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Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging pp 171–189Cite as

Integrative Computational Biology, AI, and Radiomics: Building Explainable Models by Integration of Imaging, Omics, and Clinical Data

Abstract

Addressing important clinical questions requires systematic knowledge management and analysis of the large volume of information. Understanding and successfully treating multi-genic diseases requires systems-oriented research approach focused on the implication of disease-perturbed molecular interaction networks and pathways. These networks represent crucial relationships among genes and proteins, their mutations, chromosomal aberrations, microRNA deregulation, and other epigenetic and metabolic changes.

Artificial intelligence research focuses on the development of diverse algorithms, their application in multiple areas, and system usability questions. Decision support systems in medicine need to be robust across assays, instruments, and cohorts, handling uncertainty and missing data gracefully. However, quality of data and literature-based evidence may be questionable, leading to low reproducibility and errors. Creating explainable models is essential to ensure trust in recommendations and decision support. Patient-centric, data-driven medicine requires high quality, comprehensive data sets, multiple levels of independent validation, and explainable models. Integration of multiple and diverse data sets and algorithms can improve confidence in findings, and reduce both type I and II errors. It is also essential to determine who and how will use the system and optimize it accordingly, as even useful applications may result in negative outcomes when used improperly or in an incorrect context.

When solving complex problems, one solution is usually not enough. Neither one method, nor one data set can cover the complexities. Integrating methods and data sets provides a better solution. Artificial intelligence, multimodal imaging, integrative computational biology, and wearable devices are transforming translational research. We are moving from diagnosing and treating disease to preventing it. Data mining and machine learning algorithms identify trends and interesting patterns in the cohorts, ensemble methods then apply it with confidence for each individual, and calibrated trends provide predicted preventive measures for each patient.

Keywords

  • Precision medicine
  • Personalized medicine
  • Radiomics
  • Integrative computational biology
  • Artificial intelligence
  • Data mining
  • Machine learning

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Abbreviations

18F-FDG:

18Fluoro-deoxy-glucose

AD:

Alzheimer’s disease

AI:

Artificial intelligence

AUC:

Area under the curve

cDNA:

Complementary DNA

CNIL:

National Commission on Informatics and Liberty

CPU:

Central processing unit

CT:

Computer tomography

DLBCL:

Diffuse large B cell lymphoma

DNA:

Deoxyribonucleic acid

ECG:

Electrocardiography

EEG:

Electroencephalography

EFS:

Event-free survival

EIM:

Electrical impedance myography

EOG:

Electrooculography

EPR:

Electronic patient record

FCSRT:

Free and cued selective reminding test

fMRI:

Functional magnetic resonance imaging

GLCM:

Grey level co-occurrence matrix

GLNU:

Grey-level non-uniformity

GLSZM:

Grey-level size zone matrix

GPU:

Graphical processing unit

HGZE:

High grey-level zone emphasis

HT:

High-throughput

ICD:

International classification of diseases

IID:

Integrated interactions database

IPIaa:

Age-adjusted international prognostic index

LASSI-L :

Loewenstein-Acevedo scale for semantic interference and learning

LGZE:

Low grey-level zone emphasis

LUAD:

Lung adenocarcinoma

LZE:

Long-zone emphasis

LZHGE:

Long-zone high grey-level emphasis

LZLGE:

Long-zone low grey-level emphasis

mirDIP :

microRNA data integration portal

miRNA:

microRNA

ML:

Machine learning

MRI:

Magnetic resonance imaging

MSK:

Musculoskeletal

MTV:

Metabolic tumor volume

NDD:

Neurodegenerative diseases

NHL:

Non-Hodgkin’s lymphoma

OS:

Overall survival

OSEM:

Ordered subset expectation maximisation

pathDIP:

Pathway data integration portal

PET:

Positron emission tomography

piRNA:

Piwi-interacting RNA

PPI:

Protein–protein interaction

PSF:

Point spread function

RNA:

Ribonucleic acid

RNAseq:

RNA sequencing

ROC:

Receiver operating characteristic

scRNAseq:

Single-cell RNA sequencing

SD:

Standard deviation

SNOMED CT:

Standard nomenclature of medicine clinical terms

SUV:

Standardised uptake value

SZE:

Short-zone emphasis

SZHGE:

Short-zone high grey-level emphasis

SZLGE:

Short-zone low grey-level emphasis

TCGA:

The cancer genome atlas

TILs:

Tumor infiltrated lymphocytes

VOI:

Volume of interest

ZLNU:

Zone length non-uniformity

ZP:

Zone percentage

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Jurisica, I. (2022). Integrative Computational Biology, AI, and Radiomics: Building Explainable Models by Integration of Imaging, Omics, and Clinical Data. In: Veit-Haibach, P., Herrmann, K. (eds) Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging. Springer, Cham. https://doi.org/10.1007/978-3-031-00119-2_13

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