AI-Directed Radiation Therapy for Lung Cancer

Overseen By Study Coordinator

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Northwestern University

Must not be taking: Genotoxic chemotherapy

Disqualifiers: Prior radiotherapy, Active infection, Unstable angina, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Approved in 4 Jurisdictions This treatment is already approved in other countries

Compensation: Varies

Number of Visits: 2

Duration: 1-2 weeks

70 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial explores how artificial intelligence (AI) can enhance the precision of radiation therapy for lung cancer. The focus is on using AI to make smarter dose recommendations during stereotactic body radiation therapy (SBRT), which targets tumors with high precision to spare healthy tissue. The goal is to determine if AI can reduce the chance of cancer recurrence by optimizing the treatment plan. This trial suits individuals diagnosed with lung cancer or those with cancer that has spread to the lungs, particularly if they have measurable disease that can be tracked. As an unphased trial, it offers a unique opportunity to contribute to cutting-edge research that could improve future cancer treatments.

Will I have to stop taking my current medications?

The trial requires patients to pause targeted therapy medications 3 days before and restart them at least 3 days after the radiation therapy. Other medications, including non-investigational immunotherapy and COVID-19 vaccinations, are allowed.

What prior data suggests that this AI-directed dose recommendation method is safe for lung cancer patients?

Research shows that Stereotactic Body Radiation Therapy (SBRT) is generally safe for treating lung cancer. Studies indicate it usually has low toxicity, causing fewer harmful side effects. Many patients tolerate the treatment well, and it effectively controls small tumors.

AI assists in planning these radiation treatments by determining the best radiation dose. In other areas of healthcare, AI has improved medical procedures. Early research suggests that AI can deliver the right amount of radiation more accurately, potentially leading to better results.

These findings suggest that using AI to plan SBRT could be a promising and safe approach. However, ongoing research is needed to confirm these benefits. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about AI-directed radiation therapy for lung cancer because it brings a new level of precision to treatment. Unlike traditional radiation therapy, which relies heavily on manual planning, this approach uses artificial intelligence to analyze imaging data and recommend optimal radiation doses. This could potentially improve targeting of cancerous tissues while minimizing damage to healthy cells, leading to better outcomes and fewer side effects. Additionally, the use of AI allows for more adaptive treatment planning, which can be adjusted in real-time as the tumor responds to therapy.

What evidence suggests that AI-directed dose recommendation is effective for lung cancer?

Research has shown that stereotactic body radiation therapy (SBRT) works well for lung cancer, particularly in its early stages. Studies have found that SBRT can precisely target tumors, resulting in good outcomes and manageable side effects. In this trial, participants will receive SBRT with AI-directed dose recommendations. Artificial intelligence (AI) is being tested to enhance targeting precision by suggesting personalized radiation doses. AI has already improved the accuracy of lung cancer diagnosis, potentially enhancing treatment planning as well. Early tests of AI-guided SBRT planning indicate it could increase precision and effectiveness. These advancements aim to improve outcomes while minimizing harm to healthy tissue. ^{1 4 5 6 7}

Who Is on the Research Team?

Mohamed E. Abazeed

Principal Investigator

Northwestern University

Are You a Good Fit for This Trial?

Adults (18+) with primary lung cancer or a few metastatic tumors in the lungs, able to perform daily activities (ECOG 0-2), and not pregnant can join. They must understand and sign consent. Excluded are those with prior overlapping radiotherapy, unresolved thoracic toxicities from past treatments, concurrent investigational drugs, other interfering malignancies, or uncontrolled illnesses.

Inclusion Criteria

Patients must have visible signs of disease on a medical scan that can be measured according to specific guidelines.

My lung scans show signs of cancer, either as a primary condition or with few spread sites.

I can take care of myself and am up and about more than half of my waking hours.

Exclusion Criteria

If you have a specific type of lung cancer that appears as only hazy spots on a scan, you will not be included in the study.

I do not have another cancer or serious illness that could affect the study.

