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Objectives: To investigate the utility of deep learning (DL) automated segmentation-based MRI radiomic features and clinical-radiological characteristics in predicting early recurrence after curative resection of single hepatocellular carcinoma (HCC). Methods: This single-center, retrospective study included consecutive patients with surgically proven HCC who underwent contrast-enhanced MRI before curative hepatectomy from December 2009 to December 2021. Using 3D U-net-based DL algorithms, automated segmentation of the liver and HCC was performed on six MRI sequences. Radiomic features were extracted from the tumor, tumor border extensions (5 mm, 10 mm, and 20 mm), and the liver. A hybrid model incorporating the optimal radiomic signature and preoperative clinical-radiological characteristics was constructed via Cox regression analyses for early recurrence. Model discrimination was characterized with C-index and time-dependent area under the receiver operating curve (tdAUC) and compared with the widely-adopted BCLC and CNLC staging systems. Results: Four hundred and thirty-four patients (median age, 52.0 years; 376 men) were included. Among all radiomic signatures, HCC with5 mmtumorborderextensionandliver showed the optimal predictive performance (training set C-index, 0.696). By incorporating this radiomic signature, rim arterial phase hyperenhancement (APHE), and incomplete tumor "capsule," a hybrid model demonstrated a validation set C-index of 0.706 and superior 2-year tdAUC (0.743) than both the BCLC (0.550; p < 0.001) and CNLC (0.635; p = 0.032) systems. This model stratified patients into two prognostically distinct risk strata (both datasets p < 0.001). Conclusion: A preoperative imaging model incorporating the DL automated segmentation-based radiomic signature with rim APHE and incomplete tumor "capsule" accurately predicted early postsurgical recurrence of a single HCC. Critical relevance statement: The DL automated segmentation-based MRI radiomic model with rim APHE and incomplete tumor "capsule" hold the potential to facilitate individualized risk estimation of postsurgical early recurrence in a single HCC. Key Points: A hybrid model integrating MRI radiomic signature was constructed for early recurrence prediction of HCC. The hybrid model demonstrated superior 2-year AUC than the BCLC and CNLC systems. The model categorized the low-risk HCC group carried longer RFS. [ABSTRACT FROM AUTHOR]

Deltamethrin (Del), a widely administered pyrethroid insecticide, has been established as a common contaminant of the freshwater environment and detected in many freshwater ecosystems. In this study, we investigated the changes in brain transcriptome and metabolome of crucian carp after exposure to 0.6 μg/L Del for 28 days. Elevated MDA levels and inhibition of SOD activity indicate damage to the antioxidant system. Moreover, a total of 70 differential metabolites (DMs) were identified using the liquid chromatography‐mass spectrometry, including 32 upregulated and 38 downregulated DMs in the Del‐exposed group. The DMs associated with chronic Del exposure were enriched in steroid hormone biosynthesis, fatty acid metabolism, and glycerophospholipid metabolism for prostaglandin G2, 5‐oxoeicosatetraenoic acid, progesterone, androsterone, etiocholanolone, and hydrocortisone. Transcriptomics analysis revealed that chronic Del exposure caused lipid metabolism disorder, endocrine disruption, and proinflammatory immune response by upregulating the pla2g4, cox2, log5, ptgis, lcn, and cbr expression. Importantly, the integrative analysis of transcriptomics and metabolomics indicated that the arachidonic acid metabolism pathway and steroid hormone biosynthesis were decisive processes in the brain tissue of crucian carp after Del exposure. Furthermore, Del exposure perturbed the tight junction, HIF‐1 signaling pathway, and thyroid hormone signaling pathway. Overall, transcriptome and metabolome data of our study offer a new insight to assess the risk of chronic Del exposure in fish brains. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to evaluate the potential of deep learning (DL)-assisted automated three-dimensional quantitative tumor burden at MRI to predict postoperative early recurrence (ER) of hepatocellular carcinoma (HCC)., Materials and Methods: This was a single-center retrospective study enrolling patients who underwent resection for BCLC A and B HCC and preoperative contrast-enhanced MRI. Quantitative total tumor volume (cm 3 ) and total tumor burden (TTB, %) were obtained using a DL automated segmentation tool. Radiologists' visual assessment was used to ensure the quality control of automated segmentation. The prognostic value of clinicopathological variables and tumor burden-related parameters for ER was determined by Cox regression analyses., Results: A total of 592 patients were included, with 525 and 67 patients assigned to BCLC A and B, respectively (2-year ER rate: 30.0% vs. 45.3%; hazard ratio (HR) = 1.8; p = 0.007). TTB was the most important predictor of ER (HR = 2.2; p < 0.001). Using 6.84% as the threshold of TTB, two ER risk strata were obtained in overall (p < 0.001), BCLC A (p < 0.001), and BCLC B (p = 0.027) patients, respectively. The BCLC B low-TTB patients had a similar risk for ER to BCLC A patients and thus were reassigned to a BCLC A n stage; whilst the BCLC B high-TTB patients remained in a BCLC B n stage. The 2-year ER rate was 30.5% for BCLC A n patients vs. 58.1% for BCLC B n patients (HR = 2.8; p < 0.001)., Conclusions: TTB determined by DL-based automated segmentation at MRI was a predictive biomarker for postoperative ER and facilitated refined subcategorization of patients within BCLC stages A and B., Clinical Relevance Statement: Total tumor burden derived by deep learning-based automated segmentation at MRI may serve as an imaging biomarker for predicting early recurrence, thereby improving subclassification of Barcelona Clinic Liver Cancer A and B hepatocellular carcinoma patients after hepatectomy., Key Points: Total tumor burden (TTB) is important for Barcelona Clinic Liver Cancer (BCLC) staging, but is heterogenous. TTB derived by deep learning-based automated segmentation was predictive of postoperative early recurrence. Incorporating TTB into the BCLC algorithm resulted in successful subcategorization of BCLC A and B patients., (© 2024. The Author(s).)