Review
Gastroenterology & Hepatology
Arnaldo Stanzione, Francesco Verde, Valeria Romeo, Francesca Boccadifuoco, Pier Paolo Mainenti, Simone Maurea
Summary: Rectal cancer is a common tumor with significant morbidity and mortality rates. Medical imaging, particularly using artificial intelligence, plays a crucial role in diagnosis and treatment, offering promising results but facing challenges in clinical translation.
WORLD JOURNAL OF GASTROENTEROLOGY
(2021)
Article
Oncology
Mitchell Chen, Susan J. Copley, Patrizia Viola, Haonan Lu, Eric O. Aboagye
Summary: Lung cancer, the leading cause of cancer-related deaths worldwide, can be captured non-invasively on medical imaging as radiomic features, which can be used in an artificial intelligence paradigm to predict clinical outcomes and improve patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity.
SEMINARS IN CANCER BIOLOGY
(2023)
Article
Biochemical Research Methods
Lalith Kumar Shiyam Sundar, Otto Muzik, Irene Buvat, Luc Bidaut, Thomas Beyer
Summary: State-of-the-art patient management requires investigating both the anatomy and physiology of patients, with hybrid imaging techniques providing both structural and functional information. Artificial intelligence algorithms show promise in facilitating analysis of multi-parametric data in medical imaging, addressing challenges in extracting clinical information from large sets of multi-dimensional imaging data.
Review
Oncology
Joanna Bidzinska, Edyta Szurowska
Summary: Lung cancer is a leading cause of avoidable deaths, and early detection through a lung cancer screening program using low-dose computed tomography is crucial. However, the healthcare system's heavy workload, shortage of specialists, and high costs necessitate new solutions, such as artificial intelligence (AI), to support hospitals. This paper presents promising results and discusses the need for AI in medicine, particularly in the field of lung cancer, to potentially save lives. Recent developments in lung cancer screening have significant implications for the European community and beyond, and AI can support the screening process for the benefit of patients, healthcare professionals, and hospital staff.
Article
Oncology
Anna-Katharina Meissner, Robin Gutsche, Norbert Galldiks, Martin Kocher, Stephanie T. Juenger, Marie-Lisa Eich, Manuel Montesinos-Rongen, Anna Brunn, Martina Deckert, Christina Wendl, Wolfgang Dietmaier, Roland Goldbrunner, Maximilian Ruge, Cornelia Mauch, Nils-Ole Schmidt, Martin Proescholdt, Stefan Grau, Philipp Lohmann
Summary: MRI radiomics can predict the intracranial BRAF V600E mutation status in patients with melanoma brain metastases noninvasively, and the method shows high diagnostic performance.
Article
Biochemistry & Molecular Biology
Norman E. Sharpless, Anthony R. Kerlavage
Summary: Artificial intelligence, machine learning, and deep learning have diverse applications in cancer research and clinical care, and the National Cancer Institute (NCI) is actively involved in supporting and advancing these technologies. In addition to developing and evaluating AI tools, NCI focuses on fostering a culture of data sharing, training the next generation of scientists, promoting interdisciplinary collaborations, and ensuring ethical principles in AI research and technologies.
BIOCHIMICA ET BIOPHYSICA ACTA-REVIEWS ON CANCER
(2021)
Article
Biochemistry & Molecular Biology
Fuk-Hay Tang, Yee-Wai Fong, Shing-Hei Yung, Chi-Kan Wong, Chak-Lap Tu, Ming-To Chan
Summary: In this study, a radiomics clinical probability-weighted model is proposed for predicting the prognosis of non-small cell lung cancer (NSCLC). The model combines radiomics features from radiotherapy planning images with clinical factors such as age, gender, histology, and tumor stage. The model utilizes machine learning algorithms and a probabilistic weighted approach to generate risk scores for each patient. The model demonstrated good performance in predicting NSCLC survival, outperforming the radiomic model in 1-year survival prediction.
Article
Computer Science, Hardware & Architecture
Iacovos Ioannou, Christophoros Christophorou, Vasos Vassiliou, Andreas Pitsillides
Summary: This paper proposes a novel ML based Distributed AI (DAI) framework that aims to achieve ambitious goals in emerging 5G/6G networks. The framework utilizes BDI agents with ML capabilities on mobile devices, allowing for autonomous and flexible intercommunication and cooperation. The framework offers improved network control execution time, fast response to dynamic aspects, self-organising network functionalities, and the ability to employ multiple intelligent approaches.
