Diagnostic value of diffusion-weighted magnetic resonance imaging in discriminating between metastatic and non-metastatic pelvic lymph nodes in endometrial cancer

Clinical Utility of Diffusion-Weighted Imaging in Gynecological Imaging: Revisited

ABSTRACT

Diffusion-weighted imaging (DWI) is an increasingly valuable sequence in daily clinical practice, providing both functional and morphological information. The use of DWI can help quantify diffusion using the apparent diffusion coefficient, which reflects the physiological features of the tissue and tumor microcirculation. This knowledge is crucial for understanding and interpreting gynecological imaging. This article reviews the clinical utility of DWI for gynecological imaging, highlighting its ability to aid in the detection of endometrial and cervical cancers, as well as tumor extension and metastasis. In addition, DWI can easily detect the solid components of ovarian cancer (including dissemination), assist in the diagnosis of adnexal torsion, and potentially show bone marrow status. Apparent diffusion coefficient measurement is useful for differentiating between endometrial lesions, uterine leiomyomas, and sarcomas, and may provide important information for predicting the prognosis of gynecological cancers.

Diagnostic Value and Clinical Application of Diffusion-Weighted Magnetic Resonance Imaging for Female Pelvic Lesions

Pelvic inflammatory disease refers to a group of infectious diseases of the female upper genital tract, often caused by ascending infection of vaginitis and cervicitis, causing endometritis, salpingitis, tubo-ovarian abscess, pelvic connective tissue inflammation, and/or pelvic peritonitis. PID is the most common and important infectious disease in nonpregnant women of childbearing age, and inflammation in multiple parts often coexists and affects each other. The functional MRI techniques currently used in pelvic floor muscle injury are magnetic resonance diffusion tensor imaging, T2 mapping, and magnetic resonance elastography. Diffusion tensor imaging is a new imaging and postprocessing technology developed on the basis of magnetic resonance diffusion-weighted imaging. Due to the lack of specificity of clinical symptoms, many subclinical patients are often not detected and diagnosed in time, so it is very difficult to accurately estimate the incidence of PID. This article retrospectively analyzed 72 patients with pelvic inflammatory disease confirmed by surgical pathology from February 2020 to 2022, who had undergone pelvic MRI examination before surgery, including 25 patients with chronic pelvic inflammation (hydrosalpinx), 25 patients with acute pelvic inflammation, and 47 cases (including 21 cases of hydrosalpinx, 19 cases of tubo-ovarian abscess, and 7 cases of pelvic abscess). The age range was 13 to 59 years old. The clinical data and MRI findings were analyzed, the ADC value of the cystic part of the lesion was measured, and the differences in age, maximum diameter of the lesion, thickness of the vessel wall/separation, and the ADC value of the cystic part of chronic and acute pelvic inflammation were compared. In this part of the cases, there were 25 cases of chronic pelvic inflammation and 47 cases of acute pelvic inflammation. The average ADC value of the cystic component of chronic inflammation was significantly higher than that of acute inflammation, which were (2.86 ± 0.20) × 10−3 mm2/s and (1.07 ± 0.38) ×10−3 mm2/s, respectively, $P$ value <0.001.

Computer-Aided Segmentation and Machine Learning of Integrated Clinical and Diffusion-Weighted Imaging Parameters for Predicting Lymph Node Metastasis in Endometrial Cancer

Simple Summary

Computer-aided segmentation and machine learning added values of clinical parameters and diffusion-weighted imaging radiomics for predicting nodal metastasis in endometrial cancer, with a diagnostic performance superior to criteria based on lymph node size or apparent diffusion coefficient.

Abstract

Precise risk stratification in lymphadenectomy is important for patients with endometrial cancer (EC), to balance the therapeutic benefit against the operation-related morbidity and mortality. We aimed to investigate added values of computer-aided segmentation and machine learning based on clinical parameters and diffusion-weighted imaging radiomics for predicting lymph node (LN) metastasis in EC. This prospective observational study included 236 women with EC (mean age ± standard deviation, 51.2 ± 11.6 years) who underwent magnetic resonance (MR) imaging before surgery during July 2010–July 2018, randomly split into training (n = 165) and test sets (n = 71). A decision-tree model was constructed based on mean apparent diffusion coefficient (ADC) value of the tumor (cutoff, 1.1 × 10⁻³ mm²/s), skewness of the relative ADC value (cutoff, 1.2), short-axis diameter of LN (cutoff, 1.7 mm) and skewness ADC value of the LN (cutoff, 7.2 × 10⁻²), as well as tumor grade (1 vs. 2 and 3), and clinical tumor size (cutoff, 20 mm). The sensitivity and specificity of the model were 94% and 80% for the training set and 86%, 78% for the independent testing set, respectively. The areas under the receiver operating characteristics curve (AUCs) of the decision-tree was 0.85—significantly higher than the mean ADC model (AUC = 0.54) and LN short-axis diameter criteria (AUC = 0.62) (both p < 0.0001). We concluded that a combination of clinical and MR radiomics generates a prediction model for LN metastasis in EC, with diagnostic performance surpassing the conventional ADC and size criteria.

Preoperative assessment of endometrial cancer

Endometrial cancer (EC), the most common among gynaecological malignancies occurs predominantly after the menopause. The diagnosis is most commonly (in about 75-80%) set up at early stage when surgical therapy and if necessary postoperative radiotherapy results in an excellent prognosis, with a 90-95% 5-year overall survival (OS) and a locoregional recurrence rates of 4-8%. Accurate preoperative assessment of the lymph nodes would ideally identify those patients with advanced stage disease, who might benefit from more extensive surgical procedures and adjuvant therapies. magnetic resonance imaging (MRI), hysteroscopic excisional biopsy (HEB) and high resolution 2D or 3D ultrasound performed by expert operator are considered to add valuable information for preoperative staging of EC. The use of biomarkers could be beneficial in decreasing inter-observer variability between the histology of the diagnostic specimen and the final operative sample, as well as to avoid overtreatment in a part of the high-grade tumors with excellent prognosis. The goal of surgical management of EC is to remove the primary tumor and to identify definite prognostic factors to determine whether adjuvant therapy is required. Extended surgery, including para-aortic lymphadenectomy has a significant morbidity, and with the future selection of cases it can be safely avoided, we can decrease complications without compromising oncological safety. In the future, the importance of hysteroscopy guided sampling may increase to gain a representative sample for biomarker detection.