Ghada Mohamed Abdel-Salam

Ghada Mohamed Abdel-Salam

orcid

Research Focus & Metrics

Academic Career Length

8Years (2019-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 November 2020 in British Journal of Radiology
Verified
Journal Article
Closed access
MSMahmoud Mohamed Saleh
Mahmoud Mohamed Saleh
TATamer Moustafa Abdelrahman
Tamer Moustafa Abdelrahman
YMYouusef Madney
Youusef Madney
GAGhada Mohamed Abdel-Salam
Ghada Mohamed Abdel-Salam
ASAhmed Mohammed Shokry
Ahmed Mohammed Shokry
AMAmr Farouk Moustafa
Amr Farouk Moustafa
Objective: To evaluate the multiparametric MRI in predicting chemotherapy response in pathologically proven cases of osteosarcoma and Ewing’s sarcoma. Correlation between the tumor size changes and internal breakdown using RECIST 1.1, modified RECIST, quantitative apparent diffusion coefficient (ADC) and tumor volume as well as dynamic contrast-enhanced MRI (DCE-MRI). Methods: The study included 104 patients pathologically proved osteosarcoma (53) and Ewing`s sarcoma (51) underwent MRI examinations; before and after chemotherapy. All patients were assessed using the RECIST 1.1 criteria, m-RECIST, quantitative ADC, and tumor volume evaluation. 21 patients underwent DCE-MRI curve type with quantitative parameters. Correlation between the different evaluations was carried out. Results were correlated with the post-operative pathology in 42 patients who underwent surgery and for statistical evaluation, Those patients were classified into responders (≥90% necrosis) and non-responders (<90% necrosis). Results: The initial mean ADC of 104 patients of osteosarcoma and Ewing’s sarcoma (0.90 ± 0.29) and (0.71 ± 0.16) respectively, differed significantly from that after treatment (1.62 ± 0.46) and (1.6 ± 0.39) respectively with (p<0.001). ADC variations (ADC%) in the non-progressive group were higher than those of the progressive group (128.3 ± 63.49 vs 36.34 ± 78.7) % with (p<0.001). ADC values and ADC variations were inversely correlated with morphologic changes, regardless of the effectiveness of chemotherapy expressed as changes in tumor size based on (RECIST 1.1, RECIST, and 3D volume). Linear regression analysis revealed a Pearson correlation coefficient of r=-0.427, -0.498 and -0.408, respectively with (p<0.001). An increase in the ADC value was not always associated with a reduction in tumor volume. The disease control rate (defined as the percentage of CR+PR+SD patients) was 89.4% and 93.9% according to RECIST 1.1 and m-RECIST respectively. 42 out of the 104 patients had postsurgical histological evaluation as regards the chemotherapeutic response divided into two groups. ADC values showed a statistically significant difference between Group A and Group B being more evident with minimum ADC% (p<0.001). Conclusion: Quantitative diffusion-weighted imaging with ADC mapping and ADC % after chemotherapy allows a detailed analysis of the treatment response in osteosarcoma and Ewing’s sarcoma. The therapeutic response can be underestimated using RECIST 1.1, so the modified RECIST should be also considered. Advances in knowledge: Quantitative ADC especially ADC% provided an accurate non-invasive tool in the assessment of post-therapeutic cases of osteosarcoma and Ewing's sarcoma

9 February 2019 in Pathology and Oncology Research
Verified
Journal Article
Closed access
PDF file
GAGhada Mohamed Abdel-Salam
Ghada Mohamed Abdel-Salam
STSoha Talima
Soha Talima
LLLili Li
Lili Li
WWWenbin Wei
Wenbin Wei
ZRZbigniew Rudzki
Zbigniew Rudzki
PMPaul G Murray
Paul G Murray

1 June 2021 in Clinical Breast Cancer
Verified
Journal Article
Closed access
HFHagar H. Fahim
Hagar H. Fahim
GAGhada Mohamed Abdel-Salam
Ghada Mohamed Abdel-Salam
GSGehan Safwat
Gehan Safwat
AAAsmaa Abo-Bakr
Asmaa Abo-Bakr
MIMaher H. Ibraheem
Maher H. Ibraheem
Abdallah M. Gameel AbdallahAbdallah M. Gameel Abdallah
Abdallah M. Gameel Abdallah

6 April 2025 in Pathology - Research and Practice
Verified
Journal Article
Closed access
GAGhada Mohamed Abdel-Salam
Ghada Mohamed Abdel-Salam
OHOmar Hamdy
Omar Hamdy
AAAnwar Alkallas
Anwar Alkallas
YTYoussef Tahoun
Youssef Tahoun
MGMohammed Mohammed Gomaa
Mohammed Mohammed Gomaa
MEMahitab Ibrahim Eltohamy
Mahitab Ibrahim Eltohamy

11 September 2022 in International Journal of Breast Cancer
Verified
Journal Article
Open access
AEAya Elmahs
Aya Elmahs
GAGhada Mohamed Abdel-Salam
Ghada Mohamed Abdel-Salam
MSMostafa Salem
Mostafa Salem
DODina Omar
Dina Omar
AHAmany Mohamed Helal
Amany Mohamed Helal
NSNahed Soliman
Nahed Soliman
To avoid unnecessary neoadjuvant chemotherapy in case anticipating a poor therapy response, it is essential to find the pathological parameters that would predict pathological complete response or at least a decrease in tumor burden following neoadjuvant chemotherapy. The purpose of this study is to investigate the hypothesis that tumor infiltrating lymphocytes can predict the efficacy of neoadjuvant chemotherapy and to find the Ki67 cutoff value that best predicts the benefit of chemotherapy. 153 cases of breast cancer were chosen, based on their molecular subtype: triple negative subtype (77) and luminal, HER2-ve subtype (76). Histopathological assessment of pretherapy core biopsies was conducted to assess variable pathological parameters including TILs rates with the aid of immunohistochemical staining for CD20 and CD3. Moreover, core biopsies were stained for Ki67, and the findings were compared to the residual cancer burden following neoadjuvant chemotherapy. On analyzing and contrasting the two groups, a significant association between molecular subtype and pathological complete response was confirmed, while tumor-infiltrating lymphocytes in either group had no effect on therapy response. We used receiver operating characteristic curve analysis to determine that a cutoff of 36% for Ki67 is the most accurate value to predict complete therapy response.

12 November 2024 in The Journal of Steroid Biochemistry and Molecular Biology
Verified
Journal Article
Closed access
BABelal M. Ali
Belal M. Ali
HEHanan S. Elabhar
Hanan S. Elabhar
GAGhada Mohamed Abdel-Salam
Ghada Mohamed Abdel-Salam
HNHanan R. Nassar
Hanan R. Nassar
NANelly Alieldin
Nelly Alieldin
MKMarwa Wagih Kamel
Marwa Wagih Kamel