Value of diffusion-weighted MRI in diagnosis of uterine cervical cancer: a prospective study evaluating the benefits of DWI compared to conventional MR sequences in a 3T environment

Functional Magnetic Resonance Imaging in Cervical Cancer Diagnosis and Treatment

Cervical Cancer is the fourth most common cancer in women worldwide. Treatment with chemoradiotherapy followed by brachytherapy achieves high local control, but recurrence with metastatic disease impacts survival. This highlights the need for predictive and prognostic biomarkers identifying populations at risk of poorer treatment response and survival. Magnetic resonance imaging (MRI) is routinely used in cervical cancer and is a potential source for biomarkers. Functional MRI (fMRI) can characterise tumour beyond anatomical MRI, which is limited to the assessment of morphology. This review summarises fMRI techniques used in cervical cancer and examines the role of fMRI parameters as predictive or prognostic biomarkers. Different techniques characterise different tumour factors, which helps to explain the variation in patient outcomes. These can impact simultaneously on outcomes, making biomarker identification challenging. Most studies are small, focussing on single MRI techniques, which raises the need to investigate combined fMRI approaches for a more holistic characterisation of tumour.

Diffusion-weighted imaging in the assessment of cervical cancer: comparison of reduced field-of-view diffusion-weighted imaging and conventional techniques

BACKGROUND

Cervical cancer (CC) is the second most common cancer in women worldwide. Diffusion-weighted imaging (DWI) plays an important role in the diagnosis of CC, but the conventional techniques are affected by many factors.

PURPOSE

To compare reduced-field-of-view (r-FOV) and full-field-of-view (f-FOV) DWI in the diagnosis of CC.

MATERIAL AND METHODS

Preoperative magnetic resonance imaging (MRI) with r-FOV and f-FOV DWI images were collected. Two radiologists reviewed the images using a subjective 4-point scale for anatomical features, magnetic susceptibility artifacts, visual distortion, and overall diagnostic confidence for r-FOV and f-FOV DWI. The objective features included the region of interest (ROI) signal intensity of the cervical lesion (SIlesion) and gluteus maximus muscle (SIgluteus), standard deviation of the background noise (SDbackground), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). The differences of measured apparent diffusion coefficient (ADC) values between the two examinations in pathological grades and FIGO tumor stages were compared.

RESULTS

A total of 200 patients were included (170 with squamous cell carcinoma and 30 with adenocarcinoma). The scores of anatomical features, magnetic susceptibility artifacts, visual distortion, and overall diagnostic confidence for r-FOV DWI were significantly higher than those for f-FOV DWI. There was no difference in SNR and CNR between r-FOV DWI and f-FOV DWI. There were significant differences in ADC values between the two groups in all comparisons (P < 0.05).

CONCLUSION

Compared with f-FOV DWI, r-FOV DWI might provide clearer imaging, fewer artifacts, less distortion, and higher image quality for the diagnosis of CC and might assist in the detection of CC.

Texture Analysis in Uterine Cervix Carcinoma: Primary Tumour and Lymph Node Assessment

The conventional magnetic resonance imaging (MRI) evaluation and staging of cervical cancer encounters several pitfalls, partially due to subjective evaluations of medical images. Fifty-six patients with histologically proven cervical malignancies (squamous cell carcinomas, n = 42; adenocarcinomas, n = 14) who underwent pre-treatment MRI examinations were retrospectively included. The lymph node status (non-metastatic lymph nodes, n = 39; metastatic lymph nodes, n = 17) was assessed using pathological and imaging findings. The texture analysis of primary tumours and lymph nodes was performed on T2-weighted images. Texture parameters with the highest ability to discriminate between the two histological types of primary tumours and metastatic and non-metastatic lymph nodes were selected based on Fisher coefficients (cut-off value > 3). The parameters' discriminative ability was tested using an k nearest neighbour (KNN) classifier, and by comparing their absolute values through an univariate and receiver operating characteristic analysis. Results: The KNN classified metastatic and non-metastatic lymph nodes with 93.75% accuracy. Ten entropy variations were able to identify metastatic lymph nodes (sensitivity: 79.17-88%; specificity: 93.48-97.83%). No parameters exceeded the cut-off value when differentiating between histopathological entities. In conclusion, texture analysis can offer a superior non-invasive characterization of lymph node status, which can improve the staging accuracy of cervical cancers.

Value of MRI and diffusion-weighted imaging in diagnosing normal-sized pelvic lymph nodes metastases in patients with cervical cancer

OBJECTIVES

To investigate the value of conventional MRI and diffusion-weighted imaging (DWI) in diagnosing normal-sized pelvic lymph nodes metastases in patients with cervical cancer.

METHODS

102 patients with cervical cancer who underwent MRI and DWI scan were included. 137 lymph nodes were analyzed, including 44 metastatic lymph nodes (MLNs) and 93 non-metastatic lymph nodes (non-MLNs). The morphology and apparent diffusion coefficient (ADC) value of lymph nodes were measured including short-axis diameter (DS), long-axis diameter (DL), ratio of short-to-long-axis diameter (DR), fatty hilum, asymmetry, ADCmax, ADCmean and ADCmin. The Mann-Whitney U-test, independent sample t-test and Chi-square test were employed to compare the differences of all criteria between MLNs and non-MLNs. Logistic regression and decision tree were used to develop the combined diagnostic model. ROC analyses were used to evaluate the diagnostic performance.

RESULTS

The DS and DR of MLNs were significantly higher than those of non-MLNs (p < 0.05), the ADCmax, ADCmean and ADCmin of MLNs were significantly lower than those of non-MLNs (p < 0.05). Presence of fatty hilum and asymmetric lymph nodes between MLNs and non-MLNs were significantly different (p＜0.05). Combined measurement of ADCmin, DS and DR had the highest AUC 0.937 with 90.9% sensitivity and 87.1% specificity. The accuracy of decision tree was 88.3%.

CONCLUSION

MRI with DWI had potential in diagnosing normal-sized pelvic lymph nodes metastases in patients with cervical cancer. The combined evaluation of DS, DR and ADCmin of lymph nodes and decision tree of the combined measure showed better diagnostic performances than sole criteria.

