Category: Nevin Manimala

Support Care Cancer. 2025 Aug 21;33(9):802. doi: 10.1007/s00520-025-09862-3.

ABSTRACT

BACKGROUND: Hospitalization for gynecological cancer surgery represents a critical window for assessing and addressing psychological and nutritional vulnerabilities. This prospective observational study investigated changes in emotional distress, anxiety, depression, body-image dissatisfaction, orthorexic tendencies, and nutritional status from admission to discharge, and explored associations between psychological and nutritional variables.

METHODS: A total of 220 women hospitalized for surgical treatment of gynecological cancer were enrolled, with 181 (82.3%) completing both baseline (T0) and discharge (T1) assessments. Psychological outcomes were evaluated using the Distress Thermometer (DT), Hospital Anxiety and Depression Scale (HADS), Body-Image Scale (BIS), and Teruel Orthorexia Scale (TOS). Nutritional status was assessed through the Mini Nutritional Assessment (MNA). Changes between T0 and T1 were analyzed using paired t-tests. Pearson’s correlations examined associations between psychological and nutritional variables. A multivariable logistic regression identified predictors of clinically relevant distress (DT ≥ 4) at discharge.

RESULTS: Significant improvements were observed in anxiety (p < 0.001), depression (p < 0.001), emotional distress (p < 0.001), and orthorexic tendencies (p < 0.001) between admission and discharge. Conversely, body-image dissatisfaction increased significantly (p < 0.001). Nutritional risk remained high throughout hospitalization, with no statistically significant change (p = 0.221). Higher body-image dissatisfaction at admission predicted a greater likelihood of clinically relevant distress at discharge (p = 0.003).

CONCLUSIONS: Hospitalization offers a pivotal opportunity to identify and address emotional and nutritional needs in women with gynecological cancers. Integrated, multidisciplinary supportive care models targeting both psychological and nutritional vulnerabilities are crucial to promote holistic recovery during and beyond the surgical course.

PMID:40836028 | DOI:10.1007/s00520-025-09862-3

Eur J Hum Genet. 2025 Aug 20. doi: 10.1038/s41431-025-01927-5. Online ahead of print.

ABSTRACT

Access to large patient cohort data and biobanked resources is a catalyst for progress in genomics and biomedical research, increasing statistical power, and unlocking deeper insights-especially in areas like rare diseases and mental health. Responsible research necessitates maintenance of data privacy, regulatory compliance, and research standardization. It can appear that these guiding principles oppose each other and present barriers to responsible Open science. To address these critical challenges, we developed MINDDS-Connect, a federated data collaboration platform that integrates a web-based interface with decentralized Docker instances via a REST API. This architecture allows registered users to securely query samples across the platform’s network, and offers a tool to facilitate the formation of virtual multi-centric meta-cohorts and research collaboration. MINDDS-Connect allows institutions to retain data control while enabling collaborative research and meta-cohort analysis through standardized metadata fields. Its implementation across five European centers enhanced the accessibility of 900 samples, demonstrating its effectiveness in enabling cohort construction and promoting collaborative research. The platform provides a secure, open-source solution consistent with EU Open Science policies, advancing large-scale mental health research.

PMID:40836024 | DOI:10.1038/s41431-025-01927-5

Eur Radiol. 2025 Aug 20. doi: 10.1007/s00330-025-11936-z. Online ahead of print.

NO ABSTRACT

PMID:40836022 | DOI:10.1007/s00330-025-11936-z

Eur Radiol. 2025 Aug 20. doi: 10.1007/s00330-025-11923-4. Online ahead of print.

ABSTRACT

OBJECTIVES: To assess the reproducibility and diagnostic accuracy of the Node Reporting and Data System 1.0 (Node-RADS) for detecting pelvic nodal metastases by endometrial cancer (EC) using CT and MRI, among readers with different levels of expertise.

MATERIALS AND METHODS: This IRB-approved, single-center retrospective study included 128 patients with EC who underwent preoperative MRI at our Institution (Jan 2020-Dec 2023). Six readers with different levels of expertise in pelvic MRI (2 dedicated pelvic radiologists, 2 residents in their fourth year of training, and 2 residents in their second year of training) independently evaluated preoperative CTs and MRIs and assigned Node-RADS scores. Inter-observer agreement and inter-method agreement were calculated. Node-RADS was compared with post-surgical pathology data.

RESULTS: At surgery, pelvic nodal metastases were detected in 12.5% of the patients. Interobserver agreement in nodal status assessment using Node-RADS varied from κ = 0.783 to κ = 0.426 using MRI, and from κ = 0.936 to κ = 0.295 using CT, with worse results among less experienced readers. MRI and CT were concordant in the N definition in 94-98% of the cases. Using MRI, the most experienced readers showed 63% sensitivity and 100% specificity in the detection of nodal metastases, compared to 44% sensitivity and 96% specificity for poorly experienced readers. Using CT, the most experienced readers showed 50% sensitivity and 100% specificity; the less experienced readers showed 43% sensitivity and 94% specificity.

