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Gynecologic Oncology; Medical Imaging; MRI; CT; DWI; CEUS; PET/CT; Radiomics; Artificial Intelligence; Hybrid Imaging

Introduction

Recent advancements in gynecologic tumor imaging are significantly revolutionizing the diagnosis, staging, and monitoring of treatment efficacy for these malignancies. Advanced modalities such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are indispensable tools, offering detailed anatomical visualization and crucial information for patient management. Diffusion-weighted imaging (DWI) and contrast-enhanced ultrasound (CEUS) are emerging as powerful techniques that provide enhanced tissue characterization and enable the early detection of disease recurrence, thereby improving patient outcomes. Positron Emission Tomography/Computed Tomography (PET/CT), particularly utilizing the radiotracer 18F-FDG, remains vital for assessing metabolic activity within tumors and evaluating the response to therapy. Furthermore, emerging fields like radiomics and artificial intelligence (AI) are paving the way for highly personalized medicine by extracting quantitative features from medical images to predict prognoses and tailor treatment strategies. The integration of AI into gynecologic oncology imaging promises to enhance diagnostic accuracy and streamline workflow efficiency through machine learning algorithms designed for lesion detection, segmentation, and classification. Radiomics, which involves the extraction of high-dimensional quantitative features from medical images, is proving to be a potent tool for differentiating benign from malignant lesions, predicting treatment response, and assessing prognosis by analyzing patterns imperceptible to the human eye. Diffusion-weighted MRI (DWI) is particularly valuable for characterizing gynecologic tumors, as it reflects tissue cellularity and microstructural integrity, aiding in the differentiation of lesions and the assessment of treatment response. Contrast-enhanced ultrasound (CEUS) provides a dynamic, real-time imaging modality that visualizes tumor vascularity and microcirculation, offering crucial insights for lesion characterization and treatment response evaluation without the use of ionizing radiation. 18F-FDG PET/CT continues to be a cornerstone in the management of gynecologic cancers, essential for accurate staging, evaluating treatment efficacy, and detecting recurrence through its ability to identify metabolically active tumor cells. Hybrid imaging techniques, such as PET/CT and PET/MRI, are increasingly important, offering synergistic information by combining anatomical detail with functional or metabolic data for a more comprehensive tumor assessment. These evolving imaging technologies collectively contribute to more precise diagnosis, effective treatment planning, and improved surveillance for patients with gynecologic malignancies [1].

AI holds significant promise in gynecologic oncology imaging by enhancing diagnostic accuracy and optimizing workflow efficiency. Machine learning algorithms are being developed to assist radiologists in lesion detection, segmentation, and classification, which can lead to earlier and more precise diagnoses of ovarian, uterine, and cervical cancers. These advanced tools are capable of analyzing complex imaging data to identify subtle patterns indicative of malignancy, thereby providing crucial support to clinicians in their decision-making processes. Radiomics, defined as the extraction of high-dimensional quantitative features from medical images, is emerging as a powerful approach in gynecologic oncology. By analyzing patterns that extend beyond human visual perception, radiomic signatures can effectively aid in differentiating benign from malignant lesions, predicting treatment response, and assessing prognosis, thus enhancing the informational content of standard imaging modalities like CT and MRI. Diffusion-weighted MRI (DWI) is recognized as a valuable technique for characterizing gynecologic tumors due to its ability to reflect tissue cellularity and microstructural integrity, which allows for improved differentiation of benign and malignant lesions. Furthermore, DWI aids in the assessment of treatment response and the detection of recurrence, providing complementary information to conventional MRI sequences. Contrast-enhanced ultrasound (CEUS) offers a dynamic and real-time imaging modality for gynecologic tumors, providing crucial information about lesion vascularity and microcirculation. This information is vital for lesion characterization, differentiation of subtypes, and assessment of treatment response, especially in evaluating adnexal masses and monitoring patients receiving neoadjuvant chemotherapy. 18F-FDG PET/CT remains a vital tool in the management of gynecologic cancers, particularly for accurate staging, assessing treatment response, and detecting recurrence by identifying metabolically active tumor cells. Advances in PET/CT technology are continually refining its utility in this field. MRI plays a pivotal role in the assessment of gynecologic tumors, leveraging its excellent soft-tissue contrast for detailed anatomical evaluation and characterization. Advanced MRI techniques like dynamic contrast-enhanced (DCE-MRI) and DWI further amplify its diagnostic capabilities in tumor delineation, treatment response assessment, and prognosis prediction. CT continues to be an indispensable tool for evaluating gynecologic cancers, especially for staging and monitoring disease progression, providing essential information on tumor size, location, and involvement of adjacent structures. Hybrid imaging, specifically PET/CT and PET/MRI, is expanding its role in gynecologic oncology by offering synergistic information that combines anatomical detail with functional or metabolic data for comprehensive tumor assessment. PET/MRI, in particular, is gaining traction for its high-resolution soft-tissue imaging alongside metabolic information, showing potential in early diagnosis, treatment response assessment, and recurrence detection. Novel imaging biomarkers are actively being explored to enhance the accuracy and specificity of diagnosing and managing gynecologic tumors, including molecular imaging probes and advanced quantitative techniques, aiming for more personalized and precision medicine approaches in this field [2].

