Craniocaudal Views Research Articles

Digital Mammography, Tomosynthesis, and Contrast-Enhanced Mammography: Intraindividual Comparison of Mean Glandular Dose for Screening Examinations.

Background: Contrast-enhanced mammography (CEM) is growing in clinical use due to its increased sensitivity and specificity compared to full-field digital mammography (FFDM) and/or digital breast tomosynthesis (DBT), particularly in patients with dense breasts. Objective: To perform an intraindividual comparison of MGD between FFDM, DBT, a combination protocol using both FFDM and DBT (combined FFDM-DBT), and CEM, in patients undergoing breast cancer screening. Methods: This retrospective study included 389 women (median age, 57.4 years) at elevated risk of breast cancer who, as part of participation in an earlier prospective clinical trial, underwent breast cancer screening by combined FFDM-DBT and CEM between February 2019 and April 2021. A total of 764 breasts (383 left, 381 right) were evaluated. One craniocaudal (CC) view and one mediolateral oblique (MLO) view were evaluated per breast for each of FFDM, DBT, and CEM. MGD values were extracted from DICOM metadata. BI-RADS breast density categories were extracted from clinical radiology reports. Data were summarized descriptively, including determination of corresponding effective doses. Results: Breast density category was A in no patients, B in 44 patients (88 breasts), C in 306 patients (599 breasts), and D in 39 patients (77 breasts). Median MGD per breast (CC and MLO views combined) was 4.07 mGy for FFDM alone, 4.97 mGy for DBT alone, 9.38 mGy for combined FFDM-DBT, 3.96 mGy for low-energy CEM, 1.90 mGy for high-energy CEM, and 5.87 for CEM overall. Corresponding effective dose values were 0.49 mSv, 0.60 mSv, 1.13 mSv, 0.48 mSv, 0.23 mSv, and 0.70 mSv, respectively. Median MGD for density categories B, C, and D was 4.01 mGy, 4.22 mGy, and 2.70 mGy for FFDM; 5.93 mGy, 4.93 mGy, and 3.17 mGy for DBT; and 5.90 mGy, 6.02 mGy, and 4.52 mGy for CEM. Conclusion: In this intraindividual comparative study of screening examinations, MGD per breast was higher for CEM than for FFDM or DBT alone. However, these differences were small, and MGD was lower for CEM than for combined FFDM-DBT. Clinical Impact: These findings are relevant to ongoing considerations of the role of CEM in breast cancer screening pathways.

Radiographic Assessment of the Hind Limb Bone Development in Maned Wolves (Chrysocyon brachyurus).

This study aimed to analyse the growth plate fusion and secondary ossification centres of the hind limbs in maned wolves (Chrysocyon brachyurus) using radiographs. Data from three maned wolves estimated to be 3-4 months old were utilised. The right and left hind limbs were radiographed in the mediolateral and craniocaudal views once a month until 11-12 months of age and then every 2-3 months until 18-19 months of age. The growth plates identified in the hind limbs and their closure times were as follows: proximal femur (13-15 months), distal femur (13-15 months), proximal tibia (17-19 months), tibial tuberosity (17-19 months), distal tibia (11-13 months), proximal fibula (13-15 months) and calcaneal tuber (8-9 months). Measurements of the areas of the secondary ossification centres of the proximal epiphysis of the femur, distal epiphysis of the tibia, patella and fibular tarsal bone epiphysis showed a significant difference between the first assessment and 6-7 months. The distal femoral epiphysis, proximal tibial epiphysis and tibial tuberosity showed a statistically significant difference between the first assessment and 7-8 months. The difference in the proximal epiphysis of the fibula was observed between 4 to 5 months and 7 to 8 months. In conclusion, the information obtained regarding the growth plates and secondary ossification centres of the maned wolves may support other studies and help understand the normal patterns of the species.

Evaluating Factors Affecting Mean Glandular Dose in Mammography: Insights from a Retrospective Study in Dubai.

