Article
Health Care Sciences & Services
Melania Costantini, Rino Aldo Montella, Maria Paola Fadda, Vincenzo Tondolo, Gianluca Franceschini, Sonia Bove, Giorgia Garganese, Pierluigi Maria Rinaldi
Summary: Invasive lobular carcinoma is the second most common histologic form of breast cancer, with an insidious proliferative pattern making it difficult to diagnose clinically and radiologically. Breast MR is considered the most accurate imaging modality for detecting and staging invasive lobular carcinoma, while CESM is a promising new diagnostic method that can accurately detect malignant breast lesions similar to breast MR and has potential for preoperative surgical planning.
JOURNAL OF PERSONALIZED MEDICINE
(2022)
Review
Cardiac & Cardiovascular Systems
Russell Franks, Sven Plein, Amedeo Chiribiri
Summary: Dynamic contrast enhanced CMR perfusion imaging is a reliable non-invasive tool for the evaluation and risk stratification of patients with coronary artery disease. It offers high spatial and temporal resolution, and does not involve ionizing radiation, making it recommended by international guidelines.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Nan Huang, Yu Chen, Dejun She, Zhen Xing, Tanhui Chen, Dairong Cao
Summary: The combined use of DKI and DCE-MRI can help differentiate different types of parotid gland tumors, improving diagnostic accuracy.
EUROPEAN RADIOLOGY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Weiwei Wang, Siqiang Lv, Jing Xun, Lei Wang, Fan Zhao, Jiehuan Wang, Zhe Zhou, Yueqin Chen, Zhanguo Sun, Laimin Zhu
Summary: This study explored the clinical value of diffusional kurtosis imaging (DKI) and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) in predicting genotypes and prognostic factors of breast cancer. The results showed that these two techniques have good diagnostic performance in predicting the prognostic factors of breast cancer, and their combination can improve the efficiency of predicting breast cancer genotypes.
EUROPEAN JOURNAL OF RADIOLOGY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Lorenz A. Kapsner, Sabine Ohlmeyer, Lukas Folle, Frederik B. Laun, Armin M. Nagel, Andrzej Liebert, Hannes Schreiter, Matthias W. Beckmann, Michael Uder, Evelyn Wenkel, Sebastian Bickelhaupt
Summary: This study aims to use deep learning to automatically detect artifacts on dynamic contrast-enhanced (DCE) maximum intensity projections (MIPs) of the breast. Results show that the ResNet and DenseNet ensembles perform well in detecting artifacts. Future studies can explore the potential of neural networks to improve the application of DCE MIPs in a clinical setting.
EUROPEAN RADIOLOGY
(2022)
Article
Oncology
Tianfu Lai, Xiaofeng Chen, Zhiqi Yang, Ruibin Huang, Yuting Liao, Xiangguang Chen, Zhuozhi Dai
Summary: The quantitative parameter K-ep could predict LVI status in breast cancer patients. LVI status, N stage, and the combined-predicted LVI model were predictors of poor recurrence-free survival.
Article
Radiology, Nuclear Medicine & Medical Imaging
Jingfeng Cheng, Shuo Shao, Weibo Chen, Ning Zheng
Summary: The combination of DKI and DCE-MRI showed superior accuracy in differentiating benign and malignant head and neck lesions and their subgroups compared to using DKI or DCE-MRI alone. The parameters MD, MK, T-peak, and WR demonstrated significant differences among the different subgroups.
JOURNAL OF MAGNETIC RESONANCE IMAGING
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Shuqian Feng, Jiandong Yin
Summary: In this study, a nomogram model combining clinical factors and a radiomics score was developed and validated to predict the luminal type of breast cancer, showing good performance in prediction.
