Identification Of Tumors Research Articles

Segmentation Synergy with a Dual U-Net and Federated Learning with CNNRF Models for Enhanced Brain Tumor Analysis

Background: Brain tumours represent a diagnostic challenge, especially in the imaging area, where the differentiation of normal and pathologic tissues should be precise. The use of up-to-date machine learning techniques would be of great help in terms of brain tumor identification accuracy from MRI data. Objective: This research paper aims to check the efficiency of a federated learning method that joins two classifiers, such as convolutional neural networks (CNNs) and random forests (R.F.F.), with dual U-Net segmentation for federated learning. This procedure benefits the image identification task on preprocessed MRI scan pictures that have already been categorized. Methods: In addition to using a variety of datasets, federated learning was utilized to train the CNN-RF model while taking data privacy into account. The processed MRI images with Median, Gaussian, and Wiener filters are used to filter out the noise level and make the feature extraction process easy and efficient. The surgical part used a dual U-Net layout, and the performance assessment was based on precision, recall, F1-score, and accuracy. Results: The model achieved excellent classification performance on local datasets as CRPs were high, from 91.28% to 95.52% for macro, micro, and weighted averages. Throughout the process of federated averaging, the collective model outperformed by reaching 97% accuracy compared to those of 99%, which were subjected to different clients. The correctness of how data is used helps the federated averaging method convert individual model insights into a consistent global model while keeping all personal data private. Conclusion: The combined structure of the federated learning framework, CNN-RF hybrid model, and dual U-Net segmentation is a robust and privacypreserving approach for identifying MRI images from brain tumors. The results of the present study exhibited that the technique is promising in improving the quality of brain tumor categorization and provides a pathway for practical utilization in clinical settings.

Glioma Identification Based on Digital Multimodal Spectra Integrated With Deep Learning Feature Fusion Using a Miniature Raman Spectrometer.

The miniature fiber Raman spectroscopy detection technology can reflect the properties of biomolecules through spectral characteristics and has the advantages of noninvasiveness, real-time, safety, label-free operation, and potential for early cancer diagnosis. This technology holds promise for developing portable, low-cost, intraoperative tumor detection instruments. Glioma is one of the most common malignant tumors of the central nervous system with rapid growth and a short disease course. However, the considerable heterogeneity of the glioma sample leads to substantial intraclass variance in collected spectra, coupled with the miniature Raman spectrometer's low signal-to-noise ratio. These factors diminish the accuracy of the brain glioma recognition model. To address this issue, a glioma identification method based on digital multimodal spectra integrated with deep learning features fusion (DMS-DLFF) using the miniature Raman spectrometer is proposed. Different from existing multimodal tumor detection methods employing multiple spectral instruments, DMS-DLFF enhances tumor identification accuracy without increasing hardware costs. The method mathematically decomposes the original spectra to Raman and fluorescence spectra, so as to augment the biospectral information. Then, the deep learning method is used to extract the feature information of the two kinds of spectra, respectively, and the digital multimodal spectral fusion is realized at the feature level. Moreover, a two-layer pattern recognition model is constructed based on the ensemble strategy, amalgamating the strengths of diverse classifiers. Meanwhile, the bagging strategy is introduced to improve support vector machine algorithms, one of the basic classifiers. Compared with traditional methodologies, DMS-DLFF operates at both the feature level and decision level, employing high-information-density feature vectors to train ensemble classification models for increasing overall recognition accuracy. This study collected 260 Raman spectra of glioma and 151 Raman spectra of normal brain tissue. The accuracy, sensitivity, and specificity were 91.9%, 96.7%, and 80.8%, respectively. The proposed method outperforms traditional algorithms in brain glioma detection, which helps doctors formulate precise surgical plans and thereby improve patient prognosis.

