Pancreatic cancer remains one of the most lethal cancers, primarily due to its late diagnosis and limited treatment options. This review examines the challenges and potential of using immunotherapy to treat pancreatic cancer, highlighting the role of artificial intelligence (AI) as a promising tool to enhance early detection and monitor the effectiveness of these therapies. By synthesizing recent advancements and identifying gaps in the current research, this review aims to provide a comprehensive overview of how AI and immunotherapy can be integrated to develop more personalized and effective treatment strategies. The insights from this review may guide future research efforts and contribute to improving patient outcomes in pancreatic cancer management.

Colorectal liver metastasis (CRLM) poses a significant challenge in oncology due to its high incidence and poor prognosis in unresectable cases. Current treatments, including surgical resection, systemic chemotherapy, and liver-directed therapies, often fail to effectively target hypoxic tumor regions, which are inherently more resistant to these interventions. This review examines the potential of a novel therapeutic strategy combining irreversible electroporation (IRE) ablation and Clostridium novyi-nontoxic (C. novyi-NT) bacterial therapy. IRE is a non-thermal tumor ablation technique that uses high-voltage electric pulses to create permanent nanopores in cell membranes, leading to cell death while preserving surrounding structures, and is often associated with temporary tumor hypoxia due to disrupted perfusion. C. novyi-NT is an attenuated, anaerobic bacterium engineered to selectively germinate and proliferate in hypoxic tumor regions, resulting in localized tumor cell lysis while sparing healthy, oxygenated tissue. The synergy between IRE-induced hypoxia and hypoxia-sensitive C. novyi-NT may enhance tumor destruction and stimulate systemic antitumor immunity. Furthermore, the integration of advanced imaging and artificial intelligence can support precise treatment planning and real-time monitoring. This integrated approach holds promise for improving outcomes in patients with CRLM, though further preclinical and clinical validation is needed.

Background: Assessing the efficacy of combination therapies in hepatocellular carcinoma (HCC) requires both accurate tumor delineation and biologically validated prediction of therapeutic response. Conventional MRI-based criteria, which rely primarily on tumor size, often fail to capture treatment efficacy due to tumor heterogeneity and pseudo-progression. This study aimed to develop and biologically validate a multi-task deep learning model that simultaneously segments HCC tumors and predicts treatment outcomes using clinically relevant multi-parametric MRI in a preclinical rat model. Methods: Orthotopic HCC tumors were induced in rats assigned to Control, Sorafenib, NK cell immunotherapy, and combination treatment groups. Multi-parametric MRI (T1w, T2w, and contrast enhanced MRI) scans were performed weekly. We developed a U-Net++ architecture incorporating a pre-trained EfficientNet-B0 encoder, enabling simultaneous segmentation and classification tasks. Model performance was evaluated through Dice coefficients and area under the receiver operator characteristic curve (AUROC) scores, and histological validation (H&E for viability, TUNEL for apoptosis) assessed biological correlations using linear regression analysis. Results: The multi-task model achieved precise tumor segmentation (Dice coefficient = 0.92, intersection over union (IoU) = 0.86) and reliably predicted therapeutic outcomes (AUROC = 0.97, accuracy = 85.0%). MRI-derived deep learning biomarkers correlated strongly with histological markers of tumor viability and apoptosis (root mean squared error (RMSE): viability = 0.1069, apoptosis = 0.013), demonstrating that the model captures biologically relevant imaging features associated with treatment-induced histological changes. Conclusions: This multi-task deep learning framework, validated against histology, demonstrates the feasibility of leveraging widely available clinical MRI sequences for non-invasive monitoring of therapeutic response in HCC. By linking imaging features with underlying tumor biology, the model highlights a translational pathway toward more clinically applicable strategies for evaluating treatment efficacy.

Background/Objectives: Hepatocellular carcinoma (HCC) remains a significant global health issue due to its high mortality rate and resistance to standard treatments. Sorafenib, the first-line systemic therapy for unresectable HCC, shows limited effectiveness due to resistance and severe side effects. Recent studies suggest that combining sorafenib with immunotherapy, particularly natural killer (NK) cells, may improve treatment outcomes. Methods: This study examined the effectiveness of sorafenib combined with NK cells pretreated with interleukin-12 (IL-12) and interleukin-18 (IL-18) in a rat HCC model. Tumor progression and treatment outcomes were assessed using MRI and histological analysis. Results: The results show that combination therapy significantly reduced tumor growth, increased tumor cell density, and inhibited angiogenesis and fibrosis in the tumor microenvironment. The sorafenib- and IL-12/IL-18-pretreated NK cell combination enhanced tumor inhibition by overcoming drug resistance and modulating the immune response. Conclusions: This study suggests that this combination therapy could be a promising strategy for treating HCC, offering both direct antitumor effects and modification of the tumor microenvironment for better clinical outcomes.