The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.

Cytocompatibility of MG-63 osteosarcoma cells on chitosan/hydroxyapatite/lignin hybrid composite scaffoldin vitro.

This study aims at fabricating promising cytocompatible hybrid biocomposite scaffolds from chitosan (CS), hydroxyapatite (HAP) and lignin (L) for bone tissue engineering by using freeze-drying technique. Different ratios of HAP to L (50:0, 37.5:12.5, 25:25 and 12.5:37.5) were taken to determine the optimum ratio for obtaining a composite with superior properties. The mechanical and biological properties of the resulting composites were investigated. The mechanical results showed that the prepared composite with a ratio of 25:25 of HAP/L exhibited a remarkable enhancement in the mechanical properties compared to the others. Additionally, it was found from thein vitroresults that the addition of L enhanced the water uptake value of the resulting scaffolds indicating their increased hydrophilicity. As a result, a significant increase in the attachment and proliferation of MG-63 cell line (osteoblast like cells) was observed in composite scaffolds with L over the scaffold without L (CS/HAP). From these results, it could be suggested that the prepared composite scaffold with 25:25 of HAP/L is very promising biomaterials in bone tissue-engineering as it exhibited a better mechanical and biological properties than the other prepared composites.

State of the Art: Lung Cancer Staging Using Updated Imaging Modalities.

Lung cancer is among the most common mortality causes worldwide. This scientific article is a comprehensive review of current knowledge regarding screening, subtyping, imaging, staging, and management of treatment response for lung cancer. The traditional imaging modality for screening and initial lung cancer diagnosis is computed tomography (CT). Recently, a dual-energy CT was proven to enhance the categorization of variable pulmonary lesions. The National Comprehensive Cancer Network (NCCN) recommends usage of fluorodeoxyglucose positron emission tomography (FDG PET) in concert with CT to properly stage lung cancer and to prevent fruitless thoracotomies. Diffusion MR is an alternative to FDG PET/CT that is radiation-free and has a comparable diagnostic performance. For response evaluation after treatment, FDG PET/CT is a potent modality which predicts survival better than CT. Updated knowledge of lung cancer genomic abnormalities and treatment regimens helps to improve the radiologists' skills. Incorporating the radiologic experience is crucial for precise diagnosis, therapy planning, and surveillance of lung cancer.

How AI Can Help in the Diagnostic Dilemma of Pulmonary Nodules.

Pulmonary nodules are the precursors of bronchogenic carcinoma, its early detection facilitates early treatment which save a lot of lives. Unfortunately, pulmonary nodule detection and classification are liable to subjective variations with high rate of missing small cancerous lesions which opens the way for implementation of artificial intelligence (AI) and computer aided diagnosis (CAD) systems. The field of deep learning and neural networks is expanding every day with new models designed to overcome diagnostic problems and provide more applicable and simply used models. We aim in this review to briefly discuss the current applications of AI in lung segmentation, pulmonary nodule detection and classification.