Abstract
Recently, the concept of radiomics has emerged from radiation oncology. It is a novel approach for solving the issues of precision medicine and how it can be performed, based on multimodality medical images that are noninvasive, fast and low in cost. Radiomics is the comprehensive analysis of massive numbers of medical images in order to extract a large number of phenotypic features (radiomic biomarkers) reflecting cancer traits, and it explores the associations between the features and patients' prognoses in order to improve decision-making in precision medicine. Individual patients can be stratified into subtypes based on radiomic biomarkers that contain information about cancer traits that determine the patient's prognosis. Machine-learning algorithms of AI are boosting the powers of radiomics for prediction of prognoses or factors associated with treatment strategies, such as survival time, recurrence, adverse events, and subtypes. Therefore, radiomic approaches, in combination with AI, may potentially enable practical use of precision medicine in radiation therapy by predicting outcomes and toxicity for individual patients.
| Original language | English |
|---|---|
| Pages (from-to) | 150-157 |
| Number of pages | 8 |
| Journal | Journal of radiation research |
| Volume | 60 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 1 2019 |
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All Science Journal Classification (ASJC) codes
- Radiation
- Radiology Nuclear Medicine and imaging
- Health, Toxicology and Mutagenesis
Cite this
Radiomics with artificial intelligence for precision medicine in radiation therapy. / Arimura, Hidetaka; Soufi, Mazen; Kamezawa, Hidemi; Ninomiya, Kenta; Yamada, Masahiro.
In: Journal of radiation research, Vol. 60, No. 1, 01.01.2019, p. 150-157.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Radiomics with artificial intelligence for precision medicine in radiation therapy
AU - Arimura, Hidetaka
AU - Soufi, Mazen
AU - Kamezawa, Hidemi
AU - Ninomiya, Kenta
AU - Yamada, Masahiro
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Recently, the concept of radiomics has emerged from radiation oncology. It is a novel approach for solving the issues of precision medicine and how it can be performed, based on multimodality medical images that are noninvasive, fast and low in cost. Radiomics is the comprehensive analysis of massive numbers of medical images in order to extract a large number of phenotypic features (radiomic biomarkers) reflecting cancer traits, and it explores the associations between the features and patients' prognoses in order to improve decision-making in precision medicine. Individual patients can be stratified into subtypes based on radiomic biomarkers that contain information about cancer traits that determine the patient's prognosis. Machine-learning algorithms of AI are boosting the powers of radiomics for prediction of prognoses or factors associated with treatment strategies, such as survival time, recurrence, adverse events, and subtypes. Therefore, radiomic approaches, in combination with AI, may potentially enable practical use of precision medicine in radiation therapy by predicting outcomes and toxicity for individual patients.
AB - Recently, the concept of radiomics has emerged from radiation oncology. It is a novel approach for solving the issues of precision medicine and how it can be performed, based on multimodality medical images that are noninvasive, fast and low in cost. Radiomics is the comprehensive analysis of massive numbers of medical images in order to extract a large number of phenotypic features (radiomic biomarkers) reflecting cancer traits, and it explores the associations between the features and patients' prognoses in order to improve decision-making in precision medicine. Individual patients can be stratified into subtypes based on radiomic biomarkers that contain information about cancer traits that determine the patient's prognosis. Machine-learning algorithms of AI are boosting the powers of radiomics for prediction of prognoses or factors associated with treatment strategies, such as survival time, recurrence, adverse events, and subtypes. Therefore, radiomic approaches, in combination with AI, may potentially enable practical use of precision medicine in radiation therapy by predicting outcomes and toxicity for individual patients.
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U2 - 10.1093/jrr/rry077
DO - 10.1093/jrr/rry077
M3 - Article
VL - 60
SP - 150
EP - 157
JO - Journal of Radiation Research
JF - Journal of Radiation Research
SN - 0449-3060
IS - 1
ER -