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Hospital Healthcare Europe
Hospital Healthcare Europe

Press Releases

Take a look at a selection of our recent media coverage:

Screening older black patients identifies elevated incidence of precursor condition for multiple myeloma

16th December 2021

Screening older black patients or those with relatives who have blood cancer identified an elevated precursor incidence for multiple myeloma

The screening of older black patients or those with a first-degree relative who has a haematological cancer led to the detection of monoclonal gammopathy of undetermined significance (MGUS), which is a precursor to multiple myeloma (MM). This was the conclusion of a study by a researchers from the Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, US, presented at ASH 2021.

MGUS a benign condition which is usually diagnosed incidentally when tests are performed to investigate other problems although MGUS is a precursor to multiple myeloma in around 1% of cases. However, the prevalence of MGUS has not been described in a population at high risk of developing MM, in particular, Black/African American (AA) individuals or first-degree relatives of patients with haematologic malignancies (HM).

In 2019, the US researchers launched the first nationwide US (PROMISE) screening older black patients study for individuals at high risk of MM to help better identify what population would benefit most from screening and early intervention for precursor MM stages. The overarching aim of the study is to assess the prevalence of MGUS in a high risk population and to characterise the clinical variables of individuals who screen positive. For the present study, the researchers reported on screening data available for the first 2960 participants.

The researcher team recruited individuals aged 40 or older with an additional MM risk factor which included Black/AAs and those with a first-degree relative diagnosed with a haematologic malignancy or a precursor condition to MM. Blood from all participants was analysed to measure the serum free light chains (sFLC), IgG, IgA and IgM. In addition and for comparative purposes, the team also identified and screened additional individuals from the Mass General Brigham (MGB) Biobank who met the PROMISE enrolment criteria. The researchers measured Heavy-Chain MGUS (HC-MGUS) as a marker for MGUS.


Screening older black patients occurred with 2960 individuals participants (1092 from PROMISE). The overall prevalence of HC-MGUS was 9.6% (95% CI 8.6 – 11%) and 10% (95% CI 8.3-12%) in PROMISE and 9.4% (95% CI 8.1 – 11%) in the MGB cohort.

The prevalence of HC-MGUS increased with age in high-risk individuals from 4.9% (CI 3.3 – 6.9%) for participants aged 40-49 to 13% (95% CI 10 – 17%) in the 70-79 range (P < 0.005 ). Among monoclonal HC-MGUS, they detected 65% IgG, 18% IgM, and 18% IgA. M-spike was quantified in 97% of samples.

The authors concluded that screening older black patients or those who have a first-degree relative with an HM have a high prevalence MGUS and may therefore benefit from precision screening approaches to allow for early detection and clinical intervention.


El-Khoury H et al. High Prevalence of Monoclonal Gammopathy in a Population at Risk: The First Results of the Promise Study. ASH Conference 2021

Artificial intelligence appears to be less accurate than radiologists in breast cancer screening

23rd September 2021

Breast cancer screening using artificial intelligence systems has been found, in the majority of cases, to be less accurate than a radiologist.

Globally, in 2020, there were an estimated 2.3 million women diagnosed with breast cancer leading to 685,000 deaths. Fortunately, improvements in survival over recent decades have been attributed to population-based breast cancer screening with mammography. In fact, a recent UK study suggested that screening reduces cancer mortality by 38% among women screened at least once.

The use of artificial intelligence (AI) systems for image recognition in breast cancer screening could lead to improvements in the detection of cases, either as a standalone system or as an aid to radiologists. Indeed, there is some evidence to support the value of AI with one retrospective analysis of an AI screening algorithm concluding that it showed better diagnostic performance than a radiologist. Nevertheless, in a 2019 review, it was concluded that while AI systems have good accuracy for breast cancer detection, methodological concerns and evidence gaps exist that limit translation into clinical breast cancer screening settings.

In light of these concerns, a team from the Division of Health Sciences, University of Warwick, UK, were commissioned by the UK National Screening Committee to undertake a systematic review to determine whether there was sufficient evidence to support the introduction of AI for mammographic image analysis in breast screening. They conducted literature searches up to May 2021 and included studies that reported the test accuracy of AI algorithms either alone or in combination with radiologists, to detect breast cancer in digital mammograms in screening practice or in test sets. The team included cancer confirmed by histological analysis of biopsy samples in cases where women were referred for further tests after screening as the reference standard or from symptomatic presentation during follow-up.

The review identified a total of 12 studies including 131,822 women undergoing breast cancer screening. In studies with a standalone AI system, the algorithm calculated a cancer risk score, categorising women at either high (recall) or low (no recall) risk. When used to assist the radiologist, the AI system simply provided a level of suspicion. In two large retrospective studies including 76,813 women, that compared the AI system with the clinical decisions of a radiologist, 96% of systems were less accurate than a single radiologist and all were less accurate than a double read.
Overall, the authors reported considerably heterogeneity in study methodology, some of which resulted in high concerns over the risk of bias and applicability. In their study, they commented that “evidence is insufficient on the accuracy or clinical effect of introducing AI to examine mammograms anywhere on the screening pathway”.

In the conclusion, the authors noted how AI systems for breast cancer screening are a long way from having the quality and quantity required for implementation into clinical practice.

Freeman K et al. Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy. BMJ 2021