Article
Geology
Harya D. Nugraha, Christopher A-L Jackson, Howard D. Johnson, David M. Hodgson, Michael A. Clare
Summary: Submarine slides are major geohazards that can trigger tsunamis and damage submarine infrastructure. Our study quantified the erosive potential of submarine slides and found that the slide volume tends to increase after the initial failure. This has important implications for hazard assessments and impact assessments of submarine infrastructure.
Article
Computer Science, Information Systems
Pranshu Saxena, Anjali Goyal, Mariyam Aysha Bivi, Sanjay Kumar Singh, Mamoon Rashid
Summary: This paper proposes a novel image segmentation technique to distinguish between large malignant cells called centroblasts and centrocytes. A new approach is introduced, providing additional information for oncologists to facilitate prognosis. The technique involves projecting a H&E-stained image onto L*a*b* color space, segmenting the transformed image using k-means clustering, and applying pre-processing techniques to the segmented components. The proposed technique achieved 92% sensitivity and 88.9% specificity in comparing manual vs. automated segmentation.
Review
Health Care Sciences & Services
Karthik Nath, Maher K. Gandhi
Summary: Follicular lymphoma, the most common indolent B-cell lymphoma, has different prognoses, but the emergence of novel targeted agents offers hope for future treatment options.
JOURNAL OF PERSONALIZED MEDICINE
(2021)
Article
Computer Science, Interdisciplinary Applications
Ahmed Fahim
Summary: The k-means method divides N objects into k clusters based on mean values, with linear time complexity and dependence on knowing the number of clusters and initial centers. This research introduces a method able to detect near-optimal values for k and initial centers without prior knowledge, resulting in improved final result quality. The proposed method combines DBSCAN and k-means to converge to global minima and has a time complexity of o(n log n).
JOURNAL OF COMPUTATIONAL SCIENCE
(2021)
Editorial Material
Hematology
James D. Phelan, Elaine S. Jaffe
Summary: In this study, the authors used whole genome sequencing to investigate the basis of follicular lymphoma (FL) transformation. They identified two genetically distinct subgroups of FL, dFL and cFL, which showed a significant difference in time to transformation.
Editorial Material
Immunology
Krystal S. A. Lianos, Anne L. Fletcher
Summary: Fibroblasts play a crucial role in shaping the immune landscape of lymph nodes, and understanding their interactions with T and B cells could uncover potential clinical targets in follicular lymphoma.
Article
Oncology
Yanan Li, Yan Zhang, Wei Wang, Chong Wei, Danqing Zhao, Wei Zhang
Summary: This study aims to evaluate prognostic predictors of follicular lymphoma among Chinese patients. The study found that LMR is a new independent predictor that has better predictive ability compared to FLIPI.
CANCER MANAGEMENT AND RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Carlo Baldassi
Summary: We introduce an evolutionary algorithm called recombinator-k-means for optimizing the highly nonconvex kmeans problem. Its defining feature is that its crossover step involves all the members of the current generation, stochastically recombining them with a repurposed variant of the k-means++ seeding algorithm. The recombination also uses a reweighting mechanism that realizes a progressively sharper stochastic selection policy and ensures that the population eventually coalesces into a single solution. We compare this scheme with a state-of-the-art alternative, a more standard genetic algorithm with deterministic pairwise-nearest-neighbor crossover and an elitist selection policy, of which we also provide an augmented and efficient implementation. Extensive tests on large and challenging datasets (both synthetic and real word) show that for fixed population sizes recombinator-k-means is generally superior in terms of the optimization objective, at the cost of a more expensive crossover step. When adjusting the population sizes of the two algorithms to match their running times, we find that for short times the (augmented) pairwise-nearest-neighbor method is always superior, while at longer times recombinator-k-means will match it and, on the most difficult examples, take over. We conclude that the reweighted whole-population recombination is more costly but generally better at escaping local minima Moreover, it is algorithmically simpler and more general (it could be applied even to k-medians or k-medoids, for example).
