Springer Nature

An integrated deep learning model for the prediction of pathological complete response to neoadjuvant chemotherapy with serial ultrasonography in breast cancer patients: a multicentre, retrospective study

Posted on 2022-11-25 - 12:16
Abstract Background The biological phenotype of tumours evolves during neoadjuvant chemotherapy (NAC). Accurate prediction of pathological complete response (pCR) to NAC in the early-stage or posttreatment can optimize treatment strategies or improve the breast-conserving rate. This study aimed to develop and validate an autosegmentation-based serial ultrasonography assessment system (SUAS) that incorporated serial ultrasonographic features throughout the NAC of breast cancer to predict pCR. Methods A total of 801 patients with biopsy-proven breast cancer were retrospectively enrolled from three institutions and were split into a training cohort (242 patients), an internal validation cohort (197 patients), and two external test cohorts (212 and 150 patients). Three imaging signatures were constructed from the serial ultrasonographic features before (pretreatment signature), during the first–second cycle of (early-stage treatment signature), and after (posttreatment signature) NAC based on autosegmentation by U-net. The SUAS was constructed by subsequently integrating the pre, early-stage, and posttreatment signatures, and the incremental performance was analysed. Results The SUAS yielded a favourable performance in predicting pCR, with areas under the receiver operating characteristic curve (AUCs) of 0.927 [95% confidence interval (CI) 0.891–0.963] and 0.914 (95% CI 0.853–0.976), compared with those of the clinicopathological prediction model [0.734 (95% CI 0.665–0.804) and 0.610 (95% CI 0.504–0.716)], and radiologist interpretation [0.632 (95% CI 0.570–0.693) and 0.724 (95% CI 0.644–0.804)] in the external test cohorts. Furthermore, similar results were also observed in the early-stage treatment of NAC [AUC 0.874 (0.793–0.955)–0.897 (0.851–0.943) in the external test cohorts]. Conclusions We demonstrate that autosegmentation-based SAUS integrating serial ultrasonographic features throughout NAC can predict pCR with favourable performance, which can facilitate individualized treatment strategies.


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