Hasil untuk "Neoplasms. Tumors. Oncology. Including cancer and carcinogens"

Xiaowen Liu, Jiejie Jin, Menglong Zhou et al.

Background: The prognostic superiority of preoperative chemoradiotherapy (pre-CRT) over preoperative chemotherapy (pre-CT) in patients with locally advanced gastric cancer remains controversial. Herein, we evaluated the efficacy and safety of pre-CRT relative to those of pre-CT in this cohort. Methods: This open-label, phase III, randomized controlled trial was conducted at 4 medical centers in China. Eligible patients with locally advanced gastric cancer or esophagogastric junction adenocarcinoma were randomly assigned (1:1) to receive either 3 cycles of oxaliplatin and S-1 (SOX), followed by surgery and 3 postoperative cycles of SOX (pre-CT), or 1 cycle of SOX, followed by concurrent chemoradiotherapy, a second cycle of SOX, surgery, and 3 postoperative cycles of SOX (pre-CRT). The primary endpoint was 3-year disease-free survival (DFS). Secondary endpoints included 3-year overall survival (OS), R0 resection rate, pathological complete response (pCR) rate, treatment-related toxicity, and postoperative complications. Results: Due to premature trial termination, only 204 patients were enrolled, and an efficacy analysis was conducted on 194 eligible patients. The baseline characteristics were well balanced between the 2 groups. The DFS and OS were indistinguishable between the 2 groups. The 3-year DFS rates were 53.6% in the pre-CRT group and 53.9% in the pre-CT group [hazard ratio (HR), 1.02; 95% confidence interval (CI), 0.70 to 1.50; log-rank P = 0.913]. The 3-year OS rates were 62.8% in the pre-CRT group and 60.5% in the pre-CT group (HR, 0.97; 95% CI, 0.63 to 1.47; log-rank P = 0.874). The R0 resection rates were 81.0% and 74.5% in the pre-CRT and pre-CT groups, respectively. Additionally, the pCR rate was higher in the pre-CRT group (12.0%) than in the pre-CT group (2.1%). Treatment-related toxic effects were comparable between the 2 groups. Conclusion: This trial did not demonstrate a survival advantage for pre-CRT over pre-CT in patients with locally advanced gastric or gastroesophageal adenocarcinoma.

Abhijeet Das, Ramray Bhat, Mohit Kumar Jolly

Breast cancer exhibits intricate morphological and dynamical heterogeneity across cellular, tissue, and tumor scales, posing challenges to conventional modeling approaches that fail to capture its nonlinear, self-similar, or self-affine, and memory-dependent behavior. Despite increasing applications of fractal geometry and fractional calculus in cancer modeling, their methodological integration and biological interpretation remain insufficiently consolidated. This review aims to synthesize these frameworks within an integrative morphological perspective to elucidate their collective potential for quantitative characterization of breast cancer complexity. Fractal geometry-based analyses quantify spatial and temporal irregularities along with spatiotemporal morphodynamics, while fractional calculus introduces non-local and memory-dependent formulations describing tumor growth. Together, these frameworks establish a mathematical link between fractal structure and fractional dynamics. Nevertheless, their application remains hindered by inconsistent methodologies and a lack of reproducible standards. This review consolidates existing evidence, delineates methodological interrelations between fractal geometry and fractional calculus, and outlines reproducibility requirements, including standardized preprocessing, parameter reporting, and benchmark datasets. Collectively, the findings emphasize that reproducible and biologically interpretable integration of these two approaches is fundamental to achieving clinically relevant modeling of breast cancer morphology and dynamics.

Mohammad Mahdi Danesh Pajouh

Cancer remains one of the leading causes of mortality worldwide, and among its many forms, brain tumors are particularly notorious due to their aggressive nature and the critical challenges involved in early diagnosis. Recent advances in artificial intelligence have shown great promise in assisting medical professionals with precise tumor segmentation, a key step in timely diagnosis and treatment planning. However, many state-of-the-art segmentation methods require extensive computational resources and prolonged training times, limiting their practical application in resource-constrained settings. In this work, we present a novel dual-decoder U-Net architecture enhanced with attention-gated skip connections, designed specifically for brain tumor segmentation from MRI scans. Our approach balances efficiency and accuracy by achieving competitive segmentation performance while significantly reducing training demands. Evaluated on the BraTS 2020 dataset, the proposed model achieved Dice scores of 85.06% for Whole Tumor (WT), 80.61% for Tumor Core (TC), and 71.26% for Enhancing Tumor (ET) in only 50 epochs, surpassing several commonly used U-Net variants. Our model demonstrates that high-quality brain tumor segmentation is attainable even under limited computational resources, thereby offering a viable solution for researchers and clinicians operating with modest hardware. This resource-efficient model has the potential to improve early detection and diagnosis of brain tumors, ultimately contributing to better patient outcomes

Haoyu Pan, Hongxin Lin, Zetian Feng et al.

