University of Oulu

Iivanainen, S., Ekström, J., Virtanen, H., Kataja, V. V., & Koivunen, J. P. (2022). Predicting objective response rate (ORR) in immune checkpoint inhibitor (ICI) therapies with machine learning (ML) by combining clinical and patient-reported data. Applied Sciences, 12(3), 1563.

Predicting Objective Response Rate (ORR) in Immune Checkpoint Inhibitor (ICI) Therapies with Machine Learning (ML) by combining clinical and patient-reported data

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Author: Iivanainen, Sanna1; Ekström, Jussi2; Virtanen, Henri2;
Organizations: 1Department of Oncology and Radiotherapy, Oulu University Hospital and MRC Oulu, 90220 Oulu, Finland
2Kaiku Health Oy, 00530 Helsinki, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.8 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2022
Publish Date: 2022-08-03


ICIs are a standard of care in several malignancies; however, according to overall response rate (ORR), only a subset of eligible patients benefits from ICIs. Thus, an ability to predict ORR could enable more rational use. In this study a ML-based ORR prediction model was built, with patient-reported symptom data and other clinical data as inputs, using the extreme gradient boosting technique (XGBoost). Prediction performance for unseen samples was evaluated using leave-one-out cross-validation (LOOCV), and the performance was evaluated with accuracy, AUC (area under curve), F1 score, and MCC (Matthew’s correlation coefficient). The ORR prediction model had a promising LOOCV performance with all four metrics: accuracy (75%), AUC (0.71), F1 score (0.58), and MCC (0.4). A rather good sensitivity (0.58) and high specificity (0.82) of the model were seen in the confusion matrix for all 63 LOOCV ORR predictions. The two most important symptoms for predicting the ORR were itching and fatigue. The results show that it is possible to predict ORR for patients with multiple advanced cancers undergoing ICI therapies with a ML model combining clinical, routine laboratory, and patient-reported data even with a limited size cohort.

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Series: Applied sciences
ISSN: 2076-3417
ISSN-E: 2076-3417
ISSN-L: 2076-3417
Volume: 12
Issue: 3
Pages: 1563 -
DOI: 10.3390/app12031563
Type of Publication: A1 Journal article – refereed
Field of Science: 3122 Cancers
Funding: This research was funded by Oulu University, Emil Aaltonen Foundation, The Finnish Medical Foundation, and Finnish Cancer Society. Kaiku Health employees were involved in the data acquisition and analysis.
Copyright information: © 2022 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (