Mammographic features differ with body composition in women with breast cancer.

OBJECTIVES: There are several breast cancer (BC) risk factors-many related to body composition, hormonal status, and fertility patterns. However, it is not known if risk factors in healthy women are associated with specific mammographic features at the time of BC diagnosis. Our aim was to assess the potential association between pre-diagnostic body composition and mammographic features in the diagnostic BC image. MATERIALS AND METHODS: The prospective Malmö Diet and Cancer Study includes women with invasive BC from 1991 to 2014 (n = 1116). BC risk factors at baseline were registered (anthropometric measures, menopausal status, and parity) along with mammography data from BC diagnosis (breast density, mammographic tumor appearance, and mode of detection). We investigated associations between anthropometric measures and mammographic features via logistic regression analyses, yielding odds ratios (OR) with 95% confidence intervals (CI). RESULTS: There was an association between high body mass index (BMI) (≥ 30) at baseline and spiculated tumor appearance (OR 1.370 (95% CI: 0.941-2.010)), primarily in women with clinically detected cancers (OR 2.240 (95% CI: 1.280-3.940)), and in postmenopausal women (OR 1.580 (95% CI: 1.030-2.440)). Furthermore, an inverse association between high BMI (≥ 30) and high breast density (OR 0.270 (95% CI: 0.166-0.438)) was found. CONCLUSION: This study demonstrated an association between obesity and a spiculated mass on mammography-especially in women with clinically detected cancers and in postmenopausal women. These findings offer insights on the relationship between risk factors in healthy women and related mammographic features in subsequent BC. CLINICAL RELEVANCE STATEMENT: With increasing numbers of both BC incidence and women with obesity, it is important to highlight mammographic findings in women with an unhealthy weight. KEY POINTS: Women with obesity and BC may present with certain mammographic features. Spiculated masses were more common in women with obesity, especially postmenopausal women, and those with clinically detected BCs. Insights on the relationship between obesity and related mammographic features will aid mammographic interpretation.

Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study.

BACKGROUND: Retrospective studies have shown promising results using artificial intelligence (AI) to improve mammography screening accuracy and reduce screen-reading workload; however, to our knowledge, a randomised trial has not yet been conducted. We aimed to assess the clinical safety of an AI-supported screen-reading protocol compared with standard screen reading by radiologists following mammography. METHODS: In this randomised, controlled, population-based trial, women aged 40-80 years eligible for mammography screening (including general screening with 1·5-2-year intervals and annual screening for those with moderate hereditary risk of breast cancer or a history of breast cancer) at four screening sites in Sweden were informed about the study as part of the screening invitation. Those who did not opt out were randomly allocated (1:1) to AI-supported screening (intervention group) or standard double reading without AI (control group). Screening examinations were automatically randomised by the Picture Archive and Communications System with a pseudo-random number generator after image acquisition. The participants and the radiographers acquiring the screening examinations, but not the radiologists reading the screening examinations, were masked to study group allocation. The AI system (Transpara version 1.7.0) provided an examination-based malignancy risk score on a 10-level scale that was used to triage screening examinations to single reading (score 1-9) or double reading (score 10), with AI risk scores (for all examinations) and computer-aided detection marks (for examinations with risk score 8-10) available to the radiologists doing the screen reading. Here we report the prespecified clinical safety analysis, to be done after 80 000 women were enrolled, to assess the secondary outcome measures of early screening performance (cancer detection rate, recall rate, false positive rate, positive predictive value [PPV] of recall, and type of cancer detected [invasive or in situ]) and screen-reading workload. Analyses were done in the modified intention-to-treat population (ie, all women randomly assigned to a group with one complete screening examination, excluding women recalled due to enlarged lymph nodes diagnosed with lymphoma). The lowest acceptable limit for safety in the intervention group was a cancer detection rate of more than 3 per 1000 participants screened. The trial is registered with ClinicalTrials.gov, NCT04838756, and is closed to accrual; follow-up is ongoing to assess the primary endpoint of the trial, interval cancer rate. FINDINGS: Between April 12, 2021, and July 28, 2022, 80 033 women were randomly assigned to AI-supported screening (n=40 003) or double reading without AI (n=40 030). 13 women were excluded from the analysis. The median age was 54·0 years (IQR 46·7-63·9). Race and ethnicity data were not collected. AI-supported screening among 39 996 participants resulted in 244 screen-detected cancers, 861 recalls, and a total of 46 345 screen readings. Standard screening among 40 024 participants resulted in 203 screen-detected cancers, 817 recalls, and a total of 83 231 screen readings. Cancer detection rates were 6·1 (95% CI 5·4-6·9) per 1000 screened participants in the intervention group, above the lowest acceptable limit for safety, and 5·1 (4·4-5·8) per 1000 in the control group-a ratio of 1·2 (95% CI 1·0-1·5; p=0·052). Recall rates were 2·2% (95% CI 2·0-2·3) in the intervention group and 2·0% (1·9-2·2) in the control group. The false positive rate was 1·5% (95% CI 1·4-1·7) in both groups. The PPV of recall was 28·3% (95% CI 25·3-31·5) in the intervention group and 24·8% (21·9-28·0) in the control group. In the intervention group, 184 (75%) of 244 cancers detected were invasive and 60 (25%) were in situ; in the control group, 165 (81%) of 203 cancers were invasive and 38 (19%) were in situ. The screen-reading workload was reduced by 44·3% using AI. INTERPRETATION: AI-supported mammography screening resulted in a similar cancer detection rate compared with standard double reading, with a substantially lower screen-reading workload, indicating that the use of AI in mammography screening is safe. The trial was thus not halted and the primary endpoint of interval cancer rate will be assessed in 100 000 enrolled participants after 2-years of follow up. FUNDING: Swedish Cancer Society, Confederation of Regional Cancer Centres, and the Swedish governmental funding for clinical research (ALF).

