Tumor Boundary Research Articles

Conversion therapy strategy: A novel GPC3-targeted multimodal organic phototheranostics platform for mid-late-stage hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is typically diagnosed at intermediate to advanced stage, making surgical treatment unfeasible. Conversion therapy aims to reduce tumor stage, improve hepatic resection feasibility, and lower recurrence rates. Since traditional therapies are often accompanied by uncertainty of efficacy, there is an urgent need to explore new treatment strategies. Near-infrared phototheranostics technology provides a new way for HCC diagnosis and treatment by its excellent optical properties. However, complex preparation and poor biocompatibility of phototheranostics probes limit clinical application. In this study, we developed a fluorescence/magnetic resonance dual-modality imaging (FLI/MRI) as well as photothermal/photodynamic therapy (PTT/PDT) GPC3-targeted multifunctional phototheranostics probe, IR820-GPC3-Gd NPs (IGD NPs), to improve the efficiency of conversion therapy for HCC. The IGD NPs were simply prepared with the IR820 as the core. Conjugating the HCC-specific targeting molecule GPC3 peptide and the MRI agent DOTA-Gd through click chemistry. IGD NPs target HCC cells through GPC3, releasing heat and reactive oxygen species (ROS) under noninvasive 808nm laser irradiation to reduce tumor size and achieve downstaging. High-sensitivity FLI/MRI precisely delineates tumor boundaries, providing real-time surgical navigation and prognosis assessment. This novel probe offers a feasible and effective treatment option for advanced HCC.

Head-to-Head Comparison of 68 Ga-DOTATATE and 18 F-FDG PET in EBV-Positive Nonkeratinizing Nasopharyngeal Carcinoma.

The aim of this study was to compare the clinical application value of 68 Ga-DOTATATE and 18 F-FDG PET/CT in Epstein-Barr virus (EBV)-positive nonkeratinizing nasopharyngeal carcinoma. Patients underwent 18 F-FDG and 68 Ga-DOTATATE PET scans. The lesion numbers, tracer parameters, and primary tumor volume derived from contrast-enhanced MRI, 18 F-FDG, and 68 Ga-DOTATATE PET were compared. The correlation between clinical characteristics and PET parameters as well as the predictive value of PET parameters were analyzed. The median maximum standard uptake values (SUV max ) of 18 F-FDG and 68 Ga-DOTATATE in all 26 primary tumors was 15.00 and 9.73, respectively ( P = 0.001). 68 Ga-DOTATATE PET was superior to 18 F-FDG PET in detecting intracranial and skull base involvement. The primary tumor volume of 68 Ga-DOTATATE with 35% SUV max as the threshold had the highest consistency with that of contrast-enhanced MRI. 68 Ga-DOTATATE and 18 F-FDG PET/CT detected 103/108 (95.4%) and 101/108 (93.5%) regional lymph nodes metastases ( P = 0.552), and the median SUV max was 6.05 and 10.81, respectively ( P < 0.001). Furthermore, 68 Ga-DOTATATE PET/CT detected more distant metastases than 18 F-FDG (89/92 [96.7%] vs 54/92 [58.7%], respectively, P < 0.001). The plasma EBV DNA was positively correlated with the total metabolic tumor volume, lesion glycolysis, somatostatin receptor-expressing tumor volume, and lesion somatostatin receptor expression (all P values <0.05). The PET parameters in the non-objective response rate group were higher than those in the objective response rate group (all P values >0.05). 68 Ga-DOTATATE PET/CT is a promising imaging modality for detecting primary and metastatic EBV-positive nonkeratinizing nasopharyngeal carcinoma and delineating primary tumor boundary.

Pathology-specific lipid alterations with triacylglycerol as a potential biomarker in Focal cortical dysplasia (FCD) and Mesial Temporal Lobe Epilepsy with Hippocampal Sclerosis (MTLE-HS).

