Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials

Background: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal carcinoma In Situ is safe (E. S. Hwang et al., BMJ Open, 9: e026797, 2019, A. Francis et al., Eur J Cancer. 51: 2296–2303, 2015, Chizuko Kanbayashi et al. The international collaboration of active surveillance trials for low-risk DCIS (LORIS, LORD, COMET, LORETTA), L. E. Elshof et al., Eur J Cancer, 51, 1497–510, 2015). Low-risk is defined as grade I or II DCIS. Because DCIS grade is a major eligibility criteria in these trials, it would be very helpful to assess DCIS grade on mammography, informed by grade assessed on DCIS histopathology in pre-surgery biopsies, since surgery will not be performed on a significant number of patients participating in these trials. Objective: To assess the performance and clinical utility of a convolutional neural network (CNN) in discriminating high-risk (grade III) DCIS and/or Invasive Breast Cancer (IBC) from low-risk (grade I/II) DCIS based on mammographic features. We explored whether the CNN could be used as a decision support tool, from excluding high-risk patients for active surveillance. Methods: In this single centre retrospective study, 464 patients diagnosed with DCIS based on pre-surgery biopsy between 2000 and 2014 were included. The collection of mammography images was partitioned on a patient-level into two subsets, one for training containing 80% of cases (371 cases, 681 images) and 20% (93 cases, 173 images) for testing. A deep learning model based on the U-Net CNN was trained and validated on 681 two-dimensional mammograms. Classification performance was assessed with the Area Under the Curve (AUC) receiver operating characteristic and predictive values on the test set for predicting high risk DCIS-and high-risk DCIS and/ or IBC from low-risk DCIS. Results: When classifying DCIS as high-risk, the deep learning network achieved a Positive Predictive Value (PPV) of 0.40, Negative Predictive Value (NPV) of 0.91 and an AUC of 0.72 on the test dataset. For distinguishing high-risk and/or upstaged DCIS (occult invasive breast cancer) from low-risk DCIS a PPV of 0.80, a NPV of 0.84 and an AUC of 0.76 were achieved. Conclusion: For both scenarios (DCIS grade I/II vs. III, DCIS grade I/II vs. III and/or IBC) AUCs were high, 0.72 and 0.76, respectively, concluding that our convolutional neural network can discriminate low-grade from high-grade DCIS.</p

Application of Deep Learning on Mammographies To Discriminate Between Low and High-Risk Dcis for Patient Participation in Active Surveillance Trials

BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal carcinoma In Situ is safe (E. S. Hwang et al., BMJ Open, 9: e026797, 2019, A. Francis et al., Eur J Cancer. 51: 2296-2303, 2015, Chizuko Kanbayashi et al. The international collaboration of active surveillance trials for low-risk DCIS (LORIS, LORD, COMET, LORETTA), L. E. Elshof et al., Eur J Cancer, 51, 1497-510, 2015). Low-risk is defined as grade I or II DCIS. Because DCIS grade is a major eligibility criteria in these trials, it would be very helpful to assess DCIS grade on mammography, informed by grade assessed on DCIS histopathology in pre-surgery biopsies, since surgery will not be performed on a significant number of patients participating in these trials. OBJECTIVE: To assess the performance and clinical utility of a convolutional neural network (CNN) in discriminating high-risk (grade III) DCIS and/or Invasive Breast Cancer (IBC) from low-risk (grade I/II) DCIS based on mammographic features. We explored whether the CNN could be used as a decision support tool, from excluding high-risk patients for active surveillance. METHODS: In this single centre retrospective study, 464 patients diagnosed with DCIS based on pre-surgery biopsy between 2000 and 2014 were included. The collection of mammography images was partitioned on a patient-level into two subsets, one for training containing 80% of cases (371 cases, 681 images) and 20% (93 cases, 173 images) for testing. A deep learning model based on the U-Net CNN was trained and validated on 681 two-dimensional mammograms. Classification performance was assessed with the Area Under the Curve (AUC) receiver operating characteristic and predictive values on the test set for predicting high risk DCIS-and high-risk DCIS and/ or IBC from low-risk DCIS. RESULTS: When classifying DCIS as high-risk, the deep learning network achieved a Positive Predictive Value (PPV) of 0.40, Negative Predictive Value (NPV) of 0.91 and an AUC of 0.72 on the test dataset. For distinguishing high-risk and/or upstaged DCIS (occult invasive breast cancer) from low-risk DCIS a PPV of 0.80, a NPV of 0.84 and an AUC of 0.76 were achieved. CONCLUSION: For both scenarios (DCIS grade I/II vs. III, DCIS grade I/II vs. III and/or IBC) AUCs were high, 0.72 and 0.76, respectively, concluding that our convolutional neural network can discriminate low-grade from high-grade DCIS