This study evaluates the mean glandular dose (MGD) in mammography screening for women aged 40-69 in Dubai, based on a retrospective analysis of a dose survey involving 2599 participants. MGD was calculated using the Dance formula. The average MGD was 0.96 ± 0.39 mGy for mediolateral oblique (MLO) views and 0.81 ± 0.33 mGy for craniocaudal (CC) views. Weak inverse correlations were found between age and organ dose (OD) for both views, while a direct relationship was observed between breast thickness and entrance skin dose (ESD). In adjusted models, ESD was strongly associated with MGD (β = 1.04, 95% CI: 0.97, 1.09), while OD showed a moderate association (β = 0.44, 95% CI: 0.40, 0.49). Significant variations in ESD, OD, and MGD were noted across age groups and breast thicknesses. Lower MGD indicates reduced radiation exposure risk, while higher MGD in MLO views suggests improved imaging quality. Monitoring and optimizing MGD are essential for enhancing patient safety and screening efficacy.

An Integrated Multimodal-Based CAD System for Breast Cancer Diagnosis.

Breast cancer has been one of the main causes of death among women recently, and it has been the focus of attention of many specialists and researchers in the health field. Because of its seriousness and spread speed, breast cancer-resisting methods, early diagnosis, diagnosis, and treatment have been the points of research discussion. Many computers-aided diagnosis (CAD) systems have been proposed to reduce the load on physicians and increase the accuracy of breast tumor diagnosis. To the best of our knowledge, combining patient information, including medical history, breast density, age, and other factors, with mammogram features from both breasts in craniocaudal (CC) and mediolateral oblique (MLO) views has not been previously investigated for breast tumor classification. In this paper, we investigated the effectiveness of using those inputs by comparing two combination approaches. The soft voting approach, produced from statistical information-based models (decision tree, random forest, K-nearest neighbor, Gaussian naive Bayes, gradient boosting, and MLP) and an image-based model (CNN), achieved 90% accuracy. Additionally, concatenating statistical and image-based features in a deep learning model achieved 93% accuracy. We found that it produced promising results that would enhance the CAD systems. As a result, this study finds that using both sides of mammograms outperformed the result of using only the infected side. In addition, integrating the mammogram features with statistical information enhanced the accuracy of the tumor classification. Our findings, based on a novel dataset, incorporate both patient information and four-view mammogram images, covering multiple classes: normal, benign, and malignant.

Value of automated breast ultrasound in screening: Standalone and as a supplemental to digital breast tomosynthesis.

We aimed to determine the value of standalone and supplemental automated breast ultrasound (ABUS) in detecting cancers in an opportunistic screening setting with digital breast tomosynthesis (DBT) and compare this combined screening method to DBT and ABUS alone in women older than 39 years with BI-RADS B-D density categories. In this prospective opportunistic screening study, 3466 women aged 39 or older with BI-RADS B-D density categories and with a mean age of 50 were included. The screening protocol consisted of DBT mediolateral-oblique views, 2D craniocaudal views, and ABUS with three projections for both breasts. ABUS was evaluated blinded to mammography findings. Statistical analysis evaluated diagnostic performance for DBT, ABUS, and combined workflows. Twenty-nine cancers were screen-detected. ABUS and DBT exhibited the same cancer detection rates (CDR) at 7.5/1000 whereas DBT + ABUS showed 8.4/1000, with ABUS contributing an additional CDR of 0.9/1000. Standalone ABUS outperformed DBT in detecting 12.5% more invasive cancers. DBT displayed better accuracy (95%) compared to ABUS (88%) and combined approach (86%). Sensitivities for DBT and ABUS were the same (84%), with DBT + ABUS showing a higher rate (94%). DBT outperformed ABUS in specificity (95% vs. 88%). DBT + ABUS exhibited a higher recall rate (14.89%) compared to ABUS (12.38%) and DBT (6.03%) (p < .001). Standalone ABUS detected more invasive cancers compared to DBT, with a higher recall rate. The combined approach showed a higher CDR by detecting one additional cancer per thousand.

Multi-modal classification of breast cancer lesions in Digital Mammography and contrast enhanced spectral mammography images

