Article
Oncology
Chang Liu, Jing Gong, Hui Yu, Quan Liu, Shengping Wang, Jialei Wang
Summary: The study developed a CT-based radiomics model to predict clinical outcomes of advanced NSCLC patients treated with nivolumab. The model showed promising results in accurately predicting the risk of PFS and OS, as well as effectively stratifying patients into high and low risk subgroups.
FRONTIERS IN ONCOLOGY
(2021)
Editorial Material
Oncology
Rivka R. Colen, Christian Rolfo, Murat Ak, Mira Ayoub, Sara Ahmed, Nabil Elshafeey, Priyadarshini Mamindla, Pascal O. Zinn, Chaan Ng, Raghu Vikram, Spyridon Bakas, Christine B. Peterson, Jordi Rodon Ahnert, Vivek Subbiah, Daniel D. Karp, Bettzy Stephen, Joud Hajjar, Aung Naing
Summary: In our study, we focused on predicting immunotherapy response in patients with rare cancers using radiomics analysis. By selecting appropriate feature selection and classifier methods, avoiding overfitting, checking for multicollinearity among features, and performing 10-fold cross-validation, we achieved effective predictive performance for our radiomics models.
JOURNAL FOR IMMUNOTHERAPY OF CANCER
(2021)
Article
Medicine, General & Internal
Vincenzo Venerito, Andreina Manfredi, Giuseppe Lopalco, Marlea Lavista, Giulia Cassone, Arnaldo Scardapane, Marco Sebastiani, Florenzo Iannone
Summary: This study investigated whether features from radiomic analysis of high-resolution CT scans could predict mortality in patients with rheumatoid arthritis (RA) and interstitial lung disease (ILD). The findings suggest that specific radiomic features can serve as biomarkers for predicting mortality in RA-ILD patients, regardless of the clinical characteristics of the disease.
FRONTIERS IN MEDICINE
(2023)
Editorial Material
Oncology
Cansu Cimen Bozkus, Nina Bhardwaj
Summary: The article highlights an ex vivo platform for assessing early responses to checkpoint blockade and the properties of tumor immune contexture in correlation to clinical responses, shedding light on the mechanisms behind checkpoint blockade efficacy.
Article
Oncology
Minghao Wu, Yanyan Zhang, Jianing Zhang, Yuwei Zhang, Yina Wang, Feng Chen, Yahong Luo, Shuai He, Yulin Liu, Qian Yang, Yanying Li, Hong Wei, Hong Zhang, Nian Lu, Sicong Wang, Yan Guo, Zhaoxiang Ye, Ying Liu
Summary: Based on non-contrast-enhanced/contrast-enhanced CT images, a combined-radiomics model was developed to predict the response to anti-PD1 immunotherapy in patients with non-small-cell lung cancer (NSCLC). The radiomics analysis showed that the combined-radiomics model had better predictive capacity compared to using single NCE or CE-CT images alone.
FRONTIERS IN ONCOLOGY
(2022)
Article
Oncology
Jing Gong, Xiao Bao, Ting Wang, Jiyu Liu, Weijun Peng, Jingyun Shi, Fengying Wu, Yajia Gu
Summary: This study developed a short-term follow-up CT-based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer (NSCLC) and investigated the prognostic value of radiomics features in predicting progression-free survival (PFS) and overall survival (OS). The results showed that the radiomics model improved the prediction performance and had prognostic value in predicting PFS and OS, especially for adenocarcinoma patients.
Article
Radiology, Nuclear Medicine & Medical Imaging
I Skarping, M. Larsson, D. Fornvik
Summary: This proof of concept study investigated a deep learning-based method using digital mammograms to predict breast cancer patients' responses to neoadjuvant chemotherapy. The initial artificial intelligence model showed potential in aiding clinical decision-making. Further research, including method refinement and a larger sample size, is needed to explore the clinical utility of AI in predicting responses to neoadjuvant chemotherapy for breast cancer.
EUROPEAN RADIOLOGY
(2022)
Article
Oncology
Emanuele Barabino, Giovanni Rossi, Silvia Pamparino, Martina Fiannacca, Simone Caprioli, Alessandro Fedeli, Lodovica Zullo, Stefano Vagge, Giuseppe Cittadini, Carlo Genova
Summary: The study aims to identify radiomic features that can predict the response to immunotherapy. By analyzing the lesions of patients with advanced non-small cell lung cancer, the study explores the features that predict the response to immune checkpoint inhibitors. The finding shows that 27 features can accurately distinguish the radiologic response to immune checkpoint inhibitors.