ADVANCES IN KNOWLEDGE

The short-axis diameter, ratio of short-to-long-axis diameter and ADCmin of lymph nodes have moderate value in the diagnosis of the metastases of the normal-sized lymph nodes for the patient with cervical cancer as the sole indices. The combined evaluation of DS, DR and ADCmin is much more valuable in the detection of metastatic lymph nodes.

With 3 Types of Respiratory Acquisition: 3.0 T Respiratory Triggered Acquisition Can Obtain Higher Quality DWI Images of the Upper Abdomen

Objective

To compare the effects of 1.5 T and 3.0 T upper abdominal magnetic resonance diffusion-weighted imaging (DWI) under three acquisition techniques of breath holding, breath triggering, and free breathing, so as to provide a reference for the usage of upper abdominal DWI scanning.

Methods

Twenty-one healthy subjects were selected from social volunteers and underwent routine magnetic resonance imaging (MRI) and DWI on 1.5 T and 3.0 T, respectively. DWI included three acquisition methods: breath triggering, breath holding, and free breathing, and b values were 100 and 800. The DWI image artifacts, image quality, apparent diffusion coefficient (ADC), and the signal-to-noise ratio (SNR) obtained through the three acquisition methods were compared.

Results

The 1.5 T free-breathing DWI image quality was the best, while the 3.0 T had the best breath-triggered DWI image quality. The 3.0 T breath-triggered DWI image quality was better than the 1.5 T free-breathing DWI image (P=0.012), and the SNR of free-breathing DWI was the highest. Between the two field intensities, the SNR of the liver in the 3.0 T group was much lower than that in the 1.5 T group, and obvious differences were not observed in ADC values of normal liver, gallbladder, kidney, spleen, and pancreas.

Conclusion

3.0 T respiratory-triggered acquisition can obtain higher quality DWI images. But in the case of only 1.5 T field strength, free-breathing acquisition of DWI images should be selected.

Diagnostic Performance of Diffusion-Weighted Imaging for Colorectal Cancer Detection: An Updated Systematic Review and Meta-Analysis

Background

Magnetic resonance imaging (MRI), which uses strong magnetic fields and radio waves (radiofrequency energy) to make images, is one of the best imaging methods for soft tissues and can clearly display unique anatomical structures. Diffusion-weighted imaging (DWI) has been developed for identifying various malignant tumors.

Aim

To investigate the diagnostic value of DWI-MRI quantitative analysis in colorectal cancer detection.

Methods

The PubMed, Cochrane Library, and Embase databases were searched from inception to May 29, 2020. Studies published in English that used DWI-MRI for diagnosing colorectal cancer were included. Case reports, letters, reviews, and studies conducted in non-humans or in-vitro experiments were excluded. The pooled diagnostic odds ratio (DOR) and hierarchical summary receiver operating characteristic (HSROC) curves were computed for DWI, and the area under the curve (AUC) and associated standard error (SE) and 95% confidence intervals (CIs) were also used.

Results

In total, 15 studies with 1,655 participants were finally included in this meta-analysis. There were four prospective studies and 11 retrospective studies. Eight studies focused on rectal cancer, six on colorectal cancer, and one on colonic cancer. The performance of DWI-MRI for diagnosing colorectal cancer was accurate, with pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of 0.88 (95% CI = 0.85–0.91), 0.92 (95% CI = 0.91–0.94), 30.36 (95% CI = 11.05–83.43), and 0.44 (95% CI = 0.30–0.64), respectively. The DOR and HSROC curves were 121 (95% CI = 56–261) and 0.92 (λ: 4.79), respectively.

Conclusion

DWI showed high diagnostic accuracy for colorectal cancer detection. Further studies with large sample sizes and prospective design are needed to confirm these results.

Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings

Objective

The objective of the study is to investigate the feasibility of using the fractional order calculus (FROC) model to reflect tumor subtypes and histological grades of cervical carcinoma.

Methods

Sixty patients with untreated cervical carcinoma underwent multi-b-value diffusion-weighted imaging (DWI) at 3.0T magnetic resonance imaging (MRI). The mono-exponential and the FROC models were fitted. The differences in the histological subtypes and grades were evaluated by the Mann–Whitney U test. Receiver operating characteristic (ROC) analyses were performed to assess the diagnostic performance and to determine the best predictor for both univariate analysis and multivariate analysis. Differences between ROC curves were tested using the Hanley and McNeil test, while the sensitivity, specificity, and accuracy were compared using the McNemar test. P-value <0.05 was considered as significant difference. The Bonferroni corrections were applied to reduce problems associated with multiple comparisons.

Results

Only the parameter β, derived from the FROC model could differentiate cervical carcinoma subtypes (P = 0.03) and the squamous cell carcinoma (SCC) lesions exhibited significantly lower β than that in the adenocarcinoma (ACA) lesions. All the individual parameters, namely, ADC, β, D, and μ derived from the FROC model, could differentiate low-grade cervical carcinomas from high-grade ones (P = 0.022, 0.009, 0.004, and 0.015, respectively). The combination of all the FROC parameters showed the best overall performance, providing the highest sensitivity (81.2%) and AUC (0.829).

Conclusion

The parameters derived from the FROC model were able to differentiate the subtypes and grades of cervical carcinoma.

Roles of DWI and T2-weighted MRI volumetry in the evaluation of lymph node metastasis and lymphovascular invasion of stage IB-IIA cervical cancer

AIM

To determine whether magnetic resonance imaging volumetry on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) could be used to assess lymph node metastases (LNM) and lymphovascular invasion (LVSI) in resectable cervical cancer.

MATERIAL AND METHODS

Sixty-five consecutive patients with cervical cancer were enrolled retrospectively. Tumour size, including maximum transverse diameter, tumour length, and gross tumour volume (GTV), was evaluated on DWI and T2WI. Apparent diffusion coefficient (ADC) values were measured. Univariate, multivariate, and receiver operating characteristic (ROC) curve analyses were performed to determine whether tumour size and ADC could be used to assess LNM and LVSI.

