Best of ASCO - 2014 Annual Meeting

 

Welcome

Attend this session at the
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.

Sub-category:
Clinical Informatics

Category:
Health Services Research, Clinical Informatics, and Quality of Care

Meeting:
2018 ASCO Annual Meeting

Abstract No:
6555

Poster Board Number:
Poster Session (Board #381)

Citation:
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

Abstract Disclosures

Abstract:

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.

Clinical
Decision
Point
Cohort
Predictive Models
Statistics
Logistic
Regression
(Lyman, 2011)
Logistic
Regression
(Current Study)
PH.AI Neural Net
(Current Study)
NNeutropenia
incidence within 6 mo.
Stage
Distribution
(I,II,III,IV)
Median time to Neutropenia (days)AUC-ROC# PredictorsAUC-ROC# PredictorsAUC-ROC# Predictors
Prior to Starting ACwith Prophylaxis7097.017.14, .50, .31, .0418.58 ± .037.73 ± .0219.78 ± .04704
no Prophylaxis1075.093.13, .51, .31, .0522.59 ± .037.69 ± .0215.74 ± .012169
Prior to Starting Twith Prophylaxis6753.084.14, .50, .31, .0431.59 ± .037.76 ± .0221.74 ± .012130
no Prophylaxis1078.074.13, .53, .29, .0515.57 ± .027.74 ± .0216.89 ± .02873

 
Other Abstracts in this Sub-Category:

 

1. PRECISE: A clinical-grade automated molecular eligibility screening and just-in-time (JIT) physician decision support solution for molecularly-selected oncology trials.

Meeting: 2018 ASCO Annual Meeting Abstract No: 6507 First Author: Jessica Tao
Category: Health Services Research, Clinical Informatics, and Quality of Care - Clinical Informatics

 

2. A cloud-based virtual tumor board to facilitate treatment recommendations for patients with advanced cancers.

Meeting: 2018 ASCO Annual Meeting Abstract No: 6508 First Author: Subha Madhavan
Category: Health Services Research, Clinical Informatics, and Quality of Care - Clinical Informatics

 

3. Clinical trajectory modeling to predict hospitalization or death after palliative chemotherapy.

Meeting: 2018 ASCO Annual Meeting Abstract No: 6509 First Author: Kenneth L. Kehl
Category: Health Services Research, Clinical Informatics, and Quality of Care - Clinical Informatics

 

More...