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First published online August 18, 2009
J Nucl Med 2009, doi:10.2967/jnumed.109.063602
 © 2009 by Society of Nuclear Medicine
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Prediction Model of Chemotherapy Response in Osteosarcoma by 18F-FDG PET and MRI

Gi Jeong Cheon 1, Min Suk Kim 2, Jun Ah Lee 3, Soo-Yong Lee 4, Wan Hyeong Cho 4, Won Seok Song 4, Jae-Soo Koh 2, Ji Young Yoo 5, Dong Hyun Oh 6, Duk Seop Shin 7, and Dae-Geun Jeon 4*

1 Department of Nuclear Medicine, Korea Cancer Center Hospital, Seoul, Korea; Department of Molecular Imaging Center, Korea Institute of Radiological and Medical Sciences, Seoul, Korea
2 Department of Pathology, Korea Cancer Center Hospital, Seoul, Korea
3 Department of Pediatrics, Korea Cancer Center Hospital, Seoul, Korea
4 Department of Orthopedic Surgery, Korea Cancer Center Hospital, Seoul, Korea
5 Department of Radiology, Korea Cancer Center Hospital, Seoul, Korea
6 Department of Nuclear Medicine, Korea Cancer Center Hospital, Seoul, Korea
7 Department of Orthopedic Surgery, Yeungnam University College of Medicine, Daegu, Korea

* To whom correspondence should be addressed. E-mail: dgjeon{at}kcch.re.kr.


  Abstract

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 18F-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 18F-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 18F-FDG PET and MRI scans, acquired before and after chemotherapy, could be used to predict histologic response to neoadjuvant chemotherapy in osteosarcoma.

Key Words: correlative imaging, oncology, PET, 18F-FDG PET, MRI, chemotherapy response, osteosarcoma






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Copyright © 2009 by the Society of Nuclear Medicine.