Search In this Thesis
   Search In this Thesis  
العنوان
Assessment of Treatment Response
in Patient of HCC by ADC Histogram
Post-TACE in Correlation with
Modified RECIST /
المؤلف
El Desouky, Rania Mostafa Mahmoud.
هيئة الاعداد
باحث / رانيا مصطفى محمود الدسوقي
مشرف / ايناس احمد عزب
مشرف / مروة سيد محمد
مشرف / على السعيد عبد الرحمن
تاريخ النشر
2024.
عدد الصفحات
134 P. :
اللغة
الإنجليزية
الدرجة
ماجستير
التخصص
الأشعة والطب النووي والتصوير
تاريخ الإجازة
1/1/2024
مكان الإجازة
جامعة عين شمس - كلية الطب - قسم الاشعة التشخيصية
الفهرس
Only 14 pages are availabe for public view

from 134

from 134

Abstract

H
epatocellular carcinoma (HCC) is the fifth most common cancers worldwide, and has a poor prognosis unless treated. Ablative therapies are promising treatment option for patients with liver HCC who are not eligible for surgery.
Evaluation of tumor response after trans arterial chemoembolization(TACE) is an important task in oncologic imaging.
Early favorable response indicates effectiveness of therapy while early identification of treatment failure is also critical in patient management as further treatment will be mandatory before disease progression.
Recent advances in the development of functional MRI techniques have provided the ability to detect microscopic changes in tumor microenvironment and microstructure that allow better assessment of treatment response.
Dynamic contrast enhanced(DCE) MRI can assess the change in the tumor vascularity and perfusion. While DWI/ADC is used in the quantification of hepatic fibrosis in chronic liver disease, in characterization and detection of focal liver lesions.
ADC histogram and average value can be used as a quantitative value to differentiate between active and inactive tumors. and in monitoring response to treatment in oncological patients.
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.
Regarding gender of the patients, the majority (53.3%) of patients were males; while 46.7 % were females.
Regarding number of TACE session , 83.3% of patients underwent 1 session , and 16.7% underwent 2 sessions.
30 HCC patients who underwent TACE and were classified into three groups based on their response to treatment according to mRECIST
• Stable response group (10 patients)
• partial response group (12 patients)
• complete response group (8 patients)
The post-TACE ADC values were significantly different among the three groups The favorable response group had the highest ADC values followed by the partial response group and the poor response group had the lowest ADC values
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
T
he findings provide valuable insights into the prognostic value of specific parameters derived from advanced tissue texture analysis techniques. In the context of well-circumscribed HCC, patients who exhibit a substantial increase in ADC values following TACE have a more favorable prognosis in terms of tumor response outcomes. This finding highlights the potential of using ADC changes as an early response indicator and a prognostic marker in well-circumscribed HCC.
These findings provide novel insights into the potential of advanced tissue texture analysis and machine learning techniques in predicting treatment response and prognostication for HCC patients. The proposed criteria offer a promising approach for assessing treatment response and stratifying patients based on their prognosis. This study lays the foundation for future investigations on the application of advanced imaging analysis techniques and machine learning algorithms in HCC management, aiming to enhance treatment decision-making and improve patient outcomes. Further validation and prospective studies are warranted to confirm and refine these criteria and to explore their applicability in larger and more diverse patient populations.