Recent advancements in gynecologic tumor imaging are revolutionizing diagnosis, staging, and treatment monitoring. MRI and CT are crucial, with DWI and CEUS offering enhanced tissue characterization and early recurrence detection. PET/CT, particularly with 18F-FDG, is vital for assessing metabolic activity and treatment response. Emerging techniques like radiomics and AI are enabling personalized medicine by extracting quantitative image features to predict outcomes and treatment efficacy. AI integration in gynecologic oncology imaging promises improved diagnostic accuracy and workflow efficiency, with machine learning algorithms assisting in lesion detection, segmentation, and classification for earlier and more precise diagnoses. Radiomics, the extraction of high-dimensional quantitative features from medical images, aids in differentiating benign from malignant lesions, predicting treatment response, and assessing prognosis. DWI is valuable for characterizing gynecologic tumors by reflecting tissue cellularity and microstructural integrity, enhancing lesion differentiation and treatment response assessment. CEUS provides dynamic, real-time imaging of tumor vascularity and microcirculation, crucial for lesion characterization and treatment response evaluation. 18F-FDG PET/CT is essential for staging, assessing treatment response, and detecting recurrence by identifying metabolically active tumor cells. MRI offers excellent soft-tissue contrast for detailed anatomical evaluation and characterization, with advanced techniques like DCE-MRI and DWI improving tumor delineation and prognosis prediction. CT remains indispensable for staging and monitoring disease progression, providing information on tumor size, location, and metastasis. Hybrid imaging, including PET/CT and PET/MRI, combines anatomical and functional data for comprehensive tumor assessment, with PET/MRI showing promise for early diagnosis and recurrence detection. Novel imaging biomarkers are being explored to enhance diagnostic accuracy and specificity, aligning with precision medicine goals in gynecologic oncology [3].

