2018 ASCO Annual Meeting!
Session: Health Services Research, Clinical Informatics, and Quality of Care
Type: Poster Session
Time: Saturday June 2, 1:15 PM to 4:45 PM
Location: Hall A
Artificial intelligence methods to predict chemotherapy-induced neutropenia in breast cancer patients.
Health Services Research, Clinical Informatics, and Quality of Care
2018 ASCO Annual Meeting
Poster Board Number:
Poster Session (Board #381)
J Clin Oncol 36, 2018 (suppl; abstr 6555)
Author(s): Peter Abdul DeWan, Orr Inbar, Catherine S. Spina, Karl Rudeen, Charles Lagor, Mark S. Walker, Edward J. Stepanski, Jennifer O. Nwankwo, Brigham Hyde; Precision Health Ai, New York, NY; Precision Health AI, New York, NY; Department of Radiation Oncology, Columbia University College of Physicians and Surgeons, New York, NY; Precision Health AI, Precision Health AI, NY; ACORN Research LLC, Memphis, TN; Vector Oncology, Memphis, TN
Background: While chemotherapy improves outcomes in breast cancer patients, it also increases the risk of neutropenia. There is a need for improved risk prediction models of chemotherapy-induced neutropenia. This study developed an artificial intelligence (AI) model to predict neutropenia risk within six months of chemotherapy and compared it to traditional logistic regression models. Methods: We obtained a cohort of 10,288 breast cancer patients from the ASCO CancerLinQ Discovery™ dataset, who were treated with doxorubicin and cyclophosphamide (AC) followed by paclitaxel (T) with or without pre-chemotherapy WBC growth factor prophylaxis. We created a hierarchy of predictors, then trained and evaluated a neural network algorithm. Results: Using only the relevant predictors for this more specific study, we demonstrate an average 27% increase across the scenarios in AUC ROC (p < 0.001) compared to existing studies (Lyman, 2011). We improve prediction by an average of 45%, achieving 0.56 positive predictive value (PPV) and 0.92 negative predictive value (NPV) prior to chemotherapy. Conclusions: These results demonstrate that this larger dataset combined with an AI algorithm enabled substantial improvement in prediction of chemotherapy-induced neutropenia. In the clinical setting, this would improve decisions, and enable early intervention for patients that would benefit most from prophylaxis, thus reducing neutropenic fever and infections requiring hospitalization.
|PH.AI Neural Net|
incidence within 6 mo.
|Median time to Neutropenia (days)||AUC-ROC||# Predictors||AUC-ROC||# Predictors||AUC-ROC||# Predictors|
|Prior to Starting AC||with Prophylaxis||7097||.017||.14, .50, .31, .04||18||.58 ± .03||7||.73 ± .02||19||.78 ± .04||704|
|no Prophylaxis||1075||.093||.13, .51, .31, .05||22||.59 ± .03||7||.69 ± .02||15||.74 ± .01||2169|
|Prior to Starting T||with Prophylaxis||6753||.084||.14, .50, .31, .04||31||.59 ± .03||7||.76 ± .02||21||.74 ± .01||2130|
|no Prophylaxis||1078||.074||.13, .53, .29, .05||15||.57 ± .02||7||.74 ± .02||16||.89 ± .02||873|