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.

Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials.

BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal carcinoma In Situ is safe (E. S. Hwang et al., BMJ Open, 9: e026797, 2019, A. Francis et al., Eur J Cancer. 51: 2296-2303, 2015, Chizuko Kanbayashi et al. The international collaboration of active surveillance trials for low-risk DCIS (LORIS, LORD, COMET, LORETTA),  L. E. Elshof et al., Eur J Cancer, 51, 1497-510, 2015). Low-risk is defined as grade I or II DCIS. Because DCIS grade is a major eligibility criteria in these trials, it would be very helpful to assess DCIS grade on mammography, informed by grade assessed on DCIS histopathology in pre-surgery biopsies, since surgery will not be performed on a significant number of patients participating in these trials. OBJECTIVE: To assess the performance and clinical utility of a convolutional neural network (CNN) in discriminating high-risk (grade III) DCIS and/or Invasive Breast Cancer (IBC) from low-risk (grade I/II) DCIS based on mammographic features. We explored whether the CNN could be used as a decision support tool, from excluding high-risk patients for active surveillance. METHODS: In this single centre retrospective study, 464 patients diagnosed with DCIS based on pre-surgery biopsy between 2000 and 2014 were included. The collection of mammography images was partitioned on a patient-level into two subsets, one for training containing 80% of cases (371 cases, 681 images) and 20% (93 cases, 173 images) for testing. A deep learning model based on the U-Net CNN was trained and validated on 681 two-dimensional mammograms. Classification performance was assessed with the Area Under the Curve (AUC) receiver operating characteristic and predictive values on the test set for predicting high risk DCIS-and high-risk DCIS and/ or IBC from low-risk DCIS. RESULTS: When classifying DCIS as high-risk, the deep learning network achieved a Positive Predictive Value (PPV) of 0.40, Negative Predictive Value (NPV) of 0.91 and an AUC of 0.72 on the test dataset. For distinguishing high-risk and/or upstaged DCIS (occult invasive breast cancer) from low-risk DCIS a PPV of 0.80, a NPV of 0.84 and an AUC of 0.76 were achieved. CONCLUSION: For both scenarios (DCIS grade I/II vs. III, DCIS grade I/II vs. III and/or IBC) AUCs were high, 0.72 and 0.76, respectively, concluding that our convolutional neural network can discriminate low-grade from high-grade DCIS.

Women's Experiences with Digital Breast Tomosynthesis and Targeted Breast Ultrasound for Focal Breast Complaints: A Survey Study.

Background: Owing to its high sensitivity, as concluded in the Breast UltraSound Trial (BUST), targeted ultrasound (US) now seems a promising accurate stand-alone modality for diagnostic evaluation of breast complaints. This approach implies omission of bilateral digital breast tomosynthesis (DBT) in women with clearly benign US findings. Within BUST, radiologists started with US followed by DBT. This side-study investigates women's experiences with DBT, their main motivation to undergo diagnostic imaging, and their view on US as a stand-alone modality. Methods: A subset of BUST participants completed a questionnaire on their DBT experiences, reason for undergoing diagnostic assessment, and view on US-only diagnostics. Responses were analyzed with descriptive statistics and logistic regression analyses. Results: In total, 778 of 838 women (response rate 92.8%) were included (M = 47, SD = 11.16). Of them, 16.8% reported no burden of DBT, 33.5% slight burden, 31.0% moderate, and 12.7% severe burden. Furthermore, 13% reported no pain, 35.3% slight pain, 33.2% moderate, and 11.3% severe pain. Moreover, 88.3% indicated that the most important reason for breast assessment was explanation of their complaint and to rule out breast cancer, whereas 3.2% wanted to "check" both breasts. And 82.4% reported satisfaction with US only in case of a nonmalignancy. Conclusions: Our study shows that most women in the diagnostic setting experience at least slight-to-moderate DBT-related burden and pain, and that explanation for their symptoms is their main interest. Also, the majority report satisfaction with US only in case of nonmalignant findings. However, exploration of women's perspectives outside this study is needed as our participants all underwent both examinations.

