Vivek Sanker
Postdoctoral Scholar, Neurosurgery
All Publications
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Artificial intelligence models in the surgical planning of low-grade gliomas: a systematic review
FRONTIERS IN ONCOLOGY
2026; 15
View details for DOI 10.3389/fonc.2025.1672289
View details for Web of Science ID 001673720200001
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Applying artificial intelligence in neurodevelopmental disorders management and research.
European journal of medical research
2026
Abstract
Artificial intelligence (AI) is increasingly being used in the diagnosis, treatment, and monitoring of neurodevelopmental disorders, enabling earlier detection, personalised interventions, and continuous support. Traditional machine-learning models such as logistic regression, random forests, and support vector machines remain valuable for their interpretability and their ability to integrate multimodal clinical data. Deep-learning (DL) approaches, including convolutional neural networks and transformer-based architectures, improve the analysis of neuroimaging and behavioural datasets and strengthen diagnostic and prognostic performance. Important challenges remain, including limited transparency in DL systems, ongoing concerns about data privacy and algorithmic bias, and a lack of large and diverse paediatric datasets that restricts generalisability. Interpretability tools such as SHAP and LIME offer partial solutions but still lack standardised evaluation. At the same time, AI-driven robotic platforms are enhancing therapeutic engagement and supporting skill acquisition in children with neurodevelopmental conditions. This review highlights that AI tools have strong potential to act as clinical adjuncts rather than replacements, providing earlier detection, personalised management, and scalable care models. Realising this potential will require rigorous validation, ethical safeguards, and thoughtful integration into human-led care pathways.
View details for DOI 10.1186/s40001-025-03740-8
View details for PubMedID 41484902
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Uncommon Encounter in Urachus: A Rare Case of Xanthogranulomatous Urachitis With Review of Literature.
Clinical case reports
2026; 14 (1): e71897
Abstract
Urachus is a fibrous remnant from embryologic development derived from the allantois, which is involved in waste elimination in the fetus. Typically, it extends from the dome of the bladder to the umbilicus. Xanthogranulomatous urachitis, or xanthogranulomatous inflammation of the urachus, is a highly unusual pathological entity characterized by large lipid-laden macrophages, with only a few cases reported worldwide. In this case, a 48-year-old male patient presented with complaints of persistent abdominal pain and watery discharge from the umbilicus for 1 month. Following en bloc resection of the urachal mass, histopathology revealed findings consistent with xanthogranulomatous urachitis. The postoperative course was uneventful, and eventually, the patient recovered to baseline health with resolution of bothersome symptoms. We report a case of xanthogranulomatous urachitis, which, despite its rarity, should be considered an important differential diagnosis of all urachal masses.
View details for DOI 10.1002/ccr3.71897
View details for PubMedID 41574153
View details for PubMedCentralID PMC12821230
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Patient-Specific Computed Tomography-Based Three-Dimensional Spine Trauma Models for Preoperative Planning in Virtual Reality and 3D Printing: An EANS Young Neurosurgeons' Network Study.
Journal of neurological surgery. Part A, Central European neurosurgery
2025
Abstract
Lately, the wide availability of open-source modelling and rendering software in neurosurgery has led to the development of a methodological pipeline for creating patient-specific three-dimensional (3D) models based on preoperative imaging data. With recent innovations in virtual reality (VR) technology and 3D printing, these models can be applied to enhance preoperative planning and medical training. The main question this paper aims to answer is whether the proposed algorithm of intensity-based CT segmentation and basic 3D modelling is adequate to create a reference library of patient-specific models, categorized according to the AO Spine Injury Classification System, and suitable for VR and 3D printing-based preoperative planning.We used the open-source medical image viewer Horos to create volumetric renderings of CT scans of trauma patients from several European centers. The models were postprocessed using 3D modelling software and exported in appropriate formats for VR or 3D printing.We created 37 models of trauma patients, spanning from the upper cervical to the thoracolumbar segment, categorized according to the AO Spine Injury Classification System. Additionally, a remote case discussion conducted by uploading these models into a collaborative VR environment was demonstrated as a proof of concept.In the present study, we demonstrated that open-source software can create a database of patient-specific 3D models. Additionally, the communication between remote departments can be facilitated by uploading these models into a collaborative VR environment, and the comprehensive evaluation of spine fractures fostered through 3D printing. Further studies are needed to assess the database's educational value.
View details for DOI 10.1055/a-2726-3537
View details for PubMedID 41461169
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Unraveling COVID-19-vaccination-induced bullous pemphigoid: a case report and review of the literature.
Journal of medical case reports
2025
Abstract
Coronavirus disease 2019 vaccines have been instrumental in combating the global pandemic, yet their potential side effects, including autoimmune conditions such as bullous pemphigoid, remain an area of concern. This case highlights the development of bullous pemphigoid following coronavirus disease 2019 vaccination and includes a comprehensive review of similar cases reported in the literature, emphasizing its novelty and clinical significance.An elderly British man in his 80s with type 2 diabetes mellitus developed blistering lesions 21 days after receiving his third dose of coronavirus disease 2019 vaccine (Moderna). Clinical examination revealed erythematous plaques and bullae on the trunk and limbs. Histopathological evaluation and immunofluorescence confirmed the diagnosis of bullous pemphigoid. Treatment included corticosteroids, doxycycline, and immunosuppressants. Despite initial improvement, a severe flare-up necessitated hospitalization and wound care management. A systematic review identified 50 reported cases of bullous pemphigoid linked to coronavirus disease 2019 vaccination, with the Pfizer-BioNTech vaccine implicated in most cases (64%), followed by Moderna (18%). Symptom onset typically occurred after the first dose in 52% of cases.This case underscores the need for vigilance regarding autoimmune phenomena such as bullous pemphigoid following coronavirus disease 2019 vaccination. Awareness of such potential adverse effects is crucial to ensure timely diagnosis and management, ultimately contributing to patient safety and guiding future vaccine development.
