Comparison of dynamic contrast-enhanced magnetic resonance imaging with T2-weighted imaging for preoperative staging of early endometrial carcinoma

Current Applications of PET/MR: Part II: Clinical Applications II

Due to the major improvements in the hardware and image reconstruction algorithms, positron emission tomography/magnetic resonance imaging (PET/MR) is now a reliable state-of-the-art hybrid modality in medical practice. Currently, it can provide a broad range of advantages in preclinical and clinical imaging compared to single-modality imaging. In the second part of this review, we discussed the further clinical applications of PET/MR. In the chest, PET/MR has particular potential in the oncology setting, especially when utilizing ultrashort/zero echo time MR sequences. Furthermore, cardiac PET/MR can provide reliable information in evaluating myocardial inflammation, cardiac amyloidosis, myocardial perfusion, myocardial viability, atherosclerotic plaque, and cardiac masses. In gastrointestinal and hepato-pancreato-biliary malignancies, PET/MR is able to precisely detect metastases to the liver, being superior over the other imaging modalities. In genitourinary and gynaecology applications, PET/MR is a comprehensive diagnostic method, especially in prostate, endometrial, and cervical cancers. Its simultaneous acquisition has been shown to outperform other imaging techniques for the detection of pelvic nodal metastases and is also a reliable modality in radiation planning. Lastly, in haematologic malignancies, PET/MR can significantly enhance lymphoma diagnosis, particularly in detecting extra-nodal involvement. It can also comprehensively assess treatment-induced changes. Furthermore, PET/MR may soon become a routine in multiple myeloma management, being a one-stop shop for evaluating bone, bone marrow, and soft tissues.

Nuclear Medicine and Molecular Imaging Applications in Gynecologic Malignancies: A Comprehensive Review

Gynecologic malignancies, consisting of endometrial, cervical, ovarian, vulvar, and vaginal cancers, pose significant diagnostic and management challenges due to their complex anatomic location and potential for rapid progression. These tumors cause substantial morbidity and mortality, often because of their delayed diagnosis and treatment. An estimated 19% of newly diagnosed cancers among women are gynecologic in origin. In recent years, there has been growing evidence supporting the integration of nuclear medicine imaging modalities in the diagnostic work-up and management of gynecologic cancers. The sensitivity of fluorine-18 fluorodeoxyglucose positron emission tomography (18F-FDG PET) combined with the anatomical specificity of computed tomography (CT) and magnetic resonance imaging (MRI) allows for the hybrid evaluation of metabolic activity and structural abnormalities that has become an indispensable tool in oncologic imaging. Lymphoscintigraphy, using technetium 99m (99mTc) based radiotracers along with single photon emission computed tomography/ computed tomography (SPECT/CT), holds a vital role in the identification of sentinel lymph nodes to minimize the surgical morbidity from extensive lymph node dissections. While not yet standard for gynecologic malignancies, promising therapeutic nuclear medicine agents serve as specialized treatment options for patients with advanced or recurrent disease. This article aims to provide a comprehensive review on the nuclear medicine applications in gynecologic malignancies through the following objectives: 1) To describe the role of nuclear medicine in the initial staging, lymph node mapping, response assessment, and recurrence/surveillance imaging of common gynecologic cancers, 2) To review the limitations of 18F-FDG PET/CT and promising applications of 18F-FDG PET/MRI in gynecologic malignancy, 3) To underscore the promising theragnostic applications of nuclear medicine, 4) To highlight the current role of nuclear medicine imaging in gynecologic cancers as per the National Comprehensive Cancer Network (NCCN), European Society of Surgical Oncology (ESGO), and European Society of Medical Oncology (ESMO) guidelines.

Added Value of FDG PET/MRI in Gynecologic Oncology: A Pictorial Review

Fluorine 18-fluorodeoxyglucose (FDG) PET and MRI independently play a valuable role in the management of patients with gynecologic malignancies, particularly endometrial and cervical cancer. The PET/MRI hybrid imaging technique combines the metabolic information obtained from PET with the excellent soft-tissue resolution and anatomic details provided by MRI in a single examination. MRI is the modality of choice for assessment of local tumor extent in the pelvis, whereas PET is used to assess for local-regional spread and distant metastases. The authors discuss the added value of FDG PET/MRI in imaging gynecologic malignancies of the pelvis, with a focus on the role of FDG PET/MRI in diagnosis, staging, assessing treatment response, and characterizing complications. PET/MRI allows better localization and demarcation of the extent of disease, characterization of lesions and involvement of adjacent organs and lymph nodes, and improved differentiation of benign from malignant tissues, as well as detection of the presence of distant metastasis. It also has the advantages of decreased radiation dose and a higher signal-to-noise ratio of a prolonged PET examination of the pelvis contemporaneous with MRI. The authors provide a brief technical overview of PET/MRI, highlight how simultaneously performed PET/MRI can improve stand-alone MRI and PET/CT in gynecologic malignancies, provide an image-rich review to illustrate practical and clinically relevant applications of this imaging technique, and review common pitfalls encountered in clinical practice. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

A Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging and T2-Weighted Imaging in Determining the Depth of Myometrial Invasion in Endometrial Carcinoma—A Retrospective Study

This study aims to compare dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with T2-weighted imaging (T2WI) in defining the depth of myometrial invasion in endometrial carcinoma. This retrospective study included 32 subjects with endometrial carcinoma who underwent 3.0T magnetic resonance imaging (MRI) prior to hysterectomy. DCE-MRI and T2WI were evaluated to determine the depth of myometrial invasion in endometrial carcinoma. A set of data consisting of the sensitivity, specificity, predictive values, and accuracy of DCE-MRI and T2WI were obtained and compared with the histopathological results. Out of the 32 cases included, the histopathological examination revealed that 50% myometrial invasion was found in 11 patients and ≥50% myometrial invasion was found in 21 patients. In the assessment of the tumor invasion, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of T2WI were 45.45%, 90.48 %, 71.43%, 76.0%, and 75.0%, respectively. The corresponding values for DCE-MRI were 81.82%, 76.19%, 64.29%, 88.89 %, and 78.12%, respectively. When T2WI were read together with DCE-MRI, the values were 90.91%, 90.48%, 83.33%, 95.0%, and 90.62%, respectively. Thus, the sensitivity and accuracy of DCE-MRI were greater compared to T2WI in defining the depth of myometrial invasion. However, the merging of T2WI and DCE-MRI increased the specificity and PPV value and improved the sensitivity, NPV and accuracy in detecting myometrial invasion. DCE-MRI was more sensitive but less specific than T2WI in defining the depth of myometrial invasion. In conclusion, combining DCE-MRI and T2WI further improves the diagnostic performance for myometrial invasion in endometrial carcinoma.

Endometrial carcinoma: use of tracer kinetic modeling of dynamic contrast-enhanced MRI for preoperative risk assessment

Background

To compare two tracer kinetic models in predicting of preoperative risk types in endometrial carcinoma (EC) using DCE-MRI.

Methods

A prospective study of patients with EC was conducted with institutional ethics approval and written informed consent. DCE-MRI data was analyzed using the extended Tofts (ET) and the distributed parameter (DP) models. DCE parameters blood flow (F), mean transit time, blood volume (Vp), extravascular extracellular volume (Ve), permeability surface area product (PS), extraction fraction, transfer constant (Ktrans), and efflux rate (Kep) between high- and low-risk EC were compared using the Mann–Whitney test. Bland–Altman analysis was utilized to compare parameter consistency and Spearman test to assess parameter correlation. Diagnostic performance of DCE parameters was analyzed by receiver-operating characteristic curve and compared with traditional MRI assessment.

Results

Fifty-one patients comprised the study group. Patients with high-risk EC exhibited significantly lower Ktrans, Kep, F, Vp and PS (P < 0.001). ET-derived Ktrans and DP-derived F attained AUC of 0.92 and 0.91, respectively. Bland–Altman analysis showed that the consistency of Ve or Vp between the two models was low (P < 0.001) while Spearman test showed a strong correlation (r = 0.719, 0.871). Both Ktrans and F showed higher accuracy in predicting EC risk types than traditional MRI assessment.

Conclusions

Kinetic parameters derived from DCE-MRI revealed a more hypovascular microenvironment for high risk EC than to low- risk ones, providing potential imaging biomarkers in preoperative risk assessment that might improve individualized surgical planning and management of EC.

Evaluation of the Depth of Myometrial Invasion of Endometrial Carcinoma: Comparison of Orthogonal Pelvis-axial Contrast-enhanced and Uterus-axial Dynamic Contrast-enhanced MRI Protocols

RATIONALE AND OBJECTIVES

To compare the diagnostic performance of orthogonal pelvis-axial (OPA) contrast-enhanced (CE) and orthogonal uterus-axial (OUA) dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) protocols in assessing the depth of myometrial invasion (MI) of endometrial carcinoma (EC).

MATERIALS AND METHODS

Preoperative MRI of 398 consecutive EC patients (197 patients with OPA CE-MRI protocol and 201 patients with OUA DCE-MRI protocol) was analyzed. Two radiologists independently interpreted the depth of MI, with postoperative histopathology as the reference standard. The chi-square test, Fisher's exact test, and receiver operating characteristic curve analysis were used for diagnostic performance comparison.

RESULTS

OUA DCE-MRI showed a significantly larger area under the curve than OPA CE-MRI in detecting the presence of MI for radiologist 1 (0.71 versus 0.49, p < 0.05) but not for radiologist 2 or deep MI (all p > 0.05). Compared to OPA CE-MRI, OUA DCE-MRI significantly improved the diagnostic accuracy of non-MI and superficial MI (radiologist 1: 45.5% versus 0 and 88.7% versus 86.4%, p = 0.045 and 0.567, respectively; radiologist 2: 45.5% versus 12.5% and 88.7% versus 78.8%, p = 0.177 and 0.027, respectively) and of EC with adenomyosis/submucous myomas, cornual tumor, and antero-posterior diameter ≤ 10 mm (radiologist 1: 86.4% versus 71.4%, 91.2% versus 67.7%, and 90.1% versus 81.1%, p = 0.048, 0.018, and 0.081, respectively; radiologist 2: 86.4% versus 64.3%, 88.2% versus 64.5%, and 87.0% versus 71.6%, p = 0.006, 0.023, and 0.019, respectively).

