Construction and Clinical Validation of a Predictive Model for Postoperative Adjuvant Therapy in Hepatocellular Carcinoma Based on Whole-Slide Digital Pathological Images and Deep Learning

Study Background Hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors worldwide, ranking sixth in global incidence and third in mortality, responsible for approximately 480,000 deaths annually. China accounts for more than 45% of global HCC cases, imposing an extremely heavy disease burden. Curative surgical resection is the primary curative approach for achieving long-term survival in patients with early-stage HCC. However, the postoperative recurrence rate reaches 50%-70% within five years after surgery, severely undermining patient prognosis. Postoperative adjuvant therapy has become a core strategy to delay tumor recurrence and improve survival outcomes. Transarterial chemoembolization (TACE) and tyrosine kinase inhibitors (TKIs), including sorafenib and lenvatinib, are widely administered for high-risk postoperative HCC patients.

Nevertheless, both therapeutic modalities have notable clinical limitations. The objective response rate of TACE is only 50%-60%, with a substantial proportion of patients failing to derive clinical benefits and suffering from treatment-related liver function injury. Although TKIs can prolong recurrence-free survival (RFS) by 3 to 5 months in high-risk postoperative HCC populations, the treatment response rate in unselected patients is less than 20%. Additionally, the incidence of grade 3-4 adverse events, such as hypertension, hand-foot skin reaction, and proteinuria, exceeds 50%, leading to treatment discontinuation in approximately 20% of patients due to intolerable toxicities. Currently, there is a lack of efficient and reliable biomarker systems to screen potential treatment-responsive populations. Clinical treatment decisions still rely on empirical indicators, including tumor size and vascular invasion, resulting in limited individualization, wasted medical resources, and unnecessary treatment burdens for patients.

Emerging studies have demonstrated that the tumor immune microenvironment (TIME) serves as a critical biological determinant of therapeutic sensitivity to TACE and TKI in HCC. Preoperative contrast-enhanced CT and MRI can intuitively reflect tumor vascularity, boundary integrity, peritumoral infiltration, and satellite lesion status. These imaging characteristics are closely correlated with postoperative pathological features such as microvascular invasion (MVI) and tumor differentiation, which further determine adjuvant treatment responses. For instance, preoperative MRI radiomic features, including textual heterogeneity and irregular tumor margins, have been proven to be significantly associated with postoperative HCC recurrence risk. Moreover, CT perfusion parameters can accurately predict the degree of tumor necrosis after TACE treatment. Furthermore, the integration of preoperative imaging and postoperative pathological images enables the construction of tumor spatiotemporal evolution models, revealing dynamic biological alterations before and after therapeutic intervention. Therefore, multimodal data integrating preoperative imaging, postoperative pathological slides, textual pathological reports, and clinical indicators can overcome the limitations of single-source data and substantially improve the accuracy of adjuvant therapy response prediction.

Advancements in artificial intelligence, particularly deep learning techniques, have provided a novel approach to excavate in-depth biological information from routine hematoxylin and eosin (H&E) stained whole-slide imaging (WSI). As standard postoperative pathological materials, H&E WSIs are routinely generated for all HCC patients without additional sampling or testing. The cellular and structural morphological details embedded in WSIs can effectively reflect core TIME characteristics. Specifically, convolutional neural networks (CNN) and Vision Transformer (ViT) architectures can automatically identify morphological patterns associated with CD8⁺ T cell infiltration density and PD-L1 expression, realizing cross-modal prediction of immune status based on routine H&E morphology. Recent studies have validated the high diagnostic efficacy of WSI-based deep learning models in multiple malignancies, including the prediction of microsatellite instability (MSI) in colorectal cancer (AUC = 0.88), PD-L1 expression in non-small cell lung cancer (AUC = 0.80), and tumor mutation burden (TMB) (AUC = 0.91). In HCC, WSI-driven deep learning models have achieved favorable performance in predicting postoperative recurrence risk (AUC = 0.82) and immune cell infiltration (AUC = 0.78). However, no studies have focused on the critical clinical challenge of multimodal prediction of TACE and TKI adjuvant therapy responses by integrating preoperative imaging and postoperative pathological data, and large-scale multicenter prospective clinical validation remains absent.

Accordingly, this study intends to integrate multicenter clinicopathological data with artificial intelligence algorithms to construct a novel multimodal predictive model based on preoperative imaging, postoperative H&E-stained WSI, textual pathological reports, and clinical indicators. This model aims to clarify the correlation between preoperative tumor characteristics and postoperative adjuvant treatment responses and develop a clinically applicable digital decision-making tool, promoting multi-dimensional and full-cycle precise management for HCC.