Meta analysis of breast cancer imaging in pregnant and lactating patients

Author

Lu Mao

Statistical analysis

As descriptive analysis, sensitivity and/or specificity of each primary study, along with 95% confidence intervals (CI), were calculated, provided the study has corresponding data. For studies with both types of data, the diagnostic odds ratio (DOR) was also calculated. Meta analysis was performed on studies with complete data using the recommended bivariate modeling approach (Reitsma et al. 2005), with the R-package mada (Sousa-Pinto 2022). Summary receiver operating characteristic (SROC) analysis was used to combine these studies to generate a summary area under the curve (AUC). All analyses were performed in R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria).

Note: another approach is to use multiple imputations to fill in the missing data (mainly for specificity).

Results on available data

The numbers of primary studies with sensitivity or specificity data are tabulated below. For ultrasound, mammography, and MRI, meta analysis will be based on the 7, 3, and 2 studies with complete data, respectively.

Overview

Table 0. Number of studies with sensitivity (N_sens) or specificity (N_spec) data.
Modality N_sens N_spec
Ultrasound 22 7
Mammography 18 3
MRI 7 2

Ultrasound

Characteristics of all primary studies on ultrasound are summarized below.

Table 1. Summary of primary studies for ultrasound (after continuity correction).
Study Sensitivity (95% CI) Specificity (95% CI) DOR (95% CI)
Ahn 0.975 (0.8, 0.997)
Bock 0.5 (0.246, 0.754) 0.875 (0.669, 0.96) 7 (1.2, 41.3)
Chung 0.917 (0.517, 0.991) 0.702 (0.628, 0.767) 26 (1.4, 478.8)
Cordoba 0.935 (0.76, 0.985)
Espinosa 0.917 (0.517, 0.991)
Haliloglu 0.875 (0.396, 0.987) 0.98 (0.918, 0.995) 343 (11.7, 10028.5)
Jafari 0.984 (0.867, 0.998)
Langer 0.767 (0.659, 0.848)
Liberman 0.929 (0.561, 0.992)
Myers 0.985 (0.874, 0.998)
Nishanova 0.855 (0.692, 0.939)
Obenauer 0.5 (0.17, 0.83) 0.979 (0.828, 0.998) 47 (1.7, 1280)
Oh 0.95 (0.655, 0.995)
Qian 0.971 (0.884, 0.993) 0.593 (0.515, 0.667) 49 (9.4, 256.6)
Reyes 0.956 (0.829, 0.99)
Robbins 0.9 (0.463, 0.989) 0.853 (0.778, 0.905) 52.2 (2.7, 1012.9)
Son 0.929 (0.561, 0.992) 0.783 (0.609, 0.894) 47 (2.3, 948.5)
Taron 0.975 (0.8, 0.997)
Taskin 0.99 (0.908, 0.999)
Taylor 0.977 (0.815, 0.998)
Wang 0.857 (0.786, 0.907)
Yang 0.977 (0.815, 0.998)

Figure 1. Forest plots of study-specific sensitivities and specificities for ultrasound.

Summary ROC analysis combining the 7 primary studies with complete data is presented below.

Figure 2. Confidence regions (left) and summary ROC curve (right) for the 7 studies on ultrasound with complete data.

Mammography

Characteristics of all primary studies on mammography are summarized below.

Table 2. Summary of primary studies for mammography (after continuity correction).
Study Sensitivity Specificity DOR
Ahn 0.844 (0.604, 0.95)
Bock 0.773 (0.478, 0.927) 0.962 (0.717, 0.996) 85 (3.6, 2001.3)
Cordoba 0.66 (0.464, 0.813)
Espinosa 0.917 (0.517, 0.991)
Langer 0.806 (0.712, 0.874)
Liberman 0.771 (0.573, 0.894)
Myers 0.897 (0.752, 0.962)
Nishanova 0.79 (0.619, 0.897)
Obenauer 0.5 (0.17, 0.83) 0.967 (0.747, 0.997) 29 (1, 802)
Oh 0.9 (0.463, 0.989)
Reyes 0.761 (0.618, 0.863)
Robbins 0.9 (0.463, 0.989) 0.921 (0.842, 0.962) 104.5 (5.1, 2162.8)
Samuels 0.611 (0.309, 0.847)
Taron 0.625 (0.409, 0.8)
Taskin 0.865 (0.741, 0.935)
Taylor 0.725 (0.506, 0.872)
Wang 0.827 (0.698, 0.908)
Yang 0.881 (0.682, 0.962)

Figure 3. Forest plots of study-specific sensitivities and specificities for mammography.

