Low-energy Images Research Articles

Feasibility of PET-enabled dual-energy CT imaging: First physical phantom and initial patient study results.

Dual-energy (DE) CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging. However, this increases radiation dose and may require a hardware upgrade due to the added second x-ray CT scan. The recently proposed PET-enabled DECT method allows dual-energy imaging using a conventional PET/CT scanner without the need to change scanner hardware or increase radiation exposure. Here we demonstrate the first-time physical phantom and patient data evaluation of this method. The PET-enabled DECT method reconstructs a gamma-ray CT (gCT) image at 511keV from the time-of-flight PET data with the maximum-likelihood attenuation and activity (MLAA) approach and then combines this image with the low-energy x-ray CT images to form a dual-energy image pair for material decomposition. To improve the image quality of gCT, a kernel MLAA method was developed using the x-ray CT as a priori information. Here we developed a general open-source implementation for gCT reconstruction and used this implementation for the first real data validation using both physical phantom study and human-subject study. Results from PET-enabled DECT were compared using x-ray DECT as the reference. Further, we applied the PET-enabled DECT method in another patient study to evaluate bone lesions. Compared to the standard MLAA, results from the kernel MLAA showed significantly improved image quality. PET-enabled DECT with the kernel MLAA was able to generate fractional images that were comparable to the x-ray DECT, with high correlation coefficients for both the phantom study and human subject study (R > 0.99). The application study also indicates that PET-enabled DECT has potential to characterize bone lesions. Results from this study have demonstrated the feasibility of this PET-enabled method for CT imaging and material decomposition. PET-enabled DECT shows promise to provide comparable results to x-ray DECT.

Inter-reader agreement of the BI-RADS CEM lexicon.

The purpose of this study was to assess the inter-reader agreement of the breast imaging reporting and data system (BI-RADS) contrast-enhanced mammography (CEM) lexicon. In this IRB-approved, single-center, retrospective study, three breast radiologists, each with different levels of experience, reviewed 462 lesions in 421 routine clinical CEM according to the fifth edition of the BI-RADS lexicon for mammography and to the first version of the BI-RADS lexicon for CEM. Readers were blinded to patient outcomes and evaluated breast and lesion features on low-energy (LE) images (breast density, type of lesion, associated architectural distortion), lesion features on recombined (RC) images (type of enhancement, characteristic of mass enhancement, non-mass enhancement or enhancing asymmetry), and provided a final BI-RADS assessment. The inter-reader agreement was calculated for each evaluated feature using Fleiss' kappa coefficient. Sensitivity and specificity were calculated. The inter-reader agreement was moderate to substantial for breast density (ĸ = 0.569), type of lesion on LE images (ĸ = 0.654), and type of enhancement (ĸ = 0.664). There was a moderate to substantial agreement on CEM mass enhancement descriptors. The agreement was fair to moderate for non-mass enhancement and enhancing asymmetry descriptors. Inter-reader agreement for LE and LE with RC BI-RADS assessment was moderate (ĸ = 0.421) and fair (ĸ = 0.364). Diagnostic performance was good and comparable for all readers. Inter-reader agreement of the CEM lexicon was moderate to substantial for most features. There was a low agreement for some RC descriptors, such as non-mass enhancement and enhancing asymmetry, and BI-RADS assessment, but this did not impact the diagnostic performance. Question Data on the reproducibility and inter-reader agreement for the first version of the BI-RADS lexicon dedicated to CEM are missing. Finding The inter-reader agreement for the lexicon was overall substantial to moderate, but it was lower for the descriptors for non-mass enhancement and enhancing asymmetry. Clinical relevance A common lexicon simplifies communication between specialists in clinical practice. The good inter-reader agreement confirms the effectiveness of the CEM-BIRADS in ensuring consistent communication. Detailed definitions of some descriptors (non-mass, enhancing asymmetry) are needed to ensure higher agreements.

Multi-energy CT and iodinated contrast

Spectral CT acquires images with the emission or detection of two separate energy spectra. This enables material decomposition due to the photoelectric effect (prevalent in low-energy photons) and Compton scattering (prevalent in high-energy photons).Iodine and other materials with high atomic numbers appear more hyperdense on low-energy monoenergetic images because of the direct relation between the photoelectric effect and the Z value.Given the way iodine behaves on spectral maps, radiologists can optimise the use of contrast media in these CTs, thus allowing lower doses of radiation and lower volumes of contrast media while achieving the same CT values and even enabling lower contrast flow rates, which is especially helpful in patients with poor vascular access. Moreover, in suboptimal diagnostic cases caused by poor contrast opacification, the resolution can be improved, thus avoiding the need to repeat the study.

