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Article Abstract

The PHERGain trial investigated the potential of metabolic imaging to identify candidates for chemotherapy deescalation in human epidermal growth factor receptor 2 (HER2)-positive, invasive, operable breast cancer with at least 1 breast lesion evaluable by [F]FDG PET/CT. [F]FDG PET/CT responders were defined as patients with an SUV reduction (ΔSUV) of at least 40% in all of their target lesions after 2 cycles of trastuzumab and pertuzumab (HP) (with or without endocrine therapy). In total, 227 of 285 patients (80%) included in the HP arm showed a predefined metabolic response and received a total of 8 cycles of HP (with or without endocrine therapy). Pathologic complete response (pCR), defined as ypT0/isN0, was achieved in 37.9% of the patients. Here, we describe the secondary preplanned analysis of the best cutoff of ΔSUV for pCR prediction. Receiver-operating-characteristic analysis was applied to look for the most appropriate ΔSUV cutoff in HER2-positive early breast cancer patients treated exclusively with neoadjuvant HP (with or without endocrine therapy). The ΔSUV capability of predicting pCR in terms of the area under the receiver-operating-characteristic curve was 72.1% (95% CI, 65.1-79.2%). The optimal ΔSUV cutoff was found to be 77.0%, with a 51.2% sensitivity and a 78.7% specificity. With this cutoff, 74 of 285 patients (26%) would be classified as metabolic responders, increasing the pCR rate from 37.9% (cutoff ≥ 40%) to 59.5% (44/74 patients) ( < 0.01). With this optimized cutoff, 44 of 285 patients (15.4%) would avoid chemotherapy in either the neoadjuvant or the adjuvant setting compared with 86 of 285 patients (30.2%) using the original cutoff ( < 0.001). In the PHERGain trial, an increased SUV cutoff (≥77%) after 2 cycles of exclusive HP (with or without endocrine therapy) achieves a pCR in the range of the control arm with chemotherapy plus HP (59.5% vs. 57.7%, respectively), further identifying a subgroup of patients with HER2-addicted tumors. However, the original cutoff (≥40%) maximizes the number of patients who could avoid chemotherapy.

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http://dx.doi.org/10.2967/jnumed.123.266384DOI Listing

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