The development of robust deep learning models for breast ultrasound (BUS) image analysis is significantly constrained by the scarcity of expert-annotated data. To address this limitation, we propose a clinically controllable generative framework for synthesizing BUS images. This framework integrates clinical descriptions with structural masks to generate tumors, enabling fine-grained control over tumor characteristics such as morphology, echogencity, and shape. Furthermore, we design a semantic-curvature mask generator, which synthesizes structurally diverse tumor masks guided by clinical priors. During inference, synthetic tumor masks serve as input to the generative framework, producing highly personalized synthetic BUS images with tumors that reflect real-world morphological diversity. Quantitative evaluations on six public BUS datasets demonstrate the significant clinical utility of our synthetic images, showing their effectiveness in enhancing downstream breast cancer diagnosis tasks. Furthermore, visual Turing tests conducted by experienced sonographers confirm the realism of the generated images, indicating the framework's potential to support broader clinical applications.

Mi Jian, Zhensong Yang, Xue Hu et al.

This case describes the benefits of perioperative chemo-immunotherapy for advanced gastric cancer and incomplete pyloric obstruction, supplemented with nutritional support. Early parenteral nutrition to stabilize nutritional status and mitigate nutrition impact symptoms, and in addition, throughout the chemo-immunotherapy perioperative period also maintained oral nutrition support and a tailored dietary plan. Above nutritional support maintained the patient’s physical condition during immunotherapy. Eventually, this combination therapy plan leads to a partial response. On the other hand, a combination of therapies that focus more on immune checkpoint inhibitor may be able to mitigate the side effects of chemotherapy. Such findings may yield novel prospects for patients with advanced gastric cancer and incomplete pyloric obstruction, enabling them to achieve better outcomes.

Romane Gros, Omar Rodriguez-Nunez, Leonard Felger et al.

Neuro-oncological surgery is the primary brain cancer treatment, yet it faces challenges with gliomas due to their invasiveness and the need to preserve neurological function. Hence, radical resection is often unfeasible, highlighting the importance of precise tumor margin delineation to prevent neurological deficits and improve prognosis. Imaging Mueller polarimetry, an effective modality in various organ tissues, seems a promising approach for tumor delineation in neurosurgery. To further assess its use, we characterized the polarimetric properties by analysing 45 polarimetric measurements of 27 fresh brain tumor samples, including different tumor types with a strong focus on gliomas. Our study integrates a wide-field imaging Mueller polarimetric system and a novel neuropathology protocol, correlating polarimetric and histological data for accurate tissue identification. An image processing pipeline facilitated the alignment and overlay of polarimetric images and histological masks. Variations in depolarization values were observed for grey and white matter of brain tumor tissue, while differences in linear retardance were seen only within white matter of brain tumor tissue. Notably, we identified pronounced optical axis azimuth randomization within tumor regions. This study lays the foundation for machine learning-based brain tumor segmentation algorithms using polarimetric data, facilitating intraoperative diagnosis and decision making.

Vidhyapriya Ranganathan, Celshiya Udaiyar, Jaisree Jayanth et al.

Brain tumor is a life-threatening problem and hampers the normal functioning of the human body. The average five-year relative survival rate for malignant brain tumors is 35.6 percent. For proper diagnosis and efficient treatment planning, it is necessary to detect the brain tumor in early stages. Due to advancement in medical imaging technology, the brain images are taken in different modalities. The ability to extract relevant characteristics from magnetic resonance imaging (MRI) scans is a crucial step for brain tumor classifiers. Several studies have proposed various strategies to extract relevant features from different modalities of MRI to predict the growth of abnormal tumors. Most techniques used conventional methods of image processing for feature extraction and machine learning for classification. More recently, the use of deep learning algorithms in medical imaging has resulted in significant improvements in the classification and diagnosis of brain tumors. Since tumors are located at different regions of the brain, localizing the tumor and classifying it to a particular category is a challenging task. The objective of this project is to develop a predictive system for brain tumor detection using machine learning(ensembling).

Bin He, Yuzhe Ying, Yejiong Shi et al.