Focal Cortical Dysplasia (FCD) & Mesial Temporal Lobe Epilepsy-Hippocampal Sclerosis (MTLE-HS) are two common pathologies of drug-resistant focal epilepsy (DRE). Inappropriate localization of the epileptogenic zones (EZs) in FCD is a significant contributing factor to the unsatisfactory surgical results observed in FCD cases. Currently, no molecular or cellular indicators are available which can aid in identifying the epileptogenic zones (EZs) in FCD. Phospholipid modifications in healthy and malignant tumour tissues have been documented and used to demarcate tumour boundaries. The objective of this research was to analyze and evaluate the lipid profiles in a manner that takes into account the specific disease and subtype. The technique of liquid chromatography and tandem mass spectrometry was utilized to detect changes in lipids in surgically resected brain samples from patients with FCD and MTLE-HS, in comparison to non-epileptic controls. Significant upregulation of TAGs was seen in both FCD and MTLE-HS. Additionally, the levels of triglycerides in the plasma of peripheral blood were measured in patients with FCD, MTLE-HS, and healthy individuals as controls. These findings suggest that employing distinct lipid mass spectra could be an effective method for identifying the EZs in FCD. The unique lipid mass spectra of cortical tissues from patients with FCD can be utilized for real-time surgical guidance. Additionally, the plasma triglyceride (TAG) level has the potential to act as a biomarker once validated on a larger cohort.

Multilevel perception boundary-guided network for breast lesion segmentation in ultrasound images.

Automatic segmentation of breast tumors from the ultrasound images is essential for the subsequent clinical diagnosis and treatment plan. Although the existing deep learning-based methods have achieved considerable progress in automatic segmentation of breast tumors, their performance on tumors with similar intensity to the normal tissues is still not satisfactory, especially for the tumorboundaries. To accurately segment the non-enhanced lesions with more accurate boundaries, a novel multilevel perception boundary-guided network (PBNet) is proposed to segment breast tumors from ultrasoundimages. PBNet consists of a multilevel global perception module (MGPM) and a boundary guided module (BGM). MGPM models long-range spatial dependencies by fusing both intra- and inter-level semantic information to enhance tumor recognition. In BGM, the tumor boundaries are extracted from the high-level semantic maps using the dilation and erosion effects of max pooling; such boundaries are then used to guide the fusion of low- and high-level features. Additionally, a multi-level boundary-enhanced segmentation (BS) loss is introduced to improve boundary segmentation performance. To evaluate the effectiveness of the proposed method, we compared it with state-of-the-art methods on two datasets, one publicly available datasets BUSI containing 780 images and one in-house dataset containing 995 images. To verify the robustness of each method, a 5-fold cross-validation method was used to train and test the models. Dice score (Dice), Jaccard coefficients (Jac), Hausdorff Distance (HD), Sensitivity (Sen), and specificity(Spe) were used to evaluate the segmentation performance quantitatively. The Wilcoxon test and Benjamini-Hochberg false discovery rate (FDR) multi-comparison correction were then performed to assess whether the proposed method presents statistically significant performance ( ) difference comparing with existing methods. In addition, to comprehensively demonstrate the difference between different methods, the Cohen's d effect size and compound p-value (c-Pvalue) obtained with Fisher's method were alsocalculated. On the BUSI dataset, the mean Dice and Sen of PBNet was increased by 0.93% ( ) and 1.42% ( ), respectively, comparing against the corresponding suboptimal methods. On the in-house dataset, PBNet improved Dice, Jac and Spe by approximately 0.86% ( ), 1.42% ( ), and 0.1%, respectively, and reduced HD by 1.7% ( ) compared to the sub-optimal model. Comprehensively, in terms of all the evaluation metics, the performance of the proposed method significantly (c-Pvalue ) outperformed the others but the effect size was smaller than 0.2. Ablation results confirmed that MGPM is effective in distinguishing non-enhanced tumors, while BGM and BS loss are beneficial for refining tumor segmentationcontours. The proposed PBNet allows us to segment the non-enhanced breast lesions from ultrasound images with more accurate boundaries, which provides a valuable means for the subsequent clinicalapplications.

A proof-of-concept study for precise mapping of pigmented basal cell carcinoma in asian skin using multispectral optoacoustic tomography imaging with level set segmentation.

