bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract IA-10: Machine learning enabled analysis of high content imaging datasets: Progress and prospects
Authors: Sultan Mohammad Muneeb 
Published: March 2021

Abstract
In the last decade deep learning has had a very large impact in the field of image analysis. In this talk, I will discuss several vignettes into how deep learning when applied to biomedical imaging datasets can provide us with insights that can further drug discovery campaigns and uncover new biological insights. In particular, I will show how deep learning can learn a phenotypic manifold that allows us to interrogate the dose response behavior of compounds, measure the phenotypic similarity of compounds, and deconvolute MOAs of new compounds.
Citation Format: Mohammad Muneeb Sultan. Machine learning enabled analysis of high content imaging datasets: Progress and prospects [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-10.
Abstract IA-12: Functional analysis of BRCA regulatory variants with deep learning models
Authors: Troyanskaya Olga 
Published: March 2021

Abstract
Precision medicine in oncology depends on identifying variants that are functional and interpreting them in terms of cancer disease risk, treatment guidance, and prognosis. This is especially challenging for regulatory variants, which constitute the majority of the genome and control transcription and RNA processing. We use state-of-the-art deep learning models that predict the molecular impact of variants to systematically analyze genomes and SNPs and discover BRCA-associated SNPs that impact transcriptional and post-transcriptional regulation of gene expression, identify genes and biological processes implicated across tissues of impact, and reconcile these findings with prior genomic annotation-based analysis.
Citation Format: Olga Troyanskaya. Functional analysis of BRCA regulatory variants with deep learning models [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-12.
Abstract IA-26: Artificial intelligence for regulatory science research
Authors: Tong Weida 
Published: March 2021

Abstract
Artificial Intelligence (AI) has impacted a board range of scientific disciplines that are important to public health, ranging from clinical diagnosis and prognosis, drug and food safety, disease prevention, precision medicine and nutrition. The rise of AI has also offered both opportunities and challenges to regulatory agencies with questions such as (1) how to assess and evaluate AI-based products and (2) how to develop and implement AI-based application to improve the agencies functions. In this presentation, the current thinking and on-going efforts at FDA in the area of AI will be discussed with examples from drug safety and biomarker discovery and development. The guiding principle and best practice of applying AI in regulatory science research will also be discussed with respect to a proposed framework, called TRIAL, for AI transparency, reproducibility, interpretability, applicability and liability.
Citation Format: Weida Tong. Artificial intelligence for regulatory science 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-26.
Abstract PO-010: Feature pyramid network for revealing tumour infiltrating lymphocyte presence and distribution in a whole slide image
Authors: Mazurski Jonathan , Nofech-Mozes Sharon , Bassiouny Dina , Martel Anne L. 
Published: March 2021

Abstract
Ductal Carcinoma in Situ (DCIS) is a subtype of non-invasive breast cancer contained in the ducts of the breast. DCIS is not a life-threatening condition however a small number of cases will progress to or reoccur as invasive breast cancer. Clinical workflows lack a robust biomarker to determine which patients will reoccur. Recently pathologists have turned towards investigating the tumour microenvironment to predict cancer recurrence. Machine learning models present an opportunity to analyze these features on large amounts of data with minimal requirement of pathologist time. One feature of recent interest are Tumour infiltrating lymphocytes or TILs, lymphocytes within a small region around a cancerous duct. Through a method of bootstrapping a U-Net on top of a fully convolutional network we have designed a network that is able to segment lymphocyte and malignant cells in H&E Images of Ductal Carcinoma In Situ. The original network was trained on a private set of cellularity data in breast cancer, manually annotated by pathologists to mark the centre and type of each cell in a patch. Probabilistic output from this model was used to generate segmentation maps on a much larger dataset of DCIS images from the Ontario DCIS Cohort. These segmentation maps were used as training data for a feature pyramid network. The results of this bootstrapping method was a significant increase to speed of the model in addition to much larger flexibility in the input size and shape for the model, which came at a small cost to overall model accuracy. In addition to the original segmentation target, the use of this model to generate lower resolution WSI heat-maps has been investigated for this work. This model can generate low resolution heat-maps for a 20X magnification whole slide image showing the presence of malignant cells in addition to the location and distribution of TILs around ducts. This can provide a visual overview of differences between slides and we will be examining the potential for this output to serve as information for analytical methods to process to predict recurrence free survival.
Citation Format: Jonathan Mazurski, Sharon Nofech-Mozes, Dina Bassiouny, Anne L. Martel. Feature pyramid network for revealing tumour infiltrating lymphocyte presence and distribution in a whole slide image [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-010.
Abstract PO-016: Seeing glycolysis on PDAC: Applying deep learning convolutional neural network model
Authors: Tsou Peiling , Wu Chang-Jiun 
Published: March 2021

