Details

Autor(en) / Beteiligte

• Janssen, L M
• Janse, M H A
• Penning de Vries, B B L
• van der Velden, B H M
• Wolters-van der Ben, E J M
• van den Bosch, S M
• Sartori, A
• Jovelet, C
• Agterof, M J
• Ten Bokkel Huinink, D
• Bouman-Wammes, E W
• van Diest, P J
• van der Wall, E
• Elias, S G
• Gilhuijs, K G A

Titel

Predicting response to neoadjuvant chemotherapy with liquid biopsies and multiparametric MRI in patients with breast cancer

Ist Teil von

• NPJ breast cancer, 2024-01, Vol.10 (1), p.10-10, Article 10

Ort / Verlag

United States: Nature Publishing Group

Erscheinungsjahr

2024

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Accurate prediction of response to neoadjuvant chemotherapy (NAC) can help tailor treatment to individual patients' needs. Little is known about the combination of liquid biopsies and computer extracted features from multiparametric magnetic resonance imaging (MRI) for the prediction of NAC response in breast cancer. Here, we report on a prospective study with the aim to explore the predictive potential of this combination in adjunct to standard clinical and pathological information before, during and after NAC. The study was performed in four Dutch hospitals. Patients without metastases treated with NAC underwent 3 T multiparametric MRI scans before, during and after NAC. Liquid biopsies were obtained before every chemotherapy cycle and before surgery. Prediction models were developed using penalized linear regression to forecast residual cancer burden after NAC and evaluated for pathologic complete response (pCR) using leave-one-out-cross-validation (LOOCV). Sixty-one patients were included. Twenty-three patients (38%) achieved pCR. Most prediction models yielded the highest estimated LOOCV area under the curve (AUC) at the post-treatment timepoint. A clinical-only model including tumor grade, nodal status and receptor subtype yielded an estimated LOOCV AUC for pCR of 0.76, which increased to 0.82 by incorporating post-treatment radiological MRI assessment (i.e., the "clinical-radiological" model). The estimated LOOCV AUC was 0.84 after incorporation of computer-extracted MRI features, and 0.85 when liquid biopsy information was added instead of the radiological MRI assessment. Adding liquid biopsy information to the clinical-radiological resulted in an estimated LOOCV AUC of 0.86. In conclusion, inclusion of liquid biopsy-derived markers in clinical-radiological prediction models may have potential to improve prediction of pCR after NAC in breast cancer.