UK Housing Market: Time Series Processes with Independent and Identically Distributed Residuals

The paper examines whether a univariate data generating process can be identified which explains the data by having residuals that are independent and identically distributed, as verified by the BDS test. The stationary first differenced natural log quarterly house price index is regressed, initially with a constant variance and then with a conditional variance. The only regression function that produces independent and identically distributed standardised residuals is a mean process based on a pure random walk format with Exponential GARCH in mean for the conditional variance. There is an indication of an asymmetric volatility feedback effect but higher frequency data is required to confirm this. There could be scope for forecasting the index but this is tempered by the reduction in the power of the BDS test if there is a non-linear conditional variance process

The Neuronal EGF-Related Gene Nell2 Interacts with Macf1 and Supports Survival of Retinal Ganglion Cells after Optic Nerve Injury

Nell2 is a neuron-specific protein containing six epidermal growth factor-like domains. We have identified Nell2 as a retinal ganglion cell (RGC)-expressed gene by comparing mRNA profiles of control and RGC-deficient rat retinas. The aim of this study was to analyze Nell2 expression in wild-type and optic nerve axotomized retinas and evaluate its potential role in RGCs. Nell2-positive in situ and immunohistochemical signals were localized to irregularly shaped cells in the ganglion cell layer (GCL) and colocalized with retrogradely-labeled RGCs. No Nell2-positive cells were detected in 2 weeks optic nerve transected (ONT) retinas characterized with approximately 90% RGC loss. RT-PCR analysis showed a dramatic decrease in the Nell2 mRNA level after ONT compared to the controls. Immunoblot analysis of the Nell2 expression in the retina revealed the presence of two proteins with approximate MW of 140 and 90 kDa representing glycosylated and non-glycosylated Nell2, respectively. Both products were almost undetectable in retinal protein extracts two weeks after ONT. Proteome analysis of Nell2-interacting proteins carried out with MALDI-TOF MS (MS) identified microtubule-actin crosslinking factor 1 (Macf1), known to be critical in CNS development. Strong Macf1 expression was observed in the inner plexiform layer and GCL where it was colocalizied with Thy-1 staining. Since Nell2 has been reported to increase neuronal survival of the hippocampus and cerebral cortex, we evaluated the effect of Nell2 overexpression on RGC survival. RGCs in the nasal retina were consistently more efficiently transfected than in other areas (49% vs. 13%; n = 5, p<0.05). In non-transfected or pEGFP-transfected ONT retinas, the loss of RGCs was approximately 90% compared to the untreated control. In the nasal region, Nell2 transfection led to the preservation of approximately 58% more cells damaged by axotomy compared to non-transfected (n = 5, p<0.01) or pEGFP-transfected controls (n = 5, p<0.01)

Induction of Inflammation by West Nile virus Capsid through the Caspase-9 Apoptotic Pathway

West Nile virus (WNV) is a member of the Flaviviridae family of vector-borne pathogens. Clinical signs of WNV infection include neurologic symptoms, limb weakness, and encephalitis, which can result in paralysis or death. We report that the WNV-capsid (Cp) by itself induces rapid nuclear condensation and cell death in tissue culture. Apoptosis is induced through the mitochondrial pathway resulting in caspase-9 activation and downstream caspase-3 activation. Capsid gene delivery into the striatum of mouse brain or interskeletal muscle resulted in cell death and inflammation, likely through capsid-induced apoptosis in vivo. These studies demonstrate that the capsid protein of WNV may be responsible for aspects of viral pathogenesis through induction of the apoptotic cascade

