Diffusion volume (DV) measurement in endometrial and cervical cancer: A new MRI parameter in the evaluation of the tumor grading and the risk classification

MRI as an assessment tool for prognostic risk stratification of endometrial carcinoma patients based on molecular classification

BACKGROUND

Non-invasive risk stratification for patients with endometrial carcinoma (EC) is important for developing personalised treatment plans. Our study aimed to explore the ability of quantitative MRI parameters to predict the risk stratification of EC patients based on molecular classification.

METHODS

Fifty-three patients with histologically proven EC who underwent pelvic MRI and surgical treatment at our hospital between January 2020 and August 2022 were assessed. The tumour volume (TV) and uterine volume (UV) were estimated with the ellipsoid formula and used to calculate the tumour volume ratio (TVR). The mean apparent diffusion coefficient (ADC) of the tumour was measured on a workstation. Quantitative MRI parameters were compared among different risk groups via unpaired Student's t-tests or Mann-Whitney's U-tests.

RESULTS

The TV and TVR were significantly different between the low- and high-risk groups (p < 0.001), and cut-off values of 5342 mm3 and 0.055 allowed the differentiation of the high-risk group from the low-risk group, with 77% and 85% sensitivity and 78% and 78% specificity, respectively. There was a significant difference in the ADC between the two groups (p = 0.026), and a cut-off value of 0.65 × 10-3 mm2/s allowed differentiation of the risk groups, with 93% sensitivity and 39% specificity.

CONCLUSIONS

Quantitative MRI parameters such as the TV, TVR and ADC may be helpful in preoperatively assessing the risk stratification of patients with EC based on molecular classification.

Training radiology residents to evaluate deep myometrial invasion in endometrial cancer patients on MRI: A learning curve study

PURPOSE

To evaluate the impact of a four-month training program on radiology residents' diagnostic accuracy in assessing deep myometrial invasion (DMI) in endometrial cancer (EC) using MRI.

METHOD

Three radiology residents with limited EC MRI experience participated in the training program, which included conventional didactic sessions, case-centric workshops, and interactive classes. Utilizing a training dataset of 120 EC MRI scans, trainees independently assessed subsets of cases over five reading sessions. Each subset consisted of 30 scans, the first and the last with the same cases, for a total of 150 reads. Diagnostic accuracy metrics, assessment time (rounded to the nearest minute), and confidence levels (using a 5-point Likert scale) were recorded. The learning curve was obtained plotting the diagnostic accuracy of the three trainees and the average over the subsets. Anatomopathological results served as the reference standard for DMI presence.

RESULTS

The three trainees exhibited heterogeneous starting point, with a learning curve and a trend to more homogeneous performance with training. The diagnostic accuracy of the average trainee raised from 64 % (56 %-76 %) to 88 % (80 %-94 %) across the five subsets (p < 0.001). Reductions in assessment time (5.92 to 4.63 min, p < 0.018) and enhanced confidence levels (3.58 to 3.97, p = 0.12) were observed. Improvements in sensitivity, specificity, positive predictive value, and negative predictive value were noted, particularly for specificity which raised from 56 % (41 %-68 %) in the first to 86 % (74 %-94 %) in the fifth subset (p = 0.16). Although not reaching statistical significance, these advancements aligned the trainees with literature performance benchmarks.

CONCLUSIONS

The structured training program significantly enhanced radiology residents' diagnostic accuracy in assessing DMI for EC on MRI, emphasizing the effectiveness of active case-based training in refining oncologic imaging skills within radiology residency curricula.

Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows

Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk factors. Medical imaging, especially magnetic resonance imaging (MRI), plays a major role in EC assessment, particularly for disease staging. However, the diagnostic performance of MRI exhibits variability in the detection of clinically relevant prognostic factors (e.g., deep myometrial invasion and metastatic lymph nodes assessment). To address these challenges and enhance the value of MRI, radiomics and artificial intelligence (AI) algorithms emerge as promising tools with a potential to impact EC risk assessment, treatment planning, and prognosis prediction. These advanced post-processing techniques allow us to quantitatively analyse medical images, providing novel insights into cancer characteristics beyond conventional qualitative image evaluation. However, despite the growing interest and research efforts, the integration of radiomics and AI to EC management is still far from clinical practice and represents a possible perspective rather than an actual reality. This review focuses on the state of radiomics and AI in EC MRI, emphasizing risk stratification and prognostic factor prediction, aiming to illuminate potential advancements and address existing challenges in the field.