Metabolic parameters of [18F]FDG PET-CT before and after radiotherapy may predict survival and recurrence in cervical cancer.

BACKGROUND: Cervical cancer is the fourth most common female malignancy. [18F]-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET-CT) is routinely performed in patients with locally advanced cervical cancer for staging and treatment response evaluation. With this retrospective, observational cohort study, we wanted to investigate the prognostic value of the maximum standardised uptake value (SUVmax) and the volumetric parameters of metabolic tumour volume (MTV) and total lesion glycolysis (TLG) before and after treatment in women with cervical cancer, with overall survival (OS) and recurrence as outcome measures. METHODS: Women with cervical cancer referred for curative radiotherapy and who underwent two PET-CT scans (before treatment and approximately 7 months post-treatment) were included. SUVmax, MTV and TLG were measured at baseline and post-treatment on the primary tumour, pelvic and distant lymph node metastases, distant organ metastases, and on total tumour burden. The PET parameters were associated with OS by Cox regression and recurrence by multivariable logistic regression. Kaplan-Meier curves and C-index were used to visualise the prognostic potential of the different measures. RESULTS: A total of 133 patients were included. At the primary tumour level and on total tumour burden, age- and clinical-stage adjusted analyses showed a significant association between PET parameters and OS and recurrence when measured post-treatment. At baseline (pre-treatment), MTV and TLG were associated with OS and recurrence, whereas SUVmax was not. C-index from adjusted Cox models on total tumour burden showed higher values for the post-treatment PET compared to baseline. Kaplan-Meier curves demonstrated a greater prognostic potential for MTV and TLG compared to SUVmax, both at baseline and post-treatment. CONCLUSIONS: The FDG PET-CT-derived parameters SUVmax, MTV, and TLG measured post-treatment can predict OS and recurrence in cervical cancer. Parameters measured before treatment had overall lower prognostic potential, and only MTV and TLG showed significant association to OS and recurrence.

Radiologically enlarged cardiophrenic lymph nodes and CA-125 in relation to diaphragmatic carcinomatosis, surgical outcome, and overall survival in advanced ovarian cancer.

BACKGROUND: We primarily aimed to determine whether the presence of enlarged cardiophrenic lymph nodes (CPLNs), visualized by computed tomography (CT), and CA-125 can be used to assess diaphragmatic carcinomatosis and residual disease (RD) in advanced ovarian cancer (AOC) patients treated with upfront surgery. The secondary aim was to determine the prognostic role of CT-CPLNs in overall survival (OS). MATERIAL AND METHODS: A single-center, retrospective, population-based study was conducted of patients who underwent surgery for AOC from January 1, 2014-December 31, 2018. Suspicious CT-CPLNs were defined as having a short axis ≥5 mm. The median survival and rate of survival were calculated with the Kaplan-Meier method using multivariate Cox regression analyses, including comparisons of complete cytoreductive surgery (CCS; defined as the complete removal of all intra-abdominal tumor) versus noncomplete cytoreductive surgery (non-CCS) and analyses related to CT-CPLN status and preoperative CA-125 values. RESULTS: We included 208 patients. CT-CPLNs correlated with both diaphragmatic carcinomatosis (OR 3.59, 95% CI 1.81-7.16, p < 0.01) and RD (OR 2.54, 95% CI 1.38-4.6, p = 0.003). When CCS was achieved, no differences in survival between patients with suspicious or nonsuspicious CT-CPLNs were found. The relationships between CA-125 ≥ 500 U/ml and diaphragmatic carcinomatosis (OR 3.51, 95% CI 1.86-6.64, p < 0.01) and RD (OR 2.41, 95% CI 1.33-4.38, p = 0.004) were positive. All data were adjusted for age and ECOG performance status. Survival analyses were also adjusted for RD. CONCLUSION: Enlarged CPLNs on CT scans and CA-125 levels correlate with diaphragmatic carcinomatosis and RD at the end of the surgery. The strongest prognostic factor for OS remains CCS, regardless of the CT-CPLN status.

