Shanghai scientists use artificial intelligence to predict cancer

As recently featured in a special issue of Cancer biomarkerssenior researchers from a series of Shanghai-based universities have shown how Artificial intelligence (AI) algorithms when analyzing radiomics data can help better detect the formation and movement of cancer in a patient. The principal investigators were Shaoli Song, PhD, of Shanghai Medical College and Lisheng Wang, PhD, of Shanghai Jiao Tong University.

The challenge of detecting cancer, including its relapse and metastasis, is a monumental challenge because large data sets must be taken into account. But early detection is key. Diagnosing cancer early can give the best chance of successful treatment. When cancer care is delayed or inaccessible, the chances of survival are lower, the problems associated with treatment are greater and the costs of care are higher.

To overcome these challenges, the Chinese team of scientists combined radiomic data from preoperative positron emission tomography (PET) and CT images in patients with early-stage uterine cervical squamous cell carcinoma. Next, they used AI algorithms to develop a prognostic signature capable of predicting disease-free survival. In short, by using AI, they can better predict the extent to which cancer progress is halted.

In cancer, disease-free survival (DFS) is the length of time after completion of primary cancer treatment that the patient survives without any signs or symptoms of that cancer. In a clinical trial, measuring SSM is a way to see how well a new treatment is working.

This model could provide more accurate information about potential relapses and metastases, and could be useful for decision-making.

Radiomics is an emerging field where features are extracted from medical imaging using a variety of techniques. In this sense, it uses Deep Learning (DL) and Machine Learning (ML). Radiomic characteristics can quantify tumor intensity, shape, and heterogeneity and have been applied to oncological detection, diagnosis, therapeutic response, and prognosis.

Indeed, the combination of AI, DL and ML has transformed many industries and scientific fields. Today, these tools are applied to address the challenges of cancer biomarker discovery, where the analysis of large amounts of imaging and molecular data exceeds the capability of traditional analyzes and statistical tools.

The overwhelming data challenge has been taken up by other scientists in the field of cancer biomarker discovery. Karin Roadlan, PhD, Oregon Health and Science University, USA, clarified that the biomarker field is blessed with a plethora of imaging and molecular data. But at the same time, it contains so much data that no individual can understand everything.

And it certainly looks like a bespoke job for the AI. Roadlan said AI offers a solution to this problem and has the potential to uncover new interactions that more accurately reflect the biology of cancer and other diseases.

Already, the technology looks promising, and the Cancer biomarkers noted that. ML, DL with AI has been instrumental in identifying cancers at an early stage, inferring the site of a specific cancer and aiding in the allocation of appropriate treatment options for each patient. In addition, these digital technologies have made it possible to characterize the tumor microenvironment and to predict the response to immunotherapy.

China is at the forefront of a massive digital transformation. It is certainly putting all the pieces together to advance its digital economy. And its digital assets as a nation were showcased to the world.

As reported on OpenGov Asia, the Beijing 2022 Paralympic Games are proof of that. Emerging digital technologies have enabled athletes and the public with reduced mobility to better participate in the event.

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