CONCLUSIONS: Node-RADS is a reproducible and accurate tool for locoregional nodal staging of EC, but only for readers with specific experience in pelvic imaging. MRI outperforms CT in nodal assessment.

KEY POINTS: Question Preoperative assessment of nodal metastases by EC is difficult, but it may help in tailoring the best surgical approach for each patient. Findings Node-RADS is a reliable tool for assessing the presence of pelvic nodal metastases by EC, both on CT and MRI, among experienced readers. Clinical relevance The use of Node-RADS among experienced readers enables detection of nodal metastases with good sensitivity and excellent specificity; MRI should be preferred over CT due to its higher sensitivity.

PMID:40836021 | DOI:10.1007/s00330-025-11923-4

Eur Radiol. 2025 Aug 20. doi: 10.1007/s00330-025-11933-2. Online ahead of print.

ABSTRACT

OBJECTIVES: To create and test a locally adapted large language model (LLM) for automated scoring of radiology requisitions based on the reason for exam imaging reporting and data system (RI-RADS), and to evaluate its performance based on reference standards.

MATERIALS AND METHODS: This retrospective, double-center study included 131,683 radiology requisitions from two institutions. A bidirectional encoder representation from a transformer (BERT)-based model was trained using 101,563 requisitions from Center 1 (including 1500 synthetic examples) and externally tested on 18,887 requisitions from Center 2. The model’s performance for two different classification strategies was evaluated by the reference standard created by three different radiologists. Model performance was assessed using Cohen’s Kappa, accuracy, F1-score, sensitivity, and specificity with 95% confidence intervals.

RESULTS: A total of 18,887 requisitions were evaluated for the external test set. External testing yielded a performance with an F1-score of 0.93 (95% CI: 0.912-0.943); κ = 0.88 (95% CI: 0.871-0.884). Performance was highest in common categories RI-RADS D and X (F1 ≥ 0.96) and lowest for rare categories RI-RADS A and B (F1 ≤ 0.49). When grouped into three categories (adequate, inadequate, and unacceptable), overall model performance improved [F1-score = 0.97; (95% CI: 0.96-0.97)].

CONCLUSION: The locally adapted BERT-based model demonstrated high performance and almost perfect agreement with radiologists in automated RI-RADS scoring, showing promise for integration into radiology workflows to improve requisition completeness and communication.

KEY POINTS: Question Can an LLM accurately and automatically score radiology requisitions based on standardized criteria to address the challenges of incomplete information in radiological practice? Findings A locally adapted BERT-based model demonstrated high performance (F1-score 0.93) and almost perfect agreement with radiologists in automated RI-RADS scoring across a large, multi-institutional dataset. Clinical relevance LLMs offer a scalable solution for automated scoring of radiology requisitions, with the potential to improve workflow in radiology. Further improvement and integration into clinical practice could enhance communication, contributing to better diagnoses and patient care.

PMID:40836020 | DOI:10.1007/s00330-025-11933-2

Eur Radiol. 2025 Aug 20. doi: 10.1007/s00330-025-11961-y. Online ahead of print.

ABSTRACT

OBJECTIVE: Non-sustained ventricular tachycardia (NSVT) is an independent predictor of sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM). This meta-analysis evaluates the prognostic value of late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) for predicting NSVT and its association with SCD in HCM.

MATERIALS AND METHODS: We screened electronic databases for studies evaluating the prognostic value of LGE in predicting NSVT and SCD in HCM patients. A random-effects model estimated pooled sensitivity, specificity, accuracy, predictive values, and likelihood ratios for NSVT prediction. Association between LGE extent and NSVT incidence was analyzed using weighted mean differences (WMDs), while pooled odds ratios (ORs) with 95% CIs were calculated to assess LGE’s association with SCD.

RESULTS: Among 20 studies, LGE showed a pooled sensitivity, specificity, and accuracy of 91.33% (95% CI: 88.81-93.86), 37.45% (95% CI: 31.60-43.31), and 52.86% (95% CI: 45.73-59.98), respectively, for NSVT prediction. Positive and negative likelihood ratios and predictive values were, 1.40 and 0.23, and 36.35% and 92.03%, respectively. Patients with NSVT had a significantly greater LGE extent than those without (WMD: 5.95%, 95% CI: 3.08-8.81, p < 0.0001). NSVT prevalence was 28.73% (95% CI: 20.91-36.54). Additionally, LGE presence and SCD were significantly associated (OR 3.64, 95% CI: 2.36-5.61, p < 0.00001).

