Maria Pilar Perlaza-Jiménez

Dr. Maria Pilar Perlaza-Jiménez


Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

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

Representative Publications

View All Publications
8 August 2025 in Cancers
Verified
Journal Article
Open access
PDF file
AVAna Maria Garcia Vicente
Ana Maria Garcia Vicente
MPMaria Pilar Perlaza-Jiménez
Maria Pilar Perlaza-Jiménez
SGStefania Guzman-Ortiz
Stefania Guzman-Ortiz
MTMarta Tormo-Ratera
Marta Tormo-Ratera
ASAna Sánchez-Márquez
Ana Sánchez-Márquez
Edel Noriega-ÁlvarezEdel Noriega-Álvarez
Edel Noriega-Álvarez
Background/Objectives: Endometrial cancer (EC) is the most common gynecological tumor in developed countries, and presents a wide variety of histological and molecular characteristics that make its treatment increasingly complex. In recent years, advances in molecular imaging, particularly with [18F]FDG-PET/CT and PET/MRI, have changed clinicians’ management of diagnosis, treatment planning, and prognosis of EC. Methods: In this narrative review, a search was conducted for current evidence on the role of [18F]FDG-PET/CT and PET/MRI throughout the treatment of EC, focusing on their diagnostic performance, clinical relevance, and prognostic implications. Their use in areas such as initial staging, treatment monitoring, recurrence detection, and individualized risk assessment was also discussed. Results: [18F]FDG-PET/CT is effective in detecting lymph node and distant metastases, while [18F]FDG-PET/MRI offers greater accuracy for T and N staging. In addition, [18F]FDG-PET/CT provides early metabolic indicators of therapeutic response and facilitates differentiation between viable tumors and post-treatment changes. Radiomics-derived parameters have shown promising associations with tumor aggressiveness and lymphovascular invasion and survival, supporting their role as prognostic imaging biomarkers. In addition, the use of non-FDG radiotracers, as well as predictive models based on machine learning, can further refine preoperative stratification and treatment planning in certain subtypes of EC. Conclusions: Molecular imaging enhances the accuracy of TNM staging in EC. The incorporation of molecular imaging biomarkers into daily clinical practice could significantly improve personalized decision-making in EC. However, large prospective studies are needed to confirm their efficacy.

19 June 2025 in Preprints.org
Verified
Preprint
Open access
PDF file
AGAna María García-Vicente
Ana María García-Vicente
SOStefanía Aida Guzmán Ortiz
Stefanía Aida Guzmán Ortiz
MPMaria Pilar Perlaza-Jiménez
Maria Pilar Perlaza-Jiménez
MTMarta Tormo-Ratera
Marta Tormo-Ratera
ASAna Sánchez-Márquez
Ana Sánchez-Márquez
Edel Noriega-ÁlvarezEdel Noriega-Álvarez
Edel Noriega-Álvarez
Background/Objectives: Endometrial cancer (EC) is the most common gynecological tumor in developed countries and presents a wide variety of histological and molecular characteristics that make its treatment increasingly complex. In recent years, advances in molecular imaging, particularly with [¹⁸F]FDG-PET/CT and PET/MRI, have changed clinicians management of diagnosis, treatment planning, and prognosis of EC. Methods: In this narrative review, a search was conducted for current evidence on the role of [¹⁸F]FDG-PET/CT and PET/MRI throughout the treatment of EC, focusing on their diagnostic performance, clinical relevance, and prognostic implications. Their use in areas such as initial staging, treatment monitoring, recurrence detection, and individualized risk assessment was also discussed. Results: [¹⁸F]FDG-PET/CT is effective in detecting lymph node and distant metastases, while [¹⁸F]FDG-PET/MRI offers greater accuracy for T and N staging. In addition, [¹⁸F]FDG-PET/CT provides early metabolic indicators of therapeutic response and facilitates differentiation between viable tumors and post-treatment changes. Radiomics-derived parameters have shown promising associations with tumor aggressiveness, lymphovascular invasion and survival, supporting their role as prognostic imaging biomarkers. In addition, the use of non-FDG radiotracers, as well as predictive models based on machine learning, can further refine preoperative stratification and treatment planning in certain subtypes of EC. Conclusions: Molecular imaging enhances the accuracy of TNM staging in EC. The incorporation of molecular imaging biomarkers into daily clinical practice could significantly improve personalized decision-making in EC. However, large prospective studies are needed to confirm their efficacy.