Journal of Medical Signals & Sensors最新文献

Background: Treatment of locally advanced rectal cancer (LARC) involves neoadjuvant chemoradiotherapy (nCRT), followed by total mesorectal excision. Examining the response to treatment is one of the most important factors in the follow-up of patients; therefore, in this study, radiomics patterns derived from pretreatment computed tomography images in rectal cancer and its relationship with treatment response measurement criteria have been investigated.

Methods: Fifty patients with rectal adenocarcinoma who were candidates for nCRT and surgery were included. The information obtained from the tumor surgical pathology report, including pathological T and N, the degree of tumor differentiation, lymphovascular invasion, and perineural invasion along with radiomics characteristics to each patient, was assessed. Modeling with disturbed forest model was used for radiomics data. For other variables, Shapiro-Wilk, Chi-Square, and Pearson Chi-square tests were used.

Results: The participants of this study were 50 patients (23 males [46%] and 27 females [54%]). There was no significant difference in the rate of response to neoadjuvant treatment in between age and gender groups. According to the modeling based on combined clinical and radiomics data together, area under the curves for the nonresponders and complete respond group (responder group) was 0.97 and 0.99, respectively.

Conclusion: Random forests modeling based on combined radiomics and clinical characteristics of the pretreatment tumor images has the ability to predict the response or non-response to neoadjuvant treatment in LARC to an acceptable extent.

Purpose: This study aimed to investigate the performance of multiparametric magnetic resonance imaging (mpMRI) radiomic feature-based machine learning (ML) models in classifying the Gleason grade group (GG) of prostate cancer.

Methods: In this retrospective study, a total of 203 patients with histopathologically confirmed prostate cancer who underwent mpMRI before prostate biopsy were included. After manual segmentation, radiomic features (RFs) were extracted from T2-weighted, apparent diffusion coefficient, and high b-value diffusion-weighted magnetic resonance imaging (DWMRI). Patients were split into training sets and testing sets according to a ratio of 8:2. A pipeline considering combinations of two feature selection (FS) methods and six ML classifiers was developed and evaluated. The performance of models was assessed using the accuracy, sensitivity, precision, F1-measure, and the area under curve (AUC).

Results: On high b-value DWMRI-derived features, a combination of FS method recursive feature elimination (RFE) and classifier random forest achieved the highest performance for classification of prostate cancer into five GGs, with 97.0% accuracy, 98.0% sensitivity, 98.0% precision, and 97.0% F1-measure. The method also achieved an average AUC for GG of 98%.

Conclusion: Preoperative mpMRI radiomic analysis based on ML, as a noninvasive approach, showed good performance for classification of prostate cancer into five GGs.

Advances in knowledge: Herein, radiomic models based on preoperative mpMRI and ML were developed to classify prostate cancer into 5 GGs. Our study provides evidence that analysis of quantitative RFs extracted from high b-value DWMRI images based on a combination of FS method RFE and classifier random forest can be applied for multiclass grading of prostate cancer with an accuracy of 97.0%.

Proton therapy is a cancer treatment method that uses high-energy proton beams to target and destroy cancer cells. In recent years, the use of proton therapy in cancer treatment has increased due to its advantages over traditional radiation methods, such as higher accuracy and reduced damage to healthy tissues. For accurate planning and delivery of proton therapy, advanced software tools are needed to model and simulate the interaction between the proton beam and the patient's body. One of these tools is the Monte Carlo simulation software called Geant4, which provides accurate modeling of physical processes during radiation therapy. The purpose of this study is to investigate the effectiveness of the Geant4 toolbox in proton therapy in the conducted research. This review article searched for published articles between 2002 and 2023 in reputable international databases including Scopus, PubMed, Scholar, Google Web of Science, and ScienceDirect. Geant4 simulations are reliable and accurate and can be used to optimize and evaluate the performance of proton therapy systems. Obtaining some data from experiments carried out in the real world is very effective. This makes it easy to know how close the values obtained from simulations are to the behavior of ions in reality.

Background: Different dose calculation methods vary in accuracy and speed. While most methods sacrifice precision for efficiency Monte Carlo (MC) simulation offers high accuracy but slower calculation. ISOgray treatment planning system (TPS) uses Clarkson, collapsed cone convolution (CCC), and fast Fourier transform (FFT) algorithms for dose distribution. This study's primary goal is to evaluate the dose calculation accuracy for ISOgray TPS algorithms in the presence of a wedge.

