Development and comprehensive evaluation of a national DBCG consensus-based auto-segmentation model for lymph node levels in breast cancer radiotherapy.

BACKGROUND AND PURPOSE: This study aimed at training and validating a multi-institutional deep learning (DL) auto segmentation model for nodal clinical target volume (CTVn) in high-risk breast cancer (BC) patients with both training and validation dataset created with multi-institutional participation, with the overall aim of national clinical implementation in Denmark. MATERIALS AND METHODS: A gold standard (GS) dataset and a high-quality training dataset were created by 21 BC delineation experts from all radiotherapy centres in Denmark. The delineations were created according to ESTRO consensus delineation guidelines. Four models were trained: One per laterality and extension of CTVn internal mammary nodes. The DL models were tested quantitatively in their own test-set and in relation to interobserver variation (IOV) in the GS dataset with geometrical metrics, such as the Dice Similarity Coefficient (DSC). A blinded qualitative evaluation was conducted with a national board, presented to both DL and manual delineations. RESULTS: A median DSC > 0.7 was found for all, except the CTVn interpectoral node in one of the models. In the qualitative evaluation 'no corrections needed' were acquired for 297 (36 %) in the DL structures and 286 (34 %) for manual delineations. A higher rate of 'major corrections' and 'easier to start from scratch' was found in the manual delineations. The models performed within the IOV of an expert group, with two exceptions. CONCLUSION: DL models were developed on a national consensus cohort and performed on par with the IOV between BC experts and had a comparable or higher clinical acceptance than expert manual delineations.

Abnormal recognition-assisted and onset-offset aware network for pathological wearable ECG delineation.

Electrocardiogram (ECG) delineation is essential to the identification of abnormal cardiac status, especially when ECG signals are remotely monitored with wearable devices. The complexity and diversity of cardiac conditions generate numerous pathological ECG patterns, not only requiring the recognition of normal ECG but also addressing an extensive range of abnormal ECG patterns, posing a challenging task. Therefore, we propose an abnormal recognition-assisted network to integrate supplementary information on diverse ECG patterns. Simultaneously, we design an onset-offset aware loss to enhance precise waveform localization. Specifically, we establish a two-branch framework where ECG delineation serves as the target task, producing the final segmentation results. Additionally, the abnormal recognition-assisted network serves as an auxiliary task, extracting multi-label pathological information from ECGs. This joint learning approach establishes crucial correlations between ECG delineation and associated ECG abnormalities. The correlations enable the model to demonstrate sufficient generalization in the presence of diverse abnormal ECG patterns. Besides, onset-offset aware loss focuses intensively on wave onsets and offsets by applying biased weights to various waveform positions. This approach ensures a focus on precise localization, facilitating seamless integration into cross-entropy loss function. A large-scale wearable 12-lead dataset containing 4,913 signals is collected, offering an extensive range of ECG data for model training. Results demonstrate that our method achieves outstanding performance on two test datasets, attaining sensitivity of 94.97% and 94.27% and an error tolerance lower than 20 ms. Furthermore, our method is effective for various aberrant ECG signals, including ST-segment changes, atrial premature beats, and right and left bundle branch blocks.

DRA-UNet for Coal Mining Ground Surface Crack Delineation with UAV High-Resolution Images.

Coal mining in the Loess Plateau can very easily generate ground cracks, and these cracks can immediately result in ventilation trouble under the mine shaft, runoff disturbance, and vegetation destruction. Advanced UAV (Unmanned Aerial Vehicle) high-resolution mapping and DL (Deep Learning) are introduced as the key methods to quickly delineate coal mining ground surface cracks for disaster prevention. Firstly, the dataset named the Ground Cracks of Coal Mining Area Unmanned Aerial Vehicle (GCCMA-UAV) is built, with a ground resolution of 3 cm, which is suitable to make a 1:500 thematic map of the ground crack. This GCCMA-UAV dataset includes 6280 images of ground cracks, and the size of the imagery is 256 × 256 pixels. Secondly, the DRA-UNet model is built effectively for coal mining ground surface crack delineation. This DRA-UNet model is an improved UNet DL model, which mainly includes the DAM (Dual Dttention Dechanism) module, the RN (residual network) module, and the ASPP (Atrous Spatial Pyramid Pooling) module. The DRA-UNet model shows the highest recall rate of 77.29% when the DRA-UNet was compared with other similar DL models, such as DeepLabV3+, SegNet, PSPNet, and so on. DRA-UNet also has other relatively reliable indicators; the precision rate is 84.92% and the F1 score is 78.87%. Finally, DRA-UNet is applied to delineate cracks on a DOM (Digital Orthophoto Map) of 3 km2 in the mining workface area, with a ground resolution of 3 cm. There were 4903 cracks that were delineated from the DOM in the Huojitu Coal Mine Shaft. This DRA-UNet model effectively improves the efficiency of crack delineation.

Enhanced head and neck radiotherapy target definition through multidisciplinary delineation and peer review: A prospective single-center study.

This study evaluates the benefit of weekly delineation and peer review by a multidisciplinary team (MDT) of radiation oncologists (ROs), radiologists (RXs), and nuclear medicine (NM) physicians in defining primary and lymph node tumor volumes (GTVp and GTVn) for head and neck cancer (HNC) radiotherapy. This study includes 30 consecutive HNC patients referred for definitive curative (chemo)-radiotherapy. Imaging data including head and neck MRI, [18F]-FDG-PET and CT scan were evaluated by the MDT. The RO identified the 'undeniable' tumor as GTVp_core and determined GTVp_max, representing the maximum tumoral volume. The MDT delineation (MDT-D) by RX and NM physicians outlined their respective primary GTVs (GTVp_RX and GTVp_NM). During the MDT meeting (MDT-M), these contours were discussed to reach a consensus on the final primary GTV (GTVp_final). In the comparative analysis of various GTVp delineations, we performed descriptive statistics and assessed two MDT-M factors: 1) the added value of MDT-M, which includes the section of GTVp_final outside GTVp_core but within GTVp_RX or GTVp_NM, and 2) the part of GTVp_final that deviates from GTVp_max, representing the area missed by the RO. For GTVn, discussions evaluated lymph node extent and malignancy, documenting findings and the frequency of disagreements. The average GTVp core and max volumes were 19.5 cc (range: 0.4-90.1) and 22.1 cc (range: 0.8-106.2), respectively. Compared to GTVp_core, MDT-D to GTVp_final added an average of 3.3 cc (range: 0-25.6) and spared an average of 1.3 cc (0-15.6). Compared to GTVp_max, MDT-D and -M added an average of 2.7 cc (range: 0-20.3) and removed 2.3 cc (0-21.3). The most frequent GTVn discussions included morphologically suspicious nodes not fixing on [18F]-FDG-PET and small [18F]-FDG-PET negative retropharyngeal lymph nodes. Multidisciplinary review of target contours in HNC is essential for accurate treatment planning, ensuring precise tumor and lymph node delineation, potentially improving local control and reducing toxicity.

Validation of different automated segmentation models for target volume contouring in postoperative radiotherapy for breast cancer and regional nodal irradiation.

Introduction: Target volume delineation is routinely performed in postoperative radiotherapy (RT) for breast cancer patients, but it is a time-consuming process. The aim of the present study was to validate the quality, clinical usability and institutional-specific implementation of different auto-segmentation tools into clinical routine. Methods: Three different commercially available, artificial intelligence-, ESTRO-guideline-based segmentation models (M1-3) were applied to fifty consecutive reference patients who received postoperative local RT including regional nodal irradiation for breast cancer for the delineation of clinical target volumes: the residual breast, implant or chestwall, axilla levels 1 and 2, the infra- and supraclavicular regions, the interpectoral and internal mammary nodes. Objective evaluation metrics of the created structures were conducted with the Dice similarity index (DICE) and the Hausdorff distance, and a manual evaluation of usability. Results: The resulting geometries of the segmentation models were compared to the reference volumes for each patient and required no or only minor corrections in 72 % (M1), 64 % (M2) and 78 % (M3) of the cases. The median DICE and Hausdorff values for the resulting planning target volumes were 0.87-0.88 and 2.96-3.55, respectively. Clinical usability was significantly correlated with the DICE index, with calculated cut-off values used to define no or minor adjustments of 0.82-0.86. Right or left sided target and breathing method (deep inspiration breath hold vs. free breathing) did not impact the quality of the resulting structures. Conclusion: Artificial intelligence-based auto-segmentation programs showed high-quality accuracy and provided standardization and efficient support for guideline-based target volume contouring as a precondition for fully automated workflows in radiotherapy treatment planning.