RESULTS

Tumour length on both T2WI and DWI, and T2WI-based and DWI-based GTVs could be used to assess LNM (p=0.002, 0.004, 0.001, and <0.001, respectively). Tumour length on T2WI, T2WI-based GTV, DWI-based GTV, and ADC value could be used assess LVSI (p=0.039, 0.038, 0.012, 0.039, respectively). Multivariate analyses showed both T2WI-based GTV (odds ratio [OR] = 1.044; p=0.008) and DWI-based GTV (OR=1.941; p=0.019) were independent risk factors for LNM. T2WI-based GTV (OR=1.023, p=0.038) and DWI-based GTV (OR=3.275, p=0.008) were independent risk factors for LVSI. No statistically significant difference was identified between the area under the ROC curve (AUC) of the DWI-based GTV and the T2WI-based GTV (0.790 versus 0.775, p=0.113), or the tumour length on both T2WI (0.790 versus 0.734, p=0.185) and DWI (0.790 versus 0.737, p=0.333) for LNM. For LVSI, the AUC of DWI-based GTV was higher than T2WI-based GTV (0.720 versus 0.682, p=0.006).

CONCLUSION

GTV on both T2WI and DWI could be used assess LNM and LVSI. DWI-based GTV might show the greatest potential for assessing LNM and LVSI in resectable cervical cancer.

An MRI-based radiomics signature and clinical characteristics for survival prediction in early-stage cervical cancer

OBJECTIVES

To investigate the prognostic role of magnetic resonance imaging (MRI)-based radiomics signature and clinical characteristics for overall survival (OS) and disease-free survival (DFS) in the early-stage cervical cancer.

METHODS

A total of 207 cervical cancer patients (training cohort: n = 144; validation cohort: n = 63) were enrolled. 792 radiomics features were extracted from T2W and diffusion-weighted imaging (DWI). 19 clinicopathological parameters were collected from the electronic medical record system. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select significant features to construct prognostic model for OS and DFS. Kaplan-Meier (KM) analysis and log-rank test were applied to identify the association between the radiomics score (Rad-score) and survival time. Nomogram discrimination and calibration were evaluated as well. Associations between radiomics features and clinical parameters were investigated by heatmaps.

RESULTS

A radiomics signature derived from joint T2W and DWI images showed better prognostic performance than that from either T2W or DWI image alone. Higher Rad-score was associated with worse OS (p < 0.05) and DFS (p < 0.05) in the training and validation set. The joint models outperformed both radiomics model and clinicopathological model alone for 3-year OS and DFS estimation. The calibration curves reached an agreement. Heatmap analysis demonstrated significant associations between radiomics features and clinical characteristics.

CONCLUSIONS

The MRI-based radiomics nomogram showed a good performance on survival prediction for the OS and DFS in the early-stage cervical cancer. The prediction of the prognostic models could be improved by combining with clinical characteristics, suggesting its potential for clinical application.

ADVANCES IN KNOWLEDGE

This is the first study to build the radiomics-derived models based on T2W and DWI images for the prediction of survival outcomes on the early-stage cervical cancer patients, and further construct a combined risk scoring system incorporating the clinical features.

Value of diffusion-weighted imaging in preoperative evaluation and prediction of postoperative supplementary therapy for patients with cervical cancer

Background

With the continuous progress of medical imaging technology, evaluation of cervical cancer is increasingly dependent on imaging methods. Diffusion-weighted imaging (DWI) plays an important role, and apparent diffusion coefficient (ADC) value is a unique quantitative parameter in the research of cervical cancer.

Methods

In this prospective study, a total of 273 patients diagnosed with stage IB1 to IIIC1 cervical cancer based on the International Federation of Gynecology and Obstetrics (FIGO) 2018 staging guidelines who underwent pelvic 3.0T magnetic resonance imaging (MRI), including MRI and DWI, were enrolled, and the diagnostic value of preoperative staging of cervical cancer was compared between the MRI and DWI groups. The DWI group was used to explore the potential association of mean ADC (ADC_mean) with different pathological characteristics and receiver operating characteristic (ROC) curves of ADC_mean generated to predict the appropriate postoperative supplementary therapy.

Results

The diagnostic coincidence rate of DWI was higher than that of MRI in preoperative staging of cervical cancer (χ², P<0.05) and determined as stages IB1 + IB2 + IIA1 (90.91%), IB3 + IIA2 (93.48%), and IIIC1p (95.16%). The DWI staging results were consistent with postoperative pathological staging (Kappa value =0.865, P<0.001). We observed significant differences in ADC_mean values in relation to pathological type, histological grade, depth of stromal infiltration, tumor diameter, lymphovascular invasion, and pelvic lymph node metastasis of cervical cancer (all P<0.05). The area under the ROC curve (AUC) was 0.815, with the best predictive value for postoperative supplementary therapy in cervical cancer (sensitivity 80.0%, specificity 74.0%) at ADC_mean of 0.910×10^-3 mm²/s.

Conclusions

The DWI is a useful tool for preoperative evaluation of cervical cancer. In local cervical lesions, ADC_mean varies in relation to different clinicopathological characteristics and a reference index of <0.910×10^-3 mm²/s can be effectively applied to predict the need for postoperative supplementary therapy.

The value of the added diffusion-weighted images to multiparametric MRI in the early diagnosis of uterine cervix cancers and nodal assessment

Background

Cervical cancer still one of the most common causes of tumor-related death in developing countries presented in younger women. In this study, we aimed to evaluate the value of diffusion-weighted MRI in early diagnosis of malignant cervical lesions, to assess metastatic adenopathy, peritoneal dissemination, and possible tumor recurrence, and determine treatment response. This study included 60 patients with abnormal vaginal bleeding and suspected cervical lesion by US. A histopathological biopsy was done. Pelvic MR with DWI and dynamic contrast-enhanced MRI were done for all patients.

Results

According to the histopathological findings, we divided our studied 60 patents into two groups: group I, malignant lesions (46 lesions; 76.7%), and group II, benign lesions (14 lesions; 23.3%). Multiparametric MRI could detect all cervical lesions but with poor pathologic characterization, achieving 72.37% sensitivity, 37.50% specificity, 63.33% accuracy, 76.19% PPV, and 33.33% NPV. When compared with DWI with ADC value measurements at high b value (b = 800) to MRI exam, it showed a higher diagnostic accuracy with good lesion pathological characterization that achieved 95.65% sensitivity, 71.43% specificity, 90% accuracy, 91.67 PPV, and 83.33% NPV. The mean ADC value for malignant lesions was 0.86–1.1, mean = 0.92 ± 0.71 × 10−3 mm2/s, while the mean ADC value in the benign lesion group was 1.18 ± 0.1 × 10–3 mm2/s.

