Brain-GCN-Net: Graph-Convolutional Neural Network for brain tumor identification.

BACKGROUND: The intersection of artificial intelligence and medical image analysis has ushered in a new era of innovation and changed the landscape of brain tumor detection and diagnosis. Correct detection and classification of brain tumors based on medical images is crucial for early diagnosis and effective treatment. Convolutional Neural Network (CNN) models are widely used for disease detection. However, they are sometimes unable to sufficiently recognize the complex features of medical images. METHODS: This paper proposes a fused Deep Learning (DL) model that combines Graph Neural Networks (GNN), which recognize relational dependencies of image regions, and CNN, which captures spatial features, is proposed to improve brain tumor detection. By integrating these two architectures, our model achieves a more comprehensive representation of brain tumor images and improves classification performance. The proposed model is evaluated on a public dataset of 10847 MRI images. The results show that the proposed model outperforms the existing pre-trained models and traditional CNN architectures. RESULTS: The fused DL model achieves 93.68% accuracy in brain tumor classification. The results indicate that the proposed model outperforms the existing pre-trained models and traditional CNN architectures. CONCLUSION: The numerical results suggest that the model should be further investigated for potential use in clinical trials to improve clinical decision-making.

Pressure-enhanced sensing of tissue oxygenation via endogenous porphyrin: Implications for dynamic visualization of cancer in surgery.

Fluorescence guidance is routinely used in surgery to enhance perfusion contrast in multiple types of diseases. Pressure-enhanced sensing of tissue oxygenation (PRESTO) via fluorescence is a technique extensively analyzed here, that uses an FDA-approved human precursor molecule, 5-aminolevulinic acid (ALA), to stimulate a unique delayed fluorescence signal that is representative of tissue hypoxia. The ALA precontrast agent is metabolized in most tissues into a red fluorescent molecule, protoporphyrin IX (PpIX), which has both prompt fluorescence, indicative of the concentration, and a delayed fluorescence, that is amplified in low tissue oxygen situations. Applied pressure from palpation induces transient capillary stasis and a resulting transient PRESTO contrast, dominant when there is near hypoxia. This study examined the kinetics and behavior of this effect in both normal and tumor tissues, with a prolonged high PRESTO contrast (contrast to background of 7.3) across 5 tumor models, due to sluggish capillaries and inhibited vasodynamics. This tissue function imaging approach is a fundamentally unique tool for real-time palpation-induced tissue response in vivo, relevant for chronic hypoxia, such as vascular diseases or oncologic surgery.

Tri-modality in vivo imaging for tumor detection with combined ultrasound, photoacoustic, and photoacoustic elastography.

A comprehensive understanding of a tumor is required for accurate diagnosis and effective treatment. However, currently, there is no single imaging modality that can provide sufficient information. Photoacoustic (PA) imaging is a hybrid imaging technique with high spatial resolution and detection sensitivity, which can be combined with ultrasound (US) imaging to provide both optical and acoustic contrast. Elastography can noninvasively map the elasticity distribution of biological tissue, which reflects pathological conditions. In this study, we incorporated PA elastography into a commercial US/PA imaging system to develop a tri-modality imaging system, which has been tested for tumor detection using four mice with different physiological conditions. The results show that this tri-modality imaging system can provide complementary information on acoustic, optical, and mechanical properties. The enabled visualization and dimension estimation of tumors can lead to a more comprehensive tissue characterization for diagnosis and treatment.

Engineering of a DNA/γPNA Hybrid Nanoreporter for ctDNA Mutation Detection via γPNA Urinalysis.

Detection of circulating tumor DNA (ctDNA) mutations, which are molecular biomarkers present in bodily fluids of cancer patients, can be applied for tumor diagnosis and prognosis monitoring. However, current profiling of ctDNA mutations relies primarily on polymerase chain reaction (PCR) and DNA sequencing and these techniques require preanalytical processing of blood samples, which are time-consuming, expensive, and tedious procedures that increase the risk of sample contamination. To overcome these limitations, here the engineering of a DNA/γPNA (gamma peptide nucleic acid) hybrid nanoreporter is disclosed for ctDNA biosensing via in situ profiling and recording of tumor-specific DNA mutations. The low tolerance of γPNA to single mismatch in base pairing with DNA allows highly selective recognition and recording of ctDNA mutations in peripheral blood. Owing to their remarkable biostability, the detached γPNA strands triggered by mutant ctDNA will be enriched in kidneys and cleared into urine for urinalysis. It is demonstrated that the nanoreporter has high specificity for ctDNA mutation in peripheral blood, and urinalysis of cleared γPNA can provide valuable information for tumor progression and prognosis evaluation. This work demonstrates the potential of the nanoreporter for urinary monitoring of tumor and patient prognosis through in situ biosensing of ctDNA mutations.

