RESUMO
BACKGROUND: Given its high recurrence risk, guidelines recommend systemic therapy for most patients with early-stage triple-negative breast cancer (TNBC). While some clinicopathologic factors and tumor-infiltrating lymphocytes (TILs) are known to be prognostic in patients receiving chemotherapy, their prognostic implications in systemically untreated patients remain unknown. METHODS: From a cohort of 9982 women with surgically treated non-metastatic breast cancer, all patients with clinically reported ER-negative/borderline (≤10%) disease were selected for central assessment of ER/PR/HER2, histopathology, Ki-67, and TILs. The impact of these parameters on invasive disease-free survival (IDFS) and overall survival (OS) was assessed using Cox proportional hazards models. RESULTS: Six hundred five patients met the criteria for TNBC (ER/PR < 1% and HER2 negative). Most were T1-2 (95%), N0-1 (86%), grade 3 (88%), and had a Ki-67 >15% (75%). Histologically, 70% were invasive carcinoma of no special type, 16% medullary, 8% metaplastic, and 6% apocrine. The median stromal TIL content was 20%. Four hundred twenty-three (70%) patients received adjuvant chemotherapy. Median OS follow-up was 10.6 years. On multivariate analysis, only higher nodal stage, lower TILs, and the absence of adjuvant chemotherapy were associated with worse IDFS and OS. Among systemically untreated patients (n = 182), the 5-year IDFS was 69.9% (95% CI 60.7-80.5) [T1a: 82.5% (95% CI 62.8-100), T1b: 67.5% (95% CI 51.9-87.8) and T1c: 67.3% (95% CI 54.9-82.6)], compared to 77.8% (95% CI 68.3-83.6) for systemically treated T1N0. Nodal stage and TILs remained strongly associated with outcomes. CONCLUSIONS: In early-stage TNBC, nodal involvement, TILs, and receipt of adjuvant chemotherapy were independently associated with IDFS and OS. In systemically untreated TNBC, TILs remained prognostic and the risk of recurrence or death was substantial, even for T1N0 disease.
Assuntos
Linfócitos do Interstício Tumoral/patologia , Recidiva Local de Neoplasia/tratamento farmacológico , Prognóstico , Neoplasias de Mama Triplo Negativas/tratamento farmacológico , Adulto , Idoso , Biomarcadores Tumorais/genética , Quimioterapia Adjuvante/efeitos adversos , Intervalo Livre de Doença , Receptor alfa de Estrogênio/genética , Feminino , Humanos , Pessoa de Meia-Idade , Invasividade Neoplásica/genética , Invasividade Neoplásica/patologia , Recidiva Local de Neoplasia/genética , Recidiva Local de Neoplasia/patologia , Estadiamento de Neoplasias , Modelos de Riscos Proporcionais , Receptor ErbB-2/genética , Receptores de Progesterona/genética , Neoplasias de Mama Triplo Negativas/epidemiologia , Neoplasias de Mama Triplo Negativas/genética , Neoplasias de Mama Triplo Negativas/patologiaRESUMO
OBJECTIVE: To develop an algorithm for the discovery of drug treatment patterns for endocrine breast cancer therapy within an electronic medical record and to test the hypothesis that information extracted using it is comparable to the information found by traditional methods. MATERIALS: The electronic medical charts of 1507 patients diagnosed with histologically confirmed primary invasive breast cancer. METHODS: The automatic drug treatment classification tool consisted of components for: (1) extraction of drug treatment-relevant information from clinical narratives using natural language processing (clinical Text Analysis and Knowledge Extraction System); (2) extraction of drug treatment data from an electronic prescribing system; (3) merging information to create a patient treatment timeline; and (4) final classification logic. RESULTS: Agreement between results from the algorithm and from a nurse abstractor is measured for categories: (0) no tamoxifen or aromatase inhibitor (AI) treatment; (1) tamoxifen only; (2) AI only; (3) tamoxifen before AI; (4) AI before tamoxifen; (5) multiple AIs and tamoxifen cycles in no specific order; and (6) no specific treatment dates. Specificity (all categories): 96.14%-100%; sensitivity (categories (0)-(4)): 90.27%-99.83%; sensitivity (categories (5)-(6)): 0-23.53%; positive predictive values: 80%-97.38%; negative predictive values: 96.91%-99.93%. DISCUSSION: Our approach illustrates a secondary use of the electronic medical record. The main challenge is event temporality. CONCLUSION: We present an algorithm for automated treatment classification within an electronic medical record to combine information extracted through natural language processing with that extracted from structured databases. The algorithm has high specificity for all categories, high sensitivity for five categories, and low sensitivity for two categories.