CONCLUSION

The OUA DCE-MRI protocol was superior to the OPA CE-MRI protocol in assessing the depth of MI of EC.

Injection rate of contrast medium affects diagnostic ability of dynamic contrast-enhanced magnetic resonance imaging for endometrial carcinoma: a prospective cohort study

Background

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) can obtain semi-quantitative or quantitative parameters of tumors by capturing the images before and after injection of contrast medium. However, there has been no further research on the effect of flow rate of contrast medium on image quality and parameter sensitivity of DCE-MRI in endometrial carcinoma (EC).

Methods

This was a prospective cohort study enrolling patients who were suspected of EC between January 2018 and June 2020. The baseline data of participants were collected. Post-surgical histological examination acted as the gold standard of EC diagnosis and some characteristics of tumors were recorded. We calculated 3 important parameters of DCE-MRI, including volume transfer constant (K^trans), flux rate constant (K_ep), and extravascular extracellular volume fraction (V_e), according to the MRI system. The image quality in DCE-MRI imaging was evaluated according to contrast, resolution, artifact, signal-to-noise ratio, and scanning time. To evaluate the diagnostic ability of DCE-MRI with different injection rate, receiver operating characteristic (ROC) curve was generated and the area under curve (AUC) was calculated.

Results

According to the different injection rate of contrast medium, participants were divided into three groups, including 2, 3, and 4 mL/s group. It was found that there were more grade 1 EC in the 3 mL/s group (52.4%) than other two groups (34.3% and 23.3%, respectively), and the difference was significant (P=0.021). No other significant differences were found among all other variables. It was found that K^trans was much higher in the 4 mL/s group than in other two groups (P<0.001). Also, V_e was much higher in the 4 mL/s group than in other two groups (P<0.001). However, no significant difference was found in K_ep between three groups (P=0.633). Besides, the 4 mL/s group had the highest quality of all three groups (P<0.001). The sensitivity, specificity, and accuracy were highest in 4 mL/s group. The AUC in three groups were 0.822, 0.832, and 0.888 in the 2, 3, and 4 mL/s group, respectively.

Conclusions

The DCE-MRI measurement is useful for the diagnosis of EC, and faster injection rate may be beneficial to improve diagnostic accuracy and image quality.

A machine learning approach for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted MRI parameters

PURPOSE

To propose a computer-assisted method for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted magnetic resonance imaging (PWI).

MATERIALS AND METHODS

Forty-two women confirmed to have a total of 60 masses (10 uterine sarcomas and 50 benign leiomyomas) were included. The reference diagnosis was based on postoperative histopathological examination. All women underwent the standard MRI protocol with 3-Tesla MR imager (Magnetom Trio, Siemens, Erlangen, Germany) for assessment of myometrial masses, followed by PWI. For each mass, two regions of interest (ROI) were outlined manually by an experienced radiologist; one (ROI_L) represented the entire tumor while the other (ROIs) was placed on the area of the lesion with the most marked contrast enhancement. Two additional ROI_s with diameters similar to ROI_s (3.0 to 3.1 mm) were placed on psoas muscle (ROI_P) and myometrium (ROI_M) in order to provide baselines for comparisons. The obtained ROIs of PWI images were then analyzed using the DCE Tool plug-in (version 2.0SP1) within ClearCanvas (Toronto, Ontario, Canada) framework. The DCE Tool provides seven parameters (K^trans, k_ep, V_b, IAUC, initial slope, peak, the mean squared error) for modelling contrast uptake within an ROI using the modified Tofts model. Parameters extracted from the ROIs were fed into a decision tree ensemble, which classified the corresponding lesions either as malignant or benign. The leave-one-out cross validation (LOOCV) was utilized to evaluate the performance of the classifier.

RESULTS

None of the parameters extracted from ROI_L or ROI_s differed significantly between uterine sarcoma and benign leiomyomas (all p > 0.05). The overall accuracy of 66.7% was obtained by feeding seven parameters extracted from ROI_L to the classifier. When 21 features extracted from ROI_L, ROI_M, and ROI_P were fed into the classifier an accuracy of 91.7%, sensitivity of 100%, and specificity of 90% were achieved in the optimal operating point of classifier.

CONCLUSION

Although none of the PWI parameters differed significantly between benign and malignant lesions, when the information provided by the extracted features was aggregated using a machine learning method, a promising discriminative power was obtained. This suggests that the proposed model for combining the PWI parameters is potentially useful for differentiating uterine sarcoma from leiomyomas.