Universal and extensible language-vision models for organ segmentation and tumor detection from abdominal computed tomography.

The advancement of artificial intelligence (AI) for organ segmentation and tumor detection is propelled by the growing availability of computed tomography (CT) datasets with detailed, per-voxel annotations. However, these AI models often struggle with flexibility for partially annotated datasets and extensibility for new classes due to limitations in the one-hot encoding, architectural design, and learning scheme. To overcome these limitations, we propose a universal, extensible framework enabling a single model, termed Universal Model, to deal with multiple public datasets and adapt to new classes (e.g., organs/tumors). Firstly, we introduce a novel language-driven parameter generator that leverages language embeddings from large language models, enriching semantic encoding compared with one-hot encoding. Secondly, the conventional output layers are replaced with lightweight, class-specific heads, allowing Universal Model to simultaneously segment 25 organs and six types of tumors and ease the addition of new classes. We train our Universal Model on 3410 CT volumes assembled from 14 publicly available datasets and then test it on 6173 CT volumes from four external datasets. Universal Model achieves first place on six CT tasks in the Medical Segmentation Decathlon (MSD) public leaderboard and leading performance on the Beyond The Cranial Vault (BTCV) dataset. In summary, Universal Model exhibits remarkable computational efficiency (6× faster than other dataset-specific models), demonstrates strong generalization across different hospitals, transfers well to numerous downstream tasks, and more importantly, facilitates the extensibility to new classes while alleviating the catastrophic forgetting of previously learned classes. Codes, models, and datasets are available at https://github.com/ljwztc/CLIP-Driven-Universal-Model.

Tumor-specific enhanced NIR-II photoacoustic imaging via photothermal and low-pH coactivated AuNR@PNIPAM-VAA nanogel.

BACKGROUND: Properly designed second near-infrared (NIR-II) nanoplatform that is responsive tumor microenvironment can intelligently distinguish between normal and cancerous tissues to achieve better targeting efficiency. Conventional photoacoustic nanoprobes are always "on", and tumor microenvironment-responsive nanoprobe can minimize the influence of endogenous chromophore background signals. Therefore, the development of nanoprobe that can respond to internal tumor microenvironment and external stimulus shows great application potential for the photoacoustic diagnosis of tumor. RESULTS: In this work, a low-pH-triggered thermal-responsive volume phase transition nanogel gold nanorod@poly(n-isopropylacrylamide)-vinyl acetic acid (AuNR@PNIPAM-VAA) was constructed for photoacoustic detection of tumor. Via an external near-infrared photothermal switch, the absorption of AuNR@PNIPAM-VAA nanogel in the tumor microenvironment can be dynamically regulated, so that AuNR@PNIPAM-VAA nanogel produces switchable photoacoustic signals in the NIR-II window for tumor-specific enhanced photoacoustic imaging. In vitro results show that at pH 5.8, the absorption and photoacoustic signal amplitude of AuNR@PNIPAM-VAA nanogel in NIR-II increases up obviously after photothermal modulating, while they remain slightly change at pH 7.4. Quantitative calculation presents that photoacoustic signal amplitude of AuNR@PNIPAM-VAA nanogel at 1064 nm has ~ 1.6 folds enhancement as temperature increases from 37.5 °C to 45 °C in simulative tumor microenvironment. In vivo results show that the prepared AuNR@PNIPAM-VAA nanogel can achieve enhanced NIR-II photoacoustic imaging for selective tumor detection through dynamically responding to thermal field, which can be precisely controlled by external light. CONCLUSIONS: This work will offer a viable strategy for the tumor-specific photoacoustic imaging using NIR light to regulate the thermal field and target the low pH tumor microenvironment, which is expected to realize accurate and dynamic monitoring of tumor diagnosis and treatment.