Exploring the significance of tumor volume in endometrial cancer: Clinical pathological features, prognosis, and adjuvant therapies

To assist clinicians in formulating treatment strategies for endometrial cancer (EC), this retrospective study explores the relationship between tumor volume and clinical pathological features, as well as prognosis, in patients undergoing staging surgery. Preoperative pelvic MRI examinations were conducted on 234 histologically confirmed EC patients. The ITK-SNAP software was employed to manually delineate the region of interest in the MRI images and calculate the tumor volume (MRI-TV). The analysis focused on investigating the relationship between MRI-TV and the clinical pathological features and prognosis of EC patients. Larger MRI-TV was found to be associated with various adverse prognostic factors (G3, deep myometrial invasion, cervical stromal invasion, lymphovascular space invasion, lymph node metastasis, advanced international federation of gynecology and obstetrics staging, and receipt of adjuvant therapy). The receiver operating characteristic curve indicated that MRI-TV ≥ 8 cm3 predicted deep myometrial invasion, and MRI-TV ≥ 12 cm3 predicted lymph node metastasis. Penalized spline (P-spline) regression analysis identified 14 cm3 of MRI-TV as the optimal prognostic cutoff value. MRI-TV ≥ 14 cm3 was an independent prognostic factor for overall survival and disease-free survival. For patients with MRI-TV ≥ 14 cm3, the disease-free survival rate with adjuvant therapy was superior to that of the sole staging surgery group. This study demonstrates a significant correlation between MRI-TV and clinical pathological features and prognosis in EC. For patients with MRI-TV ≥ 14 cm3, staging surgery followed by adjuvant therapy was superior to sole staging surgery.

The role of diffusion magnetic resonance imaging in determining tumor aggressiveness during preoperative surgical planning in early-stage endometrial cancer

PURPOSE

The present study aimed to evaluate the relationship between tumor volume and apparent diffusion coefficient (ADC) in preoperative magnetic resonance imaging and deep myometrial invasion, tumor grade, and lymphovascular space invasion (LVSI) in patients with early-stage endometrial cancer.

METHODS

The study included 73 patients diagnosed with early-stage endometrial cancer based on histopathological examination between May 2014 and July 2019. Receiver operating characteristic (ROC) curve analysis was used to estimate the accuracy of ADC and tumor volume in predicting the LVSI, the depth of myometrial invasion (DMI), and the histopathological tumor grade in these patients.

RESULTS

The areas under the ROC curves (AUCs) of ADC and tumor volume in predicting LVI, DMI, and high tumor grade were significantly greater than those for superficial myometrial invasion and low-grade tumors. The ROC analysis revealed that higher tumor volume was significantly associated with the prediction of DMI and tumor grade (p = 0.002 and p = 0.015). The corresponding cut-off values of tumor volume were > 7.12 and > 9.38 mL. The sensitivity of ADC in predicting DMI was higher than its sensitivity in predicting LVSI and grade 1 tumors. Furthermore, tumor volume was significantly associated with the prediction of DMI and tumor grade.

CONCLUSION

In the absence of pathological pelvic lymph nodes in early-stage endometrial cancer, tumor volume in DWI sequences determines the active tumor load and tumor aggressiveness. Furthermore, a low ADC indicates deep myometrial invasion and helps differentiate stage IA and stage IB tumors.

Diagnostic value of the apparent diffusion coefficient in differentiating malignant from benign endometrial lesions

Introduction

Magnetic resonance imaging (MRI) with its innovative techniques, such as diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC), increases the diagnostic accuracy in distinguishing between malignant and benign lesions of the endometrium. The aim of the study was MRI differentiation between malignant and benign endometrial lesions and correlation with histopathological findings with a special emphasis on quantitative analysis. An additional aim was to correlate the ADC values and histological tumor grades.

Methods

The prospective study included 119 female patients with or without vaginal bleeding and pathological values of endometrial thickness, who underwent MRI examinations. According to MRI reports the patients were divided into 45 suspicious malignant and 74 suspicious benign endometrial lesions. The radiological diagnosis was compared to the histopathological evaluation, which confirmed 37 malignant lesions while the rest were benign.