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
(2023)
Article
Multidisciplinary Sciences
Hsueh-Ying Chen, C. Blake Wilson, Robert Tycko
Summary: The spatial resolution of MRI is limited by signal detection sensitivity, but can be improved by performing measurements at low temperature and using dynamic nuclear polarization (DNP) technique. This allows for higher resolution and shorter data acquisition times, offering a promising direction for high-resolution MRI studies.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Weibo Gao, Jixin Chen, Bin Zhang, Xiaocheng Wei, Jinman Zhong, Xiaohui Li, Xiaowei He, Fengjun Zhao, Xin Chen
Summary: A deep learning-based system with a cascade feature pyramid network was developed for the detection and classification of breast lesions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The system outperformed other systems in terms of detecting and classifying breast lesions, especially small lesions.
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Jin Joo Kim, Jin You Kim, Hie Bum Suh, Lee Hwangbo, Nam Kyung Lee, Suk Kim, Ji Won Lee, Ki Seok Choo, Kyung Jin Nam, Taewoo Kang, Heeseung Park
Summary: Intratumoral heterogeneity assessed via DCE-MRI and DWI reflects the molecular subtypes of invasive breast cancers. Different tumor subtypes in breast cancer patients show varying levels of kinetic and ADC heterogeneity values.
EUROPEAN RADIOLOGY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Daniel Son, Jordana Phillips, Tejas S. Mehta, Rashmi Mehta, Alexander Brook, Vandana M. Dialani
Summary: The study aimed to gain understanding of patient preferences towards contrast-enhanced imaging such as CEM or MRI for breast cancer screening. An anonymous survey was conducted among patients undergoing screening mammography at a single academic institution. Results showed that a significant proportion of patients with dense breasts and prior CEM/MRI were willing to accept the risks and costs associated with CEM or MRI as a screening exam. Concerns related to aspects of the imaging procedure varied among patients.
ACADEMIC RADIOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Brandon M. Bordeau, Joseph Ryan Polli, Ferdinand Schweser, Hans Peter Grimm, Wolfgang F. Richter, Joseph P. Balthasar
Summary: Predicting the disposition of monoclonal antibodies (mAb) within solid tumors for individual patients is challenging due to variability in tumor physiology. This study used dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with gadobutrol as a surrogate to improve the prediction of cetuximab pharmacokinetics in epidermal growth factor receptor (EGFR) positive xenografts. The results showed that incorporating DCE-MRI parameters into a physiologically based pharmacokinetic model (PBPK) improved the characterization of cetuximab distribution in individual tumors.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Oncology
Antonello Vidiri, Andrea Ascione, Francesca Piludu, Eleonora Polito, Enzo Gallo, Renato Covello, Paola Nistico, Vittoria Balzano, Barbara Pichi, Raul Pellini, Simona Marzi
Summary: In this study, the correlation between imaging parameters obtained from magnetic resonance imaging (MRI) and pathological factors in oral cavity squamous cell carcinomas (OSCCs) was investigated. The results showed that pathological factors such as inflammatory infiltrate, tumor grading, and desmoplastic reaction affected the MRI parameters. Significant relationships were found between DKI parameters and tumor-infiltrating lymphocytes (TILs). Differences in DCE-MRI parameters were also observed according to tumor grading. These findings contribute to a better understanding of OSCCs and can be used for diagnostic and prognostic purposes.
Article
Medicine, General & Internal
Ou Ou Yang, Tian Lan, Jun Ling He, Hai Bin Xu, Liang Hao, Chang Shu, Zu Jian Hu, Hua Luo
Summary: Primary breast angiosarcoma is a rare and aggressive entity that is difficult to diagnose, leading to recurrence and metastasis. Combining contrast-enhanced ultrasound and MRI can help in the diagnosis of breast angiosarcomas.
Article
Radiology, Nuclear Medicine & Medical Imaging
Cory Robinson-Weiss, Jay Patel, Bernardo C. Bizzo, Daniel I. Glazer, Christopher P. Bridge, Katherine P. Andriole, Borna Dabiri, John K. Chin, Keith Dreyer, Jayashree Kalpathy-Cramer, William W. Mayo-Smith
Summary: A machine learning algorithm was developed to segment and classify adrenal glands on contrast-enhanced CT images. The algorithm demonstrated high accuracy and sensitivity in segmenting and categorizing normal and mass-containing glands.