Conclusion

Comparing DWI with ADC values measurements at high b value to the multiparametric MRI examination of the female pelvis increases the sensitivity, specificity, and diagnostic accuracy of characterization and early diagnosis of cervical malignant focal lesions and reduces the need for intravenous contrast administration.

Building a predictive model to assist in the diagnosis of cervical cancer

Aim: Cervical cancer is still one of the most common gynecologic cancers in the world. Since cervical cancer is a potentially preventive cancer, earlier detection is the most effective technique for decreasing the worldwide incidence of the illness. Materials and methods: This research presents a novel ensemble technique for predicting cervical cancer risk. Specifically, the authors introduce a voting classifier that aggregates prediction probabilities from multiple machine-learning models: logistic regression, K-nearest neighbor, decision tree, XGBoost and multilayer perceptron. Results: The average accuracy, precision, recall and f1-score of the voting classifier were 96.6, 97.4, 95.9 and 96.6, respectively. Furthermore, the voting algorithm gains average high values for all evaluation metrics (accuracy, precision, recall and f1-score). The f1-score of the algorithm is 96%, which demonstrates the robustness of the model. Conclusion: The findings suggest that the probability of having cervical cancer can be accurately predicted utilizing the voting technique.

Interobserver agreement of transvaginal ultrasound and magnetic resonance imaging in local staging of cervical cancer

OBJECTIVE

To evaluate interobserver agreement for the assessment of local tumor extension in women with cervical cancer, among experienced and less experienced observers, using transvaginal ultrasound (TVS) and magnetic resonance imaging (MRI).

METHODS

The TVS observers were all gynecologists and consultant ultrasound specialists, six with and seven without previous experience in cervical cancer imaging. The MRI observers were five radiologists experienced in pelvic MRI and four less experienced radiology residents without previous experience in MRI of the pelvis. The less experienced TVS observers and all MRI observers underwent a short basic training session in the assessment of cervical tumor extension, while the experienced TVS observers received only a written directive. All observers were assigned the same images from cervical cancer patients at all stages (n = 60) and performed offline evaluation to answer the following three questions: (1) Is there a visible primary tumor? (2) Does the tumor infiltrate > ⅓ of the cervical stroma? and (3) Is there parametrial invasion? Interobserver agreement within the four groups of observers was assessed using Fleiss kappa (κ) with 95% CI.

RESULTS

Experienced and less experienced TVS observers, respectively, had moderate interobserver agreement with respect to tumor detection (κ (95% CI), 0.46 (0.40-0.53) and 0.46 (0.41-0.52)), stromal invasion > ⅓ (κ (95% CI), 0.45 (0.38-0.51) and 0.53 (0.40-0.58)) and parametrial invasion (κ (95% CI), 0.57 (0.51-0.64) and 0.44 (0.39-0.50)). Experienced MRI observers had good interobserver agreement with respect to tumor detection (κ (95% CI), 0.70 (0.62-0.78)), while less experienced MRI observers had moderate agreement (κ (95% CI), 0.51 (0.41-0.62)), and both experienced and less experienced MRI observers, respectively, had good interobserver agreement regarding stromal invasion (κ (95% CI), 0.80 (0.72-0.88) and 0.71 (0.61-0.81)) and parametrial invasion (κ (95% CI), 0.69 (0.61-0.77) and 0.71 (0.61-0.81)).

CONCLUSIONS

We found interobserver agreement for the assessment of local tumor extension in patients with cervical cancer to be moderate for TVS and moderate-to-good for MRI. The level of interobserver agreement was associated with experience among TVS observers only for parametrial invasion. © 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Implications of the new FIGO staging and the role of imaging in cervical cancer

International Federation of Gynecology and Obstetrics (FIGO) staging, which is the fundamentally important cancer staging system for cervical cancer, has changed in 2018. New FIGO staging includes considerable progress in the incorporation of imaging findings for tumour size measurement and evaluating lymph node (LN) metastasis in addition to tumour extent evaluation. MRI with high spatial resolution is expected for tumour size measurements and the high accuracy of positron emmision tomography/CT for LN evaluation. The purpose of this review is firstly review the diagnostic ability of each imaging modality with the clinical background of those two factors newly added and the current state for LN evaluation. Secondly, we overview the fundamental imaging findings with characteristics of modalities and sequences in MRI for accurate diagnosis depending on the focus to be evaluated and for early detection of recurrent tumour. In addition, the role of images in treatment response and prognosis prediction is given with the development of recent technique of image analysis including radiomics and deep learning.

Imaging modalities in pregnant cancer patients

Cancer during pregnancy is increasingly diagnosed due to the trend of delaying pregnancy to a later age and probably also because of increased use of non-invasive prenatal testing for fetal aneuploidy screening with incidental finding of maternal cancer. Pregnant women pose higher challenges in imaging, diagnosis, and staging of cancer. Physiological tissue changes related to pregnancy makes image interpretation more difficult. Moreover, uncertainty about the safety of imaging modalities, fear of (unnecessary) fetal radiation, and lack of standardized imaging protocols may result in underutilization of the necessary imaging tests resulting in suboptimal staging. Due to the absence of radiation exposure, ultrasound and MRI are obvious first-line imaging modalities for detailed locoregional disease assessment. MRI has the added advantage of a more reproducible comprehensive organ or body region assessment, the ability of distant staging through whole-body evaluation, and the combination of anatomical and functional information by diffusion-weighted imaging which obviates the need for a gadolinium-based contrast-agent. Imaging modalities with inherent radiation exposure such as CT and nuclear imaging should only be performed when the maternal benefit outweighs fetal risk. The cumulative radiation exposure should not exceed the fetal radiation threshold of 100 mGy. Imaging should only be performed when necessary for diagnosis and likely to guide or change management. Radiologists play an important role in the multidisciplinary team in order to select the most optimal imaging strategies that balance maternal benefit with fetal risk and that are most likely to guide treatment decisions. Our aim is to provide an overview of possibilities and concerns in current clinical applications and developments in the imaging of patients with cancer during pregnancy.

