Radiomics of Liver Metastases : A Systematic Review

Multidisciplinary management of patients with liver metastases (LM) requires a precision medicine approach, based on adequate profiling of tumor biology and robust biomarkers. Radiomics, defined as the high-throughput identification, analysis, and translational applications of radiological textural features, could fulfill this need. The present review aims to elucidate the contribution of radiomic analyses to the management of patients with LM. We performed a systematic review of the literature through the most relevant databases and web sources. English language original articles published before June 2020 and concerning radiomics of LM extracted from CT, MRI, or PET-CT were considered. Thirty-two papers were identified. Baseline higher entropy and lower homogeneity of LM were associated with better survival and higher chemotherapy response rates. A decrease in entropy and an increase in homogeneity after chemotherapy correlated with radiological tumor response. Entropy and homogeneity were also highly predictive of tumor regression grade. In comparison with RECIST criteria, radiomic features provided an earlier prediction of response to chemotherapy. Lastly, texture analyses could differentiate LM from other liver tumors. The commonest limitations of studies were small sample size, retrospective design, lack of validation datasets, and unavailability of univocal cut-off values of radiomic features. In conclusion, radiomics can potentially contribute to the precision medicine approach to patients with LM, but interdisciplinarity, standardization, and adequate software tools are needed to translate the anticipated potentialities into clinical practice.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:12

Enthalten in:

Cancers - 12(2020), 10 vom: 07. Okt.

Sprache:

Englisch

Beteiligte Personen:

Fiz, Francesco [VerfasserIn]
Viganò, Luca [VerfasserIn]
Gennaro, Nicolò [VerfasserIn]
Costa, Guido [VerfasserIn]
La Bella, Ludovico [VerfasserIn]
Boichuk, Alexandra [VerfasserIn]
Cavinato, Lara [VerfasserIn]
Sollini, Martina [VerfasserIn]
Politi, Letterio S [VerfasserIn]
Chiti, Arturo [VerfasserIn]
Torzilli, Guido [VerfasserIn]

Links:

Volltext

Themen:

Computer-assisted diagnosis
Gray level matrices
Journal Article
Liver metastases
Overall and recurrence-free survival
Radiomics
Response to chemotherapy
Review
Texture analysis

Anmerkungen:

Date Revised 03.11.2020

published: Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.3390/cancers12102881

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM316064408