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Abstract Background To investigate whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) are associated with Ki-67 expression of breast cancer. Materials and methods This is a prospective ethically approved study of 70 women diagnosed with invasive breast cancer in 2018, including 40 low Ki-67 expression (Ki-67 proliferation index 0.5. Conclusion The present study showed that quantitative radiomic imaging features of breast tumour extracted from DBT images are associated with breast cancer Ki-67 expression. Larger studies are needed in order to further evaluate these findings.

Abstract We investigated whether the internal gantry components of our computed tomography (CT) scanner contain severe acute respiratory syndrome 2 (SARS-CoV-2) ribonucleic acid (RNA), bacterial or fungal agents. From 1 to 27 March 2020, we performed 180 examinations of patients with confirmed SARS-CoV-2 infection using a dedicated CT scanner. On 27 March 2020, this CT gantry was opened and sampled in each of the following components: (a) gantry case; (b) inward airflow filter; (c) gantry motor; (d) x-ray tube; (e) outflow fan; (f) fan grid; (g) detectors; and (h) x-ray tube filter. To detect SARS-CoV-2 RNA, samples were analysed using reverse transcriptase-polymerase chain reaction (RT-PCR). To detect bacterial or fungal agents, samples have been collected using “replicate organism detection and counting” contact plates of 24 cm2, containing tryptic soy agar, and subsequently cultured. RT-PCR detected SARS-CoV-2 RNA in the inward airflow filter sample. RT-PCR of remaining gantry samples did not reveal the presence of SARS-CoV-2 RNA. Neither bacterial nor fungal agents grew in the agar-based growth medium after the incubation period. Our data showed that SARS-Cov-2 RNA can be found inside the CT gantry only in the inward airflow filter. All remaining CT gantry components were devoid of SARS-CoV-2 RNA.

Abstract Background Computed tomography (CT) enables quantification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, helping in outcome prediction. Methods From 1 to 22 March 2020, patients with pneumonia symptoms, positive lung CT scan, and confirmed SARS-CoV-2 on reverse transcription-polymerase chain reaction (RT-PCR) were consecutively enrolled. Clinical data was collected. Outcome was defined as favourable or adverse (i.e., need for mechanical ventilation or death) and registered over a period of 10 days following CT. Volume of disease (VoD) on CT was calculated semi-automatically. Multiple linear regression was used to predict VoD by clinical/laboratory data. To predict outcome, important features were selected using a priori analysis and subsequently used to train 4 different models. Results A total of 106 consecutive patients were enrolled (median age 63.5 years, range 26–95 years; 41/106 women, 38.7%). Median duration of symptoms and C-reactive protein (CRP) was 5 days (range 1–30) and 4.94 mg/L (range 0.1–28.3), respectively. Median VoD was 249.5 cm3 (range 9.9–1505) and was predicted by lymphocyte percentage (p = 0.008) and CRP (p < 0.001). Important variables for outcome prediction included CRP (area under the curve [AUC] 0.77), VoD (AUC 0.75), age (AUC 0.72), lymphocyte percentage (AUC 0.70), coronary calcification (AUC 0.68), and presence of comorbidities (AUC 0.66). Support vector machine had the best performance in outcome prediction, yielding an AUC of 0.92. Conclusions Measuring the VoD using a simple CT post-processing tool estimates SARS-CoV-2 burden. CT and clinical data together enable accurate prediction of short-term clinical outcome.