Factors affecting the precision of lesion sizing with contrast-enhanced spectral mammography

Factors Influencing Background Parenchymal Enhancement in Contrast-Enhanced Mammography Images

Background: The aim of this study is to evaluate the correlation between background parenchymal enhancement (BPE) and various patient-related and technical factors in recombined contrast-enhanced spectral mammography (CESM) images. Material and Methods: We assessed CESM images from 62 female patients who underwent CESM between May 2017 and October 2019, focusing on factors influencing BPE. A total of 235 images, all acquired using the same mammography machine, were analyzed. A region of interest (ROI) with a standard size of 0.75 to 1 cm2 was used to evaluate the minimal, maximal, and average pixel intensity enhancement. Additionally, the images were qualitatively assessed on a scale from 1 (minimal BPE) to 4 (marked BPE). We examined correlations with body mass index (BMI), age, hematocrit, hemoglobin levels, cardiovascular conditions, and the amount of pressure applied during the examination. Results: Our study identified a significant correlation between the amount of pressure applied during the examination and the BPE (Spearman's ρ = 0.546). Additionally, a significant but weak correlation was observed between BPE and BMI (Spearman's ρ = 0.421). No significant associations were found between BPE and menopausal status, cardiovascular preconditions, hematocrit, hemoglobin levels, breast density, or age. Conclusions: Patient-related and procedural factors significantly influence BPE in CESM images. Specifically, increased applied pressure and BMI are associated with higher BPE.

Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography

Background.Breast background parenchymal enhancement (BPE) is correlated with the risk of breast cancer. BPE level is currently assessed by radiologists in contrast-enhanced mammography (CEM) using 4 classes: minimal, mild, moderate and marked, as described inbreast imaging reporting and data system(BI-RADS). However, BPE classification remains subject to intra- and inter-reader variability. Fully automated methods to assess BPE level have already been developed in breast contrast-enhanced MRI (CE-MRI) and have been shown to provide accurate and repeatable BPE level classification. However, to our knowledge, no BPE level classification tool is available in the literature for CEM.Materials and methods.A BPE level classification tool based on deep learning has been trained and optimized on 7012 CEM image pairs (low-energy and recombined images) and evaluated on a dataset of 1013 image pairs. The impact of image resolution, backbone architecture and loss function were analyzed, as well as the influence of lesion presence and type on BPE assessment. The evaluation of the model performance was conducted using different metrics including 4-class balanced accuracy and mean absolute error. The results of the optimized model for a binary classification: minimal/mild versus moderate/marked, were also investigated.Results.The optimized model achieved a 4-class balanced accuracy of 71.5% (95% CI: 71.2-71.9) with 98.8% of classification errors between adjacent classes. For binary classification, the accuracy reached 93.0%. A slight decrease in model accuracy is observed in the presence of lesions, but it is not statistically significant, suggesting that our model is robust to the presence of lesions in the image for a classification task. Visual assessment also confirms that the model is more affected by non-mass enhancements than by mass-like enhancements.Conclusion.The proposed BPE classification tool for CEM achieves similar results than what is published in the literature for CE-MRI.

The Role of Contrast-Enhanced Mammography After Cryoablation of Breast Cancer

Image-guided cryoablation is an emerging therapeutic technique for the treatment of breast cancer and is a treatment strategy that is an effective alternate to surgery in select patients. Tumor features impacting the efficacy of cryoablation include size, location in relation to skin, and histology (e.g., extent of intraductal component), underscoring the importance of imaging for staging and workup in this patient population. Contrast-enhanced mammography (CEM) utilization is increasing in both the screening and diagnostic settings and may be useful for follow-up imaging after breast cancer cryoablation, given its high sensitivity for cancer detection and its advantages in terms of PPV, time, cost, eligibility, and accessibility compared with contrast-enhanced MRI. This Clinical Perspective describes the novel use of CEM after breast cancer cryoablation, highlighting the advantages and disadvantages of CEM compared with alternate imaging modalities, expected benign postablation CEM findings, and CEM findings suggestive of residual or recurrent tumor.

AI-Based Cancer Detection Model for Contrast-Enhanced Mammography

BACKGROUND

The recent development of deep neural network models for the analysis of breast images has been a breakthrough in computer-aided diagnostics (CAD). Contrast-enhanced mammography (CEM) is a recent mammography modality providing anatomical and functional imaging of the breast. Despite the clinical benefits it could bring, only a few research studies have been conducted around deep-learning (DL) based CAD for CEM, especially because the access to large databases is still limited. This study presents the development and evaluation of a CEM-CAD for enhancing lesion detection and breast classification.

MATERIALS & METHODS

A deep learning enhanced cancer detection model based on a YOLO architecture has been optimized and trained on a large CEM dataset of 1673 patients (7443 images) with biopsy-proven lesions from various hospitals and acquisition systems. The evaluation was conducted using metrics derived from the free receiver operating characteristic (FROC) for the lesion detection and the receiver operating characteristic (ROC) to evaluate the overall breast classification performance. The performances were evaluated for different types of image input and for each patient background parenchymal enhancement (BPE) level.

RESULTS

The optimized model achieved an area under the curve (AUROC) of 0.964 for breast classification. Using both low-energy and recombined image as inputs for the DL model shows greater performance than using only the recombined image. For the lesion detection, the model was able to detect 90% of all cancers with a false positive (non-cancer) rate of 0.128 per image. This study demonstrates a high impact of BPE on classification and detection performance.

CONCLUSION

The developed CEM CAD outperforms previously published papers and its performance is comparable to radiologist-reported classification and detection capability.

Association of Clinical Factors and Degree of Early Background Parenchymal Enhancement on Contrast-Enhanced Mammography

BACKGROUND. Background parenchymal enhancement (BPE) may impact contrast-enhanced mammography (CEM) interpretation, although factors influencing the degree of BPE on CEM are poorly understood. OBJECTIVE. The purpose of our study was to evaluate relationships between clinical factors and the degree of early BPE on CEM. METHODS. This retrospective study included 207 patients (median age, 46 years) who underwent CEM between April 2020 and September 2021. Two radiologists independently assessed the degree of BPE on CEM as minimal, mild, moderate, or marked on the basis of two criteria (criterion 1, using the first of four obtained views; criterion 2, using the first two of four obtained views). The radiologists reached consensus for breast density on CEM. The EMR was reviewed for clinical factors. Radiologists' agreement for degree of BPE was assessed using weighted kappa coefficients. Univariable and multivariable analyses were performed to assess relationships between clinical factors and degree of BPE, treating readers' independent assessments as repeated measurements. RESULTS. Interreader agreement for degree of BPE, expressed as kappa, was 0.80 for both criteria. For both criteria, univariable analyses found degree of BPE to be negatively associated with age (both OR = 0.94), personal history of breast cancer (OR = 0.22-0.30), history of chemotherapy (OR = 0.18-0.21), history of radiation therapy (OR = 0.20-0.21), perimenopausal status (OR = 0.22-0.34), and postmenopausal status (OR = 0.10-0.11) and to be positively associated with dense breasts (OR = 4.13-4.26) and premenopausal status with irregular menstrual cycles (OR = 7.94-14.02). Among premenopausal patients with regular menstrual cycles, degree of BPE was lowest (using postmenopausal patients as reference) for patients in menstrual cycle days 8-14 (OR = 2.56-3.30). In multivariable analysis for both criteria, the only independent predictors of degree of BPE related to menstrual status and time of menstrual cycle (e.g., using premenopausal patients in days 1-7 as reference: OR = 0.21 for both criteria for premenopausal patients in days 8-14 and OR = 0.03-0.04 for postmenopausal patients). CONCLUSION. Clinical factors, including history of breast cancer or breast cancer treatment, breast density, menstrual status, and time of menstrual cycle, are associated with degree of early BPE on CEM. In premenopausal patients, the degree of BPE is lowest on days 8-14 of the menstrual cycle. CLINICAL IMPACT. Given the potential impact of BPE on diagnostic performance, the findings have implications for CEM scheduling and interpretation.

Background enhancement in contrast-enhanced spectral mammography (CESM): are there qualitative and quantitative differences between imaging systems?

OBJECTIVE

To evaluate the impact of the digital mammography imaging system on overall background enhancement on recombined contrast-enhanced spectral mammography (CESM) images, the overall background enhancement of two different mammography systems was compared.

METHODS

In a retrospective single-center study, CESM images of n = 129 female patients who underwent CESM between 2016 and 2019 were analyzed independently by two radiologists. Two mammography machines of different manufacturers were compared qualitatively using a Likert-scale from 1 (minimal) to 4 (marked overall background enhancement) and quantitatively by placing a region of interest and measuring the intensity enhancement. Lesion conspicuity was analyzed using a Likert-scale from 1 (lesion not reliably distinguishable) to 5 (excellent lesion conspicuity). A multivariate regression was performed to test for potential biases on the quantitative results.

