processes, such as dose calculation and auto-contouring as examples, we have translated lessons learned from aviation. It remains crucial to strike a balance between automation and human involvement. While automation offers the potential for increased efficiency and accuracy, it must be complemented by human oversight, expertise, and critical decision-making. The irreplaceable value of human judgment remains, particularly in complex clinical situations. Learning from aviation, we identify a need for human factors engineering research in radiation oncology and a continued requirement for proactive incident learning.

Quantitative Assessment of Full-Time Equivalent Effort for kV-CBCT Guided Online Adaptive RT for Medical Physicists.

INTRODUCTION: With recent clinical adoption of online adaptive radiotherapy and the increased workload associated with adaptive radiotherapy, proper staffing for medical physicists is paramount to safe clinical operation. However, there is currently no consensus on the full-time equivalent (FTE) requirements for safe administration of CBCT-guided online adaptive radiotherapy. This study aims to quantitatively assess medical physics workload and staffing needs of a CBCT-guided online adaptive radiotherapy program. METHODS: We conducted a detailed analysis of the CBCT-guided adaptive planning and treatment workflows, encompassing tasks such as patient consultation, treatment planning, plan review, training, quality assurance, and treatment delivery. Utilizing data from machine logs, clinical database queries, and staff surveys, we present a framework for estimating FTE values for different staffing scenarios, considering medical physicists' roles as planners, adaptors, or both. RESULTS: FTE calculations, based on an example workload of 100 adaptive and 200 nonadaptive patients per year, for three staffing scenarios were provided: medical physicists as planners and adaptors (2.9 FTE), medical physicists as planners but not adaptors (2.6 FTE), and medical physicists as adaptors but not planners (1.4 FTE). These findings offer calculation guidance and benchmarks for staffing requirements in CBCT-guided online adaptive radiotherapy programs, emphasizing the need for specific staffing models to accommodate the complexities of adaptive radiotherapy. CONCLUSION: This study outlines a framework for calculating FTE requirements for medical physicists in a CBCT-guided online adaptive radiotherapy program. By analyzing the processes for three common adaptive radiotherapy workflows, this work can provide effective workforce planning and resource allocation estimates. This analysis can be used either prior to the implementation of an online adaptive radiotherapy program, for program development, or as a review of current practices to ensure operational efficiency and proper staffing levels are maintained.

RADseq datasets of native beans from Mexico.

Forty-five accessions of the genus Phaseolus from the orthodox seed collection of the National Center for Genetic Resources (CNRG) of the National Institute of Forestry, Agricultural, and Livestock Research (INIFAP) of Mexico were sequenced using RADseq. The species utilized were: P. acutifolius (14), P. coccineus (12), P. lunatus (8), P. dumosus (6), P. leptostachyus (2), P. filiformis (2), and P. vulgaris (1). A variant call file (VCF) was generated using GATK with the P. vulgaris reference genome GCF_000499845.1, identifying 97,103 shared SNPs among the species. These data have the potential to be used for studies of genetic diversity intra and interspecies, phylogeny, evolution, genetic resource conservation, and agricultural improvement.

De Novo Transcriptome Assembly of Cedar (Cedrela odorata L.) and Differential Gene Expression Involved in Herbivore Resistance.

Timber trees are targets of herbivorous attacks. The identification of genes associated with pest resistance can be accomplished through differential expression analysis using transcriptomes. We reported the de novo assembly of cedar (Cedrela odorata L.) transcriptome and the differential expression of genes involved in herbivore resistance. The assembly and annotation of the transcriptome were obtained using RNAseq from healthy cedar plants and those infested with Chrysobothris yucatanensis. A total of 325.6 million reads were obtained, and 127,031 (97.47%) sequences were successfully assembled. A total of 220 herbivory-related genes were detected, of which 170 genes were annotated using GO terms, and 161 genes with 245 functions were identified-165, 75, and 5 were molecular functions, biological processes, and cellular components, respectively. To protect against herbivorous infestation, trees produce toxins and volatile compounds which are modulated by signaling pathways and gene expression related to molecular functions and biological processes. The limited number of genes identified as cellular components suggests that there are minimal alterations in cellular structure in response to borer attack. The chitin recognition protein, jasmonate ZIM-domain (JAZ) motifs, and response regulator receiver domain were found to be overexpressed, whereas the terpene synthase, cytochrome P450, and protein kinase domain gene families were underexpressed. This is the first report of a cedar transcriptome focusing on genes that are overexpressed in healthy plants and underexpressed in infested plants. This method may be a viable option for identifying genes associated with herbivore resistance.