Abstract
The reprogramming of cellular metabolism to support continuous proliferation is a hallmark of cancer. Accumulating evidence indicates that the reprogramming of tumor metabolism is under the control of various oncogenic signals. The KRAS oncogene is known to induce aerobic glycolysis, and pancreatic adenocarcinoma (PDAC) cells have been shown previously to have metabolism consistent with elevated aerobic glycolysis. Survival analysis showed that higher glycolysis activity was associated with worse clinical outcome in TCGA PDAC. Several glycolytic inhibitors are currently in preclinical and clinical development. It had been reported that cancer metabolic status could play a role in the interaction with tumor microenvironment. We hypothesized that cancer glycolysis activity could affect tumor histopathological architecture in PDAC. As a proof-of-concept study, we trained a deep learning convolutional neural network (CNN) model (Google Inception v3) on histopathological images obtained from TCGA PAAD cohort to classify tumors with high and low glycolysis pathway activity. We aimed to answer whether CNNs can predict PDAC glycolysis status using images as the only input. Our model was tested on separate tissue samples from the same cohort. Aggregated accuracy was 75% on 48 cases with area under ROC curve as 0.751. Prediction scores significantly correlated with transcriptome-based glycolysis signature (Pearson correlation = 0.352, p=0.007). The current study demonstrates the potential of deep learning approaches for histopathologically classifying cancer based on metabolic status. This information could be of value in assisting clinical decisions on targeting cancer metabolism.
Citation Format: Peiling Tsou, Chang-Jiun Wu. Seeing glycolysis on PDAC: Applying deep learning convolutional neural network model [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-016.
Abstract PO-022: Automated liver tissue segmentation in point of care ultrasound b-mode images using U-Net
Authors: Garcia Raul Blazquez , Lightstone Adi , Ibrahim Miriam Naim , El Kaffas Ahmed 
Published: March 2021

Abstract
Introduction/Purpose: We have recently developed and demonstrated that tissue acoustic properties can facilitate early detection of liver cancer during period abdominal ultrasound surveillance exams in high risk patients. Automated segmentation of the liver is a necessary step in increasing the accuracy, sensitivity, and specificity of Machine Learning Models which detect cancer using the lower quality data generated from Point-of-Care Ultrasound (PoCUS). We have developed deep learning algorithms which can successfully segment the data generated by these PoCUS systems. Methods: A total of 1000 patients from 4 collaboration sites were recruited, though for this study only 25 of which were manually segmented were used.. For this HIPPA compliant study, all data was obtained through IRBs approved at the local data acquisition site; all patients were consented before data acquisition. The patient's mean age and BMI were 43(11 STD) and 23 (5 STD). All patients were scanned using the same acquisition protocol, capturing the same 5 scan visualization windows of the liver using a PoCUS system, with capabilities to capture raw signal data. We use a deep learning algorithm to automatically segment out the liver. The initial version of the U-net Deep learning architecture was trained on a subset of our data containing B-mode images from 20 patients (500 frames) and tested on a dataset with 5 patients (125 frames). The Regions of interest that served as training and testing labels for the algorithm were outlined on an image-by-image basis by expert sonographers. U-Net is a fully convolutional neural network that consists of a block of contraction layers followed by a block of expansion layers. The architecture contains feature channels to relay context information to higher resolution layers. U-Net has been used at multiple medical segmentations tasks in which it performs particularly well even with small datasets. Results: The architecture successfully segmented the images both in the train and test datasets with a great degree of accuracy and generalization. Test accuracies surpassed 86% with a sensitivity of 80% and specificity of 95% and results can be further improved by applying thresholds to the algorithm’s output. Conclusion: This automated segmentation algorithm is a clear next step in the improvement of any automated disease detection method in the liver. Once in place it will greatly aid in the early screening of all liver diseases and improve the outcomes for at risk patients.
Citation Format: Raul Blazquez Garcia, Adi Lightstone, Miriam Naim Ibrahim, Ahmed El Kaffas. Automated liver tissue segmentation in point of care ultrasound b-mode images using U-Net [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-022.
Abstract PO-033: The impact of the variation of CT scanner on the prediction of HPV status in head & neck cancer patients
Authors: Reiazi Reza , Arrowsmith Collin , Abbas-Aghababazadeh Farnoosh , Eles Christopher , Rezaie Aria , Bratman Scott V. , Hope Andrew J. , Haibe-Kains Benjamin 
Published: March 2021