US and Digital Breast Tomosynthesis in Women with Focal Breast Complaints: Results of the Breast US Trial (BUST).

Background Digital breast tomosynthesis (DBT) followed by targeted US is commonly performed to evaluate women with localized breast complaints. However, the added value of DBT in addition to targeted US is unknown. Omitting DBT may be cost-effective and improve patient comfort but may miss potential breast cancer. Purpose To assess whether an imaging protocol consisting of targeted US alone may be feasible for the diagnostic work-up of women with localized symptoms and to assess the supplemental value of DBT in this reversed setting. Materials and Methods This prospective study enrolled consecutive women aged 30 years or older with focal breast complaints in three hospitals in the Netherlands between September 2017 and June 2019. In all participants, first, targeted US was evaluated, and if needed, biopsy was performed, followed by DBT. The primary outcome was the frequency of breast cancer detected with DBT when US was negative. Secondary outcomes were frequency of cancer detected with DBT elsewhere in the breast and combined overall sensitivity of US plus DBT. The reference standard was 1 year follow-up or histopathologic examination. Results There were 1961 women (mean age ± SD, 47 years ± 12) enrolled. Based on initial US alone, 1587 participants (81%) had normal or benign findings and 1759 (90%) had a definitive accurate diagnosis. In total, 204 breast cancers were detected during initial work-up. The frequency of malignancy was 10% (192 of 1961 participants) with US (US sensitivity, 98.5% [95% CI: 96, 100]; US specificity, 90.8% [95% CI: 89, 92]). DBT depicted three unobserved malignant lesions at the complaint site and 0.41% (eight of 1961 participants) of incidental malignant findings in participants without symptomatic cancer. Conclusion Compared with combined US and DBT, US was accurate as a stand-alone breast imaging modality in the assessment of focal breast complaints. The rate of cancer detection of cancers elsewhere in the breast with DBT is comparable to cancer detection rate of screening mammography. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Newell in this issue.

The Effect of Targeted Ultrasound as Primary Imaging Modality on Quality of Life in Women with Focal Breast Complaints: A Comparative Cohort Study.

Background: The high diagnostic performance of modern breast ultrasound (US) opens the possibility to shift toward targeted US as initial imaging test in women with breast complaints. This comparative cohort study investigates the effects of starting with US followed by digital breast tomosynthesis (DBT), as practiced in the breast ultrasound study (BUST), on women's health-related quality of life (QoL). Methods: Fifty BUST participants and 50 "controls" who underwent DBT and US in regular order filled out the EQ-5D-3L three times during their visit: BUST participants before US (T1), after US (T2), and after DBT (T3) and non-BUST participants before DBT (T1), after DBT (T2), and after US (T3). Changes in QoL from baseline to T2 and T3 were assessed using generalized least squares, also taking into account the effects of biopsy, age, and complaint type. Results: Participants' mean age was 50.6 years (BUST: SD = 12.1, controls: SD = 11.5). At T2 the overall QoL was higher [t(102.9) = 2.4, p = 0.017] and anxiety levels were lower [t(98.7) = -2.4, p = 0.020] in BUST participants compared with controls. However, from T2 to T3 these effects equalize, resulting in similar performances in QoL and anxiety at T3, respectively [t(97.6) = -2.3, p = 0.023] and [t(97.2) = 3.1, p = 0.002]. Compared with BUST participants, controls show a clear decrease in pain after US [t(106.5) = -2.8, p = 0.006]. Women undergoing biopsy had lower QoL [t(167.1) = -2.4, p = 0.017] and pain [t(154.1) = -2.1, p = 0.038], and more anxiety [t(187.4) = 4.3, p = 0.000]. Conclusions: The results suggest that changing the radiological order by starting with US has a short-term positive effect on overall QoL, anxiety, and DBT pain experience in symptomatic women. Owing to its negative impact, biopsies should be performed cautiously. In conclusion, the moment of reassurance for women advances by reversing the radiological order according to the BUST, showing the high importance of human interaction in diagnostic care in addition to the clinical performance of imaging modalities.