Prospective evaluation of 92 serum protein biomarkers for early detection of ovarian cancer.

BACKGROUND: CA125 is the best available yet insufficiently sensitive biomarker for early detection of ovarian cancer. There is a need to identify novel biomarkers, which individually or in combination with CA125 can achieve adequate sensitivity and specificity for the detection of earlier-stage ovarian cancer. METHODS: In the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we measured serum levels of 92 preselected proteins for 91 women who had blood sampled ≤18 months prior to ovarian cancer diagnosis, and 182 matched controls. We evaluated the discriminatory performance of the proteins as potential early diagnostic biomarkers of ovarian cancer. RESULTS: Nine of the 92 markers; CA125, HE4, FOLR1, KLK11, WISP1, MDK, CXCL13, MSLN and ADAM8 showed an area under the ROC curve (AUC) of ≥0.70 for discriminating between women diagnosed with ovarian cancer and women who remained cancer-free. All, except ADAM8, had shown at least equal discrimination in previous case-control comparisons. The discrimination of the biomarkers, however, was low for the lag-time of >9-18 months and paired combinations of CA125 with any of the 8 markers did not improve discrimination compared to CA125 alone. CONCLUSION: Using pre-diagnostic serum samples, this study identified markers with good discrimination for the lag-time of 0-9 months. However, the discrimination was low in blood samples collected more than 9 months prior to diagnosis, and none of the markers showed major improvement in discrimination when added to CA125.

The role of computed tomography in the assessment of tumour extent and the risk of residual disease after upfront surgery in advanced ovarian cancer (AOC).

PURPOSE: Epithelial ovarian cancer is usually diagnosed in the advanced stages. To choose the best therapeutic approach, an accurate preoperative assessment of the tumour extent is crucial. This study aimed to determine whether the peritoneal cancer index (PCI), the amount of ascites, and the presence of cardiophrenic nodes (CPLNs) visualized by computed tomography (CT) can assess the tumour extent (S-PCI) and residual disease (RD) for advanced ovarian cancer (AOC) patients treated with upfront surgery. METHODS: In total, 118 AOC cases were included between January 2016 and December 2018 at Skåne University Hospital, Lund, Sweden. Linear regression and interclass correlation (ICC) analyses were used to determine the relationship between CT-PCI and S-PCI. The patients were stratified in complete cytoreductive surgery (CCS) with no RD or to non-CCS with RD of any size. The amount of ascites on CT (CT-ascites), CA-125 and the presence of radiological enlarged CPLNs (CT-CPLN) were analysed to evaluate their impact on estimating RD. RESULTS: CT-PCI correlated well with S-PCI (0.397; 95% CI 0.252-0.541; p < 0.001). The risk of RD was also related to CT-PCI (OR 1.069 (1.009-1.131), p < 0.023) with a cut-off of 21 for CT-PCI (0.715, p = 0.000). The sensitivity, specificity, positive predictive value and negative predictive value were 58.5, 70.3, 52.2 and 75.4%, respectively. CT-ascites above 1000 ml predicted RD (OR 3.510 (1.298-9.491) p < 0.013). CONCLUSION: CT is a reliable tool to assess the extent of the disease in advanced ovarian cancer. Higher CT-PCI scores and large volumes of ascites estimated on CT predicted RD of any size.

Proteogenomic Workflow Reveals Molecular Phenotypes Related to Breast Cancer Mammographic Appearance.

Proteogenomic approaches have enabled the generat̲ion of novel information levels when compared to single omics studies although burdened by extensive experimental efforts. Here, we improved a data-independent acquisition mass spectrometry proteogenomic workflow to reveal distinct molecular features related to mammographic appearances in breast cancer. Our results reveal splicing processes detectable at the protein level and highlight quantitation and pathway complementarity between RNA and protein data. Furthermore, we confirm previously detected enrichments of molecular pathways associated with estrogen receptor-dependent activity and provide novel evidence of epithelial-to-mesenchymal activity in mammography-detected spiculated tumors. Several transcript-protein pairs displayed radically different abundances depending on the overall clinical properties of the tumor. These results demonstrate that there are differentially regulated protein networks in clinically relevant tumor subgroups, which in turn alter both cancer biology and the abundance of biomarker candidates and drug targets.

Metabolic signatures of greater body size and their associations with risk of colorectal and endometrial cancers in the European Prospective Investigation into Cancer and Nutrition.