Phosphorylation of Ubc9 by Cdk1 Enhances SUMOylation Activity

Increasing evidence has pointed to an important role of SUMOylation in cell cycle regulation, especially for M phase. In the current studies, we have obtained evidence through in vitro studies that the master M phase regulator CDK1/cyclin B kinase phosphorylates the SUMOylation machinery component Ubc9, leading to its enhanced SUMOylation activity. First, we show that CDK1/cyclin B, but not many other cell cycle kinases such as CDK2/cyclin E, ERK1, ERK2, PKA and JNK2/SAPK1, specifically enhances SUMOylation activity. Second, CDK1/cyclin B phosphorylates the SUMOylation machinery component Ubc9, but not SAE1/SAE2 or SUMO1. Third, CDK1/cyclin B-phosphorylated Ubc9 exhibits increased SUMOylation activity and elevated accumulation of the Ubc9-SUMO1 thioester conjugate. Fourth, CDK1/cyclin B enhances SUMOylation activity through phosphorylation of Ubc9 at serine 71. These studies demonstrate for the first time that the cell cycle-specific kinase CDK1/cyclin B phosphorylates a SUMOylation machinery component to increase its overall SUMOylation activity, suggesting that SUMOylation is part of the cell cycle program orchestrated by CDK1 through Ubc9

Evaluation of polygenic risk scores for breast and ovarian cancer risk prediction in BRCA1 and BRCA2 mutation carriers

Background: Genome-wide association studies (GWAS) have identified 94 common single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk and 18 associated with ovarian cancer (OC) risk. Several of these are also associated with risk of BC or OC for women who carry a pathogenic mutation in the high-risk BC and OC genes BRCA1 or BRCA2. The combined effects of these variants on BC or OC risk for BRCA1 and BRCA2 mutation carriers have not yet been assessed while their clinical management could benefit from improved personalized risk estimates. Methods: We constructed polygenic risk scores (PRS) using BC and OC susceptibility SNPs identified through population-based GWAS: for BC (overall, estrogen receptor [ER]-positive, and ER-negative) and for OC. Using data from 15 252 female BRCA1 and 8211 BRCA2 carriers, the association of each PRS with BC or OC risk was evaluated using a weighted cohort approach, with time to diagnosis as the outcome and estimation of the hazard ratios (HRs) per standard deviation increase in the PRS. Results: The PRS for ER-negative BC displayed the strongest association with BC risk in BRCA1 carriers (HR = 1.27, 95% confidence interval [CI] = 1.23 to 1.31, P = 8.2 x 10(53)). In BRCA2 carriers, the strongest association with BC risk was seen for the overall BC PRS (HR = 1.22, 95% CI = 1.17 to 1.28, P = 7.2 x 10(-20)). The OC PRS was strongly associated with OC risk for both BRCA1 and BRCA2 carriers. These translate to differences in absolute risks (more than 10% in each case) between the top and bottom deciles of the PRS distribution; for example, the OC risk was 6% by age 80 years for BRCA2 carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS. Conclusions: BC and OC PRS are predictive of cancer risk in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management

Obscured Activity: AGN, Quasars, Starbursts and ULIGs observed by the Infrared Space Observatory

Some of the most active galaxies in the Universe are obscured by large quantities of dust and emit a substantial fraction of their bolometric luminosity in the infrared. Observations of these infrared luminous galaxies with the Infrared Space Observatory (ISO) have provided a relatively unabsorbed view to the sources fuelling this active emission. The improved sensitivity, spatial resolution and spectroscopic capability of ISO over its predecessor Infrared Astronomical Satellite (IRAS), has enabled significant advances in the understanding of the infrared properties of active galaxies. ISO surveyed a wide range of active galaxies which, in the context of this review, includes those powered by intense bursts of star-formation as well as those containing a dominant active galactic nucleus (AGN). Mid infrared imaging resolved for the first time the dust enshrouded nuclei in many nearby galaxies, while a new era in infrared spectroscopy was opened by probing a wealth of atomic, ionic and molecular lines as well as broad band features in the mid and far infrared. This was particularly useful since it resulted in the understanding of the power production, excitation and fuelling mechanisms in the nuclei of active galaxies including the intriguing but so far elusive ultraluminous infrared galaxies. Detailed studies of various classes of AGN and quasars greatly improved our understanding of the unification scenario. Far-infrared imaging and photometry also revealed the presence of a new very cold dust component in galaxies and furthered our knowledge of the far-infrared properties of faint starbursts, ULIGs and quasars. We summarise almost nine years of key results based upon ISO data spanning the full range of luminosity and type of active galaxies.Comment: Accepted for publication in 'ISO science legacy - a compact review of ISO major achievements', Space Science Reviews - dedicated ISO issue. To be published by Springer in 2005. 62 pages (low resolution figures version). Higher resolution PDFs available from http://users.physics.uoc.gr/~vassilis/papers/VermaA.pdf or http://www.iso.vilspa.esa.es/science/SSR/Verma.pd