RESULTS

Significant differences in qualitative background enhancement measurements between machines A and B were observed for both readers (p = 0.003 and p < 0.001). The quantitative evaluation showed significant differences in background enhancement with an average difference of 75.69 (99%-CI [74.37, 77.02]; p < 0.001). Lesion conspicuity was better for machine A for the first and second reader respectively (p = 0.009 and p < 0.001). The factor machine was the only influencing factor (p < 0.001). The factors contrast agent, breast density, age, and menstrual cycle could be excluded as potential biases.

CONCLUSION

Mammography machines seem to significantly influence overall background enhancement qualitatively and quantitatively; thus, an impact on diagnostic accuracy appears possible.

KEY POINTS

• Overall background enhancement on CESM differs between different vendors qualitatively and quantitatively. • Our retrospective single-center study showed consistent results of the qualitative and quantitative data analysis of overall background enhancement. • Lesion conspicuity is higher in cases of lower background enhancement on CESM.

Accuracy and precision of contrast enhanced mammography versus MRI for predicting breast cancer size: how "good" are they really?

OBJECTIVE

To evaluate and compare the accuracy and precision of contrast-enhanced mammography (CEM) vs MRI to predict the size of biopsy-proven invasive breast cancer.

METHODS

Prospective study, 59 women with invasive breast cancer on needle biopsy underwent CEM and breast MRI. Two breast radiologists read each patient's study, with access limited to one modality. CEM lesion size was measured using low-energy and recombined images and on MRI, the first post-contrast series. Extent of abnormality per quadrant was measured for multifocal lesions. Reference standards were size of largest invasive malignant lesion, invasive (PathInvasive) and whole (PathTotal). Pre-defined clinical concordance ±10 mm.

RESULTS

Mean patient age 56 years, 42 (71%) asymptomatic. Lesions were invasive ductal carcinoma 40 (68%) with ductal carcinoma in situ (31/40) in 78%, multifocal in 12 (20%). Median lesion size was 17 mm (invasive) and 27 mm (total), range (5-125 mm). Lin's concordance correlation coefficients for PathTotal 0.75 (95% CI 0.6, 0.84) and 0.71 (95% CI 0.56, 0.82) for MRI and contrast-enhanced spectral mammography (CESM) respectively. Mean difference for total size, 3% underestimated and 4% overestimated, and for invasive 41% and 50% overestimate on MRI and CESM respectively. LOAs for PathTotal varied from 60% under to a 2.4 or almost threefold over estimation. MRI was concordant with PathTotal in 36 (64%) cases compared with 32 (57%) for CESM. Both modalities concordant in 26 (46%) cases respectively.

CONCLUSION

Neither CEM nor MRI have sufficient accuracy to direct changes in planned treatment without needle biopsy confirmation.

ADVANCES IN KNOWLEDGE

Despite small mean differences in lesion size estimates using CEM or MRI, the 95% limits of agreement do not meet clinically acceptable levels.

Contrast-enhanced mammography: what the radiologist needs to know

Contrast-enhanced mammography (CEM) is a combination of standard mammography and iodinated contrast material administration. During the last decade, CEM has found its place in breast imaging protocols: after i.v. administration of iodinated contrast material, low-energy and high-energy images are retrieved in one acquisition using a dual-energy technique, and a recombined image is constructed enabling visualisation of areas of contrast uptake. The increased incorporation of CEM into everyday clinical practice is reflected in the installation of dedicated equipment worldwide, the (commercial) availability of systems from different vendors, the number of CEM examinations performed, and the number of scientific articles published on the subject. It follows that ever more radiologists will be confronted with this technique, and thus be required to keep up to date with the latest developments in the field. Most importantly, radiologists must have sufficient knowledge on how to interpret CEM images and be acquainted with common artefacts and pitfalls. This comprehensive review provides a practical overview of CEM technique, including CEM-guided biopsy; reading, interpretation and structured reporting of CEM images, including the accompanying learning curve, CEM artefacts and interpretation pitfalls; indications for CEM; disadvantages of CEM; and future developments.

