Digital Mammograms Research Articles

"Artificial intelligence Reading Digital Mammogram: Enhancing Detection and Differentiation of Suspicious Microcalcifications".

To investigate the impact of artificial intelligence (AI) on enhancing the sensitivity of digital mammograms in the detection and specification of grouped microcalcifications. The study is a retrospective analysis of grouped microcalcifications for 447 patients. Grouped microcalcifications detected were correlated with AI, which was applied to the initial mammograms. AI provided a heat map, demarcation, and quantitative evaluation for abnormalities according to the degree of suspicion of malignancy. Histopathology was the standard for confirmation of malignancy. AI showed a high correlation percentage of 67.5% between the red color of the color hue bar and malignant microcalcifications (p value <0.001). The scoring of probable cancer was suggested (ie, more than 50% abnormality scoring) in 39.5% of true cancer lesions. The diagnostic performance of mammography for grouped microcalcifications revealed a sensitivity of 94.7% and a negative predictive value of 82.1%. False negatives were only 12 out of 228 that proved malignant calcifications. The agreement of cancer probability between standard mammograms and examinations read by AI presented a Kappa value of -0.094 and a p value of < 0.001. The used AI system enhanced the sensitivity of mammograms in detecting suspicious microcalcifications, yet an expert human reader is required for proper specification. Grouped calcifications could be early breast cancer on a mammogram. The morphology and distribution are correlated with the nature of breast diseases. AI is a potential decision support for the detection and classification of grouped microcalcifications and thus positively affects the control of breast cancer.

Utilizing Pseudo Color Image to Improve the Performance of Deep Transfer Learning-Based Computer-Aided Diagnosis Schemes in Breast Mass Classification.

The purpose of this study is to investigate the impact of using morphological information in classifying suspicious breast lesions. The widespread use of deep transfer learning can significantly improve the performance of themammogram based CADx schemes.However, digital mammograms are grayscale images, while deep learning models are typically optimized using the natural images containing three channels. Thus, it is needed to convert the grayscale mammograms into three channel images for the input of deep transfer models. This study aims to developa novel pseudo color image generation methodwhich utilizesthe mass contour information to enhance the classification performance.Accordingly, a total of 830 breast cancer cases were retrospectively collected, which contains 310 benign and 520 malignant cases, respectively. For each case, a total of four regions of interest (ROI) are collected from the grayscale images captured for both the CC and MLO views of the two breasts. Meanwhile, a total of seven pseudo color image sets are generated as the input of the deep learning models, which arecreated through a combination of the original grayscale image, a histogram equalized image, a bilaterally filtered image, and a segmented mass. Accordingly, the output features from four identical pre-trained deep learning models are concatenated and then processed by a support vector machine-based classifier to generate the final benign/malignant labels. The performance of each image set was evaluated and compared. The results demonstrate that the pseudo color sets containing the manually segmented mass performed significantly better than all other pseudo color sets, which achieved an AUC (area under the ROC curve) up to 0.889 ± 0.012 and an overall accuracy up to 0.816 ± 0.020, respectively. At the same time, the performance improvement is also dependent on the accuracy of the mass segmentation. The results of this study support our hypothesis that adding accurately segmented mass contours can provide complementary information, thereby enhancing the performance of the deep transfer model in classifying suspicious breast lesions.

Mammographic density and breast cancer risk among Black American women.

High mammographic density is a well-established risk factor for breast cancer; however, data from Black women are limited. It is largely unknown how mammographic density is associated with breast cancer subtypes among Black women. We examined the association between percent mammographic density (PMD) and breast cancer risk among participants in the Black Women's Health Study. Digital screening mammograms were available for 363 cases and 5541 non-cases. Cumulus software was used to assess PMD. We used inverse probability of sampling weights and Cox proportional hazards models, adjusted for age and body mass index, to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) overall and by age at mammography and estrogen receptor (ER) status of the breast tumors. Multivariable models included additional breast cancer risk factors. Tests of statistical significance were 2-sided. In simple models, women in the highest quartile of PMD had 53% increased odds of breast cancer compared to those in the lowest quartile (HR 1.53; 95% CI: 1.11, 2.11). HRs were 1.37 (95% CI: 0.83, 2.24) among women <55 years of age and 1.68 (95% CI: 1.10, 2.56) among women aged ≥55 years. HRs were 1.49 (95% CI: 1.02, 2.16) for ER+ cancer and 1.45 (95% CI: 0.73, 2.87) for ER- cancer. Associations were largely unchanged in multivariable models. In this study of U.S. Black women, higher PMD was associated with ER+ and ER- breast cancer risk. Findings from this study reinforce the importance of breast density as a risk factor for breast cancer in Black women.

