Breast Cancer Diagnostics Market to reach US$ 9 bn by 2027

AI Systems with Advanced Analytics to Identify Unmet Patient Needs

Breast cancer is one of the most common types of cancer found in women worldwide. However, breast cancer does not always show obvious symptoms in the early stages. Hence, companies in the breast cancer diagnostics market are increasing their R&D in AI technologies to identify unmet patient needs. For instance, Paragon Biosciences LLC announced the launch of its seventh portfolio company Qlarity Imaging LLC, to gain FDA clearance for their AI system used for breast cancer diagnostics in radiology.

Companies in the breast cancer diagnostics market are harnessing the advantages of AI to improve medical outcomes. The AI technology is being increasingly used in additional image modalities with the goal of lowering costs for hospitals as well as payers. Companies in the market for breast cancer diagnostics are directing their investments in developing AI systems with intuitive displays and machine learning (ML).

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Blood Screening Tests Revolutionize Breast Cancer Diagnostics with Early Detection Possibilities

Most types of breast cancer are treatable and survival rates are high for patients that detect cancer at an early stage. Hence, researchers in the market for breast cancer diagnostics are increasing their focus in novel methods of blood screening that are reinventing techniques for early detection of breast cancer. For instance, researchers from the University of Nottingham are innovating in new blood screening methods that can help detect breast cancer years before it gets severe. Such discoveries are bolstering market growth, as the breast cancer diagnostics market is expected to reach a revenue of ~US$ 9 Bn by the end of 2027. Moreover, molecular testing is anticipated for exponential growth during the forecast period.

On the other hand, researchers are examining antigens and auto-antibodies to determine the presence of a tumor. Tumor-associated antigens (TAAs) are considered as a reliable indicator of breast cancer. Thus, companies in the breast cancer diagnostics market should collaborate with researchers to advance in clinical studies.

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Simplifying Synthesis Steps and Nanoprobe Functionalization Bolster Innovations in Nanotechnology

Nanotechnology holds promising potential for early detection of breast cancer. This technology is being highly publicized for its high sensitivity, specificity, and multiplexed measurement capacity in the breast cancer diagnostics market. However, translation of nanotechnology-based diagnostic methods into routine clinical applications poses as a challenge for healthcare providers.

The breast cancer diagnostics market is consolidated with three major players accounting for ~66% of the global market share. This indicates that companies are increasing their research efforts in new diagnostic methods such as nanotechnology and deep learning to improve clinical numbers. However, the reliability of nanotechnology-based cancer diagnosis in regular clinical practices is still at the nascent stage in the market for breast cancer diagnostics. Moreover, high costs associated with the long-term storage of nanoprobes that are highly sensitive is another barrier for market growth. Hence, companies in the market for breast cancer diagnostics are simplifying the synthesis steps and nanoprobe functionalization and are increasing R&D to launch cost-efficient ways of introducing nanotechnology-based diagnosis in clinical practices.

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Automated Diagnosis Techniques with ML Algorithms Help Distinguish Tumors

Automated breast cancer diagnostics based on ML algorithms are bringing about a change in the market landscape. Soft computing techniques are being increasingly used to produce accurate and faster diagnosis of breast cancer amongst patients. Hence, healthcare companies in the breast cancer diagnostics market are collaborating with researchers to combine imaging techniques with ML algorithms to increase diagnosis accuracy. Preprocessing, feature extraction, and classification are becoming the key focus points for healthcare companies in order to advance in ML-based imaging techniques.

Several data mining and ML techniques are being used to facilitate careful interpretation and analysis of peripheral areas of the patient. As such, feature extraction plays a pivotal role in breast cancer detection using ML techniques. Such novel techniques are helping practitioners in the breast cancer diagnostics market to distinguish between benign and malignant tumors. Transform-based texture analysis techniques are being facilitated to convert the image into new analysis studies using spatial frequency properties.