Article
Gastroenterology & Hepatology
Qiu-Ping Liu, Jie Tang, Yi-Zhang Chen, Fen Guo, Ling Ma, Lan-Lan Pan, Yi-Tong Tian, Xiao-Feng Wu, Yu-Dong Zhang, Xiao-Feng Chen
Summary: Radiomics can serve as a non-invasive immuno-genomic surrogate for BTC and aid in predicting responses to immunotherapy. However, further multicenter and larger sample studies are needed to validate these findings.
Article
Oncology
Ying Liu, Minghao Wu, Yuwei Zhang, Yahong Luo, Shuai He, Yina Wang, Feng Chen, Yulin Liu, Qian Yang, Yanying Li, Hong Wei, Hong Zhang, Chenwang Jin, Nian Lu, Wanhu Li, Sicong Wang, Yan Guo, Zhaoxiang Ye
Summary: The study aimed to identify imaging biomarkers for assessing treatment response status in patients with advanced NSCLC undergoing anti-PD1 immunotherapy. The results showed that a Delta-radiomics nomogram combining radiomics signature and clinical factors had satisfactory performance in distinguishing responders from non-responders. Overall, early response assessment using combined Delta-radiomics nomograms could help oncologists modify treatments tailored individually to each patient under therapy.
FRONTIERS IN ONCOLOGY
(2021)
Article
Oncology
Marion Tonneau, Kim Phan, Venkata S. K. Manem, Cecile Low-Kam, Francis Dutil, Suzanne Kazandjian, Davy Vanderweyen, Justin Panasci, Julie Malo, Francois Coulombe, Andreanne Gagne, Arielle Elkrief, Wiam Belkaid, Lisa Di Jorio, Michele Orain, Nicole Bouchard, Thierry Muanza, Frank J. Rybicki, Kam Kafi, David Huntsman, Philippe Joubert, Florent Chandelier, Bertrand Routy
Summary: Recent developments in artificial intelligence suggest that radiomics may be a promising biomarker for predicting response to immune checkpoint inhibitors in NSCLC patients. This study demonstrated the importance of image harmonization in achieving external model generalizability using radiomics. The results support the potential of radiomics as a non-invasive strategy for predicting ICI response in advanced NSCLC.
FRONTIERS IN ONCOLOGY
(2023)
Review
Oncology
Virginia Liberini, Annapaola Mariniello, Luisella Righi, Martina Capozza, Marco Donatello Delcuratolo, Enzo Terreno, Mohsen Farsad, Marco Volante, Silvia Novello, Desiree Deandreis
Summary: Lung cancer, especially non-small cell lung cancer, remains a primary cause of cancer-related death. New therapeutic approaches, including immunotherapy, have shown significant improvement in patient survival and quality of life. However, the identification of predictive biomarkers is crucial to determine which patients will benefit most from immunotherapy-based treatments.
Article
Oncology
Riccardo Laudicella, Albert Comelli, Virginia Liberini, Antonio Vento, Alessandro Stefano, Alessandro Spataro, Ludovica Croce, Sara Baldari, Michelangelo Bambaci, Desiree Deandreis, Demetrio Arico', Massimo Ippolito, Michele Gaeta, Pierpaolo Alongi, Fabio Minutoli, Irene A. Burger, Sergio Baldari
Summary: Machine learning techniques offer new opportunities for radiomics analysis to predict treatment response in patients with neuroendocrine tumors.
Article
Radiology, Nuclear Medicine & Medical Imaging
Ricarda Hinzpeter, Livia Baumann, Roman Guggenberger, Martin Huellner, Hatem Alkadhi, Bettina Baessler
Summary: This study successfully differentiated bone metastases not visible on CT but detected by (68) Ga-PSMA PET imaging using radiomics analysis, with a classification accuracy of 0.90, high sensitivity and specificity for bone metastases.
EUROPEAN RADIOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Juan Luis Onieva, Qingyang Xiao, Miguel-angel Berciano-Guerrero, Aurora Laborda-Illanes, Carlos de Andrea, Patricia Chaves, Pilar Pineiro, Alicia Garrido-Aranda, Elena Gallego, Belen Sojo, Laura Galvez, Rosario Chica-Parrado, Daniel Prieto, Elisabeth Perez-Ruiz, Angela Farngren, Maria Jose Lozano, Martina Alvarez, Pedro Jimenez, Alfonso Sanchez-Munoz, Javier Oliver, Manuel Cobo, Emilio Alba, Isabel Barragan
Summary: Resistance to immune checkpoint blockade (ICB) is a challenge in cancer therapy. In this study, the researchers analyzed the gene expression and cellular levels to identify biomarkers for predicting response to Nivolumab and prognosis. They validated their findings using single-cell RNA-seq data and immunofluorescence. They also developed a prediction algorithm and a 15-gene model that outperformed the current reference score. The study discovered the significant role of IGKC and plasma cells in the efficacy of ICB treatment for metastatic melanoma.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