Exploring the impact of work experience on the detection of specific cancers across different breast density levels on screening mammograms

PurposeThis study explores the attributes of true positive and false positive rates in screening mammogram test sets assessed by breast screening radiologists in order to identify the combined impact of prior images, breast density and lesion features with experience factors linked to diagnostic performance. Methods869 radiologists’ first-time readings across nine mammogram BREAST test sets with 361 normal and 179 cancer mammograms were collected between 2014 and 2023. Participants viewed digital mammograms on diagnostic monitors and localized abnormal lesions. The performances of readers in normal and cancer cases were compared with the ground truth and analyzed in four quartiles of breast density, lesion types and the availability of prior images using Man-Whitney U and Kruskal Wallis tests. The general linear model was applied to determine independent and significant covariates that affected the true positives and false positives. The correlation of the readers’ experience with the performances in different case and lesion features was explored using Spearman test. ResultsThe combination of lesion appearance and the availability of prior images had a significant impact on false positive results (P=0.033). The model that included lesion appearance, breast density, and no prior image status significantly influenced case true positives of readers (P=0.026). Meanwhile, the model considering only lesion appearance and breast density (P=0.002) had a significant effect on lesion true positives. There was a positive correlation observed between the number of cases read per week and readers’ performances, including TP rates and lesion sensitivity across various lesion types (P<0.05). Radiologists reading over 100 cases weekly achieved 80 % true positive rate for architectural distortion, asymmetric density, and masses, while this threshold increased to over 150 cases for calcifications. Detecting mixed lesion types required reading more than 60 cases weekly. Radiologists with over 5 years’ experience achieved 70–75 % accuracy in localizing cancer lesions. ConclusionFindings highlight the significant combined impact of breast density, prior image availability, lesion characteristics, and breast screening readers’ experience on breast cancer detection.

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.

Breast cancer characterization using region-based convolutional neural network with screening and diagnostic mammogram

Background: Detection and classification of microcalcifications in breast tissues is crucial for early breast cancer diagnosis and long-term treatment. Objective: This paper aims to propose a robust model capable of detection and classification of breast cancer calcifications in digital mammogram images using Deep Convolutional Neural Networks (DCNN). Materials and methods: An expert breast radiologist annotated the 3,265 clinical mammogram images to create a comprehensive ground truth dataset comprising 2,500 annotations for malignant and benign calcifications. This dataset was utilized to train our model, a two-stage detection system incorporating a Region-based Convolutional Neural Network (RCNN) with AlexNet and support vector machines to enhance the system’s robustness. The proposed model was compared to the one-stage detection, utilizing YOLOv4 combined with the Cross-Stage Partial Darknet53 (CSPDarknet53) architecture. A separate dataset of 504 mammogram images was explicitly set aside for model testing. The efficacy of the proposed model was evaluated based on key performance metrics, including precision, recall, F1 score, and mean average precision (mAP). Results: The results showed that the proposed RCNN-2 model could automatically identify and categorize calcifications as malignant or benign, outperforming the YOLOv4 models. The RCNN-2’s overall effectiveness, as evaluated by precision, recall, F1 score, and mean average precision (mAP), achieved scores of 0.82, 0.85, 0.83, and 0.74, respectively. Conclusion: The proposed RCNN-2 model demonstrates very effective detection and classification of calcification in mammogram images, especially in high-dense breast images. The performance of the proposed model was compared to that of YOLOv4, and it can be concluded that the proposed RCNN model yields outstanding performance. The model can be a helpful tool for radiologists.

Current status of prepectoral breast reconstruction in Argentina.

Breast cancer is among the most common cancers diagnosed in women, affecting one in eight women per year. Immediate implant-based breast reconstruction has emerged as the predominant approach for postmastectomy reconstruction, with a growing preference for the direct-to-implant (DTI) method over the traditional tissue expander technique. While conventionally, implants were typically positioned beneath the pectoralis major muscle, recent advancements have paved the way for implant placement above the muscle, in the prepectoral plane. Nipple-sparing mastectomy (NSM) and skin-sparing mastectomy (SSM) techniques can be combined with prepectoral breast reconstruction. The presence of sufficient fatty tissue coverage is considered one of the foremost independent factors influencing the success of immediate breast reconstruction and flap viability. DTI is a safe approach for prepectoral implant-based reconstruction with a number of advantages. However, careful patient selection and judicious assessment of flap perfusion help identify an appropriate subset of patients for prepectoral DTI reconstruction. Proposed breast tissue coverage classification (BTCC) and rigorous perfusion assessment techniques will aid to minimize postoperative complications and reconstruction failure. Based on the obtained range of coverage values (distance between the Cooper's ligaments and the skin) of preoperative digital mammogram evaluation, a three-type BTCC is as follows: Type 1: <1 cm (poor coverage), Type 2: between 1 and 2 cm (medium coverage), Type 3: >2 cm (good coverage). Prepectoral DTI reconstruction provides good results with complication rates similar to those of subpectoral techniques, eliminating breast animation. A meticulous surgical technique is essential to preserve the vascular network that guarantees the survival of the skin flap and nipple-areola complex (NAC). In the good coverage group (Type 3), an immediate DTI reconstruction could be safely performed. Aesthetic complications as rippling can occur if prepectoral implants are placed in Type 1 patients. Preoperative planning for prepectoral placement should not depend on breast volume, but on breast tissue coverage. Flap evaluation based on preoperative imaging measurements may be helpful when planning a conservative mastectomy. Patient selection, preoperative and intraoperative mastectomy flap evaluation, and modifications in implant technology play a critical role in this new and rapidly growing method for implant-based breast reconstruction.

