[18F]FDG PET/CT for predicting triple-negative breast cancer outcomes after neoadjuvant chemotherapy with or without pembrolizumab

European journal of nuclear medicine and molecular imaging(2023)

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摘要
Purpose To determine if pretreatment [18F]FDG PET/CT could contribute to predicting complete pathological complete response (pCR) in patients with early-stage triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy with or without pembrolizumab. Methods In this retrospective bicentric study, we included TNBC patients who underwent [18F]FDG PET/CT before neoadjuvant chemotherapy (NAC) or chemo-immunotherapy (NACI) between March 2017 and August 2022. Clinical, biological, and pathological data were collected. Tumor SUVmax and total metabolic tumor volume (TMTV) were measured from the PET images. Cut-off values were determined using ROC curves and a multivariable model was developed using logistic regression to predict pCR. Results N = 191 patients were included. pCR rates were 53 and 70% in patients treated with NAC ( N = 91) and NACI ( N = 100), respectively ( p < 0.01). In univariable analysis, high Ki67, high tumor SUVmax (> 12.3), and low TMTV (≤ 3.0 cm 3 ) were predictors of pCR in the NAC cohort while tumor staging classification (< T3), BRCA1/2 germline mutation, high tumor SUVmax (> 17.2), and low TMTV (≤ 7.3 cm 3 ) correlated with pCR in the NACI cohort. In multivariable analysis, only high tumor SUVmax (NAC: OR 8.8, p < 0.01; NACI: OR 3.7, p = 0.02) and low TMTV (NAC: OR 6.6, p < 0.01; NACI: OR 3.5, p = 0.03) were independent factors for pCR in both cohorts, albeit at different thresholds. Conclusion High tumor metabolism (SUVmax) and low tumor burden (TMTV) could predict pCR after NAC regardless of the addition of pembrolizumab. Further studies are warranted to validate such findings and determine how these biomarkers could be used to guide neoadjuvant therapy in TNBC patients.
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关键词
Early triple-negative breast cancer,Neoadjuvant chemotherapy ± pembrolizumab,Pathologic response,[18F]FDG PET/CT,Tumor metabolism,Metabolic tumor burden
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