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Locally advanced breast cancer (LABC) often uses neoadjuvant chemotherapy (NAC).
Breast cancer
This study aims to develop and validate a deep learning radiographic nomogram (DLRN) based on pre- and post-treatment ultrasound examinations for preoperative assessment of the pathological complete remission (pCR) of breast cancer after NAC.
The aim is to develop and validate a deep learning radiographic nomogram (DLRN) based on pre- and post-treatment ultrasound examinations for preoperative assessment of the pathological complete remission (pCR) of breast cancer after NAC.
The histologically clear LABC patients who planned to undergo preoperative NAC were recruited from two hospitals (training cohort, n=356; independent external verification cohort, n=236).
Comparison of performance of different models
Comparison of performance of different modelsDLRN can accurately determine the pCR status .
DLRN can accurately determine the pCR status DLRN can accurately determine the pCR status DLRN performance is better than clinical model and single RS (p<0.
The performance of this model in different subtypes of breast cancer
The performance of this model in different subtypes of breast cancerIn addition, the model has also achieved good evaluation and recognition performance in the hormone receptor positive/human epidermal growth factor receptor 2 (HER2) negative, HER2+, and triple negative subgroups of breast cancer patients in the external validation cohort, and the corresponding AUCs are respectively 0.
This model has also achieved good evaluation and recognition performance in the hormone receptor positive/human epidermal growth factor receptor 2 (HER2) negative, HER2+ and triple negative subgroups of breast cancer patients in the external validation cohort, and the corresponding AUCs are 0.
This deep learning nomogram of ultrasound images before and after gene neoadjuvant therapy can accurately assess the pCR of LABC patients after NAC and provide information for individualized treatment.
Original source:
Meng Jiang, et al.
org/10.
1016/j.
ejca.
2021.
01.
028">Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer in this message