Delineation of core roles of oncology social workers in inpatient settings: a secondary data analysis.

There is limited literature on the roles and tasks conducted by oncology social workers (OSW) who work with cancer patients in inpatient units. The purpose of this study was to delineate the roles reported to be significant to practice among OSWs who practice in inpatient settings and to identify the domains into which these roles fall. The data used in this secondary data analysis were collected in a large national study of OSWs to delineate the roles and tasks across all cancer settings. The sample extracted for this study were 240 OSWs who endorsed providing direct care to cancer patients in inpatient settings. Exploratory factor analysis revealed eight factors made up of 34 tasks. The roles were aligned with three of the four service areas in the Association of Oncology Social Scope of Practice and seven of the nine competencies set forth by the Council of Social Work Education. The findings can be used to enhance communications about the roles of inpatient OSWs across OSW constituencies, increase awareness of the role supervision and consultation to ensure equitable and just practice, enhance social work coursework to prepare students to work in healthcare inpatient settings, and in future research.

Evaluating the Genome-Based Average Nucleotide Identity Calculation for Identification of Twelve Yeast Species.

Classifying a yeast strain into a recognized species is not always straightforward. Currently, the taxonomic delineation of yeast strains involves multiple approaches covering phenotypic characteristics and molecular methodologies, including genome-based analysis. The aim of this study was to evaluate the suitability of the Average Nucleotide Identity (ANI) calculation through FastANI, a tool created for bacterial species identification, for the assignment of strains to some yeast species. FastANI, the alignment of in silico-extracted D1/D2 sequences of LSU rRNA, and multiple alignments of orthologous genes (MAOG) were employed to analyze 644 assemblies from 12 yeast genera, encompassing various species, and on a dataset of hybrid Saccharomyces species. Overall, the analysis showed high consistency between results obtained with FastANI and MAOG, although, FastANI proved to be more discriminating than the other two methods applied to genomic sequences. In particular, FastANI was effective in distinguishing between strains belonging to different species, defining clear boundaries between them (cutoff: 94-96%). Our results show that FastANI is a reliable method for attributing a known yeast species to a particular strain. Moreover, although hybridization events make species discrimination more complex, it was revealed to be useful in the identification of these cases. We suggest its inclusion as a key component in a comprehensive approach to species delineation. Using this approach with a larger number of yeasts would validate it as a rapid technique to identify yeasts based on whole genome sequences.

Comparison of diagnostic tools to assess the feasibility of programmatic use of rapid diagnostic tests for onchocerciasis: A dataset from Gabon.

Due to the success of large-scale ivermectin mass drug administration (MDA), the aim of onchocerciasis intervention efforts have shifted from control of the disease to elimination of transmission. This has necessitated a greater understanding and comparison of the performance of diagnostic tools in hypoendemic (low prevalence) settings which had not been incorporated into large-scale MDA programmes before the goal switched from onchocerciasis elimination as a public health problem to elimination (interruption) of transmission (EOT). Data on age, sex and duration of residence were collected, prior to ivermectin treatment, across Gabon in 2015 from 5,829 participants in 67 communities from 14 districts. Skin-snip samples (for detection of Onchocerca volvulus microfilariae) were obtained from 4,350 (75 %) and blood samples (for detection of presence of IgG4 antibodies against the O. volvulus Ov16 antigen) from 4,257 of those skin-snip tested (98 %). Whole blood was tested in the field using the SD Ov16 Rapid Diagnostic Test Prototype (Ov16 RDT). Dried blood spots (DBS) were prepared for all blood-sampled individuals. After assessing DBS quality, 2,990 (70 %) samples underwent valid analysis in the lab using horseradish peroxidase (HRP) Ov16 enzyme-linked immunosorbent assay (Ov16 ELISA). The number of positive individuals varied between diagnostic tools with skin-snip microscopy, Ov16 RDT and Ov16 ELISA detecting 337/4,350 (8 %, 95 % CI =7 %-9 %), 383/4,257 (9 %, 8 %-10 %) and 348/2,990 (12 %, 10 %-13 %), respectively. Data were analysed to understand the age profiles of microfilarial and IgG4 antibody prevalence by diagnostic and mapped across Gabon. These data have reuse potential for policy makers, test manufacturers and country programmes when making determinations at community level of the suitability of using Ov16 RDT for conducting delineation mapping or evaluating the current stage of a community or, more generally, an evaluation unit along the EOT path. Further, these data are of use to transmission dynamics modellers who can fit models to these data to better understand the stage(s) in the O. volvulus lifecycle likely responsible for IgG4 antibody seroconversion in the presence of Ov16 antigen. This is crucial for incorporation of antibody prevalence as an output of onchocerciasis transmission models to permit evaluation of currently proposed serological thresholds to inform decisions about Start- or Stop-MDA in the context of onchocerciasis elimination mapping (OEM) and verification of EOT, respectively.