Illuminate Resection Pathways with Fluorescence Guidance in Glioma Surgery: Case Reports and Systematic Review

Gliomas are the most common brain tumor in adults, with a poor prognosis despite intensive treatments. Complete surgical resection is difficult due to its infiltrative growth, but aggressive surgery improves outcomes. Fluorescence-guided surgery (FGS) is used to distinguish tumor tissue during surgery. 5-Aminolevulinic Acid (5-ALA) is a crucial fluorescent agent in FGS, transforming into a molecule that accumulates in tumor cells. We presented a 34-year-old female with a high-grade glioma in the left parietal lobe who underwent fluorescence-guided tumor resection using 5-ALA was reported. In addition, a review of the literature on fluorescence in glioma surgery, searching databases like PubMed and SCOPUS from 2021 to 2023, was performed. Fifteen papers were included in our review. This technique ensured gross-total tumor resection while preserving neurological function. FGS improves tumor identification, surgical outcomes, and survival.

Radiotracer Innovations in Breast Cancer Imaging: A Review of Recent Progress.

This review focuses on the pivotal role of radiotracers in breast cancer imaging, emphasizing their importance in accurate detection, staging, and treatment monitoring. Radiotracers, labeled with radioactive isotopes, are integral to various nuclear imaging techniques, including positron emission tomography (PET) and positron emission mammography (PEM). The most widely used radiotracer in breast cancer imaging is 18F-fluorodeoxyglucose (18F-FDG), which highlights areas of increased glucose metabolism, a hallmark of many cancer cells. This allows for the identification of primary tumors and metastatic sites and the assessment of tumor response to therapy. In addition to 18F-FDG, this review will explore newer radiotracers targeting specific receptors, such as estrogen receptors or HER2, which offer more personalized imaging options. These tracers provide valuable insights into the molecular characteristics of tumors, aiding in tailored treatment strategies. By integrating radiotracers into breast cancer management, clinicians can enhance early disease detection, monitor therapeutic efficacy, and guide interventions, ultimately improving patient outcomes. Ongoing research aimed at developing more specific and sensitive tracers will also be highlighted, underscoring their potential to advance precision medicine in breast cancer care.

Enhanced tumor targeting and penetration of fluorophores via iRGD peptide conjugation: a strategy for the precision targeting of lung cancer.

Accurate real-time tumor delineation is essential for achieving curative resection (R0 resection) during non-small cell lung cancer (NSCLC) surgery. The unique characteristics of lung tissue structure significantly challenge the use of video-assisted thoracoscopic surgery in the identification of lung nodules. This difficulty often results in an inability to discern the margins of lung nodules, necessitating either an expansion of the resection scope, or a transition to open surgery. Due to its high spatial resolution, ease of operation, and capacity for real-time observation, near-infrared fluorescence (NIRF) navigation in oncological surgery has emerged as a focal point of clinical research. Targeted NIRF probes, which accumulate preferentially in tumor tissues and are rapidly cleared from normal tissues, enhance diagnostic sensitivity and surgical outcomes. The imaging effect of the clinically approved NIRF probe indocyanine green (ICG) varies significantly from person to person. Therefore, we hope to develop a new generation of targeted NIRF probes targeting lung tumor-specific targets. First, the peptide iRGD (sequence: CRGDKGPDC) fluorescent tracer was synthesized, and characterized through mass spectrometry (MS), proton nuclear magnetic resonance (1H NMR), and high-performance liquid chromatography (HPLC). Fluorescence properties were tested subsequently. Safety was performed in vitro using both human normal liver cells and human normal breast cells. Second, Metabolism and optimal imaging time were determined by tail vein injection of iRGD fluorescent tracer. Finally, Orthotopic and metastatic lung tumor models were used to evaluate the targeting properties of the iRGD fluorescent tracer. We successfully synthesized an iRGD fluorescent tracer specifically designed to target NSCLC. The molecular docking analyses indicated that this tracer has receptor affinity comparable to that of iRGD for αvβ3 integrin, with a purity ≥98%. Additionally, the tracer is highly soluble in water, and its excitation and emission wavelengths are 767 and 799 nm, respectively, positioning it within the near-infrared spectrum. The cellular assays confirmed the tracer's minimal cytotoxicity, underscoring its excellent biosafety profile. In vivo studies further validated the tracer's capacity for specific NSCLC detection at the cellular level, alongside a prolonged imaging window of 6 days or more. Notably, the tracer demonstrated superior specificity in localizing very small lung nodules, which are otherwise clinically indiscernible, outperforming non-targeted ICG. Fluorescence intensity analyses across various organs revealed that the tracer is predominantly metabolized by the liver and kidneys, with excretion via bile and urine, and exhibits minimal toxicity to these organs as well as the lungs. The iRGD fluorescent tracer selectively accumulates in NSCLC tissues by specifically targeting αvβ3 receptors, which are overexpressed on the surface of tumor cells. This targeted approach facilitates the real-time intraoperative localization of NSCLC, presenting an improved strategy for intraoperative tumor identification with significant potential for clinical application.