Capability and reliability of deep learning models to make density predictions on low-dose mammograms.

Breast density is associated with the risk of developing cancer and can be automatically estimated using deep learning models from digital mammograms. Our aim is to evaluate the capacity and reliability of such models to predict density from low-dose mammograms taken to enable risk estimates for younger women. We trained deep learning models on standard-dose and simulated low-dose mammograms. The models were then tested on a mammography dataset with paired standard- and low-dose images. The effect of different factors (including age, density, and dose ratio) on the differences between predictions on standard and low doses is analyzed. Methods to improve performance are assessed, and factors that reduce the model quality are demonstrated. We showed that, although many factors have no significant effect on the quality of low-dose density prediction, both density and breast area have an impact. The correlation between density predictions on low- and standard-dose images of breasts with the largest breast area is 0.985 (0.949 to 0.995), whereas that with the smallest is 0.882 (0.697 to 0.961). We also demonstrated that averaging across craniocaudal-mediolateral oblique (CC-MLO) images and across repeatedly trained models can improve predictive performance. Low-dose mammography can be used to produce density and risk estimates that are comparable to standard-dose images. Averaging across CC-MLO and model predictions should improve this performance. The model quality is reduced when making predictions on denser and smaller breasts.

Hybrid Feature Mammogram Analysis: Detecting and Localizing Microcalcifications Combining Gabor, Prewitt, GLCM Features, and Top Hat Filtering Enhanced with CNN Architecture.

Breast cancer is a prevalent malignancy characterized by the uncontrolled growth of glandular epithelial cells, which can metastasize through the blood and lymphatic systems. Microcalcifications, small calcium deposits within breast tissue, are critical markers for early detection of breast cancer, especially in non-palpable carcinomas. These microcalcifications, appearing as small white spots on mammograms, are challenging to identify due to potential confusion with other tissues. This study hypothesizes that a hybrid feature extraction approach combined with Convolutional Neural Networks (CNNs) can significantly enhance the detection and localization of microcalcifications in mammograms. The proposed algorithm employs Gabor, Prewitt, and Gray Level Co-occurrence Matrix (GLCM) kernels for feature extraction. These features are input to a CNN architecture designed with maxpooling layers, Rectified Linear Unit (ReLU) activation functions, and a sigmoid response for binary classification. Additionally, the Top Hat filter is used for precise localization of microcalcifications. The preprocessing stage includes enhancing contrast using the Volume of Interest Look-Up Table (VOI LUT) technique and segmenting regions of interest. The CNN architecture comprises three convolutional layers, three ReLU layers, and three maxpooling layers. The training was conducted using a balanced dataset of digital mammograms, with the Adam optimizer and binary cross-entropy loss function. Our method achieved an accuracy of 89.56%, a sensitivity of 82.14%, and a specificity of 91.47%, outperforming related works, which typically report accuracies around 85-87% and sensitivities between 76 and 81%. These results underscore the potential of combining traditional feature extraction techniques with deep learning models to improve the detection and localization of microcalcifications. This system may serve as an auxiliary tool for radiologists, enhancing early detection capabilities and potentially reducing diagnostic errors in mass screening programs.

Lifecourse growth and development determinants of mammographic density in Black women.

High mammographic density is one of the strongest breast cancer risk factors; however, determinants of high mammographic density are understudied in Black women. We assessed growth and development factors across the lifecourse in relation to mammographic density. Within the Black Women's Health Study (BWHS), we used Cumulus software to assess percent mammographic density from digital screening mammograms for 5,905 women ages 40-74. We fit linear regression models to quantify the association of lifecourse characteristics including birth weight, childhood somatotype, age at menarche, body mass index (BMI) at age 18, height, BMI at mammography, and adulthood waist-to-hip ratio with density overall and by age. We also performed a path analysis to assess the total and mediating effects of the growth and development factors on density. BMI at age 18, height, BMI at mammography, and waist-to-hip ratio were significantly and inversely associated with density. On path analysis, total effects of childhood somatotype (standardized  = -0.05, p <0.001), BMI at age 18 (standardized  = -0.13, p <0.001), BMI at mammography (standardized  = -0.22, p <0.001), and waist-to-hip ratio (standardized  = -0.04, p <0.001) were associated with density. Several factors across the lifecourse - greater childhood somatotype, BMI at age 18, height, BMI at mammography, and waist-to-hip ratio - were associated with lower mammographic density in this cohort of Black women. Body size closer to the time of mammography may be more meaningful in determining mammographic density, though early life adiposity also influences mammographic density.

