Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis.

The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a "digital biopsy". As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.

Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study.

BACKGROUND: The diagnosis performance of B-mode ultrasound (US) for focal liver lesions (FLLs) is relatively limited. We aimed to develop a deep convolutional neural network of US (DCNN-US) for aiding radiologists in classification of malignant from benign FLLs. MATERIALS AND METHODS: This study was conducted in 13 hospitals and finally 2143 patients with 24,343 US images were enrolled. Patients who had non-cystic FLLs with pathological results were enrolled. The FLLs from 11 hospitals were randomly divided into training and internal validations (IV) cohorts with a 4:1 ratio for developing and evaluating DCNN-US. Diagnostic performance of the model was verified using external validation (EV) cohort from another two hospitals. The diagnosis value of DCNN-US was compared with that of contrast enhanced computed tomography (CT)/magnetic resonance image (MRI) and 236 radiologists, respectively. FINDINGS: The AUC of ModelLBC for FLLs was 0.924 (95% CI: 0.889-0.959) in the EV cohort. The diagnostic sensitivity and specificity of ModelLBC were superior to 15-year skilled radiologists (86.5% vs 76.1%, p = 0.0084 and 85.5% vs 76.9%, p = 0.0051, respectively). Accuracy of ModelLBC was comparable to that of contrast enhanced CT (both 84.7%) but inferior to contrast enhanced MRI (87.9%) for lesions detected by US. INTERPRETATION: DCNN-US with high sensitivity and specificity in diagnosing FLLs shows its potential to assist less-experienced radiologists in improving their performance and lowering their dependence on sectional imaging in liver cancer diagnosis.

Effects of dual arterial blood supply on liver regeneration in the graft and the host following heterotopic auxiliary liver transplantation.

This study aimed to investigate the effect of the dual arterial blood supply method used in auxiliary liver transplantation on the regeneration of grafted and host liver. A total of 72 male Sprague-Dawley rats were randomly assigned to three experimental groups, namely the 68% hepatectomy group (group A), the 68% hepatectomy with dual arterial blood supply group (group B) and the auxiliary liver transplantation with dual arterial blood supply group (group C). Group C was further divided into the host liver subgroup (group Ca) and the transplanted liver subgroup (group Cb). Six animals from each group were sacrificed at 1, 2 and 7 days after surgery. The calculation of the liver regeneration rate (LRR) was based on measuring liver weight. Liver function was assessed by measuring serum alanine aminotransferase (ALT) levels. Immunohistochemistry was employed to detect the expression of proliferating cell nuclear antigen (PCNA). Apoptotic changes in the grafts and host livers were evaluated using TUNEL staining. The LRR in each group exhibited a tendency to increase over time. At each time point, the LRR of transplanted livers in group C exhibited no significant difference from that of host livers in group C (P>0.05). The ALT levels for each group exhibited a time-dependent decreasing tendency. The ALT level in group C was significantly higher compared to that in groups A and B at each time point (P<0.05). The expression of PCNA in transplanted and host livers in group C was significantly lower compared to that in groups A and B at the same time point (P<0.001). Although the number of apoptotic cells in each group varied at different time points, there was no statistically significant difference (P>0.05). In auxiliary liver transplantation with the dual arterial blood supply method, the capacity of the liver regeneration in the grafts was similar to that of the host livers. Therefore, this technique may reduce the potential risk of graft liver atrophy caused by functional competition.