Prediction Models and Decision Aids for Women with Ductal Carcinoma In Situ: A Systematic Literature Review.

Even though Ductal Carcinoma in Situ (DCIS) can potentially be an invasive breast cancer (IBC) precursor, most DCIS lesions never will progress to IBC if left untreated. Because we cannot predict yet which DCIS lesions will and which will not progress, almost all women with DCIS are treated by breast-conserving surgery +/- radiotherapy, or even mastectomy. As a consequence, many women with non-progressive DCIS carry the burden of intensive treatment without any benefit. Multiple decision support tools have been developed to optimize DCIS management, aiming to find the balance between over- and undertreatment. In this systematic review, we evaluated the quality and added value of such tools. A systematic literature search was performed in Medline(ovid), Embase(ovid), Scopus and TRIP. Following the PRISMA guidelines, publications were selected. The CHARMS (prediction models) or IPDAS (decision aids) checklist were used to evaluate the tools' methodological quality. Thirty-three publications describing four decision aids and six prediction models were included. The decision aids met at least 50% of the IPDAS criteria. However, most lacked tools to facilitate discussion of the information with healthcare providers. Five prediction models quantify the risk of an ipsilateral breast event after a primary DCIS, one estimates the risk of contralateral breast cancer, and none included active surveillance. Good quality and external validations were lacking for all prediction models. There remains an unmet clinical need for well-validated, good-quality DCIS risk prediction models and decision aids in which active surveillance is included as a management option for low-risk DCIS.

Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features.

Background Improving diagnosis of ductal carcinoma in situ (DCIS) before surgery is important in choosing optimal patient management strategies. However, patients may harbor occult invasive disease not detected until definitive surgery. Purpose To assess the performance and clinical utility of mammographic radiomic features in the prediction of occult invasive cancer among women diagnosed with DCIS on the basis of core biopsy findings. Materials and Methods In this Health Insurance Portability and Accountability Act-compliant retrospective study, digital magnification mammographic images were collected from women who underwent breast core-needle biopsy for calcifications that was performed at a single institution between September 2008 and April 2017 and yielded a diagnosis of DCIS. The database query was directed at asymptomatic women with calcifications without a mass, architectural distortion, asymmetric density, or palpable disease. Logistic regression with regularization was used. Differences across training and internal test set by upstaging rate, age, lesion size, and estrogen and progesterone receptor status were assessed by using the Kruskal-Wallis or χ2 test. Results The study consisted of 700 women with DCIS (age range, 40-89 years; mean age, 59 years ± 10 [standard deviation]), including 114 with lesions (16.3%) upstaged to invasive cancer at subsequent surgery. The sample was split randomly into 400 women for the training set and 300 for the testing set (mean ages: training set, 59 years ± 10; test set, 59 years ± 10; P = .85). A total of 109 radiomic and four clinical features were extracted. The best model on the test set by using all radiomic and clinical features helped predict upstaging with an area under the receiver operating characteristic curve of 0.71 (95% CI: 0.62, 0.79). For a fixed high sensitivity (90%), the model yielded a specificity of 22%, a negative predictive value of 92%, and an odds ratio of 2.4 (95% CI: 1.8, 3.2). High specificity (90%) corresponded to a sensitivity of 37%, positive predictive value of 41%, and odds ratio of 5.0 (95% CI: 2.8, 9.0). Conclusion Machine learning models that use radiomic features applied to mammographic calcifications may help predict upstaging of ductal carcinoma in situ, which can refine clinical decision making and treatment planning. © RSNA, 2022.