Methods: This study evaluates the dose calculation algorithms using the ISOgray TPS in the context of radiation therapy. The authors compare ISOgray TPS algorithms on an Elekta Compact LINAC through MC simulations. The study compares MC simulations for open and wedge fields with ISOgray algorithms by using gamma index analysis for validation.

Results: The percentage depth dose results for all open and wedge fields showed a more than 98% pass rate for points. However, there were differences in the dose profile gamma index results. Open fields passed the gamma index analysis in the in-plane direction, but not all points passed in the cross-plane direction. Wedge fields passed in the cross-plane direction, but not all in the in-plane direction, except for the Clarkson algorithms.

Conclusion: In all investigated algorithms, error increases in the penumbra areas, outside the field, and at cross-plane of open fields and in-plane direction of wedged fields. By increasing the wedge angle, the discrepancy between the TPS algorithms and MC simulations becomes more pronounced. This discrepancy is attributed to the increased presence of scattered photons and the variation in the delivered dose within the wedge field, consequently impacts the beam quality. While the CCC and FFT algorithms had better accuracy, the Clarkson algorithm, particularly at larger effective wedge angles, exhibited greater effectiveness than the two mentioned algorithms.

Background: Autism spectrum disorders are a type of developmental disorder that primarily disrupt social interactions and communications. Autism has no treatment, but early diagnosis of it is crucial to reduce these effects. The incidence of autism is represented in repetitive patterns of children's motion. When walking, these children tighten their muscles and cannot control and maintain their body position. Autism is not only a mental health disorder but also a movement disorder.

Method: This study aims to identify autistic children based on data recorded from their gait patterns using a Kinect sensor. The database used in this study comprises walking information, such as joint positions and angles between joints, of 50 autistic and 50 healthy children. Two groups of features were extracted from the Kinect data in this study. The first one was statistical features of joints' position and angles between joints. The second group was the features based on medical knowledge about autistic children's behaviors. Then, extracted features were evaluated through statistical tests, and optimal features were selected. Finally, these selected features were classified by naïve Bayes, support vector machine, k-nearest neighbors, and ensemble classifier.

Results: The highest classification accuracy for medical knowledge-based features was 87% with 86% sensitivity and 88% specificity using an ensemble classifier; for statistical features, 84% of accuracy was obtained with 86% sensitivity and 82% specificity using naïve Bayes.

Conclusion: The dimension of the resulted feature vector based on autistic children's medical knowledge was 16, with an accuracy of 87%, showing the superiority of these features compared to 42 statistical features.

Background: In this study, we want to evaluate the response to Lutetium-177 (¹⁷⁷Lu)-DOTATATE treatment in patients with neuroendocrine tumors (NETs) using single-photon emission computed tomography (SPECT) and computed tomography (CT), based on image-based radiomics and clinical features.

Methods: The total volume of tumor areas was segmented into 61 SPECT and 41 SPECT-CT images from 22 patients with NETs. A total of 871 radiomics and clinical features were extracted from the SPECT and SPECT-CT images. Subsequently, a feature reduction method called maximum relevance minimum redundancy (mRMR) was used to select the best combination of features. These selected features were modeled using a decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) classifiers to predict the treatment response in patients. For the SPECT and SPECT-CT images, ten and eight features, respectively, were selected using the mRMR algorithm.

Results: The results revealed that the RF classifier with feature selection algorithms through mRMR had the highest classification accuracies of 64% and 83% for the SPECT and SPECT-CT images, respectively. The accuracy of the classifications of DT, KNN, and SVM for SPECT-CT images is 79%, 74%, and 67%, respectively. The poor accuracy obtained from different classifications in SPECT images (≈64%) showed that these images are not suitable for predicting treatment response.

Conclusions: Modeling the selected features of SPECT-CT images based on their anatomy and the presence of extensive gray levels makes it possible to predict responses to the treatment of ¹⁷⁷Lu-DOTATATE for patients with NETs.

Background: Cytokine storms and inflammation lead to heart failure (HF). Bioactive compounds, as complementary medicine, can be the primary source of compounds with anti-inflammatory properties. Linum usitatissimum (LiU) has antioxidant capacity and anti-inflammatory activity. Here, candidate hugeness was selected based on the in silico studies, bio-cheminformatics, and bioinformatic analysis for excremental validation.