Analyzing the Relationship between Dose and Geometric Agreement Metrics for Auto-Contouring in Head and Neck Normal Tissues.

This study aimed to determine the relationship between geometric and dosimetric agreement metrics in head and neck (H&N) cancer radiotherapy plans. A total 287 plans were retrospectively analyzed, comparing auto-contoured and clinically used contours using a Dice similarity coefficient (DSC), surface DSC (sDSC), and Hausdorff distance (HD). Organs-at-risk (OARs) with ≥200 cGy dose differences from the clinical contour in terms of Dmax (D0.01cc) and Dmean were further examined against proximity to the planning target volume (PTV). A secondary set of 91 plans from multiple institutions validated these findings. For 4995 contour pairs across 19 OARs, 90% had a DSC, sDSC, and HD of at least 0.75, 0.86, and less than 7.65 mm, respectively. Dosimetrically, the absolute difference between the two contour sets was <200 cGy for 95% of OARs in terms of Dmax and 96% in terms of Dmean. In total, 97% of OARs exhibiting significant dose differences between the clinically edited contour and auto-contour were within 2.5 cm PTV regardless of geometric agreement. There was an approximately linear trend between geometric agreement and identifying at least 200 cGy dose differences, with higher geometric agreement corresponding to a lower fraction of cases being identified. Analysis of the secondary dataset validated these findings. Geometric indices are approximate indicators of contour quality and identify contours exhibiting significant dosimetric discordance. For a small subset of OARs within 2.5 cm of the PTV, geometric agreement metrics can be misleading in terms of contour quality.

Introducing electron correlation in solid-state calculations for superconducting states.

Analyzing the electronic localization of superconductors has been recently shown to be relevant for understanding their critical temperature [Nature Communications, 12, 5381, (2021)]. However, these relationships have only been shown at the Kohn-Sham density functional theory (DFT) level, where the onset of strong correlation linked to the superconducting state is missing. In this contribution, we approximate the superconducting gap in order to reconstruct the superconducting the one-reduced density matrix (1RDM) from a DFT calculation. This allows us to analyse the electron density and localization in the strong correlation regime. The method is applied to two well-known superconductors. Electron localization features along the electron-phonon coupling directions and hydrogen cluster formations are observed for different solids. However, in both cases we see that the overall localization channels are not affected by the onset of superconductivity, explaining the ability of DFT localization channels to characterize the superconducting ones.

Landmark-based auto-contouring of clinical target volumes for radiotherapy of nasopharyngeal cancer.