Multi-class Breast Cancer Classification Using CNN Features Hybridization

Breast cancer has become the leading cause of cancer mortality among women worldwide. The timely diagnosis of such cancer is always in demand among researchers. This research pours light on improving the design of computer-aided detection (CAD) for earlier breast cancer classification. Meanwhile, the design of CAD tools using deep learning is becoming popular and robust in biomedical classification systems. However, deep learning gives inadequate performance when used for multilabel classification problems, especially if the dataset has an uneven distribution of output targets. And this problem is prevalent in publicly available breast cancer datasets. To overcome this, the paper integrates the learning and discrimination ability of multiple convolution neural networks such as VGG16, VGG19, ResNet50, and DenseNet121 architectures for breast cancer classification. Accordingly, the approach of fusion of hybrid deep features (FHDF) is proposed to capture more potential information and attain improved classification performance. This way, the research utilizes digital mammogram images for earlier breast tumor detection. The proposed approach is evaluated on three public breast cancer datasets: mammographic image analysis society (MIAS), curated breast imaging subset of digital database for screening mammography (CBIS-DDSM), and INbreast databases. The attained results are then compared with base convolutional neural networks (CNN) architectures and the late fusion approach. For MIAS, CBIS-DDSM, and INbreast datasets, the proposed FHDF approach provides maximum performance of 98.706%, 97.734%, and 98.834% of accuracy in classifying three classes of breast cancer severities.

Detection of Breast Cancer in Digital Mammograms by Using Artificial Intelligence

Abstract Background Daily reading of a large number of normal mammograms ni breast cancer screening programs is a tedious task reducing the attractiveness of the field. The advent of Artificial Intelligence (AI) in medical imaging could, however, provide means to improve the efficiency of mammography screening. Objective Our study aims to emphasize the role of AI system as an aid in detection of breast cancer in digital mammograms. Patients and Methods The main source of data for this study were the mammography studies on the picture archiving and communications system (PACS) of theradiology department, i n Shams university hospitals of the patients referred to the radiology department at Ain Shams university hospitals for screening mammography. Results Our study showed that the improvement in diagnosticperformance and reading times with AI support was observed for the radiologists who were least experienced with mammography while the Al is increased the reading times and had no effect on diagnostic performance on the more experienced radiologists. Conclusion Using the AI as an aid has proven to improve the overall performance of less experienced radiologists allowing them to reach expert like levels of diagnostic performance in the detection of breast cancer at mammography by using an AI computer system for support without the need for additional reading time.

Mammographic density assessment: comparison of radiologists, automated volumetric measurement, and artificial intelligence-based computer-assisted diagnosis.

Artificial intelligence-based computer-assisted diagnosis (AI-CAD) is increasingly used for mammographic exams, and its role in mammographic density assessment should be evaluated. To assess the inter-modality agreement between radiologists, automated volumetric density measurement program (Volpara), and AI-CAD system in breast density categorization using the Breast Imaging-Reporting and Data System (BI-RADS) density categories. A retrospective review was conducted on 1015 screening digital mammograms that were performed in Asian female patients (mean age = 56 years ± 10 years) in our health examination center between December 2022 and January 2023. Four radiologists with two different levels of experience (expert and general radiologists) performed density assessments. Agreement between the radiologists, Volpara, and AI-CAD (Lunit INSIGHT MMG) was evaluated using weighted kappa statistics and matched rates. Inter-reader agreement between expert and general radiologists was substantial (k = 0.65) with a matched rate of 72.8%. The agreement was substantial between expert or general radiologists and Volpara (k = 0.64-0.67) with a matched rate of 72.0% but moderate between expert or general radiologists and AI-CAD (k = 0.45-0.58) with matched rates of 56.7%-67.0%. The agreement between Volpara and AI-CAD was moderate (k = 0.53) with a matched rate of 60.8%. The agreement in breast density categorization between radiologists and automated volumetric density measurement program (Volpara) was higher than the agreement between radiologists and AI-CAD (Lunit INSIGHT MMG).

