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  • Title: Prediction model of chemotherapy response in osteosarcoma by 18F-FDG PET and MRI.
    Author: Cheon GJ, Kim MS, Lee JA, Lee SY, Cho WH, Song WS, Koh JS, Yoo JY, Oh DH, Shin DS, Jeon DG.
    Journal: J Nucl Med; 2009 Sep; 50(9):1435-40. PubMed ID: 19690035.
    Abstract:
    UNLABELLED: Response to neoadjuvant chemotherapy is a significant prognostic factor for osteosarcoma; however, this information can be determined only after surgical resection. If we could predict histologic response before surgery, it might be helpful for the planning of surgeries and tailoring of treatment. We evaluated the usefulness of (18)F-FDG PET for this purpose. METHODS: A total of 70 consecutive patients with a high-grade osteosarcoma treated at our institute were prospectively enrolled. All patients underwent (18)F-FDG PET and MRI before and after neoadjuvant chemotherapy. We analyzed the predictive values of 5 parameters, namely, maximum standardized uptake values (SUVs), before and after (SUV2) chemotherapy, SUV change ratio, tumor volume change ratio, and metabolic volume change ratio (MVCR) in terms of their abilities to discriminate responders from nonresponders. RESULTS: Patients with an SUV2 of less than or equal to 2 showed a good histologic response, and patients with an SUV2 of greater than 5 showed a poor histologic response. The histologic response of a patient with an intermediate SUV2 (2 < SUV2 </= 5) was found to be predictable using MVCR. A patient with an MVCR of less than 0.65 is likely to be a good responder, whereas a patient with an MVCR of greater than or equal to 0.65 is likely to be a poor responder. According to our model, the predictive values for good responders and poor responders were 97% (31/32) and 95% (36/38), respectively. CONCLUSION: We found that combined information on (18)F-FDG PET and MRI scans, acquired before and after chemotherapy, could be used to predict histologic response to neoadjuvant chemotherapy in osteosarcoma.
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