Analytics leads to life-saving cancer treatments


From targeted therapy to precision medicine

Harrison continued a low dose of chemo until 2015 and remained in remission until 2016, with cancer returning to its central nervous system. This time, targeted treatments had gained even more traction in health care, and doctors found a new new treatment protocol, CAR T-cell therapy.

“CAR T therapy enables the immune system to recognize and kill cancer cells,” McKinion says. Your own white cells attack cancer cells the same way they would attack bacteria. “

Thanks to an astonishing combination of factors – including endurance, targeted treatment and immunotherapy – Harrison is today an active, cancer-free teenager.

“At every step, Harrison was just months after a deadline,” McKinion says. If he had presented with cancer earlier in both cases, the treatment that saved his life would not have been available. “

McKinion, who has a doctorate in divinity, has become a spokesman for families affected by cancer in children. He testified before Congress in 2016 to recommend more funding for cancer research in children, and he co-founded the Harrison’s house, a nonprofit organization in Wake Forest, NC that provides camp experiences for cancer teens, serious illnesses and special needs.

“If it wasn’t for data and the use of big data in research, Harrison would have died seven years ago,” McKinion says. “Now he is graduating from high school. He plays baseball and football and he wants to change the world.”

What career plans does Harrison have after college? “He wants to study data and analysis,” McKinion says. “His goal is to change the world through data because he knows what it means to him and his life.”

As cancer research and cancer treatment evolve in this new world of precision medicine, analysis plays a vital role. From treatment innovation to clinical research and from medical imaging to early efficacy tests – the importance of the analyzes in cancer care and diagnosis cannot be overlooked.

“Analytics not only helps to find new therapies, but also measures their impact when in use, and it helps to reward the therapies that deliver the highest patient value financially,” Lambrecht says.

The type of therapy that eventually puts Harrison in remission when his cancer returned, CAR T-cell therapy, works by strengthening a patient’s T-cells to fight the most resistant cancer cells. The treatment uses a combination of cellular therapy, gene therapy and immunotherapy to strengthen a patient’s immune system to become an advanced cancer fighting machine.

Currently, CAR T-cell therapy is more effective in treating blood cancers such as leukemia and lymphoma. However, the development of CAR-T cells for non-soft or “solid” cancers has been more difficult.

Researchers at Oslo Cancer Cluster have discovered a new way to model solid cancer cells to test CAR-T cells on these models

The consortium’s ultimate goal is to uncover the best treatment for each unique patient. Analytics is an important part of this effort.

“In cancer treatment right now, there is a digitalization of oncology,” explains Ketil Widerberg, General Manager of the Oslo Cancer Cluster. Analytics can accomplish two things for cancer research. One is to understand cancer better, be able to see patterns we have not seen before. Second, we use analytics to understand how to treat patients better, to provide the right treatment to the right patient at the right time. ”

But researchers today are aware that the answers to many complex questions are beyond the controlled study environments – and one solution is to open clinical trial data for analysis across multiple clinical trials. Consider these three programs that combine advanced research data:

  1. Projektdatasfæren cancer research platform sparks innovation by opening up data for new research opportunities. To support this effort, SAS hosts the research platform and provides access to analytics technology at no cost to researchers. The data in the platform is identified and complies with industry requirements.
  2. BioGrid Australia, a nonprofit organization that combines data from hospitals and research institutions to improve clinical research into various cancers, such as bowel cancer. The project collects data from patient screenings in 19 locations across the country.
  3. Latin America Cooperative Oncology Group (LACOG) examines data from thousands of cancer patients in more than 150 hospitals and 15 countries across the region. The project helps identify known roadblocks for optimal care, such as poor access to treatment, medication and preventative care. The long-term goals are to serve patients better, facilitate the development of new techniques and technologies to improve cancer care, and even influence public policy.

Greater access to patient-level clinical data is good for patients, science and business. From a business perspective, pooling data can significantly reduce the cost of drug development while improving the effectiveness of clinical trials.

“A large number of children with immunotherapy respond immediately. But some do not. You have to figure out for some kids why it doesn’t take. The possible answers are so great that there is no way to do it without big data, ”McKinion says.

Ultimately, the sooner doctors can identify what works or doesn’t work, the sooner they can put patients on a path to a treatment that works for them.

by Amsterdam University Medical Center, a team of radiologists and surgeons recently began using computer vision, a form of artificial intelligence, to improve the process of identifying cancerous tumors and measuring the changes in tumors after treatment. Their original project uses object detection to identify and measure tumors in CT scans of liver from patients with colon cancer spreading to the liver.

Radiologists typically measure the size of tumors manually in the scans before and after treatment. This is very time consuming and prone to subjectivity, but it is crucial work. If the patient’s tumors respond to treatment, it also makes the patient a good candidate for surgery.

Leaders at the hospital saw this as a perfect pilot project to test the capabilities of analytics and AI. Computer vision models are designed to analyze the medical images for a fraction of the time, and object detection is used to recognize tumors and tumor sizes almost immediately. AI models are more objective and accurate than the radiologist’s measurements alone.

This use of AI not only frees radiologists to do more hands-on work with patients, but also improves decisions and can therefore potentially save lives. By finding results faster and more accurately, computer vision can improve treatment strategies with the potential to lead to better cancer patient outcomes.

Lambrecht predicts many other uses for AI in the fight against cancer, including the ability to generate deep learning biomarkers that can better determine end points for treatment based on safety and efficacy.

Of course, umbrella studies and other recent precision medicine research formats generate more data and more complex data than standard trials. But today’s advances in analytics – including new machine learning algorithms and faster processing speeds – are up to the task.

Cancer patients and their families are grateful like McKinion.



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