Journal
EUROPEAN JOURNAL OF CANCER
Volume 147, Issue -, Pages 95-105Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ejca.2021.01.028
Keywords
PPathological complete response; DDeep learning; RRadiomic nomogram; Locally advanced breast cancer
Categories
Funding
- China Postdoctoral Science Foundation [2020M682422]
- Wuhan Science and Technology Bureau [2017060201010181]
- Health Commission of Hubei Province [WJ2019M077, WJ2019H227]
- Shihezi Science and Technology Bureau [2019ZH11]
- Xinjiang Construction Corps [2019DB012]
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The study developed and validated a deep learning radiomic nomogram for predicting pathological complete response in breast cancer patients after neoadjuvant chemotherapy using pre- and post-treatment ultrasound images. The model demonstrated high accuracy, good calibration, and outperformed clinical models and experts' predictions. The deep learning-based radiomic nomogram showed good predictive value and clinical usefulness for individualized treatment in locally advanced breast cancer.
Purpose: 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. Methods: 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. Results: 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 < 0.05, as per the DeLong test) and performed better than two experts' prediction of pCR (both P < 0.01 for comparison of total accuracy). Besides, prediction within the hormone receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative, HER2+ and triple-negative subgroups also achieved good discrimination performance, with an AUC of 0.90, 0.95 and 0.93, respectively, in the external validation cohort. Decision curve analysis confirmed that the model was clinically useful. Conclusion: A deep learning-based radiomic nomogram had good predictive value for pCR in LABC, which could provide valuable information for individual treatment. (C) 2021 Elsevier Ltd. All rights reserved.
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