Imaging biomarkers of breast cancers originating from the major lactiferous ducts: Ductal adenocarcinoma of the breast, DAB.

PURPOSE: As we have previously demonstrated, breast cancers originating in the major lactiferous ducts and propagating through the process of neoductgenesis are a distinct subtype of invasive breast cancers, although by current practice they are placed within the group termed ductal carcinoma in situ (DCIS) and are consequently underdiagnosed and undertreated. Imaging biomarkers provide a reliable indication of the site of origin of this breast cancer subtype (Ductal Adenocarcinoma of the breast, DAB) and have excellent concordance with long-term patient outcome. In the present paper, the imaging biomarkers of DAB are described in detail to encourage and facilitate its recognition as a distinct, invasive breast cancer subtype. METHODS: Correlation of breast imaging biomarkers with the corresponding histopathological findings using large format technology, with additional evidence from subgross, thick section histopathology to demonstrate the complex three-dimensional structure of the newly formed duct-like structures, neoducts. RESULTS: There are six imaging biomarkers (mammographic tumour features) of DAB. Four subgroups have characteristic malignant-type calcifications on the mammogram. Two of these are characterized by intraluminal necrosis producing fragmented or dotted casting type calcifications on the mammogram; another two subgroups are characterized by intraductal fluid production which may eventually calcify, producing skipping stone-like or string of pearl-like calcifications. A fifth DAB subgroup presents with bloody or serous nipple discharge and is usually occult on mammography but is detectable with galactography and magnetic resonance imaging (MRI). The sixth subgroup presents as architectural distortion on the mammogram without associated calcifications. CONCLUSIONS: Radiologists can use these well-defined imaging biomarkers to readily detect Ductal Adenocarcinoma of the Breast, DAB. Immunochemical biomarkers are generally not determined from the DAB itself, due to the erroneous assumption that DAB is non-invasive. MRI plays a crucial role in determining disease extent and guiding surgical management. The accumulating evidence that this disease subtype is, in fact, an invasive cancer, necessitates an urgent re-evaluation of the diagnostic and management criteria for this poorly understood malignancy.

Estimating global burden of COVID-19 with disability-adjusted life years and value of statistical life metrics.

BACKGROUND: Global burden of COVID-19 has not been well studied, disability-adjusted life years (DALYs) and value of statistical life (VSL) metrics were therefore proposed to quantify its impacts on health and economic loss globally. METHODS: The life expectancy, cases, and death numbers of COVID-19 until 30th April 2021 were retrieved from open data to derive the epidemiological profiles and DALYs (including years of life lost (YLL) and years loss due to disability (YLD)) by four periods. The VSL estimates were estimated by using hedonic wage method (HWM) and contingent valuation method (CVM). The estimate of willingness to pay using CVM was based on the meta-regression mixed model. Machine learning method was used for classification. RESULTS: Globally, DALYs (in thousands) due to COVID-19 was tallied as 31,930 from Period I to IV. YLL dominated over YLD. The estimates of VSL were US$591 billion and US$5135 billion based on HWM and CVM, respectively. The estimate of VSL increased from US$579 billion in Period I to US$2160 billion in Period IV using CVM. The higher the human development index (HDI), the higher the value of DALYs and VSL. However, there exits the disparity even at the same level of HDI. Machine learning analysis categorized eight patterns of global burden of COVID-19 with a large variation from US$0.001 billion to US$691.4 billion. CONCLUSION: Global burden of COVID-19 pandemic resulted in substantial health and value of life loss particularly in developed economies. Classifications of such health and economic loss is informative to early preparation of adequate resource to reduce impacts.