bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract PO-003: Deep learning identifies conserved pan-cancer tumor features
Authors: Noorbakhsh Javad , Farahmand Saman , Foroughi pour Ali , Namburi Sandeep , Caruana Dennis , Rimm David , Soltanieh-ha Mohammad , Zarringhalam Kourosh , Chuang Jeffrey H. 
Published: March 2021

Abstract
Histopathological images are an integral data type for studying cancer. We show pre-trained convolutional neural networks (CNNs) can be systematically applied across cancer types, enabling comparisons to reveal shared spatial behaviors. We develop CNNs with a common architecture trained on 19 cancer types of The Cancer Genome Atlas (TCGA), analyzing 14459 hematoxylin and eosin scanned frozen tissue images. Our CNNs are based on the Inception-V3 network and classify TCGA pathologist-annotated tumor/normal status of whole slide images in all 19 cancer types with consistently high AUCs (0.995±0.008). Remarkably, CNNs trained on one tissue are effective in others (AUC 0.88±0.11), with classifier relationships recapitulating known adenocarcinoma, carcinoma, and developmental biology. Moreover, classifier comparisons reveal intra-slide spatial similarities, with an average tile-level correlation of 0.45±0.16 between classifier pairs on the TCGA test sets. In particular, the TCGA-trained classifiers had average tile-level correlation of 0.52±0.09 and 0.58±0.08 on hold-out TCGA lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) test sets, respectively. These relations are reflected on two external datasets, i.e., LUAD and LUSC whole slide images of Clinical Proteomic Tumor Analysis Consortium. The CNNs trained on TCGA achieved cross-classification AUCs of 0.75±0.12 and 0.73±0.13 on LUAD and LUSC external validation sets, respectively. These CNNs had average tile-level correlations of 0.38±0.09 and 0.39±0.08 on LUAD and LUSC validation sets, respectively. Breast cancers, bladder cancers, and uterine cancers have spatial patterns that are particularly easy to detect, suggesting these cancers can be canonical types for image analysis. This study illustrates pre-trained CNNs can detect tumor features across a wide range of cancers, suggesting presence of pan-cancer tumor features. These shared features allow combining datasets when analyzing small samples to narrow down the parameter search space of CNN models.
Citation Format: Javad Noorbakhsh, Saman Farahmand, Ali Foroughi pour, Sandeep Namburi, Dennis Caruana, David Rimm, Mohammad Soltanieh-ha, Kourosh Zarringhalam, Jeffrey H. Chuang. Deep learning identifies conserved pan-cancer tumor features [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-003.
Abstract PO-004: A deep convolutional neural network for segmentation of whole-slide pathology images in glioblastoma
Authors: Shirazi Amin Zadeh , McDonnell Mark D. , Fornaciari Eric , Bagherian Narjes Sadat , Scheer Kaitlin G. , Samuel Michael S. , Yaghoobi Mahdi , Ormsby Rebecca J. , Poonnoose Santosh , Tumes Damon , Gomez Guillermo A. 
Published: March 2021

Abstract
Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. Here, we have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading-edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Analysis of segmentation results from tumour images together with matched RNA expression data identified genetic signatures that are specific to these different tumour regions. We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Moreover, in silico cell ontology analysis and single-cell RNA sequencing data from resected glioblastoma tissue samples, showed that these tumour regions had different gene signatures, suggesting they are driven by different cell types in the tumour microenvironment. This points to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival. Overall, this work identifies key histopathological features that are indicative of patient survival and detected spatially associated genetic signatures that mediate tumour-stroma interactions that should be investigated as new targets in glioblastoma.
Citation Format: Amin Zadeh Shirazi, Mark D. McDonnell, Eric Fornaciari, Narjes Sadat Bagherian, Kaitlin G. Scheer, Michael S. Samuel, Mahdi Yaghoobi, Rebecca J. Ormsby, Santosh Poonnoose, Damon Tumes, Guillermo A. Gomez. A deep convolutional neural network for segmentation of whole-slide pathology images in glioblastoma [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-004.
Abstract PO-039: Artificial intelligence-based extracapsular extension prediction in head and neck cancer analysis
Authors: Wang Yibin , Duggar William N. , Thomas Toms V. , Roberts Paul , Bian Linkan , Wang Haifeng 
Published: March 2021

