A novel prediction model for immunotherapy induced pneumonitis prediction based on Chest CT and electronic health record
Lyu, Q.; Yuan, H.; Lin, Z.; Ponnatapura, J.; Whitlow, C. T.
Show abstract
Immune checkpoint inhibitors (ICIs) have been extensively used for the treatment of non-small cell lung cancer patients in recent years, providing a significant survival benefit. However, a major drawback of ICIs-related immunotherapy is the risk of developing post-surgical pneumonitis. In this study, we propose a deep learning-embedded, multi-modality prediction approach to assess whether patients will develop ICI-pneumonitis after receiving ICIs-based immunotherapy. This approach utilizes multi-modal data, including clinical data and pre-treatment lung screening computed tomography (CT) images. We extracted three types of features: 1) deep learning features from CT scans using a pretrained vision transformer, 2) radiomic features from CT scans using predefined radiomic algorithms, and 3) clinical features from patients clinical records. We then compared multiple machine learning algorithms for prediction based on these extracted features. Our results demonstrated a prediction accuracy of 0.823 and an area under the receiver operating characteristic curve of 0.895.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- High-Dimensional Multinomial Multiclass Severity Scoring of COVID-19 Pneumonia Using CT Radiomics Features and Machine Learning Algorithms 96%
- Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS 94%
- An ML prediction model based on clinical parameters and automated CT scan features for COVID-19 patients 94%
Similar papers in this journal
Similar papers in this journal
- Predicting EGFR mutation status in lung adenocarcinoma presenting as ground-glass opacity: utilizing radiomics model in clinical translation 96%
- Prediction of oncogene mutation status in non-small cell lung cancer: A systematic review and meta-analysis with a special focus on artificial-intelligence-based methods 93%
- A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19) 93%
Similar papers in this journal
- TCCIA: A Comprehensive Resource for Exploring CircRNA in Cancer Immunotherapy 92%
- QVT Score, a radiomic biomarker of vascular complexity, enables prognostication and monitoring of NSCLC immunotherapy 90%
- Nicotinamide combined with gemcitabine is an immunomodulatory therapy that restrains pancreatic cancer in mice 90%
Similar papers in this journal
- Towards Predicting 30-Day Readmission among Oncology Patients: Identifying Timely and Actionable Risk Factors 92%
- Imaging-Genomics Study Of Head-Neck Squamous Cell Carcinoma: Associations Between Radiomic Phenotypes And Genomic Mechanisms Via Integration Of TCGA And TCIA 92%
- Descriptive and prognostic value of a computational model of metastasis in high-risk neuroblastoma 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.