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Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis.

ABSTRACT: BACKGROUND:The Gail model has been widely used and validated with conflicting results. The current study aims to evaluate the performance of different versions of the Gail model by means of systematic review and meta-analysis with trial sequential analysis (TSA). METHODS:Three systematic review and meta-analyses were conducted. Pooled expected-to-observed (E/O) ratio and pooled area under the curve (AUC) were calculated using the DerSimonian and Laird random-effects model. Pooled sensitivity, specificity and diagnostic odds ratio were evaluated by bivariate mixed-effects model. TSA was also conducted to determine whether the evidence was sufficient and conclusive. RESULTS:Gail model 1 accurately predicted breast cancer risk in American women (pooled E/O = 1.03; 95% CI 0.76-1.40). The pooled E/O ratios of Caucasian-American Gail model 2 in American, European and Asian women were 0.98 (95% CI 0.91-1.06), 1.07 (95% CI 0.66-1.74) and 2.29 (95% CI 1.95-2.68), respectively. Additionally, Asian-American Gail model 2 overestimated the risk for Asian women about two times (pooled E/O = 1.82; 95% CI 1.31-2.51). TSA showed that evidence in Asian women was sufficient; nonetheless, the results in American and European women need further verification. The pooled AUCs for Gail model 1 in American and European women and Asian females were 0.55 (95% CI 0.53-0.56) and 0.75 (95% CI 0.63-0.88), respectively, and the pooled AUCs of Caucasian-American Gail model 2 for American, Asian and European females were 0.61 (95% CI 0.59-0.63), 0.55 (95% CI 0.52-0.58) and 0.58 (95% CI 0.55-0.62), respectively. The pooled sensitivity, specificity and diagnostic odds ratio of Gail model 1 were 0.63 (95% CI 0.27-0.89), 0.91 (95% CI 0.87-0.94) and 17.38 (95% CI 2.66-113.70), respectively, and the corresponding indexes of Gail model 2 were 0.35 (95% CI 0.17-0.59), 0.86 (95% CI 0.76-0.92) and 3.38 (95% CI 1.40-8.17), respectively. CONCLUSIONS:The Gail model was more accurate in predicting the incidence of breast cancer in American and European females, while far less useful for individual-level risk prediction. Moreover, the Gail model may overestimate the risk in Asian women and the results were further validated by TSA, which is an addition to the three previous systematic review and meta-analyses. TRIAL REGISTRATION:PROSPERO CRD42016047215 .

SUBMITTER: Wang X

PROVIDER: S-EPMC5850919 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:There is a growing interest in delivering more personalised, risk-based breast cancer screening protocols. This requires population-level validation of practical models that can stratify women into breast cancer risk groups. Few studies have evaluated the Gail model (NCI Breast Cancer Risk Assessment Tool) in a population screening setting; we validated this tool in a large, screened population. METHODS:We used data from 40,158 women aged 50-69?years (via the lifepool cohort) participating in Australia's BreastScreen programme. We investigated the association between Gail scores and future invasive breast cancer, comparing observed and expected outcomes by Gail score ranked groups. We also used machine learning to rank Gail model input variables by importance and then assessed the incremental benefit in risk prediction obtained by adding variables in order of diminishing importance. RESULTS:Over a median of 4.3?years, the Gail model predicted 612 invasive breast cancers compared with 564 observed cancers (expected/observed (E/O)?=?1.09, 95% confidence interval (CI) 1.00-1.18). There was good agreement across decile groups of Gail scores (?2?=?7.1, p?=?0.6) although there was some overestimation of cancer risk in the top decile of our study group (E/O?=?1.65, 95% CI 1.33-2.07). Women in the highest quintile (Q5) of Gail scores had a 2.28-fold increased risk of breast cancer (95% CI 1.73-3.02, p?<?0.0001) compared with the lowest quintile (Q1). Compared with the median quintile, women in Q5 had a 34% increased risk (95% CI 1.06-1.70, p?=?0.014) and those in Q1 had a 41% reduced risk (95% CI 0.44-0.79, p?<?0.0001). Similar patterns were observed separately for women aged 50-59 and 60-69?years. The model's overall discrimination was modest (area under the curve (AUC) 0.59, 95% CI 0.56-0.61). A reduced Gail model excluding information on ethnicity and hyperplasia was comparable to the full Gail model in terms of correctly stratifying women into risk groups. CONCLUSIONS:This study confirms that the Gail model (or a reduced model excluding information on hyperplasia and ethnicity) can effectively stratify a screened population aged 50-69 years according to the risk of future invasive breast cancer. This information has the potential to enable more personalised, risk-based screening strategies that aim to improve the balance of the benefits and harms of screening.

