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. 2020 Jun;181(2):423-434.
doi: 10.1007/s10549-020-05611-8. Epub 2020 Apr 11.

Prediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts

Daniele Giardiello  1   2 , Michael Hauptmann  3   4 , Ewout W Steyerberg  2   5 , Muriel A Adank  6 , Delal Akdeniz  7 , Jannet C Blom  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 , Linetta B Koppert  36 , Iris Kramer  1 , Diether Lambrechts  37   38 , Loic Le Marchand  39 , Annika Lindblom  40   41 , Jan Lubiński  34 , Mehdi Manoochehri  31 , Luigi Mariani  42 , Heli Nevanlinna  43 , Hester S A Oldenburg  44 , Saskia Pelders  7 , Paul D P Pharoah  13   20 , Mitul Shah  20 , Sabine Siesling  45 , Vincent T H B M Smit  18 , Melissa C Southey  46   47 , William J Tapper  48 , Rob A E M Tollenaar  49 , Alexandra J van den Broek  1 , Carolien H M van Deurzen  50 , Flora E van Leeuwen  51 , Chantal van Ongeval  52 , Laura J Van't Veer  1 , Qin Wang  13 , Camilla Wendt  53 , Pieter J Westenend  54 , Maartje J Hooning  7 , Marjanka K Schmidt  55   56   57
Affiliations

Prediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts

Daniele Giardiello et al. Breast Cancer Res Treat. 2020 Jun.

Abstract

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: Clinical decision-making; Contralateral breast cancer; Risk prediction; Validation.

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Conflict of interest statement

Conflict of interest Author DG, MH, EW, MAA, DA, JCB, CB, SEB, MKB, JCC, KC, PD, AMD, DFE, JF, HF, MGC, LH, CAH, PH, UH, JLH, AJ, AJ2, AJ3, RK, LBK, IK, DL, LLN, AL, JL, MM, LM, HN, HSAO, SP, PDPP, MS, SS, VTHBMS, MCS, WJT, RAEMT, AJvdB, CHMvD, FEvL, CvO, LvV, QW, CW, PJW, MJH declares that he has no conflict of interest. Author DMM declares that she receives a lecture fee from Pierre Fabre and personal fees for consultancy from Astra Zeneca. Author PAF reports grants from Novartis, grants from Biontech, personal fees from Novartis, personal fees from Roche, personal fees from Pfizer, personal fees from Celgene, personal fees from Daiichi-Sankyo, personal fees from TEVA, personal fees from Astra Zeneca, personal fees from Merck Sharp & Dohme, personal fees from Myelo Therapeutics, personal fees from Macrogenics, personal fees from Eisai, personal fees from Puma, grants from Cepheid.

Figures

Fig. 1
Fig. 1
Prediction performance of the CBCrisk model (Chowdhury et al. [7]). The upper and lower panel show the discrimination assessed by a time-dependent Area-Under-the-Curve at 5 and 10 years, respectively. The black squares indicate the estimated accuracy of a model built on all remaining studies or geographic areas. The black horizontal lines indicate the corresponding 95% confidence intervals of the estimated accuracy (interval whiskers). The black diamonds indicate the mean with the corresponding 95% confidence interval of the predictive accuracy
Fig. 2
Fig. 2
Prediction performance of the Manchester formula (Basu et al. [8]) The upper and lower panel show the discrimination assessed by a time-dependent Area-Under-the-Curve at 5 and 10 years, respectively. The black squares for each dataset indicate the estimated accuracy of a model built on all remaining studies or geographic areas. The black horizontal lines indicate the corresponding 95% confidence intervals of the estimated accuracy (interval whiskers). The black diamonds indicate the mean with the corresponding 95% confidence interval of the predictive accuracy
See this image and copyright information in PMC

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