Article
Radiology, Nuclear Medicine & Medical Imaging
Mishka Gidwani, Ken Chang, Jay Biren Patel, Katharina Viktoria Hoebel, Syed Rakin Ahmed, Praveer Singh, Clifton David Fuller, Jayashree Kalpathy-Cramer
Summary: This study aims to uncover methodologic errors in radiomic machine learning studies, identifying inconsistent data partitioning and unproductive feature associations as common flaws. Simulations demonstrate the impact of these errors on accuracy and provide a review template to avoid them.
Article
Health Care Sciences & Services
Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge, Brian Befano, Silvia De Sanjose, Didem Egemen, Ana Cecilia Rodriguez, Mark Schiffman, John Peter Campbell, Jayashree Kalpathy-Cramer
Summary: The integration of AI into clinical workflows requires reliable and robust models, with repeatability being a key attribute. This study evaluates the repeatability of different model types on images from the same patient and proposes the use of Monte Carlo predictions to improve repeatability.
NPJ DIGITAL MEDICINE
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
William Hunter, Sergei Dolinsky, Paul Kinahan, Robert Miyaoka
Summary: We evaluated the 3-D spatial, energy, and timing resolution of the BING PET detector. The BING detector consists of stacked slats of LYSO scintillator with silicon-photomultiplier (SiPM) arrays to determine interaction positions and timing. The performance of the BING detector was measured and it showed effective resolution for a small field of view PET system.
IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES
(2023)
Article
Ophthalmology
Mallory A. deCampos-Stairiker, Aaron S. Coyner, Aditi Gupta, Minn Oh, Parag K. Shah, Prema Subramanian, Narendran Venkatapathy, Praveer Singh, Jayashree Kalpathy-Cramer, Michael F. Chiang, R. V. Paul Chan, J. Peter Campbell
Summary: This study evaluates whether an artificial intelligence algorithm can be used to assess changes in the epidemiology of retinopathy of prematurity (ROP) in babies from South India. The study found a significant decrease in the proportion of babies developing moderate to severe ROP over the past 5 years among babies with similar birthweight and gestational age. This suggests significant improvements in the primary prevention of ROP.
Article
Medicine, General & Internal
Vanessa Schmithorst, Rafael Ceschin, Vincent Lee, Julia Wallace, Aurelia Sahel, Thomas L. Chenevert, Hemant Parmar, Jeffrey I. Berman, Arastoo Vossough, Deqiang Qiu, Nadja Kadom, Patricia Ellen Grant, Borjan Gagoski, Peter S. LaViolette, Mohit Maheshwari, Lynn A. Sleeper, David C. Bellinger, Dawn Ilardi, Sharon O'Neil, Thomas A. Miller, Jon Detterich, Kevin D. Hill, Andrew M. Atz, Marc E. Richmond, James Cnota, William T. Mahle, Nancy S. Ghanayem, J. William Gaynor, Caren S. Goldberg, Jane W. Newburger, Ashok Panigrahy
Summary: Patients with hypoplastic left heart syndrome who have undergone the Fontan procedure may experience adverse neurodevelopmental outcomes, lower quality of life, and reduced employability. To study this, the SVRIII Brain Connectome study was conducted to analyze brain connectome measures and their associations with neurocognitive measures and clinical risk factors using advanced neuroimaging techniques. The study faced challenges in recruitment and technical aspects, which were addressed by adding more study sites, increasing coordination with site coordinators, and implementing additional recruitment strategies.