Basal Cell Carcinoma (BCC), the most common subtype of non-melanoma skin cancers (NMSC), is prevalent worldwide and poses significant challenges due to their increasing incidence and complex treatment considerations. Existing clinical approaches, such as Mohs micrographic surgery, are time-consuming and labour-intensive, requiring meticulous layer-by-layer excision and examination, which can significantly extend the duration of the procedure. Current optical imaging solutions also lack the necessary spatial resolution, penetration depth, and contrast for effective clinical use. Here, we introduce photoacoustic imaging, also known as optoacoustic imaging, based Multispectral Optoacoustic Tomography (MSOT) as a promising solution for non-invasive, high-resolution imaging in dermatology, which also measures hemodynamic changes. MSOT offers high isotropic resolution (80μm), increased tissue penetration, and contrast-enhanced 3D spatial imaging map. For the first time, we integrated an automated level set image segmentation methodology on optoacoustic images to further enhance the precision in delineating tumor boundaries. Through this proof-of-concept study in 30 subjects, we demonstrate that this segmentation allows for precise measurement of tumor width, depth, and volume, aiding in preoperative tumor mapping and surgical planning. The MSOT measurements, validated against histology, achieved a correlation coefficient of 0.84 and 0.81 for width and depth respectively, ensuring reliable tumor metrics with a low margin of error. Clinicians can use these tumor metrics to optimize treatment efficacy, while preserving healthy tissue and cosmetic outcomes. This advancement has the potential to revolutionize diagnostics and treatment, significantly improving the patient outcomes in managing NMSC.

Deep learning-based synthetic CT for dosimetric monitoring of combined conventional radiotherapy and lattice boost in large lung tumors

PurposeConventional radiotherapy (CRT) has limited local control and poses a high risk of severe toxicity in large lung tumors. This study aimed to develop an integrated treatment plan that combines CRT with lattice boost radiotherapy (LRT) and monitors its dosimetric characteristics.MethodsThis study employed cone-beam computed tomography from 115 lung cancer patients to develop a U-Net + + deep learning model for generating synthetic CT (sCT). The clinical feasibility of sCT was thoroughly evaluated in terms of image clarity, Hounsfield Unit (HU) consistency, and computational accuracy. For large lung tumors, accumulated doses to the gross tumor volume (GTV) and organs at risk (OARs) during 20 fractions of CRT were precisely monitored using matrices derived from the deformable registration of sCT and planning CT (pCT). Additionally, for patients with minimal tumor shrinkage during CRT, an sCT-based adaptive LRT boost plan was introduced, with its dosimetric properties, treatment safety in high dose regions, and delivery accuracy quantitatively assessed.ResultsThe image quality and HU consistency of sCT improved significantly, with dose deviations ranging from 0.15% to 1.25%. These results indicated that sCT is feasible for inter-fraction dose monitoring and adaptive planning. After rigid and hybrid deformable registration of sCT and pCT, the mean distance-to-agreement was 0.80 ± 0.18 mm, and the mean Dice similarity coefficient was 0.97 ± 0.01. Monitoring dose accumulation over 20 CRT fractions showed an increase in high-dose regions of the GTV (P < 0.05) and a reduction in low-dose regions (P < 0.05). Dosimetric parameters of all OARs were significantly higher than those in the original treatment plan (P < 0.01). The sCT based adaptive LRT boost plan, when combined with CRT, significantly reduced the dose to OARs compared to CRT alone (P < 0.05). In LRT plan, high-dose regions for the GTV and D95% exhibited displacements greater than 5 mm from the tumor boundary in 19 randomly scanned sCT sequences under free breathing conditions. Validation of dose delivery using TLD phantom measurements showed that more than half of the dose points in the sCT based LRT plan had deviations below 2%, with a maximum deviation of 5.89%.ConclusionsThe sCT generated by the U-Net + + model enhanced the accuracy of monitoring the actual accumulated dose, thereby facilitating the evaluation of therapeutic efficacy and toxicity. Additionally, the sCT-based LRT boost plan, combined with CRT, further minimized the dose delivered to OARs while ensuring safe and precise treatment delivery.

Composite spin probes with adjustable oxygen sensitivity for pulse electron paramagnetic resonance imaging.