Impact of virtual monochromatic images of different low-energy levels in dual-energy CT on radiomics models for predicting muscle invasion in bladder cancer.

The purpose of this study was to investigate the impact of different low-energy virtual monochromatic images (VMIs) in dual-energy CT on the performance of radiomics models for predicting muscle invasive status in bladder cancer (BCa). A total of 127 patients with pathologically proven muscle-invasive BCa (n = 49) and non-muscle-invasive BCa (n = 78) were randomly allocated into the training and test cohorts at a ratio of 7:3. Feature extraction was performed on the venous phase images reconstructed at 40, 50, 60 and 70-keV (single-energy analysis) or in combination (multi-energy analysis). Recursive feature elimination (RFE) and the least absolute shrinkage and selection operator(LASSO) were employed to select the most relevant features associated with BCa. Models were built using a support vector machine(SVM) classifier. Diagnostic performance was assessed through receiver operating characteristic curves, evaluating sensitivity, specificity, accuracy, precision, and the area-under-the curve (AUC) values. In the test cohort, the multi-energy model achieved the best diagnostic performance with AUC, sensitivity, specificity, accuracy, and precision of 0.917, 0.800, 0.833, 0.821, and 0.750, respectively. Conversely, the single-energy model exhibited lower AUC and sensitivity in predicting the muscle invasion status. By combining information from VMIs of various energies, the multi-energy model displays superior performance in preoperatively predicting the muscle invasion status of bladder cancer.

Performance of dual-energy subtraction in contrast-enhanced mammography for three different manufacturers: a phantom study

BackgroundDual-energy subtraction (DES) imaging is critical in contrast-enhanced mammography (CEM), as the recombination of low-energy (LE) and high-energy (HE) images produces contrast enhancement while reducing anatomical noise. The study's purpose was to compare the performance of the DES algorithm among three different CEM systems using a commercial phantom.MethodsA CIRS Model 022 phantom, designed for CEM, was acquired using all available automatic exposure modes (AECs) with three CEM systems from three different manufacturers (CEM1, CEM2, and CEM3). Three studies were acquired for each system/AEC mode to measure both radiation dose and image quality metrics, including estimation of measurement error. The mean glandular dose (MGD) calculated over the three acquisitions was used as the dosimetry index, while contrast-to-noise ratio (CNR) was obtained from LE and HE images and DES images and used as an image quality metric.ResultsOn average, the CNR of LE images of CEM1 was 2.3 times higher than that of CEM2 and 2.7 times higher than that of CEM3. For HE images, the CNR of CEM1 was 2.7 and 3.5 times higher than that of CEM2 and CEM3, respectively. The CNR remained predominantly higher for CEM1 even when measured from DES images, followed by CEM2 and then CEM3. CEM1 delivered the lowest MGD (2.34 ± 0.03 mGy), followed by CEM3 (2.53 ± 0.02 mGy) in default AEC mode, and CEM2 (3.50 ± 0.05 mGy). The doses of CEM2 and CEM3 increased by 49.6% and 8.0% compared with CEM1, respectively.ConclusionOne system outperformed others in DES algorithms, providing higher CNR at lower doses.Relevance statementThis phantom study highlighted the variability in performance among the DES algorithms used by different CEM systems, showing that these differences can be translated in terms of variations in contrast enhancement and radiation dose.Key PointsDES images, obtained by recombining LE and HE images, have a major role in CEM.Differences in radiation dose among CEM systems were between 8.0% and 49.6%.One DES algorithm achieved superior technical performance, providing higher CNR values at a lower radiation dose.Graphical

Diagnostic Accuracy of Screening Contrast-enhanced Mammography for Women with Extremely Dense Breasts at Increased Risk of Breast Cancer.