Abstract
Radiomics involves the use of high-dimensional quantitative imaging features for predictive purposes. However, studies showed that these radiomic features are sensitive to the variability of imaging parameters (e.g., scanner model). One of the major challenges in radiomics lies in improving the robustness of quantitative features against the variation in the imaging dataset in multi-center studies. Here, we assess the impact of scanner choice on the computed tomography (CT)-derived radiomic features to predict association of oropharyngeal squamous cell carcinoma with human papillomavirus (HPV), which has a well-established impact on CT-derived radiomic features. This experiment was performed on CT image datasets acquired with two different scanner types. We demonstrate strong scanner dependency by developing a machine learning model to classify HPV status from radiological images. These experiments revealed the effect of scanner type on the robustness of the radiomic features, and the extent of this dependency is reflected on the performance of HPV prediction models. The result of this study highlighted the importance of implementing an appropriate approach to reduce the impact of the imaging domain radiomic features and consequently on the machine learning models.
Citation Format: Reza Reiazi, Collin Arrowsmith, Farnoosh Abbas-Aghababazadeh, Christopher Eles, Aria Rezaie, Scott V. Bratman, Andrew J. Hope, Benjamin Haibe-Kains. The impact of the variation of CT scanner on the prediction of HPV status in head & neck 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 PO-033.
Abstract PO-041: Shape features predicting intrahepatic progression-free and overall survival for SBRT treated HCC patients using radiomics and deep learning based survival models
Authors: Wei Lise , Jiang Jue , Veeraraghavan Harini , El Naqa Issam 
Published: March 2021

Abstract
Purpose: Intrahepatic failure is a major concern in the treatment of hepatocellular carcinoma (HCC) but lacks clinically reliable predictive markers. This work aims to identify a radiomic signature for risk prediction of intrahepatic progression in HCC patients treated with stereotactic body radiation therapy (SBRT) by extracting magnetic resonance imaging (MRI) and contrast-enhanced CT phenotypes from tumor(s) and liver parenchyma. Materials/Methods: 94 HCC patients treated with SBRT were retrospectively analyzed. The range of follow-up time is 57-1768 days. Samples were right censored if there was no intrahepatic progression within the follow-up time. The event rates is 43/94=45.7%. 112 radiomics features were extracted from tumors and liver parenchyma from CECT and MRI using pyradiomics platform. CT images were first added a 1000 HU shift and MRI images were standardized by the mean and standard deviation. Univariate Cox analysis was applied for radiomics features analysis. Multivariate Cox models and survival neural networks were also used to predict the outcome. Multiple folds cross-validation was used for model evaluation. Models’ performances were assessed using Harrell’s c-index and Kaplan-Meier plot. Results: Sphericity, small dependence emphasis, small dependence high gray level emphasis, run high gray level emphasis and coarseness are significant predictors from MR tumors for the outcome. The Cox models showed c-index of 0.600 (CI 95%: 0.558-0.642). Sphericity and maximal correlation coefficient are significant predictors from CT tumors for the outcome, with a c-index of 0.613 (CI 95%: 0.576-0.649). Volume, elongation and sphericity are significant predictors for overall survival, with a c-index of 0.584 (CI 95%: 0.532-0.636). None of the liver parenchyma features were important predictors. Interestingly, convolutional neural networks (CNN) were applied on the contour of the tumors and the c-index was 0.668 (CI 95%: 0.612-0.723). The 2 channel (MR-tumor region and contour mask) CNN network showed better performance of 0.698 (CI 95%: 0.658-0.749). Conclusions: Shape features showed predictive power for both intrahepatic progression-free and overall survival endpoints, which was proven using both radiomics feature based Cox modeling and CNN based contour methods. Adding MR imaging improved the performance.
Citation Format: Lise Wei, Jue Jiang, Harini Veeraraghavan, Issam El Naqa. Shape features predicting intrahepatic progression-free and overall survival for SBRT treated HCC patients using radiomics and deep learning based survival models [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-041.
Abstract PO-046: MethylationToActivity: A deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors
Authors: Williams Justin , Xu Beisi , Putnam Daniel , Thrasher Andrew , Li Chunliang , Yang Jun , Chen Xiang 
Published: March 2021

Abstract
Although genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers for tumor detection, subtyping, and classification, their direct biological impacts at the individual gene level remain elusive. Here we present MethylationToActivity (M2A), a machine learning framework that uses convolutional neural networks to infer promoter activities based on H3K4me3 and H3K27ac enrichment, from DNA methylation patterns for individual genes. Using publicly available datasets in real-world test scenarios, we demonstrate that M2A is highly accurate and robust in revealing promoter activity landscapes in various pediatric and adult cancers, including both solid and hematologic malignant neoplasms.
Citation Format: Justin Williams, Beisi Xu, Daniel Putnam, Andrew Thrasher, Chunliang Li, Jun Yang, Xiang Chen. MethylationToActivity: A deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors [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-046.
Abstract PO-049: Pseudo-alignment resolution in discriminating mouse reads
Authors: Jahangiri-Tazehkand Soheil , Agha-Babazadeh Farnoosh , Haibe-Kains Benjamin 
Published: March 2021