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Editorial Material
Hematology
Connie Lee Batlevi
Summary: The tolerability and efficacy of obinutuzumab and lenalidomide in frontline therapy of follicular lymphoma are reported in this study.
Article
Automation & Control Systems
Uri Stemmer
Summary: This research presents a new algorithm operating in the local model of differential privacy for solving the Euclidean k-means problem, significantly reducing additive error while maintaining multiplicative error. The study shows that the obtained additive error in handling the k-means objective is almost optimal in terms of its dependency on the database size.
JOURNAL OF MACHINE LEARNING RESEARCH
(2021)
Review
Oncology
Rada Amin, Mounia S. Braza
Summary: This review provides a detailed description of the impact of epigenomic alterations on the immune microenvironment in follicular lymphoma (FL) and discusses the latest progress in targeting epigenetic pathways to inhibit the FL microenvironment. The article highlights unexplored research areas and key questions that need to be addressed.
JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH
(2022)
Review
Pharmacology & Pharmacy
Michael Northend, William Townsend
Summary: Follicular lymphoma is the most common form of indolent non-Hodgkin lymphoma, with a long median overall survival and high response rates to current therapies. Some patients show aggressive behavior or resistance to treatment, leading to poor outcomes, but new therapies like lenalidomide and immunotherapies are offering hope for the future.
Article
Oncology
Rachel Dobson, Andrew Wotherspoon, Shizhang Alexander Liu, Francesco Cucco, Zi Chen, Yuan Tang, Ming-Qing Du
Summary: The study found widespread distribution of ISFN in lymph nodes with follicular lymphoma and revealed FL-associated mutations in early lesions. The results indicate the dynamic nature of ISFN lesions, with neoplastic cells undergoing active trafficking and clonal evolution.
JOURNAL OF PATHOLOGY
(2022)
Article
Oncology
Fen Zhang, Wenyu Li, Qian Cui, Yu Chen, Yanhui Liu
Summary: We report a case of Angioimmunoblastic T-cell lymphoma (AITL) with extensive spindle cell meshwork that can be misdiagnosed as follicular dendritic cell sarcoma (FDCS) due to its unusual morphologic features. Next-generation sequencing revealed common mutations associated with AITL, while mutations related to FDCS were not detected. This study highlights the importance of careful morphological observation and immunohistochemical and molecular examinations in accurately diagnosing such cases.
FRONTIERS IN ONCOLOGY
(2022)
Review
Oncology
Danielle Wallace, Carla Casulo
Summary: Follicular lymphoma is an indolent lymphoma with most patients having normal life expectancy; however, approximately 20% of patients will experience disease progression within 24 months of diagnosis, leading to inferior survival outcomes. Recent studies have revealed novel prediction models and clinical trials utilizing novel therapies to identify and manage high-risk patients. Ongoing research efforts aim to improve management and survival outcomes for early progressing follicular lymphoma patients.
CURRENT ONCOLOGY REPORTS
(2021)
Review
Biochemistry & Molecular Biology
Meredith A. Jones, William M. MacCuaig, Alex N. Frickenstein, Seda Camalan, Metin N. Gurcan, Jennifer Holter-Chakrabarty, Katherine T. Morris, Molly W. McNally, Kristina K. Booth, Steven Carter, William E. Grizzle, Lacey R. McNally
Summary: Inflammatory diseases are highly prevalent conditions with high mortality rates in severe cases. Imaging of the immune system and inflammatory response can provide insight into disease progression and severity, leading to improved accuracy of diagnostics and patient monitoring. High specificity molecular imaging of inflammatory biomarkers allows for earlier diagnosis to prevent irreversible damage.
Article
Medicine, General & Internal
Thomas E. Tavolara, M. K. K. Niazi, Adam C. Gower, Melanie Ginese, Gillian Beamer, Metin N. Gurcan
Summary: Machine learning methodology was proposed to predict gene expression values from histopathology images for identifying supersusceptible mice infected with fulminant-like pulmonary tuberculosis. The model showed high accuracy and replicated gene expression predictions to identify supersusceptible mice effectively, with high sensitivity and specificity in both cross validation and external testing sets.