Background Mammogram interpretation is challenging in female patients with extremely dense breasts (Breast Imaging Reporting and Data System [BI-RADS] category D), who have a higher breast cancer risk. Contrast-enhanced mammography (CEM) has recently emerged as a potential alternative; however, data regarding CEM utility in this subpopulation are limited. Purpose To evaluate the diagnostic performance of CEM for breast cancer screening in female patients with extremely dense breasts. Materials and Methods This retrospective single-institution study included consecutive CEM examinations in asymptomatic female patients with extremely dense breasts performed from December 2012 to March 2022. From CEM examinations, low-energy (LE) images were the equivalent of a two-dimensional full-field digital mammogram. Recombined images highlighting areas of contrast enhancement were constructed using a postprocessing algorithm. The sensitivity and specificity of LE images and CEM images (ie, including both LE and recombined images) were calculated and compared using the McNemar test. Results This study included 1299 screening CEM examinations (609 female patients; mean age, 50 years ± 9 [SD]). Sixteen screen-detected cancers were diagnosed, and two interval cancers occured. Five cancers were depicted at LE imaging and an additional 11 cancers were depicted at CEM (incremental cancer detection rate, 8.7 cancers per 1000 examinations). CEM sensitivity was 88.9% (16 of 18; 95% CI: 65.3, 98.6), which was higher than the LE examination sensitivity of 27.8% (five of 18; 95% CI: 9.7, 53.5) (P = .003). However, there was decreased CEM specificity (88.9%; 1108 of 1246; 95% CI: 87.0, 90.6) compared with LE imaging (specificity, 96.2%; 1199 of 1246; 95% CI: 95.0, 97.2) (P < .001). Compared with specificity at baseline, CEM specificity at follow-up improved to 90.7% (705 of 777; 95% CI: 88.5, 92.7; P = .01). Conclusion Compared with LE imaging, CEM showed higher sensitivity but lower specificity in female patients with extremely dense breasts, although specificity improved at follow-up. © RSNA, 2024 See also the editorial by Lobbes in this issue.

Background parenchymal enhancement on contrast-enhanced mammography: associations with breast density and patient's characteristics.

To evaluate if background parenchymal enhancement (BPE) on contrast-enhanced mammography (CEM), graded according to the2022 CEM-dedicated Breast Imaging Reporting and Data System (BI-RADS) lexicon, is associated with breast density, menopausal status, and age. This bicentric retrospective analysis included CEM examinations performed for thework-up of suspicious mammographic findings. Three readers independently and blindly evaluated BPE on recombined CEM images and breast density on low-energy CEM images. Inter-reader reliability was estimated using Fleiss κ. Multivariable binary logistic regression was performed, dichotomising breast density and BPE as low (a/b BI-RADS categories, minimal/mild BPE) and high (c/d BI-RADS categories, moderate/marked BPE). A total of 200 women (median age 56.8years, interquartile range 50.5-65.6, 140/200 in menopause) were included. Breast density was classified as a in 27/200 patients (13.5%), as b in 110/200 (55.0%), as c in 52/200 (26.0%), and as d in 11/200 (5.5%), with moderate inter-reader reliability (κ = 0.536; 95% confidence interval [CI] 0.482-0.590). BPE was minimal in 95/200 patients (47.5%), mild in 64/200 (32.0%), moderate in 25/200 (12.5%), marked in 16/200 (8.0%), with substantial inter-reader reliability (κ = 0.634; 95% CI 0.581-0.686). At multivariable logistic regression, premenopausal status and breast density were significant positive predictors of high BPE, with adjusted odds ratios of 6.120 (95% CI 1.847-20.281, p = 0.003) and 2.416 (95% CI 1.095-5.332, p = 0.029) respectively. BPE on CEM is associated with well-established breast cancer risk factors, being higher in women with higher breast density and premenopausal status.

Optimal Spectral Performance on Pediatric Photon-Counting CT: Investigating Phantom-Based Size-Dependent kV Selection for Spectral Body Imaging.

