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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. 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. 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. 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. Swedish Cancer Society, Confederation of Regional Cancer Centres, and the Swedish governmental funding for clinical research (ALF).

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. 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. 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). 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. 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.

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.

Background Despite known benefits of physical activity in reducing breast cancer risk, its impact on mammographic characteristics remain unclear and understudied. This study aimed to investigate associations between pre-diagnostic physical activity and mammographic features at breast cancer diagnosis, specifically mammographic breast density (MBD) and mammographic tumor appearance (MA), as well as mode of cancer detection (MoD). Methods Physical activity levels from study baseline (1991–1996) and mammographic information from the time of invasive breast cancer diagnosis (1991–2014) of 1116 women enrolled in the Malmö Diet and Cancer Study cohort were used. Duration and intensity of physical activity were assessed according to metabolic equivalent of task hours (MET-h) per week, or World Health Organization (WHO) guideline recommendations. MBD was dichotomized into low-moderate or high, MA into spiculated or non-spiculated tumors, and MoD into clinical or screening detection. Associations were investigated through logistic regression analyses providing odds ratios (OR) with 95% confidence intervals (CI) in crude and multivariable-adjusted models. Results In total, 32% of participants had high MBD at diagnosis, 37% had non-spiculated MA and 50% had clinical MoD. Overall, no association between physical activity and MBD was found with increasing MET-h/week or when comparing women who exceeded WHO guidelines to those subceeding recommendations (OR adj 1.24, 95% CI 0.78–1.98). Likewise, no differences in MA or MoD were observed across categories of physical activity. Conclusions No associations were observed between pre-diagnostic physical activity and MBD, MA, or MoD at breast cancer diagnosis. While physical activity is an established breast cancer prevention strategy, it does not appear to modify mammographic characteristics or screening detection.

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.

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.

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 (OR 1-sd 1.50, 95% CI 1.30–1.74), WC (OR 1-sd 1.46, 95% CI 1.27–1.69), and WHR (OR 1-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 (OR 1-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.

Background Imaging ovarian cancer (OC) includes evaluating peritoneal carcinomatosis (PC) and enlarged cardio phrenic lymph nodes (CPLN) by computed tomography (CT), and thorough evaluation is tedious work. A “CT short score” with high-risk CT parameters might be a more pragmatic approach, but it is not known if such a short score associates with aggressive OC subtypes and impaired OC survival. Further, it is not known if certain established OC risk factors are linked to high-risk CT-findings which would be important in image evaluation. Herein, we investigate a CT short score and its relation to baseline characteristics, OC subtypes, and survival. Methods The Malmö Diet and Cancer Study is a prospective cohort that included 17,035 women (1991–1996). Baseline characteristics and tumor information on 159 OC and information on OC specific survival (last follow-up, 2017-12-31) was registered. A CT short score (CPLN and PC-index (PCI) in seven regions) was registered and associations with clinical stage [stage I vs. advanced stage (II-IV), histological type/grade (high grade serous and endometrioid vs. other subtypes], and OC-specific survival were analyzed with logistic and Cox regression, respectively. Parity and menopausal status were analyzed in relation to short score and PCI. Results There was an association between higher short score and advanced clinical stage (adjusted OR 2.76 (1.42—5.38)), adjusted for age at diagnosis and histological type/grade. Higher short score was associated with impaired OC specific survival (adjusted HR 1.17 (1.01—1.35)), adjusted for age at diagnosis, histological type/grade, and clinical stage. There were no significant associations between parity, menopausal status, and short score/PCI. Conclusions CT short score was significantly associated with advanced clinical stages and impaired OC survival. A pragmatic approach (based on CT) to evaluate high risk image findings in OC could help reduce radiologists’ workload and at the same time provide structured reports to surgeons and oncologists involved in OC care.

Background Ovarian cancer (OC) is usually detected in late clinical stages, and imaging at diagnosis is crucial. Peritoneal carcinomatosis (PC) and cardio phrenic lymph nodes (CPLN) are pathological findings of computed tomography (CT) and are relevant for surgical planning. Furthermore, mammographic breast density (BD) has shown an association with OC risk and might be prognostically relevant. However, it is not known if PC, CPLN, and BD are associated with aggressive OC subtypes and impaired OC survival. Herein, we investigated associations between three comprehensive image parameters and OC subtypes and survival. Methods The Malmö Diet and Cancer Study is a prospective study that included 17,035 women (1991–1996). Tumor information on 159 OC and information on OC specific survival (last follow-up, 2017-12-31) was registered. The CT and mammography closest to diagnosis were evaluated (Peritoneal Carcinomatosis Index PCI, CPLN, and BD). Associations between CT-PCI, CPLN, and BD vs. clinical stage [stage I vs. advanced stage (II-IV), histological type/grade (high grade serous and endometrioid vs. other subtypes], and OC-specific survival were analyzed by logistic and Cox regression. Results There was a significant association between higher CT-PCI score and advanced clinical stage (adjusted OR 1.26 (1.07–1.49)), adjusted for age at diagnosis and histological type/grade. Increasing CT-PCI was significantly associated with impaired OC specific survival (adjusted HR 1.04 (1.01–1.07)), adjusted for age at diagnosis, histological type/grade, and clinical stage. There was no significant association between PCI and histological type/grade, nor between BD or CPLN vs. the studied outcomes. Conclusions Image PCI score was significantly associated with advanced clinical stages and impaired OC survival. An objective approach (based on imaging) to scoring peritoneal carcinomatosis in ovarian cancer could help surgeons and oncologists to optimize surgical planning, treatment, and care.

Despite some epidemiological evidence on the protective effects of polyphenol intake on epithelial ovarian cancer (EOC) risk from case-control studies, the evidence is scarce from prospective studies and non-existent for several polyphenol classes. Therefore, we aimed to investigate the associations between the intake of total, classes and subclasses of polyphenols and EOC risk in a large prospective study. The study was conducted in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, which included 309,129 adult women recruited mostly from the general population. Polyphenol intake was assessed through validated country-specific dietary questionnaires and the Phenol-Explorer database. During a mean follow-up of 14 years, 1469 first incident EOC cases (including 806 serous, 129 endometrioid, 102 mucinous, and 67 clear cell tumours) were identified. In multivariable-adjusted Cox regression models, the hazard ratio in the highest quartile of total polyphenol intake compared with the lowest quartile (HRQ4vsQ1) was 1.14 (95% CI 0.94–1.39; p-trend = 0.11). Similarly, the intake of most classes and subclasses of polyphenols were not related to either overall EOC risk or any EOC subtype. A borderline statistically significant positive association was observed between phenolic acid intake (HRQ4vsQ1 = 1.20, 95% CI 1.01–1.43; p-trend = 0.02) and EOC risk, especially for the serous subtype and in women with obesity, although these associations did not exceed the Bonferroni correction threshold. The current results do not support any association between polyphenol intake and EOC in our large European prospective study. Results regarding phenolic acid intake need further investigation