Review
Ophthalmology
Saif Aldeen AlRyalat, Praveer Singh, Jayashree Kalpathy-Cramer, Malik Y. Kahook
Summary: There has been a recent increase in publications on using artificial intelligence (AI) for diagnosing systemic diseases, with FDA approval for AI algorithms in clinical practice. In ophthalmology, AI advancements are mainly focused on diabetic retinopathy, which has clear diagnostic criteria, unlike glaucoma, which is a complex disease without agreed-upon criteria. Inconsistent label quality in current glaucoma datasets complicates efficient training of AI algorithms. This perspective paper discusses details and suggests steps to overcome current limitations in developing AI models for glaucoma.
CLINICAL OPHTHALMOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Abhejit Rajagopal, Yutaka Natsuaki, Kristen Wangerin, Mahdjoub Hamdi, Hongyu An, John J. Sunderland, Richard Laforest, Paul E. Kinahan, Peder E. Z. Larson, Thomas A. Hope
Summary: In this article, a deep learning technique is demonstrated to generate realistic PET sinograms from whole-body MRI data for PET/MRI reconstruction algorithm development. The synthetic PET data generated can be used interchangeably with real PET data for PET quantification, achieving small errors in comparison between CTAC and MRAC methods.
IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES
(2023)
Article
Cardiac & Cardiovascular Systems
Ashok Panigrahy, Vanessa Schmithorst, Rafael Ceschin, Vince Lee, Nancy Beluk, Julia Wallace, Olivia Wheaton, Thomas Chenevert, Deqiang Qiu, James N. Lee, Andrew Nencka, Borjan Gagoski, Jeffrey I. Berman, Weihong Yuan, Christopher Macgowan, James Coatsworth, Lazar Fleysher, Christopher Cannistraci, Lynn A. Sleeper, Arvind Hoskoppal, Candice Silversides, Rupa Radhakrishnan, Larry Markham, John F. Rhodes, Lauryn M. Dugan, Nicole Brown, Peter Ermis, Stephanie Fuller, Timothy Brett Cotts, Fred Henry Rodriguez, Ian Lindsay, Sue Beers, Howard Aizenstein, David C. Bellinger, Jane W. Newburger, Laura Glass Umfleet, Scott Cohen, Ali Zaidi, Michelle Gurvitz
Summary: Advances in the management of congenital heart disease have led to improved survival, but adult patients with congenital heart disease still face neurocognitive deficits that affect their quality of life. This study aims to investigate the brain injuries and cognitive reserve factors in these patients through neuroimaging and other data, with the goal of shaping better care for them.
JOURNAL OF CARDIOVASCULAR DEVELOPMENT AND DISEASE
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Stephen M. Moore, James D. Quirk, Andrew W. Lassiter, Richard Laforest, Gregory D. Ayers, Cristian T. Badea, Andriy Y. Fedorov, Paul E. Kinahan, Matthew Holbrook, Peder E. Z. Larson, Renuka Sriram, Thomas L. Chenevert, Dariya Malyarenko, John Kurhanewicz, A. McGarry Houghton, Brian D. Ross, Stephen Pickup, James C. Gee, Rong Zhou, Seth T. Gammon, Henry Charles Manning, Raheleh Roudi, Heike E. Daldrup-Link, Michael T. Lewis, Daniel L. Rubin, Thomas E. Yankeelov, Kooresh I. Shoghi
Summary: Preclinical imaging is critical for translational research in cancer, but there are complexities and regional differences. The NCI emphasizes the use of oncology models to study the biological and molecular basis of cancer prevention and treatment. To address the issues, the NCI conducted a survey and summarized the metadata requirements for reproducible quantitative co-clinical imaging to support translational imaging research.
Article
Radiology, Nuclear Medicine & Medical Imaging
Emel Alkim, Heidi Dowst, Julie DiCarlo, Lacey E. Dobrolecki, Anadulce Hernandez-Herrera, David A. Hormuth II, Yuxing Liao, Apollo McOwiti, Robia Pautler, Mothaffar Rimawi, Ashley Roark, Ramakrishnan Rajaram Srinivasan, Jack Virostko, Bing Zhang, Fei Zheng, Daniel L. Rubin, Thomas E. Yankeelov, Michael T. Lewis
Summary: Co-clinical trials involve evaluating therapeutics in both patients and patient-derived xenografts (PDX) to determine how well PDX responses match patient responses, in order to inform pre-clinical and clinical trials. The challenge lies in managing and analyzing the vast amount of data generated across different scales and species. To overcome this challenge, a web-based tool called MIRACCL is being developed to correlate MRI-based changes in tumor characteristics with mRNA expression data in a co-clinical trial setting.