Breast cancer ranks as the second most prevalent cancer in women, recognized as one of the most dangerous types of cancer, and is on the rise globally. Regular screenings are essential for early-stage treatment. Digital mammography (DM) is the most recognized and widely used technique for breast cancer screening. Contrast-Enhanced Spectral Mammography (CESM or CM) is used in conjunction with DM to detect and identify hidden abnormalities, particularly in dense breast tissue where DM alone might not be as effective. In this work, we explore the effectiveness of each modality (CM, DM, or both) in detecting breast cancer lesions using deep learning methods. We introduce an architecture for detecting and classifying breast cancer lesions in DM and CM images in Craniocaudal (CC) and Mediolateral Oblique (MLO) views. The proposed architecture (JointNet) consists of a convolution module for extracting local features, a transformer module for extracting long-range features, and a feature fusion layer to fuse the local features, global features, and global features weighted based on the local ones. This significantly enhances the accuracy of classifying DM and CM images into normal or abnormal categories and lesion classification into benign or malignant. Using our architecture as a backbone, three lesion classification pipelines are introduced that utilize attention mechanisms focused on lesion shape, texture, and overall breast texture, examining the critical features for effective lesion classification. The results demonstrate that our proposed methods outperform their components in classifying images as normal or abnormal and mitigate the limitations of independently using the transformer module or the convolution module. An ensemble model is also introduced to explore the effect of each modality and each view to increase our baseline architecture's accuracy. The results demonstrate superior performance compared with other similar works. The best performance on DM images was achieved with the semi-automatic AOL Lesion Classification Pipeline, yielding an accuracy of 98.85 %, AUROC of 0.9965, F1-score of 98.85 %, precision of 98.85 %, and specificity of 98.85 %. For CM images, the highest results were obtained using the automatic AOL Lesion Classification Pipeline, with an accuracy of 97.47 %, AUROC of 0.9771, F1-score of 97.34 %, precision of 94.45 %, and specificity of 97.23 %. The semi-automatic ensemble AOL Classification Pipeline provided the best overall performance when using both DM and CM images, with an accuracy of 94.74 %, F1-score of 97.67 %, specificity of 93.75 %, and sensitivity of 95.45 %. Furthermore, we explore the comparative effectiveness of CM and DM images in deep learning models, indicating that while CM images offer clearer insights to the human eye, our model trained on DM images yields better results using Attention on Lesion (AOL) techniques. The research also suggests a multimodal approach using both DM and CM images and ensemble learning could provide more robust classification outcomes.

Whole tumour- and subregion-based radiomics of contrast-enhanced mammography in differentiating HER2 expression status of invasive breast cancers: A double-centre pilot study.

To explore the value of whole tumour- and subregion-based radiomics of contrast-enhanced mammography (CEM) in differentiating the HER2 expression status of breast cancers. 352 patients underwent preoperative CEM from two centres were consecutively enroled and divided into the training, internal validation, and external validation cohorts. The lesions were divided into HER2-positive and HER2-negative groups. Besides the radiological features, radiomics features capturing the whole tumour-based (wITH) and subregion-based intratumoral heterogeneity (sITH) were extracted from the craniocaudal view of CEM recombined images. The XGBoost classifier was applied to develop the radiological, sITH, and wITH models. A combined model was constructed by fusing the prediction results of the three models. The mean age of the patients was 51.1 ± 10.7 years. Two radiological features, four wITH features, and three sITH features were selected to establish the models. The combined model significantly improved the AUC to 0.80 ± 0.03 (95% CI: 0.73-0.86), 0.79 ± 0.06 (95% CI: 0.67-0.90), and 0.79 ± 0.05 (95% CI: 0.69-0.89) in the training, internal validation, and external validation cohorts, respectively (All P < 0.05). The combined model showed good agreement between the predicted and observed probabilities and favourable net clinical benefit in the validation cohorts. Both whole tumour- and subregion-based ITH radiomics features of CEM exhibited potential for differentiating the HER2 expression status. Combining conventional radiological features and ITH features can improve the model's performance.

Uncertainty Estimation for Dual View X-ray Mammographic Image Registration Using Deep Ensembles.

Techniques are developed for generating uncertainty estimates for convolutional neural network (CNN)-based methods for registering the locations of lesions between the craniocaudal (CC) and mediolateral oblique (MLO) mammographic X-ray image views. Multi-view lesion correspondence is an important task that clinicians perform for characterizing lesions during routine mammographic exams. Automated registration tools can aid in this task, yet if the tools also provide confidence estimates, they can be of greater value to clinicians, especially in cases involving dense tissue where lesions may be difficult to see. A set of deep ensemble-based techniques, which leverage a negative log-likelihood (NLL)-based cost function, are implemented for estimating uncertainties. The ensemble architectures involve significant modifications to an existing CNN dual-view lesion registration algorithm. Three architectural designs are evaluated, and different ensemble sizes are compared using various performance metrics. The techniques are tested on synthetic X-ray data, real 2D X-ray data, and slices from real 3D X-ray data. The ensembles generate covariance-based uncertainty ellipses that are correlated with registration accuracy, such that the ellipse sizes can give a clinician an indication of confidence in the mapping between the CC and MLO views. The results also show that the ellipse sizes can aid in improving computer-aided detection (CAD) results by matching CC/MLO lesion detects and reducing false alarms from both views, adding to clinical utility. The uncertainty estimation techniques show promise as a means for aiding clinicians in confidently establishing multi-view lesion correspondence, thereby improving diagnostic capability.

