Article
Oncology
Xinyi Zha, Yuanqing Liu, Xiaoxia Ping, Jiayi Bao, Qian Wu, Su Hu, Chunhong Hu
Summary: A nomogram model combining radiomics and clinical features is effective in non-invasively predicting visceral pleural invasion (VPI) in patients with lung adenocarcinoma.
FRONTIERS IN ONCOLOGY
(2022)
Article
Biology
Hwan-ho Cho, Ho Yun Lee, Eunjin Kim, Geewon Lee, Jonghoon Kim, Junmo Kwon, Hyunjin Park
Summary: A radiomics-guided deep-learning approach is used to model the prognosis of lung adenocarcinoma from CT scan data, demonstrating its utility as a predictive approach for stratifying clinical prognostic groups.
COMMUNICATIONS BIOLOGY
(2021)
Article
Oncology
Hongwei Si, Xinzhong Hao, Huiqin Xu, Shuqin Xue, Ruonan Wang, Li Li, Jianzhong Cao, Sijin Li
Summary: The purpose of this study was to explore the correlation between individual heterogeneity among malignancies (IHAM) and the prognosis of lung cancer. The results showed that the standard deviation of CT features was a powerful prognostic factor for lung cancer patients.
JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yuanqing Liu, Yue Chang, Xinyi Zha, Jiayi Bao, Qian Wu, Hui Dai, Chunhong Hu
Summary: The study aimed to develop a combined model based on radiomic features, imaging characteristics, and serum tumor biomarkers to predict the possibility of preoperative high-grade subtypes in lung adenocarcinomas (LADC). By extracting radiomic features and imaging characteristics, 4 features were selected and used to construct the combined model. The results showed that the combined model had good performance in predicting high-grade subtypes in both the training and validation cohorts.
ACADEMIC RADIOLOGY
(2022)
Article
Oncology
Yanqing Ma, Jie Li, Xiren Xu, Yang Zhang, Yi Lin
Summary: The delta-radiomics based machine learning algorithms in CT images can effectively differentiate between multiple primary lung adenocarcinoma (MPLA) and solitary primary lung adenocarcinoma (SPLA). With longer follow-up duration, the delta-radiomics classifiers can better distinguish between these two types of lung adenocarcinoma.
Review
Radiology, Nuclear Medicine & Medical Imaging
Vito Chianca, Domenico Albano, Carmelo Messina, Gabriele Vincenzo, Stefania Rizzo, Filippo Del Grande, Luca Maria Sconfienza
Summary: In the last two decades, significant progress has been made in the diagnosis of musculoskeletal tumors with the development of new imaging tools and artificial intelligence software, which have advanced imaging techniques and provided advantages in clinical practice.
Article
Radiology, Nuclear Medicine & Medical Imaging
Jooae Choe, Sang Min Lee, Wooil Kim, Kyung-Hyun Do, Seonok Kim, Sehoon Choi, Joon Beom Seo
Summary: The study developed a CT radiomics-based model for simultaneous diagnosis of ALK rearrangements and EGFR mutation status in lung adenocarcinoma, with evaluation of the added value of peritumoural radiomic features. Results showed that the intratumoural radiomic model performed well in predicting ALK rearrangements and EGFR mutations.
EUROPEAN JOURNAL OF RADIOLOGY
(2021)
Article
Multidisciplinary Sciences
Wufei Chen, Ming Li, Dingbiao Mao, Xiaojun Ge, Jiaofeng Wang, Mingyu Tan, Weiling Ma, Xuemei Huang, Jinjuan Lu, Cheng Li, Yanqing Hua, Hao Wu
Summary: This study aimed to develop and validate a radiomics signature to identify the invasiveness of lung adenocarcinoma presented as subcentimeter ground glass nodules. The radiomics signature built on contrast-enhanced CT data showed better predictive performance, with a radiographic-radiomics nomogram demonstrating good clinical utility. Overall, the radiomics signature on CECT could aid in preoperative prediction of invasiveness in patients with subcentimeter lung adenocarcinomas.
SCIENTIFIC REPORTS
(2021)
Article
Medicine, General & Internal
Hongyu Qiao, Zhongxiang Ding, Youcai Zhu, Yuguo Wei, Baochen Xiao, Yongzhen Zhao, Qi Feng
Summary: This study investigated the correlation between TP53 gene expression and radiomic features in lung cancer, showing a high level of association between the two. Through model prediction, the type of TP53 gene mutation in lung cancer lesions can be accurately predicted.
