Purpose: To develop a biological framework for adapting radiation therapy (RT) treatment plans of lung cancer patients by integrating functional imaging, machine learning, and convex optimization.
Methods: Seventy-four non-small cell lung cancer (NSCLC) patients treated with passive scattered proton (PSP, n=28) and intensity modulated RT (IMRT, n=46) were retrospectively analyzed. Baseline and mid-treatment F-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging information was integrated with clinicopathological/dosimetric information to estimate the patient-specific response. A novel Bayesian Network (BN) was trained and validated for predicting grade2+ radiation pneumonitis (RP) at three months post-RT. BN predictions were used to estimate an individualized normal tissue toxicity probability (NTCP) function for each patient. The updated NTCP functions were then integrated in the RT inverse optimization and a convex optimization algorithm was developed to solve the resulting problem. The framework was tested on 20 patients (PSP, n=10; IMRT, n=10) to optimally adapt the dose distribution according to mid-treatment biological information.
Results: BN model was able to predict the RP risk with an AUC of 0.82 (95% CI=[0.72-0.91]). The treatment plans for 17 patients (85%) were adapted to keep the updated RP risk below 20% (9 in PST and 8 in IMRT groups). Treatment plans for patients whose predicted RP risk exceed the predefined threshold were de-escalated (n=14, average target dose reduction = 5.4Gy). Patients with predicted NTCP lower than 20% saw an increase in average tumor dose after RT adaptation (n=6, average target dose increase = 4.1Gy). Overall, 17% decrease in predicted RP risk was achieved using the proposed methodology (SD=7.4%). No difference between PSP and IMRT groups were observed in terms of RP risk prediction or plan adaptation.
Conclusion: Mid-treatment adaptation could significantly lower the risk of RP without compromising tumor control. The proposed methodology could help the design of personalized clinical trials for NSCLC patients.
Not Applicable / None Entered.