Summary ROC analysis combining the 3 primary studies with complete data is presented below.

Figure 4. Confidence regions (left) and summary ROC curve (right) for the 3 studies on mammography with complete data.

MRI

Characteristics of all primary studies on MRI are summarized below.

Table 3. Summary of primary studies for MRI (after continuity correction).
Study Sensitivity Specificity DOR
Espinosa 0.917 (0.517, 0.991) 0.833 (0.31, 0.982) 55 (0.8, 3650.7)
Myers 0.972 (0.888, 0.994)
Obenauer 0.9 (0.463, 0.989) 0.917 (0.517, 0.991) 99 (1.6, 6052.7)
Oh 0.95 (0.655, 0.995)
Taron 0.975 (0.8, 0.997)
Taskin 0.974 (0.791, 0.997)
Taylor 0.929 (0.561, 0.992)

Figure 5. Forest plots of study-specific sensitivities and specificities for MRI.

Summary ROC analysis combining the 2 primary studies with complete data is presented below.

Figure 6. Confidence regions (left) and summary ROC curve (right; too few data points) for the 2 studies on MRI with complete data.

Summary and publication bias

Overall sensitivity and specificity
Table 4. Overall sensitivity and specificity across studies.
Modality TP FN FP TN Sensitivity Specificity
Ultrasound 532 49 137 443 0.916 0.764
Mammography 359 86 6 101 0.807 0.944
MRI 113 1 0 7 0.991 1
Funnel plot and publication bias

For each modality, the following shows the funnel plot for study-specific log sensitivity/specificity odds centered by the pooled estimates in Table 4 (hence log-odds ratio) vs their standard errors. The Egger et al. (1997) test for asymmetry suggests a potential bias for larger sensitivities/specificities in ultrasound (skewed to the right; P = 0.001). There is no visible bias in mammography (P = 0.376) or MR (P = 0.152).

Other summaries combining sensitivity and specificity
  • Positive likelihood ratio (LR+): probability of a case testing positive over that of a non-case testing positive, i.e., sensitivity/(1-specificity);
  • Negative likelihood ratio (LR-): probability of a case testing negative over that of a non-case testing negative, i.e., (1-sensitivity)/specificity;
  • Diagnostic odds ratio (DOR = LR+/LR-).

These are tabulated below.

Table 6. Summary performance metrics by meta analysis.
Modality N LR+ LR- 1/LR- DOR
Ultrasound 7 5.14 (3.09, 8.87) 0.22 (0.08, 0.5) 4.49 (2.02, 12.4) 23.8 (9.48, 59.9)
Mammography 3 9.83 (4.62, 20.9) 0.31 (0.13, 0.57) 3.25 (1.75, 7.44) 32.8 (9.27, 117)
MRI 2 7.41 (1.61, 54.7) 0.11 (0.01, 0.56) 9.12 (1.79, 67.2) 74.9 (4, 1410)

Results of imputation analysis

To be filled.

References

Egger, M., G. D. Smith, M. Schneider, and C. Minder. 1997. “Bias in Meta-Analysis Detected by a Simple, Graphical Test.” BMJ 315 (7109): 629–34. https://doi.org/10.1136/bmj.315.7109.629.
Reitsma, Johannes B., Afina S. Glas, Anne W. S. Rutjes, Rob J. P. M. Scholten, Patrick M. Bossuyt, and Aeilko H. Zwinderman. 2005. “Bivariate Analysis of Sensitivity and Specificity Produces Informative Summary Measures in Diagnostic Reviews.” Journal of Clinical Epidemiology 58 (10): 982–90. https://doi.org/10.1016/j.jclinepi.2005.02.022.
Sousa-Pinto, Philipp Doebler with contributions from Bernardo. 2022. “Mada: Meta-Analysis of Diagnostic Accuracy.” https://CRAN.R-project.org/package=mada.