Prediction of Underestimation Using Contrast-Enhanced Spectral Mammography in Patients Diagnosed as Ductal Carcinoma In Situ on Preoperative Core Biopsy

BACKGROUND

To assess the performance of contrast-enhanced spectral mammography (CESM) for the prediction of DCIS underestimation in comparison with mammography, breast US, and breast MRI.

PATIENTS AND METHODS

We prospectively enrolled patients diagnosed with DCIS on preoperative core biopsy. Visibility, lesion type, and extent on each imaging modality, CESM gray values (CGV) were evaluated. Pathologic features of core biopsy and surgery were recorded. Chi-square or Fisher's exact test were used for univariate analysis. Multivariate logistic regression analysis was used to find independent predictors for DCIS underestimation and receiver operating characteristic (ROC) curve analysis was performed.

RESULTS

A total of 113 lesions in 108 patients were analyzed (50 pure DCIS; 63 underestimated DCIS). Visibility on mammography, breast US, CESM, and breast MRI were 44%, 76%, 58%, and 80% for pure DCIS, and 73%, 81%, 86%, and 92% for underestimated DCIS. Tumor extents on surgical pathology of pure and underestimated DCIS were 1.11 ± 1.35 cm and 2.61 ± 2.09 cm. On multivariate analysis, nuclear grade and suspected invasion on core biopsy, visibility on mammography, and extent on breast MRI were independent factors for the model 1, whereas nuclear grade on core biopsy, extent on CESM, and mean CGV on MLO-recombined image were independent factors for the model 2. Area under ROC curve (AUC) was 0.843 for model 1 including breast MRI, whereas AUC was 0.823 for model 2 including CESM, which didn't show a significant difference (P = .968).

CONCLUSION

For detecting underestimated DCIS, CESM was superior to mammography and breast US, and comparable to breast MRI.

Contrast-enhanced Mammography: State of the Art

Contrast-enhanced mammography (CEM) has emerged as a viable alternative to contrast-enhanced breast MRI, and it may increase access to vascular imaging while reducing examination cost. Intravenous iodinated contrast materials are used in CEM to enhance the visualization of tumor neovascularity. After injection, imaging is performed with dual-energy digital mammography, which helps provide a low-energy image and a recombined or iodine image that depict enhancing lesions in the breast. CEM has been demonstrated to help improve accuracy compared with digital mammography and US in women with abnormal screening mammographic findings or symptoms of breast cancer. It has also been demonstrated to approach the accuracy of breast MRI in preoperative staging of patients with breast cancer and in monitoring response after neoadjuvant chemotherapy. There are early encouraging results from trials evaluating CEM in the screening of women who are at an increased risk of breast cancer. Although CEM is a promising tool, it slightly increases radiation dose and carries a small risk of adverse reactions to contrast materials. This review details the CEM technique, diagnostic and screening uses, and future applications, including artificial intelligence and radiomics.

Microscopic Tumour Classification by Digital Mammography

In this paper, we investigate the classification of microscopic tumours using full digital mammography images. Firstly, to address the shortcomings of traditional image segmentation methods, two different deep learning methods are designed to achieve the segmentation of uterine fibroids. The deep lab model is used to optimize the lesion edge detailed information by using the void convolution algorithm and fully connected CRF, and the two semantic segmentation networks are compared to obtain the best results. The Mask RCNN case segmentation model is used to effectively extract features through the ResNet structure, combined with the RPN network to achieve effective use and fusion of features, and continuously optimize the network training to achieve a fine segmentation of the lesion area, and demonstrate the accuracy and feasibility of the two models in medical image segmentation. Histopathology was used to obtain ER, PR, HER scores, and Ki-67 percentage values for all patients. The Kaplan-Meier method was used for survival estimation, the Log-rank test was used for single-factor analysis, and Cox proportional risk regression was used for multifactor analysis. The prognostic value of each factor was calculated, as well as the factors affecting progression-free survival. This study was done to compare the imaging characteristics and diagnostic value of mammography and colour Doppler ultrasonography in nonspecific mastitis, improve the understanding of the imaging characteristics of nonspecific mastitis in these two examinations, improve the accuracy of the diagnosis of this type of disease, improve the ability of distinguishing it from breast cancer, and reduce the rate of misdiagnosis.

