Topic: Artificial Intelligence in Minimally Invasive Tumor Treatment: From Preoperative Planning to Intraoperative Navigation

The integration of artificial intelligence (AI) in minimally invasive cancer treatment represents a transformative paradigm in modern oncological surgery. As healthcare systems strive to improve patient outcomes while minimizing morbidity and reducing costs, AI-driven approaches offer unprecedented opportunities to enhance the precision, safety, and effectiveness of cancer care.

This Special Issue focuses on two key applications of AI in minimally invasive cancer treatment. The first is preoperative planning, where unimodal and multimodal machine learning and deep learning models are employed to analyze clinical data, medical imaging, and laboratory results. These systems aid in differentiating benign from malignant tumors and support treatment decision making. By accurately characterizing tumors, AI helps clinicians select the most appropriate treatment modality—whether ablation, laparoscopic surgery, or open surgery—based on patient-specific factors and predicted outcomes.

The second focus is intraoperative navigation, where AI technologies enhance surgical precision and safety in real time. These include image-guided surgery, robotic assistance, and augmented reality platforms that offer enhanced visualization and data-driven decision support during procedures.

Topics of interest for this Special Issue include, but are not limited to: deep learning for tumor characterization, multimodal data fusion for treatment planning, real-time AI-based navigation systems, outcome prediction models, and validation studies of AI-assisted surgical workflows. We particularly welcome contributions addressing clinical validation, regulatory considerations, and the practical challenges of implementing these technologies across diverse healthcare settings.

Artificial intelligence, minimally invasive surgery, cancer treatment, preoperative planning, intraoperative navigation, ablation, machine learning, deep learning, medical imaging