Raman and autofluorescence spectroscopy for in situ identification of neoplastic tissue during surgical treatment of brain tumors.

Raman spectroscopy (RS) is a promising method for brain tumor detection. Near-infrared autofluorescence (AF) acquired during RS provides additional useful information for tumor identification and was investigated in comparison with RS for delineating brain tumors in situ. Raman spectra were acquired together with AF in situ within the solid tumor and at the tumor border during routine brain tumor surgeries (218 spectra; glioma WHO II-III, n = 6; GBM, n = 10; metastases, n = 10; meningioma, n = 3). Tissue classification for tumor identification in situ was trained on ex vivo data (375 spectra; glioma/GBM patients, n = 20; metastases, n = 11; meningioma, n = 13; and epileptic hippocampi, n = 4). Both in situ and ex vivo data showed that AF intensity in brain tumors was lower than that in border regions and normal brain tissue. Moreover, a positive correlation was observed between the AF intensity and the intensity of the Raman band corresponding to lipids at 1437cm- 1, while a negative correlation was found with the intensity of the protein band at 1260cm- 1. The classification of in situ AF and RS datasets matched the surgeon's evaluation of tissue type, with correct rates of 0.83 and 0.84, respectively. Similar correct rates were achieved in comparison to histopathology of tissue biopsies resected in selected measurement positions (AF: 0.80, RS: 0.83). Spectroscopy was successfully integrated into existing neurosurgical workflows, and in situ spectroscopic data could be classified based on ex vivo data. RS confirmed its ability to detect brain tumors, while AF emerged as a competitive method for intraoperative tumor delineation.

A Linear time shrinking-SL(t)-ViT approach for brain tumor identification and categorization

Automated brain tumor detection and classification systems have gained popularity in recent years because traditional diagnosis procedures are time-consuming and costly in nature. Deep learning(DL) methods, specifically pre-trained convolutional neural networks (CNNs), have shown promising results in accurately and rapidly classifying brain tumors. However, the lack of diverse magnetic resonance imaging (MRI) datasets has hindered the ability of DL algorithms to generalize effectively. To address this issue, this paper proposes a DL model using generative adversarial networks (GANs) in conjunction with data augmentation strategies and a structural similarity loss function employed for generating annotated images. A novel DL model inspired from Vision Transformer, Shrinking Linear Time Vision Transformer (SL(t)-ViT) network model is proposed for disease data classification. The model underwent extensive evaluation across multiple datasets, employing standard performance metrics to assess its efficacy in brain tumor identification. The proposed model achieved remarkable testing accuracies of 0.995, 0.996, 0.9954, 0.998, and 0.997 for binary classification tasks across multiple datasets, and 0.986, 0.982, 0.985, and 0.993 for multi-class classification tasks. These results underscore the superior performance of our proposed model, showcasing its capability to outperform state-of-the-art techniques. Specifically, it demonstrated a substantial margin of improvement, ranging from 1-2% for binary classification and 9-10% for multi-class classification, solidifying its position as a leading approach in brain tumor identification. Results demonstrate the efficacy of the proposed model, outperforming other models. Overall, this study highlights the potential of SL(t)-ViT and GANs in improving the accuracy, resource consumption, and efficiency of brain tumor diagnosis.

