Medicine and Health Sciences Commons^™

The Development Of Artificial Intelligence-Based Tools For Expert Peer Review Of Radiotherapy Treatment Plans, Mary Gronberg

Dissertations & Theses (Open Access)

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Creating a patient-specific radiation treatment plan is a time-consuming and operator-dependent manual process. The treatment planner adjusts the planning parameters in a trial-and-error fashion in an effort to balance the competing clinical objectives of tumor coverage and normal tissue sparing. Often, a plan is selected because it meets basic organ at risk dose thresholds for severe toxicity; however, it is evident that a plan with a decreased risk of normal tissue complication probability could be achieved. This discrepancy between “acceptable” and “best possible” plan is magnified if either the physician or treatment planner lacks focal expertise in the disease site. …

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Head And Neck Tumor Histopathological Image Representation With Pre- Trained Convolutional Neural Network And Vision Transformer, Ranny Rahaningrum Herdiantoputri, Daisuke Komura, Tohru Ikeda, Shumpei Ishikawa

Journal of Dentistry Indonesia

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Image representation via machine learning is an approach to quantitatively represent histopathological images of head and neck tumors for future applications of artificial intelligence-assisted pathological diagnosis systems. Objective: This study compares image representations produced by a pre-trained convolutional neural network (VGG16) to those produced by a vision transformer (ViT-L/14) in terms of the classification performance of head and neck tumors. Methods: W hole-slide images of five oral t umor categories (n = 319 cases) were analyzed. Image patches were created from manually annotated regions at 4096, 2048, and 1024 pixels and rescaled to 256 pixels. Image representations were …

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A Machine Learning Model Of Response To Hypomethylating Agents In Myelodysplastic Syndromes, Nathan Radakovich, David A. Sallman, Rena Buckstein, Andrew Brunner, Amy Dezern, Sudipto Mukerjee, Rami Komrokji, Najla Al-Ali, Jacob Shreve, Yazan Rouphail, Anne Parmentier, Alexandre Mamedov, Mohammed Siddiqui, Yihong Guan, Teodora Kuzmanovic, Metis Hasipek, Babal Jha, Jaroslaw P. Maciejewski, Mikkael A. Sekeres, Aziz Nazha

Department of Medical Oncology Faculty Papers

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Hypomethylating agents (HMA) prolong survival and improve cytopenias in individuals with higher-risk myelodysplastic syndrome (MDS). Only 30-40% of patients, however, respond to HMAs, and responses may not occur for more than 6 months after HMA initiation. We developed a model to more rapidly assess HMA response by analyzing early changes in patients’ blood counts. Three institutions’ data were used to develop a model that assessed patients’ response to therapy 90 days after the initiation using serial blood counts. The model was developed with a training cohort of 424 patients from2 institutions and validated on an independent cohort of 90 patients. …

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Artificial Intelligence In The Radiomic Analysis Of Glioblastomas: A Review, Taxonomy, And Perspective, Ming Zhu, Sijia Li, Yu Kuang, Virigina B. Hill, Amy B. Heimberger, Lijie Zhai, Shenjie Zhai

Electrical & Computer Engineering Faculty Research

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Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications …

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Scope Of Artificial Intelligence In Gastrointestinal Oncology, Hermant Goyal Md, Syed A.A. Sheraz, Rupinder Mann, Zainab Gandhi, Abhilash Perisetti Md, Muhammad Aziz, Saurabh Chandan, Jonathan Kopel, Benjamin Tharian Md, Neil Sharma Md, Nirav Thosani

PCI Publications and Projects

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Simple Summary

Gastrointestinal cancers cause over 2.8 million deaths annually worldwide. Currently, the diagnosis of various gastrointestinal cancer mainly relies on manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. Artificial intelligence (AI) may be useful in screening, diagnosing, and treating various cancers by accurately analyzing diagnostic clinical images, identifying therapeutic targets, and processing large datasets. The use of AI in endoscopic procedures is a significant breakthrough in modern medicine. Although the diagnostic accuracy of AI systems has markedly increased, it still needs collaboration with physicians. In the near future, AI-assisted systems will become a vital …