BACKGROUND: The mechanisms underlying the obesity-cancer relationship are incompletely understood. This study aimed to characterise metabolic signatures of greater body size and to investigate their association with two obesity-related malignancies, endometrial and colorectal cancers, and with weight loss within the context of an intervention study. METHODS: Targeted mass spectrometry metabolomics data from 4326 participants enrolled in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort and 17 individuals from a single-arm pilot weight loss intervention (Intercept) were used in this analysis. Metabolic signatures of body size were first determined in discovery (N = 3029) and replication (N = 1297) sets among EPIC participants by testing the associations between 129 metabolites and body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR) using linear regression models followed by partial least squares analyses. Conditional logistic regression models assessed the associations between the metabolic signatures with endometrial (N = 635 cases and 648 controls) and colorectal (N = 423 cases and 423 controls) cancer risk using nested case-control studies in EPIC. Pearson correlation between changes in the metabolic signatures and weight loss was tested among Intercept participants. RESULTS: After adjustment for multiple comparisons, greater BMI, WC, and WHR were associated with higher levels of valine, isoleucine, glutamate, PC aa C38:3, and PC aa C38:4 and with lower levels of asparagine, glutamine, glycine, serine, lysoPC C17:0, lysoPC C18:1, lysoPC C18:2, PC aa C42:0, PC ae C34:3, PC ae C40:5, and PC ae C42:5. The metabolic signature of BMI (OR1-sd 1.50, 95% CI 1.30-1.74), WC (OR1-sd 1.46, 95% CI 1.27-1.69), and WHR (OR1-sd 1.54, 95% CI 1.33-1.79) were each associated with endometrial cancer risk. Risk of colorectal cancer was positively associated with the metabolic signature of WHR (OR1-sd: 1.26, 95% CI 1.07-1.49). In the Intercept study, a positive correlation was observed between weight loss and changes in the metabolic signatures of BMI (r = 0.5, 95% CI 0.06-0.94, p = 0.03), WC (r = 0.5, 95% CI 0.05-0.94, p = 0.03), and WHR (r = 0.6, 95% CI 0.32-0.87, p = 0.01). CONCLUSIONS: Obesity is associated with a distinct metabolic signature comprising changes in levels of specific amino acids and lipids which is positively associated with both colorectal and endometrial cancer and is potentially reversible following weight loss.

Mammographic density as an image-based biomarker of therapy response in neoadjuvant-treated breast cancer patients.

PURPOSE: Personalized cancer treatment requires predictive biomarkers, including image-based biomarkers. Breast cancer (BC) patients receiving neoadjuvant chemotherapy (NACT) are in a clinically vulnerable situation with the tumor present. This study investigated whether mammographic density (MD), assessed pre-NACT, is predictive of pathological complete response (pCR). METHODS: A total of 495 BC patients receiving NACT in Sweden 2005-2019 were included, merged from two different cohorts. Cohort 1 was retrospectively collected (n = 295) and cohort 2 was prospectively collected (n = 200). Mammograms were scored for MD pre-NACT according to the Breast Imaging-Reporting and Data System (BI-RADS), 5th Edition. The association between MD and accomplishing pCR post-NACT was analyzed using logistic regression models-for the whole cohort, stratified by menopausal status, and in different St. Gallen surrogate subtypes. RESULTS: In comparison to patients with low MD (BI-RADS a), the multivariable-adjusted odds ratio (OR) of accomplishing pCR following NACT was on a descending scale: 0.62 (95% confidence interval (CI) 0.24-1.57), 0.38 (95% CI 0.14-1.02), and 0.32 (95% CI 0.09-1.08) for BI-RADS b, c, and d, respectively. For premenopausal patients selectively, the corresponding point estimates were lower, although wider CIs: 0.31 (95% CI 0.06-1.62), 0.24 (95% CI 0.04-1.27), and 0.13 (95% CI 0.02-0.88). Subgroup analyses based on BC subtypes resulted in imprecise estimates, i.e., wide CIs. CONCLUSIONS: It seemed as though patients with higher MD at baseline were less likely to reach pCR after NACT-a finding more pronounced in premenopausal women. Larger multicenter studies are needed to enable analyses and interpretation for different BC subtypes.

Patterns of pathologic lymph nodes in anal cancer: a PET-CT-based analysis with implications for radiotherapy treatment volumes.