Impact of Infrared Indocyanine Green Fluorescence imaging-guided Laparoscopic Hepatectomy on Securing the Resection Margin for Colorectal Liver Metastasis.

Laparoscopic hepatectomy for colorectal liver metastases (CRLM) is performed worldwide. However, owing to a lack of palpatory information and difficulties associated with accurate intraoperative ultrasonographic diagnosis, the tumor may be exposed at the hepatic transection margin. This study aimed to investigate the pathological significance of near-infrared (NIR) fluorescence imaging with indocyanine green (ICG)-guided laparoscopic hepatectomy and determine its usefulness in securing the resection margin for CRLMs. Fifty-nine patients who underwent laparoscopic hepatectomy for CRLM using NIR fluorescence imaging between February 2017 and June 2021 at Sapporo Medical University Hospital were included. Generally, all patients received intravenous ICG (2.5mg/body) as a fluorescence agent 1 to 2 days before surgery. During the surgical procedure, real-time NIR fluorescence imaging was repeatedly performed to assess the surgical margins. Of the 94 tumors in 59 patients, laparoscopic NIR fluorescence imaging identified 56 tumors (59.6%) on the liver surface. Pathological analysis indicated clear margins in 96.6% (57/59) of patients. Examination of paraffin-embedded sections, which were successful in only 20 of 94 cases (21.3%), revealed that there were no tumor cells positive for NIR fluorescence, and the median distance of the continuous fluorescent signal from the tumor margin was 1.074mm. We demonstrated a high R0 rate using NIR fluorescence-guided hepatectomy. This technique has the potential to improve intraoperative tumor identification and tumor margin assurance and reduce the rate of positive resection margins in patients with CRLMs.

Strategies, Challenges, and Prospects of Nanoparticles in Gynecological Malignancies.

Gynecologic cancers are a significant health issue for women globally. Early detection and successful treatment of these tumors are crucial for the survival of female patients. Conventional therapies are often ineffective and harsh, particularly in advanced stages, necessitating the exploration of new therapy options. Nanotechnology offers a novel approach to biomedicine. A novel biosensor utilizing bionanotechnology can be employed for early tumor identification and therapy due to the distinctive physical and chemical characteristics of nanoparticles. Nanoparticles have been rapidly applied in the field of gynecologic malignancies, leading to significant advancements in recent years. This study highlights the significance of nanoparticles in treating gynecological cancers. It focuses on using nanoparticles for precise diagnosis and continuous monitoring of the disease, innovative imaging, and analytic methods, as well as multifunctional drug delivery systems and targeted therapies. This review examines several nanocarrier systems, such as dendrimers, liposomes, nanocapsules, and nanomicelles, for gynecological malignancies. The review also examines the enhanced therapeutic potential and targeted delivery of ligand-functionalized nanoformulations for gynecological cancers compared to nonfunctionalized anoformulations. In conclusion, the text also discusses the constraints and future exploration prospects of nanoparticles in chemotherapeutics. Nanotechnology will offer precise methods for diagnosing and treating gynecological cancers.

Prompt Multi-level Segmentation with Denoising Model with Fragile Correlated Feature Subset for Brain Tumor Classification.

