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Radiology Today
E-Newsletter  •  September 2026
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Editor's E-Note

The challenges associated with treating lung cancer have been well documented, and it continues to be the leading cause of cancer death in the United States. Screening programs can be effective for catching it in earlier, more treatable, stages but screening continues to be an underutilized tool.

Increased screening, however, presents its own challenges. Even if screening programs are expanded, the volume of images and the ability to differentiate benign nodules from those that warrant follow-up remain significant hurdles for a health care workforce that is already stretched thin. This month, we’re looking at how AI can help improve the efficiency of lung cancer screening programs.

For more of the latest imaging news, visit us on X, formerly known as Twitter, and/or Facebook.

Enjoy the newsletter.

— Dave Yeager, editor
In This E-Newsletter
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Sustainable Screening

Improving lung cancer survival depends on effective screening.

By Chris Wood

Lung cancer remains the leading cause of cancer death in the United States, despite meaningful advances in targeted therapies, immunotherapies, and other treatments for advanced disease. That reality points to an opportunity that deserves attention as the next breakthrough therapy, which is finding lung cancer earlier and managing suspicious lung nodules more effectively.

The history of cancer care consistently demonstrates the power of early detection. When cancer is identified while it is still localized, treatment is more likely to be curative. For lung cancer, the difference is particularly significant. Patients diagnosed with localized disease have substantially better survival outcomes than those diagnosed after the cancer has spread, the prominent issue being that we are not reaching enough people early enough.

Lung cancer screening is one of the clearest tools we have for shifting diagnosis toward earlier stages, yet screening participation remains remarkably low. The CDC estimates that only about 18% of US adults who meet screening recommendations have been screened. While we work to bring compliance up, there is also increasing interest in expanding the guidelines for who should receive screening. This is because much more than half of those diagnosed with lung cancer do not meet the screening criteria. One group recently highlighted for possible inclusion are women with breast cancer (the most diagnosed cancer in women). These women are at increased risk for subsequent lung cancer, and lung cancer screening could provide protection against follow-on cancers.

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Other Imaging News
Imaging Use Higher When Ordering Clinicians Interpret Their Own Imaging
A recent study by the Harvey L. Nieman Health Policy Institute found that clinicians who interpret their own imaging studies are significantly more likely to order imaging than practices who referred all of their imaging to radiologists.

Psychedelics and Anesthetics Mirror Each Other
Researchers used MRI to demonstrate that psychedelic and anesthetic drugs create mirror image patterns of large-scale brain organization with opposite network features.

PSMA PET/CT May Reduce Unnecessary Prostate Biopsies
Prostate-specific membrane antigen PET/CT enables suspicious areas on the prostate to be examined in detail. A recent study found that the scan detected 91% of clinically relevant prostate cancers, as well as a high predictive value in normal cases, potentially pointing toward the need for fewer biopsies in the future.

Raising Breast Cancer Awareness
October is just around the corner, and the National Education Telecommunications Association is once again promoting the Breakthrough Women in Science & Medicine breast cancer series.

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Worth Repeating
“These findings demonstrate signals, invisible to the human eye, in the image alone can predict future risk. This is exciting because 85% of women diagnosed with breast cancer do not have a significant family history of breast cancer or known genetic mutations.”

— Constance D. Lehman, MD, PhD, a professor of radiology at Harvard Medical School and CEO of Clairity, Inc, discussing the findings of a study that used AI to help determine that image-based risk scores derived from screening mammograms evolve over time and differ between women who develop breast cancer and women who do not

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