bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development of a genomic predictive model for cholangiocarcinoma using copy number alteration data
Authors: Tavares Inês , Martins Ricardo , Ribeiro Ilda Patrícia , Esteves Luísa , Caramelo Francisco , Abrantes Ana Margarida , Neves Rita , Caetano-Oliveira Rui , Botelho Maria Filomena , Barbosa de Melo Joana , Diogo Dulce , Tralhão José Guilherme , Carreira Isabel Marques 
Published: March 2021

AimsCholangiocarcinoma (CC) is a rare tumour arising from the biliary tract epithelium. The aim of this study was to perform a genomic characterisation of CC tumours and to implement a model to differentiate extrahepatic (ECC) and intrahepatic (ICC) cholangiocarcinoma.MethodsDNA extracted from tumour samples of 23 patients with CC, namely 10 patients with ECC and 13 patients with ICC, was analysed by array comparative genomic hybridisation. A support vector machine algorithm for classification was applied to the genomic data to distinguish between ICC and ECC. A survival analysis comparing both groups of patients was also performed.ResultsWith these whole genome results, we observed several common alterations between tumour samples of the same CC anatomical type, namely gain of Xp and loss of 3p, 11q11, 14q, 16q, Yp and Yq in ICC tumours, and gain of 16p25.3 and loss of 3q26.1, 6p25.3–22.3, 12p13.31, 17p, 18q and Yp in ECC tumours. Gain of 2q37.3 was observed in the samples of both tumour subtypes, ICC and ECC. The developed genomic model comprised four chromosomal regions that seem to enable the distinction between ICC and ECC, with an accuracy of 71.43% (95% CI 43% to 100%). Survival analysis revealed that in our cohort, patients with ECC survived on average 8 months less than patients with ICC.ConclusionsThis genomic characterisation and the introduction of genomic models to clinical practice could be important for patient management and for the development of targeted therapies. The power of this genomic model should be evaluated in other CC populations.
Clinicopathological differences in attached versus loose infarcted epiploic appendages: an analysis of 52 cases
Authors: Dignan Caroline , Senior Adya , Gonzalez Raul S 
Published: March 2021

BackgroundEpiploic appendages are fatty peritoneal structures on the external surface of the colon that can infarct and become necrotic in situ or autoamputate.AimsTo describe clinicopathological features of infarcted epiploic appendages (IEAs).MethodsWe reviewed 52 IEAs from 49 patients, recording numerous clinical and pathological characteristics, which were compared across attached and loose IEAs.ResultsTwenty-seven IEAs were attached, and 23 were loose; location was unclear in 2. Most were incidental; 3 attached cases caused ‘appendagitis’. Most (31, 60%) had a classic ‘egg-like’ appearance. Common findings included fat necrosis (84%), calcification (67%) and fibrosis (58%). Attached cases had a larger mean size (1.8 cm vs 1.3 cm, p=0.030) and were more often haemorrhagic (37% vs 4%, p=0.0064) and inflamed (67% vs 13%, p=0.0002). Loose cases were more often necrotic (100% vs 74%, p=0.011).ConclusionsIEAs have different morphology whether they remain attached to peritoneum or become necrotic and detached. Attached cases may cause symptoms.
Hospital admissions for stroke and bleeding in Hounslow following a quality improvement initiative
Authors: Wong Kam Ying , Davies Bethan , Adeleke Yewande , Woodcock Thomas , Matthew Dionne , Sekelj Sara , Orlowski Andi , Porter Bradley , Hashmy Sophia , Mathew Ammu , Grant Ron , Kaba Agnes , Unger-Graeber Brigitte , Petrungaro Bruno , Wallace Jordan , Bell Derek , Cowie Martin R , Khan Sadia 
Published: March 2021