The comprehensive evaluation of kV selection on photon-counting computed tomography (PCCT) has yet to be performed. The aim of the study is to evaluate and determine the optimal kV options for variable pediatric body sizes on the PCCT unit. In this study, 4 phantoms of variable sizes were utilized to represent abdomens of newborn, 5-year-old, 10-year-old, and adult-sized pediatric patients. One solid water and 4 solid iodine inserts with known concentrations (2, 5, 10, and 15 mg I/mL) were inserted into phantoms. Each phantom setting was scanned on a PCCT system (Siemens Alpha) with 4 kV options (70 and 90 kV under Quantum Mode, 120 and 140 kV under QuantumPlus Mode) and clinical dual-source (3.0 pitch) protocol. For each phantom setting, radiation dose (CTDIvol) was determined by clinical dose settings and matched for all kV acquisitions. Sixty percent clinical dose images were also acquired. Reconstruction was matched across all acquisitions using Qr40 kernel and QIR level 3. Virtual monoenergetic images (VMIs) between 40 and 80 keV with 10 keV interval were generated on the scanner. Low-energy and high-energy images were reconstructed from each scan and subsequently used to generate an iodine map (IM) using an image-based 2-material decomposition method. Image noise of VMIs from each kV acquisition was calculated and compared between kV options. Absolute percent error (APE) of iodine CT number accuracy in VMIs was calculated and compared. Root mean square error (RMSE) and bias of iodine quantification from IMs were compared across kV options. At the newborn size and 50 keV VMI, noise is lower at low kV acquisitions (70 kV: 10.5 HU, 90 kV: 10.4 HU), compared with high kV acquisitions (120 kV: 13.8 HU, 140 kV: 13.9 HU). At the newborn size and 70 keV VMI, the image noise from different kV options is comparable (9.4 HU for 70 kV, 8.9 HU for 90 kV, 9.7 HU for 120 kV, 10.2 HU for 140 kV). For APE of VMI, high kV (120 or 140 kV) performed overall better than low kV (70 or 90 kV). At the 5-year-old size, APE of 90 kV (median: 3.6%) is significantly higher (P < 0.001, Kruskal-Wallis rank sum test with Bonferroni correction) than 140 kV (median: 1.6%). At adult size, APE of 70 kV (median: 18.0%) is significantly higher (P < 0.0001, Kruskal-Wallis rank sum test with Bonferroni correction) than 120 kV (median: 1.4%) or 140 kV (median: 0.8%). The high kV also demonstrated lower RMSE and bias than the low kV across all controlled conditions. At 10-year-old size, RMSE and bias of 120 kV are 1.4 and 0.2 mg I/mL, whereas those from 70 kV are 1.9 and 0.8 mg I/mL. The high kV options (120 or 140 kV) on the PCCT unit demonstrated overall better performance than the low kV options (70 or 90 kV), in terms of image quality of VMIs and IMs. Our results recommend the use of high kV for general body imaging on the PCCT.

Diagnostic Imaging Performance of Dual-Energy Computed Tomography Compared with Conventional Computed Tomography and Magnetic Resonance Imaging for Uterine Cervical Cancer.

Objective This article evaluates the ability of low-energy (40 keV) virtual monoenergetic images (VMIs) in the local diagnosis of cervical cancer compared with that of conventional computed tomography (C-CT) and magnetic resonance imaging (MRI), using clinicopathologic staging as a reference. Methods This prospective study included 33 patients with pathologically confirmed cervical cancer who underwent dual-energy CT and MRI between 2021 and 2022. The contrast-to-noise ratio (CNR) of the tumor-to-myometrium was compared between C-CT and VMI. Additionally, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for each local diagnostic parameter were compared between C-CT, VMI, and MRI. Interradiologist agreement was also assessed. Results The mean CNR was significantly higher on VMI ( p = 0.002). No significant difference in AUC was found between C-CT and VMI for all local diagnostic parameters, and the specificity of VMI was often significantly less than that of MRI. For parametrial invasion, mean sensitivity, specificity, and AUC for C-CT, VMI, and MRI were 0.81, 0.99, 0.93; 0.64, 0.35, 0.79; and 0.73, 0.67, 0.86, respectively, and MRI had significantly higher specificity and AUC than that of VMI ( p = 0.013 and 0.008, respectively). Interradiologist agreement was higher for VMI than C-CT and for MRI than VMI. Conclusion The CNR of VMI was significantly higher than C-CT and interradiologist agreement was better than with C-CT; however, the overall diagnostic performance of VMI did not significantly differ from C-CT and was inferior to MRI. VMI was characterized by low specificity, which should be understood and used for reading.

Image quality of lung perfusion with photon-counting-detector CT: comparison with dual-source, dual-energy CT.