Comparison of reduced field-of-view diffusion-weighted imaging (DWI) and conventional DWI techniques in the assessment of Cervical carcinoma at 3.0T: Image quality and FIGO staging

OBJECTIVE

To evaluate imaging quality (IQ) and International Federation of Gynecology and Obstetrics (FIGO) staging of reduced field-of-view (r-FOV) diffusion-weighted imaging (DWI) in cervical carcinoma (CC).

MATERIALS AND METHODS

Sixty patients with pathologically proven CC who underwent both pre-treatment r-FOV DWI and full field-of-view (f-FOV) DWI on a 3.0T MRI scanner were retrospectively reviewed. The subjective qualitative image scores were compared using the Wilcoxon signed-rank test. Objective quality values and apparent diffusion coefficient (ADC) were estimated by paired t-test or Wilcoxon signed-rank test for the two DWI sequences according to Normality test. Spearman rank correlation analysis was used to evaluate the relationship between pathological results and mean ADC value.

RESULTS

The subjective IQ scores for r-FOV DWI were significantly higher than those for f-FOV DWI (P < 0.001). Similarly, the contrast-to-noise (CNR) value of r-FOV DWI was superior to that of f-FOV DWI (10.30 ± 3.676, 8.91 ± 3.008, P = 0.021). However, the signal-to-noise ratio (SNR) value of r-FOV DWI was considerably lower than that of f-FOV DWI (27.80 ± 6.056, 33.67 ± 7.833, P＜0.001). No significant difference was found between mean ADC values of f-FOV DWI and r-FOV DWI. There was a significant tendency for a negative correlation between the ADC values and FIGO stages of CC for both two sequences (r=-0. 436, P＜0.01; r=-0.470, P＜0.01, respectively).

CONCLUSIONS

The rFOV DWI sequence provided significantly better IQ and lesion conspicuity than the fFOV DWI sequence. In addition, rFOV sequences can be used in evaluation of FIGO staging of cervical cancer.

A prospective comparative dosimetric study between diffusion weighted MRI (DWI) & T2-weighted MRI (T2W) for target delineation and planning in cervical cancer brachytherapy

Aim

To evaluate the difference between GTVBT (Gross Tumor Volume at Brachytherapy) and HR CTV (High Risk Clinical Tumor Volume) delineated with DWI and T2W MRI. To evaluate doses to organs at risk and targets from plans generated using T2W and DWI.

Background

Functional imaging with DWI can improve cervical tumor distinction as it is more sensitive than T2W MRI even in detecting parametrial invasion. This study does a dosimetric comparison between a T2W and DWI based plan.

Methods

Fifty carcinoma cervix patients were subjected to MRI based brachytherapy. T2W and a diffusion weighted sequence were acquired. Target delineation and brachytherapy planning was done on both T2W and DWI. Standard DVH parameters were recorded and the treatment was given using the plan generated from T2W images.

Results

GTVBT and HRCTV contours on DWI were different when compared with T2W. Mean GTVBT volume on T2W and DWI was 5.25 and 5.23, respectively (p value 0.8). Mean HRCTV on T2W and DWI was 28.3 and 27 cc, respectively (p value 0.003). Planning on the above volumes resulted in a superior coverage in terms of HRCTV D90 and D100 for DWI based plan, HRCTV D90 - 735.1 and 741 cGy for T2W and DWI, respectively (p value 0.006), HRCTV D100 - 441.05 and 444.5 for T2W and DWI plans, respectively (p value = 0.006). Doses to the OAR were not significantly increased.

Conclusion

GEC ESTRO based contouring guidelines cover all the functionally abnormal areas on DWI. DWI should only be used as a supplement to T2W for contouring target volumes.

Evaluation and evolution of apparent diffusion coefficient (ADC) in image-guided adaptive brachytherapy (IGABT) for cervical cancer

PURPOSE

Image-guided adaptive brachytherapy (IGABT) recently has shown excellent clinical outcomes with superior local control and less toxicity. For IGABT, T2W (T2-weighted) MRI is the gold standard. However, studies have shown that target delineation with the same results in uncertainties, poor interobserver variabilities, and low conformity indices for high-risk clinical target volume contours. In this study, we investigate the role of diffusion-weighted imaging-derived apparent diffusion coefficient (ADC) maps to aid in IGABT. We also evaluated ADC from the baseline to brachytherapy.

METHODS AND MATERIALS

Thirty selected patients were enrolled for this study, and two MRIs were taken at diagnosis and before brachytherapy. Patients were divided into two groups, Group 1 being patients with parametrial involvement before external beam radiotherapy and no parametrial involvement before brachytherapy. Group 2 included patients with parametrial involvement before external beam radiotherapy and persistent parametrial involvement before brachytherapy. ADC was measured at the center, edge, and 1 cm from the edge.

RESULTS

The measured ADC increased from diagnosis to brachytherapy, and this increase was more for the patients in Group 1 than in Group 2. The mean TDadc (diagnosis ADC, center), TEadc (tumor edge ADC diagnosis), and T1cmDadc (1 cm from edge at diagnosis) were 0.884, 1.45, and 1.9 × 10^-3 mm²/s, respectively. The TBadc (ADC at brachytherapy, center), TEBadc (tumor edge ADC at brachytherapy), and TE1cmBadc (1 cm from edge brachytherapy) were 1.2, 1.8, and 2.3 × 10^-3 mm²/s, respectively, p-value <0.00001. No abnormal ADC was present outside the high-risk clinical target volume contours.

CONCLUSION

MRI-based IGABT using T2W imaging essentially covers all functionally abnormal zones at brachytherapy. Diffusion-weighted imaging, along with ADC maps, should only be used as a supplement for target delineation.

Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods

Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical cancer prediction model (CCPM) that offers early prediction of cervical cancer using risk factors as inputs. The CCPM first removes outliers by using outlier detection methods such as density-based spatial clustering of applications with noise (DBSCAN) and isolation forest (iForest) and by increasing the number of cases in the dataset in a balanced way, for example, through synthetic minority over-sampling technique (SMOTE) and SMOTE with Tomek link (SMOTETomek). Finally, it employs random forest (RF) as a classifier. Thus, CCPM lies on four scenarios: (1) DBSCAN + SMOTETomek + RF, (2) DBSCAN + SMOTE+ RF, (3) iForest + SMOTETomek + RF, and (4) iForest + SMOTE + RF. A dataset of 858 potential patients was used to validate the performance of the proposed method. We found that combinations of iForest with SMOTE and iForest with SMOTETomek provided better performances than those of DBSCAN with SMOTE and DBSCAN with SMOTETomek. We also observed that RF performed the best among several popular machine learning classifiers. Furthermore, the proposed CCPM showed better accuracy than previously proposed methods for forecasting cervical cancer. In addition, a mobile application that can collect cervical cancer risk factors data and provides results from CCPM is developed for instant and proper action at the initial stage of cervical cancer.

MRI texture features differentiate clinicopathological characteristics of cervical carcinoma

OBJECTIVES

To evaluate MRI texture analysis in differentiating clinicopathological characteristics of cervical carcinoma (CC).

METHODS

Patients with newly diagnosed CC who underwent pre-treatment MRI were retrospectively reviewed. Texture analysis was performed using commercial software (TexRAD). Largest single-slice ROIs were manually drawn around the tumour on T2-weighted (T2W) images, apparent diffusion coefficient (ADC) maps and contrast-enhanced T1-weighted (T1c) images. First-order texture features were calculated and compared among histological subtypes, tumour grades, FIGO stages and nodal status using the Mann-Whitney U test. Feature selection was achieved by elastic net. Selected features from different sequences were used to build the multivariable support vector machine (SVM) models and the performances were assessed by ROC curves and AUC.

RESULTS

Ninety-five patients with FIGO stage IB~IVB were evaluated. A number of texture features from multiple sequences were significantly different among all the clinicopathological subgroups (p < 0.05). Texture features from different sequences were selected to build the SVM models. The AUCs of SVM models for discriminating histological subtypes, tumour grades, FIGO stages and nodal status were 0.841, 0.850, 0.898 and 0.879, respectively.

CONCLUSIONS

Texture features derived from multiple sequences were helpful in differentiating the clinicopathological signatures of CC. The SVM models with selected features from different sequences offered excellent diagnostic discrimination of the tumour characteristics in CC.

KEY POINTS

• First-order texture features are able to differentiate clinicopathological signatures of cervical carcinoma. • Combined texture features from different sequences can offer excellent diagnostic discrimination of the tumour characteristics in cervical carcinoma.

Diffusion Kurtosis Imaging in the Assessment of Cervical Carcinoma

RATIONALE AND OBJECTIVES

To evaluate the additional value of diffusion kurtosis imaging (DKI) in the characterization of cervical carcinoma.

MATERIALS AND METHODS

Seventy-five patients (56.9 ± 13.4 years) with histologic-confirmed cervical carcinoma were included. Diffusion-weighted imaging (DWI) was acquired on a 3T MRI with five b values (0, 500, 800, 1000, and 1500 s/mm²). Data were analyzed based on DKI model (5 b values) and conventional DWI (0 and 1000 s/mm²). Largest single-slice region of interest (ROI) and volume of interest (VOI) were drawn around the tumor. Mean diffusivity (MD), mean kurtosis (MK), and apparent diffusion coefficient (ADC) of cervical carcinoma and normal myometrium were measured and compared. MD, MK, and ADC of cervical carcinoma were compared among histologic subtypes, tumor grades, and FIGO stages.

RESULTS

ROI- and VOI-derived DKI parameters and ADC were all in excellent consistency (intraclass correlation coefficient, ICC > 0.90, respectively). Cervical carcinoma had significantly lower MD, ADC, and higher MK than normal myometrium (p < 0.001). MD and ADC showed significant differences between histologic subtypes and FIGO stages, lower in squamous cell carcinoma than adenocarcinoma and higher in FIGO I-II than FIGO III-IV (p < 0.050), but not with tumor grade. No difference was observed in MK for different clinicopathologic features tested.

CONCLUSION

ROI and VOI analyses were in excellent consistency. MD and ADC were able to distinguish histologic subtypes and separating FIGO stages, MK could not. DKI showed no clear added value over conventional DWI in the characterization of cervical carcinoma.

What Is the Role of Imaging at Primary Diagnostic Work-Up in Uterine Cervical Cancer?

PURPOSE OF REVIEW

For uterine cervical cancer, the recently revised International Federation of Gynecology and Obstetrics (FIGO) staging system (2018) incorporates imaging and pathology assessments in its staging. In this review we summarize the reported staging performances of conventional and novel imaging methods and provide an overview of promising novel imaging methods relevant for cervical cancer patient care.

RECENT FINDINGS

Diagnostic imaging during the primary diagnostic work-up is recommended to better assess tumor extent and metastatic disease and is now reflected in the 2018 FIGO stages 3C1 and 3C2 (positive pelvic and/or paraaortic lymph nodes). For pretreatment local staging, imaging by transvaginal or transrectal ultrasound (TVS, TRS) and/or magnetic resonance imaging (MRI) is instrumental to define pelvic tumor extent, including a more accurate assessment of tumor size, stromal invasion depth, and parametrial invasion. In locally advanced cervical cancer, positron emission tomography-computed tomography (PET-CT) or computed tomography (CT) is recommended, since the identification of metastatic lymph nodes and distant metastases has therapeutic consequences. Furthermore, novel imaging techniques offer visualization of microstructural and functional tumor characteristics, reportedly linked to clinical phenotype, thus with a potential for further improving risk stratification and individualization of treatment. Diagnostic imaging by MRI/TVS/TRS and PET-CT/CT is instrumental for pretreatment staging in uterine cervical cancer and guides optimal treatment strategy. Novel imaging techniques may also provide functional biomarkers with potential relevance for developing more targeted treatment strategies in cervical cancer.

