A New Approach to Breast Specimen Orientation: Avoiding Pitfalls with the Specimen Plate Concept.

Accurate specimen marking is crucial during breast cancer surgery to avoid misorientation, which can lead to inadequate re-excision and tumor recurrence. We studied the marking methods at various breast cancer centers to create a tool that would prevent specimen misorientation. An online questionnaire was used to survey marking procedures at major breast cancer centers in Hungary, and a tool was developed using a troubleshooting method. Twelve out of twenty units responded (60%). Nine use an institutionally standardized marking system. Less than half of the surgical teams found specimen mammograms to be unambiguous. In more than 70% of departments, pathologists were uncertain about breast specimen orientation. Ambiguous marking methods caused orientation errors in half of the cases, while unclear marking directions caused the rest. Most pathologists (85%) and surgeons (75%) believed that coronal plane specimen mammography would help solve the problem. A plastic specimen plate has been developed to anchor breast tissue to a coronal breast scheme as seen in mammography images, providing clear localization information throughout the surgical process. There is a lack of standardization in breast specimen orientation and marking in Hungary. An optimized orientation toolkit is being developed to ensure consistent interpretation of specimen mammograms by surgeons and pathologists.

Initial interpretation scores of screening mammograms and cancer detection in BreastScreen Norway.

PURPOSE: To explore the association between radiologists' interpretation scores, early performance measures and cumulative reading volume in mammographic screening. METHOD: We analyzed 1,689,731 screening examinations (3,379,462 breasts) from BreastScreen Norway 2012-2020, all breasts scored 1-5 by two independent radiologists. Score 1 was considered negative/benign and score ≥2 positive in this scoring system. We performed descriptive analyses of recall, screen-detected cancer, positive predictive value (PPV) 1, mammographic features and histopathological characteristics by breast-based interpretation scores, and cumulative reading volume by examination-based interpretation scores. RESULTS: Counting breasts and not women, 3.9 % (132,570/3,379,462) had a score of ≥2 by one or both radiologists. Of these, 84.8 % (112,440/132,570) were given a maximum score 2. Total recall rate was 1.6 % (53,735/3,379,462), 69.3 % (37,220/53,735) given maximum score 2. Among the 0.3 % (9733/3,379,462) diagnosed with screen-detected cancer, 34.6 % (3369/9733) had maximum score 3. The percentages of recall, screen-detected cancer and PPV-1 increased by increasing the sum of scores assigned by two radiologists (p < 0.001 for trend). Higher proportions of masses were observed among recalls and screen-detected cancers with low scores, and higher proportions of spiculated masses were observed for high scores (p < 0.001). Proportions of invasive carcinoma, histological grade 3 and lymph node positive tumors were higher for high versus low scores (p < 0.001). The proportion of examinations scored 1 increased by cumulative reading volume. CONCLUSIONS: We observed higher rates of recall and screen-detected cancer and less favorable histopathological tumor characteristics for high versus low interpretation scores. However, a considerable number of recalls and screen-detected cancers had low interpretation scores.

Automated system utilizing non-invasive technique mammograms for breast cancer detection.

In order to increase the likelihood of obtaining treatment and achieving a complete recovery, early illness identification and diagnosis are crucial. Artificial intelligence is helpful with this process by allowing us to rapidly start the necessary protocol for treatment in the early stages of disease development. Artificial intelligence is a major contributor to the improvement of medical treatment for patients. In order to prevent and foresee this problem on the individual, family, and generational levels, Monitoring the patient's therapy and recovery is crucial. This study's objective is to outline a non-invasive method for using mammograms to detect breast abnormalities, classify breast disorders, and identify cancerous or benign tumor tissue in the breast. We used classification models on a dataset that has been pre-processed so that the number of samples is balanced, unlike previous work on the same dataset. Identifying cancerous or benign breast tissue requires the use of supervised learning techniques and algorithms, such as random forest (RF) and decision tree (DT) classifiers, to examine up to thirty features, such as breast size, mass, diameter, circumference, and the nature of the tumor (solid or cystic). To ascertain if the tissue is malignant or benign, the examination's findings are employed. These features are mostly what determines how effectively anything may be categorized. The DT classifier was able to get a score of 95.32%, while the RF satisfied a far higher 98.83 percent.

Accuracy of an Artificial Intelligence System for Interval Breast Cancer Detection at Screening Mammography.