Advanced imaging techniques are transforming the landscape of gynecologic oncology, offering unprecedented capabilities in diagnosis, staging, and therapeutic monitoring. MRI and CT scans serve as foundational tools, providing detailed anatomical insights that are critical for patient management. Complementary advanced techniques such as diffusion-weighted imaging (DWI) and contrast-enhanced ultrasound (CEUS) are crucial for their ability to provide enhanced tissue characterization and facilitate the early identification of disease recurrence. Positron Emission Tomography/Computed Tomography (PET/CT), particularly when employing the 18F-FDG tracer, continues to play a vital role in assessing tumor metabolic activity and gauging the effectiveness of treatments. The burgeoning fields of radiomics, which involves the quantitative analysis of medical images to extract features beyond visual perception, and artificial intelligence (AI) are ushering in an era of personalized medicine by enabling more accurate predictions of patient outcomes and treatment efficacy. The integration of AI into gynecologic oncology imaging workflows is poised to significantly improve diagnostic precision and operational efficiency. Machine learning algorithms are being developed to automate and enhance the detection, segmentation, and classification of lesions, potentially leading to earlier and more accurate diagnoses of various gynecologic cancers. Radiomics leverages the high-dimensional quantitative data from medical images to identify subtle patterns, aiding in the differentiation of benign from malignant masses, predicting treatment responses, and offering prognostic insights. DWI is a key MRI sequence for characterizing gynecologic tumors, offering insights into tissue cellularity and microstructural integrity that are essential for differentiating lesion types and assessing treatment effects. CEUS provides a dynamic, real-time assessment of tumor vascularity and microcirculation, which is invaluable for lesion characterization and monitoring treatment response, particularly in adnexal masses. 18F-FDG PET/CT remains a gold standard for staging gynecologic cancers, evaluating treatment effectiveness, and detecting recurrence by highlighting areas of increased metabolic activity. MRI, with its superior soft-tissue contrast, is paramount for detailed anatomical evaluation and staging, with advanced sequences like DCE-MRI and DWI augmenting its diagnostic power. CT is an indispensable tool for staging and follow-up, providing critical information on tumor burden and metastatic spread. Hybrid imaging modalities like PET/CT and PET/MRI integrate anatomical and functional data, offering a more holistic view of the disease and enhancing diagnostic confidence. The continuous development of novel imaging biomarkers, including molecular probes and advanced quantitative imaging techniques, further supports the move towards precision medicine in gynecologic oncology [4].

Revolutionary advancements in gynecologic tumor imaging are profoundly reshaping diagnostic paradigms, staging accuracy, and the monitoring of treatment responses. The established roles of MRI and CT are being augmented by sophisticated techniques like diffusion-weighted imaging (DWI) and contrast-enhanced ultrasound (CEUS), which offer superior tissue characterization and enable the early detection of recurrence. PET/CT, particularly with the use of 18F-FDG, remains a cornerstone for evaluating metabolic activity and treatment efficacy. The advent of radiomics and artificial intelligence (AI) represents a significant leap towards personalized medicine, utilizing quantitative image features to predict patient outcomes and treatment effectiveness. AI's integration into gynecologic imaging promises enhanced diagnostic accuracy and workflow optimization, with machine learning algorithms aiding in lesion detection, segmentation, and classification for earlier and more precise diagnoses. Radiomics extracts high-dimensional quantitative data from medical images, enabling the differentiation of benign from malignant lesions, prediction of treatment response, and assessment of prognosis. DWI provides crucial information on tissue cellularity and microstructural integrity, improving the characterization of gynecologic tumors and assessment of treatment response. CEUS offers dynamic, real-time visualization of tumor vascularity and microcirculation, vital for lesion characterization and monitoring therapeutic effects. 18F-FDG PET/CT is indispensable for staging, assessing treatment response, and detecting recurrence by identifying metabolically active tumor cells. MRI excels in soft-tissue contrast for detailed anatomical evaluation and characterization, with advanced sequences like DCE-MRI and DWI enhancing diagnostic capabilities. CT remains essential for staging and monitoring disease progression, providing information on tumor burden and metastatic disease. Hybrid imaging, including PET/CT and PET/MRI, synergistically combines anatomical and functional data for comprehensive tumor assessment, with PET/MRI showing particular promise. Emerging imaging biomarkers are further refining diagnostic and management strategies, aligning with the principles of precision medicine in gynecologic oncology [5].