Results

The mean ADC value for malignant lesions was 0.761 ± 0.13×10^-3 mm²/s and for benign lesions was 1.318 ± 0.20×10^-3 mm²/s. The ADC values for malignant lesions were expectedly lower than those of benign lesions (p<0.001). The ADC cut-off value was 1.007×10^-3 mm²/s with a sensitivity of 100%, specificity of 92.7%, a positive predictive value of 60.3%, and a negative predictive value of 100%. In comparison with the histopathological findings, the sensitivity of MRI was 100%, specificity 90.2%, positive predictive value was 82.2%, and negative predictive value was 100%. Observing the histological grades 1, 2, and 3 of endometrial carcinoma, no statistically significant differences of mean ADC values were found. The mean ADC values for histological tumor grades 1,2 and 3 were 0.803 ± 0.13×10^-3 mm²/s, 0.754 ± 0.12×10^-3 mm²/s and 0.728 ± 0.13×10^-3 mm²/s, respectively.

Conclusion

DWI and ADC values represent clinically useful tools for the differentiation between malignant and benign endometrial lesions with high sensitivity and good specificity, but the results failed to demonstrate their usefulness in differentiating histological grades of endometrial cancer.

Greater increases in intratumoral apparent diffusion coefficients after chemoradiotherapy predict better overall survival of patients with cervical cancer

PURPOSE

To evaluate whether 1) the intratumoral apparent diffusion coefficients (ADCs) change during cervical cancer treatment and 2) the pretreatment ADC values or their change after treatment predict the treatment outcome or overall survival of patients with cervical cancer.

METHODS

We retrospectively enrolled 52 patients with inoperable cervical cancer treated with chemoradiotherapy, who had undergone diffusion weighted MRI before treatment and post external beam radiotherapy (EBRT) and concurrent chemotherapy. A subgroup of patients (n = 28) underwent altogether six consecutive diffusion weighted MRIs; 1) pretreatment, 2) post-EBRT and concurrent chemotherapy; 3-5) during image-guided brachytherapy (IGBT) and 6) after completing the whole treatment course. To assess interobserver and intertechnique reproducibility two observers independently measured the ADCs by drawing freehand a large region of interest (L-ROI) covering the whole tumor and three small ROIs (S-ROIs) in areas with most restricted diffusion.

RESULTS

Reproducibility was equally good for L-ROIs and S-ROIs. The pretreatment ADCs were higher in L-ROIs (883 mm2/s) than in S-ROIs (687 mm2/s, P < 0.001). The ADCs increased significantly between the pretreatment and post-EBRT scans (L-ROI: P < 0.001; S-ROI: P = 0.001). The ADCs remained significantly higher than pretreatment values during the whole IGBT. Using S-ROIs, greater increases in ADCs between pretreatment and post-EBRT MRI predicted better overall survival (P = 0.018).

CONCLUSION

ADC values significantly increase during cervical cancer treatment. Greater increases in ADC values between pretreatment and post-EBRT predicted better overall survival using S-ROIs. Standardized methods for timing and delineation of ADC measurements are advocated in future studies.

Update on MRI in Evaluation and Treatment of Endometrial Cancer

Endometrial cancer is the second most common gynecologic cancer worldwide and the most common gynecologic cancer in the United States, with an increasing incidence in high-income countries. Although the International Federation of Gynecology and Obstetrics (FIGO) staging system for endometrial cancer is a surgical staging system, contemporary published evidence-based data and expert opinions recommend MRI for treatment planning as it provides critical diagnostic information on tumor size and depth, extent of myometrial and cervical invasion, extrauterine extent, and lymph node status, all of which are essential in choosing the most appropriate therapy. Multiparametric MRI using a combination of T2-weighted sequences, diffusion-weighted imaging, and multiphase contrast-enhanced imaging is the mainstay for imaging assessment of endometrial cancer. Identification of important prognostic factors at MRI improves both treatment selection and posttreatment follow-up. MRI also plays a crucial role for fertility-preserving strategies and in patients who are not surgical candidates by helping guide therapy and identify procedural complications. This review is a product of the Society of Abdominal Radiology Uterine and Ovarian Cancer Disease-Focused Panel and reflects a multidisciplinary international collaborative effort to summarize updated information highlighting the role of MRI for endometrial cancer depiction and delineation, treatment planning, and follow-up. The article includes information regarding dedicated MRI protocols, tips for MRI reporting, imaging pitfalls, and strategies for image quality optimization. The roles of MRI-guided radiation therapy, hybrid PET/MRI, and advanced MRI techniques that are applicable to endometrial cancer imaging are also discussed. Online supplemental material is available for this article. ^©RSNA, 2022.