Background Artificial intelligence (AI) systems can be used to identify interval breast cancers, although the localizations are not always accurate. Purpose To evaluate AI localizations of interval cancers (ICs) on screening mammograms by IC category and histopathologic characteristics. Materials and Methods A screening mammography data set (median patient age, 57 years [IQR, 52-64 years]) that had been assessed by two human readers from January 2011 to December 2018 was retrospectively analyzed using a commercial AI system. The AI outputs were lesion locations (heatmaps) and the highest per-lesion risk score (range, 0-100) assigned to each case. AI heatmaps were considered false positive (FP) if they occurred on normal screening mammograms or on IC screening mammograms (ie, in patients subsequently diagnosed with IC) but outside the cancer boundary. A panel of consultant radiology experts classified ICs as normal or benign (true negative [TN]), uncertain (minimal signs of malignancy [MS]), or suspicious (false negative [FN]). Several specificity and sensitivity thresholds were applied. Mann-Whitney U tests, Kruskal-Wallis tests, and χ2 tests were used to compare groups. Results A total of 2052 screening mammograms (514 ICs and 1548 normal mammograms) were included. The median AI risk score was 50 (IQR, 32-82) for TN ICs, 76 (IQR, 41-90) for ICs with MS, and 89 (IQR, 81-95) for FN ICs (P = .005). Higher median AI scores were observed for invasive tumors (62 [IQR, 39-88]) than for noninvasive tumors (33 [IQR, 20-55]; P < .01) and for high-grade (grade 2-3) tumors (62 [IQR, 40-87]) than for low-grade (grade 0-1) tumors (45 [IQR, 26-81]; P = .02). At the 96% specificity threshold, the AI algorithm flagged 121 of 514 (23.5%) ICs and correctly localized the IC in 93 of 121 (76.9%) cases, with 48 FP heatmaps on the mammograms for ICs (rate, 0.093 per case) and 74 FP heatmaps on normal mammograms (rate, 0.048 per case). The AI algorithm correctly localized a lower proportion of TN ICs (54 of 427; 12.6%) than ICs with MS (35 of 76; 46%) and FN ICs (four of eight; 50% [95% CI: 13, 88]; P < .001). The AI algorithm localized a higher proportion of node-positive than node-negative cancers (P = .03). However, no evidence of a difference by cancer type (P = .09), grade (P = .27), or hormone receptor status (P = .12) was found. At 89.8% specificity and 79% sensitivity thresholds, AI detection increased to 181 (35.2%) and 256 (49.8%) of the 514 ICs, respectively, with FP heatmaps on 158 (10.2%) and 307 (19.8%) of the 1548 normal mammograms. Conclusion Use of a standalone AI system improved early cancer detection by correctly identifying some cancers missed by two human readers, with no differences based on histopathologic features except for node-positive cancers. © RSNA, 2024 Supplemental material is available for this article.

Integrated and Interoperable Platform for Detecting Masses on Mammograms.

The screening and diagnosis of breast cancer is a major public health issue. Although deep learning models are proving highly effective in breast imaging, these models are not yet readily accessible to a wide audience. In order to promote the widespread dissemination of such models, this article introduces a free and open-source, integrated platform for the automated detection of masses on mammograms. A state-of-the-art RetinaNet model is trained on this task and the results of the inference are encoded using the DICOM-SR interoperable format. These contributions present a significant step towards overcoming the accessibility gap in deep learning for breast imaging.

Pain Experience and Image Quality with Curved versus Standard Compression for Breast Cancer Screening Mammography: A Randomized Controlled Trial.

Background A curve-shaped compression paddle could reduce the pain experienced by some women at breast cancer screening. Purpose To compare curved and standard compression systems in terms of pain experience and image quality in mammography screening. Materials and Methods In this randomized controlled trial conducted between October 2021 and February 2022, participants screened at three screening sites in the Netherlands were randomized to either a curved-paddle or sham-paddle group. The sham paddle was a standard paddle that was presented as a new paddle. At a standard screening examination, one additional image was acquired with a curved or sham paddle. Pain was measured on a numerical rating scale (range, 0-10). Participants provided a pain score after compression with the standard and test paddles, resulting in two scores per participant. Differences in pain scores were compared between groups using analysis of covariance, adjusting for pain score after standard-paddle compression. Two radiographers and two radiologists performed unblinded paired comparisons of curved-paddle vs standard-paddle images, using standard image quality criteria (radiographers evaluated 1246 image pairs using 12 criteria; radiologists evaluated 320 image pairs using six criteria). The one-sample Wilcoxon signed-rank test was used to determine if there was a significant preference for either paddle. Results In total, 2499 female participants (mean age, 61.6 years ± 7.1 [SD]) were studied; 1250 in the curved-paddle group and 1249 in the sham-paddle group. The mean pain score decreased by an additional 0.19 points in the curved-paddle group compared with the sham-paddle group (95% CI: 0.09, 0.28; P < .001). In terms of image quality, the observers showed no preference or a preference for the standard paddle. Decreased image contrast (range Bonferroni-corrected P values: P < .001 to P > .99) and visibility of structures were the main concerns for curved-paddle images. Conclusion The use of the curved paddle resulted in a minimal pain reduction during mammography breast compression but image quality was reduced. © RSNA, 2024 Supplemental material is available for this article.

