Association of DCIS size and margin status with risk of developing breast cancer post-treatment: multinational, pooled cohort study.

OBJECTIVE: To examine the association between size and margin status of ductal carcinoma in situ (DCIS) and risk of developing ipsilateral invasive breast cancer and ipsilateral DCIS after treatment, and stage and subtype of ipsilateral invasive breast cancer. DESIGN: Multinational, pooled cohort study. SETTING: Four large international cohorts. PARTICIPANTS: Patient level data on 47 695 women with a diagnosis of pure, primary DCIS between 1999 and 2017 in the Netherlands, UK, and US who underwent surgery, either breast conserving or mastectomy, often followed by radiotherapy or endocrine treatment, or both. MAIN OUTCOME MEASURES: The main outcomes were 10 year cumulative incidence of ipsilateral invasive breast cancer and ipsilateral DCIS estimated in relation to DCIS size and margin status, and adjusted hazard ratios and 95% confidence intervals, estimated using multivariable Cox proportional hazards analyses with multiple imputed data RESULTS: The 10 year cumulative incidence of ipsilateral invasive breast cancer was 3.2%. In women who underwent breast conserving surgery with or without radiotherapy, only adjusted risks for ipsilateral DCIS were significantly increased for larger DCIS (20-49 mm) compared with DCIS <20 mm (hazard ratio 1.38, 95% confidence interval 1.11 to 1.72). Risks for both ipsilateral invasive breast cancer and ipsilateral DCIS were significantly higher with involved compared with clear margins (invasive breast cancer 1.40, 1.07 to 1.83; DCIS 1.39, 1.04 to 1.87). Use of adjuvant endocrine treatment was not significantly associated with a lower risk of ipsilateral invasive breast cancer compared to treatment with breast conserving surgery only (0.86, 0.62 to 1.21). In women who received breast conserving treatment with or without radiotherapy, higher DCIS grade was not significantly associated with ipsilateral invasive breast cancer, only with a higher risk of ipsilateral DCIS (grade 1: 1.42, 1.08 to 1.87; grade 3: 2.17, 1.66 to 2.83). Higher age at diagnosis was associated with lower risk (per year) of ipsilateral DCIS (0.98, 0.97 to 0.99) but not ipsilateral invasive breast cancer (1.00, 0.99 to 1.00). Women with large DCIS (≥50 mm) more often developed stage III and IV ipsilateral invasive breast cancer compared to women with DCIS <20 mm. No such association was found between involved margins and higher stage of ipsilateral invasive breast cancer. Associations between larger DCIS and hormone receptor negative and human epidermal growth factor receptor 2 positive ipsilateral invasive breast cancer and involved margins and hormone receptor negative ipsilateral invasive breast cancer were found. CONCLUSIONS: The association of DCIS size and margin status with ipsilateral invasive breast cancer and ipsilateral DCIS was small. When these two factors were added to other known risk factors in multivariable models, clinicopathological risk factors alone were found to be limited in discriminating between low and high risk DCIS.

Surveillance Imaging after Primary Diagnosis of Ductal Carcinoma in Situ.

Background Guidelines recommend annual surveillance imaging after diagnosis of ductal carcinoma in situ (DCIS). Guideline adherence has not been characterized in a contemporary cohort. Purpose To identify uptake and determinants of surveillance imaging in women who underwent treatment for DCIS. Materials and Methods A stratified random sample of women who underwent breast-conserving surgery for primary DCIS between 2008 and 2014 was retrospectively selected from 1330 facilities in the United States. Imaging examinations were recorded from date of diagnosis until first distant recurrence, death, loss to follow-up, or end of study (November 2018). Imaging after treatment was categorized into 10 12-month periods starting 6 months after diagnosis. Primary outcome was per-period receipt of asymptomatic surveillance imaging (mammography, MRI, or US). Secondary outcome was diagnosis of ipsilateral invasive breast cancer. Multivariable logistic regression with repeated measures and generalized estimating equations was used to model receipt of imaging. Rates of diagnosis with ipsilateral invasive breast cancer were compared between women who did and those who did not undergo imaging in the 6-18-month period after diagnosis using inverse probability-weighted Kaplan-Meier estimators. Results A total of 12 559 women (median age, 60 years; IQR, 52-69 years) were evaluated. Uptake of surveillance imaging was 75% in the first period and decreased over time (P < .001). Across the first 5 years after treatment, 52% of women participated in consistent annual surveillance. Surveillance was lower in Black (adjusted odds ratio [OR], 0.80; 95% CI: 0.74, 0.88; P < .001) and Hispanic (OR, 0.82; 95% CI: 0.72, 0.94; P = .004) women than in White women. Women who underwent surveillance in the first period had a higher 6-year rate of diagnosis of invasive cancer (1.6%; 95% CI: 1.3, 1.9) than those who did not (1.1%; 95% CI: 0.7, 1.4; difference: 0.5%; 95% CI: 0.1, 1.0; P = .03). Conclusion Half of women did not consistently adhere to imaging surveillance guidelines across the first 5 years after treatment, with racial disparities in adherence rates. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Rahbar and Dontchos in this issue.

Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysis.

BACKGROUND: We propose a decision-referral approach for integrating artificial intelligence (AI) into the breast-cancer screening pathway, whereby the algorithm makes predictions on the basis of its quantification of uncertainty. Algorithmic assessments with high certainty are done automatically, whereas assessments with lower certainty are referred to the radiologist. This two-part AI system can triage normal mammography exams and provide post-hoc cancer detection to maintain a high degree of sensitivity. This study aimed to evaluate the performance of this AI system on sensitivity and specificity when used either as a standalone system or within a decision-referral approach, compared with the original radiologist decision. METHODS: We used a retrospective dataset consisting of 1 193 197 full-field, digital mammography studies carried out between Jan 1, 2007, and Dec 31, 2020, from eight screening sites participating in the German national breast-cancer screening programme. We derived an internal-test dataset from six screening sites (1670 screen-detected cancers and 19 997 normal mammography exams), and an external-test dataset of breast cancer screening exams (2793 screen-detected cancers and 80 058 normal exams) from two additional screening sites to evaluate the performance of an AI algorithm on sensitivity and specificity when used either as a standalone system or within a decision-referral approach, compared with the original individual radiologist decision at the point-of-screen reading ahead of the consensus conference. Different configurations of the AI algorithm were evaluated. To account for the enrichment of the datasets caused by oversampling cancer cases, weights were applied to reflect the actual distribution of study types in the screening programme. Triaging performance was evaluated as the rate of exams correctly identified as normal. Sensitivity across clinically relevant subgroups, screening sites, and device manufacturers was compared between standalone AI, the radiologist, and decision referral. We present receiver operating characteristic (ROC) curves and area under the ROC (AUROC) to evaluate AI-system performance over its entire operating range. Comparison with radiologists and subgroup analysis was based on sensitivity and specificity at clinically relevant configurations. FINDINGS: The exemplary configuration of the AI system in standalone mode achieved a sensitivity of 84·2% (95% CI 82·4-85·8) and a specificity of 89·5% (89·0-89·9) on internal-test data, and a sensitivity of 84·6% (83·3-85·9) and a specificity of 91·3% (91·1-91·5) on external-test data, but was less accurate than the average unaided radiologist. By contrast, the simulated decision-referral approach significantly improved upon radiologist sensitivity by 2·6 percentage points and specificity by 1·0 percentage points, corresponding to a triaging performance at 63·0% on the external dataset; the AUROC was 0·982 (95% CI 0·978-0·986) on the subset of studies assessed by AI, surpassing radiologist performance. The decision-referral approach also yielded significant increases in sensitivity for a number of clinically relevant subgroups, including subgroups of small lesion sizes and invasive carcinomas. Sensitivity of the decision-referral approach was consistent across the eight included screening sites and three device manufacturers. INTERPRETATION: The decision-referral approach leverages the strengths of both the radiologist and AI, demonstrating improvements in sensitivity and specificity surpassing that of the individual radiologist and of the standalone AI system. This approach has the potential to improve the screening accuracy of radiologists, is adaptive to the requirements of screening, and could allow for the reduction of workload ahead of the consensus conference, without discarding the generalised knowledge of radiologists. FUNDING: Vara.

AI-based prevention of interval cancers in a national mammography screening program.

PURPOSE: To demonstrate that artificial intelligence (AI) can detect and correctly localise retrospectively visible cancers that were missed and diagnosed as interval cancers (false negative (FN) and minimal signs (MS) interval cancers), and to characterise AI performance on non-visible occult and true interval cancers. METHOD: Prior screening mammograms from N = 2,396 women diagnosed with interval breast cancer between March 2006 and May 2018 in north-western Germany were analysed with an AI system, producing a model score for all studies. All included studies previously underwent independent radiological review at a mammography reference centre to confirm interval cancer classification. Model score distributions were visualised with histograms. We computed the proportion and accompanying 95% confidence intervals (CI) of retrospectively visible and true interval cancers detected and correctly localised by AI at different operating points representing recall rates < 3%. Clinicopathological characteristics of retrospectively visible cancers detected by AI and not were compared using the Chi-squared test and binary logistic regression. RESULTS: Following radiological review, 15.6% of the interval cancer cases were categorised as FN, 19.5% MS, 11.4% occult, and 53.4% true interval cancers. At an operating point of 99.0% specificity, AI could detect and correctly localise 27.5% (95% CI: 23.3-32.3%), and 12.2% (95% CI: 9.5-15.5%) of the FN and MS cases on the prior mammogram, respectively. 228 of these retrospectively visible cases were advanced/metastatic at diagnosis; 21.1% (95% CI: 16.3-26.8%) were found by AI on the screening mammogram. Increased likelihood of detection of retrospectively visible cancers with AI was observed for lower-grade carcinomas and those with involved lymph nodes at diagnosis. Among true interval cancers, AI could detect and correctly localise in the screening mammogram where subsequent malignancies would appear in 2.8% (95% CI: 2.0-3.9%) of cases. CONCLUSIONS: AI can support radiologists by detecting a greater number of carcinomas, subsequently decreasing the interval cancer rate and the number of advanced and metastatic cancers.