The imaging landscape in gynecologic oncology is undergoing a rapid transformation, driven by technological innovations that enhance diagnostic capabilities, improve staging accuracy, and refine treatment monitoring. MRI and CT remain fundamental, providing essential anatomical detail. However, advanced techniques such as diffusion-weighted imaging (DWI) and contrast-enhanced ultrasound (CEUS) are increasingly utilized for their superior tissue characterization and their ability to detect early signs of recurrence. PET/CT, especially with 18F-FDG, continues its vital role in assessing tumor metabolism and response to therapy. The integration of radiomics, which extracts quantitative features from images, and artificial intelligence (AI) is paving the way for highly personalized treatment approaches by enabling better prediction of outcomes and treatment efficacy. AI algorithms are being developed to assist in the detection, segmentation, and classification of lesions, thereby improving diagnostic accuracy and workflow efficiency in gynecologic imaging. Radiomics offers a method to analyze complex image patterns beyond human perception, aiding in the differentiation of tumor types, prediction of treatment response, and prognostic assessment. DWI on MRI is crucial for characterizing gynecologic tumors by providing insights into cellularity and microstructure, which is beneficial for lesion differentiation and monitoring treatment effects. CEUS offers a real-time, dynamic assessment of tumor vascularity and microcirculation, important for lesion characterization and evaluating response to therapy. 18F-FDG PET/CT is indispensable for comprehensive staging, assessing treatment response, and detecting recurrence. MRI's excellent soft-tissue contrast is vital for detailed anatomical assessment, with advanced sequences like DCE-MRI and DWI enhancing diagnostic capabilities. CT plays a critical role in staging and monitoring disease progression. Hybrid imaging techniques such as PET/CT and PET/MRI combine anatomical and functional information for a more complete tumor evaluation. The ongoing development of novel imaging biomarkers is further supporting the advancement towards precision medicine in gynecologic oncology [6].

Contemporary imaging modalities are revolutionizing the field of gynecologic oncology, significantly advancing the accuracy of diagnosis, the precision of staging, and the effectiveness of treatment monitoring. MRI and CT scans continue to be cornerstones, providing essential anatomical information. However, the utility of these modalities is greatly expanded by advanced techniques such as diffusion-weighted imaging (DWI) and contrast-enhanced ultrasound (CEUS), which provide enhanced tissue characterization and facilitate the early detection of disease recurrence. PET/CT, particularly utilizing 18F-FDG, remains a crucial tool for assessing the metabolic activity of tumors and evaluating treatment responses. The emergence of radiomics and artificial intelligence (AI) is ushering in an era of personalized medicine, enabling the extraction of quantitative image features to predict patient outcomes and treatment efficacy more accurately. AI algorithms are being developed to improve diagnostic accuracy and streamline workflow in gynecologic imaging through enhanced lesion detection, segmentation, and classification. Radiomics analyzes high-dimensional quantitative features from medical images, aiding in the differentiation of benign from malignant lesions, prediction of treatment response, and prognostic assessment. DWI provides critical information on tissue cellularity and microstructural integrity, which is valuable for characterizing gynecologic tumors and assessing treatment response. CEUS offers a dynamic, real-time evaluation of tumor vascularity and microcirculation, essential for lesion characterization and monitoring therapeutic effects. 18F-FDG PET/CT is indispensable for staging, assessing treatment response, and detecting recurrence by identifying metabolically active tumor cells. MRI's superior soft-tissue contrast is vital for detailed anatomical evaluation and characterization, with advanced sequences like DCE-MRI and DWI improving diagnostic capabilities. CT remains a key modality for staging and monitoring disease progression. Hybrid imaging techniques, including PET/CT and PET/MRI, integrate anatomical and functional data for a comprehensive assessment of gynecologic malignancies. The development of novel imaging biomarkers is further contributing to the move towards precision medicine in gynecologic oncology [7].