Solid crystalline spin probes, such as lithium phthalocyanine (LiPc) and lithium octa-n-butoxynaphthalocyanine (LiNc-BuO), allow repeated oxygen measurement using electron paramagnetic resonance (EPR). Due to their short relaxation times, their use for pulse EPR oxygen imaging is limited. In this study, we developed and tested a new class of solid composite spin probes that modified the relaxation rates R1 and R2 of LiPc or LiNc-BuO probes, which allowed pO2 measurements in the full dynamic (0-760 torr) range. The composite probes were developed by embedding LiPc or LiNc-BuO with bonewax, beeswax, or petroleum jelly. All experiments were performed using a 25-mT EPR imager, JIVA-25®. A selected composite probe, LiPc-BW5 (LiPc + 5-times bonewax), was tested for its in vivo stability and robustness using oxygen-enhanced EPR oxygen imaging. As another application, a LiPc-BW5-based marker was tested to outline the SCC7 tumor in a C3H mouse. The composite probes showed an increased oxygen measurement range compared with unaltered probes. The in vivo experiments with LiPc-BW5 showed the stability of the probes for repeated oxygen imaging up to 4 weeks of measurements. The in vivo pO2 at the subcutaneous site changed from 26.1 ± 1.9 torr to 118.9 ± 1.9 torr when the breathing gas was changed from 21% O2 to 100% O2. The use of LiPc-BW5 as a fiducial marker showed its use in outlining the tumor boundaries. We developed a new class of robust and versatile solid composite probes with adjustable oxygen sensitivity that allows pO2 imaging in the broad dynamic range.

Minimum resection length to ensure a pathologically negative distal margin and a larger remnant stomach for esophagogastric junction cancer.

Ensuring a pathologically negative distal margin (DM) and preserving a larger remnant stomach is important for proximal gastrectomy (PG) in patients with esophagogastric junction (EGJ) cancer. However, the minimum DM length for ensuring negative margins has not been identified. We enrolled patients undergoing PG or total gastrectomy for EGJ cancer. A parameter ΔDM, representing the pathological extension distally beyond the gross tumor boundary, was evaluated. The maximum ΔDM, which indicates the minimum length ensuring a pathologically negative DM, was determined in all patients. Subgroup analyses were performed according to factors associated with ΔDM > 10mm. The possible incidences of pathologically positive DM based on gross DM length were also calculated. Among 253 eligible patients, the maximum ΔDM was 55mm. Growth and pathological types were significantly associated with ΔDM > 10mm. In subgroup analyses, the maximum ΔDM was 30/20/55mm for the superficial/expansive/infiltrative growth types, and 55/40mm for the differentiated/undifferentiated types. In the infiltrative growth type alone, the maximum ΔDM remained 55/40mm for the differentiated/undifferentiated types. However, even if the gross DM length was reduced to 30mm, the possible incidence of pathologically positive DM only increased to 2.6% in the infiltrative differentiated type. We recommend a minimum DM length of 30/20/55mm for the superficial/expansive/ infiltrative growth types. Specifically in the infiltrative growth type, we alternatively recommend 30/40mm for the differentiated/undifferentiated types, with a mandatory intraoperative frozen section analysis. Mini-abstract This study proposes a distal margin length for safe resection of esophagogastric junction cancer, ensuring pathologically negative margins while preserving a larger remnant stomach, based on growth and pathological types.

Silicone Fiducial Markers Improve Precision in Uveal Melanoma Radiation Therapy.