View details for DOI 10.1186/s13256-025-05652-x
View details for PubMedID 41316355
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A lightweight machine learning tool for Alzheimer's disease prediction.
Alzheimer's & dementia (Amsterdam, Netherlands)
2025; 17 (4): e70187
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that needs better predictive tools. Using the National Alzheimer's Coordinating Center Uniform Data Set, this study developed machine learning (ML) models and a practical clinical tool for AD prediction.Data from 52,537 individuals (22,371 with AD) and more than 200 variables were processed with MissForest imputation and genetic algorithm-based selection. Multiple ML models were trained, and interpretability was performed using SHAP and permutation importance. A LightGBM model was refined through iterative backward feature elimination (IBFE) followed by manual refinement.LightGBM performed best (receiver operating characteristic-area under the curve [ROC-AUC] 0.91, accuracy 82.0%). Key predictors included arthritis, age, body mass index, and heart rate. A 19-feature model retained accuracy (81.2%) and ROC-AUC (0.90).This lightweight tool predicts AD using mostly routine variables. Limitations include its cross-sectional nature, and would need external validation. An interactive web app and GitHub resource are available.Developed a lightweight ML based tool using 19 routinely available features.The lightweight model achieved an ROC-AUC of 0.90 for Alzheimer's disease prediction on NACC multicenter data.Genetic algorithm, IBFE, and manual refinement enabled optimal feature selection.Tool hosted on an open-access platform for clinical and research use.SHAP analysis provided model interpretability and feature-level insights.
View details for DOI 10.1002/dad2.70187
View details for PubMedID 41256012
View details for PubMedCentralID PMC12620993
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Stereotactic radiosurgery (SRS) for primary spinal bone tumors: a systematic review and meta-analysis.
Neurosurgical review
2025; 48 (1): 760
Abstract
While surgery is a vital component in the treatment of most primary spinal bone tumors (PSBTs), it is only one aspect of a comprehensive management plan. Optimizing patient outcomes often requires a multidisciplinary approach, incorporating medical oncology and interventional radiology. The use of stereotactic radiosurgery (SRS) is typically considered when patients are either poor surgical candidates or have recurrent or unresectable tumors. However, the use of SRS and its efficacy remains relatively unclear. Our aim with this study is to investigate the therapeutic response among PSBTs treated with SRS. We performed a systematic literature search in the databases Medline, Embase (Ovid), Scopus, Web of Science Advance and Cochrane Central from inception to July 8, 2024. We included studies that reported on outcomes of SRS in patients with PSBTs and meta-analyzed the proportions of recurrence, progression, and local control (LC). The quality of included studies was assessed using the MINORS tool for non-randomized studies. We identified 5 studies with relevant data for our meta-analysis, and our results consist of overall analyses that compile all the relevant data, and a subgroup analysis for chordomas. The compiled pooled proportion of LC was 0.81 [95% CI: 0.55; 0.94], and in the chordoma subgroup, it was 0.82 [95% CI: 0.59; 0.93]. The compiled pooled proportion of recurrence was 0.14 [95% CI: 0.03; 0.47], and in the chordoma subgroup, it was 0.14 [95% CI: 0.03: 0.47]. The compiled pooled proportion of progression was 0.26 [0.15; 0.42], and in the chordoma subgroup, it was 0.22 [95% CI: 0.10; 0.41]. Our meta-analysis suggests that treating PSBTs with SRS is associated with promising results in terms of LC (81%), but 14% of the cases had recurrence, and 26% showed tumor progression. Thus, SRS as a primary treatment modality for PSBTs still requires further modifications to further enhance results and improve patient outcomes.
View details for DOI 10.1007/s10143-025-03848-9
View details for PubMedID 41191153
View details for PubMedCentralID 2696242
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Current trends and future prospects of language models and processing systems in spine surgery - a scoping review.
Neurosurgical review
2025; 48 (1): 633
Abstract
Natural language processing (NLPs) and Large language models (LLM), such as ChatGPT, represent transformative advancements in artificial intelligence (AI). Their implementation into the medical field has a broad potential, and this review discusses the current trends and prospects of NLPs and LLMs in spine surgery, assessing their potential benefits, applications, and limitations. The methodology involved a comprehensive narrative review of existing English literature related to the use of NLPs and LLMs in spine surgery. We searched the databases PubMed, EMBASE, Web of Science and Scopus from inception until 16th June 2025 using keywords evolving around LLM, natural language processing and spine surgery. Original studies, clinical reports, and case series were included, while abstracts or unpublished studies were excluded. From 221 initial records, 37 studies were included: 18 evaluated LLMs and 19 evaluated NLP-based tools. LLMs were commonly used for clinical decision-making (n = 8), patient counseling (n = 7), classification (n = 2), and in research (n = 1). NLPs were applied in classification tasks (n = 12), clinical decision-making (n = 3), patient counseling (n = 1), postoperative opioid monitoring (n = 2), and research registry development (n = 1). ChatGPT-4 achieved up to 92% accuracy in clinical recommendations, outperforming GPT-3.5 in multiple tasks. Comparative analyses have found that newer versions of LLMs, such as ChatGPT-4, outperform previous versions, evident by greater accuracy and to a lesser extent of artificial hallucination. However, limitations persist, including overconfident outputs, adherence gaps to clinical guidelines, and inconsistent patient readability. While this review suggests that NLPs and LLMs can have a significant impact on spine practice, it is important to keep their limitations in mind and implement them with caution. To maximize the benefits of these models in spine surgery, future research should focus on improving model sensitivity and specificity, promoting multi-disciplinary collaborations, and addressing ethical considerations regarding the use of language models in medical practice, including the inherent issue of hallucination of these models.