Disappearance and dissemination of sleep symptoms: the importance of sleep medicine expertise for psychiatry. A comment on Forbes et al.

Commentary of 'Elemental psychopathology: distilling constituent symptoms and patterns of repetition in the diagnostic criteria of the DSM-5' Vincent P. Martin 1, Régis Lopez 2,3, Jean-Arthur Micoulaud-Franchi 4,5, Christophe Gauld 4,6,.

Analysis of re-recurrent rectal cancer after curative treatment of locally recurrent rectal cancer.

PURPOSE: Substantiating data guiding clinical decision making in locally recurrent rectal cancer (LRRC) is lacking, specifically in target volume (TV) definition for chemoradiotherapy (CRT). A case-by-case review of local re-recurrences (re-LRRC) after multimodal treatment for LRRC was performed, to determine location of re-LRRC and assess whether treatment could have been improved. METHODS: All patients treated with curative intent for LRRC at the Catharina Hospital Eindhoven from October 2016 onwards, in whom complete imaging of (re-)LRRC and radiotherapy was available, were retrieved. Patients were discussed in plenary meetings with expert colorectal surgeons, radiation oncologists and radiologists. Each case was classified based on re-LRRC location, whether it was in accordance with the (current) radiotherapy protocol, and whether multimodal management would have been different in retrospect. RESULTS: Thirty-three cases were discussed. LRRC treatment was deemed suboptimal in 17/33 patients, due to different target volumes (13/17) and/or different surgery (9/17). 15/33 (46 %) of re-LRRC developed in-field of the prior radiotherapy TV, possibly showing RT-resistant disease. Other re-LRRCs developed out-field (n = 5, 15 %), marginally (n = 6, 18 %), or in a combined fashion (n = 7, 21 %). In retrospect, 48 % of cases were irradiated in line with current TV recommendations. TVs of 13/33 cases would have been altered if irradiated today. CONCLUSION: This study highlights room for improvement within current standard-ofcare treatment for LRRC. Different surgical management or TVs may have improved outcome in up to half of discussed cases. Further delineation guideline development, incorporating the results from this study, may improve oncological outcome, specifically local control, for LRRC patients.

Clinical evaluation of the convolutional neural network­based automatic delineation tool in determining the clinical target volume and organs at risk in rectal cancer radiotherapy.

Delineating the clinical target volume (CTV) and organs at risk (OARs) is crucial in rectal cancer radiotherapy. However, the accuracy of manual delineation (MD) is variable and the process is time consuming. Automatic delineation (AD) may be a solution to produce quicker and more accurate contours. In the present study, a convolutional neural network (CNN)-based AD tool was clinically evaluated to analyze its accuracy and efficiency in rectal cancer. CT images were collected from 148 supine patients in whom tumor stage and type of surgery were not differentiated. The rectal cancer contours consisted of CTV and OARs, where the OARs included the bladder, left and right femoral head, left and right kidney, spinal cord and bowel bag. The MD contours reviewed and modified together by a senior radiation oncologist committee were set as the reference values. The Dice similarity coefficient (DSC), Jaccard coefficient (JAC) and Hausdorff distance (HD) were used to evaluate the AD accuracy. The correlation between CT slice number and AD accuracy was analyzed, and the AD accuracy for different contour numbers was compared. The time recorded in the present study included the MD time, AD time for different CT slice and contour numbers and the editing time for AD contours. The Pearson correlation coefficient, paired-sample t-test and unpaired-sample t-test were used for statistical analyses. The results of the present study indicated that the DSC, JAC and HD for CTV using AD were 0.80±0.06, 0.67±0.08 and 6.96±2.45 mm, respectively. Among the OARs, the highest DSC and JAC using AD were found for the right and left kidney, with 0.91±0.06 and 0.93±0.04, and 0.84±0.09 and 0.88±0.07, respectively, and HD was lowest for the spinal cord with 2.26±0.82 mm. The lowest accuracy was found for the bowel bag. The more CT slice numbers, the higher the accuracy of the spinal cord analysis. However, the contour number had no effect on AD accuracy. To obtain qualified contours, the AD time plus editing time was 662.97±195.57 sec, while the MD time was 3294.29±824.70 sec. In conclusion, the results of the present study indicate that AD can significantly improve efficiency and a higher number of CT slices and contours can reduce AD efficiency. The AD tool provides acceptable CTV and OARs for rectal cancer and improves efficiency for delineation.