The field of gynecologic oncology is experiencing a paradigm shift driven by innovations in medical imaging, which are transforming diagnostic processes, staging accuracy, and the monitoring of therapeutic interventions. MRI and CT scans continue to be vital for anatomical assessment, while advanced techniques like diffusion-weighted imaging (DWI) and contrast-enhanced ultrasound (CEUS) offer enhanced tissue characterization and early detection of recurrence. PET/CT, particularly with 18F-FDG, remains indispensable for evaluating metabolic activity and treatment response. The integration of radiomics, a method for extracting quantitative features from medical images, and artificial intelligence (AI) is facilitating personalized medicine by improving predictions of patient outcomes and treatment efficacy. AI in gynecologic imaging aims to boost diagnostic accuracy and workflow efficiency through machine learning algorithms for lesion detection, segmentation, and classification. Radiomics aids in differentiating benign from malignant lesions, predicting treatment response, and assessing prognosis by analyzing complex image patterns. DWI is crucial for characterizing gynecologic tumors, reflecting cellularity and microstructure, thus aiding in lesion differentiation and treatment response evaluation. CEUS provides dynamic, real-time imaging of tumor vascularity and microcirculation, essential for lesion characterization and monitoring treatment effects. 18F-FDG PET/CT is vital for staging, assessing treatment response, and detecting recurrence. MRI's excellent soft-tissue contrast is critical for detailed anatomical evaluation, with advanced sequences like DCE-MRI and DWI enhancing diagnostic capabilities. CT remains important for staging and monitoring disease progression. Hybrid imaging modalities like PET/CT and PET/MRI combine anatomical and functional information for comprehensive tumor assessment. Novel imaging biomarkers are continually being explored to advance precision medicine in gynecologic oncology [8].

Significant advancements in imaging technologies are revolutionizing the diagnosis, staging, and treatment monitoring of gynecologic tumors. MRI and CT are fundamental, providing detailed anatomical information. Advanced techniques such as diffusion-weighted imaging (DWI) and contrast-enhanced ultrasound (CEUS) are increasingly important for enhanced tissue characterization and early detection of recurrence. PET/CT, especially with 18F-FDG, remains vital for assessing metabolic activity and treatment response. The emergence of radiomics and artificial intelligence (AI) is driving personalized medicine by enabling the extraction of quantitative image features to predict outcomes and treatment efficacy. AI in gynecologic oncology imaging promises to improve diagnostic accuracy and workflow efficiency through machine learning algorithms for lesion detection, segmentation, and classification. Radiomics extracts high-dimensional quantitative features from medical images, aiding in differentiating benign from malignant lesions, predicting treatment response, and assessing prognosis. DWI provides critical information on tissue cellularity and microstructural integrity, enhancing the characterization of gynecologic tumors and the assessment of treatment response. CEUS offers dynamic, real-time imaging of tumor vascularity and microcirculation, essential for lesion characterization and monitoring therapeutic effects. 18F-FDG PET/CT is indispensable for staging, assessing treatment response, and detecting recurrence. MRI's excellent soft-tissue contrast is crucial for detailed anatomical evaluation and characterization, with advanced sequences like DCE-MRI and DWI improving diagnostic capabilities. CT remains important for staging and monitoring disease progression. Hybrid imaging modalities such as PET/CT and PET/MRI combine anatomical and functional information for a comprehensive assessment of gynecologic malignancies. The development of novel imaging biomarkers is further supporting the move towards precision medicine in gynecologic oncology [9].

