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Application of Magnetic Resonance Imaging (MRI) in the Diagnosis, Staging and Follow-Up of Oncological Diseases

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Nuclear Medicine & Radiology".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 562

Special Issue Editor

Dr. Matteo Bonatti

E-Mail Website
Guest Editor
Department of Radiology, Bolzano Central Hospital, 39100 Bolzano, Italy
Interests: MRI; urogenital imaging; abdominal imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Thanks to the impressive technological developments of recent decades, magnetic resonance imaging (MRI) has become a cornerstone of the diagnosis, staging, and follow-up of many oncological diseases. Abdominal, pelvic, and muscoloskeletal applications have been developed and now feature alongside initial neuroradiological applications. Thanks to the introduction of new sequences and AI-based reconstruction techniques, classic morphological MR imaging has increased grown in its spatial and contrast resolution, and acquisition times have decreased. Both functional and structural information can now be extrapolated from the products of MRI (e.g., mappings, advanced diffusion techniques, perfusion techniques, etc.), and efforts have been made to enable tissue characterization by means of MRI. Moreover, the field of radiomics promises to revolutionize the evaluation of imaging. This Special Issue welcomes original research and review articles about the use of MRI in the diagnosis, staging, and follow-up of oncological diseases. 

Dr. Matteo Bonatti
Guest Editor

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Keywords

  • MRI
  • cancer
  • oncology
  • diagnosis
  • staging
  • follow-up
  • radiomics

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Published Papers (1 paper)

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Research

13 pages, 603 KiB  
Article
A Nomogram for Preoperative Prediction of Tumor Aggressiveness and Lymphovascular Space Involvement in Patients with Endometrial Cancer
by Riccardo Valletta, Giacomo Avesani, Vincenzo Vingiani, Bernardo Proner, Martin Steinkasserer, Sara Notaro, Francesca Vanzo, Giovanni Negri, Caterina Vercelli and Matteo Bonatti
J. Clin. Med. 2025, 14(11), 3914; https://doi.org/10.3390/jcm14113914 - 2 Jun 2025
Viewed by 204
Abstract
Background/Objectives: To develop a nomogram for predicting tumor aggressiveness and the presence of lymphovascular space involvement (LVSI) in patients with endometrial cancer (EC) using preoperative MRI and pathology–laboratory data. Methods: This IRB-approved, retrospective, multicenter study included 245 patients with histologically confirmed EC who [...] Read more.
Background/Objectives: To develop a nomogram for predicting tumor aggressiveness and the presence of lymphovascular space involvement (LVSI) in patients with endometrial cancer (EC) using preoperative MRI and pathology–laboratory data. Methods: This IRB-approved, retrospective, multicenter study included 245 patients with histologically confirmed EC who underwent preoperative MRI and surgery at participating institutions between January 2020 and December 2024. Tumor type and grade, both from preoperative biopsy and surgical specimens, as well as preoperative CA125 and HE4 levels, were retrieved from institutional databases. A preoperative MRI was used to assess tumor morphology (polypoid vs. infiltrative), maximum diameter, presence and depth (< or >50%) of myometrial invasion, cervical stromal invasion (yes/no), and minimal tumor-to-serosa distance. The EC-to-uterus volume ratio was also calculated. Results: Among the 245 patients, 27% demonstrated substantial LVSI, and 35% were classified as aggressive on final histopathology. Multivariate analysis identified independent MRI predictors of LVSI, including cervical stromal invasion (OR = 9.06; p = 0.0002), tumor infiltration depth (OR = 2.09; p = 0.0391), and minimal tumor-to-serosa distance (OR = 0.81; p = 0.0028). The LVSI prediction model yielded an AUC of 0.834, with an overall accuracy of 78.4%, specificity of 92.2%, and sensitivity of 43.1%. For tumor aggressiveness prediction, significant predictors included biopsy grade (OR = 8.92; p < 0.0001), histological subtype (OR = 12.02; p = 0.0021), and MRI-detected serosal involvement (OR = 14.39; p = 0.0268). This model achieved an AUC of 0.932, with an accuracy of 87.0%, sensitivity of 79.8%, and specificity of 91.2%. Both models showed excellent calibration (Hosmer–Lemeshow p > 0.86). Conclusions: The integration of MRI-derived morphological and quantitative features with clinical and histopathological data allows for effective preoperative risk stratification in endometrial cancer. The two nomograms developed for predicting LVSI and tumor aggressiveness demonstrated high diagnostic performance and may support individualized surgical planning and decision-making regarding adjuvant therapy. These models are practical, reproducible, and easily applicable in standard clinical settings without the need for radiomics software, representing a step toward more personalized gynecologic oncology. Full article
(This article belongs to the Special Issue Application of Magnetic Resonance Imaging (MRI) in the Diagnosis, Staging and Follow-Up of Oncological Diseases)
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