I still have side effects from cancer treatment in my chest area, except for hair loss or low blood counts.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

1 visit (in-person)

Radiation Planning

Radiation planning with AI-directed analysis for dose recommendations using Deep Profiler + iGray software

1 week

1 visit (in-person)

Treatment

Participants undergo stereotactic body radiation therapy (SBRT) with AI-directed dose recommendations

1-2 weeks

Multiple visits (in-person)

Follow-up

Participants are monitored for safety and effectiveness after treatment, including imaging and respiratory function assessment

5 years

Regular visits (in-person)

What Are the Treatments Tested in This Trial?

Interventions

• AI-Directed Dose Recommendation
• Stereotactic Body Radiation Therapy

Trial Overview The RAD-AI study is testing if AI can improve dose recommendations during SBRT for lung cancer treatment. It aims to see if personalized AI predictions lead to better outcomes by reducing local recurrence of the disease while minimizing damage to healthy tissue.

How Is the Trial Designed?

Group I: Treatment (AI-directed analysis, SBRT) Experimental Treatment 7 Interventions

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 PET, CT, MRI, and/or x-ray imaging during screening and follow-up.

Stereotactic Body Radiation Therapy is already approved in United States, European Union, Canada for the following indications:

Approved in United States as Stereotactic Body Radiation Therapy for:

• Non-small cell lung cancer (NSCLC)
• Melanoma
• Renal cell carcinoma (RCC)
• Prostate cancer
• Oligoprogressive cancers

Approved in European Union as Stereotactic Body Radiation Therapy for:

• Non-small cell lung cancer (NSCLC)
• Melanoma
• Renal cell carcinoma (RCC)
• Prostate cancer
• Oligoprogressive cancers

Approved in Canada as Stereotactic Body Radiation Therapy for:

• Non-small cell lung cancer (NSCLC)
• Melanoma
• Renal cell carcinoma (RCC)
• Prostate cancer
• Oligoprogressive cancers

Who Is Running the Clinical Trial?

Northwestern University

Lead Sponsor

Trials

1,674

Recruited

989,000+

Varian Medical Systems

Industry Sponsor

Trials

63

Recruited

3,700+

Dow R. Wilson

Varian Medical Systems

Chief Executive Officer since 2012

MBA from Dartmouth's Amos Tuck School of Business, BA from Brigham Young University

Dr. Deepak Khuntia

Varian Medical Systems

Chief Medical Officer since 2020

MD from the University of Cambridge, PhD from the University of Leicester

National Cancer Institute (NCI)

Collaborator

Trials

14,080

Recruited

41,180,000+

Published Research Related to This Trial

In a study of 73 patients with metastatic lung tumors treated using the CyberKnife system, stereotactic ablative body radiotherapy (SABR) demonstrated high effectiveness, achieving a 2-year local control rate of 89.1% and an overall survival rate of 71.3%.

The treatment was associated with acceptable toxicity levels, with only two cases of grade 2 or higher radiation pneumonitis, indicating that SABR with a central high dose is a safe option for managing metastatic lung tumors.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Stereotactic ablative body radiotherapy with a central high dose using CyberKnife for metastatic lung tumors. Hayashi, K., Suzuki, O., Shiomi, H., et al.[2023]

Stereotactic ablative radiotherapy (SABR) is an effective treatment for early-stage non-small cell lung cancers (NSCLCs) that are medically inoperable, providing local control and toxicity levels comparable to surgical resection.

SABR is gaining interest as a noninvasive treatment option for patients with borderline resectable lung cancers, although further randomized studies are needed to evaluate its survival benefits in operable patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Stereotactic ablative radiotherapy (SABR) for non-small cell lung cancer. Iyengar, P., Westover, K., Timmerman, RD.[2013]

In a study of 100 patients with early-stage non-small cell lung cancer (NSCLC) treated with stereotactic ablative body radiotherapy (SABR), the 1-, 2-, and 3-year local control rates were 100%, 93.55%, and 84.33%, respectively, indicating high efficacy in tumor control.