ObjectiveAtrial fibrillation (AF) is the most common arrhythmia. Undiagnosed and poorly managed AF increases risk of stroke. The Hounslow AF quality improvement (QI) initiative was associated with improved quality of care for patients with AF through increased detection of AF and appropriate anticoagulation. This study aimed to evaluate whether there has been a change in stroke and bleeding rates in the Hounslow population following the QI initiative.MethodsUsing hospital admissions data from January 2011 to August 2018, interrupted time series analysis was performed to investigate the changes in standardised rates of admission with stroke and bleeding, following the start of the QI initiative in October 2014.ResultsThere was a 17% decrease in the rate of admission with stroke as primary diagnosis (incidence rate ratio (IRR) 0.83; 95% CI 0.712 to 0.963; p<0.014). There was an even larger yet not statistically significant decrease in admission with stroke as primary diagnosis and AF as secondary diagnosis (IRR 0.75; 95% CI 0.550 to 1.025; p<0.071). No significant changes were observed in bleeding admissions. For each outcome, an additional regression model including both the level change and an interaction term for slope change was created. In all cases, the slope change was small and not statistically significant.ConclusionReduction in stroke admissions may be associated with the AF QI initiative. However, the immediate level change and non-significant slope change suggests a lack of effect of the intervention over time and that the decrease observed may be attributable to other events.
Surface terms in effective action of O-plane at order $$\alpha '^2$$
Authors: Akou Yasser , Garousi Mohammad R. 
Published: March 2021

AbstractThe effective action of string theory has both bulk and boundary terms if the spacetime is an open manifold. Recently, the known classical effective action of string theory at the leading order of $$\alpha '$$

α
′

and its corresponding boundary action have been reproduced by constraining the effective actions to be invariant under gauge transformations and under string duality transformations. In this paper, we use this idea to find the classical effective action of the O-plane and its corresponding boundary terms in type II superstring theories at order $$\alpha '^2$$

α

′
2

and for NS–NS couplings. We find that these constraints fix the bulk action and its corresponding boundary terms up to one overall factor. They also produce three multiplets in the boundary action that their coefficients are independent of the bulk couplings under the string dualities.
Being Small and Intronic: miRNAs That Count!
Authors: Calin George A. 
Published: March 2021

Abstract
The roles of intronic miRNAs and their functional interaction with host coding genes represent a topic that is poorly explored. The study by Kwok and colleagues in this issue of Cancer Research presents solid evidence that the FTX locus at chromosome Xq13.2 contains multiple noncoding RNAs (ncRNA) with coordinated expression and concordant functional effects in colorectal cancer cells. The long ncRNA, FTX, and its intronic miRNAs, miR-374a, -374b, -421, and -545, are all interconnected in a functional network, including the downstream protein coding targets DHX9, DICER, PTEN, and RIG-I. These data prove that multigenic loci in the human genome have a complex functional landscape that modulates key signaling pathways for cancer progression and that much knowledge with potential clinical utility remains to be revealed from the largest, noncoding portion of the genome.
See related article by Kwok et al., p. 1308
Abstract IA-15: Platforms to improve reproducibility in artificial intelligence research
Authors: Haibe-Kains Benjamin , Mammoliti Anthony , Nakano Minoru 
Published: March 2021

Abstract
As artificial intelligence (AI) becomes a method of choice to analyze biomedical data, the field is facing multiple challenges around research reproducibility and transparency. Given the proliferation of studies investigating the applications of AI in research and clinical studies, it is essential for independent researchers to be able to scrutinize and reproduce the results of a study using its materials, and build upon them in future studies. Computational reproducibility is achievable when the data can easily be shared and the required computational resources are relatively common. However, the complexity of AI algorithms and their implementation, the need for specific computer hardware and the use of sensitive biomedical data represent major obstacles in healthy-related AI research. In this talk, I will describe the various aspects of an AI biomedical study that are necessary for reproducibility and the platforms that exist for sharing these materials with the scientific community.
Citation Format: Benjamin Haibe-Kains, Anthony Mammoliti, Minoru Nakano. Platforms to improve reproducibility in artificial intelligence research [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-15.
Abstract IA-18: Big pediatric cancer genomic data: Discovery, precision medicine, and data sharing
Authors: Zhang Jinghui 
Published: March 2021

