Accurate risk assessment is essential for the success of population screening programs in breast cancer. Models with high sensitivity and specificity would enable programs to target more elaborate ...screening efforts to high-risk populations, while minimizing overtreatment for the rest. Artificial intelligence (AI)-based risk models have demonstrated a significant advance over risk models used today in clinical practice. However, the responsible deployment of novel AI requires careful validation across diverse populations. To this end, we validate our AI-based model, Mirai, across globally diverse screening populations.
We collected screening mammograms and pathology-confirmed breast cancer outcomes from Massachusetts General Hospital, USA; Novant, USA; Emory, USA; Maccabi-Assuta, Israel; Karolinska, Sweden; Chang Gung Memorial Hospital, Taiwan; and Barretos, Brazil. We evaluated Uno's concordance index for Mirai in predicting risk of breast cancer at one to five years from the mammogram.
A total of 128,793 mammograms from 62,185 patients were collected across the seven sites, of which 3,815 were followed by a cancer diagnosis within 5 years. Mirai obtained concordance indices of 0.75 (95% CI, 0.72 to 0.78), 0.75 (95% CI, 0.70 to 0.80), 0.77 (95% CI, 0.75 to 0.79), 0.77 (95% CI, 0.73 to 0.81), 0.81 (95% CI, 0.79 to 0.82), 0.79 (95% CI, 0.76 to 0.83), and 0.84 (95% CI, 0.81 to 0.88) at Massachusetts General Hospital, Novant, Emory, Maccabi-Assuta, Karolinska, Chang Gung Memorial Hospital, and Barretos, respectively.
Mirai, a mammography-based risk model, maintained its accuracy across globally diverse test sets from seven hospitals across five countries. This is the broadest validation to date of an AI-based breast cancer model and suggests that the technology can offer broad and equitable improvements in care.
Purpose
To investigate the use of an affine‐variance noise model, with correlated quantum noise and spatially dependent quantum gain, for the simulation of noise in virtual clinical trials (VCT) of ...digital breast tomosynthesis (DBT).
Methods
Two distinct technologies were considered: an amorphous‐selenium (a‐Se) detector with direct conversion and a thallium‐doped cesium iodide (CsI(Tl)) detector with indirect conversion. A VCT framework was used to generate noise‐free projections of a uniform three‐dimensional simulated phantom, whose geometry and absorption match those of a polymethyl methacrylate (PMMA) uniform physical phantom. The noise model was then used to generate noisy observations from the simulated noise‐free data, while two clinically available DBT units were used to acquire projections of the PMMA physical phantom. Real and simulated projections were then compared using the signal‐to‐noise ratio (SNR) and normalized noise power spectrum (NNPS).
Results
Simulated images reported errors smaller than 4.4% and 7.0% in terms of SNR and NNPS, respectively. These errors are within the expected variation between two clinical units of the same model. The errors increase to 65.8% if uncorrelated models are adopted for the simulation of systems featuring indirect detection. The assumption of spatially independent quantum gain generates errors of 11.2%.
Conclusions
The investigated noise model can be used to accurately reproduce the noise found in clinical DBT. The assumption of uncorrelated noise may be adopted if the system features a direct detector with minimal pixel crosstalk.
BackgroundAryl hydrocarbon receptor interacting protein (AIP) mutations (AIPmut) cause aggressive pituitary adenomas in young patients, usually in the setting of familial isolated pituitary adenomas. ...The prevalence of AIPmut among sporadic pituitary adenoma patients appears to be low; studies have not addressed prevalence in the most clinically relevant population. Hence, we undertook an international, multicenter, prospective genetic, and clinical analysis at 21 tertiary referral endocrine departments.MethodsWe included 163 sporadic pituitary macroadenoma patients irrespective of clinical phenotype diagnosed at <30 years of age.ResultsOverall, 19/163 (11.7%) patients had germline AIPmut; a further nine patients had sequence changes of uncertain significance or polymorphisms. AIPmut were identified in 8/39 (20.5%) pediatric patients. Ten AIPmut were identified in 11/83 (13.3%) sporadic somatotropinoma patients, in 7/61 (11.5%) prolactinoma patients, and in 1/16 non-functioning pituitary adenoma patients. Large genetic deletions were not seen using multiplex ligation-dependent probe amplification. Familial screening was possible in the relatives of seven patients with AIPmut and carriers were found in six of the seven families. In total, pituitary adenomas were diagnosed in 2/21 AIPmut-screened carriers; both had asymptomatic microadenomas.ConclusionGermline AIPmut occur in 11.7% of patients <30 years with sporadic pituitary macroadenomas and in 20.5% of pediatric patients. AIPmut mutation testing in this population should be considered in order to optimize clinical genetic investigation and management.
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