Journal of Medical Signals and Sensors
Volume:14 Issue: 3, Mar 2024

Brain tumor segmentation is highly contributive in diagnosing and treatment planning. Manual brain tumor delineation is a time‑consuming and tedious task and varies depending on the radiologist’s skill. Automated brain tumor segmentation is of high importance and does not depend on either inter‑ or intra‑observation. The objective of this study is to automate the delineation of brain tumors from the Fluid-attenuated inversion recovery (FLAIR), T1‑weighted (T1W), T2‑weighted (T2W), and T1W contrast‑enhanced (T1ce) magnetic resonance (MR) sequences through a deep learning approach, with a focus on determining which MR sequence alone or which combination thereof would lead to the highest accuracy therein.

The BraTS‑2020 challenge dataset, containing 370 subjects with four MR sequences and manually delineated tumor masks, is applied to train a residual neural network. This network is trained and assessed separately for each one of the MR sequences (single‑channel input) and any combination thereof (dual‑ or multi‑channel input).

The quantitative assessment of the single‑channel models reveals that the FLAIR sequence would yield higher segmentation accuracy compared to its counterparts with a 0.77 ± 0.10 Dice index. As to considering the dual‑channel models, the model with FLAIR and T2W inputs yields a 0.80 ± 0.10 Dice index, exhibiting higher performance. The joint tumor segmentation on the entire four MR sequences yields the highest overall segmentation accuracy with a 0.82 ± 0.09 Dice index.

The FLAIR MR sequence is considered the best choice for tumor segmentation on a single MR sequence, while the joint segmentation on the entire four MR sequences would yield higher tumor delineation accuracy.

Noninvasive therapies such as focused ultrasound were developed to be used for cancer therapies, vessel bleeding, and drug delivery. The main purpose of focused ultrasound therapy is to affect regions of interest (ROI) of tissues without any injuries to surrounding tissues. In this regard, an appropriate monitoring method is required to control the treatment.

This study is aimed to develop a noninvasive monitoring technique of focused ultrasound (US) treatment using sparse representation of US radio frequency (RF) echo signals. To this end, reasonable results in temperature change estimation in the tissue under focused US radiation were obtained by utilizing algorithms related to sparse optimization as orthogonal matching pursuit (OMP) and accompanying Shannon’s entropy. Consequently, ex vivo tissue experimental tests yielded two datasets, including low‑intensity focused US (LIFU) and high‑intensity focused US (HIFU) data. The proposed processing method analyzed the ultrasonic RF echo signal and expressed it as a sparse signal and calculated the entropy of each frame.

The results indicated that the suggested approach could noninvasively estimate temperature changes between 37°C and 47°C during LIFU therapy. In addition, it represented temperature changes during HIFU ablation at various powers, ranging from 10 to 130 W. The normalized mean square error of the proposed method is 0.28, approximately 2.15 on previous related methods.

These results demonstrated that this novel proposed approach, including the combination of sparsity and Shanoon’s entropy, is more feasible and effective in temperature change estimation than its predecessors.

Glioma is one of the most drug and radiation‑resistant tumors. Gliomas suffer from inter‑ and intratumor heterogeneity which makes the outcome of similar treatment protocols vary from patient to patient. This article is aimed to overview the potential imaging markers for individual diagnosis, prognosis, and treatment response prediction in malignant glioma. Furthermore, the correlation between imaging findings and biological and clinical information of glioma patients is reviewed.

The search strategy in this study is to select related studies from scientific websites such as PubMed, Scopus, Google Scholar, and Web of Science published until 2022. It comprised a combination of keywords such as Biomarkers, Diagnosis, Prognosis, Imaging techniques, and malignant glioma, according to Medical Subject Headings.

Some imaging parameters that are effective in glioma management include: ADC, FA, Ktrans, regional cerebral blood volume (rCBV), cerebral blood flow (CBF), ve, Cho/NAA and lactate/lipid ratios, intratumoral uptake of 18F‑FET (for diagnostic application), RD, ADC, ve, vp, Ktrans, CBFT1, rCBV, tumor blood flow, Cho/NAA, lactate/lipid, MI/Cho, uptakes of 18F‑FET, 11C‑MET, and 18F‑FLT (for prognostic and predictive application). Cerebral blood volume and Ktrans are related to molecular markers such as vascular endothelial growth factor (VEGF). Preoperative ADCmin value of GBM tumors is associated with O6‑methylguanine‑DNA methyltransferase (MGMT) promoter methylation status. 2‑hydroxyglutarate metabolite and dynamic 18F‑FDOPA positron emission tomography uptake are related to isocitrate dehydrogenase (IDH) mutations.

Parameters including ADC, RD, FA, rCBV, Ktrans, vp, and uptake of 18F‑FET are useful for diagnosis, prognosis, and treatment response prediction in glioma. A significant correlation between molecular markers such as VEGF, MGMT, and IDH mutations with some diffusion and perfusion imaging parameters has been identified.