Abstract
High throughput next generation sequencing (NGS) has enabled tremendous advances in our understanding of the genomic landscape of pediatric cancer, leading to discoveries of new mechanisms of tumor initiation and progression, novel targets, and diagnostic and prognostic markers. By focusing on the development of innovative computational analysis tools, we have investigated the genomic variants in pediatric cancer in the following areas: 1) genomic landscapes of &gt;20 subtypes of pediatric cancer; 2) a pan-cancer study of genomes and transcriptomes of pediatric cancer which unveiled that &gt;50% of the driver genes are absent in adult cancer; 3) clonal evolution of relapsed pediatric leukemia driven by therapy-induced variants bearing novel mutational signature; and 4) pathogenic germline mutations in cancer predisposition genes. These insights led to implementation of clinical cancer genomic profiling by three-platform of whole genome, whole exome and transcriptome sequencing for all eligible pediatric cancer patients at St Jude. Use of these data sets improved molecular diagnosis of pediatric cancer. Since 2015, we have analyzed &gt;1,200 pediatric oncology patients providing critical data that may affect patient care. The omics data generated from our research and clinical programs can be accessed on St. Jude Cloud (https://www.stjude.cloud), a cloud-based data sharing ecosystem for accessing, analyzing and visualizing genomic data generated from &gt;10,000 pediatric cancer patients, long-term survivors of pediatric cancer and &gt;800 pediatric sickle cell patients. Access to three interconnected Apps on St. Jude Cloud, i.e. Genomics Platform, Pediatric Cancer (PeCan) Knowledgebase and Visualization Community, provides a unique experience for simultaneously performing advanced data analysis and enhancing the knowledgebase for pediatric cancer.
Citation Format: Jinghui Zhang. Big pediatric cancer genomic data: Discovery, precision medicine, and data sharing [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-18.
Abstract IA-23: Multi-scale modeling of cancer patients
Authors: Gevaert Olivier 
Published: March 2021

Abstract
Recently biomedical analysis in both imaging and non-imaging data has advanced rapidly aided by the modeling power of deep learning networks. Complex diseases such as cancer may reveal different aspects of disease characteristics in different data modalities, and thus integrating imaging with non-imaging biomarkers and clinical data has the potential to discover information that is not obvious in any single data modality. We are developing methods for integrating image, genomic and clinical data to improve precision medicine applications for cancer patients. Our results suggest that the integration strategies achieve improved prediction results than using each single data modality alone. We further show that information is transferrable across age groups or tumor subtypes under the data fusion framework.
Citation Format: Olivier Gevaert. Multi-scale modeling of cancer patients [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-23.
Abstract IA-24: Towards robust image based models for cancer risk assessment
Authors: Barzilay Regina , Yala Adam 
Published: March 2021

Abstract
To enable targeted personalized screening, we need to advance cancer risk models. Despite significant research on this topic, statistical models used today in clinical practice routinely undeperform. In this talk, I explore the potential of AI-based models for breast cancer prediction based on mammographic images. These models have already shown significantt performance gains over their counterparts. However, to bring deep learning models to clinical practice, we need to further refine their accuracy, validate them across diverse populations, and demonstrate their potential to improve clinical workflows. To this end, we propose Mirai, a new risk algorithm designed to predict risk at multiple time points, leverage potentially missing risk-factor information, and produce predictions that are consistent across mammography machines. The architectture of the new model will be covered in detail in the talk. Mirai was trained on a large dataset from Massachusetts General Hospital (MGH) in the US and was tested on held-out test sets from MGH, Karolinska in Sweden and Chang Gung Memorial Hospital in Taiwan, obtaining C-indices of 0.76 (95% CI 0.74, 0.80), 0.81 (0.79, 0.82), 0.79 (0.79, 0.83), respectively. Mirai obtained significantly higher five-year ROC AUCs than the Tyrer-Cuzick model (p&lt;0.001) and prior deep learning models, Hybrid DL (p&lt;0.001) and ImageOnly DL (p&lt;0.001), trained on the same MGH dataset.
Citation Format: Regina Barzilay, Adam Yala. Towards robust image based models for cancer risk assessment [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-24.
Abstract PO-008: Federated learning on whole slide images using weakly supervised computational pathology
Authors: Lu Ming Yang , Kong Dehan , Lipkova Jana , Chen Richard J. , Singh Rajendra , Chen Tiffany Y. , Williamson Drew F. K. , Mahmood Faisal 
Published: March 2021

