Review
Clinical Neurology
Norbert Galldiks, Frank Angenstein, Jan-Michael Werner, Elena K. Bauer, Robin Gutsche, Gereon R. Fink, Karl-Josef Langen, Philipp Lohmann
Summary: Anatomical cross-sectional imaging methods are standard for the diagnosis, treatment planning, and follow-up of meningioma patients, and advanced neuroimaging provides detailed information about the molecular and metabolic characteristics of meningiomas. Artificial intelligence methods like radiomics can extract imaging features from routine MRI and CT scans, linking imaging phenotypes to meningioma characteristics.
Article
Radiology, Nuclear Medicine & Medical Imaging
Peng Lin, Wei-Jun Wan, Tong Kang, Lian-feng Qin, Qiu-xue Meng, Xiao-xin Wu, Hong-yan Qin, Yi-qun Lin, Yun He, Hong Yang
Summary: This study aimed to identify the molecular basis of four parameters obtained from dynamic contrast-enhanced magnetic resonance imaging. The results showed that functional tumor volume (FTV) was positively correlated with proliferation-related pathways, longest diameter (LD) was positively correlated with signal transmission-related pathways, and sphericity was positively correlated with immune-related pathways. The decrease in sphericity was negatively correlated with baseline sphericity and immune signatures, and could serve as a predictor for response to neoadjuvant chemotherapy.
Article
Radiology, Nuclear Medicine & Medical Imaging
Rodrigo Delgadillo, Benjamin O. Spieler, John C. Ford, Deukwoo Kwon, Fei Yang, Matthew Studenski, Kyle R. Padgett, Matthew C. Abramowitz, Alan Dal Pra, Radka Stoyanova, Alan Pollack, Nesrin Dogan
Summary: This study investigated the quality of CBCT-based radiomic features and their relationship with reconstruction and preprocessing methods. It was found that CBCT radiomic features are generally more repeatable than reproducible, and certain features from specific classes showed high levels of repeatability and reproducibility. The study suggests that improving repeatability of CBCT radiomic features through reconstruction and preprocessing methods may decrease their reproducibility.
Article
Biochemistry & Molecular Biology
Jacobo Porto-Alvarez, Eva Cernadas, Rebeca Aldaz Martinez, Manuel Fernandez-Delgado, Emilio Huelga Zapico, Victor Gonzalez-Castro, Sandra Baleato-Gonzalez, Roberto Garcia-Figueiras, J. Ramon Antunez-Lopez, Miguel Souto-Bayarri
Summary: This article aims to prove that CT-based radiomics can predict KRAS mutation in CRC patients. The study used 56 CRC patients from the Hospital of Santiago de Compostela in Spain and obtained radiomics features through abdominal contrast enhancement CT. The results showed that AdaBoost ensemble on clinical patient data had the most reliable prediction ability, with a kappa and accuracy of 53.7% and 76.8% for KRAS mutation.
Article
Radiology, Nuclear Medicine & Medical Imaging
Giuseppe Corrias, Giulio Micheletti, Luigi Barberini, Jasjit S. Suri, Luca Saba
Summary: Texture analysis and radiomics are tools used to explore the amount of data in images. Texture analysis extracts features to uncover disease characteristics, while radiomics extracts quantitative data from medical images to correlate with clinical outcomes. In recent years, these methods have been widely used in various fields, providing clinical radiologists with tools for data processing and identifying important papers.
EUROPEAN JOURNAL OF RADIOLOGY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Jim Zhong, Russell Frood, Alan McWilliam, Angela Davey, Jane Shortall, Martin Swinton, Oliver Hulson, Catharine M. West, David Buckley, Sarah Brown, Ananya Choudhury, Peter Hoskin, Ann Henry, Andrew Scarsbrook
Summary: This study aimed to develop a machine learning model based on radiomic features extracted from whole prostate gland MRI for prediction of tumor hypoxia pre-radiotherapy. The results showed that radiomic features can be used to assist in individualized treatment optimization.
Article
Oncology
Clement Acquitter, Lucie Piram, Umberto Sabatini, Julia Gilhodes, Elizabeth Moyal Cohen-Jonathan, Soleakhena Ken, Benjamin Lemasson
Summary: This study explored the potential of multiparametric MRI harmonization in improving the classification of radionecrosis and tumor progression. The results showed that harmonization reduced scanner-related variability and improved the predictive performance of radiomics-based models. Radiomics features extracted from MRI perfusion had the highest accuracy, while features from T1-weighted MRI alone also achieved high accuracy before contrast injection.
Article
Radiology, Nuclear Medicine & Medical Imaging
Yueqiang Zhu, Yue Ma, Yuwei Zhang, Aidi Liu, Yafei Wang, Mengran Zhao, Haijie Li, Ni He, Yaopan Wu, Zhaoxiang Ye
Summary: The study aimed to develop and validate a nomogram based on clinical factors and contrast-enhanced CBBCT radiomics features to predict axillary lymph node metastasis and complement limited axilla coverage. The nomogram showed potential in distinguishing ALN positive from negative and addressing the limitation of axilla coverage in CBBCT.
