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.

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.

One label is all you need: Interpretable AI-enhanced histopathology for oncology.

Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that require only one overall label per hematoxylin and eosin (H&E) slide with no tissue-level annotations. We present a structured review of these methods organized by their degree of verifiability and by commonly recurring application areas in oncological characterization. First, we discuss morphological markers (tumor presence/absence, metastases, subtypes, grades) in which AI-identified regions of interest (ROIs) within whole slide images (WSIs) verifiably overlap with pathologist-identified ROIs. Second, we discuss molecular markers (gene expression, molecular subtyping) that are not verified via H&E but rather based on overlap with positive regions on adjacent tissue. Third, we discuss genetic markers (mutations, mutational burden, microsatellite instability, chromosomal instability) that current technologies cannot verify if AI methods spatially resolve specific genetic alterations. Fourth, we discuss the direct prediction of survival to which AI-identified histopathological features quantitatively correlate but are nonetheless not mechanistically verifiable. Finally, we discuss in detail several opportunities and challenges for these one-label-per-slide methods within oncology. Opportunities include reducing the cost of research and clinical care, reducing the workload of clinicians, personalized medicine, and unlocking the full potential of histopathology through new imaging-based biomarkers. Current challenges include explainability and interpretability, validation via adjacent tissue sections, reproducibility, data availability, computational needs, data requirements, domain adaptability, external validation, dataset imbalances, and finally commercialization and clinical potential. Ultimately, the relative ease and minimum upfront cost with which relevant data can be collected in addition to the plethora of available AI methods for outcome-driven analysis will surmount these current limitations and achieve the innumerable opportunities associated with AI-driven histopathology for the benefit of oncology.

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.

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.

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 ).

Artificial Intelligence-Based Tool for Tumor Detection and Quantitative Tissue Analysis in Colorectal Specimens.

Digital pathology adoption allows for applying computational algorithms to routine pathology tasks. Our study aimed to develop a clinical-grade artificial intelligence (AI) tool for precise multiclass tissue segmentation in colorectal specimens (resections and biopsies) and clinically validate the tool for tumor detection in biopsy specimens. The training data set included 241 precisely manually annotated whole-slide images (WSIs) from multiple institutions. The algorithm was trained for semantic segmentation of 11 tissue classes with an additional module for biopsy WSI classification. Six case cohorts from 5 pathology departments (4 countries) were used for formal and clinical validation, digitized by 4 different scanning systems. The developed algorithm showed high precision of segmentation of different tissue classes in colorectal specimens with composite multiclass Dice score of up to 0.895 and pixel-wise tumor detection specificity and sensitivity of up to 0.958 and 0.987, respectively. In the clinical validation study on multiple external cohorts, the AI tool reached sensitivity of 1.0 and specificity of up to 0.969 for tumor detection in biopsy WSI. The AI tool analyzes most biopsy cases in less than 1 minute, allowing effective integration into clinical routine. We developed and extensively validated a highly accurate, clinical-grade tool for assistive diagnostic processing of colorectal specimens. This tool allows for quantitative deciphering of colorectal cancer tissue for development of prognostic and predictive biomarkers and personalization of oncologic care. This study is a foundation for a SemiCOL computational challenge. We open-source multiple manually annotated and weakly labeled test data sets, representing a significant contribution to the colorectal cancer computational pathology field.

Targeted therapies in non-small cell lung cancer and the potential role of AI interventions in cancer treatment.

Non-small cell lung cancer is the most prevalent lung cancer, and almost three-fourths of patients are diagnosed in the advanced stage directly. In this stage, chemotherapy gives only a 15% 5-year survival rate. As people have varied symptoms and reactions to a specific cancer type, treatment for the tumor is likely to fall short, complicating cancer therapy. Immunotherapy is a breakthrough treatment involving drugs targeting novel immune checkpoint inhibitors like CTLA-4 and PD-1/PD-L1, along with combination therapies. In addition, the utility of engineered CAR-T and CAR-NK cells can be an effective strategy to promote the immune response against tumors. The concept of personalized cancer vaccines with the discovery of neoantigens loaded on dendritic cell vectors can also be an effective approach to cure cancer. Advances in genetic engineering tools like CRISPR/Cas9-mediated gene editing of T cells to enhance their effector function is another ray of hope. This review aims to provide an overview of recent developments in cancer immunotherapy, which can be used in first- and second-line treatments in the clinical space. Further, the intervention of artificial intelligence to detect cancer tumors at an initial stage with the help of machine learning techniques is also explored.

