SNM: Investigational biomarker helps predict breast cancer survival

SALT LAKE CITY—Molecular imaging continues to expand its role in determining the best course of treatment for recurring breast cancer patients and also offers a means of evaluating certain therapies for potentially positive impact on their chance of survival, researchers at the SNM annual meeting said. A study presented June 6 at SNM shows an investigational molecular imaging biomarker may determine how certain recurrent breast cancer patients will respond to therapy.

Only a minority of women with recurrent breast tumors characterized by the presence of estrogen receptors benefit from second line aromatase inhibitor therapy, according to the researchers. Conventional imaging techniques, however, do not help physicians predict which patients will respond to hormone therapy.

“As the therapeutic arsenal against breast and other cancers continues to grow, the ability to predict and monitor response to therapy will become increasingly important,” said Félix-Nicolas Roy, MD, BC Cancer Agency in Vancouver, British Columbia.

The study of Roy and colleagues examined the predictive value of PET imaging with 18F-16alpha-[18F]fluoro-17beta-estradiol (FES), an investigational molecular imaging biomarker which consists of an analog form of an estrogen known as estradiol.

The long-range prospective study followed 60 patients with suspected recurrence of estrogen-receptor positive breast cancer up to 83 months after time of suspected recurrence. Patients underwent FES-PET studies before and two months after initiating therapy.

Roy noted that patients who showed a favorable response to therapy on PET scans lived significantly longer than those who didn’t, and women with evidence of disease on FES-PET scans two months after the initiation of treatment had a significantly lower rate of survival.

In conclusion, Roy said that FES-PET could help predict the outcome of treatment and enable clinicians to discontinue futile therapies and initiate alternative therapy earlier.

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