Objectives: Accurate target definition, treatment planning and delivery increases local tumor control for radiotherapy by minimizing collateral damage. To achieve this goal for uveal melanoma (UM), tantalum fiducial markers (TFMs) were previously introduced in proton and photon beam radiotherapy. However, TFMs cause pronounced scattering effects in imaging that make the delineation of small tumors difficult. The aim of this study was to evaluate silicone fiducial markers (SFMs) for the guiding of stereotactic radiosurgery (SRS) for UM. Methods: In this retrospective interventional pilot case series, three patients with small UMs 3 mm or less in tumor thickness and ≤10 mm in largest basal diameter received silicone fiducial markers. The fiducial markers were punched out (3 mm) from conventional silicone encircling bands for buckle surgery. The markers were sutured onto the sclera at the tumor margins according to the use of TFMs. MRI and CT images were used for the localization of the tumor and the markers before robotic-guided SRS. Results: The silicone fiducial markers were punched out easily from the original band, better to handle than TFMs and easy to suture onto the sclera. They could be visualized in both MRI and CT, but were more visible in CT. In the absence of scattering effects, both the markers and thus the tumor boundaries could be clearly delineated. Conclusions: This is the first report that introduces fiducial markers intraoperatively shaped from conventional silicone encircling bands usually used for retinal detachment surgery. The SFMs allow more accurate tumor delineation, resulting in the more precise planning and administration of SRS when compared to TFMs. This simple modification has a major impact on a well-known treatment approach.

Enhanced CATBraTS for Brain Tumour Semantic Segmentation.

The early and precise identification of a brain tumour is imperative for enhancing a patient's life expectancy; this can be facilitated by quick and efficient tumour segmentation in medical imaging. Automatic brain tumour segmentation tools in computer vision have integrated powerful deep learning architectures to enable accurate tumour boundary delineation. Our study aims to demonstrate improved segmentation accuracy and higher statistical stability, using datasets obtained from diverse imaging acquisition parameters. This paper introduces a novel, fully automated model called Enhanced Channel Attention Transformer (E-CATBraTS) for Brain Tumour Semantic Segmentation; this model builds upon 3D CATBraTS, a vision transformer employed in magnetic resonance imaging (MRI) brain tumour segmentation tasks. E-CATBraTS integrates convolutional neural networks and Swin Transformer, incorporating channel shuffling and attention mechanisms to effectively segment brain tumours in multi-modal MRI. The model was evaluated on four datasets containing 3137 brain MRI scans. Through the adoption of E-CATBraTS, the accuracy of the results improved significantly on two datasets, outperforming the current state-of-the-art models by a mean DSC of 2.6% while maintaining a high accuracy that is comparable to the top-performing models on the other datasets. The results demonstrate that E-CATBraTS achieves both high segmentation accuracy and elevated generalisation abilities, ensuring the model is robust to dataset variation.

Adaptive Fusion and Edge‐Oriented Enhancement for Brain Tumor Segmentation With Missing Modalities

ABSTRACTMagnetic resonance imaging (MRI) offers comprehensive information about brain structures, enabling excellent performance in brain tumor segmentation using multimodal MRI in many methods. Nonetheless, missing modalities are common in clinical practice, which can significantly degrade segmentation performance. Current brain tumor segmentation methods often struggle to maintain feature consistency and robustness in multimodal feature fusion when modalities are missing and face difficulties in accurately capturing tumor boundaries. In this study, we propose an adaptive fusion and edge‐oriented enhancement method to address these challenges. Our approach introduces learnable parameters and a masked attention mechanism in the transformer model to achieve cross‐modal adaptive fusion, ensuring consistent feature representation even with missing data. To aggregate more information, we integrate multimodal and multi‐level features through a hierarchical context integration module. Additionally, to tackle the complex morphology of brain tumor regions, we design an edge‐enhanced deformable convolution module that captures deformation information and edge features from incomplete multimodal images, enabling precise tumor localization. Evaluations on the widely recognized BRATS2018 and BRATS2020 datasets demonstrate that our approach significantly surpasses existing brain tumor segmentation techniques in scenarios with missing modalities.

Segmentation of Brain Tumor Resections in Intraoperative 3D Ultrasound Images Using a Semisupervised Cross nnSU‐Net

ABSTRACTIntraoperative ultrasound (iUS) has been widely used in recent years to track intraoperative brain tissue deformation. Outlining tumor boundaries on iUS not only facilitates the robustness and accuracy of brain shift correction but also enables the direct use of iUS information for neurosurgical navigation. We developed a semisupervised cross nnU‐Net with depthwise separable convolution (SSC nnSU‐Net) for real‐time segmentation of 3D iUS images by two networks with different initialization but consistent network structure networks. Unlike previous methods, RESECT as labeled data and ReMIND as unlabeled data for hybrid dataset training selected break down the barriers between different datasets and further alleviate the problem of “data hunger.” The SSC nnSU‐Net method was evaluated by ablation of semisupervised learning, comparison with other state‐of‐the‐art methods, and model complexity. The results indicate that the proposed framework achieves a certain balance in terms of computation time, GPU memory utilization, and segmentation performance. This motivates segmentation of 3D iUS images for real‐time application in clinical surgery. The method can assist surgeons in identifying brain tumors through iUS.