Methods: We selected the vital genes with differential expression from the GSE26887 dataset. Based on the bioinformatics analysis, several parameters are determined to choose switchable genes involved in diabetic HF (DHF). We designed the protein-protein interactions network to consider the nodes' degree, modularity, and betweenness centrality. Hence, we selected the interleukin (IL)-6 protein as a target for drug design and discovery to reduce diabetes complications in the heart. Here, H9c2 cell lines of rat embryonic cardiomyocytes induce HF using hyperglycemic and hyperlipidemic conditions. Real-time polymerase chain reaction evaluated the relative expression of SMAD7/NRF-2/STAT3. Furthermore, we assessed the concentration of IL-6 using the enzyme-linked immunosorbent assay technique.

Results: Based on the bioinformatic analysis, we found that IL-6 with the highest network parameters score might be presented as a druggable protein in the DHF. Bioactive compounds and phytochemicals have potential strategies to manage DHF. LiUs decreased the expression level of the SMAD7 (P <0.0001) and STAT3 (P < 0.0001), and increased the expression level of the NRF2 (P < 0.0001). In addition, LiUs significantly reduced the concentration of IL-6 (P < 0.0001).

Conclusion: Our data proposed that LiUs regulated inflammation and triggered the antioxidant defense in HF. Moreover, LiUs could have potential approaches to managing and preventing DHF.

Background: Using three-dimensional conformal radiation treatment (3D-CRT) and helical tomotherapy (HT), this study examines and contrasts the dosage received by the mandible, maxilla, and teeth.

Methods: Sixteen patients with head-and-neck cancer (H and NC) were the subject of treatment planning at the Seyyed Al-Shohada Hospital in Isfahan, Iran. This study examined target coverage quality, exposure of healthy tissue, and radiation delivery effectiveness.

Results: In terms of a number of measures, including D_2%, D_50%, D_mean, V_95%, conformity index (CI), and homogeneity index (HI) for the planning target volume (PTV) and D_2%, D_98%, D_mean, V_95%, CI, and HI for the nodal PTV, HT showed considerable gains over 3D-CRT. The brainstem, D_1cc, and D_10cc received considerably lower maximum dosages in HT. Measurements of the right and left cochleas (D_mean, V55, and D_max) revealed decreases in HT, with D_mean revealing the most significant variations. The D_mean and D_max values for HT significantly decreased in constrictors as well. In terms of several HT-related indicators, the larynx, optic chiasm, optic nerves, oral cavity, mandible, thyroid, and parotid glands all showed considerable decreases.

Conclusion: The findings of the comparison of the two treatment approaches revealed that the HT method was more than 50% more effective than the 3D-CRT method in sustaining organs at risk (OARs) and the target volume dose. In general, dosimetric coverage, homogeneity, conformity indices, and the absence of cold and hot patches showed that HT produced targets with greater accuracy than 3D-CRT. In addition, HT outperformed 3D-CRT in protecting important structures (OARs). HT as a result has the potential to be a more effective method of treatment for those with H and NC and involvement of regional lymph nodes.

Artificial intelligence (AI) has become increasingly prevalent and significant across many industries, including the dental field. AI has shown accuracy and precision in detecting, evaluating, and predicting diseases. It can imitate human intelligence to carry out sophisticated predictions and decision-making in the health-care industry, especially in endodontics. AI models have demonstrated a wide range of applications in the field of endodontics. These include examining the anatomy of the root canal system, predicting the survival of dental pulp stem cells, gauging working lengths, identifying per apical lesions and root fractures, and predicting the outcome of retreatment treatments. Future uses of this technology were discussed in terms of robotic endodontic surgery, drug-drug interactions, patient care, scheduling, and prognostic diagnosis.

Unlike other functional integration methods that examine the relationship and correlation between two channels, effective connection reports the direct effect of one channel on another and expresses their causal relationship. In this article, we investigate and classify electroencephalographic (EEG) signals based on effective connectivity. In this study, we leverage the Granger causality (GC) relationship, a method for measuring effective connectivity, to analyze EEG signals from both healthy individuals and those with autism. The EEG signals examined in this article were recorded during the presentation of abstract images. Given the nonstationary nature of EEG signals, a vector autoregression model has been employed to model the relationships between signals across different channels. GC is then used to quantify the influence of these channels on one another. Selecting regions of interest (ROI) is a critical step, as the quality of the time periods under consideration significantly impacts the outcomes of the connectivity analysis among the electrodes. By comparing these effects in the ROI and various areas, we have distinguished healthy subjects from those suffering from autism. Furthermore, through statistical analysis, we have compared the results between healthy individuals and those with autism. It has been observed that the causal relationship between these two hemispheres is significantly weaker in healthy individuals compared to those with autism.