Contrast-Enhanced Mammography Implementation, Performance, and Use for Supplemental Breast Cancer Screening

Contrast-enhanced mammography (CEM) is an emerging breast imaging technology that provides recombined contrast-enhanced images of the breast in addition to low-energy images analogous to a 2-dimensional full-field digital mammogram. Because most breast imaging centers do not use CEM at this time, a detailed overview of CEM implementation and performance is presented. Thereafter, the potential use of CEM for supplemental screening is discussed in detail, given the importance of this topic for the future of the CEM community. Diagnostic performance, safety, and cost considerations of CEM for dense breast tissue supplemental screening are discussed.

Background Parenchymal Enhancement on Contrast-Enhanced Spectral Mammography: Influence of Age, Breast Density, Menstruation Status, and Menstrual Cycle Timing

To evaluate the relationship of the extent and quantitative intensity of background parenchymal enhancement (BPE) on contrast-enhanced spectral mammography (CESM) with age, breast density, menstruation status, and menstrual cycle timing. This retrospective study included women who underwent CESM from July 2017 to March 2019 and who had menstruation status records. BPE category assessment was performed subjectively. BPE intensity was quantitatively measured using regions-of-interest. 208 subjects were included (150 were regular menstrual cycle and 58 were postmenopausal). The breast density was classified as category B in 11 subjects, category C in 231 subjects, and category D in 23 subjects. Subjects based on menstrual cycle timing, 24 at days 1-7, 55 at days 8-14, 48 at days 15-21, and 23 at days 22-28. Both quantitative and categorical analyses show a weak negative correlation between BPE and age in all subjects, but there was no significant correlation in premenopausal patients. Both the BPE pixel intensity value and BPE category was significantly lower in postmenopausal patients than in premenopausal patients, and there was no significant difference in breast density according to BPE. The minimum and maximum pixel values of BPE on days 8-14 of the menstrual cycle was significantly lower than those on days 15-21. There was no correlation between BPE level and menstrual cycle timing. Breast density with category D was more likely to have a lower BPE level than category C. We show here that BPE level is affected by menstruation status and menstrual cycle timing. We suggest that CESM should not be performed on days 15-21 of the menstrual cycle, but on days 8-14.

Contrast-enhanced spectral mammography (CESM) in women presenting with palpable breast findings

OBJECTIVE

Palpable breast abnormalities in women warrant diagnostic evaluation. Contrast-enhanced spectral mammography (CESM) is a novel technique which has demonstrated early promising results in the diagnostic imaging setting. The purpose of this study was to evaluate the role of CESM for imaging of palpable breast abnormalities and compare it to the current routine imaging work-up.

MATERIALS AND METHODS

This retrospective study included women with palpable breast masses who underwent diagnostic CESM and ultrasound between 2012 and 2019. Diagnostic parameters for low-energy images, CESM and targeted ultrasound were calculated and compared. Analysis was performed at the lesion level. Additional incidental findings were reported separately.

RESULTS

Included in this study were 138 women with 147 palpable breast abnormalities, of which 38 were cancers. Standard 2D mammography revealed 36/38 cancers (sensitivity 94.7%). All 38 cancers (100%) were detected at CESM and at targeted ultrasound. Negative predictive value for 2D mammography was 97.8% (91/93), and 100% for both ultrasound (74/74) and for CESM (80/80). None of the palpable masses that were negative at CESM but positive at ultrasound (n = 13) were malignant. Two additional incidental cancers were detected with CESM at the contralateral breast to the palpable lump.

CONCLUSION

CESM could be useful for assessment of palpable breast abnormalities, potentially decreasing the number of unnecessary benign biopsies performed.

Technique, protocols and adverse reactions for contrast-enhanced spectral mammography (CESM): a systematic review

We reviewed technical parameters, acquisition protocols and adverse reactions (ARs) for contrast-enhanced spectral mammography (CESM). A systematic search in databases, including MEDLINE/EMBASE, was performed to extract publication year, country of origin, study design; patients; mammography unit/vendor, radiation dose, low-/high-energy tube voltage; contrast molecule, concentration and dose; injection modality, ARs and acquisition delay; order of views; examination time. Of 120 retrieved articles, 84 were included from 22 countries (September 2003-January 2019), totalling 14012 patients. Design was prospective in 44/84 studies (52%); in 70/84 articles (83%), a General Electric unit with factory-set kVp was used. Per-view average glandular dose, reported in 12/84 studies (14%), ranged 0.43-2.65 mGy. Contrast type/concentration was reported in 79/84 studies (94%), with Iohexol 350 mgI/mL mostly used (25/79, 32%), dose and flow rate in 72/84 (86%), with 1.5 mL/kg dose at 3 mL/s in 62/72 studies (86%). Injection was described in 69/84 articles (82%), automated in 59/69 (85%), manual in 10/69 (15%) and flush in 35/84 (42%), with 10-30 mL dose in 19/35 (54%). An examination time < 10 min was reported in 65/84 studies (77%), 120 s acquisition delay in 65/84 (77%) and order of views in 42/84 (50%) studies, beginning with the craniocaudal view of the non-suspected breast in 7/42 (17%). Thirty ARs were reported by 14/84 (17%) studies (26 mild, 3 moderate, 1 severe non-fatal) with a pooled rate of 0.82% (fixed-effect model). Only half of CESM studies were prospective; factory-set kVp, contrast 1.5 mL/kg at 3 mL/s and 120 s acquisition delay were mostly used; only 1 severe AR was reported. CESM protocol standardisation is advisable.

