SIIM: Quantitative imaginga grand opportunity
WASHINGTON, D.C.--Quantitative imaging offers a grand opportunity for radiology. However, key prerequisites including systems to support development and integration into clinical workflow are required, according to the Quantitative Imaging session June 4 at the annual meeting of the Society for Imaging Informatics in Medicine (SIIM).
“Quantitative imaging in cancer could launch the concept,” explained Mia Levy, MD, PhD of Vanderbilt University in Nashville, Tenn.
Oncology applications of quantitative imaging include tumor treatment response assessment. Oncologists want tumor measurements at baseline and follow-up to see changes over time. “We’re predominantly interested in how the tumor is responding to therapy,” said Levy.
Levy differentiated protocols for clinical trials and clinical practice. In clinical trials, oncologists manually create flow sheets as a record of lesion characteristics over time. In contrast, clinical practice depends on less precise measurements such as tumor shrinkage or growth and uses this information to inform treatment decisions.
Both methods leave room for improvement. Studies have suggested that current clinical trial response criteria methods result in 24 to 29 percent variance in radiologists’ measurement of tumor burden. “It’s a complex set of rules with insufficient specification,” stated Levy. In practice, as different radiologists review images over time, they may measure different lesions or different areas of lesions, resulting in less than reliable data.
“We would like to see quantitative imaging metadata on these calculations. They could become a first class data element that could be shared and entered into the electronic record for clinical decision making,” offered Levy.
The method holds promise both for individual patients and for entire groups of patients studied in a clinical trial setting. With quantitative data, physicians could better estimate how an entire group of patients is responding to treatment.
In addition to supporting treatment decision making, quantitative imaging also could play a role in the development and application of reproducible biomarkers that could help oncologists detect outcomes earlier.
“Anatomic-based determinants of tumor size may not suffice for novel targeted therapies because [these therapies] don’t cause cancer cell death. They cause them to stop growing. However, patients may live longer [on these therapies]. We want a biomarker that can detect the endpoint earlier.”
Novel quantitative imaging biomarkers look at physiologic change via MRI, PET/CT and PET/MR, continued Levy. “The goal is a biomarker that can improve signal detection for novel therapies,” she said.
“Quantitative imaging in cancer could launch the concept,” explained Mia Levy, MD, PhD of Vanderbilt University in Nashville, Tenn.
Oncology applications of quantitative imaging include tumor treatment response assessment. Oncologists want tumor measurements at baseline and follow-up to see changes over time. “We’re predominantly interested in how the tumor is responding to therapy,” said Levy.
Levy differentiated protocols for clinical trials and clinical practice. In clinical trials, oncologists manually create flow sheets as a record of lesion characteristics over time. In contrast, clinical practice depends on less precise measurements such as tumor shrinkage or growth and uses this information to inform treatment decisions.
Both methods leave room for improvement. Studies have suggested that current clinical trial response criteria methods result in 24 to 29 percent variance in radiologists’ measurement of tumor burden. “It’s a complex set of rules with insufficient specification,” stated Levy. In practice, as different radiologists review images over time, they may measure different lesions or different areas of lesions, resulting in less than reliable data.
“We would like to see quantitative imaging metadata on these calculations. They could become a first class data element that could be shared and entered into the electronic record for clinical decision making,” offered Levy.
The method holds promise both for individual patients and for entire groups of patients studied in a clinical trial setting. With quantitative data, physicians could better estimate how an entire group of patients is responding to treatment.
In addition to supporting treatment decision making, quantitative imaging also could play a role in the development and application of reproducible biomarkers that could help oncologists detect outcomes earlier.
“Anatomic-based determinants of tumor size may not suffice for novel targeted therapies because [these therapies] don’t cause cancer cell death. They cause them to stop growing. However, patients may live longer [on these therapies]. We want a biomarker that can detect the endpoint earlier.”
Novel quantitative imaging biomarkers look at physiologic change via MRI, PET/CT and PET/MR, continued Levy. “The goal is a biomarker that can improve signal detection for novel therapies,” she said.