To evaluate the quality of lung perfusion imaging obtained with photon-counting-detector CT (PCD-CT) in comparison with dual-source, dual-energy CT (DECT). Seventy-one consecutive patients scanned with PCD-CT were compared to a paired population scanned with dual-energy on a 3rd-generation DS-CT scanner using (a) for DS-CT (Group 1): collimation: 64 × 0.6 × 2 mm; pitch: 0.55; (b) for PCD-CT (Group 2): collimation: 144 × 0.4 mm; pitch: 1.5; single-source acquisition. The injection protocol was similar in both groups with the reconstruction of perfusion images by subtraction of high- and low-energy virtual monoenergetic images. Compared to Group 1, Group 2 examinations showed: (a) a shorter duration of data acquisition (0.93 ± 0.1 s vs 3.98 ± 0.35 s; p < 0.0001); (b) a significantly lower dose-length-product (172.6 ± 55.14 vs 339.4 ± 75.64 mGy·cm; p < 0.0001); and (c) a higher level of objective noise (p < 0.0001) on mediastinal images. On perfusion images: (a) the mean level of attenuation did not differ (p = 0.05) with less subjective image noise in Group 2 (p = 0.049); (b) the distribution of scores of fissure visualization differed between the 2 groups (p < 0.0001) with a higher proportion of fissures sharply delineated in Group 2 (n = 60; 84.5% vs n = 26; 26.6%); (c) the rating of cardiac motion artifacts differed between the 2 groups (p < 0.0001) with a predominance of examinations rated with mild artifacts in Group 2 (n = 69; 97.2%) while the most Group 1 examinations showed moderate artifacts (n = 52; 73.2%). PCD-CT acquisitions provided similar morphologic image quality and superior perfusion imaging at lower radiation doses. The improvement in the overall quality of perfusion images at lower radiation doses opens the door for wider applications of lung perfusion imaging in clinical practice. The speed of data acquisition with PCD-CT accounts for mild motion artifacts. Sharply delineated fissures are depicted on PCD-CT perfusion images. High-quality perfusion imaging was obtained with a 52% dose reduction.

Contrast-enhanced Mammography: An Emerging Modality in Breast Imaging

Contrast-enhanced mammography (CEM) is emerging as a viable alternative to contrast-enhanced breast MRI, effectively reducing examination cost. Intravenous iodinated contrast material is 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. The utility of Contrast enhanced Mammography is under study and is substituting as Poor Man MRI, easier to interpret with wider accessibility available.

A deep learning approach for virtual contrast enhancement in Contrast Enhanced Spectral Mammography

Contrast Enhanced Spectral Mammography (CESM) is a dual-energy mammographic imaging technique that first requires intravenously administering an iodinated contrast medium. Then, it collects both a low-energy image, comparable to standard mammography, and a high-energy image. The two scans are combined to get a recombined image showing contrast enhancement. Despite CESM diagnostic advantages for breast cancer diagnosis, the use of contrast medium can cause side effects, and CESM also beams patients with a higher radiation dose compared to standard mammography. To address these limitations, this work proposes using deep generative models for virtual contrast enhancement on CESM, aiming to make CESM contrast-free and reduce the radiation dose. Our deep networks, consisting of an autoencoder and two Generative Adversarial Networks, the Pix2Pix, and the CycleGAN, generate synthetic recombined images solely from low-energy images. We perform an extensive quantitative and qualitative analysis of the model’s performance, also exploiting radiologists’ assessments, on a novel CESM dataset that includes 1138 images. As a further contribution to this work, we make the dataset publicly available. The results show that CycleGAN is the most promising deep network to generate synthetic recombined images, highlighting the potential of artificial intelligence techniques for virtual contrast enhancement in this field.

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 in breast 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.

Abstract PO1-07-03: An Accurate Breast Cancer Diagnosis Method Using a Multi-Feature Fusion Neural Network on Contrast-Enhanced Spectral Mammography

Abstract Objectives: Our study aims to develop an accurate and comprehensive deep neural network capable of classifying CESM images to aid in the early detection and diagnosis of breast cancer in clinical settings. Methods: We enrolled diagnostic CESM examinations conducted between January 1, 2019 and January 17, 2021. We developed and tested the performance of a multi-feature fusion network for breast lesion classification by combining low-energy (LE) and dual-energy subtracted (DES) images, dual-view, and bilateral information using a large and diverse CESM dataset. We have evaluated the ability of the proposed network to generalize on external datasets and the results were reported using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results: In the study period 1973CESMs were included. Mean age was 53 years ± 12 (standard deviation). In the internal test dataset, the model with CESM (LE +DES) inputs combined not only bilateral information (left and right breasts) but also dualview information (CC and MLO), achieved best diagnosis performance with AUC of 0.96 [95% confidence interval (CI): 0.95–0.97], accuracy of 0.9 [95% confidence interval (CI): 0.88–0.92], sensitivity of 0.84 [95% confidence interval (CI): 0.80–0.88] and specificity of 0.93 [95% confidence interval (CI): 0.91–0.95], while the model with LE inputs only got an AUC of 0.94 [95% (CI): 0.93–0.95], accuracy of 0.88 [95% confidence interval (CI): 0.86–0.90], sensitivity of 0.79 [95% confidence interval (CI): 0.74–0.83] and specificity of 0.93 [95% confidence interval (CI): 0.91–0.95] (external dataset: an AUC of 0.90 [95% (CI): 0.85–0.94], accuracy of 0.84 [95% confidence interval (CI): 0.78–0.89], sensitivity of 0.77 [95% confidence interval (CI): 0.61–0.88] and specificity of 0.87 [95% confidence interval (CI):0.80–0.92]). Conclusion: CESM is a promising technique in breast cancer diagnosis due to its high feasibility and potential. The results demonstrate that left-right breast fusion and dual-view fusion are helpful for accurate diagnosis on CESM images. Specifically, dual-energy subtracted (DES) images generated by CESM improve diagnostic accuracy when compared to low-energy (LE) images alone. Keywords: Contrast-enhanced spectral mammography; Deep neural network; Breast cancer diagnosis; Multi-feature fusion Citation Format: Yipeng Song, Wei Jiang. An Accurate Breast Cancer Diagnosis Method Using a Multi-Feature Fusion Neural Network on Contrast-Enhanced Spectral Mammography [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-03.