INTERNATIONAL JOURNAL OF GENERAL MEDICINE
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Hanna Muenzfeld, Claus Nowak, Stefanie Riedlberger, Alexander Hartenstein, Bernd Hamm, Paul Jahnke, Tobias Penzkofer
Summary: This study evaluated the impact of clinical imaging techniques on the stability of radiomics features using a 3D printed anthropomorphic CT phantom. Results showed that most radiomics features did not have sufficient intra-scanner repeatability to serve as reliable diagnostic tools.
EUROPEAN JOURNAL OF RADIOLOGY
(2021)
Review
Oncology
Hishan Tharmaseelan, Alexander Hertel, Shereen Rennebaum, Dominik Noerenberg, Verena Haselmann, Stefan O. Schoenberg, Matthias F. Froelich
Summary: Modern personalized therapy approaches are transforming advanced cancer into a chronic disease. Novel omics methodologies in molecular biology enable individual characterization of cancerous lesions. Combined with other non-invasive methods, such as liquid profiling, quantitative imaging biomarkers provide a more individual assessment of tumor biology and potential therapies. Emerging techniques for evaluating oncologic imaging are transitioning from traditional response assessment to comprehensive cancer characterization via imaging, allowing for truly personalized and optimized cancer diagnosis and treatment.
Review
Health Care Sciences & Services
Anne-Noelle Frix, Francois Cousin, Turkey Refaee, Fabio Bottari, Akshayaa Vaidyanathan, Colin Desir, Wim Vos, Sean Walsh, Mariaelena Occhipinti, Pierre Lovinfosse, Ralph T. H. Leijenaar, Roland Hustinx, Paul Meunier, Renaud Louis, Philippe Lambin, Julien Guiot
Summary: This article reviews the recent literature on the application of radiomics in radiology, with a focus on its role and potential in lung diseases. Radiomics, through the high-throughput extraction of imaging data, can help in developing personalized treatments.
JOURNAL OF PERSONALIZED MEDICINE
(2021)
Article
Oncology
Yunyu Xu, Wenbin Ji, Liqiao Hou, Shuangxiang Lin, Yangyang Shi, Chao Zhou, Yinnan Meng, Wei Wang, Xiaofeng Chen, Meihao Wang, Haihua Yang
Summary: The study demonstrates that combining clinical features and radiomics features can more accurately predict MPP in lung IAC, yielding better diagnostic performance compared to a simple radiomics model.
FRONTIERS IN ONCOLOGY
(2021)
Article
Oncology
Guojin Zhang, Yuntai Cao, Jing Zhang, Jialiang Ren, Zhiyong Zhao, Xiaodi Zhang, Shenglin Li, Liangna Deng, Junlin Zhou
Summary: This study developed a nomogram combining radiomics features with clinical and radiological features to predict EGFR mutation status in lung adenocarcinoma, achieving high predictive performance and clinical usefulness.
AMERICAN JOURNAL OF CANCER RESEARCH
(2021)
Article
Oncology
Ran Cao, Huanhuan Chen, Huan Wang, Yan Wang, E-Nuo Cui, Wenyan Jiang
Summary: This study investigates the use of multiparameter MRI-based radiomics in predicting EGFR mutation and subtypes based on spinal metastasis in patients with lung adenocarcinoma. The radiomics models combining MRI features and clinical factors showed good prediction capabilities. These clinical-radiomics nomogram models can be used as non-invasive tools to assist clinicians in making personalized treatment plans.
FRONTIERS IN ONCOLOGY
(2023)
Article
Engineering, Biomedical
Ahmet Karagoz, Albert Guvenis
Summary: This study aimed to improve the discrimination power of clearing cell renal cell carcinoma (ccRCC) tumor grades using three-dimensional radiomic features and ensemble learning models. The results showed that the proposed model achieved high reliability in predicting the tumor grade from 3D CT images, despite the limited dataset. The algorithm demonstrated moderate robustness against segmentation deviations by observers, while features from the peritumour area did not significantly improve the results.
COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION
(2023)
Editorial Material
Oncology
Jaileene Perez-Morales, Kristy K. Broman, Deepti Bettampadi, Mary Katherine Haver, Jonathan S. Zager, Matthew B. Schabath
ANNALS OF SURGICAL ONCOLOGY
(2023)
Article
Oncology
Jaileene Perez-Morales, Kristy K. Broman, Deepti Bettampadi, Mary Katherine Haver, Jonathan S. Zager, Matthew B. Schabath
Summary: This systematic review aims to examine the timing and patterns of recurrence for patients with regionally metastatic melanoma based on nodal management and adjuvant therapy. The study found that adjuvant treatment improved recurrence-free survival (RFS), but did not alter the patterns of recurrence. Future studies should report the specific sites of disease recurrence based on adjuvant systemic treatment and surveillance practices to provide better advice to patients about their recurrence patterns and risks.