Deep learning of mammogram images to reduce unnecessary breast biopsies: a preliminary study

BackgroundPatients with a Breast Imaging Reporting and Data System (BI-RADS) 4 mammogram are currently recommended for biopsy. However, 70–80% of the biopsies are negative/benign. In this study, we developed a deep learning classification algorithm on mammogram images to classify BI-RADS 4 suspicious lesions aiming to reduce unnecessary breast biopsies.Materials and methodsThis retrospective study included 847 patients with a BI-RADS 4 breast lesion that underwent biopsy at a single institution and included 200 invasive breast cancers, 200 ductal carcinoma in-situ (DCIS), 198 pure atypias, 194 benign, and 55 atypias upstaged to malignancy after excisional biopsy. We employed convolutional neural networks to perform 4 binary classification tasks: (I) benign vs. all atypia + invasive + DCIS, aiming to identify the benign cases for whom biopsy may be avoided; (II) benign + pure atypia vs. atypia-upstaged + invasive + DCIS, aiming to reduce excision of atypia that is not upgraded to cancer at surgery; (III) benign vs. each of the other 3 classes individually (atypia, DCIS, invasive), aiming for a precise diagnosis; and (IV) pure atypia vs. atypia-upstaged, aiming to reduce unnecessary excisional biopsies on atypia patients.ResultsA 95% sensitivity for the “higher stage disease” class was ensured for all tasks. The specificity value was 33% in Task I, and 25% in Task II, respectively. In Task III, the respective specificity value was 30% (vs. atypia), 30% (vs. DCIS), and 46% (vs. invasive tumor). In Task IV, the specificity was 35%. The AUC values for the 4 tasks were 0.72, 0.67, 0.70/0.73/0.72, and 0.67, respectively.ConclusionDeep learning of digital mammograms containing BI-RADS 4 findings can identify lesions that may not need breast biopsy, leading to potential reduction of unnecessary procedures and the attendant costs and stress.

Artificial intelligence as a negative predictive tool for breast cancer postoperative recurrence

BackgroundMammography alone is an ineffective method for breast cancer surveillance and diagnosing cancer recurrence. The aim was to evaluate the ability of artificial intelligence (AI) to read digital mammograms as an additive tool to exclude recurrence in the operative bed of known breast cancer patients following the different surgical procedures.MethodsWe used a retrospective cohort study of post-surgery mammograms (n = 577). Imaging was performed within 6 months after the surgery or more. The AI solution used to read mammograms (AI-MMG) provided a targeted heat map of the operative bed, which was supported by a decision likelihood score percentage of cancer recurrence. The reference for suspicious or malignant-looking abnormalities (n = 62, 12.3%) was diagnosed by biopsy. A clear operative bed and benign-looking changes (n = 442) were confirmed by ultrasound characterization patterns and one year of intermittent follow-up.ResultsThe AI scoring percentage for a clear operative bed ranged between 0 and 26%, with a mean of 15% ± 5.4%. Operative bed benign changes ranged from 10 to 88%, with a mean of 48.2% ± 21.2%, while malignancy recurrence ranged from 65 to 99%, with an average of 87.7% ± 10.5%. The “ROC: Receiver Operating Characteristic” curve for AI to predict cancer in the surgical bed on mammograms was 0.906. The optimum cutoff value to distinguish between benign postoperative alterations and malignancy recurrence was 56.5% (95%, CI 0.824–1.060, p value < 0.001).Excellent agreement between AI-MMG and pathology or ultrasound results was observed, and Kappa was 0.894, p value < 0.001.ConclusionsThe use of artificial intelligence has enhanced the diagnostic performance of the postoperative mammograms to rule out recurrent malignancies in breast cancer surveillance.

Architectural distortion on digital breast tomosynthesis mammograms in symptomatic breast clinics: what are the result outcomes?

In April 2020, standard two-dimensional (2D) full-field digital mammograms were replaced with digital breast tomosynthesis (DBT) and synthesised 2D views for symptomatic breast clinics. This study aimed to evaluate the positive predictive value (PPV) for malignancy in DBT-detected Architectural distortion (AD). All mammogram reports with the word "distortion" were assessed between April 2020 and October 2022. There were 458 mammograms with the word "distortion." After excluding mammograms with no distortion (n = 128), post-surgical distortion (n = 173), distortion with mass (n = 33), and unchanged distortion (n = 14), there were 111 patients with pure distortion. Correlation with histopathology was obtained where possible. All patients were followed for a minimum of 2 years. Forty-two out of 111 patients (37.84%) with AD had a normal ultrasound (US) and were discharged. Fifty-five (49.5%) patients had sonographic correlation corresponding to the distortion, leading to US-guided biopsy. Thirteen (23.6%) had tomosynthesis-guided biopsy, and one had a skin biopsy. The PPV for malignancy was 42.34%. Malignancy diagnoses were higher with US-guided biopsies than tomosynthesis-guided biopsies, 78.1% and 30%, respectively. With a total malignancy rate of 42.34%, DBT-detected AD has a high enough PPV for malignancy to justify selective tissue sampling if a sonographic correlate is present or with suspicious mammograms. The chances of malignancy are higher when a sonographic correlate corresponding to AD is present. AD on DBT/synthesized mammograms views in symptomatic breast clinic patients justifies selective sampling.