Current surgical procedures for spinal cord tumors lack in vivo high-resolution, high-speed multifunctional imaging systems, posing challenges for precise tumor resection and intraoperative decision-making. This study introduces the Fast Adaptive Focus Tracking Robotic Optical Coherence Tomography (FACT-ROCT) system,designed to overcome these obstacles by providing real-time, artifact-free multifunctional imaging of spinal cord tumors during surgery. By integrating cross-scanning, adaptive focus tracking and robotics, the system addresses motion artifacts and resolution degradation from tissue movement, achieving wide-area, high-resolution imaging. We conducted intraoperative imaging on 21 patients, including 13 with spinal gliomas and 8 with other tumors. This study marks the first demonstration of OCT in situ imaging of human spinal cord tumors, providing micrometer-scale in vivo structural images and demonstrating FACT-ROCT's potential to differentiate various tumor types in real-time. Analysis of the attenuation coefficients of spinal gliomas revealed increased heterogeneity with higher malignancy grades. So, we proposed the standard deviation of the attenuation coefficient as a physical marker, achieving over 90% accuracy in distinguishing high- from low-grade gliomas intraoperatively at a threshold. FACT-ROCT even enabled extensive in vivo microvascular imaging of spinal cord tumors, covering 70 mm * 13 mm * 10 mm within 2 minutes. Quantitative vascular tortuosity comparisons confirmed greater tortuosity in higher-grade tumors. The ability to perform extensive vascular imaging and real-time tumor grading during surgery provides critical information for surgical strategy, such as minimizing intraoperative bleeding and optimizing tumor resection while preserving functional tissue.

Rostyslav Hnatyshyn, Jiayi Hong, Ross Maciejewski et al.

The development of cancer is difficult to express on a simple and intuitive level due to its complexity. Since cancer is so widespread, raising public awareness about its mechanisms can help those affected cope with its realities, as well as inspire others to make lifestyle adjustments and screen for the disease. Unfortunately, studies have shown that cancer literature is too technical for the general public to understand. We found that musification, the process of turning data into music, remains an unexplored avenue for conveying this information. We explore the pedagogical effectiveness of musification through the use of an algorithm that manipulates a piece of music in a manner analogous to the development of cancer. We conducted two lab studies and found that our approach is marginally more effective at promoting cancer literacy when accompanied by a text-based article than text-based articles alone.

Yiqing Shen, Guannan He, Mathias Unberath

Brain tumor analysis in Magnetic Resonance Imaging (MRI) is crucial for accurate diagnosis and treatment planning. However, the task remains challenging due to the complexity and variability of tumor appearances, as well as the scarcity of labeled data. Traditional approaches often address tumor segmentation and image generation separately, limiting their effectiveness in capturing the intricate relationships between healthy and pathological tissue structures. We introduce a novel promptable counterfactual diffusion model as a unified solution for brain tumor segmentation and generation in MRI. The key innovation lies in our mask-level prompting mechanism at the sampling stage, which enables guided generation and manipulation of specific healthy or unhealthy regions in MRI images. Specifically, the model's architecture allows for bidirectional inference, which can segment tumors in existing images and generate realistic tumor structures in healthy brain scans. Furthermore, we present a two-step approach for tumor generation and position transfer, showcasing the model's versatility in synthesizing realistic tumor structures. Experiments on the BRATS2021 dataset demonstrate that our method outperforms traditional counterfactual diffusion approaches, achieving a mean IoU of 0.653 and mean Dice score of 0.785 for tumor segmentation, outperforming the 0.344 and 0.475 of conventional counterfactual diffusion model. Our work contributes to improving brain tumor detection and segmentation accuracy, with potential implications for data augmentation and clinical decision support in neuro-oncology. The code is available at https://github.com/arcadelab/counterfactual_diffusion.

Samta Rani, Tanvir Ahmad, Sarfaraz Masood et al.