Abstract PO3-20-08: Case Studies on Detecting Ductal Carcinoma in Situ Using Contrast-Enhanced Spectral Mammography and Predictive Indicators for Underestimation

Abstract Purpose: To introduce clinical cases of ductal carcinoma in situ (DCIS) underestimation focusing on additional benefits of contrast-enhanced spectral mammography (CESM). Background: DCIS, a noninvasive breast cancer, is often underestimated during diagnosis. Predicting which cases will upgrade to invasive cancer is crucial. CESM offers superior diagnostic performance compared to mammography and mammography with targeted ultrasound (US). Its low-energy images improve microcalcification conspicuity, enable accurate tumor extent measurement, and provide information on enhancements associated with nonmass microcalcifications. Enhanced areas on CESM can be easily compared with mammographic microcalcifications, for they are obtained during the same session. Indicators heralding DCIS underestimation 1. Detectability: underestimated DCIS is more likely to be detected on CESM and mammogram than pure DICS, whereas detection rates do not statistically differ on US and MRI. 2. Lesion type: underestimated DCIS mostly presents as enhancing mass or nonmass lesions on CESM and calcifications on low-energy conventional mammography. 3. Disease extent: on CESM, pure DCIS show smaller tumor extent than the underestimated ones. 4. CESM gray value (CGV) of the lesion on recombined image: the pure DCIS types show lower mean, standard deviation, maximum, and difference values, whereas the underestimated types demonstrate lower minimum values, and vice versa. Case 1: Screen detected pure DCIS without postsurgical upgrade in an asymptomatic 44-year-old woman. US-guided core biopsy yielded DCIS, intermediate nuclear grade with necrosis. Postsurgical pathology confirmed a 1.5 cm pure ductal carcinoma in situ, intermediate nuclear grade with necrosis. (A) US shows a 1.3 cm irregular hypoechoic mass in the right breast. (B) Routine mammography demonstrates no definite abnormality in the right breast. (C) CESM shows a 1.2 cm irregular enhancing mass in the upper central portion of right breast and CGV was 2034 AU. (D) Contrast-enhanced MR images show a 2.1 cm irregular enhancing mass in 12 o’clock direction of the right breast. Case 2: Screen detected DCIS with postsurgical upgrade to microinvasive ductal carcinoma in a 64-year-old woman with nonspecific discomfort in her right breast. US-guided core biopsy yielded DCIS, high nuclear grade with necrosis. Postsurgical pathology confirmed a 2.8 cm sized microinvasive ductal carcinoma, high nuclear grade, histologic grade 3 with necrosis. (A) US demonstrates a 1.9 cm irregular hypoechoic mass with calcifications in 9 o’clock direction 2.1 cm from the nipple in the right breast. (B) Right magnification view shows a 2.5 cm-extent segmentally distributed fine pleomorphic calcifications in the right breast. (C) CESM shows a 3 cm irregular enhancing mass with calcifications in the right breast, and CGV was 2045 AU. (D) Contrast-enhanced MR images shows a 2.3 cm irregular enhancing mass in the right breast. Case 3: Screen detected DCIS with postsurgical upgrade to invasive ductal carcinoma with DCIS in a 47-year-old woman who had a family history of breast cancer. US-guided core biopsy yielded DCIS, intermediate nuclear grade with necrosis. Postsurgical pathology confirmed a 0.2 cm invasive ductal carcinoma with 2.4 cm DCIS. (A) US demonstrates a 2.3 cm irregular hypoechoic mass in the left breast. (B) Routine mammography shows asymmetry in the upper portion of the left breast. (C) CESM shows a 3 cm enhancing mass in the left breast, and CGV of the lesion on MLO-recombined image was 2072 AU. (D) Contrast-enhanced MR image shows a 6 cm irregular enhancing mass in the upper portion of the left breast. Conclusion: CESM provides added advantages over mammography and breast US in detecting underestimated DCIS, and it is comparable to breast MRI. Key indicators to consider include detectability, enhancing lesion, larger tumor extent, higher mean CGV on CESM. Citation Format: Minji Song, Hee Jung Shin. Case Studies on Detecting Ductal Carcinoma in Situ Using Contrast-Enhanced Spectral Mammography and Predictive Indicators for Underestimation [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-20-08.

