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Prediction and clinical utility of a contralateral breast cancer risk model.
Breast Cancer Research ( IF 6.1 ) Pub Date : 2019-12-17 , DOI: 10.1186/s13058-019-1221-1
Daniele Giardiello 1, 2 , Ewout W Steyerberg 2, 3 , Michael Hauptmann 4, 5 , Muriel A Adank 6 , Delal Akdeniz 7 , Carl Blomqvist 8, 9 , Stig E Bojesen 10, 11, 12 , Manjeet K Bolla 13 , Mariël Brinkhuis 14 , Jenny Chang-Claude 15, 16 , Kamila Czene 17 , Peter Devilee 18, 19 , Alison M Dunning 20 , Douglas F Easton 13, 20 , Diana M Eccles 21 , Peter A Fasching 22, 23 , Jonine Figueroa 24, 25, 26 , Henrik Flyger 27 , Montserrat García-Closas 26, 28 , Lothar Haeberle 23 , Christopher A Haiman 29 , Per Hall 17, 30 , Ute Hamann 31 , John L Hopper 32 , Agnes Jager 33 , Anna Jakubowska 34, 35 , Audrey Jung 15 , Renske Keeman 1 , Iris Kramer 1 , Diether Lambrechts 36, 37 , Loic Le Marchand 38 , Annika Lindblom 39, 40 , Jan Lubiński 34 , Mehdi Manoochehri 31 , Luigi Mariani 41 , Heli Nevanlinna 42 , Hester S A Oldenburg 43 , Saskia Pelders 7 , Paul D P Pharoah 13, 20 , Mitul Shah 20 , Sabine Siesling 44 , Vincent T H B M Smit 18 , Melissa C Southey 45, 46 , William J Tapper 47 , Rob A E M Tollenaar 48 , Alexandra J van den Broek 1 , Carolien H M van Deurzen 49 , Flora E van Leeuwen 50 , Chantal van Ongeval 51 , Laura J Van't Veer 1 , Qin Wang 13 , Camilla Wendt 52 , Pieter J Westenend 53 , Maartje J Hooning 7 , Marjanka K Schmidt 1, 50
Affiliation  

BACKGROUND Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. METHODS We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. RESULTS In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. CONCLUSIONS We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.

中文翻译:


对侧乳腺癌风险模型的预测和临床实用性。



背景乳腺癌幸存者有患对侧乳腺癌(CBC)的风险,从而带来进一步治疗的负担和可能较差的预后。我们的目的是开发和验证 CBC 风险预测模型,并评估其对临床决策的适用性。方法 我们纳入了来自 20 项研究的 132,756 名侵袭性非转移性乳腺癌患者的数据,涉及 4682 次 CBC 事件,中位随访时间为 8.8 年。我们开发了多变量 Fine and Gray 预测模型 (PredictCBC-1A),包括患者、原发肿瘤、治疗特征以及 BRCA1/2 种系突变状态,考虑了死亡和远处转移的竞争风险。我们还开发了一个没有 BRCA1/2 突变状态的模型 (PredictCBC-1B),因为此信息仅适用于 6% 的患者,并且在一般乳腺癌人群中通常无法获得。使用校准和辨别来评估预测性能,通过原发性乳腺癌诊断后 5 年和 10 年的时间依赖性曲线下面积 (AUC) 计算,以及内部-外部交叉验证程序。进行决策曲线分析以评估模型的净效益以量化临床效用。结果在多变量模型中,BRCA1/2 种系突变状态、家族史和全身辅助治疗显示与 CBC 风险的关联最强。 PredictCBC-1A 的 AUC 为 0.63(5 年 95% 预测区间 (PI),0.52-0.74;10 年,0.53-0.72)。大型校准为-0.13(95% PI:-1.62-1.37),校准斜率为0.90(95% PI:0.73-1.08)。 Predict-1B 10 年时的 AUC 为 0.59(95% PI:0.52-0.66);校准稍低。 预防性对侧乳房切除术的决策曲线分析显示,对于 BRCA1/2 突变携带者和非携带者,PredictCBC-1A 在 4-10% 10 年 CBC 风险阈值之间具有潜在的临床效用。结论 我们开发了一个合理校准的模型来预测欧洲裔女性的 CBC 风险;然而,预测准确性中等。我们的模型显示了改进风险咨询的潜力,但有关对侧预防性乳房切除术的决策仍然具有挑战性,特别是在一般乳腺癌人群中,可获得的 BRCA1/2 突变状态信息有限。
更新日期:2020-04-22
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