Radiomics Approaches for the Prediction of Pathological Complete Response after Neoadjuvant Treatment in Locally Advanced Rectal Cancer: Ready for Prime Time?

Abstract

In recent years, neoadjuvant therapy of locally advanced rectal cancer has seen tremendous modifications. Adding neoadjuvant chemotherapy before or after chemoradiotherapy significantly increases loco-regional disease-free survival, negative surgical margin rates, and complete response rates. The higher complete rate is particularly clinically meaningful given the possibility of organ preservation in this specific sub-population, without compromising overall survival. However, all locally advanced rectal cancer most likely does not benefit from total neoadjuvant therapy (TNT), but experiences higher toxicity rates. Diagnosis of complete response after neoadjuvant therapy is a real challenge, with a risk of false negatives and possible under-treatment. These new therapeutic approaches thus raise the need for better selection tools, enabling a personalized therapeutic approach for each patient. These tools mostly focus on the prediction of the pathological complete response given the clinical impact. In this article, we review the place of different biomarkers (clinical, biological, genomics, transcriptomics, proteomics, and radiomics) as well as their clinical implementation and discuss the most recent trends for future steps in prediction modeling in patients with locally advanced rectal cancer.

Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model: A dual-center study

“…Noninvasive assessment of the prognosis of RC is challenging and has always been a research hotspot[ 26 , 27 ]. Research has shown that PNI is not only characterized by tumor cell infiltration and growth along the nerves but also involves an interaction between various neurotrophic and chemotactic factors released from tumor cells and the surrounding microenvironment.…”

Application research of radiomics in colorectal cancer: A bibliometric study

“…[ 17 – 19 ] With the continuous development of radiomics, the application of radiomics in the evaluation of CRC efficacy is gradually increasing. [ 20 ] Shi et al [ 21 ] retrospectively collected T2-weighted Imaging (T2WI), diffusion weighted imaging, and dynamic contrast-enhanced sequences of patients with locally advanced rectal cancer (LARC) who received neoadjuvant chemoradiotherapy (nCRT), and extracted radiomics features from them to predict the pathological complete response of LARC patients after nCRT. The results showed that in the prospective validation set, the radiomics model AUC = 0.84 (95% CI: 0.70–0.94), and the radiomics model could effectively predict pathological complete response after nCRT.…”

Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes

“…Previous NSCLC radiomic analyses have primarily focused on establishing non-invasive biomarkers for specific clinical endpoints, including diagnosis [ 28 , 29 ], treatment response [ 30 , 31 ], and prognostic assessment [ 32 , 33 ]. While these studies have confirmed that radiomic features can capture relevant molecular biomarkers, their clinical utility could be improved by the fundamental limitation that these imaging features are only modest predictors of specific molecular characteristics.…”