Despite significant research efforts and advancements, cancer remains a leading cause of mortality. Early cancer prediction has become a crucial focus in cancer research to streamline patient care and improve treatment outcomes. Manual tumor detection by histopathologists can be time consuming, prompting the need for computerized methods to expedite treatment planning. Traditional approaches to tumor detection rely on supervised learning, necessitates a large amount of annotated data for model training. However, acquiring such extensive labeled data can be laborious and time\-intensive. This research examines the three learning environments: supervised learning (SL), semi\-supervised learning (Semi\-SL), and self\-supervised learning (Self\-SL): to predict kidney, lung, and breast cancer. Three pre\-trained deep learning models (Residual Network\-50, Visual Geometry Group\-16, and EfficientNetB0) are evaluated based on these learning settings using seven carefully curated training sets. To create the first training set (TS1), SL is applied to all annotated image samples. Five training sets (TS2\-TS6) with different ratios of labeled and unlabeled cancer images are used to evaluateSemi\-SL. Unlabeled cancer images from the final training set (TS7) are utilized for Self\-SL assessment. Among different learning environments, outcomes from the Semi\-SL setting show a strong degree of agreement with the outcomes achieved in the SL setting. The uniform pattern of observations from the pre\-trained models across all three datasets validates the methodology and techniques of the research. Based on modest number of labeled samples and minimal computing cost, our study suggests that the Semi\-SL option can be a highly viable replacement for the SL option under label annotation constraint scenarios.

Fritz F Parl

Different tumor types are characterized by unique histopathological patterns including distinctive nuclear architectures. I hypothesized that the difference in nuclear appearance is reflected in different nuclear maps of chromosome territories, the discrete regions occupied by individual chromosomes in the interphase nucleus. To test this hypothesis, I used interchromosomal translocations (ITLs) as an analytical tool to map chromosome territories in 11 different tumor types from the TCGA PanCancer database encompassing 6003 tumors with 5295 ITLs. For each chromosome I determined the number and percentage of all ITLs for any given tumor type. Chromosomes were ranked according to the frequency and percentage of ITLs per chromosome. The ranking showed similar patterns for all tumor types. Chromosomes 1, 8, 11, 17, and 19 were ranked in the top quarter, accounting for 35.2% of 5295 ITLs, whereas chromosomes 13, 15, 18, 21, and X were in the bottom quarter, accounting for only 10.5% ITLs. The correlation between the chromosome ranking in the total group of 6003 tumors and the ranking in individual tumor types was significant, ranging from P  < .0001 to .0033. Thus, contrary to my hypothesis, different tumor types share a common nuclear map of chromosome territories. Based on the large number of ITLs in 11 different types of malignancy one can discern a shared pattern of chromosome territories in cancer and propose a probabilistic model of chromosomes 1, 8, 11, 17, 19 in the center of the nucleus and chromosomes 13, 15, 18, 21, X at the periphery.

Azim Ansari, Afzal Hussain, Raju Wadekar et al.

Cancer is still a global challenge for healthcare professional and scientists due to complicated pathological pathways, inefficient early diagnosis, and limited safe delivery system at economic treatment cost. Despite these, other factors (life style, environmental problem, socio-economic issues, patient related complications, expensive therapy, and genetic history of oncogene) played significant role to spread and complicate treatment. However, various novel carriers have been explored and reported for effective and efficient drug delivery using polymers and lipid. Among them, vesicular systems are considered as the most biocompatible and safe for delivery of hydrophilic and lipophilic drug candidates. Therefore, the present review addressed various forms of nanovesicular systems with their benefits, progressive development stages, and mechanistic insights for drug targeting (active and passive), specific cancer wise nanovesicles, exosomes, and commercial products with potential clinical applications. The review primarily highlighted the major findings of nanovesicles employed to control solid tumor when a chemotherapeutic drug was used in specific vesicles based nanocarriers. Notably, miscellaneous exosomes, blood cells-based drug delivery (neutrophils and leukocytes), pH-responsive nanovesicles improved drug therapy by targeting tumor tissues and high drug access in the site of action. Finally, co-administration of chemotherapeutic drugs (combination therapy) further revealed convincing therapeutic outcomes as compared to standalone.

Najla A. Lakkis, Umayya M. Musharafieh, Hanane G. Issa et al.