Pathological characteristics and risk stratification in patients with stage I endometrial cancer: utility of apparent diffusion coefficient histogram analysis

OBJECTIVES

Accurate pre-operative prediction of risk stratification using a non-invasive imaging tool is clinically important for planning optimal treatment strategies, particularly in early-stage endometrial cancer (EC). This study aimed to investigate the utility of apparent diffusion coefficient (ADC) histogram analysis in evaluating the pathological characteristics and risk stratification in patients with Stage I EC.

METHODS

Between October 2009 and December 2014, a total of 108 patients with surgically proven Stage I EC (endometrioid type = 91; non-endometrioid type = 17) excluding stage ≥II that underwent preoperative 3T-diffusion-weighted imaging without administration of contrast medium were enrolled in this retrospective study. Risk stratification was divided into four risk categories based on the ESMO-ESGO-ESTRO Guidelines: low, intermediate, high-intermediate, and high risk. The ADC histogram parameters (minimum, mean [ADCmean], 10th-90th percentile, and maximum [ADCmax]) of the tumor were generated using an in-house software. The ADC histogram parameters were compared between patients with endometrioid type and non-endometrioid type, between Stage IA and IB, between histological grades, and evaluated for differentiating non-high risk group from high risk group. Inter-reader agreement for tumor ADC measurements was also evaluated. Statistical analyses were performed using the Student's t-test, Mann-Whitney U test, receiver operating characteristics (ROC) analysis, or intraclass correlation coefficient (ICC).

RESULTS

In differentiating endometrioid type from non-endometrioid type EC, all ADC histogram parameters were statistically significant (p < 0.05). In differentiating histological grades, 90th percentile ADC and ADCmax showed significantly higher values in tumor Grade III than in tumor Grade I-II (p < 0.05). In differentiating superficial myometrial invasion from deep myometrial invasion, all ADC histogram parameters were statistically significant (p < 0.05), except ADCmax. In differentiating non-high risk group from high risk group, ADCmean, 75th-90th percentile ADC, and ADCmax were statistically significant (p < 0.05). For predicting the high risk group, the area under the ROC curve of ADCmax was 0.628 and the highest among other histogram parameters. All histogram parameters revealed moderate to good inter-reader reliability (ICC = 0.581‒0.769).

CONCLUSION

The ADC histogram analysis as reproducible tool may be useful for evaluating the pathological characteristics and risk stratification in patients with early-stage EC.

ADVANCES IN KNOWLEDGE

ADC histogram analysis may be useful for evaluating risk stratification in early-stage endometrial cancer patients.

Radiomics and machine learning applications in rectal cancer: Current update and future perspectives

The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define the best treatment option and optimize patient outcome, several rectal cancer biological variables must be evaluated. Currently, medical imaging plays a crucial role in the characterization of this disease, and it often requires a multimodal approach. Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors. Computed tomography is widely adopted for the detection of distant metastases. However, conventional imaging has recognized limitations, and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation. There is a growing interest in artificial intelligence applications in medicine, and imaging is by no means an exception. The introduction of radiomics, which allows the extraction of quantitative features that reflect tumor heterogeneity, allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers. To manage such a huge amount of data, the use of machine learning algorithms has been proposed. Indeed, without prior explicit programming, they can be employed to build prediction models to support clinical decision making. In this review, current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented, with an imaging modality-based approach and a keen eye on unsolved issues. The results are promising, but the road ahead for translation in clinical practice is rather long.

The value of the apparent diffusion coefficient in differentiating type II from type I endometrial carcinoma

BACKGROUND

Diagnostic type II endometrial carcinoma (EC) is considered more aggressive and has a poorer prognosis than type I EC; differentiation between them is helpful for preoperative clinical decision-making. However, the diagnostic value of the apparent diffusion coefficient (ADC) in differentiating them remains unclear.

PURPOSE

To investigate the value of ADC in differentiating type II EC from type I EC.

MATERIAL AND METHODS

Ninety-four patients with EC who underwent diffusion-weighted imaging (DWI) were retrospectively included and divided into type I and type II subgroups, based on the postoperative pathologic results. We analyzed the clinical characteristics, conventional magnetic resonance imaging manifestations, and ADC mean values (ADCmean), ADC minimum values (ADCmin), and ADC max values (ADCmax). Receiver operating characteristic (ROC) curve analysis was further used to assess the predictive performance.