Detecting the location of lung cancer on thoracoscopic images using deep convolutional neural networks.

OBJECTIVES: The prevalence of minimally invasive surgeries has increased the need for tumor detection using thoracoscopic images during lung cancer surgery. We conducted this study to analyze the efficacy of a deep convolutional neural network (DCNN) for tumor detection using recorded thoracoscopic images of pulmonary surfaces. MATERIALS AND METHODS: We collected 644 intraoperative thoracoscopic images of changes in pulmonary appearance from 427 patients with lung cancer between 2012 and 2021. The lesion areas on the thoracoscopic images were detected by bounding boxes using an advanced version of YOLO, a well-known DCNN for object detection. The DCNN model was trained and evaluated by a 15-fold cross-validation scheme. Each predicted bounding box was considered successful detection when it overlapped more than 50% of the lesion areas annotated by board-certified surgeons. RESULTS AND CONCLUSIONS: Precision, recall, and F1-measured values of 91.9%, 90.5%, and 91.1%, respectively, were obtained. The presence of lymphatic vessel invasion was associated with successful detection (p = 0.045). The presence of pathological pleural invasion also showed a tendency toward successful detection (p = 0.081). The proposed DCNN-based algorithm yielded an accuracy of more than 90% tumor detection. These algorithms will help surgeons detect lung cancer displayed on a screen automatically.

BirCat Optimization for Automatic Segmentation of Brain Tumors and Pixel Change Detection Using Post-operative MRI Images.

There is an emerging need for medical imaging data to provide patients with timely diagnosis. Magnetic resonance imaging (MRI) images based on brain tumor segmentation approaches possess greater importance in planning treatment. Though, mechanizing the process with different imaging conditions and accuracy is a major challenge due to variations in tumor structures. Hence, an efficient optimization-driven classifier, called BirCat optimization-based deep belief network (BirCat-based DBN) is developed to detect brain tumors. The introduced BirCat is devised by incorporating birdswarm algorithm (BSA) into cat swarm optimization (CSO) algorithm and is employed in tuning the DBN classifier. Here, the first step is pre-processing, where noises, as well as artifacts in input image, are eliminated by means of ROI extraction and filtering method. Then, for segmentation, region growing algorithm is used in which the distance is calculated by the modified Bhattacharya measure. Afterward, each segment is adapted for mining the segment-based features and pixel-based features used for classification. Then, the feature vector is formed and given to the DBN classifier, which is tuned with the help of the introduced BirCat for brain tumor detection. The introduced technique effectively determines the regions with the tumor in the input MRI image. Finally, the change detection is evaluated by analyzing the post-operative MRI image and the segmented image by means of pixel mapping strategy with respect to SURF features. The pixel mapping is utilized to evaluate the percentage change in tumor pixels. The proposed BirCat surpassed other prevailing approaches by producing maximal values of specificity, accuracy, sensitivity, F1-score, and Dice score at 0.92, 0.927, 0.938, 0.909, and 0.937, correspondingly, for dataset 2.

[Paraneoplastic syndromes in rheumatology]. / Paraneoplastische Syndrome in der Rheumatologie.