Background Classifying brain tumors with extraordinary precision using images is critical for prognosis and treatment planning. The aberrant proliferation of brain cells characterizes brain tumors. Variations in neuronal development may occur among individuals. The classification of tumors as benign or malignant is contingent upon their rate of growth. A benign tumor remains localized at its site of origin; one that has spread to distant sites is malignant. Brain tumor identification may be difficult due to the unique characteristics of brain tumor cells. Objective This study presents a method that methodically improves the identification of brain tumor cells and the analysis of functional structures through the utilization of sample training that incorporates features extracted from Magnetic Resonance Imaging (MRI) images. In the image enhancement phase, the color information of the MRI image is converted to greyscale, and its margins are sharpened to facilitate the detection of finer details. For specialists or general practitioners to accurately diagnose life-threatening conditions, such as brain tumors, medical images are required. Picture denoising has been identified in recent research as a potentially fruitful area of study. It is critical to perform image cleanup while preserving the sharpness of the boundaries. Methods In this research, a Prompt Multi Level Segmentation Denoising model with a Fragile Correlated Feature Subset (PMLSD-FCFS) model is proposed for accurate denoising of MRI images and to extract the most relevant features set by applying a feature dimensionality reduction model for better brain tumor predictions. Results The proposed model achieves 98.2% accuracy in Multi-Level Image Segmentation and 98.4% accuracy in Fragile Correlated Feature Subset Generation. Conclusion The experimental findings indicated that the model proposed exhibits superior performance compared to the traditional algorithms. Furthermore, it successfully eliminates the noise from the MRI images, and most relevant features are only considered for brain tumor detection, thereby enhancing the accuracy of classification.

Enhancing liver surgery and transplantation: the role of 3D printing and virtual reality

This review explores the significant advancements in liver surgery and transplantation, particularly focusing on the integration of 3D printing and virtual reality (VR) technologies. The core objective is to enhance preoperative planning, simulation, and intraoperative navigation. The review discusses several studies that underscore the accuracy and utility of 3D printed models derived from medical imaging, which are instrumental in identifying small liver lesions, improving surgical education, and facilitating patient comprehension. Additionally, the role of VR in surgical simulation is examined, highlighting its superiority in tumor identification and its potential in training systems. While clinical outcomes data suggest a need for further randomized trials to establish the impact on surgical efficiency and recovery, the review also touches upon the promising future of augmented reality (AR) for intraoperative guidance and liver segment identification, with prospects of artificial intelligence (AI) integration. The conclusion underscores the importance of continued clinical evidence and technological advancements for wider adoption in liver surgery and transplantation.

Research on tumors segmentation based on image enhancement method

One of the most effective ways to treat liver cancer is to perform precise liver resection surgery, the key step of which includes precise digital image segmentation of the liver and its tumor. However, traditional liver parenchymal segmentation techniques often face several challenges in performing liver segmentation: lack of precision, slow processing speed, and computational burden. These shortcomings limit the efficiency of surgical planning and execution. In this work, the model initially describes in detail a new image enhancement algorithm that enhances the key features of an image by adaptively adjusting the contrast and brightness of the image. Then, a deep learning-based segmentation network was introduced, which was specially trained on the enhanced images to optimize the detection accuracy of tumor regions. In addition, multi-scale analysis techniques have been incorporated into the study, allowing the model to analyze images at different resolutions to capture more nuanced tumor features. In the presentation of the experimental results, the study used the 3Dircadb dataset to test the effectiveness of the proposed method. The experimental results show that compared with the traditional image segmentation method, the new method using image enhancement technology has significantly improved the accuracy and recall rate of tumor identification.

Development of a Plasma-Based Nanosensor Platform for Triage of Neurological Tumors

Neurological tumors have diverse etiologies and treatments. Presently, diagnostic technologies such as MRI or CT scans are limited by poor resolution, and thus imprecise identification of tumor classes. As a result, diagnostic methods rely on highly invasive intracranial biopsies for conclusive tumor identification. Development of non-invasive methods to accurately identify tumor class may lead to earlier and more accurate diagnoses, enabling prompt and targeted treatment strategies. To address this unmet clinical need, we report the development a molecular-recognition-based nanosensor array that can accurately identify a molecular fingerprint of glioma and meningioma from plasma. We developed this capability using sensor arrays of chemically modified carbon nanotubes that can transduce subtle differences in the physicochemical properties of molecules in biofluids via sensitive molecular binding interactions. Using high-throughput near-infrared fluorescence spectroscopy, we screened 23 chemically modified (6,5) carbon nanotubes sensors in 324 patient plasma samples. We trained machine learning models to classify sensor responses as coming from either glioma or meningioma tumors. The best performing models were able to achieve a positive predictive value of 0.75. Sensor feature analysis revealed a set of spectral features positively associated with class differentiation. This work has the potential to aid clinical care by facilitating the early triage of different types of tumors to provide better treatment options for patients.