View details for DOI 10.1007/s10143-025-03785-7
View details for PubMedID 40911114
View details for PubMedCentralID 8027892
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Cost-effectiveness of Surgery for Spinal Metastasis: A Systematic Review.
Spine
2025
Abstract
Systematic review.The purpose of this study was to assess the cost-effectiveness of surgery for spinal metastasis therapy.The optimal treatment for many cases of spinal metastasis (SM) is surgery followed by adjuvant radiotherapy (RT). However, the cost-effectiveness of combined therapy (CT; surgery & RT) is unclear due to the short median survival time among SM patients and the higher costs of combined therapy compared to RT alone.We performed a systematic literature search from inception to 01/21/2024. We included studies that reported on the cost-effectiveness of surgical intervention for SM and assessed their quality using Quality of Health Economic Studies instrument.We identified 5,024 studies of which 8 met our inclusion. All included studies were of fair to high quality. Of 7 studies that compared CT to definitive RT, six concluded that CT was cost-effective. Of note, one of the studies concluding that CT was cost-effective, only found CT to be cost effectiveness when considering patients with a 3-month survival probability above 50%. An additional study compared their calculated Incremental Cost Effectiveness Ratio (ICER) value to the standard Willingness to Pay (WTP) threshold in Thailand and concluded that CT was not cost-effective in Thailand. After comparing their reported ICER value to a commonly used WTP in the United States, we found CT to be cost-effective.We found CT consisting of surgery and RT to be cost effective in 6 out of 7 (85.7%) studies. Cost effectiveness might be even more pronounced in certain patient subgroups, such as patients with a high predicted survival. However, most studies did not report therapy details - a factor which could greatly influence cost-effectiveness.
View details for DOI 10.1097/BRS.0000000000005486
View details for PubMedID 40905258
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Histomorphological Changes in Breast Lesions: A Retrospective Observational Study.
Cureus
2025; 17 (9): e92801
Abstract
Background Breast lesions can occur in individuals of all age groups and may include a range of conditions, from benign abnormalities to malignant tumors. Among these, breast cancer is one of the most frequently diagnosed cancers worldwide. Methods used to diagnose breast lesions include histopathology studies and flow cytometry DNA analysis. Some lesions, like cellular fibroadenoma and benign phyllodes tumors, are difficult to differentiate from each other. As their treatment modalities differ from each other, it is important to manage the condition with care. This study emphasizes the importance of timely diagnosis and treatment of various breast conditions to ensure the best possible outcome for patients. Objective To retrospectively analyze the histopathological patterns of breast lesions, along with their demographic and clinical characteristics, among patients presenting to a specialty hospital over a 5-year period. Methods A retrospective study was conducted by including the breast biopsy, mastectomy, and Fine Needle Aspiration Cytology (FNAC) reports of all patients who presented with various breast lesions at Terna Speciality Hospital and research center during the period of January 2017 to December 2022. Various demographic and clinical information was gathered through the histopathology request forms and registry. The authors also looked at histopathology slides of various instances that occurred during the research period. Results The mean age of diagnosis of various breast lesions was found to be 34.18 (SD +/- 14.92). 83% of lesions presented at less than 50 years of age and 17% at more than 50 years. 97% of the total lesions studied were found among the female population. 98% of all lesions presented with a palpable mass, out of which most (64%) lesions were between 1 and 5 cm in size. The majority of the lesions (45.9%) were right-sided, and only 7.2% were bilateral. Most of the lesions studied presented in the upper outer quadrant. Most of the benign and inflammatory lesions studied presented in individuals aged 21-30 years, and malignant lesions were found predominantly among individuals aged 61-70 years. No inflammatory lesions were found beyond the age of 60 years, and only one malignant lesion was found below 20 years. Among all lesions studied, benign lesions were most common, found in 406 subjects. 18.5% of all lesions were malignant in nature, among which the majority were invasive breast cancer. Conclusion Breast cancer is a complex and heterogeneous disease with varying patterns. Most of the lesions studied were benign in nature, of which the most common was fibroadenoma. The most common malignant lesion in the study was invasive breast cancer - nonspecific type. Understanding the histopathological patterns of breast lesions is essential for improving the diagnostic and treatment outcomes of this disease.
View details for DOI 10.7759/cureus.92801
View details for PubMedID 41122577
View details for PubMedCentralID PMC12536952
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Applications and Performance of Artificial Intelligence in Spinal Metastasis Imaging: A Systematic Review.
Journal of clinical medicine
2025; 14 (16)
Abstract
Background: Spinal metastasis is the third most common site for metastatic localization, following the lung and liver. Manual detection through imaging modalities such as CT, MRI, PET, and bone scintigraphy can be costly and inefficient. Preliminary artificial intelligence (AI) techniques and computer-aided detection (CAD) systems have attempted to improve lesion detection, segmentation, and treatment response in oncological imaging. The objective of this review is to evaluate the current applications of AI across multimodal imaging techniques in the diagnosis of spinal metastasis. Methods: Databases like PubMed, Scopus, Web of Science Advance, Cochrane, and Embase (Ovid) were searched using specific keywords like 'spine metastases', 'artificial intelligence', 'machine learning', 'deep learning', and 'diagnosis'. The screening of studies adhered to the PRISMA guidelines. Relevant variables were extracted from each of the included articles such as the primary tumor type, cohort size, and prediction model performance metrics: area under the receiver operating curve (AUC), accuracy, sensitivity, specificity, internal validation and external validation. A random-effects meta-analysis model was used to account for variability between the studies. Quality assessment was performed using the PROBAST tool. Results: This review included 39 studies published between 2007 and 2024, encompassing a total of 6267 patients. The three most common primary tumors were lung cancer (56.4%), breast cancer (51.3%), and prostate cancer (41.0%). Four studies reported AUC values for model training, 16 for internal validation, and five for external validation. The weighted average AUCs were 0.971 (training), 0.947 (internal validation), and 0.819 (external validation). The risk of bias was the highest in the analysis domain, with 22 studies (56%) rated high risk, primarily due to inadequate external validation and overfitting. Conclusions: AI-based approaches show promise for enhancing the detection, segmentation, and characterization of spinal metastatic lesions across multiple imaging modalities. Future research should focus on developing more generalizable models through larger and more diverse training datasets, integrating clinical and imaging data, and conducting prospective validation studies to demonstrate meaningful clinical impact.