ANNALS OF SURGICAL ONCOLOGY
(2023)
Article
Oncology
Miguel Garcia-Pardo, Amy Chang, Sabine Schmid, Mei Dong, M. Catherinen Brown, David Christiani, Hilary Aurora Tindel, Paul Brennan, Chu Chen, Jie Zhang, Brid M. Ryan, David Zaridze, Matthew B. Schabath, Leticia Ferro Leal, Rui Manuel Reis, Adonina Tardon, Guillermo Fernandez-Tardon, Sanjay S. Shete, Angeline Andrew, Hermann Brenner, Wei Xu, Rayjean J. Hung, Geoffrey Liu
Summary: This study examined the association between respiratory and cardiometabolic comorbidities and overall survival (OS) and lung cancer-specific survival (LCSS) in non-small cell lung cancer (NSCLC) patients. The analysis showed that NSCLC patients with respiratory comorbidities had worse LCSS, while those with cardiometabolic comorbidities had a higher risk of death from competing causes. Patients with respiratory comorbidities were also less likely to undergo surgical resection.
JOURNAL OF THORACIC ONCOLOGY
(2023)
Review
Oncology
Erich P. Huang, James P. B. O'Connor, Lisa M. McShane, Maryellen L. Giger, Philippe Lambin, Paul E. Kinahan, Eliot L. Siegel, Lalitha K. Shankar
Summary: Integration of computer-extracted tumour characteristics into medical imaging computer-aided diagnosis (CAD) algorithms has been a long-standing practice. However, the translation of radiomics, an extension of CAD involving quantitative characterization of healthy or pathological structures and processes captured by medical imaging, into clinically useful tools has been limited. This may be due to factors such as varying imaging and radiomic feature extraction protocols, potential pitfalls in radiomic data analysis, and a lack of evidence demonstrating the clinical benefit of radiomic-based tools. The authors provide 16 criteria to guide the clinical translation of radiomics, with the aim of accelerating the use of this technology to improve patient outcomes.
NATURE REVIEWS CLINICAL ONCOLOGY
(2023)
Article
Oncology
Abdalla Ibrahim, Akshayaa Vaidyanathan, Sergey Primakov, Flore Belmans, Fabio Bottari, Turkey Refaee, Pierre Lovinfosse, Alexandre Jadoul, Celine Derwael, Fabian Hertel, Henry C. C. Woodruff, Helle D. D. Zacho, Sean Walsh, Wim Vos, Mariaelena Occhipinti, Francois-Xavier Hanin, Philippe Lambin, Felix M. M. Mottaghy, Roland Hustinx
Summary: The study aims to develop a deep learning (DL) algorithm for classifying areas of increased uptake on bone scintigraphy scans. The algorithm was trained and validated on a dataset of 2365 scans, and its performance was evaluated on an external testing set of 998 scans. The results showed that the DL algorithm achieved higher specificity and sensitivity compared to nuclear medicine physicians, and it can detect metastatic bone disease (MBD) in a shorter time.
Article
Oncology
Audrey R. Freischel, Jamie K. Teer, Kimberly Luddy, Jessica Cunningham, Yael Artzy-Randrup, Tamir Epstein, Kenneth Y. Tsai, Anders Berglund, John L. Cleveland, Robert J. Gillies, Joel S. Brown, Robert A. Gatenby
Summary: Evolution plays a crucial role in the initiation and progression of cancer. In addition to driver mutations, natural selection conserves genes that are necessary for optimal cancer cell fitness. By studying subtypes of lung adenocarcinoma, we identified highly mutated and highly conserved genes, which have common utility in adapting to similar tissue environments and are critical for optimal fitness. Targeting tumor-specific conserved genes may represent an effective treatment strategy.
Article
Oncology
Jaileene Perez-Morales, Rashmi Pathak, Monica Reyes, Haley Tolbert, Rajwantee Tirbene, Jhanelle E. Gray, Vani N. Simmons, Matthew B. Schabath, Gwendolyn P. Quinn
Summary: In order to evaluate the lung cancer screening program, a survey was conducted to measure patient experiences and satisfaction. Results showed that patients generally had positive comments, but also expressed concerns about the lack of information, long wait times, and billing issues. Suggestions for improvement included online appointments, reminders, lower costs, and clarification of eligibility criteria.