BACKGROUND: The delineation of clinical target volumes (CTVs) for radiotherapy for nasopharyngeal cancer is complex and varies based on the location and extent of disease. PURPOSE: The current study aimed to develop an auto-contouring solution following one protocol guidelines (NRG-HN001) that can be adjusted to meet other guidelines, such as RTOG-0225 and the 2018 International guidelines. METHODS: The study used 2-channel 3-dimensional U-Net and nnU-Net framework to auto-contour 27 normal structures in the head and neck (H&N) region that are used to define CTVs in the protocol. To define the CTV-Expansion (CTV1 and CTV2) and CTV-Overall (the outer envelope of all the CTV contours), we used adjustable morphological geometric landmarks and mimicked physician interpretation of the protocol rules by partially or fully including select anatomic structures. The results were evaluated quantitatively using the dice similarity coefficient (DSC) and mean surface distance (MSD) and qualitatively by independent reviews by two H&N radiation oncologists. RESULTS: The auto-contouring tool showed high accuracy for nasopharyngeal CTVs. Comparison between auto-contours and clinical contours for 19 patients with cancers of various stages showed a DSC of 0.94 ± 0.02 and MSD of 0.4 ± 0.4 mm for CTV-Expansion and a DSC of 0.83 ± 0.02 and MSD of 2.4 ± 0.5 mm for CTV-Overall. Upon independent review, two H&N physicians found the auto-contours to be usable without edits in 85% and 75% of cases. In 15% of cases, minor edits were required by both physicians. Thus, one physician rated 100% of the auto-contours as usable (use as is, or after minor edits), while the other physician rated 90% as usable. The second physician required major edits in 10% of cases. CONCLUSIONS: The study demonstrates the ability of an auto-contouring tool to reliably delineate nasopharyngeal CTVs based on protocol guidelines. The tool was found to be clinically acceptable by two H&N radiation oncology physicians in at least 90% of the cases.

Quantifying the Risk of Technology-Driven Health Disparities in Radiation Oncology.

PURPOSE: New technologies are continuously emerging in radiation oncology. Inherent technological limitations can result in health care disparities in vulnerable patient populations. These limitations must be considered for existing and new technologies in the clinic to provide equitable care. MATERIALS AND METHODS: We created a health disparity risk assessment metric inspired by failure mode and effects analysis. We provide sample patient populations and their potential associated disparities, guidelines for clinics and vendors, and example applications of the methodology. RESULTS: A disparity risk priority number can be calculated from the product of 3 quantifiable metrics: the percentage of patients impacted, the severity of the impact of dosimetric uncertainty or quality of the radiation plan, and the clinical dependence on the evaluated technology. The disparity risk priority number can be used to rank the risk of suboptimal care due to technical limitations when comparing technologies and to plan interventions when technology is shown to have inequitable performance in the patient population of a clinic. CONCLUSIONS: The proposed methodology may simplify the evaluation of how new technology impacts vulnerable populations, help clinics quantify the limitations of their technological resources, and plan appropriate interventions to improve equity in radiation treatments.

Fungal Melanonychia: A Systematic Review.

Fungal melanonychia is an uncommon condition, most typically caused by opportunistic melanin-producing pigmented filamentous fungi in the nail plate. In the present study, the clinical characteristics of patients diagnosed with fungal melanonychia were analyzed through a systematic review of cases reported in the literature. The MESH terms used for the search were "melanonychia" AND "fungal" OR "fungi" through four databases: PubMed, SciELO, Google scholar and SCOPUS. After discarding inadequate articles using the exclusion criteria, 33 articles with 133 cases were analyzed, of which 44% were women, 56% were men and the age range was between 9 and 87 years. The majority of cases were reported in Turkey followed by Korea and Italy. Frequent causal agents detected were Trichophyton rubrum as non-dematiaceous in 55% and Neoscytalidium dimidiatum as dematiaceous in 8%. Predisposing factors included nail trauma, migration history, employment and/or outdoor activities. Involvement in a single nail was presented in 45% of the cases, while more than one affected nail was identified in 21%, with a range of 2 to 10 nails. Regarding the clinical classification, 41% evidenced more than one type of melanonychia, 21% corresponded to the longitudinal pattern and 13% was of total diffuse type. Likewise, the usual dermoscopic pattern was multicolor pigmentation. It is concluded that fungal melanonychia is an uncommon variant of onychomycosis and the differential diagnosis is broad, which highlights the complexity of this disease.

Unlocking the adaptive advantage: correlation and machine learning classification to identify optimal online adaptive stereotactic partial breast candidates.