MRI of the Rectum: A Decade into DISTANCE, Moving to DISTANCED.

Over the past decade, advancements in rectal cancer research have reshaped treatment paradigms. Historically, treatment for locally advanced rectal cancer has focused on neoadjuvant long-course chemoradiotherapy, followed by total mesorectal excision. Interest in organ preservation strategies has been strengthened by the introduction of total neoadjuvant therapy with improved rates of complete clinical response. The administration of systemic induction chemotherapy and consolidation chemoradiotherapy in the neoadjuvant setting has introduced a new dimension to the treatment landscape and patients now face a more intricate decision-making process, given the expanded therapeutic options. This complexity underlines the importance of shared decision-making and brings to light the crucial role of radiologists. MRI, especially high-spatial-resolution T2-weighted imaging, is heralded as the reference standard for rectal cancer management because of its exceptional ability to provide staging and prognostic insights. A key evolution in MRI interpretation for rectal cancer is the transition from the DISTANCE mnemonic to the more encompassing DISTANCED-DIS, distal tumor boundary; T, T stage; A, anal sphincter complex; N, nodal status; C, circumferential resection margin; E, extramural venous invasion; D, tumor deposits. This nuanced shift in the mnemonic captures a wider range of diagnostic indicators. It also emphasizes the escalating role of radiologists in steering well-informed decisions in the realm of rectal cancer care.

Risk assessment and prediction of occult uterine sarcoma in patients with presumed uterine fibroids before high-intensity focused ultrasound treatment

Objective To develop a diagnostic model for predicting occult uterine sarcoma in patients with presumed uterine fibroids. Materials and methods We retrospectively reviewed 41631 patients with presumed uterine fibroids who presented for HIFU treatment in 13 hospitals between November 2008 and October 2023. Of these patients, 27 with occult uterine sarcoma and 54 with uterine fibroids were enrolled. Univariate analysis and multivariate logistics regression analysis were used to determine the independent risk factors for the diagnosis of occult uterine sarcoma. A prediction model was constructed based on the coefficients of the risk factors. Results The multivariate analysis revealed abnormal vaginal bleeding, ill-defined boundary of tumor, hyperintensity on T2WI, and central unenhanced areas as independent risk factors. A scoring system was created to assess for occult uterine sarcoma risk. The score for abnormal vaginal bleeding was 56. The score for ill-defined lesion boundary was 90. The scores for lesions with hypointensity, isointensity signal/heterogeneous signal intensity, and hyperintensity on T2WI were 0, 42, and 93, respectively. The scores for lesions without enhancement on the mass margin, uniform enhancement of tumor, and no enhancement in the center of tumor were 0, 20, and 100, respectively. Patients with a higher total score implied a higher likelihood of a diagnosis of occult uterine sarcoma than that of patients with a lower score. The established model showed good predictive efficacy. Conclusions Our results demonstrated that the diagnostic prediction model can be used to evaluate the risk of uterine sarcoma in patients with presumed uterine fibroids.

Pleomorphic dermal sarcoma of the scalp with intracranial space involvement: management of a rare entity. Illustrative case.