Diagnostic value of 18F-FDG PET/CT versus diffusion-weighted MRI in detection of residual or recurrent tumors after definitive (chemo) radiotherapy for laryngeal and hypopharyngeal squamous cell carcinoma: A prospective study.

BACKGROUND: Despite advances in treatment, residual or recurrent tumors after definitive (chemo) radiotherapy for laryngeal and hypopharyngeal squamous cell carcinoma (SCC) remain a challenge in clinical management and require accurate and timely detection for optimal salvage therapy. This study aimed to compare the diagnostic value of Fluorine 18 (18F) fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) and diffusion-weighted magnetic resonance imaging (DW-MRI) in detecting residual or recurrent tumors after definitive (chemo) radiotherapy for laryngeal and hypopharyngeal SCC. METHODS: A prospective study was conducted on 30 patients who presented with new symptoms after definitive (chemo) radiotherapy for laryngeal (n = 21) and hypopharyngeal (n = 9) carcinoma. Both 18F-FDG PET/CT and DW-MRI were performed and histopathologic analysis served as the standard of reference. RESULTS: Histopathology showed 20 patients as positive and 10 as negative for tumors. 18F-FDG PET/CT detected all tumors correctly but was falsely positive in one case. DW-MRI detected tumors in 18 out of 20 positive patients and correctly excluded tumors in all negative patients. The sensitivity and specificity of 18F-FDG PET/CT were 100% and 90%, respectively, while the values for DW-MRI were 90% and 100%, respectively. CONCLUSIONS: The study concludes that 18F-FDG PET/CT is slightly superior to DW-MRI in detecting residual or recurrent tumors after definitive (chemo) radiotherapy for laryngeal and hypopharyngeal SCC. The combined use of 18F-FDG PET/CT and DW-MRI can potentially improve specificity in therapy response evaluation.

Liquid biopsy for human cancer: cancer screening, monitoring, and treatment.

Currently, tumor treatment modalities such as immunotherapy and targeted therapy have more stringent requirements for obtaining tumor growth information and require more accurate and easy-to-operate tumor information detection methods. Compared with traditional tissue biopsy, liquid biopsy is a novel, minimally invasive, real-time detection tool for detecting information directly or indirectly released by tumors in human body fluids, which is more suitable for the requirements of new tumor treatment modalities. Liquid biopsy has not been widely used in clinical practice, and there are fewer reviews of related clinical applications. This review summarizes the clinical applications of liquid biopsy components (e.g., circulating tumor cells, circulating tumor DNA, extracellular vesicles, etc.) in tumorigenesis and progression. This includes the development process and detection techniques of liquid biopsies, early screening of tumors, tumor growth detection, and guiding therapeutic strategies (liquid biopsy-based personalized medicine and prediction of treatment response). Finally, the current challenges and future directions for clinical applications of liquid biopsy are proposed. In sum, this review will inspire more researchers to use liquid biopsy technology to promote the realization of individualized therapy, improve the efficacy of tumor therapy, and provide better therapeutic options for tumor patients.

Microscope-integrated optical coherence tomography for in vivo human brain tumor detection with artificial intelligence.

OBJECTIVE: It has been shown that optical coherence tomography (OCT) can identify brain tumor tissue and potentially be used for intraoperative margin diagnostics. However, there is limited evidence on its use in human in vivo settings, particularly in terms of its applicability and accuracy of residual brain tumor detection (RTD). For this reason, a microscope-integrated OCT system was examined to determine in vivo feasibility of RTD after resection with automated scan analysis. METHODS: Healthy and diseased brain was 3D scanned at the resection edge in 18 brain tumor patients and investigated for its informative value in regard to intraoperative tissue classification. Biopsies were taken at these locations and labeled by a neuropathologist for further analysis as ground truth. Optical OCT properties were obtained, compared, and used for separation with machine learning. In addition, two artificial intelligence-assisted methods were utilized for scan classification, and all approaches were examined for RTD accuracy and compared to standard techniques. RESULTS: In vivo OCT tissue scanning was feasible and easily integrable into the surgical workflow. Measured backscattered light signal intensity, signal attenuation, and signal homogeneity were significantly distinctive in the comparison of scanned white matter to increasing levels of scanned tumor infiltration (p < 0.001) and achieved high values of accuracy (85%) for the detection of diseased brain in the tumor margin with support vector machine separation. A neuronal network approach achieved 82% accuracy and an autoencoder approach 85% accuracy in the detection of diseased brain in the tumor margin. Differentiating cortical gray matter from tumor tissue was not technically feasible in vivo. CONCLUSIONS: In vivo OCT scanning of the human brain has been shown to contain significant value for intraoperative RTD, supporting what has previously been discussed for ex vivo OCT brain tumor scanning, with the perspective of complementing current intraoperative methods for this purpose, especially when deciding to withdraw from further resection toward the end of the surgery.

Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50.

This study addresses the critical challenge of detecting brain tumors using MRI images, a pivotal task in medical diagnostics that demands high accuracy and interpretability. While deep learning has shown remarkable success in medical image analysis, there remains a substantial need for models that are not only accurate but also interpretable to healthcare professionals. The existing methodologies, predominantly deep learning-based, often act as black boxes, providing little insight into their decision-making process. This research introduces an integrated approach using ResNet50, a deep learning model, combined with Gradient-weighted Class Activation Mapping (Grad-CAM) to offer a transparent and explainable framework for brain tumor detection. We employed a dataset of MRI images, enhanced through data augmentation, to train and validate our model. The results demonstrate a significant improvement in model performance, with a testing accuracy of 98.52% and precision-recall metrics exceeding 98%, showcasing the model's effectiveness in distinguishing tumor presence. The application of Grad-CAM provides insightful visual explanations, illustrating the model's focus areas in making predictions. This fusion of high accuracy and explainability holds profound implications for medical diagnostics, offering a pathway towards more reliable and interpretable brain tumor detection tools.

Refining neural network algorithms for accurate brain tumor classification in MRI imagery.

Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods often require extensive manual intervention and are prone to human error, leading to misdiagnosis and delayed treatment. Current approaches primarily include manual examination by radiologists and conventional machine learning techniques. These methods rely heavily on feature extraction and classification algorithms, which may not capture the intricate patterns present in brain MRI images. Conventional techniques often suffer from limited accuracy and generalizability, mainly due to the high variability in tumor appearance and the subjective nature of manual interpretation. Additionally, traditional machine learning models may struggle with the high-dimensional data inherent in MRI images. To address these limitations, our research introduces a deep learning-based model utilizing convolutional neural networks (CNNs).Our model employs a sequential CNN architecture with multiple convolutional, max-pooling, and dropout layers, followed by dense layers for classification. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The precision, recall, and F1-scores ranging from 97 to 98% with a roc-auc ranging from 99 to 100% for each tumor category further substantiate the model's effectiveness. Additionally, the utilization of Grad-CAM visualizations provides insights into the model's decision-making process, enhancing interpretability. This research addresses the pressing need for enhanced diagnostic accuracy in identifying brain tumors through MRI imaging, tackling challenges such as variability in tumor appearance and the need for rapid, reliable diagnostic tools.

Brain tumor detection using proper orthogonal decomposition integrated with deep learning networks.

BACKGROUND AND OBJECTIVE: The central organ of the human nervous system is the brain, which receives and sends stimuli to the various parts of the body to engage in daily activities. Uncontrolled growth of brain cells can result in tumors which affect the normal functions of healthy brain cells. An automatic reliable technique for detecting tumors is imperative to assist medical practitioners in the timely diagnosis of patients. Although machine learning models are being used, with minimal data availability to train, development of low-order based models integrated with machine learning are a tool for reliable detection. METHODS: In this study, we focus on comparing a low-order model such as proper orthogonal decomposition (POD) coupled with convolutional neural network (CNN) on 2D images from magnetic resonance imaging (MRI) scans to effectively identify brain tumors. The explainability of the coupled POD-CNN prediction output as well as the state-of-the-art pre-trained transfer learning models such as MobileNetV2, Inception-v3, ResNet101, and VGG-19 were explored. RESULTS: The results showed that CNN predicted tumors with an accuracy of 99.21% whereas POD-CNN performed better with about 1/3rd of computational time at an accuracy of 95.88%. Explainable AI with SHAP showed MobileNetV2 has better prediction in identifying the tumor boundaries. CONCLUSIONS: Integration of POD with CNN is carried for the first time to detect brain tumor detection with minimal MRI scan data. This study facilitates low-model approaches in machine learning to improve the accuracy and performance of tumor detection.

Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering.

Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies. This paper introduces a novel AI-driven methodology for brain tumor detection from MRI images, leveraging the EfficientNetB2 deep learning architecture. Our approach incorporates advanced image preprocessing techniques, including image cropping, equalization, and the application of homomorphic filters, to enhance the quality of MRI data for more accurate tumor detection. The proposed model exhibits substantial performance enhancement by demonstrating validation accuracies of 99.83%, 99.75%, and 99.2% on BD-BrainTumor, Brain-tumor-detection, and Brain-MRI-images-for-brain-tumor-detection datasets respectively, this research holds promise for refined clinical diagnostics and patient care, fostering more accurate and reliable brain tumor identification from MRI images. All data is available on Github: https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2 ).

Deep learning for the automatic detection and segmentation of parotid gland tumors on MRI.

OBJECTIVES: Parotid gland tumors (PGTs) often occur as incidental findings on magnetic resonance images (MRI) that may be overlooked. This study aimed to construct and validate a deep learning model to automatically identify parotid glands (PGs) with a PGT from normal PGs, and in those with a PGT to segment the tumor. MATERIALS AND METHODS: The nnUNet combined with a PG-specific post-processing procedure was used to develop the deep learning model trained on T1-weighed images (T1WI) in 311 patients (180 PGs with tumors and 442 normal PGs) and fat-suppressed (FS)-T2WI in 257 patients (125 PGs with tumors and 389 normal PGs), for detecting and segmenting PGTs with five-fold cross-validation. Additional validation set separated by time, comprising T1WI in 34 and FS-T2WI in 41 patients, was used to validate the model performance. RESULTS AND CONCLUSION: To identify PGs with tumors from normal PGs, using combined T1WI and FS-T2WI, the deep learning model achieved an accuracy, sensitivity and specificity of 98.2% (497/506), 100% (119/119) and 97.7% (378/387), respectively, in the cross-validation set and 98.5% (67/68), 100% (20/20) and 97.9% (47/48), respectively, in the validation set. For patients with PGTs, automatic segmentation of PGTs on T1WI and FS-T2WI achieved mean dice coefficients of 86.1% and 84.2%, respectively, in the cross-validation set, and of 85.9% and 81.0%, respectively, in the validation set. The proposed deep learning model may assist the detection and segmentation of PGTs and, by acting as a second pair of eyes, ensure that incidentally detected PGTs on MRI are not missed.

Brain tumor detection based on a novel and high-quality prediction of the tumor pixel distributions.

The work presented in this paper is in the area of brain tumor detection. We propose a fast detection system with 3D MRI scans of Flair modality. It performs 2 functions, predicting the gray level distribution and location distribution of the pixels in the tumor regions and generating tumor masks with pixel-wise precision. To facilitate 3D data analysis and processing, we introduce a 2D histogram presentation encompassing the gray-level distribution and pixel-location distribution of a 3D object. In the proposed system, specific 2D histograms highlighting tumor-related features are established by exploiting the left-right asymmetry of a brain structure. A modulation function, generated from the input data of each patient case, is applied to the 2D histograms to transform them into coarsely or finely predicted distributions of tumor pixels. The prediction result helps to identify/remove tumor-free slices. The prediction and removal operations are performed to the axial, coronal and sagittal slice series of a brain image, transforming it into a 3D minimum bounding box of its tumor region. The bounding box is utilized to finalize the prediction and generate a 3D tumor mask. The proposed system has been tested extensively with the data of more than 1200 patient cases in BraTS2018∼2021 datasets. The test results demonstrate that the predicted 2D histograms resemble closely the true ones. The system delivers also very good tumor detection results, comparable to those of state-of-the-art CNN systems with mono-modality inputs. They are reproducible and obtained at an extremely low computation cost and without need for training.

Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier.

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study's commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification.

Mimicking Motor Proteins: Wall-Guided Self-Navigation of Microwheels.

