Early Detection is the Single Best Lever in Lung Cancer Survival

As part of today’s Learning Center Highlight, we’re taking a closer look at why timing plays such a critical role in lung cancer survival.

It is not just about advancements in treatment, but it is about how early the disease is detected, and the difference can be significant:

• When lung cancer is caught early (localized stage), the 5-year relative survival rate can be around 60% or higher ¹
• When diagnosed at a late stage (metastatic), survival drops to 6% ¹

This gap highlights a simple but critical reality: earlier detection can change the trajectory of the disease. It allows curative-intent surgery that would otherwise not be possible.

The Challenge: Not all Lung Nodules are Equal

Each year, over 1.5 million lung nodules are detected in the US alone. Most are non-cancerous (benign), but a small number can be early lung cancer.

The challenge is consistently identifying which lung nodules are high-risk and need closer attention.

In real-world care:

• Up to 31% of chest CT scans of lung nodules are discovered incidentally, often during imaging for something else ²
• Follow-up care is not always consistently tracked across healthcare systems ³
• Not every patient is assessed the same way, which can lead to delays or unnecessary procedures ³

For physicians, care teams, and patients, this creates uncertainty, delayed diagnosis, and added unnecessary stress. And who wants that? Nobody.

Lung Cancer Survival Starts with Getting the Right Patients First

Improving survival outcomes is not just about finding more nodules. It is about prioritizing the right patients earlier.

That means:

• Identify high-risk nodules more accurately
• Helping physicians and care teams make more informed and confident clinical decisions
• Ensure patients get timely, guideline-based intervention and follow-up

Even small gains can go a long way, especially for patient outcomes, and AI is helping move that progress forward.

Studies show that AI-based lung cancer prediction models can identify high-risk nodules more accurately (AUC 0.92), outperforming traditional models like Brock (AUC 0.88). ⁴ When used alongside physicians, AI also significantly improves their ability to estimate cancer (malignancy) risk of lung nodules that need further evaluation. ⁵

Earlier and More Accurate Lung Cancer Detection: Made Possible by Optellum AI

Optellum’s Virtual Nodule Clinic (VNC), powered by Lung Cancer Prediction (LCP) AI, helps make lung nodule management more clear, consistent, and easier to navigate.

With Optellum AI, physicians and care teams:

• Identify high-risk patients earlier
• Prioritize follow-up and intervention for those who need it most
• Reduce unnecessary procedures for patients with low-risk nodules

Real-world data shows that US regional health systems and academic medical centers using Optellum AI have seen over a 50% increase in biopsies and surgeries, with some sites reporting gains as high as 118%. ⁶

This reflects a shift where earlier detection and more timely intervention can make the biggest difference.

The Bottom Line

Lung cancer survival is not just about treatment. It is about how early we act.

And that starts with better, earlier detection.

Explore our Learning Center to learn more about:

• Lung nodules: how they are assessed and followed over time.
• Lung cancer: key risk factors, the role of early diagnosis, and what NSCLC and SCLC mean in practice.
• Lung cancer care: how structured pathways and advanced tools can support earlier, more consistent decisions

References

1. American Cancer Society. Lung Cancer Survival Rates https://www.cancer.org/cancer/types/lung-cancer/detection-diagnosis-staging/survival-rates.html
2. Gould MK, et al. Evaluation of Individuals with Pulmonary Nodules: CHEST Guideline and Expert Panel Report https://journal.chestnet.org/article/S0012-3692(13)60291-3/fulltext
3. Tanner NT, et al. Management of Pulmonary Nodules by Community Pulmonologists https://pubmed.ncbi.nlm.nih.gov/26087071/
4. Piskorski, L., Debic, M., von Stackelberg, O. et al. Malignancy risk stratification for pulmonary nodules: comparing a deep learning approach to multiparametric statistical models in different disease groups. Eur Radiol 35, 3812–3822 (2025). https://doi.org/10.1007/s00330-024-11256-8
5. Kim, Roger Y., et al. “Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected With CT.” Radiology, vol. 304, no. 3, May 2022, pp. 683–91. https://doi.org/10.1148/radiol.212182.
6. Lee et al. Real-world before-and-after evaluation of AI support for lung cancer diagnosis at three US lung nodule clinics. https://www.medrxiv.org/content/10.1101/2025.11.20.25340677v1