Review
Biochemistry & Molecular Biology
Matteo Ferro, Gennaro Musi, Michele Marchioni, Martina Maggi, Alessandro Veccia, Francesco Del Giudice, Biagio Barone, Felice Crocetto, Francesco Lasorsa, Alessandro Antonelli, Luigi Schips, Riccardo Autorino, Gian Maria Busetto, Daniela Terracciano, Giuseppe Lucarelli, Octavian Sabin Tataru
Summary: Renal cancer management poses challenges throughout the entire process from diagnosis to treatment and follow-up. The differentiation between benign and malignant tissues in cases of small renal masses and cystic lesions can be problematic, even with imaging or renal biopsy. Recent advances in artificial intelligence, imaging techniques, and genomics provide potential for helping clinicians with risk stratification, treatment selection, follow-up strategies, and prognosis. However, further prospective studies with larger patient cohorts are needed to validate previous results and implement these techniques into clinical practice.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Review
Chemistry, Multidisciplinary
Jacobo Porto-Alvarez, Gary T. Barnes, Alex Villanueva, Roberto Garcia-Figueiras, Sandra Baleato-Gonzalez, Emilio Huelga Zapico, Miguel Souto-Bayarri
Summary: Computed tomography (CT) revolutionized medicine with its introduction of digital imaging in the early 1970s. This shift from analog to digital medical imaging has resulted in significant changes and improvements in healthcare, which continue to evolve. By applying computer algorithms and processing digital medical images, important diagnostic details are enhanced, and significant quantitative information can aid in diagnosis. Examples include CAD and radiomics applications in the diagnosis of lung and colorectal cancer. This article highlights the key aspects of the digital medical imaging revolution, reviews its current status, discusses its clinical translation in lung and colorectal cancer, and presents future directions and challenges.
APPLIED SCIENCES-BASEL
(2023)
Review
Medicine, General & Internal
Dan Zheng, Xiujing He, Jing Jing
Summary: The heavy burden and mortality of breast cancer highlight the importance of early diagnosis and treatment. Imaging detection is a key tool in clinical practice for breast cancer screening, diagnosis, and treatment evaluation. The use of AI-assisted imaging diagnosis can improve efficiency and accuracy in recognizing, segmenting, and diagnosing tumor lesions.
JOURNAL OF CLINICAL MEDICINE
(2023)
Article
Computer Science, Hardware & Architecture
Iacovos Ioannou, Christophoros Christophorou, Vasos Vassiliou, Andreas Pitsillides
Summary: This paper builds on the Distributed Artificial Intelligence (DAI) Framework to establish efficient, distributed, autonomous and flexible Device-to-Device (D2D) communication. It focuses on D2D Transmission Mode Selection in 5G and compares the enhanced DAIS and Distributed Sum Rate (DSR) with unsupervised ML clustering approaches. The evaluation considers factors such as QoE, QoS Fairness, Spectral Efficiency (SE), Power Consumption (PC), efficiency of clusters, signalling overhead, execution delay, D2D Effectiveness, D2D Stability and D2D Productivity.
Review
Computer Science, Artificial Intelligence
Onur Dogan, Sanju Tiwari, M. A. Jabbar, Shankru Guggari
Summary: The use of AI/ML methods in addressing the COVID-19 outbreak has increased due to their significant advantages, providing satisfactory solutions to the disease. However, the diversity in these solutions can lead to confusion. This study systematically analyzes and summarizes related studies to address this issue.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Oncology
Hwa-Yen Chiu, Heng-Sheng Chao, Yuh-Min Chen
Summary: Lung cancer, being the leading cause of malignancy-related mortality worldwide, could greatly benefit from the application of artificial intelligence (AI). AI has the potential to assist in the detection, diagnosis, and prognosis prediction of lung cancer, reducing the labor work of radiologists and increasing accuracy. By integrating AI with various imaging and pathology techniques, clinicians can predict tumor properties and make better treatment decisions.
Article
Materials Science, Multidisciplinary
Davis Unruh, Venkata Surya Chaitanya Kolluru, Arun Baskaran, Yiming Chen, Maria K. Y. Chan
Summary: Advances in instrumentation have resulted in a vast amount of information on materials chemistry, structures, and transformations, but interpreting microscopy and spectroscopy data is becoming more challenging due to their growing volume and complexity. This article discusses the use of theoretical modeling, artificial intelligence/machine learning (AI/ML), and AI/ML combined with theory for interpreting microscopy and spectroscopy data.
