First Thyroid Cancer-Specific Anxiety Scale Supports Patient-Centered Care

Anxiety about monitoring, prognosis, and recurrence can shape these preferences and complicate shared decision-making. Clinicians therefore need structured ways to identify and address disease-specific distress. In response, investigators have developed a targeted instrument to measure thyroid cancer-related anxiety and support more individualized care.

The Thyroid Cancer Modified Anxiety Scale (TC-MAX) was developed by Cedars-Sinai Health Sciences University and is described as the first tool designed specifically to measure anxiety in patients with thyroid cancer. The tool aims to clarify patient expectations, helping clinicians better align treatment intensity with individual needs and support improved outcomes.

TC-MAX uses 18 questions to assess three domains. It measures general cancer-related anxiety, anxiety related to ultrasound testing used to monitor disease progression, and fear of progression or recurrence. Development involved collaboration with the Thyroid Cancer Survivors’ Association, drawing on medical literature, physician expertise, and the perspectives of more than 1,000 thyroid cancer patients.

In study findings, patient anxiety scores tracked with the extent of surgery chosen when multiple options were clinically reasonable. Patients with the highest TC-MAX scores tended to select total thyroid removal, those with lower average scores more often chose partial thyroid removal, and those with the lowest scores typically opted for active surveillance. Investigators reported that the tool is ready for use in clinical trials and patient care.

The work was published in Thyroid on August 11, 2026.

“A patient’s distress greatly influences which thyroid cancer treatment the patient prefers and what their doctor recommends,” said Allen Ho, MD, professor of surgery, co-director of the Thyroid Cancer Program at Cedars-Sinai Cancer and senior author of the study.

“We found that our tool effectively assesses patient concerns to help doctors better determine the best course of treatment. This is especially important when treatment options have similar likely outcomes but patient preferences vary widely,” Ho said.

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Cedars-Sinai Health Sciences University