MVI-TR: A Transformer-Based Deep Learning Model with Contrast-Enhanced CT for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma

Abstract

In this study, we considered preoperative prediction of microvascular invasion (MVI) status with deep learning (DL) models for patients with early-stage hepatocellular carcinoma (HCC) (tumor size ≤ 5 cm). Two types of DL models based only on venous phase (VP) of contrast-enhanced computed tomography (CECT) were constructed and validated. From our hospital (First Affiliated Hospital of Zhejiang University, Zhejiang, P.R. China), 559 patients, who had histopathological confirmed MVI status, participated in this study. All preoperative CECT were collected, and the patients were randomly divided into training and validation cohorts at a ratio of 4:1. We proposed a novel transformer-based end-to-end DL model, named MVI-TR, which is a supervised learning method. MVI-TR can capture features automatically from radiomics and perform MVI preoperative assessments. In addition, a popular self-supervised learning method, the contrastive learning model, and the widely used residual networks (ResNets family) were constructed for fair comparisons. With an accuracy of 99.1%, a precision of 99.3%, an area under the curve (AUC) of 0.98, a recalling rate of 98.8%, and an F1-score of 99.1% in the training cohort, MVI-TR achieved superior outcomes. Additionally, the validation cohort’s MVI status prediction had the best accuracy (97.2%), precision (97.3%), AUC (0.935), recalling rate (93.1%), and F1-score (95.2%). MVI-TR outperformed other models for predicting MVI status, and showed great preoperative predictive value for early-stage HCC patients.

Semi-supervised segmentation of abdominal organs and liver tumor: uncertainty rectified curriculum labeling meets X-fuse

“…MA-Net [36] and RAU-Net [37] incorporate attention modules within the bottleneck and skip connection, respectively, aiming to capture inter-dependencies between channel and spatial dimensions, thereby enhancing the delineation of ambiguous boundaries in liver tumor segmentation. In particular, the self-attention mechanism adapted from vision transformer (ViT) architectures [38], by enabling interactions between all spatial locations in the input image, can focus adaptively on organs and lesion regions by extracting informative features from across the entire 3D volume to capture irregular boundaries and complex morphology not confined to local regions [39][40][41]. Though scarce in number, recent pioneering works have kindled explorations into SSL for liver tumor segmentation, recognizing the potential of harnessing both scarce annotated and copious unannotated data.…”

Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review

“…Inspired by the success of attention mechanisms, the transformer model was proposed as a complete shift from the sequential processing nature of recurrent neural networks (RNNs) and their variants [19][20][21][22]. The transformer model leverages attention mechanisms to process the input data in parallel, allowing for faster and more efficient computations.…”

Application of New Molecular Probes in the Diagnosis and Treatment of Malignant Tumors

“…Additionally, the article outlines the future research prospects of CRISPR/Cas9 in glioma treatment while highlighting potential opportunities and challenges in this domain [9]. In a recent study conducted by Cao et al, a pioneering transformer-based end-to-end deep learning model, MVI-TR, was introduced as substantial preoperative predictive utility for early-stage hepatocellular carcinoma patients [10]. Furthermore, an examination of recent research elucidating the involvement of WT1-associated protein (WTAP) in oncogenesis and its potential therapeutic implications was undertaken [11].…”