We've updated our Privacy Policy to make it clearer how we use your personal data.

We use cookies to provide you with a better experience. You can read our Cookie Policy here.

Advertisement

AI Uses Liver Cancer MRI To Predict Treatment Responses


Want a FREE PDF version of This News Story?

Complete the form below and we will email you a PDF version of "AI Uses Liver Cancer MRI To Predict Treatment Responses"

Technology Networks Ltd. needs the contact information you provide to us to contact you about our products and services. You may unsubscribe from these communications at any time. For information on how to unsubscribe, as well as our privacy practices and commitment to protecting your privacy, check out our Privacy Policy

Read time:
 

According to ARRS’ American Journal of Roentgenology (AJR), machine learning models applied to presently underutilized imaging features could help construct more reliable criteria for organ allocation and liver transplant eligibility.


“The findings suggest that machine learning-based models can predict recurrence before therapy allocation in patients with early-stage hepatocellular carcinoma (HCC) initially eligible for liver transplant,” wrote corresponding author Julius Chapiro from the department of radiology and biomedical imaging at Yale University School of Medicine in New Haven, CT.


Chapiro and colleagues’ proof-of-concept study included 120 patients (88 men, 32 women; median age, 60 years) diagnosed with early-stage HCC between June 2005 and March 2018, who were initially eligible for liver transplant and underwent treatment by transplant, resection, or thermal ablation. Patients underwent pretreatment MRI and posttreatment imaging surveillance, and imaging features were extracted from postcontrast phases of pretreatment MRI examinations using a pretrained convolutional neural network (VGG-16). Pretreatment clinical characteristics (including laboratory data) and extracted imaging features were integrated to develop three ML models—clinical, imaging, combined—for recurrence prediction within 1–6 years posttreatment.


Ultimately, all three models predicted posttreatment recurrence for early-stage HCC from pretreatment clinical (AUC 0.60–0.78, across all six time frames), MRI (AUC 0.71–0.85), and both data combined (AUC 0.62–0.86). Using imaging data as the sole model input yielded higher predictive performance than clinical data alone; however, combining both data types did not significantly improve performance over use of imaging data alone.


Reference: Iseke S, Zeevi T, Kucukkaya AS, et al. Machine-learning models for prediction of posttreatment recurrence in early-stage hepatocellular carcinoma using pretreatment clinical and MRI features: A proof-of-concept study. Am J Roentgenol. 2022. doi: 10.2214/AJR.22.28077

  

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.


Advertisement