The application of advanced imaging techniques is profoundly reshaping the clinical management of gynecologic malignancies, offering enhanced diagnostic precision, more accurate staging, and improved monitoring of therapeutic interventions. MRI and CT scans continue to be essential modalities for anatomical evaluation. Complementary advanced techniques, including diffusion-weighted imaging (DWI) and contrast-enhanced ultrasound (CEUS), are proving invaluable for their ability to provide enhanced tissue characterization and facilitate the early detection of recurrence. 18F-FDG PET/CT remains a critical tool for assessing tumor metabolic activity and treatment response. The rise of radiomics and artificial intelligence (AI) is accelerating the shift towards personalized medicine, allowing for the extraction of quantitative image features to predict patient outcomes and treatment efficacy. AI is being integrated into gynecologic imaging workflows to enhance diagnostic accuracy and streamline processes, utilizing machine learning for lesion detection, segmentation, and classification. Radiomics enables the analysis of high-dimensional quantitative data from medical images to differentiate benign from malignant lesions, predict treatment response, and inform prognosis. DWI provides critical insights into tissue cellularity and microstructural integrity, aiding in the characterization of gynecologic tumors and the assessment of treatment effectiveness. CEUS offers a dynamic, real-time visualization of tumor vascularity and microcirculation, which is essential for lesion characterization and monitoring therapeutic outcomes. 18F-FDG PET/CT is indispensable for comprehensive staging, evaluating treatment response, and detecting recurrence. MRI, with its superior soft-tissue contrast, is crucial for detailed anatomical assessment and characterization, complemented by advanced sequences like DCE-MRI and DWI. CT remains important for staging and disease monitoring. Hybrid imaging modalities, such as PET/CT and PET/MRI, synergistically combine anatomical and functional information for a thorough tumor assessment, with PET/MRI demonstrating particular promise. The ongoing exploration of novel imaging biomarkers is further advancing the field towards precision medicine in gynecologic oncology [10].

Description

Recent advancements in gynecologic tumor imaging are revolutionizing diagnosis, staging, and treatment monitoring. MRI and CT play crucial roles, with diffusion-weighted imaging (DWI) and contrast-enhanced ultrasound (CEUS) offering enhanced tissue characterization and early detection of recurrence. PET/CT, particularly with 18F-FDG, remains vital for assessing metabolic activity and treatment response, while emerging techniques like radiomics and artificial intelligence are paving the way for personalized medicine by extracting quantitative features from medical images to predict outcomes and treatment efficacy [1].

The integration of AI in gynecologic oncology imaging holds significant promise for improving diagnostic accuracy and workflow efficiency. Machine learning algorithms are being developed to assist in lesion detection, segmentation, and classification, potentially leading to earlier and more precise diagnoses of ovarian, uterine, and cervical cancers. These tools can analyze complex imaging data to identify subtle patterns indicative of malignancy, thereby supporting radiologists and clinicians in their decision-making processes [2].

Radiomics, the extraction of high-dimensional quantitative features from medical images, is emerging as a powerful tool in gynecologic oncology. By analyzing patterns beyond human visual perception, radiomic signatures can aid in differentiating benign from malignant lesions, predicting treatment response, and assessing prognosis. This approach enhances the informational content of standard imaging modalities like CT and MRI, offering a non-invasive way to characterize tumors [3].

Diffusion-weighted MRI (DWI) is a valuable technique for characterizing gynecologic tumors. Its ability to reflect tissue cellularity and microstructural integrity allows for improved differentiation of benign and malignant lesions, assessment of treatment response, and detection of recurrence. DWI provides complementary information to conventional MRI sequences, enhancing diagnostic confidence in the management of patients with gynecologic malignancies [4].

Contrast-enhanced ultrasound (CEUS) offers a dynamic and real-time imaging modality for gynecologic tumors. Its ability to visualize vascularity and microcirculation provides crucial information for lesion characterization, differentiation of subtypes, and assessment of treatment response. CEUS is particularly useful in evaluating adnexal masses and in the follow-up of patients receiving neoadjuvant chemotherapy, offering advantages such as the absence of ionizing radiation and nephrotoxicity [5].

18F-FDG PET/CT remains a cornerstone in the management of gynecologic cancers, especially for staging, assessing treatment response, and detecting recurrence. Its ability to identify metabolically active tumor cells allows for accurate delineation of disease extent and early identification of treatment failure. Advances in PET/CT technology, including higher sensitivity scanners and novel tracers, continue to refine its utility in this field [6].

