Optimizing imaging modalities for sarcoma subtypes in radiation therapy: State of the art.

Journal: Critical reviews in oncology/hematology
Published Date:

Abstract

The choice of imaging modalities is essential in sarcoma management, as different techniques provide complementary information depending on tumor subtype and anatomical location. This narrative review examines the role of imaging in sarcoma characterization and treatment planning, particularly in the context of radiation therapy (RT). Magnetic resonance imaging (MRI) provides superior soft tissue contrast, enabling detailed assessment of tumor extent and peritumoral involvement. Computed tomography (CT) is particularly valuable for detecting osseous involvement, periosteal reactions, and calcifications, complementing MRI in sarcomas involving bone or calcified lesions. The combination of MRI and CT enhances tumor delineation, particularly for complex sites such as retroperitoneal and uterine sarcomas, where spatial relationships with adjacent organs are critical. In vascularized sarcomas, such as alveolar soft-part sarcomas, the integration of MRI with CT or MR angiography facilitates accurate mapping of tumor margins. Positron emission tomography with [18 F]-fluorodeoxyglucose ([18 F]-FDG PET) provides functional insights, identifying metabolically active regions within tumors to guide dose escalation. Although its role in routine staging is limited, [18 F]-FDG PET and emerging PET tracers offer promise for refining RT planning. Advances in artificial intelligence further enhance imaging precision, enabling more accurate contouring and treatment optimization. This review highlights how the integration of imaging modalities, tailored to specific sarcoma subtypes, supports precise RT delivery while minimizing damage to surrounding tissues. These strategies underline the importance of multidisciplinary approaches in improving sarcoma management and outcomes through multi-image-based RT planning.

Authors

  • Arnaud Beddok
    Department of Radiation Oncology, Institut Godinot, 51454 Reims, France.
  • Harleen Kaur
    Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India. harleen.unu@gmail.com.
  • Sakshi Khurana
    Department of Radiology, Columbia University Irving Medical Center, New-York, USA.
  • Laurent Dercle
    Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032; Gustave Roussy, Université Paris-Saclay, Université Paris-Saclay, Département D'imagerie Médicale, Villejuif, France.
  • Radouane El Ayachi
    Department of Radiation Oncology, Institut Curie, Paris, France.
  • Emmanuel Jouglar
    Department of Radiation Oncology, Institut Curie, Paris, France.
  • Hamid Mammar
    Department of Radiation Oncology, Institut Curie, Paris, France.
  • Mathilde Mahe
    Department of Radiation Oncology, Institut Curie, Paris, France.
  • Elie Najem
    Department of Radiology. Dana-Farber Cancer Institute, Boston, USA.
  • Laura Rozenblum
    Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA; Department of Nuclear Medicine, AP - HP Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Sorbonne Université, Paris, France.
  • Juliette Thariat
    Laboratoire de physique corpusculaire, UMR6534 IN2P3/EnsiCaen, Caen, France; Department of Radiation Oncology, Centre François-Baclesse, Caen, France. Electronic address: jthariat@gmail.com.
  • Georges El Fakhri
  • Sylvie Helfre
    Department of Radiation Oncology, Institut Curie, Paris, France.