An initial investigation into the use of virtual reality (VR) glasses on self-reported pain perception during mammography

ObjectivePain is one of the causes of refraining from mammography. This study aimed to investigate the use of virtual reality (VR) glasses for management of pain during mammography. MethodsFifty subjects who were referred to the radiology unit for mammography screening were invited to participate in the study. The study was approved by the hospital's ethics committee and participants provided written and oral informed consent. Craniocaudal (CC) and mediolateral oblique (MLO) views of the right breast were first acquired, followed by left CC and MLO mammographic views with the VR glasses on. After the examination, participants completed a questionnaire including a validated visual analog scale (VAS) scoring system to analyze pain scores. Data were analyzed using Spearman's rho and the Kruskal-Wallis test, as well as the Bland–Altman analysis to examine discrepancy. ResultsThe association of the right breast pain score with the parameters of age, breast density, previous mammography, family history of breast cancer, educational attainment, and employment status was examined using a correlation test. All the parameters showed no significant correlation with the right breast pain score except age (p: 0.016, correlation coefficient: −.340). Apart from that this study demonstrated no significant correlation between the perceived pain experienced during mammography and the utilization of VR technology. ConclusionThe findings indicate an inverse correlation between participant's age and pain perception during mammography examinations. Moreover, the utilization of Virtual Reality (VR) did not demonstrate efficacy in reducing pain perception.

The radiographic appearance of the canine femoral nutrient foramen for dogs undergoing total hip replacement is consistent, with some variations.

This study aimed to document the radiographic appearance of the femoral nutrient foramina and the variation of this in dogs undergoing total hip replacement (THR). Our hypothesis was that the radiographic appearance of the foramen would be consistent with the previously described anatomy, with some variations. 89 client-owned dogs. Preoperative radiographs were retrospectively analyzed for dogs undergoing THR at a single referral center. The signalment of all dogs was recorded. Radiographs were retrospectively examined to describe the number, direction, appearance, and foraminal index of the femoral nutrient foramen. Radiographs of 89 dogs and 102 femurs were examined. In 73 cases, a single foramen was seen; in 19 cases, no foramen was visible; and in 10 cases, 2 foramina were visible. The median foraminal index was 33.1% (range, 26% to 55.3%). On the mediolateral view, 72 were of proximocaudal-to-distocranial orientation, 19 were proximocranial to distocaudal, and 1 was atypical. On the craniocaudal or ventrodorsal views, the foramen was seen as a focal round radiolucency in 65 cases, was curved or atypical in 13 cases, and was not visible in 14. Radiolucent lines across the cortices that do not fit these criteria should raise suspicion of a femoral fissure, particularly within the context of THR.

A Survey of Mean Glandular Doses and Suggestions on National Diagnostic Reference Levels for Digital Mammography in China.

The primary purpose of this study was to report the mean glandular doses and to determine the national diagnostic reference levels for digital mammography based on data between 2016 and 2018 in China. The data from 19,076 mammograms (4,769 examinations) by random sampling from 118 digital mammography systems were compiled. Exposure factors included age, compressed breast thickness, kVp, mAs, target/filter combination, entrance surface air kerma, and mean glandular doses, which were retrospectively surveyed and recorded from the monitor. The national diagnostic reference levels (75th percentiles) in mean glandular dose were calculated across median value obtained for all included data and stratified to specific compressed breast thickness ranges. The patients' ages ranged from 22 to 88 y, with a median age of 45. The applied voltage and output medians were 28 kVp and 75.1 mAs for all exposure, respectively. The median CBTs were 45 mm and 48 mm for craniocaudal views and mediolateral oblique views, and the corresponding median mean glandular doses were 1.32 mGy and 1.40 mGy, respectively. The national diagnostic reference level at compressed breast thickness of 40-50 mm was 1.67 mGy for CC views and 1.71 mGy for MLO views. The median mean glandular doses varied significantly and increased with compressed breast thickness, demonstrating the necessity of establishing DRL according to breast thickness and optimizing the clinic's digital mammography practice in China.