Exophytic tumors of the calvaria (ETCs) remain a challenging pathology because of their complex management. The authors discuss the case of a woman with a large exophytic mass of the right frontotemporal region and underline their decision-making process on the management of this unique case and possible similar ones. Neuroradiological findings showed a calvarial tumor with both epicranial and intracranial extension involving the frontotemporal bone with a mixed component (lytic and sclerotic) and dural infiltration with a pseudonodular pattern. A wide en bloc excision from the skin to the dura mater was performed. The compound 5-aminolevulinic acid (5-ALA) was not very useful in identifying the tumor boundaries. One-step cranioplasty and a skin pedicle flap were used to reconstruct the anatomical defect. Acellular dermal matrix was used to repair the uncovered calvaria. Pathological examination confirmed the diagnosis of pleomorphic dermal sarcoma (PDS). In the evaluation of an ETC, PDS should be considered. Wide en bloc excision, if achievable, should be considered the gold standard. The 5-ALA was not helpful, and a pedicle skin flap could be considered as a less invasive alternative to microsurgical reconstruction. The use of an acellular matrix implant on the contralateral exposed galea increases the rate of reconstruction success. https://thejns.org/doi/10.3171/CASE24457.

Intraoperative variations in intra-axial brain tumor size after craniotomy: a prospective study with histopathological and tumor tissue composition correlation, introducing concepts of tumor expansion and tumor surfacing.

Intraoperative assessment of tumor margins can be challenging; as neoplastic cells may extend beyond the margins seen on preoperative imaging. Real-time intraoperative ultrasonography (IOUS) has emerged as a valuable tool for delineating tumor boundaries during surgery. However, concerns remain regarding its ability to accurately distinguish between tumor margins, peritumoral edema, and normal brain tissue. Preoperative contrast-enhanced MRI (CEMRI) and contrast-enhanced CT (CECT) were performed to assess tumor dimensions, and IOUS was used intraoperatively to further evaluate tumor characteristics. Tumor volume was estimated using the prolate ellipsoid formula. Statistical analysis was conducted to compare tumor dimensions between imaging modalities and assess tumor expansion post-craniotomy. Our study included 51 patients with intracranial tumors. IOUS revealed larger tumor dimensions compared to preoperative CEMRI and CECT, with significant differences observed in surface area and volume. Tumors exhibited varied echogenicity on IOUS, with most showing mixed echogenicity. Histopathological analysis revealed a range of tumor grades, with gliomas being the most common. Statistical analysis indicated significant differences in tumor dimensions between imaging modalities, with tumor expansion observed post-craniotomy. Tumor type and grade were predictive factors for tumor volume expansion. Tumors exhibit expansion after craniotomy, with both tumor volume and surface area increasing. This expansion phenomenon, not solely attributed to tumor edema, underscores the importance of considering tumor mass expansion during surgical planning and intraoperative decision-making. These findings highlight the utility of IOUS in accurately delineating tumor boundaries and optimizing surgical outcomes in the management of intracranial tumors.

Comparison of optical-spectral characteristics of glioblastoma at intraoperative diagnosis and ex vivo optical biopsy

The difficulty of intraoperative delineation of glial tumors is due to the peculiarities of their growth along vessels and nerve fibers with infiltration of healthy white matter. Insufficiently complete removal of tumor tissues leads to recurrences, and excessive removal is fraught with neurological complications. Optical spectroscopy methods are characterized by high speed, accuracy and non-invasiveness, which determines the prospects of their use for intraoperative demarcation of the boundaries of such tumors. Fluorescence and diffuse reflectance spectroscopy have found wide application in intraoperative neuronavigation, mainly for detecting the edges of diffuse gliomas. At the same time, in recent years the direction of &lt;i&gt;ex vivo&lt;/i&gt; spectral analysis of tumor samples using a combination of various optical spectroscopy methods, including both elastic and inelastic scattering spectroscopy, has been actively developed. Obviously, the ability to obtain spectra intraoperatively and on fresh specimens is different. The present article compares the results of the analysis of optical-spectral characteristics of intracranial tumors at intraoperative diagnosis and &lt;i&gt;ex vivo&lt;/i&gt; analysis and proposes a mathematical model for interpretation of the observed dependencies.