Preferences of Treatment Strategies among Women with Low-Risk DCIS and Oncologists.

As ongoing trials study the safety of an active surveillance strategy for low-risk ductal carcinoma in situ (DCIS), there is a need to explain why particular choices regarding treatment strategies are made by eligible women as well as their oncologists, what factors enter the decision process, and how much each factor affects their choice. To measure preferences for treatment and surveillance strategies, women with newly-diagnosed, primary low-risk DCIS enrolled in the Dutch CONTROL DCIS Registration and LORD trial, and oncologists participating in the Dutch Health Professionals Study were invited to complete a discrete choice experiment (DCE). The relative importance of treatment strategy-related attributes (locoregional intervention, 10-year risk of ipsilateral invasive breast cancer (iIBC), and follow-up interval) were discerned using conditional logit models. A total of n = 172 patients and n = 30 oncologists completed the DCE. Patient respondents had very strong preferences for an active surveillance strategy with no surgery, irrespective of the 10-year risk of iIBC. Extensiveness of the locoregional treatment was consistently shown to be an important factor for patients and oncologists in deciding upon treatment strategies. Risk of iIBC was least important to patients and most important to oncologists. There was a stronger inclination toward a twice-yearly follow-up for both groups compared to annual follow-up.

Treating (low-risk) DCIS patients: What can we learn from real-world cancer registry evidence?

PURPOSE: Results from active surveillance trials for ductal carcinoma in situ (DCIS) will not be available for > 10 years. A model based on real-world data (RWD) can demonstrate the comparative impact of non-intervention for women with low-risk features. METHODS: Multi-state models were developed using Surveillance, Epidemiology, and End Results Program (SEER) data for three treatment strategies (no local treatment, breast conserving surgery [BCS], BCS + radiotherapy [RT]), and for women with DCIS low-risk features. Eligible cases included women aged ≥ 40 years, diagnosed with primary DCIS between 1992 and 2016. Five mutually exclusive health states were modelled: DCIS, ipsilateral invasive breast cancer (iIBC) ≤ 5 years and > 5 years post-DCIS diagnosis, contralateral IBC, death preceded by and death not preceded by IBC. Propensity score-weighted Cox models assessed effects of treatment, age, diagnosis year, grade, ER status, and race. RESULTS: Data on n = 85,982 women were used. Increased risk of iIBC ≤ 5 years post-DCIS was demonstrated for ages 40-49 (Hazard ratio (HR) 1.86, 95% Confidence Interval (CI) 1.34-2.57 compared to age 50-69), grade 3 lesions (HR 1.42, 95%CI 1.05-1.91) compared to grade 2, lesion size ≥ 2 cm (HR 1.66, 95%CI 1.23-2.25), and Black race (HR 2.52, 95%CI 1.83-3.48 compared to White). According to the multi-state model, propensity score-matched women with low-risk features who had not died or experienced any subsequent breast event by 10 years, had a predicted probability of iIBC as first event of 3.02% for no local treatment, 1.66% for BCS, and 0.42% for BCS+RT. CONCLUSION: RWD from the SEER registry showed that women with primary DCIS and low-risk features demonstrate minimal differences by treatment strategy in experiencing subsequent breast events. There may be opportunity to de-escalate treatment for certain women with low-risk features: Hispanic and non-Hispanic white women aged 50-69 at diagnosis, with ER+, grade 1 + 2, < 2 cm DCIS lesions.

Cost-effectiveness analysis of the 70-gene signature compared with clinical assessment in breast cancer based on a randomised controlled trial.

