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Hospital Healthcare Europe
Hospital Healthcare Europe

Press Releases

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

Electronic noses show high diagnostic accuracy for cancer detection

4th July 2022

A review suggests that electronic noses which analyse exhaled breath have a high sensitivity and specificity for the detection of cancer

Electronic noses (e-noses) have been found to have a high level of diagnostic accuracy for the detection of cancer in exhaled breath according to a systematic review and meta-analysis by a group of Dutch researchers.

Volatile organic compounds (VOC) represent the end products of human metabolism and which can be excreted in breath, urine, and faeces. As a result, VOC can be very useful as markers of diseases and given that sampling is both non-invasive and painless, it is ideal for both clinicians and patients. Exhaled breath samples can be analysed using a combination of gas-chromatography and mass spectrometry (GS-MS) and this approach has been used to identify a specific profile of organic compounds that can be indicative of a malignancy. For example, one study has suggested that there is a specific profile of markers for ovarian cancer, whereas another revealed how VOCs exhibited significant differences between nodular goitre patients and normal controls, papillary thyroid carcinoma patients and normal controls. While GC-MS systems are expensive, enoses rely on the binding of the organic volatile components to sensors within the device and which generates a measurable electrical response. However, none of the electronic noses are currently used in clinical practice but could be of value although there has not been a systematic evaluation of the utility of these devices.

Consequently, the Dutch team sought to examine the diagnostic performance of currently available enoses for the diagnosis of cancer in exhaled breath samples and assessed the devices in terms of the sensitivity and specificity derived from an analysis of the receiver operating characteristic curve.

Electronic noses and detection of cancer

A total of 52 publications that included 3,677 patients with cancer were included in the final analysis, all of which were feasibility studies. The number of patients in studies ranged from 10 to 351 and the types of cancer identified included lung, head, neck, gastric, breast, colorectal and prostate. In most studies, patients with cancer were compared with controls, normally healthy volunteers and histopathological analysis was used for diagnostic confirmation.

The pooled sensitivity for all 52 studies was 90% (95% CI 88 – 92%) and the pooled specificity was 87% (95% CI 81 – 92%). However, there was a high degree of heterogeneity for both measures.

The authors noted that many studies did not report on the effect of various endogenous or exogenous factors that could affect the breath profile such as smoking and co-morbidities. They concluded that while electronic noses appeared to be reasonably accurate for the detection of cancer, there was a need for adequately powered studies to establish the value of the technology as part of a patient’s diagnostic work-up.

Scheepers MHMC et al. Diagnostic Performance of Electronic Noses in Cancer Diagnoses Using Exhaled Breath: A Systematic Review and Meta-analysis JAMA Netw Open 2022

Breast cancer scans detected tumours in 40% of women with no risk factors

10th February 2022

Breast cancer scans in women with no recognised risk factors identified tumours in 40% of cases highlighting a need to scan all eligible women

Breast cancer scans for women with no recognisable risk factors for the disease has identified that around 40% of these women have a tumour, emphasising the need to continue scanning eligible women. This was the conclusion of a study by researchers from the Sydney School of Public Health, University of Sydney, Australia.

A 2015 systematic review of the benefits and harms of breast cancer screening concluded that for women of all ages and at average risk, screening was associated with a reduction in breast cancer mortality of approximately 20%. However, risk-stratification of breast cancer screening might improve the cost-effectiveness of the whole scanning process and at the same time, potentially reduce any associated risks. In fact, a 2018 study found that not offering breast cancer screening to women at lower risk could improve the cost-effectiveness of the screening programme, through reducing over diagnosis and at the same time, maintaining the benefits of screening. Nevertheless, little is known about the screening outcomes for women without any known breast cancer risk factors and who are therefore assumed to be at a lower risk.

For the present study, the Australian team examined the breast cancer scan outcomes for women deemed to be at the lower end of the risk spectrum. They undertook a retrospective analysis of clinical data routinely collected in the BreastScreen Western Australia (BSWA) program and included women aged 40 years and older. Although the program does stratify women in terms of their risk, i.e., those with > 2 affected first-degree relatives etc), women age 40 and over can volunteer to participate, despite not being in the target group. The researchers collected variables such as age, need for repeat scans together with information such as a previous history of breast or ovarian cancer, use of hormone replacement therapy etc. The scans were then categorised as having none of these factors verses at least one factor and age bands of 40 -49, 50 -59, 60 – 69 and > 70 years were created. The outcomes of interest were cancer detection rates at screening (CDR) per 10,000 screens and the interval cancer rates (ICR) per 10,000 women-years.

Breast cancer scans and detection of tumours

A total of 1,026,137 screens were performed including 323,082 in women aged > 40 years, who had a mean age of 58.5 years. Among the total scans, 44.7% of women had at least one risk factor although for 55.3% of screens, the women had none of the recorded risk factors.

In the screens without any risk factors, the CDR was 50 (95% CI 48 – 52) per 10,000 screens and the ICR was 7.9 (95% CI 7.4 – 8.4). Overall, in all of the scans in which cancer was detected, for 40.9% of cases, there were no recognised risk factors present.

The authors concluded that given how many of the scans identified cancers in women without risk factors, their finding did not justify less frequent screening of women without recognised risks.

