Cancer

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Article • Early detection approach

Multi-cancer blood tests could shake up screening

New tests can identify over 50 types of cancer and boost detection of traditionally elusive cancers from tumour DNA in blood, researchers showed at the ESMO congress in September. These multi-cancer early detection (MCED) tests in development can spot common cancer signals and predict where the signal comes from in the body, results from a prospective investigation suggest.

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Article • Visceral imaging

Endosonography: AI takes on the “supreme discipline”

Endosonography poses unique challenges for medical professionals, because two demanding disciplines have to be mastered at the same time. The use of artificial intelligence (AI) could help speed up the notoriously slow learning curve of the procedure, says Prof Dr Christoph F. Dietrich. At the Visceral Medicine Congress in Hamburg, the expert explained how AI can help endosonography achieve…

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Article • Diagnostic assistant systems

AI in endoscopy: helper, trainer – influencer?

Artificial intelligence (AI) is increasing its foothold in endoscopy. Although the algorithms often detect pathologies faster than humans, their use also generates new problems. PD Dr Alexander Hann from the University Hospital Würzburg points out that the use of AI helpers can affect not only the reporting of findings – but also the person making the findings.

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Malignant tumour management

’Our machine learning model achieved 88.9% accuracy in predicting the sarcoma-specific survival rate’

Clinical management of soft tissue sarcoma is particularly challenging. Dr Sebastian Foersch, researcher at the Institute of Pathology at the University Medical Center in Mainz, Germany, has used a deep learning model for diagnosis and prognosis prediction of soft tissue sarcoma using conventional histopathology slides.

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Article • AI provides prognostic information

Next-generation deep learning models predict cancer survival

Deaths from cancer are currently estimated at 10 million each year worldwide. Conventional cancer staging systems aim to categorize patients into different groups with distinct outcomes. ‘However, even within a specific stage, there is often substantial variation in patient outcomes,’ Markus Plass, academic researcher from the Medical University of Graz, Austria, explained to Healthcare in…

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Article • Prediction for breast, ovarian, cervical, and endometrial carcinoma

New test detects four women’s cancers from cervical screening samples

What if a test analysing cervical cells from a gynaecological swab could be used to detect four different female cancers at an early stage and also predict cancer risk over a healthy woman's lifetime? Researchers at the EUTOPS Institute in Innsbruck, Austria, are developing tests to do just that for breast, ovarian, cervical, and endometrial cancer detection.

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Supervised learning approach

A new deep learning-based algorithm to predict relapse-free survival in papillary thyroid carcinoma

The tall cell variant (TCV) is an aggressive subtype of papillary thyroid carcinoma (PTC). Sebastian Stenman, researcher from the Institute for Molecular Medicine, and the Department of Pathology at the University of Helsinki, Finland, is developing and training a deep learning algorithm using supervised learning to detect and quantify the proportion of tall cells in PTC.

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