RESULTS

The ADCmean, ADCmin, and tumor size differed significantly between the two subtypes. The area under the ROC curve (AUC) for ADCmean and ADCmin was 0.787 (95% confidence interval [CI] = 0.692-0.88) and 0.835 (95% CI = 0.751-0.919) for predicting type II EC, respectively. The optimal cut-off value of ADCmean for prediction was 0.757 × 10-3 mm2/s with a sensitivity of 91%, a specificity of 58%, and an accuracy of 74%, while for ADCmin was 0.637 × 10-3 mm2/s with a sensitivity of 82%, a specificity of 73%, and an accuracy of 75%.

CONCLUSION

EC with lower ADCmean and ADCmin values derived from DWI, and a larger size, are indicative of type II EC.

Deep Myometrial Infiltration of Endometrial Cancer on MRI: A Radiomics-Powered Machine Learning Pilot Study

RATIONALE AND OBJECTIVES

To evaluate an MRI radiomics-powered machine learning (ML) model's performance for the identification of deep myometrial invasion (DMI) in endometrial cancer (EC) patients and explore its clinical applicability.

MATERIALS AND METHODS

Preoperative MRI scans of EC patients were retrospectively selected. Three radiologists performed whole-lesion segmentation on T2-weighted images for feature extraction. Feature robustness was tested before randomly splitting the population in training and test sets (80/20% proportion). A multistep feature selection was applied to the first, excluding noninformative, low variance features and redundant, highly-intercorrelated ones. A Random Forest wrapper was used to identify the most informative among the remaining. An ensemble of J48 decision trees was tuned and finalized in the training set using 10-fold cross-validation, and then assessed on the test set. A radiologist evaluated all MRI scans without and with the aid of ML to detect the presence of DMI. McNemars's test was employed to compare the two readings.

RESULTS

Of the 54 patients included, 17 had DMI. In all, 1132 features were extracted. After feature selection, the Random Forest wrapper identified the three most informative which were used for ML training. The classifier reached an accuracy of 86% and 91% and areas under the Receiver Operating Characteristic curve of 0.92 and 0.94 in the cross-validation and final testing, respectively. The radiologist performance increased from 82% to 100% when using ML (p = 0.48).

CONCLUSION

We proved the feasibility of a radiomics-powered ML model for DMI detection on MR T2-w images that might help radiologists to increase their performance.

Machine Learning-Based Integration of Prognostic Magnetic Resonance Imaging Biomarkers for Myometrial Invasion Stratification in Endometrial Cancer

BACKGROUND

Estimation of the depth of myometrial invasion (MI) in endometrial cancer is pivotal in the preoperatively staging. Magnetic resonance (MR) reports suffer from human subjectivity. Multiparametric MR imaging radiomics and parameters may improve the diagnostic accuracy.

PURPOSE

To discriminate between patients with MI ≥ 50% using a machine learning-based model combining texture features and descriptors from preoperatively MR images.

STUDY TYPE

Retrospective.

POPULATION

One hundred forty-three women with endometrial cancer were included. The series was split into training (n = 107, 46 with MI ≥ 50%) and test (n = 36, 16 with MI ≥ 50%) cohorts.

FIELD STRENGTH/SEQUENCES

Fast spin echo T2-weighted (T2W), diffusion-weighted (DW), and T1-weighted gradient echo dynamic contrast-enhanced (DCE) sequences were obtained at 1.5 or 3 T magnets.

ASSESSMENT

Tumors were manually segmented slice-by-slice. Texture metrics were calculated from T2W and ADC map images. Also, the apparent diffusion coefficient (ADC), wash-in slope, wash-out slope, initial area under the curve at 60 sec and at 90 sec, initial slope, time to peak and peak amplitude maps from DCE sequences were obtained as parameters. MR diagnostic models using single-sequence features and a combination of features and parameters from the three sequences were built to estimate MI using Adaboost methods. The pathological depth of MI was used as gold standard.

STATISTICAL TEST

Area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, precision and recall were computed to assess the Adaboost models performance.

RESULTS

The diagnostic model based on the features and parameters combination showed the best performance to depict patient with MI ≥ 50% in the test cohort (accuracy = 86.1% and AUROC = 87.1%). The rest of diagnostic models showed a worse accuracy (accuracy = 41.67%-63.89% and AUROC = 41.43%-63.13%).

