oncology.pencis.com/”>Precision medicine is transforming cancer treatment by tailoring therapies to the unique genetic makeup of each individual’s cancer.

oncology.pencis.com/”>Genetic testing and molecular profiling enable oncologists to identify specific mutations and biomarkers, allowing for targeted therapies that attack cancer cells while minimizing harm to healthy cells. This approach has shown promising results, leading to improved treatment outcomes and reduced side effects.
oncology.pencis.com/”>Conference-registration-euro/”>immunotherapy: Harnessing power of immune system

oncology.pencis.com/”>Conference-registration-euro/”>immunotherapy has emerged as a groundbreaking treatment option that utilizes the body’s immune system to fight cancer.

oncology.pencis.com/”>It involves stimulating the immune system to recognize and destroy cancer cells. Checkpoint inhibitors, CAR-T Conference-sponsor/”>cell therapy and cancer vaccines are among the notable Conference-registration-euro/”>immunotherapy techniques that have shown remarkable success in treating various cancers and prolonging patient survival.
oncology.pencis.com/”>Liquid biopsies: Non-invasive cancer detection and monitoring

oncology.pencis.com/”>Liquid biopsies offer a non-invasive method for detecting and monitoring cancer. By analyzing circulating tumor DNA (ctDNA), circulating tumor cells (CTCs) or other biomarkers present in Blood or other body fluids, liquid biopsies can provide valuable information about tumor characteristics, treatment response and potential resistance.

oncology.pencis.com/”>This approach is revolutionizing early cancer detection, monitoring treatment effectiveness and guiding personalized treatment decisions.
oncology.pencis.com/”>Targeted therapies: Disrupting cancer-specific pathways

oncology.pencis.com/”>Targeted therapies focus on specific molecules or pathways involved in cancer growth and survival.

oncology.pencis.com/”>By directly interfering with these cancer-specific targets, these therapies can effectively inhibit tumor growth and progression. Advancements in understanding tumor biology and development of targeted therapies have led to significant breakthroughs, particularly in cancers with specific mutations or genetic alterations.
oncology.pencis.com/”>Artificial intelligence and machine learning: Assisting diagnosis and treatment

oncology.pencis.com/”>Artificial intelligence and machine learning algorithms are being utilized to analyze vast amounts of patient data and assist in cancer diagnosis, prognosis and treatment planning.

oncology.pencis.com/”>These technologies can identify patterns and provide valuable insights for healthcare professionals, leading to more accurate and personalized treatment strategies. AI-powered imaging techniques also aid in early detection and precise tumor delineation.

oncology.pencis.com/”>The field of cancer Research and treatment is rapidly advancing, bringing new hope to patients and transforming the way we approach this complex disease and revolutionizing cancer care.

oncology.pencis.com/”>As these innovations continue to evolve, they hold the potential to improve patient outcomes, increase survival rates and eventually bring us closer to a world where cancer is no longer a formidable threat.

oncology.pencis.com/”>It’s important to note that while these advances have tremendous promise, they may not be applicable to all cancer types or individuals. Each patient’s treatment plan should be tailored to their specific diagnosis, characteristics and medical history.

oncology.pencis.com/”>Consulting with healthcare professionals and oncologists is crucial for personalized guidance and decision-making regarding the latest advancements in cancer Research and treatment.

June 08, 2023 – Research from the Radiological Society of North America (RSNA) indicates that artificial intelligence (AI) algorithms performed better than the Breast cancer Surveillance Consortium (BCSC) risk model in predicting the five-year risk of the disease.

Data from the Centers for Disease Control and Prevention (CDC) shows that 264,000 women and 2,400 men receive a breast cancer diagnosis annually.

Despite the various methods of predicting breast cancer, such as the BCSC risk model, their use can be taxing.
Dig DeeperDeep-Learning Tool Can Predict Lung cancer Risk Within Six Years
Genetic Mutation Data Indicates Ovarian cancer Risk
Predictive Analytics Model Helps Determine Colon cancer Risk

According to Vignesh A. Arasu, MD, PhD, a Research scientist and practicing radiologist at Kaiser Permanente Northern California, this is mainly because the information they require can be inaccessible or difficult to obtain. However, Arasu noted that technological advances and AI could make the process of evaluating mammograms more efficient.

To compare the abilities of AI to the BCSC model, Arasu conducted a retrospective study that involved negative screening 2D mammograms from Kaiser Permanente Northern California in 2016. From a pool of 324,009 women who were deemed eligible in 2016, mammograms from 13,628 were analyzed. The study also followed the 4,584 patients from the original patient population who received a cancer diagnosis within five years.