Average Nucleotide Identity and Digital DNA-DNA Hybridization Analysis Following PromethION Nanopore-Based Whole Genome Sequencing Allows for Accurate Prokaryotic Typing.

Whole-genome sequencing (WGS) is revolutionizing clinical bacteriology. However, bacterial typing remains investigated by reference techniques with inherent limitations. This stresses the need for alternative methods providing robust and accurate sequence type (ST) classification. This study optimized and evaluated a GridION nanopore sequencing protocol, adapted for the PromethION platform. Forty-eight Escherichia coli clinical isolates with diverse STs were sequenced to assess two alternative typing methods and resistance profiling applications. Multi-locus sequence typing (MLST) was used as the reference typing method. Genomic relatedness was assessed using Average Nucleotide Identity (ANI) and digital DNA-DNA Hybridization (DDH), and cut-offs for discriminative strain resolution were evaluated. WGS-based antibiotic resistance prediction was compared to reference Minimum Inhibitory Concentration (MIC) assays. We found ANI and DDH cut-offs of 99.3% and 94.1%, respectively, which correlated well with MLST classifications and demonstrated potentially higher discriminative resolution than MLST. WGS-based antibiotic resistance prediction showed categorical agreements of ≥ 93% with MIC assays for amoxicillin, ceftazidime, amikacin, tobramycin, and trimethoprim-sulfamethoxazole. Performance was suboptimal (68.8-81.3%) for amoxicillin-clavulanic acid, cefepime, aztreonam, and ciprofloxacin. A minimal sequencing coverage of 12× was required to maintain essential genomic features and typing accuracy. Our protocol allows the integration of PromethION technology in clinical laboratories, with ANI and DDH proving to be accurate and robust alternative typing methods, potentially offering superior resolution. WGS-based antibiotic resistance prediction holds promise for specific antibiotic classes.

Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application.

BACKGROUND: Radiotherapy has been crucial in prostate cancer treatment. However, manual segmentation is labor intensive and highly variable among radiation oncologists. In this study, a deep learning based automated contouring model is constructed for clinical target volumes (CTVs) of intact and postoperative prostate cancer. METHODS: Computed tomography (CT) data sets of 197 prostate cancer patients were collected. Two auto-delineation models were built for radical radiotherapy and postoperative radiotherapy of prostate cancer respectively, and each model included CTVn for pelvic lymph nodes and CTVp for prostate tumors or prostate tumor beds. RESULTS: In the radical radiotherapy model, the volumetric dice (VD) coefficient of CTVn calculated by AI, was higher than that of the one delineated by the junior physicians (0.85 vs. 0.82, p = 0.018); In the postoperative radiotherapy model, the quantitative parameter of CTVn and CTVp, counted by AI, was better than that of the junior physicians. The median delineation time for AI was 0.23 min in the postoperative model and 0.26 min in the radical model, which were significantly shorter than those of the physicians (50.40 and 45.43 min, respectively, p < 0.001). The correction time of the senior physician for AI was much shorter compared with that for the junior physicians in both models (p < 0.001). CONCLUSION: Using deep learning and attention mechanism, a highly consistent and time-saving contouring model was built for CTVs of pelvic lymph nodes and prostate tumors or prostate tumor beds for prostate cancer, which also might be a good approach to train junior radiation oncologists.

Auto-delineation of treatment target volume for radiation therapy using large language model-aided multimodal learning.

PURPOSE: Artificial intelligence (AI)-aided methods have made significant progress in the auto-delineation of normal tissues. However, these approaches struggle with the auto-contouring of radiotherapy target volume. Our goal is to model the delineation of target volume as a clinical decision-making problem, resolved by leveraging large language model-aided multimodal learning approaches. METHODS AND MATERIALS: A vision-language model, termed Medformer, has been developed, employing the hierarchical vision transformer as its backbone, and incorporating large language models to extract text-rich features. The contextually embedded linguistic features are seamlessly integrated into visual features for language-aware visual encoding through the visual language attention module. Metrics, including Dice similarity coefficient (DSC), intersection over union (IOU), and 95th percentile Hausdorff distance (HD95), were used to quantitatively evaluate the performance of our model. The evaluation was conducted on an in-house prostate cancer dataset and a public oropharyngeal carcinoma (OPC) dataset, totaling 668 subjects. RESULTS: Our Medformer achieved a DSC of 0.81 ± 0.10 versus 0.72 ± 0.10, IOU of 0.73 ± 0.12 versus 0.65 ± 0.09, and HD95 of 9.86 ± 9.77 mm versus 19.13 ± 12.96 mm for delineation of gross tumor volume (GTV) on the prostate cancer dataset. Similarly, on the OPC dataset, it achieved a DSC of 0.77 ± 0.11 versus 0.72 ± 0.09, IOU of 0.70 ± 0.09 versus 0.65 ± 0.07, and HD95 of 7.52 ± 4.8 mm versus 13.63 ± 7.13 mm, representing significant improvements (p < 0.05). For delineating the clinical target volume (CTV), Medformer achieved a DSC of 0.91 ± 0.04, IOU of 0.85 ± 0.05, and HD95 of 2.98 ± 1.60 mm, comparable to other state-of-the-art algorithms. CONCLUSIONS: Auto-delineation of the treatment target based on multimodal learning outperforms conventional approaches that rely purely on visual features. Our method could be adopted into routine practice to rapidly contour CTV/GTV.

Generalising electrocardiogram detection and delineation: training convolutional neural networks with synthetic data augmentation.

Introduction: Extracting beat-by-beat information from electrocardiograms (ECGs) is crucial for various downstream diagnostic tasks that rely on ECG-based measurements. However, these measurements can be expensive and time-consuming to produce, especially for long-term recordings. Traditional ECG detection and delineation methods, relying on classical signal processing algorithms such as those based on wavelet transforms, produce high-quality delineations but struggle to generalise to diverse ECG patterns. Machine learning (ML) techniques based on deep learning algorithms have emerged as promising alternatives, capable of achieving similar performance without handcrafted features or thresholds. However, supervised ML techniques require large annotated datasets for training, and existing datasets for ECG detection/delineation are limited in size and the range of pathological conditions they represent. Methods: This article addresses this challenge by introducing two key innovations. First, we develop a synthetic data generation scheme that probabilistically constructs unseen ECG traces from "pools" of fundamental segments extracted from existing databases. A set of rules guides the arrangement of these segments into coherent synthetic traces, while expert domain knowledge ensures the realism of the generated traces, increasing the input variability for training the model. Second, we propose two novel segmentation-based loss functions that encourage the accurate prediction of the number of independent ECG structures and promote tighter segmentation boundaries by focusing on a reduced number of samples. Results: The proposed approach achieves remarkable performance, with a F 1 -score of 99.38% and delineation errors of 2.19 ± 17.73 ms and 4.45 ± 18.32 ms for ECG segment onsets and offsets across the P, QRS, and T waves. These results, aggregated from three diverse freely available databases (QT, LU, and Zhejiang), surpass current state-of-the-art detection and delineation approaches. Discussion: Notably, the model demonstrated exceptional performance despite variations in lead configurations, sampling frequencies, and represented pathophysiology mechanisms, underscoring its robust generalisation capabilities. Real-world examples, featuring clinical data with various pathologies, illustrate the potential of our approach to streamline ECG analysis across different medical settings, fostered by releasing the codes as open source.

Optimized target delineation procedure for the radiosurgery treatment of ventricular tachycardia: observer-independent accuracy.