Addressing Breast Cancer Equity Through Virtual Community Oncology Navigation and Engagement (vCONET).

BACKGROUND: Breast cancer remains a leading cause of cancer morbidity and mortality worldwide. In the United States, Black women face significant disparities in screening mammograms, experience higher rates of breast cancer at advanced stages, and are more likely to die from the disease. AIMS: This study aimed to develop and beta-test a virtual health navigation program to enhance breast cancer care within the Black community. We identified barriers to utilizing virtual patient navigators and factors impacting the adoption of virtual navigation for breast cancer information among Black women. METHODS: The vCONET (Virtual Community Oncology Navigation and Engagement) intervention was delivered through the Second Life virtual platform. The informational content was collaboratively developed with community members. Participants engaged in an informational session on risk factors, mammography information, and preventive behaviors. Surveys (n = 18) and focus groups (n = 9) assessed knowledge and insights into perceptions. RESULTS: Findings revealed a positive impact of the intervention, with participants expressing increased knowledge and willingness to seek further information about breast cancer prevention, and highlighted the engaging nature of the virtual environment, while acknowledging potential technological challenges. CONCLUSION: Virtual health navigation shows promise in addressing breast cancer disparities by promoting awareness among Black women. Future efforts should optimize virtual navigation approaches through collaborative engagement for lasting impact, enhancing breast cancer care and equity in communities of color.

Correlations of Imaging and Therapy in Breast Cancer Based on Molecular Patterns: An Important Issue in the Diagnosis of Breast Cancer.

Breast cancer is a global health issue affecting countries worldwide, imposing a significant economic burden due to expensive treatments and medical procedures, given the increasing incidence. In this review, our focus is on exploring the distinct imaging features of known molecular subtypes of breast cancer, underlining correlations observed in clinical practice and reported in recent studies. The imaging investigations used for assessment include screening modalities such as mammography and ultrasonography, as well as more complex investigations like MRI, which offers high sensitivity for loco-regional evaluation, and PET, which determines tumor metabolic activity using radioactive tracers. The purpose of this review is to provide a better understanding as well as a revision of the imaging differences exhibited by the molecular subtypes and histopathological types of breast cancer.

Patients' perceptions of targeted breast ultrasound and digital breast tomosynthesis in the diagnostic setting: A mixed methods study.

BACKGROUND: Although DBT is the standard initial imaging modality for women with focal breast symptoms, the importance of ultrasound has grown rapidly in the past decades. Therefore, the Breast UltraSound Trial (BUST) focused on assessing the diagnostic value of ultrasound and digital breast tomosynthesis (DBT) for the evaluation of breast symptoms by reversing the order of breast imaging; first performing ultrasound followed by DBT. This side-study of the BUST evaluates patients' perceptions of ultrasound and DBT in a reversed setting. METHODS: After imaging, 1181/1276 BUST participants completed a survey consisting of open and closed questions regarding both exams (mean age 47.2, ±11.74). Additionally, a different subset of BUST participants (n = 29) participated in six focus group interviews 18-24 months after imaging to analyze their imaging experiences in depth. RESULTS: A total of 55.3% of women reported reluctance to undergoing DBT, primarily due of pain, while the vast majority also find bilateral DBT reassuring (87.3%). Thematic analysis identified themes related to 1) imaging reluctance (pain/burden, result, and breast harm) and 2) ultrasound and DBT perceptions. Regarding the latter, the theme comfort underscores DBT as burdensome and painful, while ultrasound is largely perceived as non-burdensome. Ultrasound is also particularly valued for its interactive nature, as highlighted in the theme interaction. Perceived effectiveness reflects women's interest in bilateral breast evaluation with DBT and the visibility of lesions, while they express more uncertainty about the reliability of ultrasound. Emotional impact portrays DBT as reassuring for many women, whereas opinions on the reassurance provided by ultrasound are more diverse. Additional themes include costs, protocols and privacy. CONCLUSIONS: Ultrasound is highly tolerated, and particularly valued is the interaction with the radiologist. Nearly half of women express reluctance towards DBT; nevertheless, a large portion report feeling more confident after undergoing bilateral DBT, reassuring them of the absence of abnormalities. Understanding patients' perceptions of breast imaging examinations is of great value when optimizing diagnostic pathways.