View details for DOI 10.3390/jcm14165877
View details for PubMedID 40869703
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Artificial Intelligence Models for Predicting Outcomes in Spinal Metastasis: A Systematic Review and Meta-Analysis.
Journal of clinical medicine
2025; 14 (16)
Abstract
Background: Spinal metastases can cause significant impairment of neurological function and quality of life. Hence, personalized clinical decision-making based on prognosis and likely outcome is desirable. The effectiveness of AI in predicting complications and treatment outcomes for patients with spinal metastases is assessed. Methods: A thorough search was carried out through the PubMed, Scopus, Web of Science, Embase, and Cochrane databases up until 27 January 2025. Included were studies that used AI-based models to predict outcomes for adult patients with spinal metastases. Three reviewers independently extracted the data, and screening was conducted in accordance with PRISMA principles. AUC results were pooled using a random-effects model, and the PROBAST program was used to evaluate the study's quality. Results: Included were 47 articles totaling 25,790 patients. For training, internal validation, and external validation, the weighted average AUCs were 0.762, 0.876, and 0.810, respectively. The Skeletal Oncology Research Group machine learning algorithms (SORG-MLAs) were the ones externally validated the most, continuously producing AUCs > 0.84 for 90-day and 1-year mortality. Models based on radiomics showed promise in preoperative planning, especially for outcomes of radiation and concealed blood loss. Most research concentrated on breast, lung, and prostate malignancies, which limited its applicability to less common tumors. Conclusions: AI models have shown reasonable accuracy in predicting mortality, ambulatory status, blood loss, and surgical complications in patients with spinal metastases. Wider implementation necessitates additional validation, data standardization, and ethical and regulatory framework evaluation. Future work should concentrate on creating multimodal, hybrid models and assessing their practical applications.
View details for DOI 10.3390/jcm14165885
View details for PubMedID 40869712
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Quantitative radiomic analysis of computed tomography scans using machine and deep learning techniques accurately predicts histological subtypes of non-small cell lung cancer: A retrospective analysis.
European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
2025; 51 (10): 110376
Abstract
Non-small cell lung cancer (NSCLC) histological subtypes impact treatment decisions. While pre-surgical histopathological examination is ideal, it's not always possible. CT radiomic analysis shows promise in predicting NSCLC histological subtypes.To predict NSCLC histological subtypes using machine learning and deep learning models using Radiomic features.422 lung CT scans from The Cancer Imaging Archive (TCIA) were analyzed. Primary neoplasms were segmented by expert radiologists. Using PyRadiomics, 2446 radiomic features were extracted; post-selection, 179 features remained. Machine learning models like logistic regression (LR), Support vector machine (SVM), Random Forest (RF), XGBoost, LightGBM, and CatBoost were employed, alongside a deep neural network (DNN) model.RF demonstrated the highest accuracy at 78 % (95 % CI: 70 %-84 %) and AUC-ROC at 94 % (95 % CI: 90 %-96 %). LightGBM, XGBoost, and CatBoost had AUC-ROC values of 95 %, 93 %, and 93 % respectively. The DNN's AUC was 94.4 % (95 % CI: 94.1 %-94.6 %). Logistic regression had the least efficacy. For histological subtype prediction, random forest, boosting models, and DNN were superior.Quantitative radiomic analysis with machine learning can accurately determine NSCLC histological subtypes. Random forest, ensemble models, and DNNs show significant promise for pre-operative NSCLC classification, which can streamline therapy decisions.
View details for DOI 10.1016/j.ejso.2025.110376
View details for PubMedID 40803192
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Artificial Intelligence for Non-Invasive Prediction of Molecular Signatures in Spinal Metastases: A Systematic Review.
Bioengineering (Basel, Switzerland)
2025; 12 (8)
Abstract
Background: Spinal metastases (SMs) are associated with poor prognosis and significant morbidity. We hypothesize that artificial intelligence (AI) models can enhance the identification and clinical utility of genetic and molecular signatures associated with SMs, improving diagnostic accuracy and enabling personalized treatment strategies. Methods: A systematic review of five databases was conducted to identify studies that used AI to predict genetic alterations and SMs outcomes. Accuracy, area under the receiver operating curve (AUC), and sensitivity were used for comparison. Data analysis was performed in R. Results: Eleven studies met the inclusion criteria, covering three different primary tumor origins, comprising a total of 2211 patients with an average of 201 ± 90 patients (range: 76-359 patients) per study. EGFR, Ki-67, and HER-2 were studied in ten (90.9%), two (18.1%), and one (9.1%) study, respectively. The weighted average AUC is 0.849 (95% CI: 0.835-0.863) and 0.791 (95% CI: 0.738-0.844) for internal and external validation of the established models, respectively. Conclusions: AI, through radiomics and machine learning, shows strong potential in predicting molecular markers in SMs. Our study demonstrates that AI can predict molecular markers in SMs with high accuracy.
View details for DOI 10.3390/bioengineering12080791
View details for PubMedID 40868304
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Cross-modality image-to-image translation from MR to synthetic 18F-FDOPA PET/MR fusion images using conditional GAN in brain cancer.