Objective.Online adaptive radiotherapy (OART) is a promising technique for delivering stereotactic accelerated partial breast irradiation (APBI), as lumpectomy cavities vary in location and size between simulation and treatment. However, OART is resource-intensive, increasing planning and treatment times and decreasing machine throughput compared to the standard of care (SOC). Thus, it is pertinent to identify high-yield OART candidates to best allocate resources.Approach.Reference plans (plans based on simulation anatomy), SOC plans (reference plans recalculated onto daily anatomy), and daily adaptive plans were analyzed for 31 sequential APBI targets, resulting in the analysis of 333 treatment plans. Spearman correlations between 22 reference plan metrics and 10 adaptive benefits, defined as the difference between mean SOC and delivered metrics, were analyzed to select a univariate predictor of OART benefit. A multivariate logistic regression model was then trained to stratify high- and low-benefit candidates.Main results.Adaptively delivered plans showed dosimetric benefit as compared to SOC plans for most plan metrics, although the degree of adaptive benefit varied per patient. The univariate model showed high likelihood for dosimetric adaptive benefit when the reference plan ipsilateral breast V15Gy exceeds 23.5%. Recursive feature elimination identified 5 metrics that predict high-dosimetric-benefit adaptive patients. Using leave-one-out cross validation, the univariate and multivariate models classified targets with 74.2% and 83.9% accuracy, resulting in improvement in per-fraction adaptive benefit between targets identified as high- and low-yield for 7/10 and 8/10 plan metrics, respectively.Significance.This retrospective, exploratory study demonstrated that dosimetric benefit can be predicted using only ipsilateral breast V15Gy on the reference treatment plan, allowing for a simple, interpretable model. Using multivariate logistic regression for adaptive benefit prediction led to increased accuracy at the cost of a more complicated model. This work presents a methodology for clinics wishing to triage OART resource allocation.

Removal of Mo(VI), Pb(II), and Cu(II) from wastewater using electrospun cellulose acetate/chitosan biopolymer fibers.

Environmentally friendly polymers such as cellulose acetate (CA) and chitosan (CS) were used to obtain electrospun fibers for Cu2+, Pb2+, and Mo6+ capture. The solvents dichloromethane (DCM) and dimethylformamide (DMF) allowed the development of a surface area of 148 m2 g-1 for CA fibers and 113 m2 g-1 for cellulose acetate/chitosan (CA/CS) fibers. The fibers were characterized by IR-DRIFT, SEM, TEM, CO2 sorption isotherms at 273 K, Hg porosimetry, TGA, stress-strain tests, and XPS. The CA/CS fibers had a higher adsorption capacity than CA fibers without affecting their physicochemical properties. The capture capacity reached 102 mg g-1 for Cu2+, 49.3 mg g-1 for Pb2+, and 13.1 mg g-1 for Mo6+. Furthermore, optimal pH, adsorption times qt, and C0 were studied for the evaluation of kinetic models and adsorption isotherms. Finally, a proposal for adsorbate-adsorbent interactions is presented as a possible capture mechanism where, in the case of Mo6+, a computational study is presented. The results demonstrate the potential to evaluate the fibers in tailings wastewater from copper mining.

Longitudinal brain volumetrics in glioma survivors.