Diagnosis of acute pulmonary embolism: when photon-counting-detector CT replaces energy-integrating-detector CT in daily routine.

To compare the diagnostic approach of acute pulmonary embolism (PE) with photon-counting-detector CT (PCD-CT) and energy-integrating-detector CT (EID-CT). Two cohorts underwent CT angiographic examinations with EID-CT (Group 1; n = 158) and PCD-CT (Group 2; n = 172), (b) with two options in Group 1, dual energy (Group 1a) or single energy (Group 1b) and a single option in Group 2 (spectral imaging with single source). In Group 2, all patients benefited from spectral imaging, only accessible to 105 patients (66.5%) in Group 1, with a mean acquisition time significantly shorter (0.9 ± 0.1 s vs 4.0 ± 0 .3 s; p < 0.001) and mean values of CTDIvol and DLP reduced by 46.3% and 47.7%, respectively. Comparing the quality of 70 keV (Group 2) and averaged (Group 1a) images: (a) the mean attenuation within pulmonary arteries did not differ (p = 0.13); (b) the image noise was significantly higher (p < 0.001) in Group 2 with no difference in subjective image noise (p = 0.29); and (c) 89% of examinations were devoid of artifacts in Group 2 vs 28.6% in Group 1a. The percentage of diagnostic examinations was 95.2% (100/105; Group 1a), 100% (53/53; Group 1b), and 95.3% (164/172; Group 2). There were 4.8% (5/105; Group 1a) and 4.7% (8/172; Group 2) of non-diagnostic examinations, mainly due to the suboptimal quality of vascular opacification with the restoration of a diagnostic image quality on low-energy images. Compared to EID-CT, morphology and perfusion imaging were available in all patients scanned with PCD-CT, with the radiation dose reduced by 48%. PCD-CT enables scanning patients with the advantages of both spectral imaging, including high-quality morphologic imaging and lung perfusion for all patients, and fast scanning-a combination that is not simultaneously accessible with EID-CT while reducing the radiation dose by almost 50%.

Synthetic Low-Energy Monochromatic Image Generation in Single-Energy Computed Tomography System Using a Transformer-Based Deep Learning Model.

While dual-energy computed tomography (DECT) technology introduces energy-specific information in clinical practice, single-energy CT (SECT) is predominantly used, limiting the number of people who can benefit from DECT. This study proposed a novel method to generate synthetic low-energy virtual monochromatic images at 50keV (sVMI50keV) from SECT images using a transformer-based deep learning model, SwinUNETR. Data were obtained from 85 patients who underwent head and neck radiotherapy. Among these, the model was built using data from 70 patients for whom only DECT images were available. The remaining 15 patients, for whom both DECT and SECT images were available, were used to predict from the actual SECT images. We used the SwinUNETR model to generate sVMI50keV. The image quality was evaluated, and the results were compared with those of the convolutional neural network-based model, Unet. The mean absolute errors from the true VMI50keV were 36.5 ± 4.9 and 33.0 ± 4.4 Hounsfield units for Unet and SwinUNETR, respectively. SwinUNETR yielded smaller errors in tissue attenuation values compared with those of Unet. The contrast changes in sVMI50keV generated by SwinUNETR from SECT were closer to those of DECT-derived VMI50keV than the contrast changes in Unet-generated sVMI50keV. This study demonstrated the potential of transformer-based models for generating synthetic low-energy VMIs from SECT images, thereby improving the image quality of head and neck cancer imaging. It provides a practical and feasible solution to obtain low-energy VMIs from SECT data that can benefit a large number of facilities and patients without access to DECT technology.

Frequently Encountered Artifacts in the Application of Dual-Energy Computed Tomography to Cardiovascular Imaging for Urate Crystals in Gout: A Matched-Control Study.

