Clinical application of radiomics for the prediction of treatment outcome and survival in patients with renal cell carcinoma: a systematic review

被引：1

作者：

Khene, Zine-Eddine ^{[1
,2
,3
]}

Tachibana, Isamu ^{[1
]}

Bertail, Theophile ^{[2
,7
]}

Fleury, Raphael ^{[2
]}

Bhanvadia, Raj ^{[1
]}

Kapur, Payal ^{[4
]}

Rajaram, Satwik ^{[4
]}

Guo, Junyu ^{[5
]}

Christie, Alana ^{[6
]}

Pedrosa, Ivan ^{[5
]}

Lotan, Yair ^{[1
]}

Margulis, Vitaly ^{[1
]}

机构：

[1] Univ Texas Southwestern Med Ctr, Dept Urol, 2001 Inwood Rd,WCB3 Floor 4, Dallas, TX 75390 USA

[2] Univ Rennes, Dept Urol, Rennes, France

[3] Univ Rennes, Image & Signal Proc Lab, Inserm U1099, Rennes, France

[4] UT Southwestern Med Ctr, Dept Pathol, Dallas, TX USA

[5] UT Southwestern Med Ctr, Dept Radiol, Dallas, TX USA

[6] Univ Texas Southwestern Med Ctr, Simmons Comprehens Canc Ctr Biostat, Dallas, TX USA

[7] CLCC Eugene Marquis, Radiat Oncol Dept, F-35000 Rennes, France

来源：

WORLD JOURNAL OF UROLOGY | 2024年 / 42卷 / 01期

关键词：

Kidney cancer; Renal cell carcinoma; Radiomics; Artificial intelligence; Machine learning; Survival; CT;

D O I：

10.1007/s00345-024-05247-z

中图分类号：

R5 [内科学]; R69 [泌尿科学（泌尿生殖系疾病）];

学科分类号：

1002 ; 100201 ;

摘要：

PurposeThe management of renal cell carcinoma (RCC) relies on clinical and histopathological features for treatment decisions. Recently, radiomics, which involves the extraction and analysis of quantitative imaging features, has shown promise in improving RCC management. This review evaluates the current application and limitations of radiomics for predicting treatment and oncological outcomes in RCC.MethodsA systematic search was conducted in Medline, EMBASE, and Web of Science databases or studies that used radiomics to predict response to treatment and survival outcomes in patients with RCC. The study quality was assessed using the Radiomics Quality Score (RQS) tools.ResultsThe systematic review identified a total of 27 studies, examining 6,119 patients. The most used imaging modality was contrast-enhanced abdominal CT. The reviewed studies extracted between 19 and 3376 radiomics features, including Histogram, Texture, Filter, or transformation method. Radiomics-based risk stratification models provided valuable insights into treatment response and oncological outcomes. All developed signatures demonstrated at least modest accuracy (AUC range: 0.55-0.99). The studies included in this analysis reported heterogeneous results regarding radiomics methods. The range of Radiomics Quality Score (RQS) was from - 5 to 20, with a mean RQS total of 9.15 +/- 7.95.ConclusionRadiomics has emerged as a promising tool in the management of RCC. It offers the potential for improved risk stratification and response assessment. However, future trials must demonstrate the generalizability of findings to prospective cohorts before progressing towards clinical translation.

引用

页数：15