BACKGROUND: This study investigates the patterns of PET-positive lymph nodes (LNs) in anal cancer. The aim was to provide information that could inform future anal cancer radiotherapy contouring guidelines. METHODS: The baseline [18F]-FDG PET-CTs of 190 consecutive anal cancer patients were retrospectively assessed. LNs with a Deauville score (DS) of ≥3 were defined as PET-positive. Each PET-positive LN was allocated to a LN region and a LN sub-region; they were then mapped on a standard anatomy reference CT. The association between primary tumor localization and PET-positive LNs in different regions were analyzed. RESULTS: PET-positive LNs (n = 412) were identified in 103 of 190 patients (54%). Compared to anal canal tumors with extension into the rectum, anal canal tumors with perianal extension more often had inguinal (P < 0.001) and less often perirectal (P < 0.001) and internal iliac (P < 0.001) PET-positive LNs. Forty-two patients had PET-positive LNs confined to a solitary region, corresponding to first echelon nodes. The most common solitary LN region was inguinal (25 of 42; 60%) followed by perirectal (26%), internal iliac (10%), and external iliac (2%). No PET-positive LNs were identified in the ischiorectal fossa or in the inguinal area located posterolateral to deep vessels. Skip metastases above the bottom of the sacroiliac joint were quite rare. Most external iliac PET-positive LNs were located posterior to the external iliac vein; only one was located in the lateral external iliac sub-region. CONCLUSIONS: The results support some specific modifications to the elective clinical target volume (CTV) in anal cancer. These changes would lead to reduced volumes of normal tissue being irradiated, which could contribute to a reduction in radiation side-effects.

Identification of Women at High Risk of Breast Cancer Who Need Supplemental Screening.

Background Mammography screening reduces breast cancer mortality, but a proportion of breast cancers are missed and are detected at later stages or develop during between-screening intervals. Purpose To develop a risk model based on negative mammograms that identifies women likely to be diagnosed with breast cancer before or at the next screening examination. Materials and Methods This study was based on the prospective screening cohort Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA), 2011-2017. An image-based risk model was developed by using the Stratus method and computer-aided detection mammographic features (density, masses, microcalcifications), differences in the left and right breasts, and age. The lifestyle extended model included menopausal status, family history of breast cancer, body mass index, hormone replacement therapy, and use of tobacco and alcohol. The genetic extended model included a polygenic risk score with 313 single nucleotide polymorphisms. Age-adjusted relative risks and tumor subtype specific risks were estimated by using logistic regression, and absolute risks were calculated. Results Of 70 877 participants in the KARMA cohort, 974 incident cancers were sampled from 9376 healthy women (mean age, 54 years ± 10 [standard deviation]). The area under the receiver operating characteristic curve (AUC) for the image-based model was 0.73 (95% confidence interval [CI]: 0.71, 0.74). The AUCs for the lifestyle and genetic extended models were 0.74 (95% CI: 0.72, 0.75) and 0.77 (95% CI: 0.75, 0.79), respectively. There was a relative eightfold difference in risk between women at high risk and those at general risk. High-risk women were more likely to be diagnosed with stage II cancers and with tumors 20 mm or larger and were less likely to have stage I and estrogen receptor-positive tumors. The image-based model was validated in three external cohorts. Conclusion By combining three mammographic features, differences in the left and right breasts, and optionally lifestyle factors and family history and a polygenic risk score, the model identified women at high likelihood of being diagnosed with breast cancer within 2 years of a negative screening examination and in possible need of supplemental screening. © RSNA, 2020 Online supplemental material is available for this article.

The association of single nucleotide polymorphisms (SNPs) with breast density and breast cancer survival: the Malmö Diet and Cancer Study.

BACKGROUND: Genetic factors are important in determining breast density, and heritable factors account for 60% of the variation. Certain single nucleotide polymorphisms (SNPs) are associated with density and risk of breast cancer but the association with prognosis is not clear. PURPOSE: To investigate associations between selected SNPs and breast cancer survival in the Malmö Diet and Cancer Study (MDCS). MATERIAL AND METHODS: A total of 724 unrelated women with breast cancer and registered radiological and pathological data were identified in MDCS 1991-2007, with genotyping available for 672 women. Associations among 15 SNPs, density, and breast cancer-specific survival were analyzed using logistic/Cox regression, adjusted for factors affecting density and survival. Variants significantly associated with either density or survival were validated in a large independent breast cancer cohort (LIBRO-1). RESULTS: Minor homozygotes of SNPs rs9383589, CCDC170 and rs6557161, ESR1 were associated with high breast density (adjusted odds ratio [AOR] 8.97, 95% confidence interval [CI] 1.35-59.57; AOR 2.08, 95% CI 1.19-3.65, respectively) and poorer breast cancer survival (adjusted hazard ratio [HRadj] 6.46, 95% CI 1.95-21.39; HRadj 2.30, 95% CI 1.33-3.96, respectively) compared to major homozygotes. For SNP rs3757318, ESR1, minor homozygotes (HRadj 7.46, 95% CI 2.28-24.45) were associated with poorer survival. We confirmed that rs6557161, ESR1 was significantly associated with both density and survival in the LIBRO-1 study. CONCLUSION: These findings support a shared genetic basis for density and breast cancer survival. The SNP significantly associated with both density and survival in both cohorts may be of interest in future research investigating polygenic risk scores for breast cancer risk and screening stratification purposes.

Tumor Characteristics and Molecular Subtypes in Breast Cancer Screening with Digital Breast Tomosynthesis: The Malmö Breast Tomosynthesis Screening Trial.