Neuroradiology
2025
Abstract
This study aims to identify the possibility of cross-modality image-to-image translation from magnetic resonance (MR) to synthetic positron emission tomography (PET)/MR fusion images using conditional generative adversarial networks (CGAN).Retrospective study was conducted involving 32 simultaneous 6-[18F]-fluoro-L-3,4-dihydroxyphenylalanine (18F-FDOPA) PET/MR imaging examinations from 27 patients diagnosed with brain cancer. We applied paired axial T1-weighted contrast MR (T1C) and PET/T1C fusion images to translate from T1C to synthetic PET/T1C fusion images using the Pix2Pix algorithm of CGAN. To access the image similarity between real and synthetic PET/T1C fusion images, we calculated correlation coefficients for the maximum/mean tumor-to-background ratio (TBRmax/mean) and quantitative analyses were performed using peak signal-to-noise ratio (PSNR), mean squared error (MSE), structural similarity index (SSIM), and feature similarity index measure (FSIM).Total 2167 pairs of T1C and PET/T1C fusion images were obtained, which were randomly assigned to training and test datasets in 9:1 ratio (1950 and 217 pairs), and training data were further divided into training and validation datasets in 4:1 ratio (1560 and 390 pairs). The correlation coefficients were 0.706 (CI:0.533-0.822) for TBRmax (p < 0.001) and 0.901 (CI:0.831-0.943) for TBRmean (p < 0.001). The quantitative analyses were PSNR of 31.075 ± 3.976, MSE of 0.001 ± 0.001, SSIM of 0.868 ± 0.079, and FSIM of 0.922 ± 0.044, respectively.CGAN based on simultaneous 18F-FDOPA PET/MR imaging data demonstrated the potential for cross-modality image-to-image translation from T1C to PET/T1C fusion images, though limitations in small dataset and lack of external validation requiring further research.
View details for DOI 10.1007/s00234-025-03704-z
View details for PubMedID 40682663
View details for PubMedCentralID 7218205
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Exoscope-Assisted Spine Surgery: A Systematic Review From Basic to Complex Pathologies.
Cureus
2025; 17 (7): e88450
Abstract
Operative neurosurgery has greatly benefited from technological advancements over the past several decades. However, challenges such as limited visualization, intraoperative navigation difficulties, and the complexity of spinal anatomy continue to pose significant hurdles for surgeons. The utilization of advanced technologies, such as exoscopes, navigation systems, and robotics, can help overcome some of these challenges, thereby enhancing surgical precision and accuracy. Over time, spine surgery has undergone remarkable advancements. Among those, exoscope-assisted spine surgery stands out as a promising approach, providing surgeons with an unmatched visual experience and enhancing the potential for improved patient outcomes. The objective of this systematic review is to examine the current use of exoscopes in spine surgery and compare the available technologies and types. We conducted a systematic review of the literature for exoscopes in spine surgeries using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology across four major reliable databases: PubMed, ScienceDirect, Embase, and Scopus. A total of 42 studies were included in this review. We aim to present a comprehensive overview of exoscope-assisted spine surgeries, focusing on the technology's evolution, advantages, clinical applications, and potential limitations. Adequate lighting, magnification, and precision in the identification of critical surgical tissues are essential for predicting the "maximal safe resection" in neurosurgery. Vital neurovascular structures can be recognized and dissected using the high-resolution illumination provided by the operating microscope (OM). Conversely, the OM has several disadvantages, including a limited field of view, difficulty seeing around corners, and the potential to impose uncomfortable surgical postures. Additionally, an OM is difficult to maneuver around the operating room due to its size and weight. The exoscope distinguishes itself from traditional surgery by positioning the camera externally to the surgical area, providing the surgeon with an improved ergonomic vantage point to visually oversee the operative field. With more visibility, surgeons can navigate the spine and its supporting components, potentially improving treatment precision. Exoscopes offer numerous advantages over traditional OMs, including higher magnification, enhanced 3D visualization, improved ergonomics, and greater flexibility. These benefits increase precision, reduce surgeon fatigue, and enhance surgical outcomes. The use of exoscopes in spine surgery has shown promise in reducing bleeding, improving hemostasis, and potentially shortening surgical times. Additionally, the ability to record and stream surgical procedures facilitates better communication and collaboration among the surgical team, benefiting experienced surgeons and trainees. Surgeons may face a learning curve when transitioning from traditional microscopes to exoscopes, but this hurdle can be overcome with adequate training and experience. The initial high procurement costs and limited availability of exoscopes in resource-constrained areas may also pose barriers to widespread adoption.
View details for DOI 10.7759/cureus.88450
View details for PubMedID 40842795
View details for PubMedCentralID PMC12367202
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Comparative Analysis Between Cortical Bone Trajectory (CBT) Screw Fixation and Traditional Pedicle Screw Fixation in Lumbar Spine Surgery: A Systematic Review and Meta-Analysis.
Cureus
2025; 17 (7): e87944
Abstract
Cortical bone trajectory (CBT) screw fixation has been proposed as an alternative method to pedicle screw (PS) fixation for lumbar instrumented fusion. However, the benefits of this technique remain controversial. We searched PubMed, Scopus, Web of Science Advance, ScienceDirect, Embase (Ovid), and Cochrane databases from inception to July 22, 2023. Articles were included if they were related to adults undergoing lumbar spine surgery and showed comparative outcomes with cortical bone and traditional PS trajectory. Twenty-three studies with 1,929 patients (833 in the CBT group and 1,096 in the PS group) were systematically reviewed and meta-analyzed. Significantly lower complications in the CS group, including adjacent segment degeneration (p < 0.05) and screw loosening (p < 0.05), were found. Superior facet joint violation was significantly less in the CBT group (p < 0.05). The CBT group had reduced hospital stay (p < 0.05), reduced blood loss (p < 0.05), and shorter operations (p < 0.05). Clinical outcomes at a mean of 18 months (standard deviation (SD): six months) favored the CBT group in leg pain visual analogue scale (VAS) scores in some subgroups (p < 0.05). Reoperation was significantly reduced in the CBT group (p < 0.05). Our study indicates that CBT fixation has acceptable outcomes relative to PS fixation and may be advantageous in some respects. Further studies are required to ascertain optimal surgical indications for each method.