BACKGROUND: The clinical utility of the 70-gene signature (MammaPrint®) to guide chemotherapy use in T1-3N0-1M0 breast cancer was demonstrated in the Microarray in Node-Negative and 1 to 3 Positive Lymph Node Disease May Avoid Chemotherapy (MINDACT) study. One thousand four ninety seven of 3356 (46.2%) enrolled patients with high clinical risk (in accordance with the modified Adjuvant! Online clinical-pathological assessment) had a low-risk 70-gene signature. Using patient-level data from the MINDACT trial, the cost-effectiveness of using the 70-gene signature to guide adjuvant chemotherapy selection for clinical high risk, estrogen receptor positive (ER+), human epidermal growth factor 2 negative (HER2-) patients was analysed. PATIENTS AND METHODS: A hybrid decision tree-Markov model simulated treatment strategies in accordance with the 70-gene signature with clinical assessment versus clinical assessment alone, over a 10-year time horizon. Primary outcomes were quality-adjusted life years (QALYs), country-specific costs and incremental cost-effectiveness ratios (ICERs) for six countries: Belgium, France, Germany, Netherlands, UK and the US. RESULTS: Treatment strategies guided by the 70-gene signature result in more QALYs compared with clinical assessment alone. Costs of the 70-gene signature strategy were lower in five of six countries. This led to dominance of the 70-gene signature in Belgium, France, Germany, Netherlands and the US and to a cost-effective situation in the UK (ICER £22,910/QALY). Annual national cost savings were €4.2M (Belgium), €24.7M (France), €45.1M (Germany), €12.7M (Netherlands) and $244M (US). UK budget increase was £8.4M. CONCLUSION: Using the 70-gene signature to safely guide chemotherapy de-escalation in clinical high risk patients with ER+/HER2- tumours is cost-effective compared with using clinical assessment alone. Long-term follow-up and outcomes from the MINDACT trial are necessary to address uncertainties in model inputs.

Determinants of healthcare utilization and costs in COPD patients: first longitudinal results from the German COPD cohort COSYCONET.

Background: In light of overall increasing healthcare expenditures, it is mandatory to study determinants of future costs in chronic diseases. This study reports the first longitudinal results on healthcare utilization and associated costs from the German chronic obstructive pulmonary disease (COPD) cohort COSYCONET. Material and methods: Based on self-reported data of 1904 patients with COPD who attended the baseline and 18-month follow-up visits, direct costs were calculated for the 12 months preceding both examinations. Direct costs at follow-up were regressed on baseline disease severity and other co-variables to identify determinants of future costs. Change score models were developed to identify predictors of cost increases over 18 months. As possible predictors, models included GOLD grade, age, sex, education, smoking status, body mass index, comorbidity, years since COPD diagnosis, presence of symptoms, and exacerbation history. Results: Inflation-adjusted mean annual direct costs increased by 5% (n.s., €6,739 to €7,091) between the two visits. Annual future costs were significantly higher in baseline GOLD grades 2, 3, and 4 (factors 1.24, 95%-confidence interval [1.07-1.43], 1.27 [1.09-1.48], 1.57 [1.27-1.93]). A history of moderate or severe exacerbations within 12 months, a comorbidity count >3, and the presence of dyspnea and underweight were significant predictors of cost increase (estimates ranging between + €887 and + €3,679, all p<0.05). Conclusions: Higher GOLD grade, comorbidity burden, dyspnea and moderate or severe exacerbations were determinants of elevated future costs and cost increases in COPD. In addition we identified underweight as independent risk factor for an increase in direct healthcare costs over time.

Does Pharmaceutical Pricing Transparency Matter? Examining Brazil's Public Procurement System.

BACKGROUND: We review procurement and pricing transparency practices for pharmaceutical products. We specifically focus on Brazil and examine its approach to increasing pricing transparency, with the aim of determining the level of effectiveness in lower prices using a tool (Banco de Preços em Saúde, BPS) that only reveals purchase prices as compared to other tools (in other countries) that establish a greater degree of price transparency. METHODS: A general report of Preços em Saúde (BPS) and Sistema Integrado de Administração de Serviços Gerais (SIASG) pricing data was created for 25 drugs that met specific criteria. To explore the linear time trend of each of the drugs, separate regression models were fitted for each drug, resulting in a total of 19 models. Each model controlled for the state variable and the interaction between state and time, in order to accommodate expected heterogeneity in the data. Additionally, the models controlled for procurement quantities and the effect they have on the unit price. Secondary analysis using mixed effects models was also carried out to account for the impact that institutions and suppliers may have upon the unit price. Adjusting for these predictor variables (procurement quantities, supplier, purchasing institution) was important to determine the sole effect that time has had on unit prices. A total of 2 x 19 = 38 models were estimated to explore the overall effect of time on changes in unit price. All statistical analyses were performed using the R statistical software, while the linear mixed effects models were fitted using the lme4 R package. RESULTS: The findings from our analysis suggest that there is no pattern of consistent price decreases within the two Brazilian states during the five-year period for which the prices were analyzed. CONCLUSIONS: While the BPS does allow for an increase in transparency and information on drug purchase prices in Brazil, it has not shown to lead to consistent reductions in drug purchase prices for some of the most widely used medicines. This is indicative of a limited model for addressing the challenges in pharmaceutical procurement and puts into question the value of tools used globally to improve transparency in pharmaceutical pricing.