Project description:The Gail model for predicting the absolute risk of invasive breast cancer has been validated extensively in US populations, but its performance in the international setting remains uncertain. We evaluated the predictive accuracy of the Gail model in 54,649 Spanish women aged 45-68 years who were free of breast cancer at the 1996-1998 baseline mammographic examination in the population-based Navarre Breast Cancer Screening Program. Incident cases of invasive breast cancer and competing deaths were ascertained until the end of 2005 (average follow-up of 7.7 years) through linkage with population-based cancer and mortality registries. The Gail model was tested for calibration and discrimination in its original form and after recalibration to the lower breast cancer incidence and risk factor prevalence in the study cohort, and compared through cross-validation with a Navarre model fully developed from this cohort. The original Gail model overpredicted significantly the 835 cases of invasive breast cancer observed in the cohort (ratio of expected to observed cases 1.46, 95 % CI 1.36-1.56). The recalibrated Gail model was well calibrated overall (expected-to-observed ratio 1.00, 95 % CI 0.94-1.07), but it tended to underestimate risk for women in low-risk quintiles and to overestimate risk in high-risk quintiles (P = 0.01). The Navarre model showed good cross-validated calibration overall (expected-to-observed ratio 0.98, 95 % CI 0.92-1.05) and in different cohort subsets. The Navarre and Gail models had modest cross-validated discrimination indexes of 0.542 (95 % CI 0.521-0.564) and 0.544 (95 % CI 0.523-0.565), respectively. Although the original Gail model cannot be applied directly to populations with different underlying rates of invasive breast cancer, it can readily be recalibrated to provide unbiased estimates of absolute risk in such populations. Nevertheless, its limited discrimination ability at the individual level highlights the need to develop extended models with additional strong risk factors.

Project description:Despite the availability of various clinical trials that used different diagnostic methods to identify diabetic sensorimotor polyneuropathy (DSPN), no reliable studies that prove the associations among diagnostic parameters from two different methods are available. Statistically significant diagnostic parameters from various methods can help determine if two different methods can be incorporated together for diagnosing DSPN. In this study, a systematic review, meta-analysis, and trial sequential analysis (TSA) were performed to determine the associations among the different parameters from the most commonly used electrophysiological screening methods in clinical research for DSPN, namely, nerve conduction study (NCS), corneal confocal microscopy (CCM), and electromyography (EMG), for different experimental groups. Electronic databases (e.g., Web of Science, PubMed, and Google Scholar) were searched systematically for articles reporting different screening tools for diabetic peripheral neuropathy. A total of 22 studies involving 2394 participants (801 patients with DSPN, 702 controls, and 891 non-DSPN patients) were reviewed systematically. Meta-analysis was performed to determine statistical significance of difference among four NCS parameters, i.e., peroneal motor nerve conduction velocity, peroneal motor nerve amplitude, sural sensory nerve conduction velocity, and sural sensory nerve amplitude (all p < 0.001); among three CCM parameters, including nerve fiber density, nerve branch density, and nerve fiber length (all p < 0.001); and among four EMG parameters, namely, time to peak occurrence (from 0 to 100% of the stance phase) of four lower limb muscles, including the vastus lateralis (p < 0.001), tibialis anterior (p = 0.63), lateral gastrocnemius (p = 0.01), and gastrocnemius medialis (p = 0.004), and the vibration perception threshold (p < 0.001). Moreover, TSA was conducted to estimate the robustness of the meta-analysis. Most of the parameters showed statistical significance between each other, whereas some were statistically nonsignificant. This meta-analysis and TSA concluded that studies including NCS and CCM parameters were conclusive and robust. However, the included studies on EMG were inconclusive, and additional clinical trials are required.

Project description:BackgroundTo investigate the performance of primary ultrasound (P-US) screening for breast cancer, and that of supplemental ultrasound (S-US) screening for breast cancer after negative mammography (MAM).MethodsElectronic databases (PubMed, Scopus, Web of Science, and Embase) were systematically searched to identify relevant studies published between January 2003 and May 2018. Only high-quality or fair-quality studies reporting any of the following performance values for P-US or S-US screening were included: sensitivity, specificity, cancer detected rate (CDR), recall rate (RR), biopsy rate (BR), proportion of invasive cancers among screening-detected cancers (ProIC), and proportion of node-negative cancers among screening-detected invasive cancers (ProNNIC).ResultsTwenty-three studies were included, including 12 studies in which S-US screening was used after negative MAM and 11 joint screening studies in which both primary MAM (P-MAM) and P-US were used. Meta-analyses revealed that S-US screening could detect 96% [95% confidential intervals (CIs): 82 to 99%] of occult breast cancers missed by MAM and identify 93% (95% CIs: 89 to 96%) of healthy women, with a CDR of 3.0/1000 (95% CIs: 1.8/1000 to 4.6/1000), RR of 8.8% (95% CIs: 5.0 to 13.4%), BR of 3.9% (95% CIs: 2.7 to 5.4%), ProIC of 73.9% (95% CIs: 49.0 to 93.7%), and ProNNIC of 70.9% (95% CIs: 46.0 to 91.6%). Compared with P-MAM screening, P-US screening led to the recall of significantly more women with positive screening results [1.5% (95% CIs:0.6 to 2.3%), P = 0.001] and detected significantly more invasive cancers [16.3% (95% CIs: 10.6 to 22.1%), P < 0.001]. However, there were no significant differences for other performance measures between the two screening methods, including sensitivity, specificity, CDR, BR, and ProNNIC.ConclusionsCurrent evidence suggests that S-US screening could detect occult breast cancers missed by MAM. P-US screening has shown to be comparable to P-MAM screening in women with dense breasts in terms of sensitivity, specificity, cancer detection rate, and biopsy rate, but with higher recall rates and higher detection rates for invasive cancers.