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Artificial Image Objects For Classification Of Breast Cancer Biomarkers With Transcriptome Sequencing Data And Convolutional Neural Network Algorithms, Xiangning Chen, Daniel G. Chen, Zhongming Zhao, Justin M. Balko, Jingchun Chen

School of Medicine Faculty Publications

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Background: Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively for clinical applications due to the high dimension of the data and the highly correlated expression between individual genes. Methods: We proposed a method to transform RNA sequencing data into artificial image objects (AIOs) and applied convolutional neural network (CNN) algorithms to classify these AIOs. With the AIO technique, we considered each gene as a pixel in an image and its expression level as pixel intensity. Using the GSE96058 (n = 2976), GSE81538 (n = 405), and GSE163882 (n = …

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Interactive Machine Learning-Based Multi-Label Segmentation Of Solid Tumors And Organs, Dimitrios Bounias, Ashish Singh, Spyridon Bakas, Sarthak Pati, Saima Rathore, Hamed Akbari, Michel Bilello, Benjamin Greenberger, Joseph Lombardo, Rhea Chitalia, Nariman Jahani, Aimilia Gastounioti, Michelle Hershman, Leonid Roshkovan, Sharyn Katz, Bardia Yousefi, Carolyn Lou, Amber Simpson, Richard Do, Russell Shinohara, Despina Kontos, Konstantina Nikita, Christos Davatzikos

Department of Radiation Oncology Faculty Papers

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We seek the development and evaluation of a fast, accurate, and consistent method for general-purpose segmentation, based on interactive machine learning (IML). To validate our method, we identified retrospective cohorts of 20 brain, 50 breast, and 50 lung cancer patients, as well as 20 spleen scans, with corresponding ground truth annotations. Utilizing very brief user training annotations and the adaptive geodesic distance transform, an ensemble of SVMs is trained, providing a patient-specific model applied to the whole image. Two experts segmented each cohort twice with our method and twice manually. The IML method was faster than manual annotation by 53.1% …

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Application Of Artificial Intelligence In Pancreaticobiliary Diseases, Hemant Goyal, Rupinder Mann, Zainab Gandhi, Abhilash Perisetti, Zhongheng Zhang, Neil Sharma Md, Shreyas Saligram, Sumant Inamdar, Benjamin Tharian

PCI Publications and Projects

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The role of artificial intelligence and its applications has been increasing at a rapid pace in the field of gastroenterology. The application of artificial intelligence in gastroenterology ranges from colon cancer screening and characterization of dysplastic and neoplastic polyps to the endoscopic ultrasonographic evaluation of pancreatic diseases. Artificial intelligence has been found to be useful in the evaluation and enhancement of the quality measure for endoscopic retrograde cholangiopancreatography. Similarly, artificial intelligence techniques like artificial neural networks and faster region-based convolution network are showing promising results in early and accurate diagnosis of pancreatic cancer and its differentiation from chronic pancreatitis. Other …

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Spatial Organization And Molecular Correlation Of Tumor-Infiltrating Lymphocytes Using Deep Learning On Pathology Images., Joel Saltz, Rajarsi Gupta, Le Hou, Tahsin Kurc, Pankaj Singh, Vu Nguyen, Dimitris Samaras, Kenneth R Shroyer, Tianhao Zhao, Rebecca Batiste, John Van Arnam, Ilya Shmulevich, Arvind U K Rao, Alexander J Lazar, Ashish Sharma, Vésteinn Thorsson

Articles, Abstracts, and Reports

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Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched …

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Modular Machine Learning Methods For Computer-Aided Diagnosis Of Breast Cancer, Mia Kathleen Markey '94

Doctoral Dissertations

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The purpose of this study was to improve breast cancer diagnosis by reducing the number of benign biopsies performed. To this end, we investigated modular and ensemble systems of machine learning methods for computer-aided diagnosis (CAD) of breast cancer. A modular system partitions the input space into smaller domains, each of which is handled by a local model. An ensemble system uses multiple models for the same cases and combines the models' predictions.

Five supervised machine learning techniques (LDA, SVM, BP-ANN, CBR, CART) were trained to predict the biopsy outcome from mammographic findings (BIRADS™) and patient age based on a …

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