There is surging interest in using dual-energy computed tomography (DECT) to identify cardiovascular monosodium urate (MSU) deposits in patients with gout. We sought to examine the prevalence and characterization of cardiovascular DECT artifacts using non-electrocardiogram (EKG)-gated DECT pulmonary angiograms. We retrospectively reviewed non-EKG-gated DECT pulmonary angiograms performed on patients with and without gout at a single academic center. We noted the presence and locations of vascular green colorization using the default postprocessing two-material decomposition algorithm for MSU. The high- and low-energy grayscale images and advanced DECT measurements were used to determine whether they were true findings or artifacts. We classified artifacts into five categories: streak, contrast medium mixing, misregistration due to motion, foreign body, and noise. Our study included CT scans from 48 patients with gout and 48 age- and sex-matched controls. The majority of patients were male with a mean age of 67 years. Two independent observers attributed all areas of vascular green colorization to artifacts. The most common types of artifacts were streak (56% vs 57% between patients and controls, respectively) and contrast medium mixing (51% vs 65%, respectively). Whereas some of the default DECT measurements of cardiovascular green colorization were consistent with values reported for subcutaneous tophi, advanced DECT measurements were not consistent with that of tophi. Artifacts that could be misconstrued as cardiovascular MSU deposits were commonly identified in patients with and without gout on non-EKG-gated DECT pulmonary angiograms. These artifacts can inform future vascular DECT studies on patients with gout to minimize false-positive findings.

Performance of contrast-enhanced mammography for detecting multifocal and multicentric breast cancer and evaluating tumour size, and implications for surgical management: Early experience in a tertiary centre.

To compare diagnostic accuracy of contrast-enhanced mammography (CEM) with standard 2D digital mammography (equivalent to low-energy image; LEM) for detection of multifocal and multicentric breast cancer and evaluation of tumour size and disease extent for preoperative planning. Biopsy proven breast cancer patients who underwent CEM preoperatively between January 2021 and January 2023 were included in this study. CEM and LEM images were independently reviewed by at least two blinded readers. Lesion location, number, size (maximal diameter) and extension across the midline and/or nipple invasion were recorded. Tumour number and size estimated on imaging were compared with final operative histology, which served as the gold standard. Forty-nine patients (48 females and 1 male) and 50 cases (one patient had bilateral breast lesions) were included in the analysis. Median patient age was 60 (IQR 51, 69). CEM had significantly higher lesion detection rate compared with LEM, with sensitivities of 78% for LEM and 92% for CEM for the index tumour and 15% for LEM and 100% for CEM for multicentric and multifocal cancer. We found no statistically significant difference in median tumour size measurements on CEM and final surgical specimen (P value = 0.97); however, a significant difference was identified in the tumour size measured on LEM and surgical specimen (P value < 0.001). CEM is superior to standard 2D digital mammography for detection of multifocal and multicentric breast cancer and is a reliable and more accurate method for estimating tumour size.

Contrast-Enhanced Mammography Radiomics Analysis for Preoperative Prediction of Breast Cancer Molecular Subtypes

BackgroundPredicting breast cancer molecular subtypes can help guide individualized clinical treatment of patients who need the rational preoperative treatment. This study aimed to investigate the efficacy of preoperative prediction of breast cancer molecular subtypes by contrast-enhanced mammography (CEM) radiomic features. MethodsThis retrospective two-centre study included women with breast cancer who underwent CEM preoperatively between August 2016 and May 2022. We included 356 patients with 386 lesions, which were grouped into training (n=162), internal test (n=160) and external test sets (n=64). Radiomics features were extracted from low-energy (LE) images and recombined (RC) images and selected. Three dichotomous tasks were established according to postoperative immunohistochemical results: Luminal vs. non-Luminal, human epidermal growth factor receptor (HER2)-enriched vs. non-HER2-enriched, and triple-negative breast cancer (TNBC) vs. non-TNBC. For each dichotomous task, the LE, RC and LE+RC radiomics models were built by the support vector machine (SVM) classifier. The prediction performance of the models was assessed by the area under the receiver operating characteristic curve (AUC). Then, the accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for the models. DeLong’s test was utilized to compare the AUCs. ResultsRadiomics models based on CEM are valuable for predicting breast cancer molecular subtypes. The LE+RC model achieved the best performance in the test set. The LE+RC model predicted Luminal, HER2-enriched, and TNBC subtypes with AUCs of 0.93, 0.89, and 0.87 in the internal test set and 0.82, 0.83 and 0.69 in the external test set, respectively. In addition, the LE model performed more satisfactorily than the RC model. ConclusionCEM radiomics features can effectively predict breast cancer molecular subtypes preoperatively, and the LE+RC model has the best predictive performance.