目前，对侧乳腺癌发生风险预测计算工具主要有三种：曼彻斯特公式、CBCrisk、PredictCBC。
2020年4月11日，施普林格自然旗下《乳腺癌研究与治疗》在线发表荷兰癌症研究所、列文虎克医院、莱顿大学、鹿特丹大学、东荷兰病理实验室、荷兰综合癌症组织、多德雷赫特病理实验室、德国勃兰登堡医学院、德国癌症研究中心、汉堡大学、埃尔朗根纽伦堡大学、芬兰赫尔辛基大学、瑞典厄勒布鲁大学、卡罗林医学院、斯德哥尔摩南方医院、卡罗林大学医院、丹麦哥本哈根大学、英国剑桥大学、南安普敦大学、爱丁堡大学、英国癌症研究中心、伦敦大学癌症研究院、美国洛杉矶加利福尼亚大学、国家癌症研究所、南加利福尼亚大学、夏威夷大学、澳大利亚墨尔本大学、莫纳什大学、波兰波美拉尼亚医科大学、比利时法兰德斯生物技术研究中心、鲁汶大学、意大利米兰大学国家肿瘤研究所的研究报告，比较了曼彻斯特公式、CBCrisk、PredictCBC预测对侧乳腺浸润癌发生风险的性能。
该研究对20项国际研究13万2756例患者（其中对侧乳腺浸润癌4682例）中位8.8年随访数据进行分析。预测性能包括：
区分度：量化为原发乳腺癌诊断后5年和10年对侧乳腺浸润癌逆删失概率权重与时间曲线下面积
校准度：量化为原发乳腺癌诊断后5年和10年对侧乳腺浸润癌预测与实际发生率之比和校准斜率
结果，10年曲线下面积：
CBCrisk：0.58（95%置信区间：0.57～0.59）
曼彻斯特公式：0.60（95%置信区间：0.59～0.61）
PredictCBC-1A（BRCA突变）0.63（95%置信区间：0.59～0.66）
PredictCBC-1B（一般人群）0.59（95%置信区间：0.56～0.62）
10年预测与实际比值：
CBCrisk：0.82（95%置信区间：0.51～1.32）
PredictCBC-1A：1.28（95%置信区间：0.63～2.58）
PredictCBC-1B：1.35（95%置信区间：0.65～2.77）
曼彻斯特公式：1.53（95%置信区间：0.63～3.73）
校准斜率：
CBCrisk：1.26（95%置信区间：1.01～1.50）
PredictCBC-1A：0.90（95%置信区间：0.79～1.02）
PredictCBC-1B：0.81（95%置信区间：0.63～0.99）
曼彻斯特公式：0.39（95%置信区间：0.34～0.43）
因此，该研究结果表明，目前对侧乳腺癌风险预测工具区分度仅中等水平，而曼彻斯特公式的校准度最低，故有必要进一步开发更好的预测工具和重新校准，以提高对侧乳腺癌的预测能力，并确定对侧乳腺癌风险较低和较高的患者进行临床决策。
Breast Cancer Res Treat. 2020 Apr 11. [Epub ahead of print]
Prediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts.
Daniele Giardiello, Michael Hauptmann, Ewout W. Steyerberg, Muriel A. Adank, Delal Akdeniz, Jannet C. Blom, Carl Blomqvist, Stig E. Bojesen, Manjeet K. Bolla, Mariel Brinkhuis, Jenny Chang-Claude, Kamila Czene, Peter Devilee, Alison M. Dunning, Douglas F. Easton, Diana M. Eccles, Peter A. Fasching, Jonine Figueroa, Henrik Flyger, Montserrat García-Closas, Lothar Haeberle, Christopher A. Haiman, Per Hall, Ute Hamann, John L. Hopper, Agnes Jager, Anna Jakubowska, Audrey Jung, Renske Keeman, Linetta B. Koppert, Iris Kramer, Diether Lambrechts, Loic Le Marchand, Annika Lindblom, Jan Lubiński, Mehdi Manoochehri, Luigi Mariani, Heli Nevanlinna, Hester S. A. Oldenburg, Saskia Pelders, Paul D. P. Pharoah, Mitul Shah, Sabine Siesling, Vincent T. H. B. M. Smit, Melissa C. Southey, William J. Tapper, Rob A. E. M. Tollenaar, Alexandra J. van den Broek, Carolien H. M. van Deurzen, Flora E. van Leeuwen, Chantal van Ongeval, Laura J. Van't Veer, Qin Wang, Camilla Wendt, Pieter J. Westenend, Maartje J. Hooning, Marjanka K. Schmidt.
The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands; Leiden University Medical Center, Leiden, The Netherlands; Erasmus MC Cancer Institute, Rotterdam, The Netherlands; Laboratory for Pathology, East-Netherlands, Hengelo, The Netherlands; Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands; Laboratory for Pathology, Dordrecht, The Netherlands; Brandenburg Medical School, Institute of Biostatistics and Registry Research, Neuruppin, Germany; Helsinki University Hospital, University of Helsinki, Helsinki, Finland; Orebro University Hospital, Orebro, Sweden; Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark; University of Copenhagen, Copenhagen, Denmark; University of Cambridge, Cambridge, UK; German Cancer Research Center (DKFZ), Heidelberg, Germany; University Medical Center Hamburg-Eppendorf, Cancer Epidemiology, University Cancer Center Hamburg (UCCH), Hamburg, Germany; Karolinska Institutet, Stockholm, Sweden; University of Southampton, Southampton, UK; University of California At Los Angeles, Los Angeles, CA, USA; Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany; The University of Edinburgh Medical School, Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK; Cancer Research UK Edinburgh Centre, Edinburgh, UK; National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Institute of Cancer Research, London, UK; University of Southern California, Los Angeles, CA, USA; Sodersjukhuset, Stockholm, Sweden; The University of Melbourne, Melbourne, VIC, Australia; Pomeranian Medical University, Szczecin, Poland; VIB Center for Cancer Biology, Leuven, Belgium; University of Leuven, Leuven, Belgium; University of Hawaii Cancer Center, Honolulu, HI, USA; Karolinska University Hospital, Stockholm, Sweden; Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy; Monash University, Clayton, VIC, Australia; University Hospitals Leuven, Leuven, Belgium.
BACKGROUND: Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC).
METHODS: We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope.
RESULTS: The AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57-0.59) for CBCrisk; 0.60 (95% CI 0.59-0.61) for the Manchester formula; 0.63 (95% CI 0.59-0.66) and 0.59 (95% CI 0.56-0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51-1.32) for CBCrisk; 1.53 (95% CI 0.63-3.73) for the Manchester formula; 1.28 (95% CI 0.63-2.58) for PredictCBC-1A and 1.35 (95% CI 0.65-2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01-1.50) for CBCrisk; 0.90 (95% CI 0.79-1.02) for PredictCBC-1A; 0.81 (95% CI 0.63-0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34-0.43) for the Manchester formula.
CONCLUSIONS: Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.
KEYWORDS: Contralateral breast cancer, Risk prediction, Validation, Clinical decision-making
DOI: 10.1007/s10549-020-05611-8
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