Paraneoplastic syndromes in rheumatology are a group of canonical rare rheumatic diseases with musculoskeletal involvement that occur in close temporal and causal association with malignancies. Knowledge of these possibly enables a prognostically relevant early diagnosis of the underlying malignant disease. In the era of immune checkpoint inhibitor treatment, there are first indications of an increase in the incidence and severity of paraneoplastic syndromes, so that they are becoming of increasing importance for the practicing rheumatologist. These nine syndromes, paraneoplastic arthritis, palmar fasciitis and polyarthritis, remitting seronegative symmetrical synovitis with pitting edema, pancreatic panniculitis with polyarthritis, paraneoplastic vasculitis, cancer-associated myositis, hypertrophic osteoarthropathy (Marie-Bamberger), eosinophilic fasciitis and tumor-induced osteomalacia, usually occur with characteristic courses and sometimes pathognomonic clinical manifestations, which are presented in this article accompanied by the rational use of a diagnostic algorithm for tumor detection. With frequently disappointing therapeutic response to glucocorticoids, nonsteroidal antirheumatic drugs and immunosuppressants, treatment of the underlying malignant disease represents the crucial step in the treatment of paraneoplastic syndromes.

Deep convolutional neural network based hyperspectral brain tissue classification.

BACKGROUND: Hyperspectral brain tissue imaging has been recently utilized in medical research aiming to study brain science and obtain various biological phenomena of the different tissue types. However, processing high-dimensional data of hyperspectral images (HSI) is challenging due to the minimum availability of training samples. OBJECTIVE: To overcome this challenge, this study proposes applying a 3D-CNN (convolution neural network) model to process spatial and temporal features and thus improve performance of tumor image classification. METHODS: A 3D-CNN model is implemented as a testing method for dealing with high-dimensional problems. The HSI pre-processing is accomplished using distinct approaches such as hyperspectral cube creation, calibration, spectral correction, and normalization. Both spectral and spatial features are extracted from HSI. The Benchmark Vivo human brain HSI dataset is used to validate the performance of the proposed classification model. RESULTS: The proposed 3D-CNN model achieves a higher accuracy of 97% for brain tissue classification, whereas the existing linear conventional support vector machine (SVM) and 2D-CNN model yield 95% and 96% classification accuracy, respectively. Moreover, the maximum F1-score obtained by the proposed 3D-CNN model is 97.3%, which is 2.5% and 11.0% higher than the F1-scores obtained by 2D-CNN model and SVM model, respectively. CONCLUSION: A 3D-CNN model is developed for brain tissue classification by using HIS dataset. The study results demonstrate the advantages of using the new 3D-CNN model, which can achieve higher brain tissue classification accuracy than conventional 2D-CNN model and SVM model.

Liquid biopsy: a step closer to transform diagnosis, prognosis and future of cancer treatments.

Over the past decade, invasive techniques for diagnosing and monitoring cancers are slowly being replaced by non-invasive methods such as liquid biopsy. Liquid biopsies have drastically revolutionized the field of clinical oncology, offering ease in tumor sampling, continuous monitoring by repeated sampling, devising personalized therapeutic regimens, and screening for therapeutic resistance. Liquid biopsies consist of isolating tumor-derived entities like circulating tumor cells, circulating tumor DNA, tumor extracellular vesicles, etc., present in the body fluids of patients with cancer, followed by an analysis of genomic and proteomic data contained within them. Methods for isolation and analysis of liquid biopsies have rapidly evolved over the past few years as described in the review, thus providing greater details about tumor characteristics such as tumor progression, tumor staging, heterogeneity, gene mutations, and clonal evolution, etc. Liquid biopsies from cancer patients have opened up newer avenues in detection and continuous monitoring, treatment based on precision medicine, and screening of markers for therapeutic resistance. Though the technology of liquid biopsies is still evolving, its non-invasive nature promises to open new eras in clinical oncology. The purpose of this review is to provide an overview of the current methodologies involved in liquid biopsies and their application in isolating tumor markers for detection, prognosis, and monitoring cancer treatment outcomes.

Positive Versus Neutral Oral Contrast Material for Detection of Malignant Deposits in Intraabdominal Nonsolid Organs on CT.