Abstract
Deep learning has excelled in a wide range of tasks in computational pathology, including but not limited to the characterization of both common and rare morphological phenotypes and the prediction of molecular alterations from histology. However, large datasets of diverse, high-quality annotated training examples are often necessary in order to build robust and accurate deep learning-based models for real-world test scenarios. Pooling medical data from multiple institutions can alleviate this challenge but is constrained by privacy concerns associated with sharing sensitive patient information, in addition to the technical challenges of transferring and storing large whole slide image files at the terabyte or even petabyte scale. On the other hand, federated learning is a promising field of machine learning research that allows algorithms to learn from multiple decentralized data sources without requiring training data to be directly shared. We introduce a federated learning framework for computational pathology on whole slide images using attention-based multiple instance learning for weakly-supervised learning and random noise generation for preserving differential privacy. We evaluated our approach on two different classification problems using large multi-centric datasets, including renal cell carcinoma (RCC) histologic subtyping and invasive breast carcinoma (BRCA) histologic subtyping. We additionally demonstrate that our approach can be used for survival prediction and patient stratification from whole slide images. Our proposed privacy-preserving federated learning framework can be used to develop accurate, interpretable deep learning models from distributed data sources for both classification and survival prediction.
Citation Format: Ming Yang Lu, Dehan Kong, Jana Lipkova, Richard J. Chen, Rajendra Singh, Tiffany Y. Chen, Drew F. K. Williamson, Faisal Mahmood. Federated learning on whole slide images using weakly supervised computational pathology [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-008.
Abstract PO-018: Computational staining of tumor hypoxia from H&E images using convolutional neural networks
Authors: Zaidi Mark , Cui Haotian , Wang Bo , McKee Trevor D. , Wouters Bradly G. 
Published: March 2021

Abstract
Heterogeneity in the tumor environment can be driven by multiple biochemical processes, such as immune infiltration, cell-to-cell genetic variation, and oxygenation gradients. Low oxygen, termed hypoxia, has been strongly correlated with metastasis, radiation resistance, and poor prognosis. One ongoing challenge is determining the magnitude of how hypoxia influences treatment response relative to other features, such as proliferation and micro vessel density. While it is possible to stain banked tissue for these markers, it is challenging to stain for endogenous markers of hypoxia. Exogenous probes have been developed to overcome this barrier; however, most clinical samples lack such probes. Previously, we have shown that spatial distributions of hypoxia correlate with morphological features of the tumor, such as necrosis and vasculature. Some of these features can be identified in hematoxylin and eosin (H&E)-stained sections, present for nearly all tumor samples. We hypothesize that a trained machine learning algorithm can be used to accurately predict hypoxia distributions given an H&E-stained section. Colorectal, pancreatic, and ovarian cancer cell line xenografts were cut and stained for markers of hypoxia, proliferation, perfusion, vasculature, and H&E. Binary masks of necrosis were generated, and together with the H&E and hypoxia images, were used to train a convolutional neural network. Once the model had been trained, a fairly precise prediction (MSE &lt;0.012) had been obtained relative to the ground-truth hypoxia stained images. We believe a model trained on clinical H&E and hypoxia sections can be used to accurately predict hypoxia distributions from a H&E section. Predicted hypoxia distributions from existing H&E histology can be used to evaluate the influence of hypoxia on treatment outcome, by retroactively comparing a patient’s prognosis with levels of hypoxia. Correlations between hypoxia and prognostic measures such as tumor volume after irradiation, would indicate hypoxia may have modulated the efficacy of the treatment. Thus, computational stain prediction of hypoxia can serve as an invaluable tool in closing the gap between preclinical and clinical implementations of hypoxia-targeted therapies.
Citation Format: Mark Zaidi, Haotian Cui, Bo Wang, Trevor D. McKee, Bradly G. Wouters. Computational staining of tumor hypoxia from H&E images using convolutional neural networks [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-018.
Abstract PO-029: Survival prediction of non-small cell lung cancer by deep learning model integrating clinical and positron emission tomography data
Authors: Kang Sae-Ryung , Oh Seungwon , Oh In-Jae , Min Jung-Joon , Bom Hee-Seung , Yang Hyung-Jeong , Lee Guee-Sang , Kim Soo-Hyung , Kim Min Soo 
Published: March 2021

