Assessment of Treatment Response in Patient of HCC by ADC Histogram Post-TACE in Correlation with Modified RECIST
Abstract Background Hepatocellular carcinoma (HCC) is the fifth most common cancers worldwide, and has a poor prognosis unless treated. Trance arterial chemoembolization is promising treatment option for patients with liver HCC who are not eligible for surgery. Objective The aim of this study is assessment pre & post TACE imaging based on histogram analysis for predicting response of the treatment in patients diagnosed with hepatocellular carcinoma (HCC). Patients and Methods This work was a prospective study conducted on 30 patients with HCC; to assess therapeutic response of HCC cases after TACE by ADC histogram in comparison to mRECIST to evaluate its role in detecting treatment response /residual tumor. Results According to CART test, out of 30 patients, 12 (40%) showing a Complete response, indicating a favorable outcome where the treatment successfully reduced the tumor burden, 14 patients (46.67%) demonstrated a Partial response, suggesting a significant reduction in tumor size or overall improvement in disease status as well as 4 patients (13.33%) had a Stable response, indicating that their disease remained relatively stable without significant progression or regression. So, There is a statistically significant correlation between the responses categorized the mRECIST methods and CART test (P = 0.013). Conclusion changes in mean ADC and ADC kurtosis, as a measure of change in tissue heterogencity, can be used as quantitative values to assess post TACE treatment response in patient with well-circumscribed HCC.
Authors
- Enas Ahmed Azzab
- Moghniuddin Mohammed (ORCID: https://orcid.org/0000-0003-3614-4922)
- Ali El Saied Abd El-Rahman
- Rania Mostafa Mahmoud El Desouky
Institutions
- Ain Shams University (EG)
- Research Institute for Tropical Medicine (PH)
Publication Details
- Journal
- QJM
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1093/qjmed/hcae175.959
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00