Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer

The aim of the study was to develop and validate a deep learning radiomic nomogram (DLRN) for preoperatively assessing breast cancer pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) based on the pre- and post-treatment ultrasound. Patients with locally advanced breast cancer...

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Bibliographic Details
Published in:European journal of cancer (1990) 2021-04, Vol.147, p.95-105
Main Authors: Jiang, Meng, Li, Chang-Li, Luo, Xiao-Mao, Chuan, Zhi-Rui, Lv, Wen-Zhi, Li, Xu, Cui, Xin-Wu, Dietrich, Christoph F.
Format: Article
Language:English
Subjects:
Algorithms
Biopsy
Breast cancer
Calibration
Chemotherapy
Confidence intervals
DDeep learning
Decision analysis
Deep learning
Epidermal growth factor
ErbB-2 protein
Growth factors
Locally advanced breast cancer
Machine learning
Nomograms
Phenotypes
PPathological complete response
Radiomics
Receptors
Redundancy
Risk analysis
Risk factors
RRadiomic nomogram
Statistical analysis
Subgroups
Tumors
Ultrasonic imaging
Ultrasonic testing
Ultrasound
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Summary:The aim of the study was to develop and validate a deep learning radiomic nomogram (DLRN) for preoperatively assessing breast cancer pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) based on the pre- and post-treatment ultrasound. Patients with locally advanced breast cancer (LABC) proved by biopsy who proceeded to undergo preoperative NAC were enrolled from hospital #1 (training cohort, 356 cases) and hospital #2 (independent external validation cohort, 236 cases). Deep learning and handcrafted radiomic features reflecting the phenotypes of the pre-treatment (radiomic signature [RS] 1) and post-treatment tumour (RS2) were extracted. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression were used for feature selection and RS construction. A DLRN was then developed based on the RSs and independent clinicopathological risk factors. The performance of the model was assessed with regard to calibration, discrimination and clinical usefulness. The DLRN predicted the pCR status with accuracy, yielded an area under the receiver operator characteristic curve of 0.94 (95% confidence interval, 0.91–0.97) in the validation cohort, with good calibration. The DLRN outperformed the clinical model and single RS within both cohorts (P 
ISSN:0959-8049
1879-0852
DOI:10.1016/j.ejca.2021.01.028