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Abstract Background Double reading (DR) in screening mammography increases cancer detection and lowers recall rates, but has sustainability challenges due to workforce shortages. Artificial intelligence (AI) as an independent reader (IR) in DR may provide a cost-effective solution with the potential to improve screening performance. Evidence for AI to generalise across different patient populations, screening programmes and equipment vendors, however, is still lacking. Methods This retrospective study simulated DR with AI as an IR, using data representative of real-world deployments (275,900 cases, 177,882 participants) from four mammography equipment vendors, seven screening sites, and two countries. Non-inferiority and superiority were assessed for relevant screening metrics. Results DR with AI, compared with human DR, showed at least non-inferior recall rate, cancer detection rate, sensitivity, specificity and positive predictive value (PPV) for each mammography vendor and site, and superior recall rate, specificity, and PPV for some. The simulation indicates that using AI would have increased arbitration rate (3.3% to 12.3%), but could have reduced human workload by 30.0% to 44.8%. Conclusions AI has potential as an IR in the DR workflow across different screening programmes, mammography equipment and geographies, substantially reducing human reader workload while maintaining or improving standard of care. Trial registration ISRCTN18056078 (20/03/2019; retrospectively registered).

Background: Despite treatment advances, there remains a significant risk of recurrence in ovarian cancer, at which stage it is usually incurable. Consequently, there is a clear need for improved patient stratification. However, at present clinical prognosticators remain largely unchanged due to the lack of reproducible methods to identify high-risk patients. Methods: In high-grade serous ovarian cancer patients with advanced disease, we spatially define a tumour ecological balance of stromal resource and immune hazard using high-throughput image and spatial analysis of routine histology slides. On this basis an EcoScore is developed to classify tumours by a shift in this balance towards cancer-favouring or inhibiting conditions. Findings: The EcoScore provides prognostic value stronger than, and independent of, known risk factors. Crucially, the clinical relevance of mutational burden and genomic instability differ under different stromal resource conditions, suggesting that the selective advantage of these cancer hallmarks is dependent on the context of stromal spatial structure. Under a high resource condition defined by a high level of geographical intermixing of cancer and stromal cells, selection appears to be driven by point mutations; whereas, in low resource tumours featured with high hypoxia and low cancer-immune co-localization, selection is fuelled by aneuploidy. Interpretation: Our study offers empirical evidence that cancer fitness depends on tumour spatial constraints, and presents a biological basis for developing better assessments of tumour adaptive strategies in overcoming ecological constraints including immune surveillance and hypoxia. Keywords: Tumour ecology, Tumour spatial heterogeneity, Cancer evolution, Ovarian cancer, Histological image analysis

Cancer cells can actively engage in overcoming microenvironmental constraints such as tissue stiffness through adapting their shapes; however it is unclear how microenvironmental cells shape cancer nuclear morphology in human tumors in situ. Here the authors merge machine learning, digital pathology and spatial statistics to study this issue; furthermore the authors identify decreased immune infiltration in the surrounding of diversified cancer cells in a subset of ovarian tumors.

Diffusion-weighted magnetic resonance imaging (DWI) enables non-invasive, quantitative staging of prostate cancer via measurement of the apparent diffusion coefficient (ADC) of water within tissues. In cancer, more advanced disease is often characterized by higher cellular density (cellularity), which is generally accepted to correspond to a lower measured ADC. A quantitative relationship between tissue structure and in vivo measurements of ADC has yet to be determined for prostate cancer. In this study, we establish a theoretical framework for relating ADC measurements with tissue cellularity and the proportion of space occupied by prostate lumina, both of which are estimated through automatic image processing of whole-slide digital histology samples taken from a cohort of six healthy mice and nine transgenic adenocarcinoma of the mouse prostate (TRAMP) mice. We demonstrate that a significant inverse relationship exists between ADC and tissue cellularity that is well characterized by our model, and that a decrease of the luminal space within the prostate is associated with a decrease in ADC and more aggressive tumor subtype. The parameters estimated from our model in this mouse cohort predict the diffusion coefficient of water within the prostate-tissue to be 2.18 × 10−3 mm2/s (95% CI: 1.90, 2.55). This value is significantly lower than the diffusion coefficient of free water at body temperature suggesting that the presence of organelles and macromolecules within tissues can drastically hinder the random motion of water molecules within prostate tissue. We validate the assumptions made by our model using novel in silico analysis of whole-slide histology to provide the simulated ADC (sADC); this is demonstrated to have a significant positive correlation with in vivo measured ADC (r2 = 0.55) in our mouse population. The estimation of the structural properties of prostate tissue is vital for predicting and staging cancer aggressiveness, but prostate tissue biopsies are painful, invasive, and are prone to complications such as sepsis. The developments made in this study provide the possibility of estimating the structural properties of prostate tissue via non-invasive virtual biopsies from MRI, minimizing the need for multiple tissue biopsies and allowing sequential measurements to be made for prostate cancer monitoring.

Judas Iscariot prima facie seems to appear only in five NT writings: the four Gospels and Acts. In this study, the question shall be raised and answered whether the authentic Pauline epistles contain traces of this enigmatic figure. Judas’s two stereotypical designations, „one of the Twelve“ and „the one who handed Jesus over“, point to 1Cor 11:23 and 15:5 as places in need of close examination.

Judas Iscariot, one of the most controversial characters in the NT, invited – and still invites – to discussion beyond the bounds of Christianity. One tradition of Islamic exegesis of the hardly intelligible Koran Sura 4.157f. claims that God gave the traitor of Jesus his appearance, whereupon he was crucified in his master’s place. This tradition is inter alia represented by the so-called „Gospel of Barnabas“. Herein Judas is shown at his worst: as an infidel, a greedy thief possessed by the devil. God saves Jesus from his impending arrest by having him carried away to heaven by his four archangels. Judas receives Jesus’ outward appearance, which results in his passion, the latter being described in awfully great detail.

Judas Iscariot plays an important role in the Jewish polemical work called „Toledot Yeshu“: A pious and godfearing rabbi, he becomes Jesus’ ultimate antagonist, the latter being a blasphemer leading the people of Israel astray. Thereby Toledot Yeshu’s presentation of Judas and Jesus is in sharp contrast with the four canonical gospels as well as the Islamic „Gospel of Barnabas“. A short comparison of the Toledot Yeshu with the Gospel of Barnabas shall conclude this study.