Background Lung cancer (Lca) is the leading cause of cancer morbidity and mortality worldwide. This study examines the Lca incidence and trends in Lebanon and compares them to regional and global ones. It also discusses Lca risk factors in Lebanon. Methods Lung cancer data from the Lebanese National Cancer Registry for 2005 to 2016 was obtained. The age-standardized incidence rates (ASRw) and age-specific rates per 100 000 population were calculated. Results Lung cancer ranked second for cancer incidence in Lebanon from 2005-2016. Lung cancer ASRw ranged from 25.3 to 37.1 per 100 000 males and 9.8 to 16.7 per 100 000 females. Males 70-74 and females 75+ had the highest incidence. Lung cancer ASRw in males increased significantly at 3.94% per year from 2005 to 2014 ( P > .05), then decreased non-significantly from 2014 to 2016 ( P < .05). Lung cancer ASRw in females increased significantly at 11.98% per year from 2005 to 2009 ( P > .05), then increased non-significantly from 2009 to 2016 ( P < .05). Males' Lca ASRw in Lebanon was lower than the global average in 2008 and became similar in 2012 (34.1 vs 34.2 per 100 000); However, females' Lca ASRw was almost comparable to the global average in 2008 and exceeded it in 2012 (16.5 vs 13.6, respectively, per 100 000). Males’ and Females’ Lca ASRw in Lebanon were among the highest in the Middle East and North Africa (MENA) region but lower than those estimated for North America, China and Japan, and several European countries. The proportion of Lca cases attributed to smoking among Lebanese males and females was estimated at 75.7% and 66.3% for all age groups, respectively. The proportion of Lca cases attributed to air pollution with PM 10 and PM 2.5 in Lebanon was estimated at 13.5% for all age groups. Conclusion Lung cancer incidence in Lebanon is among the highest in the MENA region. The leading known modifiable risk factors are tobacco smoking and air pollution.

D.M. Fernández-Aroca, N. García-Flores, S. Frost et al.

Background and purpose: MAPKs are among the most relevant signalling pathways involved in coordinating cell responses to different stimuli. This group includes p38MAPKs, constituted by 4 different proteins with a high sequence homology: MAPK14 (p38α), MAPK11 (p38β), MAPK12 (p38γ) and MAPK13 (p38δ). Despite their high similarity, each member shows unique expression patterns and even exclusive functions. Thus, analysing protein-specific functions of MAPK members is necessary to unequivocally uncover the roles of this signalling pathway. Here, we investigate the possible role of MAPK11 in the cell response to ionizing radiation (IR). Materials and methods: We developed MAPK11/14 knockdown through shRNA and CRISPR interference gene perturbation approaches and analysed the downstream effects on cell responses to ionizing radiation in A549, HCT-116 and MCF-7 cancer cell lines. Specifically, we assessed IR toxicity by clonogenic assays; DNA damage response activity by immunocytochemistry; apoptosis and cell cycle by flow cytometry (Annexin V and propidium iodide, respectively); DNA repair by comet assay; and senescence induction by both X-Gal staining and gene expression of senescence-associated genes by RT-qPCR. Results: Our findings demonstrate a critical role of MAPK11 in the cellular response to IR by controlling the associated senescent phenotype, and without observable effects on DNA damage response, apoptosis, cell cycle or DNA damage repair. Conclusion: Our results highlight MAPK11 as a novel mediator of the cellular response to ionizing radiation through the control exerted onto IR-associated senescence.

Sheng Liu, Hong Du, Jun Wan et al.

Background Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of cells in tumor microenvironment, which suppress antitumor immunity. Expansion of various MDSC subpopulations is closely associated with poor clinical outcomes in cancer. Lysosomal acid lipase (LAL) is a key enzyme in the metabolic pathway of neutral lipids, whose deficiency (LAL-D) in mice induces the differentiation of myeloid lineage cells into MDSCs. These Lal-/- MDSCs not only suppress immune surveillance but also stimulate cancer cell proliferation and invasion. Understanding and elucidating the underlying mechanisms of MDSCs biogenesis will help to facilitate diagnosis/prognosis of cancer occurrence and prevent cancer growth and spreading.Methods Single-cell RNA sequencing (scRNA-seq) was performed to distinguish intrinsic molecular and cellular differences between normal versus Lal-/- bone marrow–derived Ly6G+ myeloid populations in mice. In humans, LAL expression and metabolic pathways in various myeloid subsets of blood samples of patients with non-small cell lung cancer (NSCLC) were assessed by flow cytometry. The profiles of myeloid subsets were compared in patients with NSCLC before and after the treatment of programmed death-1 (PD-1) immunotherapy.Results scRNA-seq of Lal-/- CD11b+Ly6G+ MDSCs identified two distinctive clusters with differential gene expression patterns and revealed a major metabolic shift towards glucose utilization and reactive oxygen species (ROS) overproduction. Blocking pyruvate dehydrogenase (PDH) in glycolysis reversed Lal-/- MDSCs’ capabilities of immunosuppression and tumor growth stimulation and reduced ROS overproduction. In the blood samples of human patients with NSCLC, LAL expression was significantly decreased in CD13+/CD14+/CD15+/CD33+ myeloid cell subsets. Further analysis in the blood of patients with NSCLC revealed an expansion of CD13+/CD14+/CD15+ myeloid cell subsets, accompanied by upregulation of glucose-related and glutamine-related metabolic enzymes. Pharmacological inhibition of the LAL activity in the blood cells of healthy participants increased the numbers of CD13+ and CD14+ myeloid cell subsets. PD-1 checkpoint inhibitor treatment in patients with NSCLC reversed the increased number of CD13+ and CD14+ myeloid cell subsets and PDH levels in CD13+ myeloid cells.Conclusion These results demonstrate that LAL and the associated expansion of MDSCs could serve as targets and biomarkers for anticancer immunotherapy in humans.