Project description:PurposeWith the in-depth application of machine learning(ML) in clinical practice, it has been used to predict the mortality risk in patients with traumatic brain injuries(TBI). However, there are disputes over its predictive accuracy. Therefore, we implemented this systematic review and meta-analysis, to explore the predictive value of ML for TBI.MethodologyWe systematically retrieved literature published in PubMed, Embase.com, Cochrane, and Web of Science as of November 27, 2022. The prediction model risk of bias(ROB) assessment tool (PROBAST) was used to assess the ROB of models and the applicability of reviewed questions. The random-effects model was adopted for the meta-analysis of the C-index and accuracy of ML models, and a bivariate mixed-effects model for the meta-analysis of the sensitivity and specificity.ResultA total of 47 papers were eligible, including 156 model, with 122 newly developed ML models and 34 clinically recommended mature tools. There were 98 ML models predicting the in-hospital mortality in patients with TBI; the pooled C-index, sensitivity, and specificity were 0.86 (95% CI: 0.84, 0.87), 0.79 (95% CI: 0.75, 0.82), and 0.89 (95% CI: 0.86, 0.92), respectively. There were 24 ML models predicting the out-of-hospital mortality; the pooled C-index, sensitivity, and specificity were 0.83 (95% CI: 0.81, 0.85), 0.74 (95% CI: 0.67, 0.81), and 0.75 (95% CI: 0.66, 0.82), respectively. According to multivariate analysis, GCS score, age, CT classification, pupil size/light reflex, glucose, and systolic blood pressure (SBP) exerted the greatest impact on the model performance.ConclusionAccording to the systematic review and meta-analysis, ML models are relatively accurate in predicting the mortality of TBI. A single model often outperforms traditional scoring tools, but the pooled accuracy of models is close to that of traditional scoring tools. The key factors related to model performance include the accepted clinical variables of TBI and the use of CT imaging.

Project description:IntroductionGlucocorticosteroids (steroids) are widely used for sepsis patients. However, the potential benefits and harms of both high and low dose steroids remain unclear. A systematic review of randomised clinical trials with meta-analysis and trial sequential analysis (TSA) might shed light on this clinically important question.MethodsA systematic review was conducted according to a published protocol and The Cochrane Handbook methodology including meta-analyses, TSA of randomised clinical trials, and external validity estimation (GRADE). Randomised clinical trials evaluating steroids were included for sepsis patients (systemic inflammatory response syndrome, sepsis, severe sepsis or septic shock) aged >18 years. Cochrane Central Register of Controlled Trials (CENTRAL), PubMed/Medline, Embase, Web of Science and Cinahl were searched until 18 February 2015. No language restrictions were applied. Primary outcomes were mortality at longest follow-up and serious adverse events.ResultsA total of 35 trials randomising 4682 patients were assessed and reviewed in full text. All trials but two had high risk of bias. No statistically significant effect was found for any dose of steroids versus placebo or no intervention on mortality at maximal follow-up [relative risk (RR) 0.89; TSA adjusted confidence interval (CI) 0.74-1.08]. Two trials with low risk of bias also showed no statistically significant difference (random-effects model RR 0.38, 95% CI 0.06-2.42). Similar results were obtained in subgroups of trials stratified according to high (>500 mg) or low (? 500 mg) dose hydrocortisone (or equivalent) (RR 0.87; TSA-adjusted CI 0.38-1.99; and RR 0.90; TSA-adjusted CI 0.49-1.67, respectively). There were also no statistically significant effects on serious adverse events other than mortality (RR 1.02; TSA-adjusted CI 0.7-1.48). The effects did not vary according to the degree of sepsis. TSA showed that many more randomised patients are needed before definitive conclusions may be drawn.ConclusionEvidence to support or negate the use of steroids in any dose in sepsis patients is lacking. The results of ongoing and future well-designed, large randomised clinical trials are needed.

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Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis.

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