Abstract
Extracapsular extension (ECE) is a decisive indication for treatment planning of patients with head and neck squamous cell carcinoma (HNSCC). ECE occurs when metastatic tumor cells within the lymph node break through the nodal capsule into surrounding tissues. It is crucial to identify whether ECE occurs in HNSCC patient treatment. Current ECE detection practice mainly relies on the visual identification of doctors and professionals, which can be extremely labor intensive and time-consuming. Therefore, we aim to automatically perform ECE detection using a machine learning technique to classify whether ECE appears or not. In this research, we propose a systematic machine learning approach to detect ECE from 3D computed tomography (CT) scans. The process includes four steps: 1) apply sliding-cube to extract small 3D samples from patient data; 2) various 3D features are extracted for each sample; and 3) three machine learning models, gradient boosting (GB), random forest (RF), and support vector machine (SVM), with feature extraction and selection approaches, are employed for sample classification task; 4) classify the patient based on sample classification result. Different training scenarios are designed for the experimental test. Based on five-fold cross-validation, the experimental results have demonstrated that our model is able to classify ECE and non-ECE patients. To check the explainability of the models, feature analysis is also conducted. The outcome of this research is expected to promote the implementation of artificial intelligence for ECE classification and detection as well as head and neck cancer diagnosis in the radiology computer vision field.
Citation Format: Yibin Wang, William N. Duggar, Toms V. Thomas, Paul Roberts, Linkan Bian, Haifeng Wang. Artificial intelligence-based extracapsular extension prediction in head and neck cancer analysis [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-039.
Abstract PO-040: Retinal disease diagnosis through an adaptive deep learning model
Authors: Wang Haifeng 
Published: March 2021

Abstract
This research presents an adaptive deep learning model for retinal disease classification based on 3D optical coherence tomography (OCT) images. Computer-aided retinal disorder diagnosis is one of the critical research topics that can benefit the patient care process. 3D medical images can provide more comprehensive information about retinal structures. However, due to the higher data dimension and more complicated data pattern compared to 2D images, 3D image analysis is challenging the performance of many machine learning models. This research aims to develop a machine learning model for retinal disease diagnosis on 3D OCT images. A public dataset, which includes age-related macular degeneration (AMD), diabetic macular edema (DME), and the normal cases, is used in this study. The model includes three main procedures: 1) Image preprocessing is conducted for each cross-sectional retinal image. A novel Hough transform-based line detection approach is developed to extract 3D retinal layers. 2) 3D retinal layer samples are constructed, which are used to optimize an adaptive deep learning model to identify the most effective and efficient model structure. A novel objective function is proposed to optimize the convergence trend in this process. The constructed deep learning structure is based on the search result of a neural architecture space. 3) The optimized deep learning model is trained using the extracted 3D samples. The experimental results show that the proposed model can significantly boost the training efficiency and improve the testing accuracy.
Citation Format: Haifeng Wang. Retinal disease diagnosis through an adaptive deep learning 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-040.
Abstract PO-048: Visual nucleotyping identifies chromatin phenotypes triggered by genome editing
Authors: Nandakumar Vivek , Stehling-Sun Sandra , Kerwin William , Funnell Alister , Stamatoyannopoulos John 
Published: March 2021

Abstract
Optical microscopy has the potential to provide rapid phenotypic readout of cellular states. Here we show that automated optical phenotyping of nuclei is capable of rapidly and reliably discriminating the effects of targeted mutations to chromatin remodelers, which are major players in oncogenesis and targets for therapeutic intervention. Smarca4 is a chromatin remodeler frequently mutated in cancer. We investigated whether nuclear morphometry and concomitant supervised machine learning approaches on standard optical microscopy images of nuclear-DNA stained cells could reliably discern Smarca4-/- lung cancer cells from isogenic Smarca4+/+ clones that were derived by precision genome editing. We used TALEN-based editing on A549 lung cancer cells to generate isogenic Smarca4+/+ clones, performed conventional 2D 40x optical imaging of DAPI-stained cell nuclei from parental (Smarca4-/-) and edited (Smarca4+/+) cells, quantified a set of numerical parameters (features) that reflected various intuitive attributes of nuclear morphology and chromatin texture of the cell nuclei, and finally tested the efficacy of supervised machine learning algorithms containing these features to distinguish between the two cell categories. We found that the parental and edited A549 cells exhibited similar nuclear size and shape attributes but showed measurable differences in subtle chromatin texture. Additionally, classifiers based on these chromatin texture features were able to accurately distinguish the smarca4-rescued cells from the smarca4-mutant cells. Our results thus demonstrate the promise of quantitative chromatin phenotyping for genome editing applications in oncology.
Citation Format: Vivek Nandakumar, Sandra Stehling-Sun, William Kerwin, Alister Funnell, John Stamatoyannopoulos. Visual nucleotyping identifies chromatin phenotypes triggered by genome editing [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-048.
Abstract PO-076: Unsupervised learning of image embeddings enables new opportunities to extract novel information from digital pathology H&E images
Authors: Hipp Jason , Xu Mona , Bordeaux Lucas , Gu Feng , Pedrinaci Carlos , Baykaner Khan 
Published: March 2021