DATA CONCLUSION

The model combining the texture features from T2W and ADC map images with the semi-quantitative parameters from DW and DCE series allow the preoperative estimation of myometrial invasion. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.

MRI to assess deep myometrial invasion in patients with endometrial cancer:A multi-reader study to evaluate the diagnostic role of different sequences

OBJECTIVE

The identification of deep myometrial invasion (DMI) represents a fundamental aspect in patients with endometrial cancer (EC) for accurate disease staging. It can be detected on MRI using T2-weighted (T2-w), diffusion weighted (DWI) and dynamic contrast enhanced sequences (DCE). Aim of the study was to perform a multi-reader evaluation of such sequences to identify the most accurate and its reliability for the best protocol.

METHODS

In this multicenter retrospective study, MRI were independently evaluated by 4 radiologists (2 senior and 2 novice) with a sequence-based approach to identify DMI. The performance of the entire protocol was also evaluated. A comparison between the different sequences assessed by the same reader was performed using receiver operating curve and post-hoc analysis. Intraclass Correlation Coefficient (ICC) was used to assess inter- and intra-observer variability.

RESULTS

A total of 92 patients were included. The performance of the readers did not show significant differences among DWI, DCE and the entire protocol. For only one senior radiologist, who reached the highest diagnostic accuracy with the entire protocol (82,6 %), both DWI (p = 0,0197) and entire protocol (p = 0,0039) were found significantly superior to T2-w. The highest inter-observer agreement was obtained with the entire protocol by expert readers (ICC = 0,77).

CONCLUSIONS

For the detection of DMI, the performances of DWI and DCE alone and that of a complete protocol do not significantly differ, even though the latter ensures the highest reliability particularly for expert readers. In cases in which T2-w and DWI are consistent, an unenhanced protocol could be proposed.

Associations Between Apparent Diffusion Coefficient Value With Pathological Type, Histologic Grade, and Presence of Lymph Node Metastases of Esophageal Carcinoma

Objective:

To investigate the application value of apparent diffusion coefficient value in the pathological type, histologic grade, and presence of lymph node metastases of esophageal carcinoma.

Materials and Methods:

Eighty-six patients with pathologically confirmed esophageal carcinoma were divided into different groups according to pathological type, histological grade, and lymph node status. All patients underwent conventional magnetic resonance imaging and diffusion-weighted imaging scan, and apparent diffusion coefficient values of tumors were measured. Independent sample t test and 1-way variance were used to compare the difference of apparent diffusion coefficient value in different pathological types, histologic grades, and lymph node status. Correlation between the apparent diffusion coefficient value and the histologic grade was evaluated using Spearman rank correlation test. Receiver operating characteristic curve of apparent diffusion coefficient value was generated to evaluate the differential diagnostic efficiency of poorly and well/moderately differentiated esophageal carcinoma.

Results:

No significant difference was observed in apparent diffusion coefficient value between esophageal squamous cell carcinoma and adenocarcinoma and in patients between those with and without lymph node metastases (P > .05). The differences of apparent diffusion coefficient value were statistically significant between different histologic grades of esophageal carcinoma (P < .05). The apparent diffusion coefficient value was positively correlated with histologic grade (rs = 0.802). The apparent diffusion coefficient value ≤1.25 × 10⁻³ mm²/s as the cutoff value for diagnosis of poorly differentiated esophageal carcinoma with the sensitivity of 84.3%, and the specificity was 94.3%.

Conclusions:

The performance of apparent diffusion coefficient value was contributing to predict the histologic grade of esophageal carcinoma, which might increase lesions characterization before choosing the best therapeutic alternative. However, they do not correlate with pathological type and the presence of lymph node metastases of esophageal carcinoma.

The value of clinical parameters combined with magnetic resonance imaging (MRI) features for preoperatively distinguishing different subtypes of uterine sarcomas: An observational study (STROBE compliant)

To investigate clinical parameters combined with magnetic resonance imaging (MRI) features including apparent diffusion coefficient (ADC) values in preoperative identification of different subtypes of uterine sarcomas including uterine leiomyosarcoma (LMS), endometrial stromal sarcoma (ESS), and carcinosarcoma (CS).Data from 71 patients with uterine sarcoma confirmed by surgery and pathology were collected. The clinical characteristics, conventional MRI features, mean ADC values, minimum ADC values, and lesion-muscle ADC ratio (rADC) values were compared with different subtypes of uterine sarcomas.Age, clinical manifestation, tumor location, shape, and T1-weighted image (T1WI) signals were significantly different between CS and LMS or ESS (all P < .01). The presence of band sign was significantly higher in ESS than in LMS or CS (both P < .001). The cystic change or necrosis and enhancement could help to differentiate LMS from ESS or CS (both P < .02). Significant differences were observed in T2-weighted image (T2WI) signals of the solid components of LMS compared with CS (P < .001). There was a significant difference between ESS and CS in the rADC values (P = .004).Clinical parameters combined with MRI features could help narrowing preoperative diagnostic possibilities in distinguishing subtypes of uterine sarcomas. These findings may be beneficial in helping guide operative decisions.

