지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2021.5
- 수록면
- 183 - 186 (4page)
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초록· 키워드
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Purpose
Identifying patients who may benefit from neoadjuvant chemotherapy will facilitate personalized treatment regarding chemotherapy and surgery. This study compares the predictive performance of an artificial neural network with nomogram to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with advanced breast cancer.
Methods
Medical records of 359 patients with advanced breast cancer who received neoadjuvant NAC prior to surgical resection between January 2017 and December 2019 were retrospectively reviewed. Random over sampling method was used to overcome data imbalance and random sampling method to divide patients into training and test sets at a split ratio of 7:3. Univariate and multivariate regression analyses were used to develop a nomogram and a machine learning model based on artificial neural network. Performance of the two models were evaluated using the validation sets in terms of area under the receiver operating characteristic curve (AUC).
Results
Multivariate logistic regression analysis showed that high level of estrogen receptor (OR 0.84, p < 0.001), positive HER2 status (OR 1.25, p < 0.001), complete response on MRI (OR 1.62, p < 0.001), abnormal CEA level after NAC (OR 0.86, p = 0.051), and abnormal CA15-3 level after NAC (OR 0.87, p = 0.074) were independent predictors of pCR. A nomogram and a machine learning model to predict pCR were developed using the five predictors. Validation test showed AUCs of 0.789 [95 % confidence interval (CI), 0.707-0.871] for the nomogram and 0.876 [95 % CI, 0.808-0.943] for the machine learning model.
Conclusion
We developed a nomogram and neural network-based machine learning model to predict pCR after NAC. Both models showed excellent performance, but machine learning model performed better in discrimination. Machine learning model could be used as an alternative predictive tool to decide surgical extent before resection.
Identifying patients who may benefit from neoadjuvant chemotherapy will facilitate personalized treatment regarding chemotherapy and surgery. This study compares the predictive performance of an artificial neural network with nomogram to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with advanced breast cancer.
Methods
Medical records of 359 patients with advanced breast cancer who received neoadjuvant NAC prior to surgical resection between January 2017 and December 2019 were retrospectively reviewed. Random over sampling method was used to overcome data imbalance and random sampling method to divide patients into training and test sets at a split ratio of 7:3. Univariate and multivariate regression analyses were used to develop a nomogram and a machine learning model based on artificial neural network. Performance of the two models were evaluated using the validation sets in terms of area under the receiver operating characteristic curve (AUC).
Results
Multivariate logistic regression analysis showed that high level of estrogen receptor (OR 0.84, p < 0.001), positive HER2 status (OR 1.25, p < 0.001), complete response on MRI (OR 1.62, p < 0.001), abnormal CEA level after NAC (OR 0.86, p = 0.051), and abnormal CA15-3 level after NAC (OR 0.87, p = 0.074) were independent predictors of pCR. A nomogram and a machine learning model to predict pCR were developed using the five predictors. Validation test showed AUCs of 0.789 [95 % confidence interval (CI), 0.707-0.871] for the nomogram and 0.876 [95 % CI, 0.808-0.943] for the machine learning model.
Conclusion
We developed a nomogram and neural network-based machine learning model to predict pCR after NAC. Both models showed excellent performance, but machine learning model performed better in discrimination. Machine learning model could be used as an alternative predictive tool to decide surgical extent before resection.
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