Computer aided automated detection and classification of brain tumors using CANFIS classification method

The development of abnormal cells in human brain leads to the formation of tumors. This article proposes an efficient approach for brain tumor detection and segmentation using image fusion and co-active adaptive neuro fuzzy inference system (CANFIS) classification method. The brain MRI images are fused and the dual tree complex wavelet transform is applied on the fused image. Then, the statistical features, local ternary pattern features and gray level co-occurrence matrix features. These extracted features are classified using CANFIS classification approach for the classification of source brain MRI image into either normal or abnormal. Further, morphological operations are applied on the abnormal brain MRI image for segmenting the tumor regions. The proposed methodology is evaluated with respect to the performance metrics sensitivity, specificity, positive predictive value, negative predictive value, tumor segmentation accuracy with detection rate. The proposed image fusion based brain tumor detection and classification methodology stated in this article achieves 96.5% of average sensitivity, 97.7% of average specificity, 87.6% of positive predictive value, 96.6% of negative predictive value, and 98.8% of average accuracy.     

Candidate Gene Discovery in Hereditary Colorectal Cancer and Polyposis Syndromes–Considerations for Future Studies

To discover novel high-penetrant risk loci for hereditary colorectal cancer (hCRC) and polyposis syndromes many whole-exome and whole-genome sequencing (WES/WGS) studies have been performed. Remarkably, these studies resulted in only a few novel high-penetrant risk genes. Given this observation, the possibility and strategy to identify high-penetrant risk genes for hCRC and polyposis needs reconsideration. Therefore, we reviewed the study design of WES/WGS-based hCRC and polyposis gene discovery studies (n = 37) and provide recommendations to optimize discovery and validation strategies. The group of genetically unresolved patients is phenotypically heterogeneous, and likely composed of distinct molecular subtypes. This knowledge advocates for the screening of a homogeneous, stringently preselected discovery cohort and obtaining multi-level evidence for variant pathogenicity. This evidence can be collected by characterizing the molecular landscape of tumors from individuals with the same affected gene or by functional validation in cell-based models. Together, the combined approach of a phenotype-driven, tumor-based candidate gene search might elucidate the potential contribution of novel genetic predispositions in genetically unresolved hCRC and polyposis.     

An optimized generic cerebral tumor growth modeling framework by coupling biomechanical and diffusive models with treatment effects

Mathematical modeling of cerebral tumor growth is of great importance in clinics. It can help in understanding the physiology of tumor growth, future prognosis of tumor shape and volume, quantify tumor aggressiveness, and the response to therapy. This can be achieved at macroscopic level using medical imaging techniques (particularly, magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI)) and complex mathematical models which are either diffusive or biomechanical. We propose an optimized generic modeling framework that couples tumor diffusivity and infiltration with the induced mass effect. Tumor cell diffusivity and infiltration are captured using a modified reaction-diffusion model with logistic proliferation term. On the other hand, tumor mass effect is modeled using continuum mechanics formulation. In addition, we consider the treatment effects of both radiotherapy and chemotherapy. The efficacy of chemotherapy is included via an adaptively modified log-kill method to consider tissue heterogeneity while the efficacy of radiotherapy is considered using the linear quadratic model. Moreover, our model efficiently utilizes the diffusion tensor of the diffusion tensor imaging. Furthermore, we optimize the tumor growth parameters to be patient-specific using bio-inspired particle swarm optimization (PSO) algorithm. Our model is tested on an atlas and real MRI scans of 8 low grade gliomas subjects. Experimental results show that our model efficiently incorporates both treatment effects in the growth modelingprocess. In addition, simulated growths of our model have high accuracy in terms of Dice coefficient (average 87.1%) and Jaccard index (77.14%) when compared with the follow up scans (ground truth) and other models as well.     

An Efficient Image Analysis Framework for the Classification of Glioma Brain Images Using CNN Approach

The identification of brain tumors is multifarious work for the separation of the similar intensity pixels from their surrounding neighbours. The detection of tumors is performed with the help of automatic computing technique as presented in the proposed work. The non-active cells in brain region are known to be benign and they will never cause the death of the patient. These non-active cells follow a uniform pattern in brain and have lower density than the surrounding pixels. The Magnetic Resonance (MR) image contrast is improved by the cost map construction technique. The deep learning algorithm for differentiating the normal brain MRI images from glioma cases is implemented in the proposed method. This technique permits to extract the linear features from the brain MR image and glioma tumors are detected based on these extracted features. Using k-mean clustering algorithm the tumor regions in glioma are classified. The proposed algorithm provides high sensitivity, specificity and tumor segmentation accuracy.     

