Biodistribution Study of Niosomes in Tumor-Implanted BALB/C Mice Using Scintigraphic Imaging

The purpose of this work was to study the biodistribution of niosomes in tumor-implanted BALB/c mice using gamma scintigraphy. Niosomes were first formulated and characterized, then radiolabeled with Technetium-99 m (99mTc). The biodistribution of 99mTc-labeled niosomes was evaluated in tumor-bearing mice through intravenous injection and imaged with gamma scintigraphy. The labeled complexes possessed high radiolabeling efficiency (98.08%) and were stable in vitro (>80% after 8 h). Scintigraphic imaging showed negligible accumulation in the stomach and thyroid, indicating minimal leaching of the radiolabel in vivo. Radioactivity was found mainly in the liver, spleen and kidneys. Tumor-to-muscle ratio indicated a higher specificity of the formulation for the tumor area. Overall, the formulated niosomes are stable both in vitro and in vivo, and show preferential tumor accumulation.

To Design a Mammogram Edge Detection Algorithm Using an Artificial Neural Network (ANN)

An artificial neural network (ANN) is used to resolve problems related to complex scenarios and logical thinking. Nowadays, a cause for concern is the mortality rate among women due to cancer. Generally, women to around 45 years old are the most vulnerable to this disease. Early detection is the only hope for the patient to survive, otherwise it may reach an unrecoverable stage. Currently, there are numerous techniques available for the diagnosis of such diseases out of which mammography is the most trustworthy method for detecting early stage cancer. The analysis of these mammogram images is always difficult to analyze due to low contrast and non-uniform background. The mammogram images are scanned, digitized for processing, nut that further reduces the contrast between region of interest (ROI) and the background. Furthermore, presence of noise, glands, and muscles leads to background contrast variations. The boundaries of the suspected tumor area are always fuzzy and improper. The aim of this article is to develop a robust edge detection technique which works optimally on mammogram images to segment a tumor area.

Comparative Analysis of Proposed Artificial Neural Network (ANN) Algorithm With Other Techniques

The mortality rate among women is increasing progressively due to cancer. Generally, women around 45 years old are vulnerable from this disease. Early detection is hope for patients to survive otherwise it may reach to unrecoverable stage. Currently, there are numerous techniques available for diagnosis of such a disease out of which mammography is the most trustworthy method for detecting early cancer stage. The analysis of these mammogram images are difficult to analyze due to low contrast and nonuniform background. The mammogram images are scanned and digitized for processing that further reduces the contrast between Region of Interest and background. Presence of noise, glands and muscles leads to background contrast variations. Boundaries of suspected tumor area are fuzzy & improper. Aim of paper is to develop robust edge detection technique which works optimally on mammogram images to segment tumor area. Output results of proposed technique on different mammogram images of MIAS database are presented and compared with existing techniques in terms of both Qualitative & Quantitative parameters.

Radiation therapy with neutrons and hydrons of the liver region using the MCNP code: المعالجة الاشعاعية بالنترونات والهدرونات لمنطقة الكبد باستخدام الكود MCNP

CT images were read and a 3D model of the tumor was created in the liver area, Then the values ​​of the radiation dose in terms of the depth resulting from (photons, neutrons and protons) were estimated and studied using the code (MCNP) after entering the data into it. The value of the radiation dose in terms of depth and curvature in photons, neutrons and protons radiation therapy was studied, from our findings in the research we note that protons are the best option for radiation therapy for high-depth liver cancer of photons and neutrons due to the lower dose at entry compared to the dose absorbed in the tumor area and its ability to deliver a greater amount of dose of neutrons and photons to the tumor area. We note that the values reached are acceptable for the treatment of tumors at a depth close to the surface. As for a large-depth tumor, it is necessary to increase high-energy radiation doses deep in the tumor area by accelerating proton therapy to protect natural organs from high-energy radiation doses.

A Bounding Box-Based Radiomics Model for Detecting Occult Peritoneal Metastasis in Advanced Gastric Cancer: A Multicenter Study

PurposeTo develop a bounding box (BBOX)-based radiomics model for the preoperative diagnosis of occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients.Materials and Methods599 AGC patients from 3 centers were retrospectively enrolled and were divided into training, validation, and testing cohorts. The minimum circumscribed rectangle of the ROIs for the largest tumor area (R_BBOX), the nonoverlapping area between the tumor and R_BBOX (peritumoral area; PERI) and the smallest rectangle that could completely contain the tumor determined by a radiologist (M_BBOX) were used as inputs to extract radiomic features. Multivariate logistic regression was used to construct a radiomics model to estimate the preoperative probability of OPM in AGC patients.ResultsThe M_BBOX model was not significantly different from R_BBOX in the validation cohort [AUC: M_BBOX model 0.871 (95% CI, 0.814–0.940) vs. R_BBOX model 0.873 (95% CI, 0.820–0.940); p = 0.937]. M_BBOX was selected as the final radiomics model because of its extremely low annotation cost and superior OPM discrimination performance (sensitivity of 85.7% and specificity of 82.8%) over the clinical model, and this radiomics model showed comparable diagnostic efficacy in the testing cohort.ConclusionsThe BBOX-based radiomics could serve as a simpler reliable and powerful tool for the preoperative diagnosis of OPM in AGC patients. And M_BBOX-based radiomics is simpler and less time consuming.

