It’s often research into new drugs that makes headlines. But examining routine data can also help doctors continually improve how they use treatments already available | CRUK Blog
To do that the NHS needs to look at how these treatments are being used and how different patients fare following treatment. Last year, we worked with Public Health England to do exactly that.
Our world-first study of patients treated for breast and lung cancer in 2014 gave us a national picture of what happened to patients following treatment with chemotherapy, ‘targeted’ drugs and immunotherapy.
The study was made possible by routine data collected by Public Health England as part of the treatment of all patients across the English NHS. And for the first time each English NHS hospital trust was able to see how well their breast and lung cancer patients fared in the first 30 days after receiving these treatments compared with other trusts.
This measure, called ‘30-day mortality’, is really useful. If a patient dies in that short window of time, it’s unlikely they benefitted from the treatment and they might still have experienced its side effects, even if it didn’t directly lead to their death.
For those patients, other types of treatment and support might have led to a better outcome. But the only way to know this is by giving treatment teams data to help them spot where they could make improvements.
Gerd Gigerenzer discusses how search engines use big data analytics to “diagnose” your state of health | BMJ Opinion
Image shows pancreatic desmoplasia. Pancreatic cancer is associated with a vast desmoplastic reaction in which the connective tissue around the tumor thickens and scars.
Imagine this warning popping up on your search engine page: “Attention! There are signs that you might have pancreatic cancer. Please visit your doctor immediately.” Just as search engines use big data analytics to detect your book and music preferences, they may also “diagnose” your state of health.
Microsoft researchers have claimed that web search queries could predict pancreatic adenocarcinoma. A retrospective study of 6.4 million users of Microsoft’s search engine Bing identified first-person queries suggestive of a recent diagnosis, such as “I was told I have pancreatic cancer, what to expect.” Then the researchers went back months before these queries were made and looked for earlier ones indicating symptoms or risk factors, such as blood clots and unexplained weight loss. They concluded that their statistical classifiers “can identify 5% to 15% of cases, while preserving extremely low false-positive rates (0.00001 to 0.0001)”, and that “this screening capability could increase 5-year survival.” The New York Times reported: “The study suggests that early screening can increase the five-year survival rate of pancreatic patients to 5 to 7 percent, from just 3 percent.”
In a project funded by Bloodwise and the Scottish Cancer Foundation, we have created LEUKomics. This online data portal brings together a wealth of CML gene expression data from specialised laboratories across the globe | Lorna Jackson & Lisa Hopcroft for The Conversation
Our intention is to eliminate the bottleneck surrounding big data analysis in CML. Each dataset is subjected to manual quality checks, and all the necessary computational processing to extract information on gene expression. This enables immediate access to and interpretation of data that previously would not have been easily accessible to academics or clinicians without training in specialised computational approaches.
Consolidating these data into a single resource also allows large-scale, computationally-intensive research efforts by bioinformaticians (specialists in the analysis of big data in biology). From a computational perspective, the fact that CML is caused by a single mutation makes it an attractive disease model for cancer stem cells. However, existing datasets tend to have small sample numbers, which can limit their potential.
For this project, doctors and data miners are specifically focusing on lung cancer patients | ScienceDaily
By flagging things like recent lab tests, radiology visits, or patient-reported symptoms, Penn’s team is hoping to come up with a formula that will predict when a patient is likely to end up visiting the emergency room. Right now, the formula can predict an estimated one out of every three ER visits, giving doctors the chance to take action before a patient gets to that point.