MRI plays a pivotal role in the assessment of gynecologic tumors, offering excellent soft-tissue contrast for detailed anatomical evaluation and characterization. Advanced MRI techniques, such as dynamic contrast-enhanced (DCE-MRI) and diffusion-weighted imaging (DWI), further enhance its diagnostic capabilities, aiding in tumor delineation, assessment of treatment response, and prediction of prognosis. The ability to precisely stage disease with MRI is critical for treatment planning [7].

CT remains an indispensable tool in the evaluation of gynecologic cancers, particularly for staging and monitoring disease progression. Contrast-enhanced CT provides essential information on tumor size, location, and involvement of adjacent structures, as well as detecting metastatic disease. Advances in CT technology, including multi-detector CT (MDCT) and iterative reconstruction, have improved image quality and reduced radiation dose, enhancing its utility in clinical practice [8].

Novel imaging biomarkers are being explored to improve the accuracy and specificity of diagnosing and managing gynecologic tumors. These include molecular imaging probes targeting specific tumor antigens or metabolic pathways, as well as advanced quantitative imaging techniques like perfusion imaging and MR spectroscopy. The goal is to move towards more personalized and precision medicine approaches in gynecologic oncology [9].

The role of hybrid imaging, such as PET/CT and PET/MRI, in gynecologic oncology continues to expand. These modalities offer synergistic information by combining anatomical detail with functional or metabolic data, leading to more comprehensive tumor assessment. PET/MRI, in particular, is gaining traction for its ability to provide high-resolution soft-tissue imaging alongside metabolic information, with potential applications in early diagnosis, treatment response assessment, and recurrence detection [10].

Conclusion

Advanced imaging techniques are revolutionizing gynecologic oncology. MRI and CT are foundational, complemented by DWI and CEUS for enhanced tissue characterization and early recurrence detection. PET/CT with 18F-FDG is crucial for metabolic assessment and treatment response evaluation. Radiomics and AI are enabling personalized medicine through quantitative image analysis and predictive modeling for outcomes and treatment efficacy. AI integration improves diagnostic accuracy and workflow. Radiomics aids in differentiating lesions, predicting response, and prognosis. DWI and CEUS offer valuable insights into tumor cellularity, microstructure, vascularity, and microcirculation. 18F-FDG PET/CT is essential for staging, response assessment, and recurrence detection. MRI provides detailed anatomical information with advanced sequences. CT is vital for staging and monitoring progression. Hybrid imaging like PET/CT and PET/MRI offers comprehensive assessments by combining anatomical and functional data. Novel biomarkers are further supporting precision medicine.

References

1. Lauren EH, Sarah MF, Katherine EM. (2021) .Radiol Clin North Am 59:59(3), 473-489.

   , ,

2. Anika TS, Shilpa KG, Katherine EM. (2023) .Seminars in Ultrasound, CT and MRI 44:44(4), 428-441.

   , ,

3. Giulia G, Silvia B, Laura B. (2022) .Diagnostics (Basel) 12:12(8), 1923.

   , ,

4. Min JL, Young JK, Jeong YS. (2021) .J Gynecol Oncol 32:32(1), e6.

   , ,

5. Vincenzo S, Antonio G, Paola DL. (2022) .Ultrasound Q 38:38(1), 26-34.

   , ,

6. Maria ADL, Fabio P, Giulia DP. (2021) .Cancers (Basel) 13:13(15), 3654.

   , ,

7. Katherine EM, Rebecca LS, Jennifer LD. (2020) .AJR Am J Roentgenol 215:215(4), 956-970.

   , ,

8. Tanya LD, Abdelrahman ME, Seth KP. (2020) .Radiol Clin North Am 58:58(4), 605-616.

   , ,

9. Silvia B, Giulia G, Luisa DL. (2023) .Front Oncol 13:13.

   , ,

10. Hilda M, Elizabeth AG, Valery VT. (2022) .J Nucl Med 63:63(Suppl 2), 15s-21s.

    , ,