Article
Radiology, Nuclear Medicine & Medical Imaging
Brian D. Ross, Dariya Malyarenko, Kevin Heist, Ghoncheh Amouzandeh, Youngsoon Jang, Christopher A. Bonham, Cyrus Amirfazli, Gary D. Luker, Thomas L. Chenevert
Summary: This study investigates the repeatability of MRI-based quantitative imaging biomarkers (QIBs) for monitoring and assessing treatment for myelofibrosis (MF) cancer, aiming to replace the painful and invasive bone-marrow biopsies. The repeatability coefficients (RCs) were determined for three defined tibia bone-marrow sections across 15 diseased mice over a period of 10 weeks. The results demonstrate the capability to derive quantitative imaging metrics from mouse tibia bone marrow for monitoring significant longitudinal MF changes.
Review
Radiology, Nuclear Medicine & Medical Imaging
Donna M. Peehl, Cristian T. Badea, Thomas L. Chenevert, Heike E. Daldrup-Link, Li Ding, Lacey E. Dobrolecki, A. McGarry Houghton, Paul E. Kinahan, John Kurhanewicz, Michael T. Lewis, Shunqiang Li, Gary D. Luker, Cynthia X. Ma, H. Charles Manning, Yvonne M. Mowery, Peter J. O'Dwyer, Robia G. Pautler, Mark A. Rosen, Raheleh Roudi, Brian D. Ross, Kooresh I. Shoghi, Renuka Sriram, Moshe Talpaz, Richard L. Wahl, Rong Zhou
Summary: The availability of high-fidelity animal models has increased, allowing for preclinical studies relevant to cancer research. Co-clinical trials conducted on animal models that mirror patients' tumors have seen increased opportunities. However, quantitative imaging in co-clinical trials still needs optimization.
Review
Radiology, Nuclear Medicine & Medical Imaging
Paul E. Kinahan
Article
Ophthalmology
Adam Hanif, Ilkay Yildiz, Peng Tian, Beyza Kalkanl, Deniz Erdogmus, Stratis Ioannidis, Jennifer Dy, Jayashree Kalpathy-Cramer, Susan Ostmo, Karyn Jonas, R. V. Paul Chan, Michael F. Chiang, J. Peter Campbell
Summary: This study compares the efficacy and efficiency of training neural networks for medical image classification using comparison labels indicating relative disease severity versus diagnostic class labels. The results show that neural networks trained with comparison labels perform better than those trained with diagnostic class labels. These findings provide a potential approach to overcome the common obstacle of data variability and scarcity when training neural networks for medical image classification tasks.
OPHTHALMOLOGY SCIENCE
(2022)
Article
Oncology
Xiaofan Pu, Chaolei Zhang, Guoping Ding, Hongpeng Gu, Yang Lv, Tao Shen, Tianshu Pang, Liping Cao, Shengnan Jia
Summary: This study demonstrated the potential utility of the sEV-miRNA d-signature in the diagnosis of PDAC via machine learning methods. A novel sEV biomarker, miR-664a-3p, was identified for the diagnosis of PDAC. It can also potentially promote angiogenesis and metastasis, provide insight into PDAC pathogenesis, and reveal novel regulators of this disease.
TRANSLATIONAL ONCOLOGY
(2024)
Article
Oncology
Jiaping Wang, Zhijuan Xu, Yanli Lai, Yanli Zhang, Ping Zhang, Qitian Mu, Shujun Yang, Yongcheng Sun, Lixia Sheng, Guifang Ouyang
Summary: This study demonstrates the significance of PD-1 in EBV-infected lymphoma cells. Silencing PD-1 enhances the tumor targeting effect of EBV-specific killer T cells on B lymphocytes and attenuates the immune escape effect.