Review
Oncology
Alberto Giovanni Leone, Dario Trapani, Matthew B. Schabath, Joshua D. Safer, N. F. N. Scout, Matteo Lambertini, Rossana Berardi, Silvia Marsoni, Francesco Perrone, Saverio Cinieri, Rosalba Miceli, Federica Morano, Filippo Pietrantonio
Summary: Transgender and gender-diverse individuals face unique challenges in cancer risk and outcomes due to barriers to health care access and inequities in treatment. Research shows a high prevalence of tobacco consumption, alcohol use, HPV and HIV infections among this population. These individuals are less likely to adhere to cancer screening programs and have a higher incidence of HIV- and HPV-associated cancers. A lack of knowledge among health care practitioners about the health needs of gender minorities and other barriers further hinder cancer prevention, care, and survivorship for transgender and gender-diverse individuals.
Article
Oncology
Zohaib Salahuddin, Yi Chen, Xian Zhong, Henry C. Woodruff, Nastaran Mohammadian Rad, Shruti Atul Mali, Philippe Lambin
Summary: Automatic delineation and detection of primary tumour (GTVp) and lymph nodes (GTVn) in head and neck cancer using PET and CT can help diagnose and stratify patient risk. This study utilized data from nine centres and developed a segmentation model that estimated uncertainty for false positive reduction. Radiomics features extracted from GTVp and GTVn in PET and CT were found to be prognostic for recurrence-free survival prediction. The framework incorporated uncertainty estimation, fairness, and explainability.
Article
Oncology
Ryan Fogarty, Dmitry Goldgof, Lawrence Hall, Alex Lopez, Joseph Johnson, Manoj Gadara, Radka Stoyanova, Sanoj Punnen, Alan Pollack, Julio Pow-Sang, Yoganand Balagurunathan
Summary: In recent years, Gleason's prostate cancer histopathological description has become a universal standard for disease diagnosis and progression. We have developed deep learning models to assist clinicians in identifying the primary cancer grade. These models have significant application value in histopathological classification.
Article
Oncology
Ulrike Boehmer, Shine Chang, Nelson F. Sanchez, Bill M. Jesdale, Matthew B. Schabath
Summary: This study compared health risk behaviors and outcomes between sexual and gender minority cancer survivors and matched controls. The findings showed that sexual and gender minority cancer survivors face higher risks in terms of mental health, physical health, and health behaviors, indicating the urgent need for preventive measures.
JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Oya Altinok, Albert Guvenis
Summary: This study developed a simple and interpretable Bayesian Network (BN) model to classify HPV status in patients with oropharyngeal cancer. The model selected two relevant predictors from patients' CT images and demonstrated good performance in predicting HPV positivity. The BN model's straightforward structure and interpretability make it useful for clinicians in treatment decision-making and non-invasive detection of HPV status.
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS
(2023)
Article
Oncology
Cassandra L. Sather, Pamela Yang, Chaomei Zhang, Matthew P. Fitzgibbon, Michelle Fournier, Eric Toloza, Amit Tandon, Matthew Schabath, Sean Yoder, Viswam S. Nair
Summary: We investigated the impact of sequencing depth, variant calling strategy, and targeted sequencing region on identifying tumor-derived variants in cell-free bronchoalveolar lavage (cfBAL) DNA compared with plasma cfDNA. Low-coverage sequencing detected tumor-derived cfBAL variants in a higher percentage of patients compared to plasma, and high-coverage sequencing did not affect the number of tumor-derived variants detected in either biospecimen type. Accounting for germline mutations eliminated false-positive plasma calls and increased the number of cfBAL calls, irrespective of coverage and the number of targeted genes.
Article
Oncology
Xian Zhong, Zohaib Salahuddin, Yi Chen, Henry C. Woodruff, Haiyi Long, Jianyun Peng, Xiaoyan Xie, Manxia Lin, Philippe Lambin
Summary: This study developed and validated an interpretable radiomics model based on two-dimensional shear wave elastography (2D-SWE) for predicting symptomatic post-hepatectomy liver failure in patients with hepatocellular carcinoma. The clinical-radiomics model outperformed the clinical model and radiomics model, and the first-order radiomics features were identified as the most important for PHLF prediction.