View details for DOI 10.7759/cureus.87944
View details for PubMedID 40821299
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Vertebral fracture following primary stereotactic body radiation therapy for spinal bone metastases: a decade of experience.
Journal of neurosurgery. Spine
2025: 1-11
Abstract
The aim of this retrospective study was to comprehensively evaluate the factors that contribute to and protect against the occurrence of vertebral fracture (VF) following stereotactic body radiation therapy (SBRT) for the treatment of spinal bone metastasis (SBM).This study focused on adult patients who underwent primary SBRT for management of solid tumor SBMs from March 2012 to January 2023 with detailed follow-up medical records for at least 6 months. Target volume delineation for sacral and spinal SBRT was conducted in accordance with International Spine Radiosurgery Consortium guidelines and international consensus recommendations. Patients with SBM showing local progression during the follow-up period were excluded. The Spine Instability Neoplastic Score (SINS) was used to assess the relationship between various factors and the occurrence of post-SBRT VF.A total of 304 patients (178 male, median age 65 years) with 450 SBMs involving 557 vertebrae were analyzed. The overall occurrence rate of VF, including post-SBRT VFs on SBRT-treated vertebrae and adjacent VFs (AVFs), was 16.6%. Post-SBRT VFs accounted for 15.6% of cases, while AVFs constituted 3.3%. Post-SBRT VFs predominantly exhibited a biconcave shape. Key factors associated with the development of post-SBRT VF included SBMs in the lumbar segment, spinal instability (SINS ≥ 7), the presence of pre-SBRT VF, and a higher radiation dose (biologically effective doses [BED3] ≥ 153.3 Gy). The use of antiresorptive agents, including bisphosphonates and denosumab, significantly reduced the occurrence rate of post-SBRT VF, with denosumab showing a particularly enhanced protective effect. Pain relief and recalcification of SBMs following SBRT were also observed.This study offers valuable insights into the occurrence of post-SBRT VF in SBM. While post-SBRT VF remains a significant concern in SBRT treatment, the potential for remineralization in SBM provides a promising avenue for enhancing spinal stability over time.
View details for DOI 10.3171/2025.3.SPINE231234
View details for PubMedID 40577849
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Patient-specific 3D reconstruction models for sacral tumor resection: illustrative cases.
Journal of neurosurgery. Case lessons
2025; 9 (23)
Abstract
The surgical methods used to resect sacral tumors are extensive and require maneuvering through complex anatomical systems such as the pelvic organs and sacral nerve roots. These procedures may result in complications and adverse patient outcomes. The technology integrating 3D reconstruction models in the field of spine surgery is rapidly evolving, and these challenging cases present a unique opportunity to leverage this technology's capability for enhanced patient outcomes.The authors present two sacral tumor cases diagnosed with synovial cell sarcoma and giant cell osteosarcoma, respectively. Both patients underwent a three-staged en bloc tumor resection assisted by 3D reconstruction models. Postoperative imaging showed a complete tumor resection, and the patients symptomatically improved, with no signs of recurrence on follow-up.Surgical planning and execution have clearly advanced significantly with the introduction of 3D modeling into spine surgery. Based on the authors' experience, this technology can be used to improve outcomes for complicated spinal tumors with successful results. Although these results are encouraging, extensive studies with larger patient cohorts must be carried out to fully appreciate the technology's advantages across a range of patient demographics and tumor types. https://thejns.org/doi/10.3171/CASE2522.
View details for DOI 10.3171/CASE2522
View details for PubMedID 40489946
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Frequency of Diffuse Axonal Injury and Its Outcomes in Severe Traumatic Brain Injury (sTBI): A Systematic Review and Meta-Analysis.
Journal of neurotrauma
2025
Abstract
While it is established that diffuse axonal injury (DAI) is a leading cause of death or disability among patients with traumatic brain injury (TBI), less is known about the frequency of DAI in patients with severe TBI (sTBI). Additionally, little is known about the mortality rate and proportion of males/females among patients with both sTBI and DAI. We conducted a systematic literature search in the databases EMBASE Ovid, PubMed, Scopus, and Web of Science Advance from inception until April 22, 2024. No filters or language restrictions were applied. Two reviewers (A.S. and P.S.) independently screened the obtained abstracts and full texts. We included full-text studies that reported the frequency of DAI after TBI or any measure of association between DAI and clinical outcome (e.g., death, Glasgow Outcome Scale). Animal studies, reviews, and non-original research articles were excluded. We qualitatively described the results of the included studies. Thirty-seven studies met our inclusion criteria: 18 retrospective, 18 prospective, and 1 was both retrospective and prospective, representing studies from 14 countries. Thirty-three were single-center studies, and four were multicenter. Five studies were exclusively conducted among pediatric patients, while the remaining 32 included adults. The pooled proportion of DAI among sTBI patients was 0.60 (95% confidence interval [CI]: 0.39, 0.78]), I2 = 98%. The pooled mortality among patients with both sTBI and DAI is 0.16 [95% CI: 0.07, 0.30], I2 = 12%. The pooled proportion of males among individuals with both sTBI and DAI was 0.81 [95% CI: 0.76, 0.85], I2 = 46%. DAI is common in patients with sTBI. The comorbid state of having both sTBI and DAI can be life-threatening and is more often seen in males than females, possibly due to the increased tendency of males to partake in risky behaviors that increase the likelihood of head trauma. There might be a difference in outcome after DAI between the pediatric and adult patient populations, possibly due to increased plasticity of brain tissue in younger patients.
View details for DOI 10.1089/neu.2024.0469
View details for PubMedID 40485292
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Advances in chromosomal microarray analysis: Transforming neurology and neurosurgery.