BACKGROUND. Data are limited regarding utility of positive oral contrast material for peritoneal tumor detection on CT. OBJECTIVE. The purpose of this article is to compare positive versus neutral oral contrast material for detection of malignant deposits in nonsolid intraabdominal organs on CT. METHODS. This retrospective study included 265 patients (133 men, 132 women; median age, 61 years) who underwent an abdominopelvic CT examination in which the report did not suggest presence of malignant deposits and a subsequent CT examination within 6 months in which the report indicated at least one unequivocal malignant deposit. Examinations used positive (iohexol; n = 100) or neutral (water; n = 165) oral agents. A radiologist reviewed images to assess whether the deposits were visible (despite clinical reports indicating no deposits) on unblinded comparison with the follow-up examinations; identified deposits were assigned to one of seven intraabdominal compartments. The radiologist also assessed adequacy of bowel filling with oral contrast material. Two additional radiologists independently reviewed examinations in blinded fashion for malignant deposits. NPV was assessed of clinical CT reports and blinded retrospective readings for detection of malignant deposits visible on unblinded comparison with follow-up examinations. RESULTS. Unblinded review identified malignant deposits in 58.1% (154/265) of examinations. In per-patient analysis of clinical reports, NPV for malignant deposits was higher for examinations with adequate bowel filling with positive oral contrast material (65.8% [25/38]) than for examinations with inadequate bowel filling with positive oral contrast material (45.2% [28/62], p = .07) or with neutral oral contrast material regardless of bowel filling adequacy (35.2% [58/165], p = .002). In per-compartment analysis of blinded interpretations, NPV was higher for examinations with adequate and inadequate bowel filling with positive oral contrast material than for examinations with neutral oral contrast regardless of bowel filling adequacy (reader 1: 94.7% [234/247] and 92.5% [382/413] vs 88.3% [947/1072], both p = .045; reader 2: 93.1% [228/245] and 91.6% [361/394] vs 85.9% [939/1093], both p = .01). CONCLUSION. CT has suboptimal NPV for malignant deposits in intraabdominal nonsolid organs. Compared with neutral material, positive oral contrast material improves detection, particularly with adequate bowel filling. CLINICAL IMPACT. Optimization of bowel preparation for oncologic CT may help avoid potentially severe clinical consequences of missed malignant deposits.

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images.

Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, and prone to error. Magnetic resonance imaging (MRI) scans are mostly used for tumor detection due to their non-invasive properties and also avoid painful biopsy. MRI scanning of one patient's brain generates many 3D images from multiple directions, making the manual detection of tumors very difficult, error-prone, and time-consuming. Therefore, there is a considerable need for autonomous diagnostics tools to detect brain tumors accurately. In this research, we have presented a novel TumorResnet deep learning (DL) model for brain detection, i.e., binary classification. The TumorResNet model employs 20 convolution layers with a leaky ReLU (LReLU) activation function for feature map activation to compute the most distinctive deep features. Finally, three fully connected classification layers are used to classify brain tumors MRI into normal and tumorous. The performance of the proposed TumorResNet architecture is evaluated on a standard Kaggle brain tumor MRI dataset for brain tumor detection (BTD), which contains brain tumor and normal MR images. The proposed model achieved a good accuracy of 99.33% for BTD. These experimental results, including the cross-dataset setting, validate the superiority of the TumorResNet model over the contemporary frameworks. This study offers an automated BTD method that aids in the early diagnosis of brain cancers. This procedure has a substantial impact on improving treatment options and patient survival.

Metamaterial Vivaldi Antenna Array for Breast Cancer Detection.

The objective of this work is the design and validation of a directional Vivaldi antenna to detect tumor cells' electromagnetic waves with a frequency of around 5 GHz. The proposed antenna is 33% smaller than a traditional Vivaldi antenna due to the use of metamaterials in its design. It has an excellent return loss of 25 dB at 5 GHz and adequate radiation characteristics as its gain is 6.2 dB at 5 GHz. The unit cell size of the proposed metamaterial is 0.058λ × 0.054λ at the operation frequency of 5 GHz. The proposed antenna was designed and optimized in CST microwave software, and the measured and simulated results were in good agreement. The experimental study demonstrates that an array composed with the presented antennas can detect the existence of tumors in a liquid breast phantom with positional accuracy through the analysis of the minimum amplitude of Sii.