Preoperative prediction of pelvic lymph nodes metastasis in early-stage cervical cancer using radiomics nomogram developed based on T2-weighted MRI and diffusion-weighted imaging

OBJECTIVE

To explore an MRI-based radiomics nomogram for preoperatively predicting of pelvic lymph node (PLN) metastasis in patients with early-stage cervical cancer (ECC).

METHODS

Ninety-six patients with ECC were enrolled in this study. All patients underwent T2WI and DWI scans before radical hysterectomy with PLN dissection surgery. Radiomics features extracted from T2WI and DWI were selected by least absolute shrinkage and selection operation regression for further radimoics signature calculation. The discrimination of this radiomics signature for PLN metastasis was then assessed using a support vector machine (SVM) model. Subsequently, a radiomics nomogram was constructed based on the radiomics signature and clinicopathologic risk factors using a multivariable logistic regression method. The performance of the radiomics nomogram for the preoperative prediction of PLN metastasis was evaluated for discrimination and calibration.

RESULTS

The radiomics signatures demonstrated a good discrimination for PLN metastasis. A radiomics signature derived from joint T2WI and DWI yielded higher AUC than the signatures derived from T2WI or DWI alone. The radiomics nomogram integrating the radiomics signature with clinicopathologic risk factors showed a significant improvement over the nomogram based only on clinicopathologic risk factors in the primary cohort(C-index, 0.893 vs. 0.616; P = 4.311×10^-5) and validation cohort(C-index, 0.922 vs. 0.799; P = 3.412 ×10^-2).The calibration curves also showed good agreement.

CONCLUSIONS

The radiomics nomogram based on joint T2WI and DWI demonstrated an improved prediction ability for PLN metastasis in ECC. This noninvasive and convenient tool may be used to facilitate preoperative identification of PLN metastasis in patients with ECC.

Diffusion-weighted MRI in the evaluation of the thyroid nodule: Comparison between integrated-shimming EPI and conventional 3D-shimming EPI techniques

This study aimed to evaluate whether a prototype echo planar imaging sequence with integrated-shimming (iShim-EPI) can improve image quality in the thyroid gland in comparison to 3D-volume shimming echo planar imaging (3D-Shim-EPI), and to compare ADC values derived from iShim-EPI with those of 3D-Shim-EPI. Twenty-one patients with thyroid disease were enrolled and underwent axial DWIs with iShim-EPI and 3D-Shim-EPI using a 3 Tesla magnetic resonance scanner in this prospective study. Both sets of DWI images were evaluated by two independent observers who identified susceptibility and ghost artifacts and evaluated the images' capacity to detect thyroid nodules using quantitative scores. The ADC values of the thyroid nodules and the normal thyroid gland were measured two times within a 4-week period. The reproducibility was evaluated using the intraclass correlation coefficient (ICC) and Bland-Altman plots. There were significant differences in the image quality scores for susceptibility (2.81 ± 0.37 vs. 1.93 ± 0.29, p < 0.001), ghost artifacts (2.95 ± 0.15 vs. 1.93 ± 0.29, p < 0.001) and the detectability of thyroid nodules (3.00 ± 0.00 vs. 2.55 ± 0.75, p = 0.008) between the iShim-EPI and 3D-Shim-EPI techniques, except for the ADC values of the thyroid nodules (1.607 ± 0.466×10⁻³ mm²/s vs. 1.561 ± 0.483 × 10⁻³ mm²/s, p = 0.184) and contralateral normal thyroid gland (1.295 ± 0.340 × 10⁻³ mm²/s vs.1.279 ± 0.411 × 10⁻³ mm²/s, p = 0.777). Both techniques demonstrated excellent agreement between the ADC values using the ICC (range, 0.963 to 0.999) and Bland-Altman plots. The iShim-EPI technique demonstrated significantly higher image quality compared with the conventional 3D-Shim-EPI technique, with no significant differences in the ADC values.

Radiomics analysis of apparent diffusion coefficient in cervical cancer: A preliminary study on histological grade evaluation

BACKGROUND

The role of apparent diffusion coefficient (ADC)-based radiomics features in evaluating histopathological grade of cervical cancer is unresolved.

PURPOSE

To determine if there is a difference between radiomics features derived from center-slice 2D versus whole-tumor volumetric 3D for ADC measurements in patients with cervical cancer regarding tumor histopathological grade, and systematically assess the impact of the b value on radiomics analysis in ADC quantifications.

STUDY TYPE

Prospective.

SUBJECTS

In all, 160 patients with histopathologically confirmed squamous cell carcinoma of uterine cervix.

FIELD STRENGTH/SEQUENCE

Conventional and diffusion-weighted MR images (b values = 0, 800, 1000 s/mm² ) were acquired on a 3.0T MR scanner.

ASSESSMENT

Regions of interest (ROIs) were drawn manually along the margin of tumor on each slice, and then the center slice of the tumor was selected with naked eyes in the course of whole-tumor segmentation. A total of 624 radiomics features were derived from T₂ -weighted images and ADC maps. We randomly selected 50 cases and did the reproducibility analysis.

STATISTICAL TESTS

Parameters were compared using Wilcoxon signed rank test, Bland-Altman analysis, t-test, and least absolute shrinkage and selection operator (LASSO) regression with crossvalidation.

RESULTS

In all, 95 radiomics features were insensitive to ROI variation among T₂ images, ADC map of b800, and ADC map of b1000 (P > 0.0002). There was a significant statistical difference between the performances of 2D center-slice and 3D whole-tumor radiomics models in both ADC feature sets of b800 and b1000 (P < 0.0001, P < 0.0001). Compared with ADC features of b800 (0.3758 ± 0.0118), the model of b1000 ADC features appeared to be slightly lower in overall misclassification error (0.3642 ± 0.0162) (P = 0.0076).

DATA CONCLUSION

Several radiomics features extracted from T₂ images and ADC maps were highly reproducible. Whole-tumor volumetric 3D radiomics analysis had a better performance than using the 2D center-slice of tumor in stratifying the histological grade of cervical cancer. A b value of 1000 s/mm² is suggested as the optimal parameter in pelvic DWI scans.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:280-290.