TRANSLATIONAL ONCOLOGY
(2024)
Article
Oncology
Qiliang Peng, Jialong Tao, Yingjie Xu, Yi Shen, Yong Wang, Yang Jiao, Yiheng Mao, Yaqun Zhu, Yulong Liu, Ye Tian
Summary: This study investigates the potential role of lipid metabolism-associated genes (LMAGs) in neoadjuvant chemoradiotherapy (nCRT) and immunotherapy for rectal cancer. The results suggest that the SREBF2 gene is a highly predictive factor for nCRT in rectal cancer and is associated with favorable prognosis. SREBF2 is also closely associated with immune cell infiltration and immunotherapy-related genes.
TRANSLATIONAL ONCOLOGY
(2024)
Article
Oncology
Shiquan Li, Nan Zhang, Yongping Yang, Tongjun Liu
Summary: This study investigated the potential molecular mechanism of SPDEF in immune evasion of colorectal cancer (CRC) and found that it suppresses immune evasion by activating CCL28 through the modulation of M2 polarization of macrophages. These findings provide a new research direction and potential therapeutic target for immunotherapy in CRC.
TRANSLATIONAL ONCOLOGY
(2024)
Article
Oncology
Manas Sehgal, Soundharya Ramu, Joel Markus Vaz, Yogheshwer Raja Ganapathy, Srinath Muralidharan, Sankalpa Venkatraghavan, Mohit Kumar Jolly
Summary: This study investigates the relationship between gene expression patterns and phenotypic plasticity and heterogeneity in colorectal cancer (CRC). The results demonstrate the interconnectedness between different Consensus Molecular Subtypes (CMS) of CRC and specific phenotypes such as epithelial and mesenchymal characteristics. Additionally, the study reveals correlations between metabolic pathways and phenotypic scores, as well as between PD-L1 activity and mesenchymal phenotype. Single-cell RNA sequencing analysis further confirms the heterogeneity of different CMS subtypes. These findings have important implications for understanding CRC heterogeneity and developing targeted therapies.
TRANSLATIONAL ONCOLOGY
(2024)
Article
Oncology
Yutong Zou, Siyao Guo, Yan Liao, Weidong Chen, Ziyun Chen, Junkai Chen, Lili Wen, Xianbiao Xie
Summary: This study found that ceramide metabolism is associated with the progression and clinical outcome of osteosarcoma by analyzing data from osteosarcoma patients. The gene ST3GAL1 plays an important role in osteosarcoma, regulating the tumor immune microenvironment and affecting T cell function. It may become a new target for the treatment of osteosarcoma.
TRANSLATIONAL ONCOLOGY
(2024)
Article
Oncology
Chuanhui Chen, Mengzhi Wan, Xiong Peng, Qing Zhang, Yu Liu
Summary: This study examines the function and mechanism of the ceRNA network centered around GPR37 in LUAD. The findings show that high expression of GPR37 in LUAD tissue samples is associated with poor prognosis, and it may regulate the expression of downstream target genes by competitively binding to lncRNA DLEU1 and miR-4458.
TRANSLATIONAL ONCOLOGY
(2024)
Article
Oncology
Junping Li, Hong Hu, Jinping He, Yuling Hu, Manting Liu, Bihui Cao, Dongni Chen, Xiaodie Ye, Jian Zhang, Zhiru Zhang, Wen Long, Hui Lian, Deji Chen, Likun Chen, Lili Yang, Zhenfeng Zhang
Summary: Sequential administration of CDC7 inhibitor XL413 after carboplatin enhances the chemotherapeutic effect of carboplatin on ovarian cancer cells, possibly by inhibiting homologous recombination repair activity and increasing the accumulation of chemotherapy-induced DNA damage.