Abstract
Introduction: In digital pathology, large amounts of time and money are spent annotating whole slide images (WSIs). By using an AI algorithm trained on thousands of WSIs, it is possible to remove the process of annotating from the workflow whilst simultaneously finding potential imaging features that correlate with drug response, molecular phenotypes, histologic features (e.g. Tertiary Lymphoid Structures), inflammation at the tumor-stroma interface, artifacts, etc. Method: A pipeline was constructed for ingesting WSI from the set of diagnostic slides available in the cancer genome archive (TCGA). The WSIs were masked for tissue areas and divided into patches extracted at multiple magnification levels. These patches were used to train a U-Net (initialised with a pre-trained ResNet34) to complete a self-supervised in-painting task and evaluated with perceptual loss using VGG16. After training, embeddings were extracted from the bottleneck layer of the U-Net, and these were evaluated for their capacity to cluster according to tissue type and magnification level. Results: The trained embeddings showed strong clustering by tissue type, magnification level, and separation of artefacts. The results were qualitatively evaluated by pathologist with consensus and exceeded the performance of a baseline pre-trained ImageNet model. Conclusion: These results highlight a novel methodology for AI algorithm development that removes the need for numerous pathologists to annotate pathology images by hand and converts their role to reviewing and quality-checking images. AI algorithms based on this approach can have a significant impact in the pathologic evaluation of tissue for oncology clinical trials by extracting a richer set of features than glass microscopy alone would permit. Furthermore, self-supervised methods, such as in-painting allow for the training of embeddings that encapsulate a richer understanding of the data and are consequently more repurposable than supervised/labelled approaches.
Citation Format: Jason Hipp, Mona Xu, Lucas Bordeaux, Feng Gu, Carlos Pedrinaci, Khan Baykaner. Unsupervised learning of image embeddings enables new opportunities to extract novel information from digital pathology H&E images [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-076.
Abstract PO-093: Evaluating dependencies by rapid image-based ex vivo cancer biosensors
Authors: Al-Jazrawe Mushriq , Molnar Csaba , Rindtorff Niklas , Eser Pinar , Misek Sean , Alimova Maria , Ursu Oana , Colgan William , Attari Adel , Tsang Natalie , Keskula Paula , Rios Carmen , Tseng Moony , Carpenter Anne , McFarland James , Bass Adam , Klempner Samuel , Boehm Jesse 
Published: March 2021

Abstract
Successful mapping of cancer dependencies requires conducting genetic and drug screens on a diversity of models. However, the difficulty in generating long-term models of many cancers limits the share of patient samples that can be studied. Such long-term models have likely also lost the cellular heterogeneity present in the original tumor due to in vitro propagation. To overcome these limitations, we are developing image-based ex vivo cancer biosensors from early patient material. Using freshly received gastroesophageal cancer ascites, we are optimizing perturbation methods and utilizing single-cell transcriptomics and label-free microscopy to infer a subpopulation-specific vulnerability profile. We show that label-free microscopy can infer cell identity and viability in heterogeneous early patient samples. Additionally, early drug perturbation recapitulates observations made in established gastroesophageal cancer organoids. Successful implementation of ex vivo biosensors will expand the cancer dependency space by making perturbational studies accessible to more diverse samples, and by identifying and validating hits in a more immediate setting to the original tumor.
Citation Format: Mushriq Al-Jazrawe, Csaba Molnar, Niklas Rindtorff, Pinar Eser, Sean Misek, Maria Alimova, Oana Ursu, William Colgan, Adel Attari, Natalie Tsang, Paula Keskula, Carmen Rios, Moony Tseng, Anne Carpenter, James McFarland, Adam Bass, Samuel Klempner, Jesse Boehm. Evaluating dependencies by rapid image-based ex vivo cancer biosensors [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-093.
Effects of Adiposity and Exercise on Breast Tissue and Systemic Metabo-Inflammatory Factors in Women at High Risk or Diagnosed with Breast Cancer
Authors: Iyengar Neil M. , Zhou Xi Kathy , Mendieta Hillary , Giri Dilip D. , El-Hely Omar , Winston Lisle , Falcone Domenick J. , Wang Hanhan , Meng Lingsong , Landa Jonathan , Pollak Michael , Kirstein Laurie , Morrow Monica , Dannenberg Andrew J. 
Published: March 2021

