Background: The purpose of this study was to investigate the value of wavelet-transformed radiomic MRI in predicting the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) for patients with locally advanced breast cancer (LABC). Methods: Fifty-five female patients with LABC who underwent contrast-enhanced MRI (CE-MRI) examination prior to NAC were collected for the retrospective study. According to the pathological assessment after NAC, patient responses to NAC were categorized into pCR and non-pCR. Three groups of radiomic textures were calculated in the segmented lesions, including (1) volumetric textures, (2) peripheral textures, and (3) wavelet-transformed textures. Six models for the prediction of pCR were Model I: group (1), Model II: group (1) + (2), Model III: group (3), Model IV: group (1) + (3), Model V: group (2) + (3), and Model VI: group (1) + (2) + (3). The performance of predicting models was compared using the area under the receiver operating characteristic (ROC) curves (AUC). Results: The AUCs of the six models for the prediction of pCR were 0.816 ± 0.033 (Model I), 0.823 ± 0.020 (Model II), 0.888 ± 0.025 (Model III), 0.876 ± 0.015 (Model IV), 0.885 ± 0.030 (Model V), and 0.874 ± 0.019 (Model VI). The performance of four models with wavelet-transformed textures (Models III, IV, V, and VI) was significantly better than those without wavelet-transformed textures (Model I and II). In addition, the inclusion of volumetric textures or peripheral textures or both did not result in any improvements in performance. Conclusions: Wavelet-transformed textures outperformed volumetric and/or peripheral textures in the radiomic MRI prediction of pCR to NAC for patients with LABC, which can potentially serve as a surrogate biomarker for the prediction of the response of LABC to NAC.

Objectives: The accurate detection and precise segmentation of lung nodules on computed tomography are key prerequisites for early diagnosis and appropriate treatment of lung cancer. This study was designed to compare detection and segmentation methods for pulmonary nodules using deep-learning techniques to fill methodological gaps and biases in the existing literature. Methods: This study utilized a systematic review with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searching PubMed, Embase, Web of Science Core Collection, and the Cochrane Library databases up to May 10, 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 criteria was used to assess the risk of bias and was adjusted with the Checklist for Artificial Intelligence in Medical Imaging. The study analyzed and extracted model performance, data sources, and task-focus information. Results: After screening, we included nine studies meeting our inclusion criteria. These studies were published between 2019 and 2023 and predominantly used public datasets, with the Lung Image Database Consortium Image Collection and Image Database Resource Initiative and Lung Nodule Analysis 2016 being the most common. The studies focused on detection, segmentation, and other tasks, primarily utilizing Convolutional Neural Networks for model development. Performance evaluation covered multiple metrics, including sensitivity and the Dice coefficient. Conclusions: This study highlights the potential power of deep learning in lung nodule detection and segmentation. It underscores the importance of standardized data processing, code and data sharing, the value of external test datasets, and the need to balance model complexity and efficiency in future research. Clinical relevance statement: Deep learning demonstrates significant promise in autonomously detecting and segmenting pulmonary nodules. Future research should address methodological shortcomings and variability to enhance its clinical utility. Key Points: Deep learning shows potential in the detection and segmentation of pulmonary nodules. There are methodological gaps and biases present in the existing literature. Factors such as external validation and transparency affect the clinical application.

This study was designed to explore the potential mechanisms of electroacupuncture (EA) in treating inflammatory bowel disease‐ (IBD‐) related anxiety and mood disorders. A colitis model was induced in rats with 2, 4, 6‐trinitrohydrosulfonic acid (TNBS), followed by ST36 and SP6 targeted therapy by EA or sham EA treatment. The elevated plus maze (EPM) and open‐field test (OFT) were performed to assess the state of anxiety and depression‐like behavior. Tests were carried out by 16S rDNA amplification sequence, ¹H nuclear magnetic resonance (¹H NMR) spectroscopy, immunofluorescence staining, and enzyme‐linked immunosorbent assay (ELISA). The analyses detailed metabolic alterations and the Toll‐like receptor 4 (TLR4) signaling pathway/NOD‐like receptor protein 3 (NLRP3) inflammasome in rats’ hippocampal region. Furthermore, the activity of the hypothalamic‐pituitary adrenal (HPA) axis and gut microbiome was assessed. As a result of treatment, EA significantly improved in the behavioral tests and altered the composition of the gut microbiome through a significant increase in the density of short chain fatty acids (SCFAs) producers mainly including Ruminococcaceae, Phascolarctobacterium, and Akkermansiaceae. EA upregulated the metabolites of the hippocampus mainly containing l‐glutamine and gamma‐aminobutyric acid (GABA), as well as ZO‐1 expression. Whereas the treatment blocked the TLR4/nuclear factor‐ kappa B (NF‐κB) signaling pathways and NLRP3 inflammasomes, along with downregulating the interleukin‐ (IL‐) 1β level. The hyperactivity of the HPA axis was also diminished. In conclusion, EA at ST36 and SP6 attenuated anxiety and depression‐like behavior in colitis model rats through their effects on the gut microbiome by modulating the hippocampal inflammatory response and metabolic disorders, as well as the HPA axis. This study provides evidence for clinical application of EA to serve as an adjunctive treatment for IBD‐related anxiety and depression.

Objectives: To provide an overarching evaluation of the value of peritumoral CT radiomics features for predicting the prognosis of non-small cell lung cancer and to assess the quality of the available studies. Methods: The PubMed, Embase, Web of Science, and Cochrane Library databases were searched for studies predicting the prognosis in patients with non-small cell lung cancer (NSCLC) using CT-based peritumoral radiomics features. Information about the patient, CT-scanner, and radiomics analyses were all extracted for the included studies. Study quality was assessed using the Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST). Results: Thirteen studies were included with 2942 patients from 2017 to 2022. Only one study was prospective, and the others were all retrospectively designed. Manual segmentation and multicenter studies were performed by 69% and 46% of the included studies, respectively. 3D-Slicer and MATLAB software were most commonly used for the segmentation of lesions and extraction of features. The peritumoral region was most frequently defined as dilated from the tumor boundary of 15 mm, 20 mm, or 30 mm. The median RQS of the studies was 13 (range 4–19), while all of included studies were assessed as having a high risk of bias (ROB) overall. Conclusions: Peritumoral radiomics features based on CT images showed promise in predicting the prognosis of NSCLC, although well-designed studies and further biological validation are still needed. Key Points: • Peritumoral radiomics features based on CT images are promising and encouraging for predicting the prognosis of non-small cell lung cancer. • The peritumoral region was often dilated from the tumor boundary of 15 mm or 20 mm because these were considered safe margins. • The median Radiomics Quality Score of the included studies was 13 (range 4–19), and all of studies were considered to have a high risk of bias overall.