An optimized model based on adaptive convolutional neural network and grey wolf algorithm for breast cancer diagnosis.

Medical image classification (IC) is a method for categorizing images according to the appropriate pathological stage. It is a crucial stage in computer-aided diagnosis (CAD) systems, which were created to help radiologists with reading and analyzing medical images as well as with the early detection of tumors and other disorders. The use of convolutional neural network (CNN) models in the medical industry has recently increased, and they achieve great results at IC, particularly in terms of high performance and robustness. The proposed method uses pre-trained models such as Dense Convolutional Network (DenseNet)-121 and Visual Geometry Group (VGG)-16 as feature extractor networks, bidirectional long short-term memory (BiLSTM) layers for temporal feature extraction, and the Support Vector Machine (SVM) and Random Forest (RF) algorithms to perform classification. For improved performance, the selected pre-trained CNN hyperparameters have been optimized using a modified grey wolf optimization method. The experimental analysis for the presented model on the Mammographic Image Analysis Society (MIAS) dataset shows that the VGG16 model is powerful for BC classification with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) of 99.86%, 99.9%, 99.7%, 97.1%, and 1.0, respectively, on the MIAS dataset and 99.4%, 99.03%, 99.2%, 97.4%, and 1.0, respectively, on the INbreast dataset.

Evaluating acute nipple inversion, imaging findings and outcomes.

PURPOSE: Acute nipple inversion can be unsettling for patients and is sometimes associated with an underlying breast malignancy. It also poses a diagnostic challenge with lack of consensus management guidelines. This study reviewed institutional experience with new nipple inversion, including malignant association, imaging utilization, and outcomes, in an effort to improve management. METHODS: A multisite institutional retrospective review was conducted of all breast imaging reports from 1/2010 to 6/2022 mentioning nipple inversion as an indication or finding. Patients with new nipple inversion, defined as arising since the time of last breast imaging exam or if reported as new by the patient/provider, were included for analysis. Retroareolar imaging findings, BI-RADS assessments/recommendations, pathology obtained from percutaneous or excisional biopsies, and follow-up imaging and clinical exams were collated. Cases of chronic or stable nipple inversion were excluded. Descriptive statistics were performed. RESULTS: A total of 414 patients had new nipple inversion, 387/414 (93.5 %) with benign or negative results at initial imaging and 27/414 (6.5 %) with malignant lesions. Diagnostic mammography/ultrasound detected 25/27 (92.6 %) cancers (sensitivity 92.6 %, specificity 75.5 %, PPV 20.8 %, NPV 99.3 %). Of 62 breast MRI exams performed in patients with negative mammogram/ultrasound, no cancers were detected in the retroareolar space with 2 incidental malignant lesions discovered distant from the nipple. CONCLUSION: Diagnostic mammography/ultrasound is reliable in workups of acute nipple inversion, with a high sensitivity and NPV for excluding malignancy. Breast MRI and surgical referral should be reserved for patients with suspicious associated symptoms or clinical findings.

'Humans think outside the pixels' - Radiologists' perceptions of using artificial intelligence for breast cancer detection in mammography screening in a clinical setting.

OBJECTIVE: This study aimed to explore radiologists' views on using an artificial intelligence (AI) tool named ScreenTrustCAD with Philips equipment) as a diagnostic decision support tool in mammography screening during a clinical trial at Capio Sankt Göran Hospital, Sweden. METHODS: We conducted semi-structured interviews with seven breast imaging radiologists, evaluated using inductive thematic content analysis. RESULTS: We identified three main thematic categories: AI in society, reflecting views on AI's contribution to the healthcare system; AI-human interactions, addressing the radiologists' self-perceptions when using the AI and its potential challenges to their profession; and AI as a tool among others. The radiologists were generally positive towards AI, and they felt comfortable handling its sometimes-ambiguous outputs and erroneous evaluations. While they did not feel that it would undermine their profession, they preferred using it as a complementary reader rather than an independent one. CONCLUSION: The results suggested that breast radiology could become a launch pad for AI in healthcare. We recommend that this exploratory work on subjective perceptions be complemented by quantitative assessments to generalize the findings.

Radiology for Ductal Carcinoma In Situ of the Breast: Updates on Invasive Cancer Progression and Active Monitoring.

