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首页> 外文期刊>Journal of Clinical Oncology >Nomograms to predict pathologic complete response and metastasis-free survival after preoperative chemotherapy for breast cancer.
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Nomograms to predict pathologic complete response and metastasis-free survival after preoperative chemotherapy for breast cancer.

机译:线型图可预测乳腺癌术前化疗后的病理完全缓解和无转移生存。

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摘要

PURPOSE: To combine clinical variables associated with pathologic complete response (pCR) and distant metastasis-free survival (DMFS) after preoperative chemotherapy (PC) into a prediction nomogram. PATIENTS AND METHODS: Data from 496 patients treated with anthracycline PC at the Institut Gustave Roussy were used to develop and calibrate a nomogram for pCR based on multivariate logistic regression. This nomogram was tested on two independent cohorts of patients treated at the M.D. Anderson Cancer Center. The first cohort (n = 337) received anthracycline; the second cohort (n = 237) received a combination of paclitaxel and anthracycline PC. A separate nomogram to predict DMFS was developed using Cox proportional hazards regression model. RESULTS: The pCR nomogram based on clinical stage, estrogen receptor status, histologic grade, and number of preoperative chemotherapy cycles had good discrimination and calibration in the training and the anthracycline-treated validation sets (concordance indices, 0.77, 0.79). In the paclitaxel plus anthracycline group, when the predicted pCR rate was less than 14%, the observed rate was 7.5%; for a predicted rate of > or = 38%, the actual rate was 85%. For a predicted rate between 14% to 38%, the observed rates were 50% with weekly and 27% with 3-weekly paclitaxel. This indicates that patients with intermediate chemotherapy sensitivity benefit the most from the optimized schedule of paclitaxel. Patients unlikely to achieve pCR to anthracylines remain at low probability for pCR, even after inclusion of paclitaxel. The nomogram for DMFS had a concordance index of 0.72 in the validation set and outperformed other prediction tools (P = .02). CONCLUSION: Our nomograms predict pCR accurately and can serve as a basis to integrate future molecular markers into a clinical prediction model.
机译:目的:将术前化疗(PC)后与病理完全缓解(pCR)和远处无转移生存期(DMFS)相关的临床变量合并到预测列线图中。患者与方法:使用来自古斯塔夫·鲁西研究所的496名接受蒽环类PC治疗的患者的数据,基于多因素Logistic回归分析开发并校正了pCR的诺模图。该诺模图在M.D. Anderson癌症中心接受治疗的两个独立队列中进行了测试。第一组(n = 337)接受蒽环类药物治疗;第二组(n = 237)接受了紫杉醇和蒽环类药物联合治疗。使用Cox比例风险回归模型开发了单独的列线图来预测DMFS。结果:基于临床分期,雌激素受体状态,组织学等级和术前化疗周期数的pCR诺模图在训练和蒽环类治疗验证组中具有良好的区分度和校准度(一致性指数,0.77,0.79)。紫杉醇加蒽环类药物的组中,当预测的pCR率低于14%时,观察到的率为7.5%。如果预测比率>或= 38%,则实际比率为85%。对于14%到38%的预测比率,观察到的比率为每周紫杉醇为50%,每周3次为27%。这表明具有中度化疗敏感性的患者从紫杉醇的优化方案中获益最大。即使在纳入紫杉醇后,不太可能达到蒽环类药物的pCR的患者仍保持较低的pCR可能性。 DMFS的诺模图在验证集中的一致性指数为0.72,优于其他预测工具(P = .02)。结论:我们的诺模图可以准确预测pCR,并可以作为将未来分子标记物整合到临床预测模型中的基础。

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