The treatment was associated with low toxicity, with only 2% of patients experiencing Grade-3 side effects, suggesting that SABR is a safe option for patients who are inoperable or refuse surgery.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Definitive Treatment of Early-Stage Non-Small Cell Lung Cancer with Stereotactic Ablative Body Radiotherapy in a Community Cancer Center Setting. Heal, C., Ding, W., Lamond, J., et al.[2020]

Stereotactic ablative radiotherapy (SABR) is an effective treatment for early-stage non-small cell lung cancer, especially for patients who cannot undergo surgery, showing high local tumor control rates with manageable side effects.

The CyberKnife system is particularly advantageous for SABR in lung tumors due to its ability to accurately target moving tumors during breathing, enhancing treatment precision.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

CyberKnife stereotactic ablative radiotherapy for lung tumors. Gibbs, IC., Loo, BW.[2022]

Stereotactic ablative radiotherapy (SABR) using Cyberknife demonstrated high effectiveness in treating stage I non-small-cell lung cancer, with 2-year local control, progression-free, and overall survival rates of 91.9%, 61.7%, and 84.8%, respectively, based on a study of 153 patients.

The treatment was generally safe, with only 8.1% of patients experiencing severe toxicities, including one case of grade 5 radiation pneumonitis, indicating that while SABR is effective, careful monitoring for side effects is necessary.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Stereotactic Ablative Radiotherapy Using CyberKnife for Stage I Non-small-cell Lung Cancer: A Retrospective Analysis. Hayashi, K., Suzuki, O., Shiomi, H., et al.[2022]

Stereotactic ablative radiation therapy (SABR) for lung oligometastases shows promising safety and efficacy, with a 24-month overall survival rate of 74.6% and minimal toxicity, as only one significant adverse event was reported among 77 patients studied over an average follow-up of 22 months.

Colorectal metastases exhibited a notably higher local failure rate compared to other types of tumors, suggesting they may be more resistant to SABR, indicating a need for potential dose escalation strategies for these patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Colorectal Histology Is Associated With an Increased Risk of Local Failure in Lung Metastases Treated With Stereotactic Ablative Radiation Therapy. Binkley, MS., Trakul, N., Jacobs, LR., et al.[2022]

Citations

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Stereotactic Body Radiation Therapy Planning With ... This phase II trial tests the effectiveness and safety of artificial intelligence (AI) to determine dose recommendation during stereotactic body radiation ...

2. cancer.gov cancer.gov/research/participate/clinical-trials-search/v?id=NCI-2023-01822

Stereotactic Body Radiation Therapy Planning with ... This phase II trial tests the effectiveness and safety of artificial intelligence (AI) to determine dose recommendation during stereotactic body radiation ...

3. withpower.com withpower.com/trial/phase-carcinoma-4-2023-85250

AI-Directed Radiation Therapy for Lung Cancer This trial tests if using AI to decide radiation doses can improve treatment for lung cancer patients. The AI aims to make radiation therapy more precise ...

4. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC10650618/

Artificial Intelligence and Lung Cancer: Impact on Improving ... AI models have become an effective tool in lung cancer diagnosis, improving the accuracy, stability, and efficiency [45,47]. This review covers ...

5. aapm.onlinelibrary.wiley.com aapm.onlinelibrary.wiley.com/doi/10.1002/acm2.70291

Evaluation of deep learning‐based automated radiotherapy ... Purpose. This study evaluates the feasibility of deep learning (DL)-based automated SBRT-VMAT planning for lung cancer.

6. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC10486721/

Artificial Intelligence in Lung Cancer Screening: The Future Is ... Particularly, in low-dose computed tomography for screening programs, AI further reduces radiation dose maintaining an optimal image quality. AI also allows ...

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A Narrative Review of Prospective Studies AI to guide radiation dose for primary lung cancer and lung metastases. Adaptive radiation using AI. AI-assisted radiation treatment planning.