Background Screening accuracy can be improved with digital breast tomosynthesis (DBT). To further evaluate DBT in screening, it is important to assess the molecular subtypes of the detected cancers. Purpose To describe tumor characteristics, including molecular subtypes, of cancers detected at DBT compared with those detected at digital mammography (DM) in breast cancer screening. Materials and Methods The Malmö Breast Tomosynthesis Screening Trial is a prospective, population-based screening trial comparing one-view DBT with two-view DM. Tumor characteristics were obtained, and invasive cancers were classified according to St Gallen as follows: luminal A-like, luminal B-like human epidermal growth factor receptor (HER)2-negative/HER2-positive, HER2-positive, and triple-negative cancers. Tumor characteristics were compared by mode of detection: DBT alone or DM (ie, DBT and DM or DM alone). χ2 test was used for data analysis. Results Between January 2010 and February 2015, 14 848 women were enrolled (mean age, 57 years ± 10; age range, 40-76 years). In total, 139 cancers were detected; 118 cancers were invasive and 21 were ductal carcinomas in situ. Thirty-seven additional invasive cancers (36 cancers with complete subtypes and stage) were detected at DBT alone, and 81 cancers (80 cancers with complete stage) were detected at DM. No differences were seen between DBT and DM in the distribution of tumor size 20 mm or smaller (86% [31 of 36] vs 85% [68 of 80], respectively; P = .88), node-negative status (75% [27 of 36] vs 74% [59 of 80], respectively; P = .89), or luminal A-like subtype (53% [19 of 36] vs 46% [37 of 81], respectively; P = .48). Conclusion The biologic profile of the additional cancers detected at digital breast tomosynthesis in a large prospective population-based screening trial was similar to those detected at digital mammography, and the majority were early-stage luminal A-like cancers. This indicates that digital breast tomosynthesis screening does not alter the predictive and prognostic profile of screening-detected cancers. © RSNA, 2019.

Mammographic density is a potential predictive marker of pathological response after neoadjuvant chemotherapy in breast cancer.

BACKGROUND: Our aim is to study if mammographic density (MD) prior to neoadjuvant chemotherapy is a predictive factor in accomplishing a pathological complete response (pCR) in neoadjuvant-treated breast cancer patients. METHODS: Data on all neoadjuvant treated breast cancer patients in Southern Sweden (2005-2016) were retrospectively identified, with patient and tumor characteristics retrieved from their medical charts. Diagnostic mammograms were used to evaluate and score MD as categorized by breast composition with the Breast Imaging-Reporting and Data System (BI-RADS) 5th edition. Logistic regression was used in complete cases to assess the odds ratios (OR) for pCR compared to BI-RADS categories (a vs b-d), adjusting for patient and pre-treatment tumor characteristics. RESULTS: A total of 302 patients were included in the study population, of which 57 (18.9%) patients accomplished pCR following neoadjuvant chemotherapy. The number of patients in the BI-RADS category a, b, c, and d were separately 16, 120, 140, and 26, respectively. In comparison to patients with BI-RADS breast composition a, patients with denser breasts had a lower OR of accomplishing pCR: BI-RADS b 0.32 (95%CI 0.07-0.1.5), BI-RADS c 0.30 (95%CI 0.06-1.45), and BI-RADS d 0.06 (95%CI 0.01-0.56). These associations were measured with lower point estimates, but wider confidence interval, in premenopausal patients; OR of accomplishing pCR for BI-RADS d in comparison to BI-RADS a: 0.03 (95%CI 0.00-0.76). CONCLUSIONS: The likelihood of accomplishing pCR is indicated to be lower in breast cancer patients with higher MD, which need to be analysed in future studies for improved clinical decision-making regarding neoadjuvant treatment.

Comparison between software volumetric breast density estimates in breast tomosynthesis and digital mammography images in a large public screening cohort.

OBJECTIVES: To compare software estimates of volumetric breast density (VBD) based on breast tomosynthesis (BT) projections to those based on digital mammography (DM) images in a large screening cohort, the Malmö Breast Tomosynthesis Screening Trial (MBTST). METHODS: DM and BT images of 9909 women (enrolled 2010-2015) were retrospectively analysed with prototype software to estimate VBD. Software calculation is based on a physics model of the image acquisition process and incorporates the effect of masking in DM based on accumulated dense tissue areas. VBD (continuously and categorically) was compared between BT [central projection (mediolateral oblique view (MLO)] and two-view DM, and with radiologists' BI-RADS density 4th ed. scores. Agreement and correlation were investigated with weighted kappa (κ), Spearman's correlation coefficient (r), and Bland-Altman analysis. RESULTS: There was a high correlation (r = 0.83) between VBD in DM and BT and substantial agreement between the software breast density categories [observed agreement, 61.3% and 84.8%; κ = 0.61 and ĸ = 0.69 for four (a/b/c/d) and two (fat involuted vs. dense) density categories, respectively]. There was moderate agreement between radiologists' BI-RADS scores and software density categories in DM (ĸ = 0.55) and BT (ĸ = 0.47). CONCLUSIONS: In a large public screening setting, we report a substantial agreement between VBD in DM and BT using software with special focus on masking effect. This automated and objective mode of measuring VBD may be of value to radiologists and women when BT is used as the primary breast cancer screening modality. KEY POINTS: • There was a high correlation between continuous volumetric breast density in DM and BT. • There was substantial agreement between software breast density categories (four groups) in DM and BT; with clinically warranted binary software breast density categories, the agreement increased markedly. • There was moderate agreement between radiologists' BI-RADS scores and software breast density categories in DM and BT.

