Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

@article{Papp2018PersonalizingMT,
  title={Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis},
  author={Laszlo Papp and Clemens P. Spielvogel and Ivo Rausch and Marcus Hacker and Thomas Beyer},
  journal={Frontiers in Physics},
  year={2018}
}
Medical imaging has evolved from a pure visualization tool to representing a primary source of analytic approaches towards in vivo disease characterization. Hybrid imaging is an integral part of this approach, as it provides complementary visual and quantitative information in the form of morphological and functional insights into the living body. As such, non-invasive imaging modalities no longer provide images only, but data, as stated recently by pioneers in the field. Today, such… 

Figures and Tables from this paper

Nuclear medicine radiomics in precision medicine: why we can't do without artificial intelligence.

TLDR
The radiomic pipeline, its applications and the increasing role of artificial intelligence within the field are described and the challenges that need to be overcome to achieve clinical translation are discussed, so that radiomics, combined with clinical data and other biomarkers, can contribute to precision medicine.

High-dimensional role of AI and machine learning in cancer research.

TLDR
The transformative value of IS applied to multimodal data acquired through various interrelated cancer domains, including high-throughput genomics, experimental biology, medical image processing, radiomics, patient electronic records, etc., is discussed.

Machine Learning Applications of Surgical Imaging for the Diagnosis and Treatment of Spine Disorders: Current State of the Art.

TLDR
It is concluded that ML in spine imaging represents a significant addition to the neurosurgeon's armamentarium-it has the capacity to directly address and manifest clinical needs and improve diagnostic and procedural quality and safety-but is yet subject to challenges that must be addressed before widespread implementation.

Data Mining in Spine Surgery: Leveraging Electronic Health Records for Machine Learning and Clinical Research

TLDR
There is no question that ML is already starting to affect surgical practice relevantly in many aspects, and the advent of open source algorithms provided by today's tech giants have largely democratized the development of ML models, and as the authors summarize, this has led to an explosion of publications reporting such algorithms.

Integrative Systems Biology Resources and Approaches in Disease Analytics

TLDR
Several database resources, standalone and web-based tools applied in disease analytics workflows based in data-driven integration of outputs of multi-omic detection platforms are described.

Multi-lesion radiomics of PET/CT for non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma

TLDR
Machine learning models trained on pre-therapeutic PET/CT radiomics enable highly accurate long-term survival prediction of patients with lung adenocarcinoma and are able to predict long- term survival outcomes in LUAD patients with high accuracy.

Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

TLDR
The current state of machine learning using electronic medical records as it applies to spine surgery is examined, and applications include clinical prognostication, preoperative planning, diagnostics, and dynamic clinical assistance.

Two-Year Event-Free Survival Prediction in DLBCL Patients Based on In Vivo Radiomics and Clinical Parameters

TLDR
Predicting 2-year event-free survival built on imaging features is feasible by utilizing high-performance automated machine learning.

References

SHOWING 1-10 OF 255 REFERENCES

Machine Learning in Medical Imaging

TLDR
This special issue focuses on major trends and challenges in this area, and it presents work aimed at identifying new cutting-edge techniques and their use in medical imaging, as well as a series of medical imaging applications of machine-learning techniques.

Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases

TLDR
The results suggest that when the individual modalities demonstrate relatively poor discriminability, many of the data fusion methods may not yield accurate, discriminatory representations either, and methodological choices for data fusion must explicitly account for the sparsity of and noise in the feature space.

Radiomics: extracting more information from medical images using advanced feature analysis.

Deep Learning in Medical Image Analysis.

TLDR
This review covers computer-assisted analysis of images in the field of medical imaging and introduces the fundamentals of deep learning methods and their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on.

Medical Image Data and Datasets in the Era of Machine Learning—Whitepaper from the 2016 C-MIMI Meeting Dataset Session

TLDR
There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data and high-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described.

The future of hybrid imaging—part 2: PET/CT

TLDR
This review summarises the state-of-the-art of dual-modality PET/CT imaging with a focus on clinical applications and highlights selected areas for potential improvement of combined imaging technologies and new applications.

PET and MRI: Is the Whole Greater than the Sum of Its Parts?

TLDR
The authors have combined multiparametric MRI and PET imaging to address the important issue of intratumoral heterogeneity in breast cancer using both preclinical and clinical data and have been able to identify multiple coexisting regions ("habitats") within living tumors.

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

TLDR
The data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer, which may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.

Radiomics: the process and the challenges.

...