Artificial Intelligence, Lymphoid Neoplasms, and Prediction of MYC, BCL2, and BCL6 Gene Expression Using a Pan-Cancer Panel in Diffuse Large B-Cell Lymphoma

被引:4
作者
Carreras, Joaquim [1 ]
Nakamura, Naoya [1 ]
机构
[1] Tokai Univ, Sch Med, Dept Pathol, 143 Shimokasuya, Isehara, Kanagawa 2591193, Japan
来源
HEMATO | 2024年 / 5卷 / 02期
基金
日本学术振兴会;
关键词
artificial intelligence; machine learning; artificial neural networks; lymphoma; hematological neoplasia; immuno-oncology; MYC; BCL2; BCL6; diffuse large B-cell lymphoma; APPROXIMATION; SURVIVAL; CLASSIFICATION; PROGNOSIS; DIAGNOSIS; DISEASE;
D O I
10.3390/hemato5020011
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: Artificial intelligence in medicine is a field that is rapidly evolving. Machine learning and deep learning are used to improve disease identification and diagnosis, personalize disease treatment, analyze medical images, evaluate clinical trials, and speed drug development. Methods: First, relevant aspects of AI are revised in a comprehensive manner, including the classification of hematopoietic neoplasms, types of AI, applications in medicine and hematological neoplasia, generative pre-trained transformers (GPTs), and the architecture and interpretation of feedforward neural net-works (multilayer perceptron). Second, a series of 233 diffuse large B-cell lymphoma (DLBCL) patients treated with rituximab-CHOP from the Lymphoma/Leukemia Molecular Profiling Project (LLMPP) was analyzed. Results: Using conventional statistics, the high expression of MYC and BCL2 was associated with poor survival, but high BCL6 was associated with a favorable overall survival of the patients. Then, a neural network predicted MYC, BCL2, and BCL6 with high accuracy using a pan-cancer panel of 758 genes of immuno-oncology and translational research that includes clinically relevant actionable genes and pathways. A comparable analysis was performed using gene set enrichment analysis (GSEA). Conclusions: The mathematical way in which neural networks reach conclusions has been considered a black box, but a careful understanding and evaluation of the architectural design allows us to interpret the results logically. In diffuse large B-cell lymphoma, neural networks are a plausible data analysis approach.
引用
收藏
页码:119 / 143
页数:25
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