Background: Part of the current stereotactic arrythmia radioablation (STAR) workflow is transfer of findings from the electroanatomic mapping (EAM) to computed tomography (CT). Here, we analyzed inter- and intraobserver variation in a modified EAM-CT registration using automatic registration algorithms designed to yield higher robustness. Materials and methods: This work is based on data of 10 patients who had previously undergone STAR. Two observers participated in this study: (1) an electrophysiologist technician (cardiology) with substatial experience in EAM-CT merge, and (2) a clinical engineer (radiotherapy) with minimum experience with EAM-CT merge. EAM-CT merge consists of 3 main steps: segmentation of left ventricle from CT (CT LV), registration of the CT LV and EAM, clinical target volume (CTV) delineation from EAM specific points. Mean Hausdorff distance (MHD), Dice Similarity Coefficient (DSC) and absolute difference in Center of Gravity (CoG) were used to assess intra/interobserver variability. Results: Intraobserver variability: The mean DSC and MHD for 3 CT LVs altogether was 0.92 ± 0.01 and 1.49 ± 0.23 mm. The mean DSC and MHD for 3 CTVs altogether was 0,82 ± 0,06 and 0,71 ± 0,22 mm. Interobserver variability: Segmented CT LVs showed great similarity (mean DSC of 0,91 ± 0,01, MHD of 1,86 ± 0,47 mm). The mean DSC comparing CTVs from both observers was 0,81 ± 0,11 and MHD was 0,87 ± 0,45 mm. Conclusions: The high interobserver similarity of segmented LVs and delineated CTVs confirmed the robustness of the proposed method. Even an inexperienced user can perform a precise EAM-CT merge following workflow instructions.

Quantifying remediation of chlorinated volatile compounds by sulfidated nano zerovalent iron treatment using numerical modeling and CSIA.

Sulfidated nanoscale zerovalent iron (S-nZVI) has demonstrated promising reactivity and longevity for remediating chlorinated volatile compounds (cVOC) contaminants in laboratory tests. However, its effectiveness in field applications remains inadequately evaluated. This study provides the first quantitative evaluation of the long-term effectiveness of carboxymethyl cellulose-stabilized S-nZVI (CMC-S-nZVI) at a cVOC-contaminated field site. A reactive transport model-based numerical approach delineates the change in cVOC concentrations and carbon isotope values (i.e., δ13C from compound-specific stable isotope analysis (CSIA)) caused by dissolution of dense non-aqueous phase liquid, sorption, and pathway-specific degradation and production, respectively. This delineation reveals quantitative insights into remediation effectiveness typically difficult to obtain, including extent of degradation, contributions of different degradation pathways, and degradation rate coefficients. Significantly, even a year after CMC-S-nZVI application, degradation remains an important process effectively removing various cVOC contaminants (i.e., chlorinated ethenes, 1,2-dichloroethanes, and chlorinated methanes) at an extent varying from 5 %-62 %. Although the impacts of CMC-S-nZVI abundance on degradation vary for different cVOC and for different sampling locations at the site, for the primary site contaminants of tetrachloroethene and trichloroethene, their predominance of dichloroelimination pathway (≥ 88 %), high degradation rate coefficient (0.4-1.7 d-1), and occurrence at locations with relatively high CMC-S-nZVI abundance strongly indicate the effectiveness of abiotic remediation. These quantitative assessments support that CMC-S-nZVI supports sustainable ZVI-based remediation. Further, the novel numerical approach presented in this study provides a powerful tool for quantitative cVOC remediation assessments at complex field sites where multiple processes co-occur to control both concentration and CSIA data.

Efficient application of deep learning-based elective lymph node regions delineation for pelvic malignancies.