Diagnosis of Orthopedic Fractures Using Craniodorsal-Caudoventral Oblique and Caudodorsal-Cranioventral Oblique Radiographic Views Made at 45° to the Frontal Plane in Four Wild Turtles

Abstract Four wild chelonians (two three-toed box turtles [Terrapene triunguis] and two red-eared sliders [Trachemys scripta elegans]) were presented to an academic wildlife rehabilitation center after presumably being hit by a car. Physical examinations revealed various carapace, bridge, and plastron fractures, as well as a lack of motor function of different limbs. A craniodorsal-caudoventral oblique view and/or caudodorsal-cranioventral oblique radiographic view made at 45° to the frontal plane (Cr45D-CdV and Cd45D-CrV oblique views, respectively), along with standard views (dorsoventral, right lateral, and craniocaudal views) were performed. The oblique views allowed for visualization of different pectoral girdle or pelvic girdle fractures that were not visualized or clearly identified on the standard views. Based on the four clinical cases described here, the addition of Cr45D-CdV and/or Cd45D-CrV oblique views to the standard views may be beneficial, especially when advance imaging such as computed tomography is not available. These oblique views can be used to better assess chelonian pectoral and pelvic girdles as these structures are superimposed in standard radiographs. Future studies investigating the diagnostic accuracy of detecting pelvic and pectoral girdle fractures using these views are warranted.

Designing an improved model of the adult female ICRP reference phantom dedicated to mammography procedure

Mammography is an x-ray-based imaging method to examine breast abnormalities. Since low-energy photons are used in mammography, doses to different organs would depend strongly on the phantom posture and anatomy. Until now, a few studies have been performed on doses delivered to different organs during mammography. However, in none of them, the correct posture of the patient has been considered. In the present study, the effect of accurate patient positioning, on doses to organs in the chest region were investigated through Monte Carlo simulations. The results show the rotation of the phantom head, may affect organ doses up to 60%. Also, ignoring the head in dosimetry calculations changes scattering effects and causes dose uncertainty of about 8% for these organs. Moreover, according to the obtained results, not compressing the breast causes serious dose misestimation. Finally, using developed phantoms dedicated for mammography, total doses received by different organs have been calculated for the tube voltages of 25, 28, 30 and 35 kVp and for craniocaudal and mediolateral oblique views.

Breast Delineation in Full-Field Digital Mammography Using the Segment Anything Model

Breast cancer is a major health concern worldwide. Mammography, a cost-effective and accurate tool, is crucial in combating this issue. However, low contrast, noise, and artifacts can limit the diagnostic capabilities of radiologists. Computer-Aided Diagnosis (CAD) systems have been developed to overcome these challenges, with the accurate outlining of the breast being a critical step for further analysis. This study introduces the SAM-breast model, an adaptation of the Segment Anything Model (SAM) for segmenting the breast region in mammograms. This method enhances the delineation of the breast and the exclusion of the pectoral muscle in both medio lateral-oblique (MLO) and cranio-caudal (CC) views. We trained the models using a large, multi-center proprietary dataset of 2492 mammograms. The proposed SAM-breast model achieved the highest overall Dice Similarity Coefficient (DSC) of 99.22% ± 1.13 and Intersection over Union (IoU) 98.48% ± 2.10 over independent test images from five different datasets (two proprietary and three publicly available). The results are consistent across the different datasets, regardless of the vendor or image resolution. Compared with other baseline and deep learning-based methods, the proposed method exhibits enhanced performance. The SAM-breast model demonstrates the power of the SAM to adapt when it is tailored to specific tasks, in this case, the delineation of the breast in mammograms. Comprehensive evaluations across diverse datasets—both private and public—attest to the method’s robustness, flexibility, and generalization capabilities.

