Objective:: Radiation planning for locally-advanced non-small cell lung cancer (NSCLC) can be time-consuming and iterative. Many cases cannot be planned satisfactorily using multisegment three-dimensional conformal radiotherapy (3DCRT). We sought to develop and validate a predictive model which could estimate the probability that acceptable target volume coverage would need intensity modulated radiotherapy (IMRT).
Methods:: Variables related to the planning target volume (PTV) and topography were identified heuristically. These included the PTV, it's craniocaudal extent, the ratio of PTV to total lung volume, distance of the centroid of the PTV from the spinal canal, and the extent PTV crossed the midline. Metrics were chosen such that they could be measured objectively, quickly and reproducibly. A logistic regression model was trained and validated on 202 patients with NSCLC. A group of patients who had both complex 3DCRT and IMRT planned was then used to derive the utility of the use of such a model in the clinic based on the time taken for planning such complex 3DCRT.
Results:: Of the 202 patients, 93 received IMRT, as they had larger volumes crossing midline. The final model showed a good rank discrimination (Harrell's C-index 0.84) and low calibration error (mean absolute error of 0.014). Predictive accuracy in an external dataset was 92%. The final model was presented as a nomogram. Using this model, the dosimetrist can save a median planning time of 168 min per case.
Conclusion:: We developed and validated a data-driven, decision aid which can reproducibly determine the best planning technique for locally-advanced NSCLC.
Advances in knowledge:: Our validated, data-driven decision aid can help the planner to determine the need for IMRT in locally advanced NSCLC saving significant planning time in the process.