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

医学 病态的 列线图 乳腺癌 队列 接收机工作特性 置信区间 肿瘤科 新辅助治疗 放射科 内科学 癌症
作者
Meng Jiang,Changli Li,Xiaomao Luo,Zhi-Rui Chuan,Wenzhi Lv,Xu Li,Xin‐Wu Cui,Christoph F. Dietrich
出处
期刊:European Journal of Cancer [Elsevier BV]
卷期号:147: 95-105 被引量:158
标识
DOI:10.1016/j.ejca.2021.01.028
摘要

Abstract

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.
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