A New Method of Identifying Pathologic Complete Response After Neoadjuvant Chemotherapy for Breast Cancer Patients Using a Population-Based Electronic Medical Record System.
Ann Surg Oncol
; 30(4): 2095-2103, 2023 Apr.
Article
em En
| MEDLINE
| ID: mdl-36542249
ABSTRACT
BACKGROUND:
Accurate identification of pathologic complete response (pCR) from population-based electronic narrative data in a timely and cost-efficient manner is critical. This study aimed to derive and validate a set of natural language processing (NLP)-based machine-learning algorithms to capture pCR from surgical pathology reports of breast cancer patients who underwent neoadjuvant chemotherapy (NAC).METHODS:
This retrospective cohort study included all invasive breast cancer patients who underwent NAC and subsequent curative-intent surgery during their admission at all four tertiary acute care hospitals in Calgary, Alberta, Canada, between 1 January 2010 and 31 December 2017. Surgical pathology reports were extracted and processed with NLP. Decision tree classifiers were constructed and validated against chart review results. Machine-learning algorithms were evaluated with a performance matrix including sensitivity, specificity, positive predictive value (PPV), negative predictive value [NPV], accuracy, area under the receiver operating characteristic curve [AUC], and F1 score.RESULTS:
The study included 351 female patients. Of these patients, 102 (29%) achieved pCR after NAC. The high-sensitivity model achieved a sensitivity of 90.5% (95% confidence interval [CI], 69.6-98.9%), a PPV of 76% (95% CI, 59.6-87.2), an accuracy of 88.6% (95% CI, 78.7-94.9%), an AUC of 0.891 (95% CI, 0.795-0.987), and an F1 score of 82.61. The high-PPV algorithm reached a sensitivity of 85.7% (95% CI, 63.7-97%), a PPV of 81.8% (95% CI, 63.4-92.1%), an accuracy of 90% (95% CI, 80.5-95.9%), an AUC of 0.888 (95% CI, 0.790-0.985), and an F1 score of 83.72. The high-F1 score algorithm obtained a performance equivalent to that of the high-PPV algorithm.CONCLUSION:
The developed algorithms demonstrated excellent accuracy in identifying pCR from surgical pathology reports of breast cancer patients who received NAC treatment.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
Tipo de estudo:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Female
/
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article