Engineering 2D Mesoporous Silica@MXene‐Integrated 3D‐Printing Scaffolds for Combinatory Osteosarcoma Therapy and NO‐Augmented Bone Regeneration

The rising concerns of the recurrence and bone deficiency in surgical treatment of malignant bone tumors have raised an urgent need of the advance of multifunctional therapeutic platforms for efficient tumor therapy and bone regeneration. Herein, the construction of a multifunctional biomaterial system is reported by the integration of 2D Nb₂C MXene wrapped with S‐nitrosothiol (R? SNO)‐grafted mesoporous silica with 3D‐printing bioactive glass (BG) scaffolds (MBS). The near infrared (NIR)‐triggered photonic hyperthermia of MXene in the NIR‐II biowindow and precisely controlled nitric oxide (NO) release are coordinated for multitarget ablation of bone tumors to enhance localized osteosarcoma treatment. The in situ formed phosphorus and calcium components degraded from BG scaffold promote bone‐regeneration bioactivity, augmented by sufficient blood supply triggered by on‐demand NO release. The tunable NO generation plays a crucial role in sequential adjuvant tumor ablation, combinatory promotion of coupled vascularization, and bone regeneration. This study demonstrates a combinatory osteosarcoma ablation and a full osseous regeneration as enabled by the implantation of MBS. The design of multifunctional scaffolds with the specific features of controllable NO release, highly efficient photothermal conversion, and stimulatory bone regeneration provides an intriguing biomaterial platform for the diversified treatment of bone tumors.     

Traceable Nanoparticles with Dual Targeting and ROS Response for RNAi‐Based Immunochemotherapy of Intracranial Glioblastoma Treatment

The chemotherapy of glioblastoma is severely hindered by the immunosuppressive tumor microenvironment, especially the tumor growth factor β (TGF‐β), an immunosuppressive cytokine. In this study, it is proposed to employ RNAi‐based immunomodulation to modify the tumor immune microenvironment and improve the effect of chemotherapy. Herein, a nanotheranostic system (Angiopep LipoPCB(Temozolomide+BAP/siTGF‐β), ALBTA) with dual targeting and ROS response is established for intracranial glioblastoma treatment. The traceable nanoparticles exhibit strong siRNA condensation, high drug loading efficiency, good serum stability, and magnetic property. They can efficiently cross the blood–brain barrier and target to glioblastoma cells via receptor‐mediated transcytosis. The zwitterionic lipid (distearoyl phosphoethanol‐amine‐polycarboxybetaine lipid) in ALBTA promotes endosomal/lysosomal escape, and thus enhances the cytotoxicity of temozolomide and improves gene silencing efficiency of siTGF‐β. ALBTA significantly improves the immunosuppressive microenvironment and prolongs the survival time of glioma‐bearing mice. Moreover, ALBTA can be accurately traced by MRI in brain tumors. The study indicates that this immunochemotherapeutic platform can serve as a flexible and powerful synergistic system for treatment with brain tumors as well as other brain diseases in central nervous system.     

Performance analysis of brain tissues and tumor detection and grading system using ANFIS classifier

Abnormal growth of cells in brain leads to the formation of tumors, which are categorized into benign and malignant. In this article, Co‐Active Adaptive Neuro Fuzzy Inference System (CANFIS) classification based brain tumor detection and its grading system is proposed. It has two phases as brain tumor segmentation and brain tissue segmentation. In brain tumor segmentation, CANFIS classifier is used to classify the test brain image into benign or malignant. Then, morphological operations are applied over the malignant image in order to segment the tumor regions in brain image. The K‐means classifier is used to classify the brain tissues into Grey Matter (GM), White Matter (WM) and Cerebro Spinal Fluid (CSF) regions as three different classes. Next, the segmented tumor is graded as mild, moderate or severe based on the presence of segmented tumor region in brain tissues.     

SN-38 loading capacity of hydrophobic polymer blend nanoparticles: formulation,optimization and efficacy evaluation

One of the most important problems in nanoencapsulation of extremely hydrophobic drugs is poor drug loading due to rapid drug crystallization outside the polymer core. The effort to use nanoprecipitation, as a simple one-step procedure with good reproducibility and FDA approved polymers like Poly(lactic-co-glycolic acid) (PLGA) and Polycaprolactone (PCL), will only potentiate this issue. Considering that drug loading is one of the key defining characteristics, in this study we attempted to examine whether the nanoparticle (NP) core composed of two hydrophobic polymers will provide increased drug loading for 7-Ethyl-10-hydroxy-camptothecin (SN-38), relative to NPs prepared using individual polymers. D-optimal design was applied to optimize PLGA/PCL ratio in the polymer blend and the mode of addition of the amphiphilic copolymer Lutrol^®F127 in order to maximize SN-38 loading and obtain NPs with acceptable size for passive tumor targeting. Drug/polymer and polymer/polymer interaction analysis pointed to high degree of compatibility and miscibility among both hydrophobic polymers, providing core configuration with higher drug loading capacity. Toxicity studies outlined the biocompatibility of the blank NPs. Increased in vitro efficacy of drug-loaded NPs compared to the free drug was confirmed by growth inhibition studies using SW-480 cell line. Additionally, the optimized NP formulation showed very promising blood circulation profile with elimination half-time of 7.4?h.