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Molecular Imaging Research in the Outcomes Era

Posted Mar 31, 2007 Updated Apr 7, 2023
By David A. Mankoff MD PhD , Finbarr O’Sullivan PhD , William E. Barlow PhD , Kenneth A. Krohn PhD
10 min read

Individualized cancer therapy

A recent trend in cancer treatment has been termed individualized or targeted therapy and has the goal of matching cancer treatment to the characteristics of the patient and the biologic features of his or her tumor ( ). As cancer treatment moves toward more targeted and individualized therapy, there is an increasing need for tools to guide therapy selection and to evaluate response. In particular, to make appropriate choices for therapy, the cancer physician needs to know the following:

  • 1 How aggressive is the cancer? How likely is it that the cancer will spread and/or cause symptoms or death?

  • 2 What are appropriate targets for cancer therapy? Are the targets expressed by the tumor? Are there resistance factors that may mitigate the success of the treatment? Has the target been suppressed?

  • 3 Is the tumor responding? Can a lack of response for ineffective therapy be identified quickly so that alternative treatment can be tried?

The current approach to patient management relies upon a combination of imaging and biopsy to detect, localize, and confirm tumors and upon in vitro assay of biopsy material to determine tumor biologic features. Relying entirely upon tissue sampling to characterize tumors has two important limitations:

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The approach to cancer imaging

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Figure 1, Illustration of cancer cell targets for cancer imaging. Targets for tumor detection ( A ) must be processes present in high levels in the tumor, but absent or present in low levels in normal tissue. There are a greater number of possible targets for cancer imaging to guide tumor therapy ( B ), where measuring both increased and decreased levels is important in choosing and monitoring treatment.

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Approaches to clinical study design and analysis

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Applications of molecular imaging to cancer prognosis, prediction, and response

Prognosis

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Figure 2, Diagram for clinical study design to test a prognostic marker. The prognostic marker may be a tissue assay, an imaging measure, or a combination of tissue and imaging results.

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Prediction

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Figure 3, Diagram for clinical study design to test a predictive assay.

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Figure 4, Coronal PET images of FDG uptake ( left ) and 18 F-fluoroestradiol (FES) uptake ( right ) are shown for two patients with recurrent and metastatic disease from estrogen receptor positive (ER+) breast cancers (30). The top patient (patient 1) showed sternal metastases that were metabolically active by FDG PET (second column) with matched uptake of FES (first column), indicated preserved ER expression ( arrow ). The bottom patient (patient 2) showed a site of bone metastasis by FDG PET ( arrow ) but no corresponding uptake by FES, suggesting a loss of ER expression. Both patients were treated with hormonal therapy subsequent to PET imaging. Patient 1 had an excellent objective response, while patient 2 had disease progression, both indicated by posttherapy FDG ( right column ). Normal liver and kidney uptake is also seen in the images for both radiopharmaceuticals. This example illustrates how uptake on FES PET can be predictive for response of breast cancer to endocrine therapy.

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Response

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Figure 5, Diagram of biologic processes involved in cancer response to successful therapy.

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Figure 6, Study design for testing a measure of cancer response. The change in the response measure is compared to the “gold standard” response measure to determine the accuracy of predicting a response and, importantly, to patient outcome including time-to-progression (TTP) and survival.

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Figure 7, Illustration of a study testing a novel imaging procedure as a measure of response to therapy. The change in uptake of 99m Tc-sestamibi (MIBI) in locally advanced breast cancer was compared to the “gold standard” response (posttherapy histopathology) (40) and posttherapy MIBI uptake was compared to patient outcome (41). Images ( A ) show the ability to distinguish pathologic complete response (CR) versus partial response (PR), and a plot of the change in MIBI uptake ratios (lesion-to-normal breast uptake [L:N ratio]) shows that values for CR versus PR are nearly completely separated ( B ). ROC analysis depicts the ability to classify CR versus PR based upon the change in MIBI uptake on a plot of the sensitivity for CR versus 1 − specificity ( C ). Comparison of residual MIBI uptake posttherapy to overall survival ( D ) demonstrates that low posttherapy MIBI uptake is predictive of survival.

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Summary

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Academic Radiology, Volume 14, Issue 4
Journals General Radiology
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