Ductal carcinoma in situ (DCIS) accounts for approximately 30% of new breast cancer diagnoses. However, our understanding of how normal breast tissue evolves into DCIS and invasive cancers remains insufficient. Further, conclusions regarding the mechanisms of disease progression in terms of histopathology, genetics, and radiology are often conflicting and have implications for treatment planning. Moreover, the increase in DCIS diagnoses since the adoption of organized breast cancer screening programs has raised concerns about overdiagnosis and subsequent overtreatment. Active monitoring, a nonsurgical management strategy for DCIS, avoids surgery in favor of close imaging follow-up to de-escalate therapy and provides more treatment options. However, the two major challenges in active monitoring are identifying occult invasive cancer and patients at risk of invasive cancer progression. Subsequently, four prospective active monitoring trials are ongoing to determine the feasibility of active monitoring and refine the patient eligibility criteria and follow-up intervals. Radiologists play a major role in determining eligibility for active monitoring and reviewing surveillance images for disease progression. Trial results published over the next few years would support a new era of multidisciplinary DCIS care.

Magnetic resonance imaging of the breast: Could it be used as a screening test?

OBJECTIVES: To investigate whether magnetic resonance imaging (MRI) best detects early malignancy in high-risk women. METHODS: A retrospective, cross-sectional study, carried out at King Abdulaziz University Hospital, Jeddah, Saudi Arabia, included 419 female breast cancer patients aged 16-84 years (mean age of 49). Data were collected from the radiological department's database to compare the MRI, ultrasound (US), and mammography results, with or without tissue biopsy. RESULTS: In diagnosing benign versus malignant lesions, MRI showed significant agreement with tissue biopsy, with high sensitivity (70%) and specificity (87%); its positive predictive value (PPV) was 92% and negative predictive value (NPV) was 56%. While US has a PPV of 84% and NPV of 63%; with a sensitivity (79%) and specificity (71%). In patients without tissue biopsy, there was little difference between mammography and US compared with MRI results. CONCLUSION: Magnetic resonance imaging is more effective than US and mammography for early detection of BC. It showed high sensitivity in detecting breast lesions and high specificity in characterizing their nature when correlated with pathological results. Ultrasound screening followed by MRI is suggested for undetected or suspected lesions. This will increase the breast lesion detection rate, reduce unneeded tissue biopsies, and enhance the disease's survival rate.

Factors Associated With Continuous Use of a Cancer Education Metaverse Platform: Mixed Methods Study.

BACKGROUND: Early detection of cancer and provision of appropriate treatment can increase the cancer cure rate and reduce cancer-related deaths. Early detection requires improving the cancer screening quality of each medical institution and enhancing the capabilities of health professionals through tailored education in each field. However, during the COVID-19 pandemic, regional disparities in educational infrastructure emerged, and educational accessibility was restricted. The demand for remote cancer education services to address these issues has increased, and in this study, we considered medical metaverses as a potential means of meeting these needs. In 2022, we used Metaverse Educational Center, developed for the virtual training of health professionals, to train radiologic technologists remotely in mammography positioning. OBJECTIVE: This study aims to investigate the user experience of the Metaverse Educational Center subplatform and the factors associated with the intention for continuous use by focusing on cases of using the subplatform in a remote mammography positioning training project. METHODS: We conducted a multicenter, cross-sectional survey between July and December 2022. We performed a descriptive analysis to examine the Metaverse Educational Center user experience and a logistic regression analysis to clarify factors closely related to the intention to use the subplatform continuously. In addition, a supplementary open-ended question was used to obtain feedback from users to improve Metaverse Educational Center. RESULTS: Responses from 192 Korean participants (male participants: n=16, 8.3%; female participants: n=176, 91.7%) were analyzed. Most participants were satisfied with Metaverse Educational Center (178/192, 92.7%) and wanted to continue using the subplatform in the future (157/192, 81.8%). Less than half of the participants (85/192, 44.3%) had no difficulty in wearing the device. Logistic regression analysis results showed that intention for continuous use was associated with satisfaction (adjusted odds ratio 3.542, 95% CI 1.037-12.097; P=.04), immersion (adjusted odds ratio 2.803, 95% CI 1.201-6.539; P=.02), and no difficulty in wearing the device (adjusted odds ratio 2.020, 95% CI 1.004-4.062; P=.049). However, intention for continuous use was not associated with interest (adjusted odds ratio 0.736, 95% CI 0.303-1.789; P=.50) or perceived ease of use (adjusted odds ratio 1.284, 95% CI 0.614-2.685; P=.51). According to the qualitative feedback, Metaverse Educational Center was useful in cancer education, but the experience of wearing the device and the types and qualities of the content still need to be improved. CONCLUSIONS: Our results demonstrate the positive user experience of Metaverse Educational Center by focusing on cases of using the subplatform in a remote mammography positioning training project. Our results also suggest that improving users' satisfaction and immersion and ensuring the lack of difficulty in wearing the device may enhance their intention for continuous use of the subplatform.