Multifunctional exosome-mimetics for targeted anti-glioblastoma therapy by manipulating protein corona

Targeted drug delivery to the glioblastoma (GBM) overcoming blood–brain barrier (BBB) has been challenging. Exosomes are promising vehicles for brain tumor drug delivery, but the production and purification hinder its application for nanomedicine. Besides, the formation of protein corona (PC) may affect the behaviour of nanocarriers. Here, multifunctional exosomes-mimetics (EM) are developed and decorated with angiopep-2 (Ang) for enhancing GBM drug delivery by manipulating PC. Docetaxel (DTX)-loaded EM with Ang modification (DTX@Ang-EM) show less absorption of serum proteins and phagocytosis by macrophages. Ang-EM show enhanced BBB penetration ability and targeting ability to the GBM. Ang-EM-mediated delivery increase the concentration of DTX in the tumor area. The multifunctional DTX@Ang-EM exhibits significant inhibition effects on orthotopic GBM growth with reduced side effects of the chemotherapeutic. Findings from this study indicate that the developed DTX@Ang-EM provide a new strategy for targeted brain drug delivery and GBM therapy. Graphical abstract

Application of MRI Image Based on Computer Semiautomatic Segmentation Algorithm in the Classification Prediction of Breast Cancer Histology

Objective. The study aimed to investigate the predictive classification accuracy of computer semiautomatic segmentation algorithm for the histological grade of breast tumors through the magnetic resonance imaging (MRI) examination. Methods. Five dynamic contrast-enhanced (DCE) MRI regions of interest (ROIs) were captured using computer semiautomatic segmentation method, referring to the entire tumor area, tumor border area, proximal gland area, middle gland area, and distal gland area. According to the mutual information maximum protocol, the corresponding five ROIs were extracted from diffusion weighted imaging (DWI) combined with DCE-MRI images. To use the features in the nonoverlapping area of DWI image and DCE-MRI image as elements, a single-variable logistic regression model was established corresponding to element characteristics. After multiple training, the model was evaluated using the receiver operating characteristic (ROC) curve and area under curve (AUC). Results. This DCE-MRI combined with DWI was superior to DCE-MRI and DW in the prediction of tumor area features. To use DCE-MRI or DWI alone was less effective than DCE-MRI combined with DWI. The DWI combined DCE-MRI demonstrated good regional segmentation effects in the tumour area, with luminal A value being 0.767 and the area under curve (AUC) value being 0.758. After optimization, the AUC value of the tumor area was 0.929, indicating that classification effects can be enhanced by combining the two imaging methods, which complemented each other. Conclusions. The DWI combined DCE-MRI imaging has improved the early diagnosis effects of breast cancer by predicting the occurrence of breast cancer through the labeling of biomarkers.

Current Strategies for Cancer Cell-Derived Extracellular Vesicles for Cancer Therapy

Cancer cell-derived extracellular vesicles (CEVs), a novel type of therapeutic agent in cancer treatment, can be prepared from the autocrine secretion of various cancer cells, the direct extraction of cancer cells and the combination of cancer cell-derived membranes with advanced materials. With various bioactive molecules, exosomes are produced by cells for intercellular communication. Although cancer cell-derived exosomes are known to inhibit tumor apoptosis and promote the progression of cancer, researchers have developed various innovative strategies to prepare anti-tumor vesicles from cancer cells. With current strategies for anti-tumor vesicles, four different kinds of CEVs are classified including irradiated CEVs, advanced materials combined CEVs, chemotherapeutic drugs loaded CEVs and genetically engineered CEVs. In this way, CEVs can not only be the carriers for anti-tumor drugs to the target tumor area but also act as immune-active agents. Problems raised in the strategies mainly concerned with the preparation, efficacy and application. In this review, we classified and summarized the current strategies for utilizing the anti-tumor potential of CEVs. Additionally, the challenges and the prospects of this novel agent have been discussed.

BIOM-28. SERIAL PLASMA AND CSF CELL-FREE TUMOR DNA (cf-tDNA) TRACKING IN DIFFUSE MIDLINE GLIOMA PATIENTS UNDERGOING TREATMENT WITH ONC201

Diffuse midline glioma (DMG) with H3K27M mutation is a lethal childhood brain cancer, with limited means of monitoring beyond serial MRI scans. We conducted a multi-site Phase 1 trial of the imipridone ONC201 for children with H3K27M-mutant glioma (NCT03416530). Patients on Arm D of the trial (n=24) underwent serial lumbar puncture (baseline, 2 and 6-months) for cell-free tumor DNA (cf-tDNA) analysis at time of MRI. Additionally, patients on all arms of the trial at the University of Michigan underwent serial plasma collection. We collected a total of 96 plasma-samples and 53 CSF-samples from 29 patients. We performed ddPCR analysis of cf-tDNA samples and compared variant allele fraction (VAF) to radiographic change (maximal tumor area on MRI). For our H3F3A-mutated (K27M) patients, cf-tDNA was positive in 53/62 plasma samples (sensitivity 85.4%) and 28/29 CSF samples (sensitivity 96.5%) and overall specificity of 100%. There was no direct correlation between percent-change in tumor-area and plasma (p=0.47) or CSF VAF (p=0.89), implying that VAF provided information supplemental to radiographic assessments. “Spikes” in plasma cf-tDNA VAF (increase of ≥25%) co-occurred with progression in 2/9 (22%) cases and preceded progression in 5/9 cases (55%) by an average of 1.22 months. In CSF, spikes preceded progression in 4/6 cases (66%) by an average of 1.8 months. Two patients had increases in tumor-area with no increase in plasma VAF; both were later confirmed as pseudo-progressors, suggesting additional potential utility of cf-tDNA VAF monitoring. A 14yo male with spinal cord glioma received concurrent bevacizumab with ONC201, which resulted in a decrease in tumor area but continued increase in plasma VAF, predicting radiologic progression at the next time. In summary, we present data which suggests monitoring serial CSF/plasma H3K27M tDNA is a promising clinical tool. Changes in cf-tDNAVAF over time appear to correlate with response, predict progression, and differentiate pseudo-progression.