Brain & spine
2025; 5: 104197
Abstract
Over the past two decades, genomics has transformed our understanding of various clinical conditions, with Chromosomal Microarray Analysis (CMA) standing out as a key technique. Offering unparalleled sensitivity, CMA detects submicroscopic chromosomal imbalances, enabling the examination of DNA for copy number variations, deletions, duplications, and other structural differences. In neurology, CMA has revolutionised diagnoses, personalised treatment plans, and patient outcomes. By identifying genetic anomalies linked to neurological conditions, CMA allows clinicians to tailor treatments based on individual genetic profiles, enhancing precision medicine. CMA's clinical utility spans numerous neurological conditions, providing crucial insights into neurodevelopmental disorders, CNS tumours, neurodegenerative diseases, cerebrovascular diseases, and epilepsy. In neurodevelopmental disorders, CMA aids in diagnosing autism and intellectual disabilities, facilitating early interventions that improve long-term outcomes. In epilepsy, CMA helps identify genetic causes of drug-resistant seizures, enabling more targeted therapies and reducing adverse reactions. CMA also aids in stratifying risk for cerebrovascular diseases, enabling preventive interventions that improve patient prognosis. Despite its potential, challenges remain, such as interpreting variants of uncertain significance (VOUS), the lack of standardised testing guidelines, and issues of cost and accessibility. Addressing these challenges will optimise CMA's impact, advancing personalised medicine and reshaping neurology. This review discusses CMA's pivotal role in bridging the gap between genomics and clinical practice, underscoring its potential to transform neurogenetics and ultimately improve patient care.
View details for DOI 10.1016/j.bas.2025.104197
View details for PubMedID 39990116
View details for PubMedCentralID PMC11847126
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Artificial intelligence models in the surgical planning of low-grade gliomas: a systematic review.
Frontiers in oncology
2025; 15: 1672289
Abstract
AI techniques like convolutional neural networks (CNN), deep learning (DL), and neural networks (NN) have made it easier to automatically extract important clinical data for glioma post-treatment monitoring and surgical planning.To systematically review and analyze the role of AI/ML models in the surgical planning of LGG.A rigorous and comprehensive systematic literature search was conducted across PubMed, Scopus, Web of Science Advance, ArXiV, and Embase (Ovid) databases from inception to July 14, 2025. Articles related to the utility of ML models in the surgical planning of LGG were included.Our review included eight studies in both preoperative and intraoperative settings with variation in the type of AI applied, such as tumor segmentation, intraoperative neuro navigation, hyperspectral imaging, and surgical recommendation. Upon comparative analysis of mean DICE coefficients of the proposed models for segmentation, the DeepMedic CNN was found to have the highest DICE for tumor segmentation. With hyperspectral imaging, the use of MLP classifiers yields high accuracy; however, when taking into consideration the quality of tiles, DL methods outperform the classical methods by ~10%. Survival Probability using the Balanced Survival lasso-network (BSL), balanced individual treatment effect (BITES), and DeepSurv models: Difference in restricted mean survival time (DRMST) between the Consis group and In-consis group [4.75 (1.54-7.95)] for BSL, [3.81 (0.63-6.98)] for Deep Surv, and [3.76 (0.57-6.96)] for BITES.AI/ML models have shown promising results in diagnostic and management approaches for glioma resection. Nonetheless, this is based on a small number of studies (n=8) and remain preliminary. Validating the findings in external datasets with a larger patient population would help enhance the predictive capacity of the existing models.
View details for DOI 10.3389/fonc.2025.1672289
View details for PubMedID 41626168
View details for PubMedCentralID PMC12851994
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Efficacy of Additional Surgical Decompression on Functional Outcome in Pyogenic Spinal Epidural Abscess With No Neurological Deficit.
Korean journal of neurotrauma
2024; 20 (4): 276-288
Abstract
The aim of this study was to investigate the efficacy of additional surgical decompression with antibiotics to treat pyogenic spinal epidural abscess (SEA) with no neurological deficits.We retrospectively reviewed the data of patients diagnosed with spontaneous pyogenic SEA in the thoracolumbosacral area who presented with sciatica and no motor deficits in the lower extremities. The treatment took place in a single tertiary hospital. The effects of additional surgical decompression (decompressive laminectomy) and other clinical variables on functional outcome were assessed using the short form 36 (SF-36).Fifty-nine patients (49 men and 10 women, mean age 65.73±12.29 [41-89] years) were included in the analysis. Surgical decompression had been performed in 31 patients (Group S, treated with additional surgical decompression and antibiotics). There were five (15.2%, 5/33) unplanned operations to control leg sciatica among the patients with initially non-surgical plans, and 28 patients were finally treated with only antibiotics (group N-S). Group S showed a statistically significant increased cost of hospitalization compared to group N-S (15,856.37±7,952.83 vs. 10,672.62±4,654.17 US dollars, p=0.004) with no superiority of 6-month functional outcome after the completion of antibiotic treatment (53.65±4.74 vs. 51.75±7.96 SF-36 scores, p=0.266).Although there is a possibility of requiring an unplanned operation to control leg sciatica during conservative antibiotic treatment, overall, additional surgical decompression in pyogenic SEA presenting with no motor deficit of the lower extremity showed increased medical burden and no greater benefit in terms of functional outcomes.
View details for DOI 10.13004/kjnt.2024.20.e48
View details for PubMedID 39803337
View details for PubMedCentralID PMC11711023
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Sonic hedgehog signalling pathway in CNS tumours: its role and therapeutic implications.