One-view breast tomosynthesis versus two-view mammography in the Malmö Breast Tomosynthesis Screening Trial (MBTST): a prospective, population-based, diagnostic accuracy study.

BACKGROUND: Digital breast tomosynthesis is an advancement of the mammographic technique, with the potential to increase detection of lesions during breast cancer screening. The main aim of the Malmö Breast Tomosynthesis Screening Trial (MBTST) was to investigate the accuracy of one-view digital breast tomosynthesis in population screening compared with standard two-view digital mammography. METHODS: In this prospective, population-based screening study, of women aged 40-74 years invited to attend national breast cancer screening at Skåne University Hospital, Malmö, Sweden, a random sample was asked to participate in the trial (every third woman who was invited to attend regular screening was invited to participate). Participants had to be able to speak English or Swedish and were excluded from the study if they were pregnant. Participants underwent screening with two-view digital mammography (ie, craniocaudal and mediolateral oblique views) followed by one-view digital breast tomosynthesis with reduced compression in the mediolateral oblique view (with a wide tomosynthesis angle of 50°) at one screening visit. Images were read with masked double reading and scoring by two separate reading groups, one for each method, made up of seven radiologists. Any cancer detected with a malignancy probability score of three or higher by any reader in either group was discussed in a consensus meeting of at least two readers, from which the decision of whether or not to recall the woman for further investigation was made. The primary outcome measures were sensitivity and specificity of breast cancer detection. Secondary outcome measures were screening performance measures of cancer detection, recall, and interval cancers (cancers clinically detected between screenings), and positive predictive value for screen recalls and negative predictive value of each method. Outcomes were analysed in the per-protocol population. Follow-up of the participants for at least 2 years allowed for identification of interval cancers. This trial is registered with ClinicalTrials.gov, number NCT01091545. FINDINGS: Between Jan 27, 2010, and Feb 13, 2015, of 21 691 women invited, 14 851 (68%) agreed to participate. Three women withdrew consent during follow-up and were excluded from the analyses. 139 breast cancers were detected in 137 (<1%) of 14 848 women. Sensitivity was higher for digital breast tomosynthesis than for digital mammography (81·1%, 95% CI 74·2-86·9, vs 60·4%, 52·3-68·0) and specificity was slightly lower for digital breast tomosynthesis than was for digital mammography (97·2%, 95% CI 97·0-97·5, vs 98·1%, 97·9-98·3). The proportion of cancers detected was significantly higher with digital breast tomosynthesis than with digital mammography (8·7 cancers per 1000 women screened, 95% CI 7·3-10·3 vs 6·5 cancers per 1000 screened, 5·2-7·9; p<0·0001). The proportion of women recalled after discussion was higher among cancers detected by digital breast tomosynthesis than for those detected by digital mammography after consensus (3·6%, 95% CI 3·3-3·9 vs 2·5%, 2·2-2·8; p<0·0001). The positive predictive value for screen recalls was 24·1% (95% CI 20·5-28·0) for digital breast tomosynthesis and 25·9% (21·6-30·7) for digital mammography, and the negative predictive value was 99·8% (99·7-99·9) and 99·6% (99·4-99·7), respectively. The proportion of women who developed interval cancers after trial screening was 1·48 cancers per 1000 women screened (95% CI 0·93-2·24). INTERPRETATION: Breast cancer screening by use of one-view digital breast tomosynthesis with a reduced compression force has higher sensitivity at a slightly lower specificity for breast cancer detection compared with two-view digital mammography and has the potential to reduce the radiation dose and screen-reading burden required by two-view digital breast tomosynthesis with two-view digital mammography. FUNDING: The Swedish Cancer Society, The Swedish Research Council, The Breast Cancer Foundation, The Swedish Medical Society, The Crafoord Foundation, The Gunnar Nilsson Cancer Foundation, The Skåne University Hospital Foundation, Governmental funding for clinical research, The South Swedish Health Care Region, The Malmö Hospital Cancer Foundation and The Cancer Foundation at the Department of Oncology, Skåne University Hospital.