Optimized brain tumor identification via graph sample and aggregate-attention network with Artificial Lizard Search Algorithm

A brain tumour is an abnormal growth of brain nerves that interferes with normal brain function.It causes a great deal of deaths. Timely detection and treatment are essential for saving the lives from this illness. Locating tumor-affected brain cells is a tedious, time-consuming process. In this manuscript, a Graph Sample and Aggregate-Attention Network with Artificial Lizard Search Optimization Algorithm for identification of brain tumour (GSAAN-ALSOA-BTI) is proposed. Initially, the input imageries are obtained from Figshare Brain Tumor Dataset. The images are preprocessed using Data-Adaptive Gaussian Average Filtering (DAGAF) to eliminate the unnecessary noise from image frames. The pre-processed image is given into the Multi kernel k-Means clustering (MKKMC) for image segmentation to identify boundaries in an image. Finally, the extracted feature features are given to Graph Sample with Aggregate-Attention Network (GSAAN) for categorizing the brain tumour identification as benign and malignant tumors. In general, GSAAN does not express any adaption of optimization techniques for determining the ideal parameters to assure exact classification of brain tumour identification. Thus, the Artificial Lizard Search Optimization Algorithm (ALSOA) is proposed to improve the weight parameter of GSAAN, which accurately categorizes the brain tumour identification of brain tumor. The proposed model is executed in Python and its efficacy is evaluated utilizing performance metrics like accuracy, precision, recall, FI-score, error rate, computation time and ROC. The GSAAN-ALSOA-BTI method provides higher accuracy, higher precision, higher recall compared with existing methods.

Neuro-XAI: Explainable deep learning framework based on deeplabV3+ and bayesian optimization for segmentation and classification of brain tumor in MRI scans

The prevalence of brain tumor disorders is currently a global issue. In general, radiography, which includes a large number of images, is an efficient method for diagnosing these life-threatening disorders. The biggest issue in this area is that it takes a radiologist a long time and is physically strenuous to look at all the images. As a result, research into developing systems based on machine learning to assist radiologists in diagnosis continues to rise daily. Convolutional neural networks (CNNs), one type of deep learning approach, have been pivotal in achieving state-of-the-art results in several medical imaging applications, including the identification of brain tumors. CNN hyperparameters are typically set manually for segmentation and classification, which might take a while and increase the chance of using suboptimal hyperparameters for both tasks. Bayesian optimization is a useful method for updating the deep CNN's optimal hyperparameters. The CNN network, however, can be considered a "black box" model because of how difficult it is to comprehend the information it stores because of its complexity. Therefore, this problem can be solved by using Explainable Artificial Intelligence (XAI) tools, which provide doctors with a realistic explanation of CNN's assessments. Implementation of deep learning-based systems in real-time diagnosis is still rare. One of the causes could be that these methods don't quantify the Uncertainty in the predictions, which could undermine trust in the AI-based diagnosis of diseases. To be used in real-time medical diagnosis, CNN-based models must be realistic and appealing, and uncertainty needs to be evaluated. So, a novel three-phase strategy is proposed for segmenting and classifying brain tumors. Segmentation of brain tumors using the DeeplabV3+ model is first performed with tuning of hyperparameters using Bayesian optimization. For classification, features from state-of-the-art deep learning models Darknet53 and mobilenetv2 are extracted and fed to SVM for classification, and hyperparameters of SVM are also optimized using a Bayesian approach. The second step is to understand whatever portion of the images CNN uses for feature extraction using XAI algorithms. Using confusion entropy, the Uncertainty of the Bayesian optimized classifier is finally quantified. Based on a Bayesian-optimized deep learning framework, the experimental findings demonstrate that the proposed method outperforms earlier techniques, achieving a 97 % classification accuracy and a 0.98 global accuracy.

