PRIMARY OBJECTIVE:~I. To obtain preliminary evidence of efficacy (reduction in local failure free survival) in patients receiving SBRT to the lung with personalized artificial intelligence dose guidance (Deep Profiler + iGray).~SECONDARY OBJECTIVES:~I. To evaluate progression free survival (PFS) per Response Evaluation Criteria in Solid Tumors (RECIST) version (v.) 1.1 in patients receiving individualized radiation doses to the lung as recommended by Deep Profiler + iGray.~II. To evaluate respiratory function per the Radiation Therapy Oncology Group (RTOG) Pulmonary Function Scale.~III. To assess toxicity per Common Terminology Criteria for Adverse Events (CTCAE) v. 5.0 in patients receiving individualized radiation doses to the lung as recommended by Deep Profiler + iGray.~IV. To evaluate the feasibility, defined as 85% receiving within 10% of the projected dose, of implementing the individualized radiation doses recommended by machine learning software (Deep Profiler + iGray) in a clinical practice.~OUTLINE:~Patients undergo radiation planning with AI-directed analysis for dose recommendations with Deep Profiler + iGray software on study. Patients then undergo SBRT on study. Patients also undergo positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), and/or x-ray imaging during screening and follow-up.
This phase II trial tests the effectiveness and safety of artificial intelligence (AI) to determine dose recommendation during stereotactic body radiation therapy (SBRT) planning in patients with primary lung cancer or tumors that has spread from another primary site to the lung (metastatic). SBRT uses special equipment to position a patient and deliver radiation to tumors with high precision. This method may kill tumor cells with fewer doses over a shorter period and cause less damage to normal tissue. Even with the high precision of SBRT, disease persistence or reappearance (local recurrence) can still occur, which could be contributed to the radiation dose. AI has been used in other areas of healthcare to automate and improve various aspects of medical science. Because the relationship of dose and local recurrence indicates that dose prescriptions matter, decision support systems to help guide dose based on personalized prediction AI algorithms could better assist providers in prescribing the radiation dose of lung stereotactic body radiation therapy treatment.