Abstract

Excess body fat and sedentary behavior are associated with increased breast cancer risk and mortality, including in normal weight women. To investigate underlying mechanisms, we examined whether adiposity and exercise impact the breast microenvironment (e.g., inflammation and aromatase expression) and circulating metabo-inflammatory factors. In a cross-sectional cohort study, breast white adipose tissue (WAT) and blood were collected from 100 women undergoing mastectomy for breast cancer risk reduction or treatment. Self-reported exercise behavior, body composition measured by dual-energy x-ray absorptiometry (DXA), and waist:hip ratio were obtained prior to surgery. Breast WAT inflammation (B-WATi) was assessed by IHC and aromatase expression was assessed by quantitative PCR. Metabolic and inflammatory blood biomarkers that are predictive of breast cancer risk and progression were measured. B-WATi was present in 56 of 100 patients and was associated with older age, elevated BMI, postmenopausal status, decreased exercise, hypertension and dyslipidemia (Ps &lt; 0.001). Total body fat and trunk fat correlated with B-WATi and breast aromatase levels (Ps &lt; 0.001). Circulating C-reactive protein, IL6, insulin, and leptin positively correlated with body fat and breast aromatase levels, while negative correlations were observed for adiponectin and sex hormone binding globulin (P &lt; 0.001). Inverse relationships were observed with exercise (Ps &lt; 0.05). In a subgroup of 39 women with normal BMI, body fat levels positively correlated with B-WATi and aromatase expression (Ps &lt; 0.05). In conclusion, elevated body fat levels and decreased exercise are associated with protumorigenic micro- and host environments in normal, overweight, and obese individuals. These findings support the development of BMI-agnostic lifestyle interventions that target adiposity.

Prevention Relevance:
We report that individuals with high body fat and low exercise levels have breast inflammation, higher breast aromatase expression, and levels of circulating metabo-inflammatory factors that have been associated with increased breast cancer risk. These findings support interventions to lower adiposity, even among normal weight individuals, to prevent tumor growth.
Randomized Phase II Trial of Polyphenon E versus Placebo in Patients at High Risk of Recurrent Colonic Neoplasia
Authors: Sinicrope Frank A. , Viggiano Thomas R. , Buttar Navtej S. , Song Louis M. Wong Kee , Schroeder Kenneth W. , Kraichely Robert E. , Larson Mark V. , Sedlack Robert E. , Kisiel John B. , Gostout Christopher J. , Kalaiger Abdul M. , Patai Árpád V. , Della'Zanna Gary , Umar Asad , Limburg Paul J. , Meyers Jeffrey P. , Foster Nathan R. , Yang Chung S. , Sontag Stephen 
Published: March 2021

Abstract

Polyphenon E (Poly E) is a green tea polyphenol preparation whose most active component is epigallocatechin gallate (EGCG). We studied the cancer preventive efficacy and safety of Poly E in subjects with rectal aberrant crypt foci (ACF), which represent putative precursors of colorectal cancers. Eligible subjects had prior colorectal advanced adenomas or cancers, and had ≥5 rectal ACF at a preregistration chromoendoscopy. Subjects (N = 39) were randomized to 6 months of oral Poly E (780 mg EGCG) daily or placebo. Baseline characteristics were similar by treatment arm (all P &gt;0.41); 32 of 39 (82%) subjects completed 6 months of treatment. The primary endpoint was percent reduction in rectal ACF at chromoendoscopy comparing before and after treatment. Among 32 subjects (15 Poly E, 17 placebo), percent change in rectal ACF number (baseline vs. 6 months) did not differ significantly between study arms (3.7% difference of means; P = 0.28); total ACF burden was also similar (−2.3% difference of means; P = 0.83). Adenoma recurrence rates at 6 months were similar by arm (P &gt; 0.35). Total drug received did not differ significantly by study arm; 31 (79%) subjects received ≥70% of prescribed Poly E. Poly E was well tolerated and adverse events (AE) did not differ significantly by arm. One subject on placebo had two grade 3 AEs; one subject had grade 2 hepatic transaminase elevations attributed to treatment. In conclusion, Poly E for 6 months did not significantly reduce rectal ACF number relative to placebo. Poly E was well tolerated and without significant toxicity at the dose studied.

Prevention Relevance:
We report a chemoprevention trial of polyphenon E in subjects at high risk of colorectal cancer. The results show that polyphenon E was well tolerated, but did not significantly reduce the number of rectal aberrant crypt foci, a surrogate endpoint biomarker of colorectal cancer.