Abstract PO5-07-06: Impact of patient-specific factors on quantitative breast parenchymal texture features

Abstract Breast parenchymal texture characterizes the spatial and structural patterns of breast tissue and is an independent predictor of breast cancer risk. Although it has conceptual similarities with another breast composition measure, breast density, parenchymal texture is more complex and is represented by multiple quantitative measures. The extent to which the epidemiology and biological basis of parenchymal texture and breast density differ has not been well described. In this cross-sectional study, we evaluated quantitative measures of parenchymal texture among 308 postmenopausal women aged ≥ 45 years who participated in a routine breast cancer screening at an academic medical center between 2020 and 2022. After providing informed consent, participants completed a short interview-based questionnaire. Additional demographic and breast cancer risk factor information was obtained from the medical record. Parenchymal texture features were measured from craniocaudal views of full-field digital mammograms. Measured texture values were standardized and averaged across left and right breasts. For this analysis, we evaluated associations between features previously validated as being associated with breast cancer risk (fractal dimension, grey-level mean, co-occurrence entropy and inverse difference moment) and patient personal and clinical characteristics reported at the time of screening. Associations between texture features and participant characteristics were evaluated using Pearson correlations (continuous variables) or the Kruskal-Wallis test (categorical variables). Generalized linear models were used to adjust for technical factors associated with image acquisition, including mammography machine, compression force, and software version. P-values less than 0.05 were considered statistically significant. Among the 308 participants, 70% were White, 24% were Black, 1% were Asian, 1% were American Indian, and 4% were another race that was not specified. 4% of participants reported Hispanic ethnicity. The mean age was 64.7 years (range 45 – 84 years). Fewer than half of the women were reported by a radiologist to have dense breasts (heterogeneously dense – 24%, extremely dense – 5%, vs. almost entirely fatty – 21%, scattered fibroglandular densities – 50%). The mean body mass index (BMI) was 29.4 kg/m2 (range 19.4 to 53.1 kg/m2) and 77% of the women were parous. BMI was strongly correlated with fractal dimension (r=0.62, P&amp;lt; 0.01) and grey-level mean (r=-0.48, P&amp;lt; 0.01), and had a weaker correlation with co-occurrence entropy (r=-0.19, P&amp;lt; 0.01). Age was weakly correlated with co-occurrence inverse difference moment (r=-0.12, P=0.03). BI-RADS breast density was inversely associated with fractal dimension, grey-level mean, and co-occurrence entropy (all P&amp;lt; 0.01). Adjustment for technical factors attenuated some associations, but BMI associations with fractal dimension (P&amp;lt; 0.01) and gray-level mean (P&amp;lt; 0.01), and breast density associations with fractal dimension, grey-level mean, and co-occurrence entropy (all P&amp;lt; 0.01) all remained statistically significant. Parity was not associated with any of the features evaluated in unadjusted or adjusted analyses (all P &amp;gt;0.05). These data suggest that, in postmenopausal women, risk-associated parenchymal texture features are associated with measures affected by body size and adiposity, but not with measures related to age or reproductive status. Longitudinal studies are needed to understand the temporality of these associations. Additionally, future studies will address the joint effects of texture features and the impact of endogenous estrogen levels on the features, which will provide insight into the biological mechanisms that influence texture feature associations with personal characteristics and breast cancer risk. Citation Format: Sarah Nyante, Yukie Kajita, Walter Mankowski, Ley Killeya, Despina Kontos, Xianming Tan, Eric Cohen, Cherie Kuzmiak. Impact of patient-specific factors on quantitative breast parenchymal texture features [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 PO5-07-06.

Diagnostic value of qualitative and quantitative enhancement parameters on contrast-enhanced mammography

To determine whether qualitative and quantitative enhancement parameters obtained from contrast-enhanced mammography (CEM) can be used in predicting malignancy. After review board approval, consecutive 136 suspicious lesions with definite diagnosis were retrospectively analyzed on CEM. Acquisition was routinely started with craniocaudal view and ended with mediolateral oblique view of the affected breast. Lesion conspicuity (low, moderate, high), internal enhancement pattern (homogeneous, heterogeneous, rim), contrast-to-noise ratio (CNR), percentage of signal difference (PSD) and relative enhancement from early to late view were analyzed. PSD and relative enhancements were used to determine patterns of descending, steady or ascending enhancements. Receiver operating characteristic analysis, Cohen's kappa statistics and Spearman correlation tests were used. There were 29 benign and 107 malignant lesions. 64% of the malignant lesions exhibited high conspicuity compared to 14% of the benign lesions (P < 0.001). CNR values were higher in malignant lesions compared to benign ones (P ≤ 0.004). CNR from early view yielded 82% sensitivity, 72% specificity and PSD yielded 79% sensitivity, 65% specificity. Descending pattern and rim enhancement observed in 44% and 21% of breast cancers, respectively, and both provided 96% positive predictive value for malignancy. Diagnostic accuracy of quantitative parameters was higher than that of qualitative parameters. High CNR, rim enhancement, and descending pattern were features commonly seen in malignant lesions, while low CNR, homogeneous enhancement, and ascending pattern were commonly seen in benign lesions.