Abstract PO3-09-01: Integrating pathomic and radiomic images to classify risk of subsequent events among women with DCIS

Abstract Background Multiple sources of ~omic data can be generated from women at different stages of developing breast cancer, the leading cancer diagnosed in women worldwide. Traditionally interrogation of risk factors to study associations and develop prediction models for future breast events has been limited to one or few risk factors, or summary scores of clinical and tumor characteristics. Methods to bring mammography images and breast biopsies of precancer lesions together to summarize risk of cancer developing in the breast are urgently needed. Integration of these two sources has not been performed to date, but has potential to increase accuracy of risk prediction. Approach The Repository of Archival Human Breast Tissue (RAHBT) was established in 2007 at Washington University School of Medicine (WUSM) and maintains biospecimens and medical record data of women treated with breast-conserving surgery (BCS) or mastectomy for breast cancer at WUSM and six other St Louis metropolitan hospitals between 1981-2016. Prospective follow-up of cases is achieved through health records and Siteman tumor registry. Among 1831 patients with pathologically confirmed DCIS who had no prior cancer, 174 developed breast events at least six months after initial DCIS diagnosis. For each case diagnosed between January 1998 and March 2016 with subsequent breast events, two DCIS controls were matched on race, year of diagnosis (±5 years), age (±5 years), and type of surgery. Tissue micro arrays (TMAs) are constructed after breast pathology review and processed for H&amp;E and imaged. Breast cancer risk factor data are uniformly retrieved form the medical records at time of processing cases of DCIS. Mammogram images are retrieved for all women. Full-field digital mammograms (FFDM) are all using the same technology (Hologic). Risk factors and outcome variables: ipsilateral breast events (IBE) was defined as the development of invasive cancer or DCIS in the treated breast at least 6 months post-diagnosis. Follow-up time was calculated from the date of DCIS diagnosis to the date of first IBE, death, or last follow-up, whichever occurred first. The predictors included commonly used clinicopathological factors: age, tumor grade (high vs. low/intermediate), comedonecrosis, surgical margins (close (≤2mm) vs. negative ( &amp;gt;2mm)), local treatment (BCS only and mastectomy vs. BCS+radiation), and endocrine therapy. BMI, menopausal status, and ER were also available for evaluation as predictors. We limited this analysis to women with digital mammograms immediately prior to diagnosis and include 128 cases and controls. Validation cohort As the RAHBT cohort continues to be followed additional second breast events have been documented after the cut off for events identified through March 2016, and the identical procedures used to review/confirm DCIS and subsequent breast events, construct TMAs, process H&amp;E and images, and assemble risk factor data, and the associated digital mammograms. We have 13 additional ipsilateral breast events with complete data and their controls for independently testing the model performance. Statistical methods Propose novel supervised learning approach to integrate image data from mammograms and TMA slides that accommodates agreement between multi-omics. Results 128 (cases and controls, 21.9% ipsilateral subsequent events) identified for development and internal cross validation. Median age at diagnosis was 52 and median time to subsequent breast event was 63 mo. Controlling for the clinical/pathologic risk factors (age, BMI, BI-RADs density, treatment, tumor grade, parous, menopausal status, ER status, and race) and integrating the whole mammogram and TMA pathology images, we observe a 5-yr ipsilateral AUC = 0.74; 10-yr AUC 0.75. The independent validation is ongoing. Conclusions Integrating heterogeneous multiomic data sources can generate significant improvement in long term risk prediction after initial DCIS diagnosis. Citation Format: Shih-Ting Huang, Debbie Bennett, Robert West, Graham Colditz, Shu Jiang. Integrating pathomic and radiomic images to classify risk of subsequent events among women with DCIS [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 PO3-09-01.

Abstract PO2-09-04: Changes in an artificial intelligence model score for breast cancer detection (Transpara) with tamoxifen and aromatase inhibitor treatment