CONCLUSION: LGE on CMR shows high sensitivity but limited specificity and accuracy for NSVT prediction. Moreover, LGE presence was significantly associated with SCD, and NSVT patients had greater LGE extent. Nonetheless, variability in predictive values and likelihood ratios underscores the need to combine LGE with other imaging biomarkers.

KEY POINTS: Question Can LGE on CMR predict NSVT and SCD in HCM patients? Findings LGE demonstrated high sensitivity but limited specificity for NSVT prediction. Moreover, LGE presence was significantly associated with SCD, and NSVT patients had greater LGE extent. Clinical relevance LGE on CMR is a valuable marker for NSVT prediction and SCD in HCM patients, but it is not widely integrated into clinical practice. Our study highlights the need to integrate LGE with other imaging biomarkers for improved risk stratification.

PMID:40836019 | DOI:10.1007/s00330-025-11961-y

Eur Radiol. 2025 Aug 20. doi: 10.1007/s00330-025-11958-7. Online ahead of print.

ABSTRACT

OBJECTIVES: Preoperative assessment of World Health Organization (WHO) meningioma grading and Ki-67 expression is crucial for treatment strategies. We aimed to develop a fully automated attention-based deep learning network to predict WHO meningioma grading and Ki-67 expression.

MATERIALS AND METHODS: This retrospective study included 952 meningioma patients, divided into training (n = 542), internal validation (n = 96), and external test sets (n = 314). For each task, clinical, radiomics, and deep learning models were compared. We used no-new-Unet (nn-Unet) models to construct the segmentation network, followed by four classification models using ResNet50 or Swin Transformer architectures with 2D or 2.5D input strategies. All deep learning models incorporated attention mechanisms.

RESULTS: Both the segmentation and 2.5D classification models demonstrated robust performance on the external test set. The segmentation network achieved Dice coefficients of 0.98 (0.97-0.99) and 0.87 (0.83-0.91) for brain parenchyma and tumour segmentation. For predicting meningioma grade, the 2.5D ResNet50 achieved the highest area under the curve (AUC) of 0.90 (0.85-0.93), significantly outperforming the clinical (AUC = 0.77 [0.70-0.83], p < 0.001) and radiomics models (AUC = 0.80 [0.75-0.85], p < 0.001). For Ki-67 expression prediction, the 2.5D Swin Transformer achieved the highest AUC of 0.89 (0.85-0.93), outperforming both the clinical (AUC = 0.76 [0.71-0.81], p < 0.001) and radiomics models (AUC = 0.82 [0.77-0.86], p = 0.002).

CONCLUSION: Our automated deep learning network demonstrated superior performance. This novel network could support more precise treatment planning for meningioma patients.

KEY POINTS: Question Can artificial intelligence accurately assess meningioma WHO grade and Ki-67 expression from preoperative MRI to guide personalised treatment and follow-up strategies? Findings The attention-enhanced nn-Unet segmentation achieved high accuracy, while 2.5D deep learning models with attention mechanisms achieved accurate prediction of grades and Ki-67. Clinical relevance Our fully automated 2.5D deep learning model, enhanced with attention mechanisms, accurately predicts WHO grades and Ki-67 expression levels in meningiomas, offering a robust, objective, and non-invasive solution to support clinical diagnosis and optimise treatment planning.

PMID:40836018 | DOI:10.1007/s00330-025-11958-7

Eur Radiol. 2025 Aug 20. doi: 10.1007/s00330-025-11921-6. Online ahead of print.

ABSTRACT

OBJECTIVE: To analyze the differences in contrast-enhanced ultrasound (CEUS) patterns among synovial pathotypes in rheumatoid arthritis (RA), aiming to provide a non-invasive basis for differentiating RA subtypes.

MATERIALS AND METHODS: A total of 151 patients with RA (involving 151 joints) were included and underwent gray-scale ultrasound, Power Doppler ultrasound (PDUS), CEUS, and ultrasound-guided synovial biopsy sequentially. The synovial tissues were classified into three pathological types by immunohistochemistry, including lympho-myeloid, diffuse-myeloid, and pauci-immune. This study analyzed the differences in ultrasound features among different synovial pathotypes and proposed five distinct synovial vascular enhancement patterns. Variables with correlations > 0.3 were selected to construct three classification models, namely Model 1 (Clinical variables), Model 2 (Clinical-PDUS), and Model 3 (Clinical-PDUS-CEUS).

RESULTS: CEUS synovial vascular enhancement patterns: the pauci-immune predominantly exhibited pattern II (35.7%), the diffuse-myeloid predominantly exhibited pattern IV (42.5%), and the lympho-myeloid predominantly exhibited pattern V (72.0%). Qualitative parameters (enhancement pattern, degree, and range) and quantitative parameters (peak intensity [PI], maximum gradient [MGrad], and wash-out area under the curve [WoAUC]) of CEUS were significantly associated with pathological types (r = 0.426-0.602, all p < 0.001). In differentiating pathological subtypes, Model 3 achieved an AUC of 0.902, which was significantly higher than Model 1 (AUC = 0.728) and Model 2 (AUC = 0.751). However, the diagnostic efficacy for the diffuse-myeloid still needs optimization (AUC = 0.818).

