Safer lung cancer radiotherapy delivery using novel artificial intelligence methods

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SUMMARY Lung cancer is the leading cause of cancer-related deaths in the U.S. Curative radiotherapy + chemotherapy is the standard of care for patients with inoperable or unresectable disease that has spread beyond the primary tumor to the lymph nodes. Unfortunately, this treatment approach has a high recurrence of 15%-40% and advanced treatments including immunotherapy combined with radiation increase toxicity to organs. Spillover radiation to normal organs at risk (OAR) results from treatment margins to account for uncertainty in localizing tumors and OARs. Despite being part of standard equipment, information from in-treatment room cone-beam computed tomography scans (CBCTs) is currently used only in limited ways for patient positioning during treatment, without simultaneous online localization of the tumor and each OAR. This proposal will use innovative artificial intelligence (AI) methods, that have been trained from both CT and magnetic resonance imaging (MRI) studies, to create auto-segmentation tools that can accurately localize the tumor and key OARs online at treatment setup. The proposed novel AI methodology is called “Cross-Modality Educed Learning” or CMEDL (‘c-medal’). The key advantage of CMEDL is that MRI datasets, even from different patients, can be used, to guide the CT/CBCT network and “learn” to extract features that emphasize the difference between tissue types and produces accurate segmentations even in areas with little inherent contrast such as the mediastinum. For the first time, the clinical utility of what could be called AI-Guided Radiotherapy (AIGRT) segmentation tools will be systematically studied in relation to their potential impact on treatment margin reduction and normal tissue toxicity modeling for longitudinally segmented tumor and healthy tissues on CBCTs. Proposed AIGRT tools would provide increased geometric confidence as well as provide a better basis for an after-delivery estimate of delivered dose, and treatment toxicity, enabling better risk-benefit assessments for potential treatment adaptations. Aim 1: Apply CMEDL methodology to develop lung tumor and OAR segmentations on planning CTs. Aim 2: Extend the CMEDL methodology to longitudinally segment tumors and OARs on weekly CBCTs, incorporating patient-specific anatomic and shape priors from planning CTs. Aim 3: Determine whether CMEDL can enable improved (safer) lung cancer radiotherapy dose characteristics by performing automated planning and delivery simulations, using in-house planning system. Project goal: To develop and rigorously test AIGRT tools for lung cancer radiotherapy treatments. Potential impact: If successful, these innovative AI tools could be deployed routinely, enabling (1) smaller margins and less radiotherapy toxicity for patients, including those with very difficult-to-treat centrally located tumors and (2) providing tools for monitoring the need for plan changes. These AIGRT tools could potentially be deployed to other disease sites, and once established be made widely available as a pragmatic, generalizable technology for geometry guidance throughout the radiation treatment.