Abstract 6619: Dynamic prediction with repeated mammogram images improves 5-year breast cancer risk performance

Abstract Background: Increasing use of digital screening mammography opens potential to add individualized dynamic measures of breast image history to predict future breast cancer risk. Current prediction models use a single mammogram measure, usually breast density, at one point in time, to add to prediction models based on established risk factors. Our previous work has shown that change in each breast over time is related to risk of diagnosis with breast cancer. Methods: We apply a dynamic prediction model that includes full field digital mammogram in each breast to predict future 5-year risk. Data from Joanne Knight Breast Health Cohort (JKBHC) serve as training population with 290 pathology-confirmed breast cancer cases and 658 controls sampled with a total number of 8,712 craniocaudal (CC) view mammograms for analysis. Nurses’ Health Study 2 (NHS2) serves as the external validation with 188 cases and 462 controls sampled with a total number of 3,712 CC-views. The dynamic risk prediction model aims to estimate the probability of future breast cancer risk conditional on the past history of the longitudinal mammograms. We train the model on the JKBH cohort, retaining 20% for testing and 15% for validation. We report a time dependent AUC when evaluating model performance. Results: The mean age of women was 56.6 years at entry to cohort; 81.3% were white. Adding trajectory of mammogram images in each breast to a model improved performance. The mean number of mammograms conditional on 5 years of observation in JKBHC was 3.36 (1.49) and in NHS2 2.36 (1.53). We obtained a 5-year dynamic AUC of 0.81 (0.05) in the JKBHC internal validation and 0.77 (0.06) in the NHS2 external validation. Model performance was comparable in pre- and postmenopausal women. Because breasts with high mammographic density hinder detection of tumors we stratify analyses by dense and non-dense at baseline. Among women with dense breasts at baseline (BIRADS C and D), we observe a dynamic 5-year risk AUC = 0.77. In the subgroup with less dense breasts (BIRADS A and B) we observe AUC = 0.74. Conclusions: With this approach we can accommodate the irregularly observed time-varying images and project only partial information prior to doing the prediction. Adding trajectory of mammogram history in each breast improves 5-year breast cancer risk prediction beyond models used in clinic today. Citation Format: Graham A. Colditz, Rulla M. Tamimi, Bernard Rosner, Shu Jiang. Dynamic prediction with repeated mammogram images improves 5-year breast cancer risk performance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6619.

Development and validation of a novel overhead method for anteroposterior radiographs of fractured rat femurs

We aimed to establish a new method of obtaining femur anteroposterior radiographs from live rats. We used five adult male Sprague–Dawley rats and created a femoral fracture model with an 8 mm segmental fragment. After the surgery, we obtained two femoral anteroposterior radiographs, a novel overhead method, and a traditional craniocaudal view. We obtained the overhead method three times, craniocaudal view once, and anteroposterior radiograph of the isolated femoral bone after euthanasia. We compared the overhead method and craniocaudal view with an isolated femoral anteroposterior view. We used a two-sample t-test and intraclass correlation coefficient (ICC) to estimate the intra-observer reliability. The overhead method had significantly smaller differences than the craniocaudal view for nail length (1.53 ± 1.26 vs. 11.4 ± 3.45, p < 0.001, ICC 0.96) and neck shaft angle (5.82 ± 3.8 vs. 37.8 ± 5.7, p < 0.001, ICC 0.96). No significant differences existed for intertrochanteric length/femoral head diameter (0.23 ± 0.13 vs. 0.23 ± 0.13, p = 0.96, ICC 0.98) or lateral condyle/medial condyle width (0.15 ± 0.16 vs. 0.13 ± 0.08, p = 0.82, ICC 0.99). A fragment displacement was within 0.11 mm (2.4%). The overhead method was closer to the isolated femoral anteroposterior view and had higher reliability.