Abstract Artificial intelligence (AI) models applied to mammography have been shown to improve breast cancer (BC) detection and risk estimation. The Transpara AI detection system has been associated with short and longer-term risk of invasive BC. Whether Transpara can identify changes in the breast associated with tamoxifen (TAM) and aromatase inhibitor (AIs) treatment is unknown. We compare changes in the Transpara malignancy score in the unaffected breast of BC cases using TAM or AIs to those of untreated women without cancer. Cases and controls were sampled from the Mayo Clinic Rochester Breast Screening Practice. Eligible cases were women with breast cancer treated with TAM and/or AIs for at least 8 months who had full field digital (FFDM) mammograms within two years prior to start of treatment (pretreatment mammogram). Controls did not have BC, were not using TAM, AIs, or postmenopausal hormones, and had at least two FFDM screening mammograms. Transpara scores (1-10) were calculated for the contralateral breast in cases and a randomly selected side for controls. Two controls were matched to each case on menopausal status at both mammograms, time between mammograms (&amp;lt; 6 months) and baseline Transpara score (±2 units). Conditional logistic regression was used to assess the association of treatment (case (TAM/AIs) vs. control) with change in Transpara score between the pretreatment mammogram and the mammogram taken closest to 13 months later. Change was classified as having a decrease in Transpara score between the two mammograms vs. no change or an increase. Models were adjusted for age and Transpara score at pretreatment mammogram, and time between the two mammograms. We performed analyses on all women combined and stratified by time on treatment, menopausal status, and treatment type (TAM vs. AIs). A total of 134 cases were identified, with 53 on TAM (21 premenopausal and 32 postmenopausal) and 81 on AIs (postmenopausal); 268 controls were matched to the cases. Characteristics of the study population are summarized (Table). Most case mammograms were obtained within six months prior to treatment start [median 2.63 months (interquartile range (IQR), 1.62, 5.72)] and median time on TAM/AIs was 10.9 months (IQR, 8.03, 16.6) at time of second mammogram. The median absolute change in Transpara score between the two mammograms was similar in cases [0 (IQR, -2, 1)] vs. controls [0 (IQR, -1.25, 1)]; 40.3% of cases and 39.2% of controls had any decrease. Conditional logistic models found no evidence for an association between TAM/AIs and decrease in Transpara score [odds ratio (OR) = 1.07; 95% confidence interval (CI), 0.65, 1.78]. When stratified by median time on treatment (10.9 months), there was suggestion of a stronger association for those on treatment for a longer duration (OR = 1.21; 95% CI, 0.61, 2.40 for &amp;gt;10.9 months vs. OR = 0.96; 95% CI, 0.43, 2.13 for &amp;lt; 10.9 months) but these results were not statistically significant. Similarly, odds ratios were stronger in postmenopausal (OR = 1.18; 95% CI, 0.67, 2.07) compared to premenopausal (OR = 0.91; 95% CI, 0.24, 3.45) women, and for AIs (OR = 1.21, 95% CI, 0.64, 2.29) vs. TAM (OR = 0.93; 95% CI, 0.39, 2.22) but none of these results reached statistical significance. In summary, we found no association between treatment with TAM/AIs and change in Transpara score. However, our study had limited power. Also, longer time on treatment as well as investigation of AI-based scores trained directly to assess BC risk may be necessary to identify relevant changes. Citation Format: Yvonne Wang, Christopher Scott, Matthew Jensen, Dan Hursh, Kathleen Brandt, Aaron Norman, Fang Fang Wu, Stacey Winham, Karla Kerlikowske, Celine Vachon. Changes in an artificial intelligence model score for breast cancer detection (Transpara) with tamoxifen and aromatase inhibitor treatment [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 PO2-09-04.

Abstract PS10-06: Contribution of a breast cancer polygenic risk score to mammography artificial intelligence models and breast density for long term breast cancer risk prediction