A Predictor of Tumor Recurrence in Patients With Endometrial Carcinoma After Complete Resection of the Tumor: The Role of Pretreatment Apparent Diffusion Coefficient

Objectives

The aim of this study was to assess the prognostic and incremental value of pretreatment apparent diffusion coefficient (ADC) values of tumors for the prediction of tumor recurrence after complete resection of the tumor in patients with endometrial cancer.

Methods

This study enrolled 210 patients with stages IA to IIIC endometrial cancer who had undergone complete resection of the tumor and pretreatment magnetic resonance imaging. The minimum and mean ADC values (ADCmin, ADCmean) of tumors and normalized ADC (nADCmin, nADCmean) were calculated from magnetic resonance imaging. The primary outcome was recurrence-free survival (RFS). Receiver operating characteristic analysis was performed to compare the diagnostic performance of ADC values of 4 types. The Kaplan-Meier method, log-rank tests, and Cox regression were used to explore associations between recurrence and the ADC values with adjustment for clinicopathological factors.

Results

In receiver operating characteristic curve analysis, the areas under the curve were significant for ADCmean and nADCmean predicting tumor recurrence but were not significant for ADCmin and nADCmin. Regarding univariate analysis, ADCmean and nADCmean were significantly associated with increased risk of recurrence. Multivariate analysis showed that ADCmean and nADCmean remained independently associated with shorter RFS. In the high-risk group, the RFS of patients with lower ADC values (ADCmean and nADCmean) was significantly shorter than that of patients in the higher ADC value group.

Conclusions

Pretreatment tumor ADCmean and nADCmean were important imaging biomarkers for predicting recurrence in patients after complete resection of the tumor. They might improve existing risk stratification.

Preoperative Magnetic Resonance Volumetry in Predicting Myometrial Invasion, Lymphovascular Space Invasion, and Tumor Grade: Is It Valuable in International Federation of Gynecology and Obstetrics Stage I Endometrial Cancer?

Objective

The aim of this retrospective single-center study was to evaluate the relationship between maximum tumor size, tumor volume, tumor volume ratio (TVR) based on preoperative magnetic resonance (MR) volumetry, and negative histological prognostic parameters (deep myometrial invasion [MI], lymphovascular space invasion, tumor histological grade, and subtype) in International Federation of Gynecology and Obstetrics stage I endometrial cancer.

Methods/Materials

Preoperative pelvic MR imaging studies of 68 women with surgical-pathologic diagnosis of International Federation of Gynecology and Obstetrics stage I endometrial cancer were reviewed for assessment of MR volumetry and qualitative assessment of MI. Volume of the tumor and uterus was measured with manual tracing of each section on sagittal T2-weighted images. Tumor volume ratio was calculated according to the following formula: TVR = (total tumor volume/total uterine volume) × 100. Receiver operating characteristics curve was performed to investigate a threshold for TVR associated with MI. The Mann-Whitney U test, Kruskal-Wallis test, and linear regression analysis were applied to evaluate possible differences between tumor size, tumor volume, TVR, and negative prognostic parameters.

Results

Receiver operating characteristics curve analysis of TVR for prediction of deep MI was statistically significant (P = 0.013). An optimal TVR threshold of 7.3% predicted deep myometrial invasion with 85.7% sensitivity, 46.8% specificity, 41.9% positive predictive value, and 88.0% negative predictive value. Receiver operating characteristics curve analyses of TVR, tumor size, and tumor volume for prediction of tumor histological grade or lymphovascular space invasion were not significant. The concordance between radiologic and pathologic assessment for MI was almost excellent (κ value, 0.799; P < 0.001). Addition of TVR to standard radiologic assessment of deep MI increased the sensitivity from 90.5% to 95.2%.

