Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview

Gastrointestinal (GI) cancers are the major cause of cancer-related mortality globally. Medical imaging is an important auxiliary means for the diagnosis, assessment and prognostic prediction of GI cancers. Radiomics is an emerging and effective technology to decipher the encoded information within medical images, and traditional machine learning is the most commonly used tool. Recent advances in deep learning technology have further promoted the development of radiomics. In the field of GI cancer, although there are several surveys on radiomics, there is no specific review on the application of deep-learning-based radiomics (DLR). In this review, a search was conducted on Web of Science, PubMed, and Google Scholar with an emphasis on the application of DLR for GI cancers, including esophageal, gastric, liver, pancreatic, and colorectal cancers. Besides, the challenges and recommendations based on the findings of the review are comprehensively analyzed to advance DLR.

Automatic detection of multiple types of pneumonia: Open dataset and a multi-scale attention network

The quick and precise identification of COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, mycoplasma pneumonia, and normal lung on chest CT images play a crucial role in timely quarantine and medical treatment. However, manual identification is subject to potential misinterpretations and time-consumption issues owing the visual similarities of pneumonia lesions. In this study, we propose a novel multi-scale attention network (MSANet) based on a bag of advanced deep learning techniques for the automatic classification of COVID-19 and multiple types of pneumonia. The proposed method can automatically pay attention to discriminative information and multi-scale features of pneumonia lesions for better classification. The experimental results show that the proposed MSANet can achieve an overall precision of 97.31%, recall of 96.18%, F1-score of 96.71%, accuracy of 97.46%, and macro-average area under the receiver operating characteristic curve (AUC) of 0.9981 to distinguish between multiple classes of pneumonia. These promising results indicate that the proposed method can significantly assist physicians and radiologists in medical diagnosis. The dataset is publicly available at https://doi.org/10.17632/rf8x3wp6ss.1.

Effects of chronic paraventricular nucleus infusion of clonidine and alpha-methyl-para-tyrosine on macronutrient intake

Earlier studies have demonstrated that acute injections of norepinephrine (NE) and clonidine (CLON) into the paraventricular nucleus (PVN) elicit feeding in satiated rats. This study examined the effects of chronic PVN infusion of alpha-methyl-paratyrosine (alpha-MpT), a catecholamine synthesis inhibitor, on intake of a mixed milk-mash diet and on the ingestion of pure macronutrients, protein, carbohydrate and fat. The impact of chronic CLON administration on intake of these macronutrient preparations was also investigated. Over a 14-day period, chronic infusion of alpha-MpT (50 nmoles/30 sec/0.5 microliter) resulted in a reduction of total daily food intake in rats maintained on a milk-mash diet. Chronic administration of CLON (3 nmoles/30 sec/0.5 microliters) was observed to produce a specific increase in ingestion of the carbohydrate, along with a suppression in the intakes of the protein and fat. These results are similar to those previously demonstrated for NE. In contrast, chronic infusion of alpha-MpT into the PVN caused a specific reduction of carbohydrate consumption and an enhancement of protein ingestion, with fat intake unaffected. This evidence suggests that the alpha 2-noradrenergic system of the PVN plays an important role in the daily regulation of macronutrient intake, specifically of carbohydrate.