Is it necessary to define new diagnostic reference levels during pandemics like the Covid19-?

Enhancing cancer stage prediction through hybrid deep neural networks: a comparative study

Efficiently detecting and treating cancer at an early stage is crucial to improve the overall treatment process and mitigate the risk of disease progression. In the realm of research, the utilization of artificial intelligence technologies holds significant promise for enhancing advanced cancer diagnosis. Nonetheless, a notable hurdle arises when striving for precise cancer-stage diagnoses through the analysis of gene sets. Issues such as limited sample volumes, data dispersion, overfitting, and the use of linear classifiers with simple parameters hinder prediction performance. This study introduces an innovative approach for predicting early and late-stage cancers by integrating hybrid deep neural networks. A deep neural network classifier, developed using the open-source TensorFlow library and Keras network, incorporates a novel method that combines genetic algorithms, Extreme Learning Machines (ELM), and Deep Belief Networks (DBN). Specifically, two evolutionary techniques, DBN-ELM-BP and DBN-ELM-ELM, are proposed and evaluated using data from The Cancer Genome Atlas (TCGA), encompassing mRNA expression, miRNA levels, DNA methylation, and clinical information. The models demonstrate outstanding prediction accuracy (89.35%-98.75%) in distinguishing between early- and late-stage cancers. Comparative analysis against existing methods in the literature using the same cancer dataset reveals the superiority of the proposed hybrid method, highlighting its enhanced accuracy in cancer stage prediction.

COVID-19 and cancer risk arising from ionizing radiation exposure through CT scans: a cross-sectional study

BACKGROUND

The surge in the utilization of CT scans for COVID-19 diagnosis and monitoring during the pandemic is undeniable. This increase has brought to the forefront concerns about the potential long-term health consequences, especially radiation-induced cancer risk. This study aimed to quantify the potential cancer risk associated with CT scans performed for COVID-19 detection.

METHODS

In this cross-sectional study data from a total of 561 patients, who were referred to the radiology center at Imam Hossein Hospital in Shahroud, was collected. CT scan reports were categorized into three groups based on the radiologist's interpretation. The BEIR VII model was employed to estimate the risk of radiation-induced cancer.

RESULTS

Among the 561 patients, 299 (53.3%) were males and the average age of the patients was 49.61 ± 18.73 years. Of the CT scans, 408 (72.7%) were reported as normal. The average age of patients with normal, abnormal, and potentially abnormal CT scans was 47.57 ± 19.06, 54.80 ± 16.70, and 58.14 ± 16.60 years, respectively (p-value < 0.001). The average effective dose was 1.89 ± 0.21 mSv, with 1.76 ± 0.11 mSv for males and 2.05 ± 0.29 mSv for females (p-value < 0.001). The average risk of lung cancer was 3.84 ± 1.19 and 9.73 ± 3.27 cases per 100,000 patients for males and females, respectively. The average LAR for all cancer types was 10.30 ± 6.03 cases per 100,000 patients.

CONCLUSIONS

This study highlights the critical issue of increased CT scan usage for COVID-19 diagnosis and the potential long-term consequences, especially the risk of cancer incidence. Healthcare policies should be prepared to address this potential rise in cancer incidence and the utilization of CT scans should be restricted to cases where laboratory tests are not readily available or when clinical symptoms are severe.

Classification of cancer cells and gene selection based on microarray data using MOPSO algorithm

PURPOSE

Microarray information is crucial for the identification and categorisation of malignant tissues. The very limited sample size in the microarray has always been a challenge for classification design in cancer research. As a result, by pre-processing gene selection approaches and genes lacking their information, the microarray data are deleted prior to categorisation. In essence, an appropriate gene selection technique can significantly increase the accuracy of illness (cancer) classification.

METHODS

For the classification of high-dimensional microarray data, a novel approach based on the hybrid model of multi-objective particle swarm optimisation (MOPSO) is proposed in this research. First, a binary vector representing each particle's position is presented at random. A gene is represented by each bit. Bit 0 denotes the absence of selection of the characteristic (gene) corresponding to it, while bit 1 denotes the selection of the gene. Therefore, the position of each particle represents a set of genes, and the linear Bayesian discriminant analysis classification algorithm calculates each particle's degree of fitness to assess the quality of the gene set that particle has chosen. The suggested methodology is applied to four different cancer database sets, and the results are contrasted with those of other approaches currently in use.

RESULTS

The proposed algorithm has been applied on four sets of cancer database and its results have been compared with other existing methods. The results of the implementation show that the improvement of classification accuracy in the proposed algorithm compared to other methods for four sets of databases is 25.84% on average. So that it has improved by 18.63% in the blood cancer database, 24.25% in the lung cancer database, 27.73% in the breast cancer database, and 32.80% in the prostate cancer database. Therefore, the proposed algorithm is able to identify a small set of genes containing information in a way choose to increase the classification accuracy.

CONCLUSION

Our proposed solution is used for data classification, which also improves classification accuracy. This is possible because the MOPSO model removes redundancy and reduces the number of redundant and redundant genes by considering how genes are correlated with each other.