Conclusions

Tumor volume ratio, based on preoperative MR volumetry, seems to predict deep MI independently in stage I endometrial cancer with insufficient sensitivity and specificity. Its value in clinical practice for risk stratification models in endometrial cancer has to be studied in larger cohort of patients.

Prediction of histological grade of endometrial cancer by means of MRI

OBJECTIVES

To evaluate the ability of MRI in predicting histological grade of endometrial cancer (EC).

METHODS

IRB-approved retrospective study; requirement for informed consent was waived. 90 patients with histologically proven EC who underwent preoperative MRI and surgery at our Institution between Sept2011 and Nov2016 were included. Myometrial invasion (</>50%) was assessed. Neoplasm and uterus volumes were estimated according to the ellipsoid formula; neoplasm/uterus volume ratio (N/U) was calculated. ADC maps were generated and histogram analysis was performed using commercially available software. MRI parameters were compared with the definitive histological grade (G1 = 28 patients, G2 = 29, G3 = 33) using ANOVA and Tukey-Kramer tests.

RESULTS

Deep myometrial invasion was significantly more frequent in G2-G3 lesions than in G1 ones (p < 0,005). N/U ratio was significantly higher for high-grade neoplasms (mean 0,08 for G1, 0,16 for G2 and 0,21 in G3; P = 0,002 for G1 vs. G2-G3); a cut off value of 0,13 enabled to distinguish G1 from G2-G3 lesions with 50% sensibility and 89% specificity. ADC values didn't show any statistically significant correlation with tumour grade.

CONCLUSIONS

N/U ratio >0.13 and deep myometrial invasion are significantly correlated with high grade EC, whereas ADC values are not useful for predicting EC grade.

The value of advanced MRI techniques in the assessment of cervical cancer: a review

OBJECTIVES

To assess the value of new magnetic resonance imaging (MRI) techniques in cervical cancer.

METHODS

We searched PubMed and MEDLINE and reviewed articles published from 1990 to 2016 to identify studies that used MRI techniques, such as diffusion weighted imaging (DWI), intravoxel incoherent motion (IVIM) and dynamic contrast enhancement (DCE) MRI, to assess parametric invasion, to detect lymph node metastases, tumour subtype and grading, and to detect and predict tumour recurrence.

RESULTS

Seventy-nine studies were included. The additional use of DWI improved the accuracy and sensitivity of the evaluation of parametrial extension. Most studies reported improved detection of nodal metastases. Functional MRI techniques have the potential to assess tumour subtypes and tumour grade differentiation, and they showed additional value in detecting and predicting treatment response. Limitations included a lack of technical standardisation, which limits reproducibility.

CONCLUSIONS

New advanced MRI techniques allow improved analysis of tumour biology and the tumour microenvironment. They can improve TNM staging and show promise for tumour classification and for assessing the risk of tumour recurrence. They may be helpful for developing optimised and personalised therapy for patients with cervical cancer.

TEACHING POINTS

• Conventional MRI plays a key role in the evaluation of cervical cancer. • DWI improves tumour delineation and detection of nodal metastases in cervical cancer. • Advanced MRI techniques show promise regarding histological grading and subtype differentiation. • Tumour ADC is a potential biomarker for response to treatment.

Role of Imaging in Fertility-sparing Treatment of Gynecologic Malignancies

Treatments for gynecologic cancer usually result in loss of fertility due to surgery or radical radiation therapy in the pelvis. In countries with an established screening program for cervical cancer, the majority of gynecologic malignancies occur in postmenopausal women. However, a substantial number of affected women are of childbearing age and have not completed their families. In these younger women, consideration of fertility preservation may be important. This article describes the fertility-sparing treatment options that are currently available and outlines the role of imaging in the selection of eligible patients on the basis of a review of the literature. In the setting of cervical cancer, magnetic resonance (MR) imaging is used to delineate the size, position, and stage of the tumor for selection of patients who are suitable for radical trachelectomy. In patients with solitary complex adnexal masses, diffusion- and perfusion-weighted MR imaging sequences are used to categorize the likelihood of invasive or borderline malignancy for consideration of unilateral ovarian resection, with fertility preservation when possible. In patients with endometrial cancer, MR imaging is used to rule out signs of invasive disease before hormone therapy is considered. Imaging is also used at patient follow-up to detect recurrent disease; however, evidence to support this application is limited. In conclusion, imaging is an essential tool in the care of patients with gynecologic malignancies who are considering fertility-preserving treatment options. ^©RSNA, 2016.