Mammography screening supported by deep learning-based artificial intelligence (AI) solutions can potentially reduce workload without compromising breast cancer detection accuracy, but the site of deployment in the workflow might be crucial. This retrospective study compared three simulated AI-integrated screening scenarios with standard double reading with arbitration in a sample of 249 402 mammograms from a representative screening population. A commercial AI system replaced the first reader (scenario 1: integrated AI
Abstract Objectives This systematic review and meta-analysis aimed to assess the stroke detection performance of artificial intelligence (AI) in magnetic resonance imaging (MRI), and additionally to identify reporting insufficiencies. Methods PRISMA guidelines were followed. MEDLINE, Embase, Cochrane Central, and IEEE Xplore were searched for studies utilising MRI and AI for stroke detection. The protocol was prospectively registered with PROSPERO (CRD42021289748). Sensitivity, specificity, accuracy, and area under the receiver operating characteristic (ROC) curve were the primary outcomes. Only studies using MRI in adults were included. The intervention was AI for stroke detection with ischaemic and haemorrhagic stroke in separate categories. Any manual labelling was used as a comparator. A modified QUADAS-2 tool was used for bias assessment. The minimum information about clinical artificial intelligence modelling (MI-CLAIM) checklist was used to assess reporting insufficiencies. Meta-analyses were performed for sensitivity, specificity, and hierarchical summary ROC (HSROC) on low risk of bias studies. Results Thirty-three studies were eligible for inclusion. Fifteen studies had a low risk of bias. Low-risk studies were better for reporting MI-CLAIM items. Only one study examined a CE-approved AI algorithm. Forest plots revealed detection sensitivity and specificity of 93% and 93% with identical performance in the HSROC analysis and positive and negative likelihood ratios of 12.6 and 0.079. Conclusion Current AI technology can detect ischaemic stroke in MRI. There is a need for further validation of haemorrhagic detection. The clinical usability of AI stroke detection in MRI is yet to be investigated. Critical relevance statement This first meta-analysis concludes that AI, utilising diffusion-weighted MRI sequences, can accurately aid the detection of ischaemic brain lesions and its clinical utility is ready to be uncovered in clinical trials. Key Points There is a growing interest in AI solutions for detection aid. The performance is unknown for MRI stroke assessment. AI detection sensitivity and specificity were 93% and 93% for ischaemic lesions. There is limited evidence for the detection of patients with haemorrhagic lesions. AI can accurately detect patients with ischaemic stroke in MRI. Graphical Abstract
Background To present the first national series of salivary duct carcinoma patients, including survival rates and an analysis of prognostic factors. Methods By merging three Danish nationwide registries that encompass an entire population, 34 patients diagnosed with salivary duct carcinoma from 1990 to 2005 were identified. Histological slides were reviewed, and data concerning demographics, tumour site, clinical stage, treatment profiles and follow‐up were retrieved. Survival estimates and prognostic factors were evaluated by comparing Kaplan–Meier plots using the Mantel–Haenszel log‐rank test. Results Salivary duct carcinoma showed an incidence of 0.04/100.000 inhabitants/year. Distant recurrence was seen in 52% of patients. Five‐year overall survival, disease‐specific survival and recurrence‐free survival were 32%, 42% and 35%, respectively. Univariate analyses suggested that overall stage ( III / IV ) and vascular invasion have a negative impact on all survival measures. Involved resection margins correlated with a poorer overall survival and disease‐specific survival, whereas adjuvant radiotherapy improved overall survival and recurrence‐free survival. Conclusions Salivary duct carcinoma incidence averages to two episodes per year in the entire Kingdom of Denmark. With half of patients in this study experiencing distant recurrences and only a third surviving at 5 years, prognosis is dismal. Advanced overall stage, vascular invasion and involved resection margins all seem to correlate with a poorer survival, while adjuvant radiotherapy significantly improved outcome. Extensive T‐site surgery, neck dissection and adjuvant radiotherapy are therefore recommended.