Diagnostic performance of contrast-enhanced dual-energy spectral mammography (CESM): a retrospective study involving 644 breast lesions

OBJECTIVE

To evaluate the diagnostic performance of contrast-enhanced dual-energy spectral mammography (CESM) in comparison with that of full-field digital mammography (FFDM), either alone or accompanied with breast ultrasound (BUS) in a large series of patients/breast lesions (n = 644).

PATIENTS AND METHODS

In this retrospective study, five radiologists evaluated the lesions by three imaging modalities: FFDM, FFDM + BUS, and CESM and compared the imaging to the gold standard (histopathology or clinical follow-up). Diagnostic performance parameters and receiver operating characteristic (ROC) curves of CESM were calculated and compared to those of FFDM or FFDM + BUS (McNemar's test). Additionally, the reliability of tumor size measurement by CESM was compared with the histopathological measurement.

RESULTS

The study included 218 benign and 426 malignant lesions. 85% of benign and 93% of malignant lesions were adequately identified using CESM. With respect to FFDM and FFDM + BUS, CESM significantly increased sensitivity to 93.2% (+ 10.7% and + 3.4%, respectively); specificity to 84.4% (+ 15.8% and + 1.7%, respectively); PPV to 92.3% (+ 26.8% and + 3.6%, respectively); NPV to 86.0% (+ 1.6% and + 1.8%, respectively); and accuracy to 90.2% (+ 15.8% and + 3.2%, respectively). In the ROC curves analyses, the comparison among the three AUC values was also statistically significant (p < 0.001). Good agreement between tumor diameters measured using CESM and histopathology was observed (Spearman's rank correlation, r = 0.891, p < 0.0001), although this technique tended to produce an overestimation of the size (+ 7 mm).

CONCLUSIONS

CESM has high diagnostic accuracy and can be considered as a useful technique for the assessment of breast lesions.

Contrast enhanced digital mammography versus magnetic resonance imaging for accurate measurement of the size of breast cancer

OBJECTIVE

To compare the accuracy of contrast-enhanced digital mammography (CEDM) and MRI, including maximal intensity projection (MIP) images, for measuring the tumour size of breast cancer.

METHODS

We included 52 females (mean age, 50.9 years) with surgery due to breast cancer, and measured maximum diameter of main mass on mediolateral oblique (MLO) and craniocaudal (CC) views of mammography and CEDM; sagittal, axial MIP images, and early dynamic contrast-enhanced MRI (CEMRI) before surgery. Bland-Altman plot, intraclass correlation coefficient, and univariate linear regression analysis were used to evaluate the maximum size between imaging and pathology including only invasive component (OP_invasive) or with carcinoma in situ (OP_max).

RESULTS

Mean OP_invasive was 15.5 mm, and overestimation rate was similar or higher than underestimation rate on all images except CC view of mammography and axial MIP image of CEDM. Mean OP_max was 21.7 mm, and underestimation rate was higher than the overestimation rate. All parameters of CEDM and CEMRI showed good agreement ( k > 0.75) with OP_invasive, with the most favourable result being the CC view of CEDM and axial MIP image of CEMRI.

CONCLUSION

All views of CEDM and MRI provided accurate measurements of tumour size. Axial plane CEDM and MRI would be the first choice for image review and treatment planning, with the highest accuracy obtained by using CC view of CEDM.

ADVANCES IN KNOWLEDGE

Previous studies have not compared the measurement of the tumour size using detailed sequences; in our study, we discovered that CEDM can be an alternative modality to CEMRI.