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e-Poster Display Session

38P - Predicting objective response rate (ORR) in immune checkpoint inhibitor (ICI) therapies with machine learning (ML) by combining clinical and patient-reported data

Date

09 Dec 2020

Session

e-Poster Display Session

Presenters

Sanna Iivanainen

Citation

Annals of Oncology (2020) 31 (suppl_7): S1428-S1440. 10.1016/annonc/annonc391

Authors

S. Iivanainen1, J. Esktrom2, H. Virtanen2, L. Lang2, V. Kataja2

Author affiliations

  • 1 Oncology And Radiotherapy, Oulu University Hospital, 90220 - Oulu/FI
  • 2 Kaiku Health Oy, Helsinki/FI
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Abstract 38P

Background

ICIs have been approved as standard of care in several malignancies. However, only a subset of eligible patients benefits from ICIs according to overall response rate (ORR) which predicts long-term benefit from therapy. Predicting ORR could enable more rational use of ICIs. The aim of this study was to investigate if ML could be used to predict ORR of patients undergoing ICIs using clinical and patient-reported data.

Methods

ML-based prediction model was built by using data from 31 patients receiving ICI therapies for various advanced cancers. Several data sources were used as inputs for the model. Clinician-assessed treatment responses (n=63), immune-related adverse events (irAEs) according to CTCAE, and laboratory measurements (9 values) were collected prospectively. Patient-reported symptom data including 18 monitored symptoms was collected using the Kaiku Health digital platform. In addition, time from treatment initiation, age and sex were included. Preceding lab values and patient-reported symptoms, as changes from the baseline, were linked to the treatment responses. The prediction model for ORR was built using gradient boosting technique (XGBoost). Prediction performance for unseen samples was evaluated using leave-one-out cross-validation (LOOCV), which trained and tested 63 models, each time iteratively leaving one sample out as a test set. The LOOCV prediction performance was evaluated with accuracy, AUC (Area Under Curve), F1 score and MCC (Matthew’s correlation coefficient).

Results

Table: 38P

Accuracy AUC F1 score MCC
XGBoost LOOCV performance 75 % 0,71 0,58 0,40

Conclusions

This study demonstrated that it is possible to predict ORR for patients undergoing ICI therapies with ML model combining clinical, routine laboratory, and patient-reported data even with a limited size cohort. These promising results indicate that ML-based approaches in treatment response prediction should be investigated and validated further with a larger dataset.

Clinical trial identification

NCT3928938.

Legal entity responsible for the study

Jussi Koivunen, Sanna Iivanainen; OYS, MRC Oulu.

Funding

Has not received any funding.

Disclosure

J. Esktrom: Full/Part-time employment: Kaiku Health Oy. H. Virtanen: Shareholder/Stockholder/Stock options, Full/Part-time employment: Kaiku Health Oy. L. Lang: Full/Part-time employment: Kaiku Health Oy. V. Kataja: Full/Part-time employment: Kaiku Health Oy. J. Koivunen: Advisory/Consultancy: Kaiku Health Oy. All other authors have declared no conflicts of interest.

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