Advanced Hybrid Brain Tumor Segmentation in MRI: Elephant Herding Optimization Combined with Entropy-Guided Fuzzy Clustering

Accurate and early detection of brain tumors is essential for improving clinical outcomes and guiding effective treatment planning. Traditional segmentation techniques in MRI often struggle with challenges such as noise, intensity variations, and complex tumor morphologies, which can hinder their effectiveness in critical healthcare scenarios. This study proposes an innovative hybrid methodology that integrates advanced metaheuristic optimization and entropy-based fuzzy clustering to enhance segmentation precision in brain tumor detection. This method combines the nature-inspired Elephant Herding Optimization (EHO) algorithm with Entropy-Driven Fuzzy C-Means (EnFCM) clustering, offering significant improvements over conventional methods. EHO is utilized to optimize the clustering process, enhancing the algorithm’s ability to delineate tumor boundaries, while entropy-based fuzzy clustering accounts for intensity inhomogeneity and diverse tumor characteristics, promoting more consistent and reliable segmentation results. This approach was evaluated using the BraTS challenge dataset, a benchmark in the field of brain tumor segmentation. The results demonstrate marked improvements across several performance metrics, including Dice similarity, mean squared error (MSE), peak signal-to-noise ratio (PSNR), and the Tanimoto coefficient (TC), underscoring this method’s robustness and segmentation accuracy. By managing image noise and reducing computational demands, the EHO-EnFCM approach not only captures intricate tumor structures but also facilitates efficient image processing, making it suitable for real-time clinical applications. Overall, the findings reveal the potential of this hybrid approach to advance MRI-based tumor detection, offering a promising tool that enhances both accuracy and computational efficiency for medical imaging and diagnosis.

Dynamic Focus on Tumor Boundaries: A Lightweight U-Net for MRI Brain Tumor Segmentation.

Accurate segmentation of brain tumors in MRI scans is critical for diagnosis and treatment planning. Traditional segmentation models, such as U-Net, excel in capturing spatial information but often struggle with complex tumor boundaries and subtle variations in image contrast. These limitations can lead to inconsistencies in identifying critical regions, impacting the accuracy of clinical outcomes. To address these challenges, this paper proposes a novel modification to the U-Net architecture by integrating a spatial attention mechanism designed to dynamically focus on relevant regions within MRI scans. This innovation enhances the model's ability to delineate fine tumor boundaries and improves segmentation precision. Our model was evaluated on the Figshare dataset, which includes annotated MRI images of meningioma, glioma, and pituitary tumors. The proposed model achieved a Dice similarity coefficient (DSC) of 0.93, a recall of 0.95, and an AUC of 0.94, outperforming existing approaches such as V-Net, DeepLab V3+, and nnU-Net. These results demonstrate the effectiveness of our model in addressing key challenges like low-contrast boundaries, small tumor regions, and overlapping tumors. Furthermore, the lightweight design of the model ensures its suitability for real-time clinical applications, making it a robust tool for automated tumor segmentation. This study underscores the potential of spatial attention mechanisms to significantly enhance medical imaging models and paves the way for more effective diagnostic tools.

3W‐MultiHier: A Three Way Multi‐Hierarchical Model Enabled Deep Learning for Brain Tumor Classification in MRI Scans

AbstractAccurate brain tumor detection and classification are vital for effective diagnosis and treatment planning in medical imaging. Despite advancements in deep learning, challenges such as multimodal complexity, small lesion segmentation, limited training data, and variability in tumor characteristics hinder precise tumor analysis in MRI scans. To address these issues, we propose the Three Way Multi‐Hierarchical Model (3W‐MultiHier) for tumor classification in MRI. 3W‐MultiHier employs a hybrid Capsule‐Transformer UNet (Capsule‐TransUNet) architecture, integrating capsule and transformer networks within the U‐Net framework. This enables the model to capture spatial hierarchies, long‐range dependencies, and global context, ensuring accurate tumor boundary segmentation. The model also incorporates Residual Network Version 2 ‐ Squeeze‐and‐Excitation Network (ResNetV2‐SENet), which excels at extracting complex features through deep hierarchical structures and feature recalibration. Additionally, the Vision Transformer ‐ Transfer Learning (ViT‐TL) pipeline enhances classification accuracy by leveraging fine‐grained hierarchical representations. Extensive evaluations on BraTS (2019, 2020, 2021) datasets demonstrate the superior performance of 3W‐MultiHier, achieving 99.8% accuracy with rapid training and low loss. These results highlight the model's efficiency in handling diverse datasets and its potential to improve clinical diagnostics by enabling precise, reliable brain tumor classification.