Mammographically detected breast clustered microcalcifications localized by chest thin-section computed tomography

BackgroundTo explore the capability and clinical significance of chest thin-section computed tomography (CT) for localization of mammographically detected clustered microcalcifications.MethodsA total of 69 patients with 71 mammographically detected clustered microcalcifications received surgical biopsy under the guidance of mammography (MG), CT was used to localize calcifications combined with MG if calcifications can be seen on CT. Intraoperative mammography of the specimens were performed in all cases for identification of the resected microcalcifications. The clinical, imaging and pathological information of these patients were analyzed.ResultsA total of 42 (59.15%) cases of calcifications were localized by CT + MG, 29 (40.85%) cases were guided only by the mammography. All suspicious calcifications on the mammography were successfully removed. Pathological results showed 42 cases were cancer, 23 cases were benign, and 6 cases were atypical hyperplasia. The mean age in the CT + MG group was older than that of the MG group (54.12 vs. 49.27 years; P = 0.014). The maximum diameter of clusters of microcalcifications on mammography in the CT + MG group was larger than that of the MG group [(cranio-caudal view, 1.52 vs. 0.61 mm, P = 0.000; mediolateral oblique (MLO) view, 1.53 vs. 0.62 mm, P = 0.000)]. The gray value ratio (calcified area / paraglandular; MLO, P = 0.004) and the gray value difference (calcified area - paraglandular; MLO, P = 0.005) in the CT + MG group was higher than that of the MG group. Multivariate analysis showed that the max diameter of clusters of microcalcifications (MLO view) was a significant predictive factor of localization by CT in total patients (P = 0.001).ConclusionsAbout half of the mammographically detected clustered microcalcifications could be localized by thin-section CT. Maximum diameter of clusters of microcalcifications (MLO view) was a predictor of visibility of calcifications by CT. Chest thin-section CT may be useful for localization of calcifications in some patients, especially for calcifications that are only visible in one view on the mammography.

How do AI markings on screening mammograms correspond to cancer location? An informed review of 270 breast cancer cases in BreastScreen Norway

ObjectivesTo compare the location of AI markings on screening mammograms with cancer location on diagnostic mammograms, and to classify interval cancers with high AI score as false negative, minimal sign, or true negative.MethodsIn a retrospective study from 2022, we compared the performance of an AI system with independent double reading according to cancer detection. We found 93% (880/949) of the screen-detected cancers, and 40% (122/305) of the interval cancers to have the highest AI risk score (AI score of 10). In this study, four breast radiologists reviewed mammograms from 126 randomly selected screen-detected cancers and all 120 interval cancers with an AI score of 10. The location of the AI marking was stated as correct/not correct in craniocaudal and mediolateral oblique view. Interval cancers with an AI score of 10 were classified as false negative, minimal sign significant/non-specific, or true negative.ResultsAll screen-detected cancers and 78% (93/120) of the interval cancers with an AI score of 10 were correctly located by the AI system. The AI markings matched in both views for 79% (100/126) of the screen-detected cancers and 22% (26/120) of the interval cancers. For interval cancers with an AI score of 10, 11% (13/120) were correctly located and classified as false negative, 10% (12/120) as minimal sign significant, 26% (31/120) as minimal sign non-specific, and 31% (37/120) as true negative.ConclusionAI markings corresponded to cancer location for all screen-detected cancers and 78% of the interval cancers with high AI score, indicating a potential for reducing the number of interval cancers. However, it is uncertain whether interval cancers with subtle findings in only one view are actionable for recall in a true screening setting.Clinical relevance statementIn this study, AI markings corresponded to the location of the cancer in a high percentage of cases, indicating that the AI system accurately identifies the cancer location in mammograms with a high AI score.Key Points• All screen-detected and 78% of the interval cancers with high AI risk score (AI score of 10) had AI markings in one or two views corresponding to the location of the cancer on diagnostic images.• Among all 120 interval cancers with an AI score of 10, 21% (25/120) were classified as a false negative or minimal sign significant and had AI markings matching the cancer location, suggesting they may be visible on prior screening.• Most of the correctly located interval cancers matched only in one view, and the majority were classified as either true negative or minimal sign non-specific, indicating low potential for being detected earlier in a real screening setting.