Abstract
Patient-Derived Xenografts (PDX) are the best preclinical models to study cancer biology preclinical drug screening. PDXs are generated by implanting a piece of fresh cancer tissue directly into an immunodeficient mouse to grow and imitate a human tumor. Previous studies have shown that PDXs are able to accurately recapitulate the (epi-)genomic characteristics of the original tumor. Yet the inclusion of the mouse cells in tumor biopsies can introduce unwanted noise and bias in high throughput (epi-)genomics analyses and interpretation. Thus, filtering the mouse reads is one of the first and important steps in analyzing high throughput PDX sequencing data. There are two main approaches to account for mouse reads in analyzing bulk RNAseq data generated from PDXs. In the first approach, an external tool is used to classify the raw or aligned reads into a human, mouse, or ambiguous reads. Examples include XenoFilter, BBsplit, disambiguate, and Xenome. In the second approach, a mixed human-mouse reference genome is used when aligning the raw reads. Multiple studies have shown that the mixed reference approach produces better or similar results compared to when an external tool is used for mouse read discrimination. However, these comparisons have employed exact alignment methods when using a mixed reference approach. Recently, the significant deluge of sequencing data has brought attention to pseudo-alignment based methods, specifically Kallisto and Salmon. By taking advantage of pseudo-alignment to a reference transcriptome, these tools are capable of analyzing millions of reads in minutes while demonstrating near-optimal quantification performance. Yet their performance in analyzing PDX RNAseq data while using a mixed reference is unclear. Moreover, among the external methods, only Xenome has a pseudo-alignment strategy for read classification and the rest of the methods use exact alignment making them labor-intensive and intractable for large datasets. In this study, we compare two main mouse read discrimination pipelines when using Kallisto and Salmon. By sampling from two human and mouse lung RNAseq datasets, we have simulated five pseudo-PDX RNAseq datasets with different contamination ratios. By comparing outputs of different discriminating strategies to the ground truth, we show that Kallisto and Salmon when used with mixed human-mouse transcriptome produce results that are more correlated with the ground truth. Moreover, by differential gene expression analyses we show that fewer genes are significantly affected when using the mixed reference approach.
Citation Format: Soheil Jahangiri-Tazehkand, Farnoosh Agha-Babazadeh, Benjamin Haibe-Kains. Pseudo-alignment resolution in discriminating mouse reads [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-049.
Abstract PO-051: Development of web-based quality-assurance tool for radiotherapy target delineation for head and neck cancer: Quality evaluation of nasopharyngeal carcinoma
Authors: Kim Jun Won , Marsilla Joseph , Kazmierski Michal , Tkachuk Denis , Haibe-Kains Benjamin , Hope Andrew 
Published: March 2021

Abstract
Purpose: We developed Label-It, a new web-application for rapid review of radiotherapy (RT) target volumes, and used it to evaluate the relationship between target delineation compliance to the international guidelines and treatment outcomes of nasopharyngeal carcinoma (NPC) patients undergoing definitive RT. Methods and Materials: Our radiographic image database consists of anonymized simulation CT scans, RT structures, and treatment data of 3,211 head and neck cancer patients treated between July 2005 and August 2017. We used 332 patients treated with intensity-modulated RT for pathologically confirmed NPC as the study cohort and imported intermediate risk clinical target volumes of the primary tumor (IR-CTVp) receiving 56 Gy into Label-It. We determined inclusion of anatomic sites within IR-CTVp in accordance with 2018 International guideline for CTV delineation for NPC and correlated the results with time to local failure (TTLF) using Cox-regression. Results: At a median follow-up of 5.6 years, 5-year TTLF and overall survival rates were 93.1% and 85.9% respectively. The most frequently non-compliant anatomic sites were sphenoid sinus (n = 69, 20.8%), followed by cavernous sinus (n = 38, 11.4%), left and right petrous apices (n = 37 and 32, 11.1% and 9.6%), clivus (n = 14, 4.2%), and right and left foramen rotundum (n = 14 and 12, 4.2% and 3.6%). Among 23 patients with a local failure (6.9%), the number of non-compliant cases were 8 for sphenoid sinus, 7 cavernous sinus, 4 left and 3 right petrous apices, and 2 clivus. In Cox regression analysis, T4 disease (p = 0.003), RT alone (p = 0.007), cavernous sinus non-conformity (p = 0.020) were independent prognostic factors for TTLF. Conclusions: Label-It was an effective tool for rapid review of target volumes in a large patient cohort. Despite a high compliance to the international guidelines, inadequate coverage of cavernous sinus was correlated with decreased TTLF.
Citation Format: Jun Won Kim, Joseph Marsilla, Michal Kazmierski, Denis Tkachuk, Benjamin Haibe-Kains, Andrew Hope. Development of web-based quality-assurance tool for radiotherapy target delineation for head and neck cancer: Quality evaluation of nasopharyngeal carcinoma [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-051.