Abstract Artificial intelligence (AI) models based on deep learning of mammogram images have been developed for lesion detection and diagnosis as well as breast cancer (BC) risk. We and others have shown that these AI models, coupled with breast density, predict long-term breast cancer risk. Common germline genetic variation, in particular a polygenic risk score (PRS) that captures the cumulative impact of hundreds of common risk variants, has been shown associated with 3-fold increased BC risk. The BC-PRS may complement imaging risk factors to improve identification of women at high BC risk and better target prevention strategies. We evaluated the contribution of a BC-PRS to two imaging-based AI models (Transpara AI cancer detection system and Mirai 5-year risk) and breast density measures to long-term BC risk. We hypothesized that the BC-PRS will improve long-term risk prediction above AI and density measures. We examined this hypothesis within a cohort of 10,271 women enrolled to the Mayo Clinic Biobank between 2009 and 2015. Eligible women had no prior cancer, were ages 35 to 85, had a full-field digital screening mammogram within two years of enrollment and genotyping by Regeneron. Incident invasive and DCIS BC was identified through the Mayo tumor registry and follow-up questionnaires, and person years were calculated from date of mammogram through date of BC, last mammogram at Mayo or last questionnaire. We estimated Transpara AI malignancy score (1-10), 5-year Mirai risk and Volpara volumetric density measures (volumetric percent density, dense volume) on screening mammograms at enrollment and obtained clinical BI-RADS density. We calculated the BC-PRS using 268 SNPs (of the 313-SNP score) and the weighted sum of the SNPs using odds ratios from the largest BC GWAS of European ancestry to date. We performed cox proportional hazards regression adjusting for age and BMI to estimate hazard ratios (HRs), 95% confidence intervals (CI), and C-statistics (AUC) to describe the contribution of BC-PRS (per standard deviation (SD)) to the association of AI scores and breast density with BC risk. Likelihood ratio tests (LRT) and bootstrapping were used to compare model performance with vs. without inclusion of BC-PRS, at 10-years follow-up. Comparisons to the Tyrer-Cuzick model are ongoing. Over a median of 9.1 years (Interquartile range, 6.8 to 10.6), 446 incident BC were identified of which 285 invasive and 103 DCIS were confirmed by pathology report (60 were self-reported); 399 (256 invasive) occurred within 10 years and were used in primary analyses. Transpara score [HR per 1 unit=1.18 (1.14, 1.23), AUC=0.655 (0.628, 0.682)] and Mirai risk [HR per SD log risk=1.54 (1.43, 1.65), AUC=0.681 (0.655, 0.706)] were both associated with BC. All three breast density measures were also significantly associated with BC, including in models with AI scores (Table). Addition of the BC-PRS to models with AI scores and breast density improved both model fit (All PLRT &amp;lt; 0.001; Table) and discrimination, with increases in AUC ranging from 0.033 to 0.034 for models with Transpara and density and 0.025 to 0.026 for models with Mirai and density (all P-values &amp;lt; 0.001; Table). Analyses restricted to invasive BC showed identical results. But, the BC-PRS contribution at 5-year follow-up was attenuated. In this cohort study, we found BC-PRS contributed to long-term risk prediction of BC above imaging AI models and breast density. Imaging and genetic risk factors are complementary and may outperform existing risk models and provide more accurate risk assessment to aid in precision prevention strategies. Citation Format: Celine Vachon, Christopher Scott, Imon Banerjee, Ramon Correa Moderno, Dan Hursh, Matthew Jensen, Yvonne Wang, Kathleen Brandt, Karla Kerlikowske, Sandhya Pruthi, Fergus Couch, Stacey Winham. Contribution of a breast cancer polygenic risk score to mammography artificial intelligence models and breast density for long term breast cancer risk prediction [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 PS10-06.

Abstract PO2-23-10: Obesity May Modulate the Relationship Between High Mammographic Breast Density and Neoadjuvant Chemoresistance in Early Breast Cancer

Abstract Background - An inverse association has been described in a number of studies between mammographic breast density (MBD) and pathologic complete response (pCR) following neoadjuvant chemotherapy (NAC) for early breast cancer (BC), implicating high MBD in chemotherapy resistance. However, this effect has not been seen in all there is a paucity of data exploring whether this relationship extends the relationship beyond pCR to overall survival. This study aims to validate the relationship between MBD and pCR in patients undergoing NAC for early BC as well as to assess the relation of MBD with clinical complete response (cCR), relapse-free (RFS) and BC-specific survival (BCSS). Further, this study examines these associations stratified by body mass index (BMI). Methods - MBD was measured in contralateral mammograms in 127 women before NAC, using Cumulus software. As the study spanned the transition period from film to digital mammography, film mammograms were digitised using a high-powered scanner and processed full-field digital mammograms were extracted from the imaging storage facility. Basic patient, tumour and treatment demographics were collated including BMI. Percent dense area (PDA) was correlated with patient and tumour characteristics, short-term (pCR/cCR incidences), and long-term chemotherapy outcomes (RFS and BCSS). Results - Overall rates of cCR and pCR of 49% and 21% were observed across the whole cohort. Mean PDA was higher in those not undergoing cCR (p=0.041) and relapsing patients (p=0.041) but did not significantly vary by pCR or BC-deaths. As a dichotomous variable, high PDA relative to low PDA corresponded to lower cCR (40 v 58%, p=0.027), but no significant difference was seen for pCR (17.5 v 25.0%, p=0.15), BC relapse (38 v 30%, p=0.15) or BC-death (32 v 25%, p=0.20). High PDA (relative to low PDA) in patients with obesity (BMI &amp;gt;30kg/m2) was associated with lower pCR (0% v 28.1%, p=0.036) and numerically higher relapse rates (56% v 28%, p=0.063) and breast cancer deaths (56 v 28%, p=0.071. No such relationship was observed in patients with a BMI&amp;lt; 30kg/m2. Conclusion - Higher MBD associated with lower cCR and higher relapse but not with pCR or BC death. The inferior outcomes with high MBD were confined to patients with obesity in our study. Further analysis of the relationship between MBD and outcomes, including by BMI, is warranted. Citation Format: Andrew Redfern, Veenoo Agarwal, Ellie Darcey, Lisa Spalding, Hilary Martin, Jennifer Stone. Obesity May Modulate the Relationship Between High Mammographic Breast Density and Neoadjuvant Chemoresistance in Early Breast Cancer [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 PO2-23-10.