Targeting lateral pelvic lymph nodes in rectal cancer: response to neoadjuvant therapy and artificial intelligence driven clinical decision support

Rectal cancer with lateral pelvic lymph node (LPLN) involvement presents significant challenges. Neoadjuvant chemoradiation (nCRT) became accepted as a standard preoperative approach in locally advanced rectal cancer, but its effectiveness in addressing LPLN metastasis remains debated, particularly when compared with prophylactic LPLN dissection (LPLND), due to patient and treatment heterogeneity. Current evidence supports reviewing pre- and post-treatment imaging to identify suspicious LPLNs, using a baseline short axis ≥ 7 mm and restaging short axis ≥ 4 mm as thresholds. The introduction of total neoadjuvant therapy (TNT) has added complexity to LPLN management, with indications for LPLND evolving alongside advances in understanding LPLN size, location, and morphology. In recent years, artificial intelligence (AI) techniques including radiomics, image segmentation (eg. conventional neural network (CNN)), multi-modal model, natural language processing (NLP), and surgery navigation have emerged as a promising solution to tackle the complex challenges for LPLN management. Accurate LPLN mapping and minimally invasive techniques enable more precise LPLND, reshaping the scale of surgical decision-making. Thus, optimizing LPLN evaluation methods and exploring AI applications in this process offer potential for tailoring treatment strategies, enhance tumor response and improve patient outcomes.