Radiomics in surgical oncology: applications and challenges

Computer Assisted Surgery

Travis L. Williams¹  Mithat Gonen¹  Richard K. G. Do²  Lily V. Saadat³  Alice Wei³  Amber L. Simpson⁴ 

[1] Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, US;Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, US;Department of Surgery – Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, US;School of Computing, Queen’s University, Kingston, ON, Canad;Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canad;

关键词: Radiomics;    neoadjuvant;    adjuvant;    chemotherapy;    machine learning;    review;    challenges in surgery;   

DOI  :  10.1080/24699322.2021.1994014

来源: Taylor & Francis

【 摘 要 】

Surgery is a curative treatment option for many patients with malignant tumors. Increased attention has focused on the combination of surgery with chemotherapy, as multimodality treatment has been associated with promising results in certain cancer types. Despite these data, there remains clinical equipoise on optimal timing and patient selection for neoadjuvant or adjuvant strategies. Radiomics, an emerging field involving the extraction of advanced features from radiographic images, has the potential to revolutionize oncologic treatment and contribute to the advance of personalized therapy by helping predict tumor behavior and response to therapy. This review analyzes and summarizes studies that use radiomics with machine learning in patients who have received neoadjuvant and/or adjuvant chemotherapy to predict prognosis, recurrence, survival, and therapeutic response for various cancer types. While studies in both neoadjuvant and adjuvant settings demonstrate above average performance on ability to predict progression-free and overall survival, there remain many challenges and limitations to widespread implementation of this technology. The lack of standardization of common practices to analyze radiomics, limited data sharing, and absence of auto-segmentation have hindered the inclusion and rapid adoption of radiomics in prospective, clinical studies.

【 授权许可】

CC BY