Improving Clinical Decision-Making in Radiotherapy: A Comparative Analysis of LQ and LQL Dose Models

Radiotherapy is an essential component of cancer treatment, requiring accurate dose planning to optimize tumor control while sparing healthy tissues. This study investigates advanced dose modeling approaches, focusing on the Linear Quadratic (LQ) and Linear Quadratic Linear (LQL) models, to refine the calculation of biologically effective doses (BED) and improve treatment personalization. Using tools such as LQL-equiv and other BED calculators, we integrated patient-specific data (e.g., fractionation schedules and organ-at-risk (OAR) constraints) to predict outcomes such as normal tissue complication probabilities (NTCP). Through a series of clinical case studies (including treatment interruptions, palliative boosts, and reirradiation scenarios), participant responses were analyzed using the Jaccard similarity index, revealing a significant lack of consensus in treatment planning decisions (mean agreement of 10%). This variability highlights a critical gap between the potential of advanced modeling and its consistent clinical application. While the LQ and LQL models offer promising tools for personalized radiotherapy, their interpretation and implementation remain highly variable. This study emphasizes the need for standardized guidelines, enhanced training programs, and decision-support systems to reduce inter-observer variability and ensure effective clinical adoption, ultimately improving patient care.