Article
Oncology
Bihong T. Chen, Taihao Jin, Sunita K. Patel, Ningrong Ye, Huiyan Ma, Chi Wah Wong, Russell C. Rockne, James C. Root, Andrew J. Saykin, Tim A. Ahles, Andrei I. Holodny, Neal Prakash, Joanne Mortimer, James Waisman, Yuan Yuan, Daneng Li, Mina S. Sedrak, Jessica Vazquez, Vani Katheria, William Dale
BREAST CANCER RESEARCH AND TREATMENT
(2019)
Article
Oncology
Bihong T. Chen, Ningrong Ye, Chi Wah Wong, Sunita K. Patel, Taihao Jin, Can-Lan Sun, Russell C. Rockne, Heeyoung Kim, James C. Root, Andrew J. Saykin, Tim A. Ahles, Andrei Holodny, Neal Prakash, Joanne Mortimer, Mina S. Sedrak, James Waisman, Yuan Yuan, Daneng Li, Jessica Vazquez, Vani Katheria, William Dale
JOURNAL OF GERIATRIC ONCOLOGY
(2020)
Article
Oncology
Bihong T. Chen, Zikuan Chen, Ningrong Ye, Isa Mambetsariev, Jeremy Fricke, Ebenezer Daniel, George Wang, Chi Wah Wong, Russell C. Rockne, Rivka R. Colen, Mohd W. Nasser, Surinder K. Batra, Andrei Holodny, Sagus Sampath, Ravi Salgia
FRONTIERS IN ONCOLOGY
(2020)
Article
Radiology, Nuclear Medicine & Medical Imaging
Bihong T. Chen, Taihao Jin, Ningrong Ye, Isa Mambetsariev, Ebenezer Daniel, Tao Wang, Chi Wah Wong, Russell C. Rockne, Rivka Colen, Andrei Holodny, Sagus Sampath, Ravi Salgia
MAGNETIC RESONANCE IMAGING
(2020)
Meeting Abstract
Oncology
Luis A. Meza, Aleksandr Filippov, Sohaib Naim, Nazli Dizman, Alex Chehrazi-Raffle, Errol James Philip, Jasnoor Maholtra, Ramya Muddasani, Neal Shiv Chawla, Chi Wah Wong, Sabrina Salgia, Misagh Karimi, Jeffrey M. Trent, Sara Ann Byron, Ammar Chaudhry, Sumanta K. Pal
JOURNAL OF CLINICAL ONCOLOGY
(2021)
Article
Oncology
Bihong T. Chen, Taihao Jin, Ningrong Ye, Isa Mambetsariev, Tao Wang, Chi Wah Wong, Zikuan Chen, Russell C. Rockne, Rivka R. Colen, Andrei I. Holodny, Sagus Sampath, Ravi Salgia
Summary: The study used radiomic analysis of brain metastases from NSCLC and found that radiomic scores can serve as non-invasive biomarkers for predicting survival duration. By utilizing radiomic features of EGFR, ALK, and KRAS mutations, each mutation-positive group could be divided into two subgroups with significantly different survival durations.
FRONTIERS IN ONCOLOGY
(2021)
Article
Oncology
Chi Wah Wong, Susan E. Yost, Jin Sun Lee, John D. Gillece, Megan Folkerts, Lauren Reining, Sarah K. Highlander, Zahra Eftekhari, Joanne Mortimer, Yuan Yuan
Summary: In a pilot study on patients with HER2+ breast cancer, specific microbiota associated with reduced risk of neratinib-induced diarrhea were identified, providing important insights for further research.
FRONTIERS IN ONCOLOGY
(2021)
Article
Neuroimaging
Bihong T. Chen, Zikuan Chen, Sunita K. Patel, Russell C. Rockne, Chi Wah Wong, James C. Root, Andrew J. Saykin, Tim A. Ahles, Andrei Holodny, Can-Lan Sun, Mina S. Sedrak, Heeyoung Kim, Ashley Celis, Vani Katheria, William Dale
Summary: Chemotherapy in older women with breast cancer can lead to alterations in the connectivity of the default mode network, with a pattern of stronger anterior connectivity and weaker posterior connectivity observed post-chemotherapy. These changes may serve as potential neuroimaging biomarkers for cancer-related cognitive impairment and accelerated aging.
BRAIN IMAGING AND BEHAVIOR
(2022)
Article
Oncology
Chi Wah Wong, Chen Chen, Lorenzo A. Rossi, Monga Abila, Janet Munu, Ryotaro Nakamura, Zahra Eftekhari
Summary: The study developed explainable predictive models for the hematology population and dynamic models to monitor readmission risk, achieving significant performance improvements with the use of clinical embeddings. Hematology models demonstrated more performance gains compared to surgery and medical oncology models.
JCO CLINICAL CANCER INFORMATICS
(2021)
Meeting Abstract
Oncology
Chi Wah Wong, Susan E. Yost, Jin Sun Lee, John D. Gillece, Megan Folkerts, Lauren Reining, Sarah K. Highlander, Zahra Eftekhari, Joanne Mortimer, Yuan Yuan
Meeting Abstract
Oncology
Chi Wah Wong, Chen Chen, Lorenzo A. Rossi, Jerry Wang, Monga Abila, Janet Munu, Zahra Eftekhari
Meeting Abstract
Oncology
Chi Wah Wong, Sohaib Naim, Vincent La, Seth Michael Hilliard, Eemon Tizpa, Rashi Ranjan, Hannah Jade Young, Kimberly Jane Bonjoc, Aleksandr Filippov, Saman Tabassum Khan, Christine Brown, Behnam Badie, Ammar Ahmed Chaudhry
Meeting Abstract
Oncology
Chi Wah Wong, Chen Chen, Lorenzo A. Rossi, Jerry Wang, Monga Abila, Janet Munu, Zahra Eftekhari
JOURNAL OF CLINICAL ONCOLOGY
(2020)
Meeting Abstract
Oncology
Chi Wah Wong, Monga Abila, Jerry Wang, Deron Johnson, Janet Munu, Susan Brown
ONCOLOGY NURSING FORUM
(2019)