Article
Oncology
Seda Camalan, Hanya Mahmood, Hamidullah Binol, Anna Luiza Damaceno Araujo, Alan Roger Santos-Silva, Pablo Agustin Vargas, Marcio Ajudarte Lopes, Syed Ali Khurram, Metin N. Gurcan
Summary: Oral cancer is among the top ten most common cancers globally, with the need for early accurate diagnosis emphasized. This study aims to develop deep learning methods for image classification and highlight decision-making regions using heat maps. Through validation on datasets from different countries, it was shown that better performance could be achieved by using patches instead of whole lesion images and analyzing predictive regions through class activation map analysis.
Article
Health Policy & Services
James E. Peacock, David M. Herrington, Sharon L. Edelstein, Austin L. Seals, Ian D. Plumb, Sharon Saydah, William H. Lagarde, Michael S. Runyon, Patrick D. Maguire, Adolfo Correa, William S. Weintraub, Thomas F. Wierzba, John W. Sanders
Summary: Prevention behaviors are crucial to limiting the spread of SARS-CoV-2, yet a survey of over 20,000 individuals in the US found that most did not fully adhere to recommended public health safety measures during holiday gatherings following Thanksgiving and the winter holidays. Women were more likely to gather with non-household members (NHM), while older individuals and non-Hispanic Whites were more likely to wear masks when NHM were present. The extent to which failure to follow these recommendations contributed to the COVID-19 surges observed post-holidays remains uncertain.
JOURNAL OF COMMUNITY HEALTH
(2022)
Review
Pathology
Claudio Luchini, Liron Pantanowitz, Volkan Adsay, Sylvia L. Asa, Pietro Antonini, Ilaria Girolami, Nicola Veronese, Alessia Nottegar, Sara Cingarlini, Luca Landoni, Lodewijk A. Brosens, Anna V. Verschuur, Paola Mattiolo, Antonio Pea, Andrea Mafficini, Michele Milella, Muhammad K. Niazi, Metin N. Gurcan, Albino Eccher, Ian A. Cree, Aldo Scarpa
Summary: Ki-67 assessment plays a key role in the diagnosis of neuroendocrine neoplasms (NENs) from all anatomic locations. Digital pathology combined with machine learning has shown to be highly accurate and reproducible for evaluating Ki-67 in NENs. In this systematic review, the advantages of digital image analysis (DIA) in assessing Ki-67 in pancreatic NENs (PanNENs) were highlighted, including improved standardization and reliability, as well as increased speed and practicality compared to manual counting. However, limitations such as higher costs and operator qualification issues need to be addressed. A comparative meta-analysis showed a high concordance between DIA and manual counting. These findings support the widespread adoption of validated DIA methods for Ki-67 assessment in PanNENs.
Article
Computer Science, Artificial Intelligence
Hamidullah Binol, M. Khalid Khan Niazi, Charles Elmaraghy, Aaron C. Moberly, Metin N. Gurcan
Summary: The lack of objective evaluation methods for the eardrum is a critical barrier to accurate diagnosis. This paper proposes a novel deep learning-based method called OtoXNet, which automatically learns features for eardrum classification from otoscope video clips. By utilizing multiple composite image generation methods, OtoXNet proves to outperform baseline approaches in qualitative results, showing the advantage of using multiple composite images in analyzing eardrum abnormalities.
NEURAL COMPUTING & APPLICATIONS
(2022)
Review
Otorhinolaryngology
Stephany Ngombu, Hamidullah Binol, Metin N. Gurcan, Aaron C. Moberly
Summary: This review discusses the state of the art applications of artificial intelligence (AI) techniques in diagnosing otitis media (OM) and highlights the potential benefits of using AI to automate and aid in diagnosis.