Article
Oncology
Anahita Fathi Kazerooni, Stephen J. Bagley, Hamed Akbari, Sanjay Saxena, Sina Bagheri, Jun Guo, Sanjeev Chawla, Ali Nabavizadeh, Suyash Mohan, Spyridon Bakas, Christos Davatzikos, MacLean P. Nasrallah
Summary: Radiomics and radiogenomics, integrated with machine learning and medical imaging, have the potential to revolutionize precision diagnostics and personalized treatments for high-grade gliomas, improving prognostication accuracy and optimizing patient care.
Review
Biochemistry & Molecular Biology
Matteo Ferro, Ottavio de Cobelli, Mihai Dorin Vartolomei, Giuseppe Lucarelli, Felice Crocetto, Biagio Barone, Alessandro Sciarra, Francesco Del Giudice, Matteo Muto, Martina Maggi, Giuseppe Carrieri, Gian Maria Busetto, Ugo Falagario, Daniela Terracciano, Luigi Cormio, Gennaro Musi, Octavian Sabin Tataru
Summary: Radiomics and genomics play crucial roles in prostate cancer research, enhancing clinical value through mathematical analysis and machine learning. Validation of recent findings in large, randomized cohorts can establish the role of radiogenomics in the future.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
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
Oncology
Yue Niu, Xiaoping Yu, Lu Wen, Feng Bi, Lian Jian, Siye Liu, Yanhui Yang, Yi Zhang, Qiang Lu
Summary: This study compared CT and MRI-based multiparametric radiomics models and validated a multi-modality, multiparametric clinical-radiomics nomogram for individual preoperative prediction of lymph node metastasis (LNM) in rectal cancer (RC) patients.
FRONTIERS IN ONCOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Tao Wan, Chunxue Wu, Ming Meng, Tao Liu, Chuzhong Li, Jun Ma, Zengchang Qin
Summary: Radiomics models based on multiparametric magnetic resonance imaging have demonstrated good performance in preoperatively evaluating the tumor consistency of pituitary macroadenomas, showing the ability to distinguish between soft and hard consistency.
JOURNAL OF MAGNETIC RESONANCE IMAGING
(2022)
Article
Oncology
Wenlong Ming, Yanhui Zhu, Yunfei Bai, Wanjun Gu, Fuyu Li, Zixi Hu, Tiansong Xia, Zuolei Dai, Xiafei Yu, Huamei Li, Yu Gu, Shaoxun Yuan, Rongxin Zhang, Haitao Li, Wenyong Zhu, Jianing Ding, Xiao Sun, Yun Liu, Hongde Liu, Xiaoan Liu
Summary: This study investigated the associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC). The researchers identified specific imaging features that were significantly associated with BC subtypes and prognosis, and developed classifiers to predict clinical receptors, PAM50 subtypes, and prognostic gene sets. These imaging features have the potential to non-invasively predict clinical characteristics and prognosis of breast cancer.
FRONTIERS IN ONCOLOGY
(2022)
Review
Medicine, General & Internal
Kuo Feng Hung, Qi Yong H. Ai, Lun M. Wong, Andy Wai Kan Yeung, Dion Tik Shun Li, Yiu Yan Leung
Summary: The increasing use of CT and CBCT in oral and maxillofacial imaging has led to the development of deep learning and radiomics applications for maxillofacial disease diagnosis and management. Deep learning models have been developed for automatic diagnosis, segmentation, and classification of various maxillofacial diseases, while radiomics applications mainly focus on diagnosing occult metastasis and osteoarthritis. These models show high performance and have the potential for clinical use, but challenges in generalizability and reproducibility need to be addressed.
Article
Radiology, Nuclear Medicine & Medical Imaging
Yihang Xu, Tejan Diwanji, Nellie Brovold, Michael Butkus, Kyle R. Padgett, Ryder M. Schmidt, Adam King, Alan Dal Pra, Matt Abramowitz, Alan Pollack, Nesrin Dogan
Summary: The study implemented a daily CBCT based dose accumulation technique to assess ideal robust optimization parameters for IMPT treatment of prostate cancer. Results indicated that plans with +/- 3mm/+/- 3% uncertainty provided satisfactory CTV coverage and superior sparing to OARs compared to plans with +/- 5 mm/+/- 3% uncertainty. Weekly dose accumulation accurately estimated the overall dose delivered to prostate cancer patients.
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Rodrigo Delgadillo, Benjamin O. Spieler, John C. Ford, Deukwoo Kwon, Fei Yang, Matthew Studenski, Kyle R. Padgett, Matthew C. Abramowitz, Alan Dal Pra, Radka Stoyanova, Alan Pollack, Nesrin Dogan
Summary: This study investigated the quality of CBCT-based radiomic features and their relationship with reconstruction and preprocessing methods. It was found that CBCT radiomic features are generally more repeatable than reproducible, and certain features from specific classes showed high levels of repeatability and reproducibility. The study suggests that improving repeatability of CBCT radiomic features through reconstruction and preprocessing methods may decrease their reproducibility.