OBJECTIVE: Radiation therapy (RT) is used selectively for patients with low-grade glioma (LGG) given the concerns for potential cognitive effects in survivors, but prior cognitive outcome studies among LGG survivors have had inconsistent findings. Translational studies that characterize changes in brain anatomy and physiology after treatment of LGG may help to both contextualize cognitive findings and improve the overall understanding of radiation effects in normal brain tissue. This study aimed to investigate the hypothesis that patients with LGG who are treated with RT will experience greater brain volume loss than those who do not receive RT. METHODS: This retrospective longitudinal study included all patients with WHO grade 2 glioma who received posttreatment surveillance MRI at the University of Alabama at Birmingham. Volumetric analysis of contralateral cortical white matter (WM), cortical gray matter (GM), and hippocampus was performed on all posttreatment T1-weighted MRI sequences using the SynthSeg script. The effect of clinical and treatment variables on brain volumes was assessed using two-level hierarchical linear models. RESULTS: The final study cohort consisted of 105 patients with 1974 time points analyzed. The median length of imaging follow-up was 4.6 years (range 0.36-18.9 years), and the median number of time points analyzed per patient was 12 (range 2-40). Resection was performed in 79 (75.2%) patients, RT was administered to 61 (58.1%) patients, and chemotherapy was administered to 66 (62.9%) patients. Age at diagnosis (ß = -0.06, p < 0.001) and use of RT (ß = -1.12, p = 0.002) were associated with the slope of the contralateral cortical GM volume model (i.e., change in GM over time). Age at diagnosis (ß = -0.08, p < 0.001), midline involvement (ß = 1.31, p = 0.006), and use of RT (ß = -1.45, p = 0.001) were associated with slope of the contralateral cortical WM volume model. Age (ß = -0.0027, p = 0.001), tumor resection (ß = -0.069, p < 0.001), use of chemotherapy (ß = -0.0597, p = 0.003), and use of RT (ß = -0.0589, p < 0.001) were associated with the slope of the contralateral hippocampus volume model. CONCLUSIONS: This study demonstrated volume loss in contralateral brain structures among LGG survivors, and patients who received RT experienced greater volume loss than those who did not. The results of this study may help to provide context for cognitive outcome research in LGG survivors and inform the design of future strategies to preserve cognition.

Floquet Engineering of a Diatomic Molecule through a Bichromatic Radiation Field.

We report on a theoretical study of a Cs2 molecule illuminated by two lasers and show how this can result in novel quantum dynamics. We reveal that these interactions facilitate the bypass of the non-crossing rule, forming light-induced conical intersections and modifiable avoided crossings. Our findings show how laser field orientation and strength, along with initial phase differences, can control molecular-state transitions, especially on the micromotion scale. We also extensively discuss how the interaction of radiation with matter gives rise to the emergence of potential energy surfaces of hybrids of radiation and molecular states. This research advances a technique for manipulating photoassociation processes in Cs2 molecules, offering potential new avenues in quantum control.

Prospective Evaluation of Automated Contouring for CT-Based Brachytherapy for Gynecologic Malignancies.

Purpose: The use of deep learning to auto-contour organs at risk (OARs) in gynecologic radiation treatment is well established. Yet, there is limited data investigating the prospective use of auto-contouring in clinical practice. In this study, we assess the accuracy and efficiency of auto-contouring OARs for computed tomography-based brachytherapy treatment planning of gynecologic malignancies. Methods and Materials: An inhouse contouring tool automatically delineated 5 OARs in gynecologic radiation treatment planning: the bladder, small bowel, sigmoid, rectum, and urethra. Accuracy of each auto-contour was evaluated using a 5-point Likert scale: a score of 5 indicated the contour could be used without edits, while a score of 1 indicated the contour was unusable. During scoring, automated contours were edited and subsequently used for treatment planning. Dice similarity coefficient, mean surface distance, 95% Hausdorff distance, Hausdorff distance, and dosimetric changes between original and edited contours were calculated. Contour approval time and total planning time of a prospective auto-contoured (AC) cohort were compared with times from a retrospective manually contoured (MC) cohort. Results: Thirty AC cases from January 2022 to July 2022 and 31 MC cases from July 2021 to January 2022 were included. The mean (±SD) Likert score for each OAR was the following: bladder 4.77 (±0.58), small bowel 3.96 (±0.91), sigmoid colon 3.92 (±0.81), rectum 4.6 (±0.71), and urethra 4.27 (±0.78). No ACs required major edits. All OARs had a mean Dice similarity coefficient > 0.86, mean surface distance < 0.48 mm, 95% Hausdorff distance < 3.2 mm, and Hausdorff distance < 10.32 mm between original and edited contours. There was no significant difference in dose-volume histogram metrics (D2.0 cc/D0.1 cc) between original and edited contours (P values > .05). The average time to plan approval in the AC cohort was 19% less than the MC cohort. (AC vs MC, 117.0 + 18.0 minutes vs 144.9 ± 64.5 minutes, P = .045). Conclusions: Automated contouring is useful and accurate in clinical practice. Auto-contouring OARs streamlines radiation treatment workflows and decreases time required to design and approve gynecologic brachytherapy plans.

