Explainable AI Reveals Key Surgical Parameters for Prostatectomy: Technical Analysis of Robot-Assisted vs. Open Surgery

This article focuses on a study using Explainable AI (XAI) to analyze prostatectomy. Through methods like SHAP and LIME, it reveals key parameters affecting surgical outcomes, compares differences between robot-assisted (RARP) and open surgery (ORP), explores the value and limitations of XAI in surgical decision support, and provides data-driven references for clinical practice.

Prostate cancer is a common malignant tumor in men, and radical prostatectomy is the main treatment method, which is divided into robot-assisted (RARP: minimally invasive, fast recovery but high cost and steep learning curve) and open surgery (ORP: direct tactile feedback but large trauma). Traditional decisions rely on experience and lack quantitative data support; AI models need to be explainable to be used in clinical decision-making.

The study uses XAI technology to analyze models, with core methods including: 1. SHAP value calculation (quantifies feature contribution to predictions, based on game theory); 2. Feature importance ranking (identifies key factors via SHAP values); 3. Individual prediction interpretation (generates personalized explanations for specific cases); 4. Feature interaction analysis (explores synergistic/antagonistic effects between features).

Key parameters identified in the study include:

• Patient factors: Age, BMI, preoperative PSA, comorbidities, previous surgical history;
• Tumor characteristics: Clinical stage, Gleason score, prostate volume;
• Surgical factors: Operation time, blood loss, lymph node dissection range. Additionally, the importance ranking of some parameters differs between RARP and ORP, suggesting that surgical method selection needs to be individualized.

Clinical Value: 1. Preoperative risk assessment (predicting complications, length of hospital stay, etc.); 2. Surgical method selection recommendations (quantifying benefits); 3. Surgical plan optimization (targeted preventive measures); 4. Postoperative management guidance (personalized rehabilitation). Limitations: 1. Correlation ≠ causation; 2. Data bias (specific hospital/physician practices); 3. Insufficient feature completeness (e.g., physician experience not captured); 4. High computational cost of XAI methods; 5. Long clinical translation cycle.

Future Directions: Real-time intraoperative decision support, surgical skill assessment, personalized surgical plans, intelligent enhancement of surgical robots, cross-hospital knowledge sharing (federated learning). Ethical Regulation: Need to clarify responsibility attribution, ensure informed consent, guarantee algorithm fairness, and comply with approval requirements of regulatory agencies like FDA/NMPA for AI medical devices (explainability is an important consideration).

Explainable AI provides data-driven decision support for prostatectomy, enhancing model transparency and providing clinical insights. However, technology is only an aid; final decisions need to combine physicians' professional judgment and patients' individual conditions. As XAI matures and clinical validation accumulates, it is expected to play a greater role in the surgical field and benefit patients.