CONCLUSIONS: CEUS features of synovium can help differentiate RA synovial pathotypes and may provide important guidance for the early diagnosis and treatment selection of RA.

KEY POINTS: Question Are there differences between RA synovial pathotype and their corresponding contrast-enhanced (CEUS) findings? Findings Five patterns of synovial vascular enhancement corresponding to pathology types were identified, and a model was constructed. Clinical relevance CEUS synovial qualitative analysis and quantitative parameters can help differentiate RA synovial pathotypes.

PMID:40836017 | DOI:10.1007/s00330-025-11921-6

Sci Rep. 2025 Aug 20;15(1):30502. doi: 10.1038/s41598-025-16364-z.

ABSTRACT

Accurate estimation of the evaporation is of great significance for the management of limited agricultural water resources. However, developing highly accurate and universal data- driven models using time-series analysis methods to achieve precise evaporation estimation remains a challenging. Specifically, integrating meta-heuristic algorithms, ensemble deep learning models, and data preprocessing techniques for evaporation prediction is notably scarce. The aim of this paper was to employ time series analysis methods to develop data-driven model with high accuracy and universality to realize accurate estimation of evaporation. To achieve this purpose, the Convolutional neural network (CNN) was integrated with Bidirectional long short-term memory network (BiLSTM) as main estimating module, and the Sparrow search algorithm (SSA) was employed to search the optimal hyperparameters of CNN-BiLSTM. To overcome the drawback that directly using measured evaporation time series to predict evaporation may lead to large error, the Variational mode decomposition (VMD) was used to extract multiscale traits of evaporation time series, and Whale optimization algorithm (WOA) was adopted to find the optimal parameters of VMD, and a novel hybrid deep learning model WOA-VMD-CNN-SSA-BiLSTM was proposed to estimate the evaporation in the Linze County, China. The estimating performance was evaluated by using the statistical accuracy metrics, including R², the mean squared error (MSE), the mean absolute error (MAE), the root mean squared error (RMSE), and the mean absolute percentage error (MAPE). The results show that the Sample entropy (SEn) remains 0.0832 when the optimal values of [Formula: see text] and [Formula: see text] of VMD are 6 and 0.1773, suggesting that VMD optimized by using WOA effectively overcomes the subjectivity in traditional VMD parameter setting and realizes amplitude-dependent feature extraction of evaporation time series in the study area. In addition, the model performance of CNN-SSA-BiLSTM can be significantly improved by coupling CNN-SSA-BiLSTM with WOA-VMD, and the hybrid model WOA-VMD-SSA-CNN-BiLSTM with MSE = 0.1258, RMSE = 0.3547, MAE = 0.2833, and MAPE = 6.17% in testing stage is superior than other hybrid models and ensemble models, which could be highly recommended for estimating evaporation in study area.

PMID:40836003 | DOI:10.1038/s41598-025-16364-z

Burns. 2025 Aug 6;51(8):107644. doi: 10.1016/j.burns.2025.107644. Online ahead of print.

ABSTRACT

BACKGROUND: For many burn survivors, the symptomatic burn scarring can limit the functionality and compromise the quality of life. Various modalities have been in use over the years to minimize the scar-related distress of the patients but none effective in totality. Due to availability of sparse data on efficacy of a most potent combination therapy, we used split-scar technique to compare Er:YAG and triamcinolone versus triamcinolone alone.

METHODS: The study was a prospective, interventional, double blind, parallel, two-arm, randomized controlled trial including 34 patients from November 2022 to April 2024. Scars were divided into two halves using lottery system; one half was given Er:YAG laser along with intralesional triamcinolone (group A) while the other half was given Er:YAG laser alone (group B). Results were compared objectively by measuring the decrease in thickness and subjectively on UNC4P scar scale and patient satisfaction index.

RESULTS: In group A, 52.9 % (n = 18) achieved > 75 % reduction in thickness as compared to 26.5 % (n = 9) in group B (p < .05). UNC4P scar scale revealed better symptomatic relief in the scars of group A and the difference was found statistically significant for pruritus, paraesthesia, and pliability; and comparable for pain. Patient satisfaction index was also higher in group A.

CONCLUSION: A combination treatment of Er:YAG laser along with intralesional corticosteroid is more efficacious in improving the scar thickness and providing symptomatic relief, better safety profile and improved patient satisfaction–thereby establishing it as a better modality of treatment compared to laser monotherapy of scars.

PMID:40834485 | DOI:10.1016/j.burns.2025.107644