PathoDuet: Foundation models for pathological slide analysis of H&E and IHC stains

Large amounts of digitized histopathological data display a promising future for developing pathological foundation models via self-supervised learning methods. Foundation models pretrained with these methods serve as a good basis for downstream tasks. However, the gap between natural and histopathological images hinders the direct application of existing methods. In this work, we present PathoDuet, a series of pretrained models on histopathological images, and a new self-supervised learning framework in histopathology. The framework is featured by a newly-introduced pretext token and later task raisers to explicitly utilize certain relations between images, like multiple magnifications and multiple stains. Based on this, two pretext tasks, cross-scale positioning and cross-stain transferring, are designed to pretrain the model on Hematoxylin and Eosin (H&E) images and transfer the model to immunohistochemistry (IHC) images, respectively. To validate the efficacy of our models, we evaluate the performance over a wide variety of downstream tasks, including patch-level colorectal cancer subtyping and whole slide image (WSI)-level classification in H&E field, together with expression level prediction of IHC marker, tumor identification and slide-level qualitative analysis in IHC field. The experimental results show the superiority of our models over most tasks and the efficacy of proposed pretext tasks. The codes and models are available at https://github.com/openmedlab/PathoDuet.

Charged Gold Nanoparticles for Target Identification-Alignment and Automatic Segmentation of CT Image-Guided Adaptive Radiotherapy in Small Hepatocellular Carcinoma.

Because of the challenges posed by anatomical uncertainties and the low resolution of plain computed tomography (CT) scans, implementing adaptive radiotherapy (ART) for small hepatocellular carcinoma (sHCC) using artificial intelligence (AI) faces obstacles in tumor identification-alignment and automatic segmentation. The current study aims to improve sHCC imaging for ART using a gold nanoparticle (Au NP)-based CT contrast agent to enhance AI-driven automated image processing. The synthesized charged Au NPs demonstrated notable in vitro aggregation, low cytotoxicity, and minimal organ toxicity. Over time, an in situ sHCC mouse model was established for in vivo CT imaging at multiple time points. The enhanced CT images processed using 3D U-Net and 3D Trans U-Net AI models demonstrated high geometric and dosimetric accuracy. Therefore, charged Au NPs enable accurate and automatic sHCC segmentation in CT images using classical AI models, potentially addressing the technical challenges related to tumor identification, alignment, and automatic segmentation in CT-guided online ART.

Assessment of imaging risks for recurrence after stereotactic radiosurgery for brain metastases (IRRaS-BM)

BackgroundThe identification of viable tumors and radiation necrosis after stereotactic radiosurgery (SRS) is crucial for patient management. Tumor habitat analysis involving the grouping of similar voxels can identify subregions that share common biology and enable the depiction of areas of tumor recurrence and treatment-induced change. This study aims to validate an imaging biomarker for tumor recurrence after SRS for brain metastasis by conducting tumor habitat analysis using multi-parametric MRI.MethodsIn this prospective study (NCT05868928), patients with brain metastases will undergo multi-parametric MRI before SRS, and then follow-up MRIs will be conducted every 3 months until 24 months after SRS. The multi-parametric MRI protocol will include T2-weighted and contrast-enhanced T1-weighted imaging, diffusion-weighted imaging, and dynamic susceptibility contrast imaging. Using k-means voxel-wise clustering, this study will define three structural MRI habitats (enhancing, solid low-enhancing, and nonviable) on T1- and T2-weighted images and three physiologic MRI habitats (hypervascular cellular, hypovascular cellular, and nonviable) on apparent diffusion coefficient maps and cerebral blood volume maps. Using RANO-BM criteria as the reference standard, via Cox proportional hazards analysis, the study will prospectively evaluate associations between parameters of the tumor habitats and the time to recurrence. The DICE similarity coefficients between the recurrence site and tumor habitats will be calculated.DiscussionThe tumor habitat analysis will provide an objective and reliable measure for assessing tumor recurrence from brain metastasis following SRS. By identifying subregions for local recurrence, our study could guide the next therapeutic targets for patients after SRS.Trial registrationThis study is registered at ClinicalTrials.gov (NCT05868928).

A 10-year retrospective study of the risks and peculiarities in pediatric patients with (para)gonadal tumors and cysts.

Gonadal pediatric tumors are rare, ranking fourth (6%) among pediatric tumors, by Surveillance, Epidemiology, and End Results Program (https:∕∕seer.cancer.gov∕). They have vague symptoms, leading to late discovery, but early detection and identifying its risk factors result in favorable prognosis and reduction of its incidence respectively. A 10-year retrospective study identified peculiarities and risk factors in 210 children till age 17 with (para)gonadal tumors. Stress, pollution (agricultural chemicals, insecticides and metal mine), obesity, breastfeeding ≤5 months, malformations [mainly non-genetic related 67∕87 (77%), especially eye malformation - 64%], hormone, smoking, positive heredo-genetic history, rural residence area, abnormal birth weight, and menstruation disorders showed an increased gonadal malignancy risk; relative risk ratio (RR): 1.33, 1.30, 1.34, 1.11, 1.65, 1.16, 1.36, 1.10, 1.00, 1.08 and 1.15 folds, respectively. RR for histopathological subtypes: immature teratoma (IT) (pollution - 1.75, Rhesus positive - 3.41), dysgerminoma (menstruation disorders - 2.80), granulosa cell tumor (stress - 2.10, menstruation disorders - 2.80), mucinous cystadenomas (obesity - 2.84, no postnatal vaccine - 3.71), mature teratomas (stress - 2.35, malformations - 2.18) and serous cystadenomas (breastfeeding ≤5 months - 2.53), dependent variables being mixed germ cell tumors (GCTs) and cysts. Children presenting with bleeding (73%), abdominal distention (62%), elevated tumor markers (91%), (multilocular) solid tumor (88% and 100%), tumor size >10 cm (65%), GCTs (74%), death (100%), metastases (100%), viruses (77%), loss of appetite (68%), and weight (85%), had gonadal malignant tumors, especially mixed GCTs and IT. Avoiding these risk factors will prevent and reduce gonadal pediatric tumors. Investigating children presenting with the listed peculiarities, especially if exposed to the mentioned risk factors, will enable early gonadal tumor identification, successful patient management, and favorable prognosis.

Blood-Derived Extracellular Vesicles as a Promising Liquid Biopsy Diagnostic Tool for Early Cancer Detection.

Cancer poses a significant public health challenge worldwide, and timely screening has the potential to mitigate cancer progression and reduce mortality rates. Currently, early identification of most tumors relies on imaging techniques and tissue biopsies. However, the use of low-cost, highly sensitive, non-invasive detection methods for early cancer screening has become more attractive. Extracellular Vesicles (EVs) released by all living cells contain distinctive biological components, such as nucleic acids, proteins, and lipids. These vesicles play crucial roles in the tumor microenvironment and intercellular communication during tumor progression, rendering liquid biopsy a particularly suitable method for diagnosis. Nevertheless, challenges related to purification methods and validation of efficacy currently hinder its widespread clinical implementation. These limitations underscore the importance of refining isolation techniques and conducting comprehensive investigations on EVs. This study seeks to evaluate the potential of liquid biopsy utilizing blood-derived EVs as a practical, cost-effective, and secure approach for early cancer detection.