Predictive Data to Help Cancer Patients Know Progress of Treatment
Researchers at Stanford
University School of Medicine have developed an algorithm that used predictive
analysis to assess the treatment progress of a patient.
Maximilian Diehn and Ash Alizadeh said that just as bookies and pundits sift through mountains of data to predict outcome of an election or a game,the new algorithm developed by the team integrates many different types of predictive data-including a tumour's reponse to treatment and the amount of cancer DNA circuating in a patient's blood during therapy. This can help patients and doctors take meaningful decisions such as attending a child's wedding next summer or whether priority should be on preparing a will.
The treatment process termed CIRI-continious individualised risk index, might also help doctors to identify people who might benefit from early, more aggressive treatments as well as those who are likely to be cured by standard methods.
The researchers began their study by looking at people previously diagnosed with diffuse large B-cell lymphoma (DLBCL), which is the most common blood cancer in the United States. Although nearly two-thirds of adults with DLBCL are cured with standard treatment protocols, the remaining third will likely die from the disease.
When a DLBCL patient is diagnosed, clinicians like Alizadeh, Diehn and Kurtz assess the initial symptoms, the cell type from which the cancer originated and the size and location of the tumor after the first imaging scan to generate an initial prognosis. More recently, clinicians have also been able to assess the amount of tumor DNA circulating in a patient’s blood after the first one or two rounds of therapy to determine how the tumor is responding and estimate a patient’s overall risk of succumbing to their disease.