OTOLARYNGOLOGY-HEAD AND NECK SURGERY
(2023)
Article
Computer Science, Artificial Intelligence
Ziyu Su, Thomas E. Tavolara, Gabriel Carreno-Galeano, Sang Jin Lee, Metin N. Gurcan, M. K. K. Niazi
Summary: This study proposes attention2majority, a weak multiple instance learning model, to automatically and efficiently process whole slide images (WSIs) of stained tissue sections for classification. By using intelligent sampling and a multi-head attention-based multiple instance learning model, slide-level classification based on high-confidence patches is achieved.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Public, Environmental & Occupational Health
Lydia E. Calamari, Ashley H. Tjaden, Sharon L. Edelstein, William S. Weintraub, Roberto Santos, Michael Gibbs, Johnathan Ward, Michele Santacatterina, Alain G. Bertoni, Lori M. Ward, Sharon Saydah, Ian D. Plumb, Michael S. Runyon
Summary: This study investigated self-reported mask use among participants in the COVID-19 Community Research Partnership (CRP) and found that mask use was higher among vaccinated participants and those aged 65 years and older, female, racial or ethnic minority group, and healthcare workers. Lower mask use was associated with a history of self-reported prior COVID-19 illness.
PREVENTIVE MEDICINE REPORTS
(2022)
Article
Oncology
Thomas E. Tavolara, Metin N. Gurcan, M. Khalid Khan Niazi
Summary: This study proposes an unsupervised method to learn meaningful features from histopathological imaging data. The method achieves high accuracy and correlation in classifying non-small cell lung cancer subtypes and scoring breast cancer proliferation. The significance of this method lies in its ability to learn meaningful features from raw imaging data without slide-level annotations.
Proceedings Paper
Computer Science, Information Systems
Thomas E. Tavolara, M. Khalid Khan Niazi, Gary Tozbikian, Robert Wesolowski, Metin N. Gurcan
Summary: This study developed an automated method to predict HER2 scores in breast cancer, using immunohistochemical staining images and tissue sections. The preliminary results showed potential for localizing and scoring HER2 using H&E images.
MEDICAL IMAGING 2022: DIGITAL AND COMPUTATIONAL PATHOLOGY
(2022)
Proceedings Paper
Computer Science, Information Systems
Thomas E. Tavolara, Arijit Dutta, Martin Burks, Wei Chen, Wendy Frankel, Metin N. Gurcan, M. Khalid Khan Niazi
Summary: This study successfully developed an automated algorithm that combines routine H&E staining with pan-cytokeratin staining to generate ground truth for tumor budding. The results demonstrated the potential feasibility of this method in identifying tumor buds.
MEDICAL IMAGING 2022: DIGITAL AND COMPUTATIONAL PATHOLOGY
(2022)
Review
Engineering, Biomedical
Diana Lim, Eric S. Renteria, Drake S. Sime, Young Min Ju, Ji Hyun Kim, Tracy Criswell, Thomas D. Shupe, Anthony Atala, Frank C. Marini, Metin N. Gurcan, Shay Soker, Joshua Hunsberger, James J. Yoo
Summary: Regenerative medicine and tissue engineering provide new therapeutic options for restoring, maintaining, or improving tissue function. To optimize the biological function of tissue-engineered clinical products, specific conditions must be maintained in a bioreactor to allow product maturation and mimic the in vivo environment. Real-time monitoring of product functional capacity is critical for quality management during manufacturing.
BIO-DESIGN AND MANUFACTURING
(2022)
Article
Health Policy & Services
Martin S. Kohn, Umit Topaloglu, Eric S. Kirkendall, Ajay Dharod, Brian J. Wells, Metin Gurcan
Summary: The nature of information in medicine has changed with the availability of massive, diverse data streams; a Learning Health System facilitates the development of medical decision-making tools and demonstrates enhanced value in decision-making; clinicians need to acquire skills necessary to work with big data in this era.
LEARNING HEALTH SYSTEMS
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Hamidullah Binol, M. Khalid Khan Niazi, Charles Elmaraghy, Aaron C. Moberly, Metin N. Gurcan
Summary: This study developed a digital otoscopy video summarization and automated diagnostic model using deep learning and natural language processing, which can effectively diagnose TM diseases by extracting key visual features from short descriptive reports.
MEDICAL IMAGING 2021: IMAGING INFORMATICS FOR HEALTHCARE, RESEARCH, AND APPLICATIONS
(2021)