Abstract
The Cox proportional hazards model (CPH), the standard method for survival analysis in cancer patients, is difficult to use maximal information of positron emission tomography (PET) images containing information that reflects underlying pathophysiology. In this retrospective study, we aimed to investigate a deep learning model integrating clinical and PET image data to improve survival prediction in non-small cell lung cancer (NSCLC) patients. We developed a bimodal learning model thorough DeepSurv-based model using 3D-CNN-based 3D-Resnet model for image data and deep neural network (DNN) model for clinical data. DeepSurv-based model combining clinical and PET image data showed the best performance among the four models, the c-index of the training and testing sets reaching 0.898 and 0.768, respectively, followed by DeepSurv-based model for single modalities (clinical (0.763/0.740) or PET (0.773/0.743)) and CPH (0.726/0.736). Deep learning-based survival prediction with combination of two modalities may improve prediction accuracy in NSCLC patients.
Citation Format: Sae-Ryung Kang, Seungwon Oh, In-Jae Oh, Jung-Joon Min, Hee-Seung Bom, Hyung-Jeong Yang, Guee-Sang Lee, Soo-Hyung Kim, Min Soo Kim. Survival prediction of non-small cell lung cancer by deep learning model integrating clinical and positron emission tomography data [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-029.
Abstract PO-031: Evaluating clinical utility of organs-at-risk segmentation in head & neck cancer by simple open-source 3D CNNs
Authors: Marsilla Joseph , Haibe-Kains Benjamin 
Published: March 2021

Abstract
Open source code helps the scientific community converge to breakthroughs at an accelerated rate. Only one of 14 previous deep learning based OAR segmentation studies met most open-source standards. Using this study as a benchmark, we will assess the segmentation quality of each network discussed and provide the community with pre-trained weights of the top-performing models. 11 open-source 3D segmentation models originally engineered for medical image segmentation in both the 2D and 3D domains were trained to automatically segment 19 OaR classes. For this study, a large internally curated dataset from the University Health Network (UHN) of 582 patient scans was used. All models were tested on a hold out set from the 582 cohort of 59 patients. Models were also tested on 98 external patient scans from publicly available sources. 10 test set contours from the winning network were assessed by an expert radiation oncologist with 10+ years of experience to identify the observer of the contour set (whether AI or human). Preliminary results show that only 13 out of 20 scans were identified correctly. Results show that simple 3D architectures consistently outcompete more complex networks by producing more accurate, clinically acceptable contours. A more thorough clinical acceptability test is underway to establish a protocol for integrating deep learning based auto contouring models into radiation therapy planning workflows.
Citation Format: Joseph Marsilla, Benjamin Haibe-Kains. Evaluating clinical utility of organs-at-risk segmentation in head & neck cancer by simple open-source 3D CNNs [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-031.