Noguchi N et al. Evidence from a BreastScreen cohort does not support a longer inter-screen interval in women who have no conventional risk factors for breast cancer Breast 2022

NMR-based metabolomic biomarkers help identify cancer in patients with non-specific symptoms

11th January 2022

NMR-based metabolomic biomarkers based on specific patterns can assist in the diagnosis of cancer in patients with non-specific symptoms

NMR-based metabolomic biomarkers which identify specific disease patterns, can be used to assist in the diagnosis of cancer in patients who present with non-specific signs and symptoms and even distinguish between those with and without metastatic disease. This was an important finding by a team from the Department of Oncology, University of Oxford, United Kingdom.

The earlier most cancers are detected, the better the prognosis. For example, colorectal cancer, when identified at stage 1 has a 97.7% survival which falls to only 43.9% if detected at stage 4. Although there are often classic symptoms and signs of a possible cancer, e.g. palpable abnormalities such as a breast lump or haematuria, diagnosis becomes more difficult where the has non-specific symptoms such as fatigue.

One potential solution to the diagnosis of a cancer in patients who have non-specific signs and symptoms is metabolomics which can rapidly supply information on thousands of molecules and hence serve as a biofluid-based diagnostic method. The technique aims to comprehensively identify endogenous metabolites in biological systems, providing a complete biochemical phenotype of a cell, tissue, or whole organism, using established analytical techniques such as nuclear magnetic resonance spectroscopy (NMR) or gas chromatography-mass spectrometry (GC-MS). Using an NMR-based metabolomic approach, the Oxford team had previously and successfully detected tumours at the micro metastatic stage based on analysis of urine metabolomics.

For the present study, they hypothesised that biomarkers within the blood metabolome could identify patients referred from primary care with suspected cancer but largely non-specific symptoms, or those deemed to be at ‘low risk, but not no-risk’. In other words, the researchers felt that they would be ale to distinguish between those with and without a cancer and even to identify patients with metastatic disease.

They recruited patients aged 40 years and over who were not referred under the specific ‘2-week wait’ cancer specific pathway and those with one of the following symptoms: unexplained weight loss, severe unexplained fatigue, persistent nausea or appetite loss, new atypical pain, an unexplained laboratory finding or finally, where the primary care physician had a suspicion (i.e., ‘gut feeling’) of cancer. Prior to the metabolomics analysis, patients were randomised into a modelling set and an independent test set which was used to determine the ability of the models to classify new patients.

Blood samples were collected and analysed by NMR-based metabolomics and receiver operator characteristic curves were constructed and the area under the curves (AUC) examined.


A total of 284 patients with a mean age of 68 years (57% male) were included in the analysis. The most common reasons for referral were weight loss (64%), ‘gut feeling’ of the referring physician (63%), unexplained laboratory results (37%), fatigue (29%), non-specific pain (28%) and nausea/appetite loss (27%). On average, referred patients had at least two of these symptoms.

For distinguishing between patients who were unwell with the above non-specific symptoms and those with a solid tumour diagnosis, the modelling plasma metabolome had an AUC of 0.91 and showed a sensitivity of 94% (95% CI 73 – 99) and a specificity of 82% (95% CI 75 – 87) at detecting cancer.

For the identification set, the AUC was 0.83 giving a sensitivity of 71% and a specificity of 70%. In addition, the model showed a sensitivity of 94% and a specificity of 88% for distinguishing between metastatic and non-metastatic disease.

Interestingly, the authors also examined whether the metabolomics model could identify early-stage cancers before conventional imaging and found that this was possible for 2 out of 5 patients.

Although a preliminary study, the authors concluded that NMR-based metabolomics represented a sensitive and specific means for the identification of solid organ tumours in patients with non-specific symptoms, who have been traditionally hard to diagnosis. They called for the technique to be tested in a larger cohort of patients.


Larkin JR et al. Metabolomic Biomarkers in Blood Samples Identify Cancers in a Mixed Population of Patients with Nonspecific Symptoms Clin Cancer Res 2022

FDA approves AI software to aid detection of prostate cancer

28th September 2021

AI software designed to identify an area on a prostate biopsy image with a high likelihood of cancer has received FDA approval.

Prostate cancer is the second most common cancer in men, with 1.3 million new cases recorded in 2018. Confirmation of a prostate cancer diagnosis can only be achieved via biopsy and subsequent examination of digitalised slides of the biopsy. Now, the first artificial intelligence (AI) software for in vitro diagnostic detection cancer in prostate biopsies has been approved by the FDA in the US. The software is designed to identify an area of interest on the prostate biopsy image with the highest likelihood of harbouring cancer. This alerts the pathologist if the area of concern has not been noticed on their initial review and thus can assist them in their overall assessment of the biopsy slides.

The AI system approved is Paige Prostate and it is anticipated to increase the number of identified prostate biopsy samples with cancerous tissue and ultimately save lives. The FDA approval was based on a study of Paige Prostate undertaken with three pathologists. In the study, which was conducted in two phases, each pathologist was required to assess 232 anonymised whole slide images and asked to dichotomise these as either cancerous or benign, with only 93 slides (40%) that were in fact cancerous. In the first phase, the pathologists assessed the scans alone, whereas in the second phase, 4-weeks later, the same scans were reviewed but this time using the AI software, Paige Prostate.


In the study, the Paige Prostate software alone, had a sensitivity for detecting cancer of 96% and a specificity of 98%. Without the use of Paige Prostate, the pathologists averaged a sensitivity of 74% but with the addition of the AI software, their average sensitivity increased significantly to 90% (p < 0.001). Addition of Paige Prostate mainly improved pathologists’ detection of grade 1 to 3 cancers. However, despite a greater sensitivity from the use of Paige Prostate, there was no significant difference in specificity (p = 0.327) since this was already high at an average of 97% without Paige Prostate.

Source. FDA Press release September 2021