Application of a Modified Combinational Approach to Brain Tumor Detection in MR Images.

For many years, brain tumor detection has been one of the most essential and competitive issues for medical researchers. Many methods have been developed to detect normal and abnormal tissues in Magnetic Resonance (MR) images. In this work, we present a novel algorithm based on iterative Co-Clustering and K-Means (ICCK). After image pre-processing and enhancement, this algorithm recognizes the part of the image that contains the tumor and eliminates the unused parts using a modification of the Co-Clustering method. Finally, the K-Means clustering method is adopted to detect the tumor area. The Co-Clustering methods cannot be used directly for the detection of brain tumors because they manipulate the image matrix for the purpose of block clustering. Furthermore, they are incapable of detecting the tumor area correctly and accurately. Such issues are addressed by our proposed methodology. The latent block model (LBM) is applied as the Co-Clustering method in this work. We evaluate the performance of our method on the images that were collected from the BraTS2019 dataset. The sensitivity, specificity, accuracy, and dice similarity coefficient values for our method are 82.41%, 99.74%, 99.28%, and 84.87%, respectively, which shows that the proposed method outperforms the existing methods in the literature. Moreover, it performs much better on complex images.

Intraoperative Tumor Detection Using Pafolacianine.

Cancer is a leading cause of death worldwide, with increasing numbers of new cases each year. For the vast majority of cancer patients, surgery is the most effective procedure for the complete removal of the malignant tissue. However, relapse due to the incomplete resection of the tumor occurs very often, as the surgeon must rely primarily on visual and tactile feedback. Intraoperative near-infrared imaging with pafolacianine is a newly developed technology designed for cancer detection during surgery, which has been proven to show excellent results in terms of safety and efficacy. Therefore, pafolacianine was approved by the U.S. Food and Drug Administration (FDA) on 29 November 2021, as an additional approach that can be used to identify malignant lesions and to ensure the total resection of the tumors in ovarian cancer patients. Currently, various studies have demonstrated the positive effects of pafolacianine's use in a wide variety of other malignancies, with promising results expected in further research. This review focuses on the applications of the FDA-approved pafolacianine for the accurate intraoperative detection of malignant tissues. The cancer-targeting fluorescent ligands can shift the paradigm of surgical oncology by enabling the visualization of cancer lesions that are difficult to detect by inspection or palpation. The enhanced detection and removal of hard-to-detect cancer tissues during surgery will lead to remarkable outcomes for cancer patients and society, specifically by decreasing the cancer relapse rate, increasing the life expectancy and quality of life, and decreasing future rates of hospitalization, interventions, and costs.

[Tumor localization and treatment of tumor-induced osteomalacia]. / Tumorlokalisation und Therapie der onkogenen Osteomalazie.

Tumor-induced osteomalacia (TIO) or oncogenic osteomalacia (OOM) is a rare paraneoplastic renal phosphate wasting syndrome. The disease is mostly triggered by small, benign mesenchymal tumors that express somatostatin receptors (SSTR) and produce excessive levels of fibroblast growth factor 23 (FGF 23) or other phosphatonins. These reduce the phosphate back resorption in the proximal tubules of the kidneys, thereby causing hypophosphatemia and lead to an absolute or relatively low calcitriol serum concentration. The main symptoms include muscle weakness, bone pain and recurrent insufficiency fractures secondary to sometimes pronounced osteomalacia. The suspected diagnosis can only be confirmed by determination of the phosphate level. It can often take years before the tumor is successfully localized. The necessary tumor localization is often the most difficult step in the treatment before the OOM can be curatively treated by open surgical resection of the tumor. In recent years new approaches for faster tumor localization and treatment of the tumor have been developed. Positron emission tomography (PET) in co-registration with computed tomography (68Ga-DOTA-TATE PET/CT) is currently the most sensitive imaging methodology for tumor detection. The application of the monoclonal FGF 23 antibody burosumab represents a promising new option in the treatment of inoperable adult OOM.