BACKGROUND: While there are established international consensuses on the delineation of pelvic lymph node regions (LNRs), significant inter- and intra-observer variabilities persist. Contouring these clinical target volumes for irradiation in pelvic malignancies is both time-consuming and labor-intensive. PURPOSE: The purpose of this study was to develop a deep learning model of pelvic LNRs delineation for patients with pelvic cancers. METHODS: Planning computed tomography (CT) studies of 160 patients with pelvic primary malignancies (including rectal, prostate, and cervical cancer) were retrospectively collected and divided into training set (n = 120) and testing set (n = 40). Six pelvic LNRs, including abdominal presacral, pelvic presacral, internal iliac nodes, external iliac nodes, obturator nodes, and inguinal nodes were delineated by two radiation oncologists as ground truth (Gt) contours. The cascaded multi-heads U-net (CMU-net) was constructed based on the Gt contours from training cohort, which was subsequently verified in the testing cohort. The automatic delineation of six LNRs (Auto) was evaluated using dice similarity coefficient (DSC), average surface distance (ASD), 95th percentile Hausdorff distance (HD95), and a 7-point scale score. RESULTS: In the testing set, the DSC of six pelvic LNRs by CMU-net model varied from 0.851 to 0.942, ASD from 0.381 to 1.037 mm, and HD95 from 2.025 to 3.697 mm. No significant differences were founded in these three parameters between postoperative and preoperative cases. 95.9% and 96.2% of auto delineations by CMU-net model got a score of 1-3 by two expert radiation oncologists, respectively, meaning only minor edits needed. CONCLUSIONS: The CMU-net was successfully developed for automated delineation of pelvic LNRs for pelvic malignancies radiotherapy with improved contouring efficiency and highly consistent, which might justify its implementation in radiotherapy work flow.

MR-CT image fusion method of intracranial tumors based on Res2Net.

BACKGROUND: Information complementarity can be achieved by fusing MR and CT images, and fusion images have abundant soft tissue and bone information, facilitating accurate auxiliary diagnosis and tumor target delineation. PURPOSE: The purpose of this study was to construct high-quality fusion images based on the MR and CT images of intracranial tumors by using the Residual-Residual Network (Res2Net) method. METHODS: This paper proposes an MR and CT image fusion method based on Res2Net. The method comprises three components: feature extractor, fusion layer, and reconstructor. The feature extractor utilizes the Res2Net framework to extract multiscale features from source images. The fusion layer incorporates a fusion strategy based on spatial mean attention, adaptively adjusting fusion weights for feature maps at each position to preserve fine details from the source images. Finally, fused features are input into the feature reconstructor to reconstruct a fused image. RESULTS: Qualitative results indicate that the proposed fusion method exhibits clear boundary contours and accurate localization of tumor regions. Quantitative results show that the method achieves average gradient, spatial frequency, entropy, and visual information fidelity for fusion metrics of 4.6771, 13.2055, 1.8663, and 0.5176, respectively. Comprehensive experimental results demonstrate that the proposed method preserves more texture details and structural information in fused images than advanced fusion algorithms, reducing spectral artifacts and information loss and performing better in terms of visual quality and objective metrics. CONCLUSION: The proposed method effectively combines MR and CT image information, allowing the precise localization of tumor region boundaries, assisting clinicians in clinical diagnosis.

Interobserver variation in tumor delineation of liver metastases using Magnetic Resonance Imaging.

Background and purpose: Magnetic Resonance Imaging (MRI) guided stereotactic body radiotherapy (SBRT) of liver metastases is an upcoming high-precision non-invasive treatment. Interobserver variation (IOV) in tumor delineation, however, remains a relevant uncertainty for planning target volume (PTV) margins. The aims of this study were to quantify IOV in MRI-based delineation of the gross tumor volume (GTV) of liver metastases and to detect patient-specific factors influencing IOV. Materials and methods: A total of 22 patients with liver metastases from three primary tumor origins were selected (colorectal(8), breast(6), lung(8)). Delineation guidelines and planning MRI-scans were provided to eight radiation oncologists who delineated all GTVs. All delineations were centrally peer reviewed to identify outliers not meeting the guidelines. Analyses were performed both in- and excluding outliers. IOV was quantified as the standard deviation (SD) of the perpendicular distance of each observer's delineation towards the median delineation. The correlation of IOV with shape regularity, tumor origin and volume was determined. Results: Including all delineations, average IOV was 1.6 mm (range 0.6-3.3 mm). From 160 delineations, in total fourteen single delineations were marked as outliers after peer review. After excluding outliers, the average IOV was 1.3 mm (range 0.6-2.3 mm). There was no significant correlation between IOV and tumor origin or volume. However, there was a significant correlation between IOV and regularity (Spearman's ρs = -0.66; p = 0.002). Conclusion: MRI-based IOV in tumor delineation of liver metastases was 1.3-1.6 mm, from which PTV margins for IOV can be calculated. Tumor regularity and IOV were significantly correlated, potentially allowing for patient-specific margin calculation.