Researchers defined three time periods based on when the diagnosis occurred: interval cancer risk, describing diagnoses between zero and one year; future cancer risk, describing diagnoses from between one and five years; and all cancer risk, encompassing the entirety of the five-year period.

Researchers used a total of five AI algorithms for the study, two of which were academic algorithms and three of which were commercially available. After comparing their performance to the abilities of the BCSC risk model, researchers found that the AI algorithms performed better than the standard risk model.

“All five AI algorithms performed better than the BCSC risk model for predicting breast cancer risk at 0 to 5 years,” said Arasu in a press release. “This strong predictive performance over the five-year period suggests AI is identifying both missed cancers and breast tissue features that help predict future cancer development. Something in mammograms allows us to track breast cancer risk. This is the ‘black box’ of AI.”

Beyond this, the AI algorithms presented several other benefits. Researchers also noted that certain AI algorithms performed well in predicting those at risk of interval cancer. This is critical, as this generally requires follow-up mammogram readings.

Also, even AI algorithms that did not have a long training duration performed well.

“We’re looking for an accurate, efficient and scalable means of understanding a women’s breast cancer risk,” said Arasu. “Mammography-based AI risk models provide practical advantages over traditional clinical risk models because they use a single data source: the mammogram itself.”

AI is playing an increasingly significant role in cancer prediction and detection, serving as the foundation of many Research efforts.

A large grant from the National cancer Institute in November 2022 led researchers from the University of California Davis to fuel AI projects to enhance breast cancer screening and risk prediction. Through these efforts, researchers aimed to reduce health disparities.

Often, certain types of regular screening can lead to false positive results. The grant, however, will support researchers as they test whether new AI and imaging features can improve risk prediction models.

Tagged Artificial Intelligence  Predictive Analytics  Risk Assessment

oncology.pencis.com/” style=”margin-left: 1em; margin-right: 1em;”>

oncology.pencis.com/”>Every year, approximately 400 women die of ovarian cancer in Finland, while the corresponding figure for Europe overall is more than 40,000. Ovarian cancer is a genetically very heterogeneous disease, which makes it exceptionally difficult to study and treat. The prognosis is particularly poor in ovarian high grade serous carcinoma (HGSC), a subtype of ovarian cancer. Less than 40% of patients with this subtype survive five years after their diagnosis.oncology.pencis.com/”>
The researchers were able to classify HGSC tumours into three groups on the basis of genomic changes. The groups differ in the intracellular signalling pathways, the ways in which the tumours grow, and response to treatment. The findings can help therapies to become more accurate and help patients with HGSC.

“Prior studies have not identified generally accepted subgroups of HGSC tumours that would enable targeted treatment in the same way as, for example, in breast cancer. Our study is a step forward in identifying effective targeted therapies,” says Professor of Systems Biology Sampsa Hautaniemi from the University of Helsinki.

Three signalling pathways
The researchers analysed genomics data on cancer tumours collected in the DECIDER project from 148 patients with HGSC treated and recruited in Turku University Hospital. Depending on the stage of development, they divided the tumours into three evolutionary states: evolving, maintaining and adaptive. The classification was based on the tumours’ pattern of spread and their development in metastases. Depending on the group, the cancer populations grew up in combinations of either genetically different or clonal cells. These combinations either continued to evolve in metastases or remained unchanged.

The researchers identified signalling pathways characteristic of each tumour group, which make these tumours biologically distinct.

“There are targeted drugs already in clinical use for many of them. We demonstrated that a single signalling pathway, PI3K/AKT, is particularly important for certain patients. While the importance of this pathway has been known, it was not known who are most likely to respond to treatment targeted at this signalling pathway. Based on our findings, we are better able to identify the subset of patients likely to benefit from such treatment,” says Postdoctoral Researcher Jaana Oikkonen from the University of Helsinki.

Tumour evolution should be investigated
The study approaches the issue from the perspective of tumour evolution, or how the tumour develops and spreads into new metastases. This approach is currently important in Research on HGSC as the knowledge available is largely based on studies with small sample or patient numbers.

“The dataset we analysed was one of the largest, if not the largest, to date in terms of HGSC tumour samples. This is yet another indication of the capacity for top-level Research in Finland in spite of our small population,” Hautaniemi says.

“Our findings bring order to the genomic chaos of HGSC. Now, the entire Research field will advance faster, making it easier to target therapies. Of course, there is still work to be done. Further Research is being carried out on, for instance, what would be the easiest way to classify patients into the three groups identified,” says Postdoctoral Researcher Alexandra Lahtinen from the University of Helsinki.

The results of the joint study carried out by the University of Helsinki, Turku University Hospital, HUS Helsinki University Hospital, the University of Turku and the Danish cancer Society Research Center were published in the cancer Cell journal in May.