Automatic end-to-end VMAT treatment planning for rectal cancers.

BACKGROUND: The treatment planning process from segmentation to producing a deliverable plan is time-consuming and labor-intensive. Existing solutions automate the segmentation and planning processes individually. The feasibility of combining auto-segmentation and auto-planning for volumetric modulated arc therapy (VMAT) for rectal cancers in an end-to-end process is not clear. PURPOSE: To create and clinically evaluate a complete end-to-end process for auto-segmentation and auto-planning of VMAT for rectal cancer requiring only the gross tumor volume contour and a CT scan as inputs. METHODS: Patient scans and data were retrospectively selected from our institutional records for patients treated for malignant neoplasm of the rectum. We trained, validated, and tested deep learning auto-segmentation models using nnU-Net architecture for clinical target volume (CTV), bowel bag, large bowel, small bowel, total bowel, femurs, bladder, bone marrow, and female and male genitalia. For the CTV, we identified 174 patients with clinically drawn CTVs. We used data for 18 patients for all structures other than the CTV. The structures were contoured under the guidance of and reviewed by a gastrointestinal (GI) radiation oncologist. The predicted results for CTV in 35 patients and organs at risk (OAR) in six patients were scored by the GI radiation oncologist using a five-point Likert scale. For auto-planning, a RapidPlan knowledge-based planning solution was modeled for VMAT delivery with a prescription of 25 Gy in five fractions. The model was trained and tested on 20 and 34 patients, respectively. The resulting plans were scored by two GI radiation oncologists using a five-point Likert scale. Finally, the end-to-end pipeline was evaluated on 16 patients, and the resulting plans were scored by two GI radiation oncologists. RESULTS: In 31 of 35 patients, CTV contours were clinically acceptable without necessary modifications. The CTV achieved a Dice similarity coefficient of 0.85 (±0.05) and 95% Hausdorff distance of 15.25 (±5.59) mm. All OAR contours were clinically acceptable without edits, except for large and small bowel which were challenging to differentiate. However, contours for total, large, and small bowel were clinically acceptable. The two physicians accepted 100% and 91% of the auto-plans. For the end-to-end pipeline, the two physicians accepted 88% and 62% of the auto-plans. CONCLUSIONS: This study demonstrated that the VMAT treatment planning technique for rectal cancer can be automated to generate clinically acceptable and safe plans with minimal human interventions.

nnUNet for Automatic Kidney and Cyst Segmentation in Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND: Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a genetic disorder that causes uncontrolled kidney cyst growth, leading to kidney volume enlargement and renal function loss over time. Total kidney volume (TKV) and cyst burdens have been used as prognostic imaging biomarkers for ADPKD. OBJECTIVE: This study aimed to evaluate nnUNet for automatic kidney and cyst segmentation in T2-weighted (T2W) MRI images of ADPKD patients. METHODS: 756 kidney images were retrieved from 95 patients in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) cohort (95 patients × 2 kidneys × 4 follow-up scans). The nnUNet model was trained, validated, and tested on 604, 76, and 76 images, respectively. In contrast, all images of each patient were exclusively assigned to either the training, validation, or test sets to minimize evaluation bias. The kidney and cyst regions defined using a semi-automatic method were employed as ground truth. The model performance was assessed using the Dice Similarity Coefficient (DSC), the intersection over union (IoU) score, and the Hausdorff distance (HD). RESULTS: The test DSC values were 0.96±0.01 (mean±SD) and 0.90±0.05 for kidney and cysts, respectively. Similarly, the IoU scores were 0.91± 0.09 and 0.81±0.06, and the HD values were 12.49±8.71 mm and 12.04±10.41 mm, respectively, for kidney and cyst segmentation. CONCLUSION: The nnUNet model is a reliable tool to automatically determine kidney and cyst volumes in T2W MRI images for ADPKD prognosis and therapy monitoring.