Molecular brain
2024; 17 (1): 83
Abstract
CNS tumours encompass a diverse group of neoplasms with significant morbidity and mortality. The SHH signalling pathway plays a critical role in the pathogenesis of several CNS tumours, including gliomas, medulloblastomas and others. By influencing cellular proliferation, differentiation and migration in CNS tumours, the SHH pathway has emerged as a promising target for therapeutic intervention. Current strategies such as vismodegib and sonidegib have shown efficacy in targeting SHH pathway activation. However, challenges such as resistance mechanisms and paradoxical effects observed in clinical settings underscore the complexity of effectively targeting this pathway. Advances in gene editing technologies, particularly CRISPR/Cas9, have provided valuable tools for studying SHH pathway biology, validating therapeutic targets and exploring novel treatment modalities. These innovations have paved the way for a better understanding of pathway dynamics and the development of more precise therapeutic interventions. In addition, the identification and validation of biomarkers of SHH pathway activation are critical to guide clinical decision making and improve patient outcomes. Molecular profiling and biomarker discovery efforts are critical steps towards personalised medicine approaches in the treatment of SHH pathway-associated CNS tumours. While significant progress has been made in understanding the role of the SHH pathway in CNS tumorigenesis, ongoing research is essential to overcome current therapeutic challenges and refine treatment strategies. The integration of molecular insights with advanced technologies and clinical expertise holds great promise for developing more effective and personalised therapies for patients with SHH pathway-driven CNS tumours.
View details for DOI 10.1186/s13041-024-01155-w
View details for PubMedID 39568072
View details for PubMedCentralID PMC11580395
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Theranostics advances in the treatment and diagnosis of neurological and neurosurgical diseases.
Archives of medical research
2024; 56 (1): 103085
Abstract
Theranostics represents a significant advance in the fields of neurology and neurosurgery, offering innovative approaches that combine the diagnosis and treatment of various neurological disorders. This innovation serves as a cornerstone of personalized medicine, where therapeutic strategies are closely integrated with diagnostic tools to enable precise and targeted interventions. Primary research results emphasize the profound impact of theranostics in Neuro Oncol. In this context, it has provided valuable insights into the complexity of the tumor microenvironment and mechanisms of resistance. In addition, in the field of neurodegenerative diseases (NDs), theranostics has facilitated the identification of distinct disease subtypes and novel therapeutic targets. It has also unravelled the intricate pathophysiology underlying conditions such as cerebrovascular disease (CVD) and epilepsy, setting the stage for more refined treatment approaches. As theranostics continues to evolve through ongoing research and refinement, its goals include further advancing the field of precision medicine, developing practical biomarkers for clinical use, and opening doors to new therapeutic opportunities. Nevertheless, the integration of these approaches into clinical settings presents challenges, including ethical considerations, the need for advanced data interpretation, standardization of procedures, and ensuring cost-effectiveness. Despite these obstacles, the promise of theranostics to significantly improve patient outcomes in the fields of neurology and neurosurgery remains a source of optimism for the future of healthcare.
View details for DOI 10.1016/j.arcmed.2024.103085
View details for PubMedID 39369666
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Novel insights into the role of TREM2 in cerebrovascular diseases.
Brain research
2024; 1846: 149245
Abstract
Cerebrovascular diseases (CVDs) include conditions such as stroke, cerebral amyloid angiopathy (CAA) and cerebral small vessel disease (CSVD), which contribute significantly to global morbidity and healthcare burden. The pathophysiology of CVD is complex, involving inflammatory, cellular and vascular mechanisms. Recently, research has focused on triggering receptor expressed on myeloid cells 2 (TREM2), an immune receptor predominantly found on microglia. TREM2 interacts with multiple signalling pathways, particularly toll-like receptor 4 (TLR4) and nuclear factor kappa B (NF-κB), inhibiting patients' inflammatory response. This receptor plays an essential role in both immune regulation and neuroprotection. TREM2 deficiency or dysfunction is associated with impaired microglial responses, exacerbated neurodegeneration and neuroinflammation. Up until recently, TREM2 related studies have focused on neurodegenerative diseases (NDs), however a shift in focus towards CVDs is beginning to take place. Advancements in CVD research have focused on developing therapeutic strategies targeting TREM2 to enhance recovery and reduce long-term deficits. These include the exploration of TREM2 agonists and combination therapies with other anti-inflammatory agents, which may synergistically reduce neuroinflammation and promote neuroprotection. The modulation of TREM2 activity holds potential for innovative treatment approaches aimed at improving patient outcomes following cerebrovascular insults. This review compiles current research on TREM2, emphasising its molecular mechanisms, therapeutic potential, and advancements in CNS disease research.
View details for DOI 10.1016/j.brainres.2024.149245
View details for PubMedID 39305972
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Unusual giant plunging sublingual epidermoid cyst: A case report and review of literature.
Clinical case reports
2024; 12 (6): e9067
Abstract
When treating a painless or asymptomatic mass in the submental or floor of the mouth, sublingual epidermoid cyst should be considered. Despite its irregularity, preventing malignant transformation is essential for a successful outcome.Dermoid and epidermoid cysts are rarely found in the head and neck region. They account for less than 0.01% of all oral cavity cysts. This is a rare case of a sublingual epidermoid cyst of the oral cavity in a 25-year-old male. The patient presented with a painless sublingual swelling for a duration of 1 month. The clinical examination revealed a non-tender swelling in the sublingual region extending to the submental triangle. Magnetic resonance imaging confirmed a 6.2 × 7.7 × 3.2 cm cystic lesion in the sublingual space. Fine needle aspiration cytology confirmed dermoid cyst contents. Intra-oral surgical excision under general anesthesia was performed successfully. Histopathological analysis revealed that the cyst wall was lined by stratified squamous epithelium. The presence of a prominent granular layer and keratin flakes confirmed the diagnosis of an epidermoid cyst. Postoperative recovery was good, and no recurrence was observed during follow-up. This case emphasizes the infrequent and unusual presentation of a case of a giant plunging sublingual epidermoid cyst and promotes awareness and potential studies in the enhancement of patient care in this area.
View details for DOI 10.1002/ccr3.9067
View details for PubMedID 38868117
View details for PubMedCentralID PMC11166552