Measuring mammographic density: comparing a fully automated volumetric assessment versus European radiologists' qualitative classification.

OBJECTIVES: Breast Imaging-Reporting and Data System (BI-RADS) mammographic density categories are associated with considerable interobserver variability. Automated methods of measuring volumetric breast density may reduce variability and be valuable in risk and mammographic screening stratification. Our objective was to assess agreement of mammographic density by a volumetric method with the radiologists' classification. METHODS: Eight thousand seven hundred and eighty-two examinations from the Malmö Breast Tomosynthesis Screening Trial were classified according to BI-RADS, 4th Edition. Volumetric breast density was assessed using automated software for 8433 examinations. Agreement between volumetric breast density and BI-RADS was descriptively analyzed. Agreement between radiologists and between categorical volumetric density and BI-RADS was calculated, rendering kappa values. RESULTS: The observed agreement between BI-RADS scores of different radiologists was 80.9 % [kappa 0.77 (0.76-0.79)]. A spread of volumetric breast density for each BI-RADS category was seen. The observed agreement between categorical volumetric density and BI-RADS scores was 57.1 % [kappa 0.55 (0.53-0.56)]. CONCLUSIONS: There was moderate agreement between volumetric density and BI-RADS scores from European radiologists indicating that radiologists evaluate mammographic density differently than software. The automated method may be a robust and valuable tool; however, differences in interpretation between radiologists and software require further investigation. KEY POINTS: • Agreement between qualitative and software density measurements has not been frequently studied. • There was substantial agreement between different radiologists´ qualitative density assessments. • There was moderate agreement between software and radiologists' density assessments. • Differences in interpretation between software and radiologists require further investigation.

Mammographic density in relation to tumor biomarkers, molecular subtypes, and mode of detection in breast cancer.

PURPOSE: Mammographic density is an established risk factor for breast cancer; however, the relation to tumor pathological parameters including the androgen receptor and molecular subtypes has not been extensively studied. METHODS: In the Malmö Diet and Cancer Study, 733 invasive breast cancers were diagnosed from 1991 to 2007. Mammographic density was defined qualitatively. Tumor biomarker information including estrogen receptor (ER), progesterone receptor, androgen receptor (AR), human epidermal growth factor 2 (HER2), and Ki67 was collected. Surrogate molecular subtypes were defined as luminal A, luminal B, HER2 positive and triple-negative breast cancer (TNBC). RESULTS: Among the 632 tumors with mammographic and pathological information, 352 tumors were screening-detected and 280 clinically detected. Higher mammographic density was associated with ER-negative tumors [ORadj 1.93 (1.04-3.59)] and TNBC [ORadj 2.44 (1.01-5.89), luminal A reference], in clinically detected breast cancer. Similarly, higher mammographic density was associated with AR-negative tumors [ORadj 1.77 (0.80-3.93)] in clinically detected breast cancer, though the evidence for this association was weak. CONCLUSIONS: In clinically detected breast cancer, but not in screening-detected, higher mammographic density was associated with ER-negative tumors including TNBC. This study highlights the need for taking mode of detection into consideration when addressing mammographic density and tumor biomarkers.

Do mammographic tumor features in breast cancer relate to breast density and invasiveness, tumor size, and axillary lymph node involvement?

BACKGROUND: Breast density and mammographic tumor features of breast cancer may carry prognostic information. The potential benefit of using the combined information obtained from breast density, mammographic tumor features, and pathological tumor characteristics has not been extensively studied. PURPOSE: To investigate how mammographic tumor features relate to breast density and pathological tumor characteristics. MATERIAL AND METHODS: This retrospective study was carried out within the Malmö Diet and Cancer Study: a population-based cohort study recruiting 17,035 women during 1991-1996. A total of 826 incident breast cancers were identified during follow-up. Mammography images were collected and analyzed according to breast density and tumor features at diagnosis. Pathological data were retrieved from medical reports. Mammographic tumor features in relation to invasiveness, tumor size, and axillary lymph node involvement were analyzed using logistic regression yielding odds ratios (OR) with 95% confidence intervals (CI) and adjusted for age at diagnosis, mode of detection, and breast density. RESULTS: Tumors presenting as an ill-defined mass or calcifications were more common in dense breasts than tumors presenting as a distinct mass or with spiculated appearance. Invasive cancer was more common in tumors with spiculated appearance than tumors presenting as a distinct mass (adjusted OR, 5.68 [1.81-17.84]). Among invasive tumors, an ill-defined mass was more often large (>20 mm) compared with a distinct mass, (adjusted OR, 3.16 [1.80-5.55]). CONCLUSION: Tumors presenting as an ill-defined mass or calcifications were more common in dense breasts. Spiculated appearance was related to invasiveness, and ill-defined mass to larger tumor size, regardless of mode of detection and breast density. The potential role of mammographic tumor features in clinical decision-making warrants further investigation.