ABSTRACT
BACKGROUND AND AIMS: Accurate preoperative prediction of microvascular invasion (MVI) and recurrence-free survival (RFS) is vital for personalised hepatocellular carcinoma (HCC) management. We developed a multitask deep learning model to predict MVI and RFS using preoperative MRI scans. METHODS: Utilising a retrospective dataset of 725 HCC patients from seven institutions, we developed and validated a multitask deep learning model focused on predicting MVI and RFS. The model employs a transformer architecture to extract critical features from preoperative MRI scans. It was trained on a set of 234 patients and internally validated on a set of 58 patients. External validation was performed using three independent sets (n = 212, 111, 110). RESULTS: The multitask deep learning model yielded high MVI prediction accuracy, with AUC values of 0.918 for the training set and 0.800 for the internal test set. In external test sets, AUC values were 0.837, 0.815 and 0.800. Radiologists' sensitivity and inter-rater agreement for MVI prediction improved significantly when integrated with the model. For RFS, the model achieved C-index values of 0.763 in the training set and ranged between 0.628 and 0.728 in external test sets. Notably, PA-TACE improved RFS only in patients predicted to have high MVI risk and low survival scores (p < .001). CONCLUSIONS: Our deep learning model allows accurate MVI and survival prediction in HCC patients. Prospective studies are warranted to assess the clinical utility of this model in guiding personalised treatment in conjunction with clinical criteria.
Subject(s)
Carcinoma, Hepatocellular , Deep Learning , Liver Neoplasms , Magnetic Resonance Imaging , Neoplasm Invasiveness , Humans , Carcinoma, Hepatocellular/diagnostic imaging , Carcinoma, Hepatocellular/pathology , Carcinoma, Hepatocellular/mortality , Liver Neoplasms/diagnostic imaging , Liver Neoplasms/pathology , Liver Neoplasms/mortality , Magnetic Resonance Imaging/methods , Retrospective Studies , Female , Male , Middle Aged , Aged , Microvessels/diagnostic imaging , Microvessels/pathology , Disease-Free Survival , Neoplasm Recurrence, LocalABSTRACT
A quantitative imaging strategy for the vicinal dithiol (VD) of arsenic-binding proteins in the mouse brain is reported. 2-p-Aminophenyl-1,3,2-dithiarsenolane (PAO-EDT) couples to gold nanoclusters Au22(GSH)18 to form conjugate Au22-PAO-EDT (APE). PAO-EDT in APE selectively binds VD with 1 : 1 stoichiometry. After tagging the mouse brain with APE, VD imaging is realized by laser ablation ICP-MS. VD correlates linearly with 197Au in APE offering a 22-fold amplification and a LOD of 5.43 nM. It is found that the cerebral cortex and hippocampus are most affected in an arsenic poisoned mouse brain. This study provides useful information for further understanding the mechanisms underlying the biological effects of arsenic on the living body.