Shashidhar Reddy Javaji, Sovesh Mohapatra, Advait Gosai et al.

Brain tumors remain a critical global health challenge, necessitating advancements in diagnostic techniques and treatment methodologies. A tumor or its recurrence often needs to be identified in imaging studies and differentiated from normal brain tissue. In response to the growing need for age-specific segmentation models, particularly for pediatric patients, this study explores the deployment of deep learning techniques using magnetic resonance imaging (MRI) modalities. By introducing a novel ensemble approach using ONet and modified versions of UNet, coupled with innovative loss functions, this study achieves a precise segmentation model for the BraTS-PEDs 2023 Challenge. Data augmentation, including both single and composite transformations, ensures model robustness and accuracy across different scanning protocols. The ensemble strategy, integrating the ONet and UNet models, shows greater effectiveness in capturing specific features and modeling diverse aspects of the MRI images which result in lesion wise Dice scores of 0.52, 0.72 and 0.78 on unseen validation data and scores of 0.55, 0.70, 0.79 on final testing data for the "enhancing tumor", "tumor core" and "whole tumor" labels respectively. Visual comparisons further confirm the superiority of the ensemble method in accurate tumor region coverage. The results indicate that this advanced ensemble approach, building upon the unique strengths of individual models, offers promising prospects for enhanced diagnostic accuracy and effective treatment planning and monitoring for brain tumors in pediatric brains.

Adrien Badré, Li Zhang, Wellington Muchero et al.

Polygenic risk scores (PRS) estimate the genetic risk of an individual for a complex disease based on many genetic variants across the whole genome. In this study, we compared a series of computational models for estimation of breast cancer PRS. A deep neural network (DNN) was found to outperform alternative machine learning techniques and established statistical algorithms, including BLUP, BayesA and LDpred. In the test cohort with 50% prevalence, the Area Under the receiver operating characteristic Curve (AUC) were 67.4% for DNN, 64.2% for BLUP, 64.5% for BayesA, and 62.4% for LDpred. BLUP, BayesA, and LPpred all generated PRS that followed a normal distribution in the case population. However, the PRS generated by DNN in the case population followed a bi-modal distribution composed of two normal distributions with distinctly different means. This suggests that DNN was able to separate the case population into a high-genetic-risk case sub-population with an average PRS significantly higher than the control population and a normal-genetic-risk case sub-population with an average PRS similar to the control population. This allowed DNN to achieve 18.8% recall at 90% precision in the test cohort with 50% prevalence, which can be extrapolated to 65.4% recall at 20% precision in a general population with 12% prevalence. Interpretation of the DNN model identified salient variants that were assigned insignificant p-values by association studies, but were important for DNN prediction. These variants may be associated with the phenotype through non-linear relationships.

Belal Badawy, Romario Sameh Samir, Youssef Tarek et al.

Brain tumors are a complex and potentially life-threatening medical condition that requires accurate diagnosis and timely treatment. In this paper, we present a machine learning-based system designed to assist healthcare professionals in the classification and diagnosis of brain tumors using MRI images. Our system provides a secure login, where doctors can upload or take a photo of MRI and our app can classify the model and segment the tumor, providing the doctor with a folder of each patient's history, name, and results. Our system can also add results or MRI to this folder, draw on the MRI to send it to another doctor, and save important results in a saved page in the app. Furthermore, our system can classify in less than 1 second and allow doctors to chat with a community of brain tumor doctors. To achieve these objectives, our system uses a state-of-the-art machine learning algorithm that has been trained on a large dataset of MRI images. The algorithm can accurately classify different types of brain tumors and provide doctors with detailed information on the size, location, and severity of the tumor. Additionally, our system has several features to ensure its security and privacy, including secure login and data encryption. We evaluated our system using a dataset of real-world MRI images and compared its performance to other existing systems. Our results demonstrate that our system is highly accurate, efficient, and easy to use. We believe that our system has the potential to revolutionize the field of brain tumor diagnosis and treatment and provide healthcare professionals with a powerful tool for improving patient outcomes.