UNLABELLED: We have compared 2-deoxy-2-[(18)F]-fluoro-D-glucose positron emission tomography (FDG-PET) images of large or locally advanced breast cancers (LABC) acquired during Anthracycline-based chemotherapy. The purpose was to determine whether there is an optimal method for defining tumour volume and an optimal imaging time for predicting pathologic chemotherapy response. METHOD: PET data were acquired before the first and second cycles, at the midpoint and at the endpoint of neoadjuvant chemotherapy. FDG uptake was quantified using the mean and maximum standardized uptake values (SUV) and the coefficient of variation within a region of interest. Receiver-operator characteristic (ROC) analysis was used to determine the discrimination between tumours demonstrating a high pathological response (i.e. those with greater than 90% reduction in viable tumour cells) and low pathological response. RESULTS: Only tumours with an initial tumour to background ratio (TBR) of greater than five showed a difference between response categories. In terms of response discrimination, there was no statistically significant advantage of any of the methods used for image quantification or any of the time points. The best discrimination was measured for mean SUV at the midpoint of therapy, which identified 77% of low responding tumours whilst correctly identifying 100% of high responding tumours and had an ROC area of 0.93. CONCLUSION: FDG-PET is efficacious for predicting the pathologic response of most primary breast tumours throughout the duration of a neoadjuvant chemotherapy regimen. However, this technique is ineffective for tumours with low image contrast on pre-therapy PET scans.
UNLABELLED: We have compared 2-deoxy-2-[(18)F]-fluoro-D-glucose positron emission tomography (FDG-PET) images of large or locally advanced breast cancers (LABC) acquired during Anthracycline-based chemotherapy. The purpose was to determine whether there is an optimal method for defining tumour volume and an optimal imaging time for predicting pathologic chemotherapy response. METHOD: PET data were acquired before the first and second cycles, at the midpoint and at the endpoint of neoadjuvant chemotherapy. FDG uptake was quantified using the mean and maximum standardized uptake values (SUV) and the coefficient of variation within a region of interest. Receiver-operator characteristic (ROC) analysis was used to determine the discrimination between tumours demonstrating a high pathological response (i.e. those with greater than 90% reduction in viable tumour cells) and low pathological response. RESULTS: Only tumours with an initial tumour to background ratio (TBR) of greater than five showed a difference between response categories. In terms of response discrimination, there was no statistically significant advantage of any of the methods used for image quantification or any of the time points. The best discrimination was measured for mean SUV at the midpoint of therapy, which identified 77% of low responding tumours whilst correctly identifying 100% of high responding tumours and had an ROC area of 0.93. CONCLUSION:FDG-PET is efficacious for predicting the pathologic response of most primary breast tumours throughout the duration of a neoadjuvant chemotherapy regimen. However, this technique is ineffective for tumours with low image contrast on pre-therapy PET scans.
Authors: Savannah C Partridge; Risa K Vanantwerp; Robert K Doot; Xiaoyu Chai; Brenda F Kurland; Peter R Eby; Jennifer M Specht; Lisa K Dunnwald; Erin K Schubert; Constance D Lehman; David A Mankoff Journal: J Magn Reson Imaging Date: 2010-11 Impact factor: 4.813
Authors: J M Cochran; S H Chung; A Leproux; W B Baker; D R Busch; A M DeMichele; J Tchou; B J Tromberg; A G Yodh Journal: Phys Med Biol Date: 2017-04-12 Impact factor: 3.609
Authors: Bruce J Tromberg; Zheng Zhang; Anaïs Leproux; Thomas D O'Sullivan; Albert E Cerussi; Philip M Carpenter; Rita S Mehta; Darren Roblyer; Wei Yang; Keith D Paulsen; Brian W Pogue; Shudong Jiang; Peter A Kaufman; Arjun G Yodh; So Hyun Chung; Mitchell Schnall; Bradley S Snyder; Nola Hylton; David A Boas; Stefan A Carp; Steven J Isakoff; David Mankoff Journal: Cancer Res Date: 2016-08-15 Impact factor: 12.701
Authors: Paul E Kinahan; Robert K Doot; Michelle Wanner-Roybal; Luc M Bidaut; Samuel G Armato; Charles R Meyer; Geoffrey McLennan Journal: Transl Oncol Date: 2009-12 Impact factor: 4.243