TRANSLATIONAL ONCOLOGY
(2024)
Article
Oncology
Madison Catalanotto, Joel Markus Vaz, Camille Abshire, Reneau Youngblood, Min Chu, Herbert Levine, Mohit Kumar Jolly, Ana -Maria Dragoi
Summary: The study demonstrates that loss of FLASH in cancer cells leads to a hybrid E/M phenotype with high epithelial scores, suggesting FLASH acts as a repressor of the epithelial phenotype. Additionally, FLASH expression is inversely correlated with the epithelial score and subsets of mesenchymal markers are distinctly up-regulated in FLASH, NPAT, or SLBP-depleted cells.
TRANSLATIONAL ONCOLOGY
(2024)
Article
Oncology
Xiaorui Wang, Na Li, Minying Zheng, Yongjun Yu, Shiwu Zhang
Summary: Adipocytes are derived from pluripotent mesenchymal stem cells and histone modifications play a key role in their differentiation. Recent studies have shown that cancer stem cells can differentiate into adipocytes, reducing the malignancy of cancer cells.
TRANSLATIONAL ONCOLOGY
(2024)
Article
Oncology
Hana Q. Sadida, Alanoud Abdulla, Sara Al Marzooqi, Sheema Hashem, Muzafar A. Macha, Ammira S. Al-Shabeeb Akil, Ajaz A. Bhat
Summary: Cancer heterogeneity and drug resistance are major obstacles to effective cancer treatment, and epigenetic modifications play a pivotal role in these processes. This review explores essential epigenetic modifications, including DNA methylation, histone modifications, and chromatin remodeling, and discusses their complex contributions to cancer biology. However, the interplay of epigenetic and genetic changes in cancer cells presents unique challenges that must be addressed to fully exploit the potential of epigenetic modifications.
TRANSLATIONAL ONCOLOGY
(2024)
Article
Oncology
Pedro De Marchi, Leticia Ferro Leal, Luciane Sussuchi da Silva, Rodrigo de Oliveira Cavagna, Flavio Augusto Ferreira da Silva, Vinicius Duval da Silva, Eduardo C. A. da Silva, Augusto O. Saito, Vladmir C. Cordeiro de Lima, Rui Manuel Reis
Summary: The TIS and IFN-gamma signatures are predictive biomarkers for identifying NSCLC patients who could potentially benefit from immune checkpoint inhibitor therapies.
TRANSLATIONAL ONCOLOGY
(2024)
Article
Oncology
Giovanni Marchi, Anna Rajavuori, Mai T. N. Nguyen, Kaisa Huhtinen, Sinikka Oksa, Sakari Hietanen, Sampsa Hautaniemi, Johanna Hynninen, Jaana Oikkonen
Summary: The study shows that ctDNA can adequately represent high-grade serous ovarian carcinoma (HGSC), and the mutations observed at relapse suggest personalized therapy options.
TRANSLATIONAL ONCOLOGY
(2024)
Article
Oncology
Yuncang Yuan, Jiawei Fan, Dandan Liang, Shijie Wang, Xu Luo, Yongjie Zhu, Nan Liu, Tingxiu Xiang, Xudong Zhao
Summary: This study demonstrates that csGRP78-directed CAR-T cells can selectively kill pancreatic cancer cells, and the combination with chemotherapy enhances cytotoxicity.
TRANSLATIONAL ONCOLOGY
(2024)
Article
Oncology
Niyati Piplani, Tanusri Roy, Neha Saxena, Shamik Sen
Summary: The glycocalyx, a protective barrier surrounding cells, has been found to play a role in cancer cell proliferation, survival, and metastasis. However, its function in maintaining DNA/nuclear integrity during migration through dense matrices has not been explored. This study shows that the bulkiness of the glycocalyx is inversely associated with nuclear stresses, and highlights its mechanical role in shielding migration-associated stresses.
TRANSLATIONAL ONCOLOGY
(2024)