Radiomics in surgical oncology: applications and challenges

Abstract

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.

Keywords: Radiomics; adjuvant; challenges in surgery; chemotherapy; machine learning; neoadjuvant; review.

Fig 1:

This graphic gives a patient a top-level view of their progression from an initial screening, chemotherapy, surgery, and surveillance after resection. Radiomics serve as a diagnostic and prognostic tool, which can be applied at many different timepoints in oncologic care. Use of this technology in the diagnosis stage, treatment planning stage, and surveillance stage can complement traditional oncologic care and help personalize treatment options for patients with solid tumors.

Fig 2:

This flowchart shows the generalized overview of the neoadjuvant and adjuvant studies from patient image acquisition (CT, MRI, PET), tumor segmentation, radiomic signature, machine learning analysis, and the predicted outcome. These studies capture prognosis, recurrence, therapeutic response, survival, and tumor volume. These various outcome categories describe different aspects of the effectiveness of either neoadjuvant or adjuvant chemotherapy.^,

Fig 3:

This flowchart shows the image data processing and radiomic analysis used to parse and validate the radiomic features. LASSO = least absolute shrinkage and selection operator, LDA = linear discriminant analysis, RF = random forest, ROC = receiver operating characteristic.

Fig 4:

This flowchart shows the generalized overview of the neoadjuvant and adjuvant studies from patient image acquisition (CT, MRI, PET), tumor segmentation, radiomic signature, machine learning analysis, and the predicted outcome in the traditional machine learning radiomics approach versus the deep learning approach.