Untethered micro/nanorobots (MNRs) show great promise in biomedicine. However, high-precision targeted in vivo navigation of MNRs into both deep and tiny microtube networks comes with big challenges because the present medical imaging cannot simultaneously meet the requirements of high resolution, high penetration depth, and high real-time performance. Inspired by intracellular motor proteins that transport cargo along cytoskeletal tracks, this study proposed a microtube inwall-guided targeted self-navigation strategy of magnetic microwheels (µ-wheels) that relies only on interactions with a microtube inwall, compared to conventional techniques that rely on real-time imaging and tracking of MNRs. By presetting the direction of the rotating magnetic field, the µ-wheel realized targeted navigation along the inwall. The propulsion principles behind it are elaborated. The targeted self-navigation of the µ-wheels in three-dimensional microtube networks, a spiral microtube, and an intrahepatic bile duct of a pig was conducted. Lastly, based on the strategy, a practical tumor early detection method was proposed and verified by means of magnetic resonance imaging. The microtube inwall-guided targeted self-navigation strategy reduces the dependence of in vivo targeted navigation of MNRs on the real-time performance of medical imaging technology and greatly contributes to the development of MNRs in biomedical applications.

IR780-based diffuse fluorescence tomography for cancer detection.

IR780 iodide is a commercially available targeted near-infrared contrast agent for in vivo imaging and cancer photodynamic or photothermal therapy, whereas the accumulation, dynamics, and retention of IR780 in biological tissue, especially in tumor is still under-explored. Diffuse fluorescence tomography (DFT) can be used for localization and quantification of the three-dimensional distribution of NIR fluorophores. Herein, a homemade DFT imaging system combined with tumor-targeted IR780 was utilized for cancer imaging and pharmacokinetic evaluation. The aim of this study is to comprehensively assess the biochemical and pharmacokinetic characteristics of IR780 with the aid of DFT imaging. The optimal IR780 concentration (20 µg/mL) was achieved first. Subsequently, the good biocompatibility and cellar uptake of IR780 was demonstrated through the mouse acute toxic test and cell assay. In vivo, DFT imaging effectively identified various subcutaneous tumors and revealed the long-term retention of IR780 in tumors and rapid metabolism in the liver. Ex vivo imaging indicated IR780 was mainly concentrated in tumor and lung with significantly different from the distribution in other organs. DFT imaging allowed sensitive tumor detection and pharmacokinetic rates analysis. Simultaneously, the kinetics of IR780 in tumors and liver provided more valuable information for application and development of IR780.

Assessment of brain tumor detection techniques and recommendation of neural network.

OBJECTIVES: Brain tumor classification is amongst the most complex and challenging jobs in the computer domain. The latest advances in brain tumor detection systems (BTDS) are presented as they can inspire new researchers to deliver new architectures for effective and efficient tumor detection. Here, the data of the multi-modal brain tumor segmentation task is employed, which has been registered, skull stripped, and histogram matching is conducted with the ferrous volume of high contrast. METHODS: This research further configures a capsule network (CapsNet) for brain tumor classification. Results of the latest deep neural network (NN) architectures for tumor detection are compared and presented. The VGG16 and CapsNet architectures yield the highest f1-score and precision values, followed by VGG19. Overall, ResNet152, MobileNet, and MobileNetV2 give us the lowest f1-score. RESULTS: The VGG16 and CapsNet have produced outstanding results. However, VGG16 and VGG19 are more profound architecture, resulting in slower computation speed. The research then recommends the latest suitable NN for effective brain tumor detection. CONCLUSIONS: Finally, the work concludes with future directions and potential new architectures for tumor detection.

Molecular Engineering of Bright NIR-I/NIR-II Nanofluorophores for High-Resolution Bioimaging and Tumor Detection in Vivo.

A comprehensive approach for the construction of NIR-I/NIR-II nanofluorophores with exceptional brightness and excellent chemo- and photostability has been developed. This study first confirmed that the amphiphilic molecules with stronger hydrophobic moieties and weaker hydrophilic moieties are superior candidates for constructing brighter nanofluorophores, which are attributed to its higher efficiency in suppressing the intramolecular charge transfer/aggregation-caused fluorescence quenching of donor-acceptor-donor type fluorophores. The prepared nanofluorophore demonstrates a fluorescence quantum yield exceeding 4.5% in aqueous solution and exhibits a strong NIR-II tail emission up to 1300 nm. The superior performance of the nanofluorophore enabled the achievement of high-resolution whole-body vessel imaging and brain vessel imaging, as well as high-contrast fluorescence imaging of the lymphatic system in vivo. Furthermore, their potential for highly sensitive fluorescence detection of tiny tumors in vivo has been successfully confirmed, thus supporting their future applications in precise fluorescence imaging-guided surgery in the early stages of cancer.