Prostate cancer most commonly metastasizes to lymph nodes, bones, the liver, and the lungs. Prostate cancer carcinomatosis with an affinity for the appendix is not well described in current literature and is usually reported with acute appendicitis as the primary presentation. A 65-year-old male with a history of recurrent prostate cancer presented with an increase in PSA value. 18F-PSMA-1007 PET/CT showed nodular tissue growth and increased PSMA uptake in the prostate, on the appendix, in the umbilicus, and in several intra- and extra pelvic lymph nodes. The patient had no symptomatic complaints at time of referral. Imaging findings of the appendix resembling characteristic findings of acute appendicitis raised doubts about the interpretation of these as inflammatory disease or peritoneal carcinomatosis secondary to prostate cancer. This case demonstrates the importance of correct differentiation between the 2 conditions based on imaging, clinical symptomatology, and patient history to provide proper care in time.
Artificial intelligence (AI) has the potential to increase quality and efficiency of breast cancer screening. Recent studies have provided comparative data on AI versus human performance in cancer detection with encouraging results, and commercially available AI systems are used worldwide as a clinical tool for mammography screening. There are, however, a number of methodological concerns in relation to the evaluation of AI systems. This review discusses these aspects as well as the opportunities and challenges of clinical validation and implementation in breast cancer screening practice.
Importance Survivors of spontaneous (ie, nontraumatic and with no known structural cause) intracerebral hemorrhage (ICH) have an increased risk of major cardiovascular events (MACEs), including recurrent ICH, ischemic stroke (IS), and myocardial infarction (MI). Only limited data are available from large, unselected population studies assessing the risk of MACEs according to index hematoma location. Objective To examine the risk of MACEs (ie, the composite of ICH, IS, spontaneous intracranial extra-axial hemorrhage, MI, systemic embolism, or vascular death) after ICH based on ICH location (lobar vs nonlobar). Design, Setting, and Participants This cohort study identified 2819 patients in southern Denmark (population of 1.2 million) 50 years or older hospitalized with first-ever spontaneous ICH from January 1, 2009, to December 31, 2018. Intracerebral hemorrhage was categorized as lobar or nonlobar, and the cohorts were linked to registry data until the end of 2018 to identify the occurrence of MACEs and separately recurrent ICH, IS, and MI. Outcome events were validated using medical records. Associations were adjusted for potential confounders using inverse probability weighting. Exposure Location of ICH (lobar vs nonlobar). Main Outcomes and Measures The main outcomes were MACEs and separately recurrent ICH, IS, and MI. Crude absolute event rates per 100 person-years and adjusted hazard ratios (aHRs) with 95% CIs were calculated. Data were analyzed from February to September 2022. Results Compared with patients with nonlobar ICH (n = 1255; 680 [54.2%] men and 575 [45.8%] women; mean [SD] age, 73.5 [11.4] years), those with lobar ICH (n = 1034; 495 [47.9%] men and 539 [52.1%] women, mean [SD] age, 75.2 [10.7] years) had higher rates of MACEs per 100 person-years (10.84 [95% CI, 9.51-12.37] vs 7.91 [95% CI, 6.93-9.03]; aHR, 1.26; 95% CI, 1.10-1.44) and recurrent ICH (3.74 [95% CI, 3.01-4.66] vs 1.24 [95% CI, 0.89-1.73]; aHR, 2.63; 95% CI, 1.97-3.49) but not IS (1.45 [95% CI, 1.02-2.06] vs 1.77 [95% CI, 1.34-2.34]; aHR, 0.81; 95% CI, 0.60-1.10) or MI (0.42 [95% CI, 0.22-0.81] vs 0.64 [95% CI, 0.40-1.01]; aHR, 0.64; 95% CI, 0.38-1.09). Conclusions and Relevance In this cohort study, spontaneous lobar ICH was associated with a higher rate of subsequent MACEs than nonlobar ICH, primarily due to a higher rate of recurrent ICH. This study highlights the importance of secondary ICH prevention strategies in patients with lobar ICH.
A causal relationship between statin use and intracerebral hemorrhage (ICH) is uncertain. We hypothesized that an association between long-term statin exposure and ICH risk might vary for different ICH locations.
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