Article
Multidisciplinary Sciences
Ryder M. Schmidt, Rodrigo Delgadillo, John C. Ford, Kyle R. Padgett, Matthew Studenski, Matthew C. Abramowitz, Benjamin Spieler, Yihang Xu, Fei Yang, Nesrin Dogan
Summary: This study quantitatively assessed the accuracy of a commercially available deformable image registration (DIR) algorithm in generating prostate contours, showing high consistency between auto-generated and manually drawn contours, as well as good robustness of radiomic features to differing contours. The majority of DIRs were within recommended tolerance, indicating promising potential for auto contour usage in radiomic feature analysis.
SCIENTIFIC REPORTS
(2021)
Article
Oncology
Miguel Angel Noy, Benjamin J. Rich, Ricardo Llorente, Deukwoo Kwon, Matthew Abramowitz, Brandon Mahal, Eric A. Mellon, Nicholas G. Zaorsky, Alan Dal Pra
Summary: The study found that only 9.7% of radiation therapy recommendations in NCCN guidelines are category I evidence, with the highest levels of evidence for radiation therapy found in breast and cervical cancers. There is a significant difference in the distribution of consensus and evidence between radiation therapy recommendations and systemic therapy recommendations. The proportion of category I evidence is significantly higher in systemic therapy recommendations compared to radiation therapy recommendations.
ADVANCES IN RADIATION ONCOLOGY
(2022)
Article
Oncology
Benjamin J. Rich, Chris Montoya, William H. Jin, Benjamin O. Spieler, Brandon A. Mahal, Rodrigo Delgadillo, Marijo Bilusic, Matthew C. Abramowitz, Alan Pollack, Alan Dal Pra
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
(2022)
Article
Multidisciplinary Sciences
Rodrigo Delgadillo, Benjamin O. Spieler, Anthony M. Deana, John C. Ford, Deukwoo Kwon, Fei Yang, Matthew T. Studenski, Kyle R. Padgett, Matthew C. Abramowitz, Alan Dal Pra, Radka Stoyanova, Nesrin Dogan
Summary: This study investigated the performance of CBCT-based delta-radiomic features (DRF) models in predicting acute and sub-acute International Prostate Symptom Scores (IPSS) and Common Terminology Criteria for Adverse Events (CTCAE) version 5 GU toxicity grades for prostate cancer (PCa) patients treated with definitive radiotherapy (RT). The results showed that the DRF models based on CBCT images could accurately predict IPSS and CTCAE grades as early as week 1 of treatment. This study highlights the potential of using CBCT-based DRF models for predicting RT outcomes.
SCIENTIFIC REPORTS
(2022)
Meeting Abstract
Oncology
L. E. D. Schumacher, A. Dal Pra, S. Hoffe, E. A. Mellon
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
(2020)
Meeting Abstract
Oncology
B. Spieler, G. Azzam, D. Kwon, D. Saravia, G. Lopes, A. Dal Pra, T. Diwanji, R. Yechieli, L. M. Freedman, I. B. Mihaylov
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
(2020)
Meeting Abstract
Oncology
R. Stoyanova, C. Lopez, A. L. Breto, I. R. Xu, D. Kwon, G. C. Franco, A. Dal Pra, M. C. Abramowitz, S. Punnen, A. Pollack
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
(2020)
Meeting Abstract
Oncology
R. Stoyanova, S. Punnen, D. Kwon, I. M. Reis, N. Soodana Prakash, S. M. Gaston, C. R. Rich, B. Nahar, M. L. Gonzalgo, B. Kava, R. P. Castillo Acosta, O. N. Kryvenko, A. Dal Pra, M. C. Abramowitz, E. Davicioni, D. J. Parekh, A. Pollack
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
(2020)
Meeting Abstract
Oncology
J. J. Meshman, B. Farnia, R. Stoyanova, A. Dal Pra, M. C. Abramowitz, I. M. Reis, D. Kwon, S. Punnen, E. M. Horwitz, A. Pollack
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
(2020)
Meeting Abstract
Oncology
T. M. Giret, R. Stoyanova, S. Ansari, T. Tulasigeri, M. Jorda, A. Dal Pra, M. C. Abramowitz, S. Punnen, A. Pollack
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
(2020)
Article
Environmental Sciences
Samantha J. Kramer, Claudia Alvarez, Anne E. Barkley, Peter R. Colarco, Lillian Custals, Rodrigo Delgadillo, Cassandra J. Gaston, Ravi Govindaraju, Paquita Zuidema
ATMOSPHERIC CHEMISTRY AND PHYSICS
(2020)