Reduced field-of-view diffusion-weighted MRI in patients with cervical cancer

OBJECTIVE

Diffusion-weighted imaging (DWI) with reduced field-of-view (FOV) has been shown to provide high spatial resolution with reduced distorsion in the spinal cord, breast, pancreas, and prostate gland. Therefore, we performed this study to evaluate the qualitative image quality and quantitative ADC value of reduced FOV DWI in patients with cervical cancer in comparison with conventional DWI.

METHODS

This study retrospectively included 22 patients (mean age, 53.9 years) with biopsy-proven cervical cancer who underwent pelvic MR imaging including conventional DWI and reduced FOV DWI before therapy. Two observers independently rated image quality for reduced FOV DWI and conventional DWI regarding anatomic detail, lesion conspicuity, presence of artifacts, and overall image quality using the following 4-point scale. Quantitative analysis was performed by measuring the ADC value of the tumor. The Wilcoxon signed-rank test was used to compare qualitative scores and mean ADC value between two DWI sequences.

RESULTS

Reduced FOV DWI achieved significantly better anatomic detail, lesion conspicuity, presence of artifacts, and overall image quality compared to conventional DWI (p < 0.05). There was no significant difference in mean tumor ADC value between the two DWI sequences (0.990 × 10^-3 mm² s^-1 ± 0.364 at reduced FOV DWI vs 1.253 × 10^-3 mm² s^-1 ± 0.387 at conventional DWI) (p = 0.067).

CONCLUSION

Reduced FOV DWI shows better image quality in terms of anatomic detail and lesion conspicuity with fewer artifacts compared to conventional DWI. Advance in knowledge: Reduced FOV DWI may enhance diagnostic performance for evaluation of cervical cancer.

Appropriate magnetic resonance imaging techniques for gross tumor volume delineation in external beam radiation therapy of locally advanced cervical cancer

Background

Accurate delineation of the gross tumor volumes (GTV) is a prerequisite for precise radiotherapy planning and delivery. Different MRI sequences have different advantages and limitations in their ability to discriminate primary cervical tumor from normal tissue. The purpose of this work is to determine appropriate MRI techniques for GTV delineation for external-beam radiation therapy of locally advanced cervical cancer (LACC).

Materials and Methods

GTVs were delineated on the MRI, CT, and PET images acquired for 23 LACC patients in treatment positions to obtain GTVs on CT (GTV-CT), on various MRI sequences including T1 (GTV-T1), T2 (GTV-T2), T1 with fat suppression and contrast (GTV-T1F+), DWI-ADC (GTV-ADC) and on PET were generated using the threshold of 40% of maximum SUV (GTV-SUV40%) as well as SUV of 2.5 (GTV-SUV2.5). MRI, CT and PET were registered for comparison. The GTVs defined by MRI were compared using the overlap ratio (OR) and relative volume ratio (RVR). The union of GTV-T2 and GTV-ADC was generated to represent the MRI-based GTV (GTV-MRI).

Results

The differences between GTV-T2 and other MRI GTVs are significant (P < 0.05). The average ORs for GTV-T1, GTV-T1F+, and GTV-ADC related to GTV-T2 were 86.3%, 81.6%, and 61.6% with the corresponding average RVRs 113.8%, 112.3% and 77.2%, respectively. There is no significant difference between GTV-T1 and GTV-T1F+. GTV-ADC was generally smaller than GTV-T2, however, encompassed suspicious regions that are uncovered in GTV-T2 (up to 16% of GTV-T2) because of different imaging mechanisms. There was significant difference between GTV-MRI, GTV-SUV2.5, GTV-SUV40%, and GTV-CT. On average, GTV-MRI is 18.4% smaller than GTV-CT.

Conclusions

MRI provides improved visualization of disease over CT or PET for cervical cancer. The GTV from the union of GTV-T2 and GTV-ADC provides a reasonable GTV including tumor region defined anatomically and functionally with MRI and substantially reduces the conventional GTV defined on CT.

A Network Meta-Analysis on the Diagnostic Value of Different Imaging Methods for Lymph Node Metastases in Patients With Cervical Cancer

PURPOSE

We performed this network meta-analysis to compare the diagnostic value of 4 imaging methods (magnetic resonance imaging, positron emission tomography, computed tomography, and diffusion-weighted imaging) for diagnosing lymph node metastases in cervical cancer.

METHOD

Diagnostic tests regarding different imaging methods to diagnose lymph node metastases in cervical cancer were retrieved from the Cochrane Library, PubMed, and Embase electronic databases from inception to December 2016. Direct and indirect evidence was performed to calculate the odds ratio and to draw the surface under the cumulative ranking curves of the 4 imaging methods for diagnosing lymph node metastases in cervical cancer.

RESULTS

Sixteen eligible diagnostic tests were included in this network meta-analysis. The results of network meta-analysis demonstrate that in comparison with the diffusion-weighted imaging, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of positron emission tomography were relatively higher. Additionally, the results further indicate that compared with other diagnosis method, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of positron emission tomography had a higher trend. The surface under the cumulative ranking curve results indicated that in terms of positive likelihood ratio and diagnostic odds ratio, positron emission tomography had a relatively higher diagnostic value for lymph node metastases in patients with cervical cancer.

CONCLUSION

Our findings indicate that positron emission tomography might have a relatively higher diagnostic value for lymph node metastases in patients with cervical cancer.

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.

Fertility-sparing for young patients with gynecologic cancer: How MRI can guide patient selection prior to conservative management

Historically, cancer treatment has emphasized measures for the "cure" regardless of the long-term consequences. Advances in cancer detection and treatment have resulted in improved outcomes bringing to the fore various quality of life considerations including future fertility. For many young cancer patients, fertility preservation is now an integral component of clinical decision-making and treatment design. Optimal fertility-sparing options for young patients with gynecologic cancer are influenced by patient age, primary cancer, treatment regimens, and patient preferences. Possible approaches include embryo or oocyte cryopreservation, ovarian transposition, conservative surgery, and conservative medical treatment to delay radical surgery. These may be used alone or in combination to maximize fertility preservation. Awareness of the various fertility-sparing options, eligibility criteria, and the central role of magnetic resonance imaging in the proper selection of patients will enable radiologists to produce complete clinically relevant imaging reports and serve as effective consultants to referring clinicians. Knowledge of the potential imaging pitfalls is essential to avoid misinterpretation and guide appropriate management.