Advanced image analytics predicting clinical outcomes in patients with colorectal liver metastases: A systematic review of the literature
| Autor: | J.H.T.M. van Waesberghe, V.J. van Goor, Nina J. Wesdorp, J. Huiskens, R. Kemna, Rutger-Jan Swijnenburg, Geert Kazemier, Cornelis J A Punt, Elise P. Jansma |
|---|---|
| Přispěvatelé: | Surgery, Radiology and nuclear medicine, AGEM - Re-generation and cancer of the digestive system, CCA - Cancer Treatment and quality of life, Internal medicine |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Oncology
medicine.medical_specialty Disease Cochrane Library 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Radiomics Internal medicine Antineoplastic Combined Chemotherapy Protocols Image Processing Computer-Assisted Humans Medicine In patient Prospective cohort study business.industry Liver Neoplasms Prognosis Advanced image analytics Survival Rate Colorectal liver metastases Analytics Homogeneous 030220 oncology & carcinogenesis Quality Score Surgery Neoplasm Recurrence Local Colorectal Neoplasms Tomography X-Ray Computed business |
| Zdroj: | Wesdorp, N J, van Goor, V J, Kemna, R, Jansma, E P, van Waesberghe, J H T M, Swijnenburg, R J, Punt, C J A, Huiskens, J & Kazemier, G 2021, ' Advanced image analytics predicting clinical outcomes in patients with colorectal liver metastases : A systematic review of the literature ', Surgical Oncology, vol. 38, pp. 101578 . https://doi.org/10.1016/j.suronc.2021.101578 Surgical Oncology, 38. Elsevier BV |
| ISSN: | 0960-7404 |
| Popis: | BACKGROUND: To better select patients with colorectal liver metastases (CRLM) for an optimal selection of treatment strategy (i.e. local, systemic or combined treatment) new prognostic models are warranted. In the last decade, radiomics has emerged as a field to create predictive models based on imaging features. This systematic review aims to investigate the current state and potential of radiomics to predict clinical outcomes in patients with CRLM.METHODS: A comprehensive literature search was conducted in the electronic databases of PubMed, Embase, and Cochrane Library, according to PRISMA guidelines. Original studies reporting on radiomics predicting clinical outcome in patients diagnosed with CRLM were included. Clinical outcomes were defined as response to systemic treatment, recurrence of disease, and survival (overall, progression-free, disease-free). Primary outcome was the predictive performance of radiomics. A narrative synthesis of the results was made. Methodological quality was assessed using the radiomics quality score.RESULTS: In 11 out of 14 included studies, radiomics was predictive for response to treatment, recurrence of disease, survival, or a combination of outcomes. Combining clinical parameters and radiomic features in multivariate modelling often improved the predictive performance. Different types of individual features were found prognostic. Noticeable were the contrary levels of heterogeneous and homogeneous features in patients with good response. The methodological quality as assessed by the radiomics quality score varied considerably between studies.CONCLUSION: Radiomics appears a promising non-invasive method to predict clinical outcome and improve personalized decision-making in patients with CRLM. However, results were contradictory and difficult to compare. Standardized prospective studies are warranted to establish the added value of radiomics in patients with CRLM. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect