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Radiomics, cancer

Winter Spence
Prostate cancer continues to be the most commonly diagnosed cancer among Canadian men. The introduction of routine screening and advanced treatment options have allowed for a decrease in prostate cancer-related mortality, but outcomes following treatment continue to vary widely. In addition, the overtreatment of indolent prostate cancers causes unnecessary treatment toxicities and burdens health care systems. Accurate identification of patients who should undergo aggressive treatment, and those which should be managed more conservatively, needs to be implemented...
December 2018: Journal of Medical Imaging and Radiation Sciences
Natally Horvat, Serena Monti, Brunna Clemente Oliveira, Camila Carlos Tavares Rocha, Romina Grazia Giancipoli, Lorenzo Mannelli
Background Liver cancer is the sixth most common cancer worldwide and the second leading cause of cancer mortality. Chronic liver disease caused by viral infection, alcohol abuse, or other factors can lead to cirrhosis. Cirrhosis is the most important clinical risk factor for hepatocellular carcinoma (HCC) whereby the normal hepatic architecture is replaced by fibrous septa and a spectrum of nodules ranging from benign regenerative nodules to HCC, each one of them with different imaging features. Conclusions Furthermore, advanced techniques including the quantification of hepatic and intralesional fat and iron, magnetic resonance elastography, radiomics, radiogenomics, and positron emission tomography (PET)-MRI are highly promising for the extraction of new imaging biomarkers that reflect the tumor microenvironment and, in the future, may add decision-making value in the management of patients with HCC...
November 26, 2018: Radiology and Oncology
Dmitry Cherezov, Samuel H Hawkins, Dmitry B Goldgof, Lawrence O Hall, Ying Liu, Qian Li, Yoganand Balagurunathan, Robert J Gillies, Matthew B Schabath
BACKGROUND: Current guidelines for lung cancer screening increased a positive scan threshold to a 6 mm longest diameter. We extracted radiomic features from baseline and follow-up screens and performed size-specific analyses to predict lung cancer incidence using three nodule size classes (<6 mm [small], 6-16 mm [intermediate], and ≥16 mm [large]). METHODS: We extracted 219 features from baseline (T0) nodules and 219 delta features which are the change from T0 to first follow-up (T1)...
December 1, 2018: Cancer Medicine
Elisabeth Pfaehler, Roelof J Beukinga, Johan R de Jong, Riemer H J A Slart, Cornelis H Slump, Rudi A J O Dierckx, Ronald Boellaard
BACKGROUND: 18 F-fluoro-2-deoxy-D-Glucose positron emission tomography (18 F-FDG PET) radiomics has the potential to guide the clinical decision making in cancer patients, but validation is required before radiomics can be implemented in the clinical setting. The aim of this study was to explore how feature space reduction and repeatability of 18 F-FDG PET radiomic features are affected by various sources of variation such as underlying data (e.g. object size and uptake), image reconstruction methods and settings, noise, discretization method, and delineation method...
December 3, 2018: Medical Physics
Linning E, Lin Lu, Li Li, Hao Yang, Lawrence H Schwartz, Binsheng Zhao
OBJECTIVES: To evaluate the performance of using radiomics method to classify lung cancer histological subtypes based on nonenhanced computed tomography images. MATERIALS AND METHODS: 278 patients with pathologically confirmed lung cancer were collected, including 181 nonsmall cell lung cancer (NSCLC) and 97 small cell lung cancers (SCLC) patients. Among the NSCLC patients, 88 patients were adenocarcinomas (AD) and 93 patients were squamous cell carcinomas (SCC)...
November 28, 2018: Academic Radiology
Ahmed Hosny, Chintan Parmar, Thibaud P Coroller, Patrick Grossmann, Roman Zeleznik, Avnish Kumar, Johan Bussink, Robert J Gillies, Raymond H Mak, Hugo J W L Aerts
BACKGROUND: Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification. METHODS AND FINDINGS: We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median = 68...
November 2018: PLoS Medicine
Andreas Meier, Harini Veeraraghavan, Stephanie Nougaret, Yulia Lakhman, Ramon Sosa, Robert A Soslow, Elizabeth J Sutton, Hedvig Hricak, Evis Sala, Hebert A Vargas
PURPOSE: To assess the associations between inter-site texture heterogeneity parameters derived from computed tomography (CT), survival, and BRCA mutation status in women with high-grade serous ovarian cancer (HGSOC). MATERIALS AND METHODS: Retrospective study of 88 HGSOC patients undergoing CT and BRCA mutation status testing prior to primary cytoreductive surgery. Associations between texture metrics-namely inter-site cluster variance (SCV), inter-site cluster prominence (SCP), inter-site cluster entropy (SE)-and overall survival (OS), progression-free survival (PFS) as well as BRCA mutation status were assessed...
November 24, 2018: Abdominal Radiology
Hongming Li, Maya Galperin-Aizenberg, Daniel Pryma, Charles B Simone, Yong Fan
BACKGROUND AND PURPOSE: To predict treatment response and survival of NSCLC patients receiving stereotactic body radiation therapy (SBRT), we develop an unsupervised machine learning method for stratifying patients and extracting meta-features simultaneously based on imaging data. MATERIAL AND METHODS: This study was performed based on an 18 F-FDG-PET dataset of 100 consecutive patients who were treated with SBRT for early stage NSCLC. Each patient's tumor was characterized by 722 radiomic features...
November 2018: Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology
Alberto Diaz de Leon, Payal Kapur, Ivan Pedrosa
Renal tumors encompass a heterogeneous disease spectrum, which confounds patient management and treatment. Percutaneous biopsy is limited by an inability to sample every part of the tumor. Radiomics may provide detail beyond what can be achieved from human interpretation. Understanding what new technologies offer will allow radiologists to play a greater role in caring for patients with renal cell carcinoma. In this article, we review the use of radiomics in renal cell carcinoma, in both the pretreatment assessment of renal masses and posttreatment evaluation of renal cell carcinoma, with special emphasis on the use of multiparametric MR imaging datasets...
February 2019: Magnetic Resonance Imaging Clinics of North America
Meghan W Macomber, Mark Phillips, Ivan Tarapov, Rajesh Jena, Aditya Nori, David Carter, Loic Le Folgoc, Antonio Criminisi, Matthew J Nyflot
Machine learning for image segmentation could provide expedited clinic workflow and better standardization of contour delineation. We evaluated a new model using deep decision forests of image features in order to contour pelvic anatomy on treatment planning CTs. 193 CT scans from one UK and two US institutions for patients undergoing radiotherapy treatment for prostate cancer from 2012-2016 were anonymized. A decision forest autosegmentation model was trained on a random selection of 94 images from Institution 1 and tested on 99 scans from Institution 1, 2, and 3...
November 22, 2018: Physics in Medicine and Biology
Sara Ramella, Michele Fiore, Carlo Greco, Ermanno Cordelli, Rosa Sicilia, Mario Merone, Elisabetta Molfese, Marianna Miele, Patrizia Cornacchione, Edy Ippolito, Giulio Iannello, Rolando Maria D'Angelillo, Paolo Soda
The primary goal of precision medicine is to minimize side effects and optimize efficacy of treatments. Recent advances in medical imaging technology allow the use of more advanced image analysis methods beyond simple measurements of tumor size or radiotracer uptake metrics. The extraction of quantitative features from medical images to characterize tumor pathology or heterogeneity is an interesting process to investigate, in order to provide information that may be useful to guide the therapies and predict survival...
2018: PloS One
Pierre Starkov, Todd A Aguilera, Daniel I Golden, David B Shultz, Nicholas Trakul, Peter G Maxim, Quynh-Thu Le, Billy W Loo, Maximillan Diehn, Adrien Depeursinge, Daniel L Rubin
OBJECTIVE: Stereotactic ablative radiotherapy (SABR) is being increasingly used as a non-invasive treatment for early-stage non-small cell lung cancer (NSCLC). A non-invasive method to estimate treatment outcomes in these patients would be valuable, especially since access to tissue specimens is often difficult in these cases. METHODS: We developed a method to predict survival following SABR in NSCLC patients using analysis of quantitative image features on pre-treatment CT images...
November 20, 2018: British Journal of Radiology
Nikkole M Weber, Chi Wan Koo, Lifeng Yu, Brian J Bartholmai, Ahmed F Halaweish, Cynthia H McCollough, Joel G Fletcher
OBJECTIVE: Chest computed tomography (CT) imaging enables detailed visualization of the pulmonary structures and diseases. This article reviews how continued innovation and improvements in modern CT system hardware and software now facilitate a wider range of image acquisition options and generate unique qualitative and quantitative information that can benefit patients RESULTS: Dual energy imaging utilizes two x-ray energies to highlight differences in tissue properties and increase iodine signal to improve diagnosis or reduce metal artifacts...
October 24, 2018: Current Problems in Diagnostic Radiology
Elyse Colard, Sarkis Delcourt, Laetitia Padovani, Sébastien Thureau, Arthur Dumouchel, Pierrick Gouel, Justine Lequesne, Bardia Farman Ara, Pierre Vera, David Taïeb, Isabelle Gardin, Dominique Barbolosi, Sébastien Hapdey
BACKGROUND: In FDG-PET, SUV images are hampered by large potential biases. Our aim was to develop an alternative method (ParaPET) to generate 3D kinetic parametric FDG-PET images easy to perform in clinical oncology. METHODS: The key points of our method are the use of a new error model of PET measurement extracted from a late dynamic PET acquisition of 15 min, centered over the lesion and an image-derived input function (IDIF). The 15-min acquisition is reconstructed to obtain five images of FDG mean activity concentration and images of its variance to model errors of PET measurement...
November 15, 2018: EJNMMI Research
Rahul Paul, Lawrence Hall, Dmitry Goldgof, Matthew Schabath, Robert Gillies
Lung cancer is the leading cause of cancer-related deaths globally, which makes early detection and diagnosis a high priority. Computed tomography (CT) is the method of choice for early detection and diagnosis of lung cancer. Radiomics features extracted from CT-detected lung nodules provide a good platform for early detection, diagnosis, and prognosis. In particular when using low dose CT for lung cancer screening, effective use of radiomics can yield a precise non-invasive approach to nodule tracking. Lately, with the advancement of deep learning, convolutional neural networks (CNN) are also being used to analyze lung nodules...
July 2018: Proceedings of ... International Joint Conference on Neural Networks
Woo Kyoung Jeong, Neema Jamshidi, Ely Richard Felker, Steven Satish Raman, David Shinkuo Lu
Concurrent advancements in imaging and genomic biomarkers have created opportunities to identify non-invasive imaging surrogates of molecular phenotypes. In order to develop such imaging surrogates radiomics and radiogenomics/imaging genomics will be necessary; there has been consistent progress in these fields for primary liver cancers. In this article we evaluate the current status of the field specifically with regards to hepatocellular carcinoma and intrahepatic cholangiocarcinoma, highlighting some of the up and coming results that were presented at the annual Radiological Society of North America Conference in 2017...
November 16, 2018: Clinical and Molecular Hepatology
Wei Wei, Yu Rong, Zhenyu Liu, Bin Zhou, Zhenchao Tang, Shuo Wang, Di Dong, Yali Zang, Yingkun Guo, Jie Tian
In order to predict the 3-year recurrence of advanced ovarian cancer before surgery, we retrospective collected 94 patients to analyze by using a novel radiomics method. A total of 575 3D imaging features used for radiomics analysis were extracted, and 7 features were selected from computed tomography (CT) images that were most strongly associated with 3-year clinical recurrence-free survival (CRFS) probability to build a radiomics signature. The area under the Receiver Operating Characteristic (ROC) curve (AUC) of 0...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Marco Bologna, Eros Montin, Valentina D A Corino, Paolo Bossi, Giuseppina Calareso, Lisa Licitra, Luca T Mainardi
The purpose of this study is to identify a set of radiomic features extracted from apparent diffusion coefficient (ADC) maps, obtained using baseline diffusion weighted magnetic resonance imaging (DW-MRI), which are able to predict the outcome of induction chemotherapy (IC) in sinonasal cancers. Such prediction could help the clinician defining the better treatment for a particular patient. Eighty-eight radiomic features were extracted from the ADC maps of 15 patients that underwent IC. A preliminary filtering of the features was made by assessing their stability to geometrical transformations of the region of interest (ROI)...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Zhiguo Zhou, Liyuan Chen, David Sher, Qiongwen Zhang, Jennifer Shah, Nhat-Long Pham, Steve Jiang, Jing Wang
Lymph node metastasis (LNM) is a significant prognostic factor in patients with head and neck cancer, and the ability to predict it accurately is essential for treatment optimization. PET and CT imaging are routinely used for LNM identification. However, uncertainties of LNM always exist especially for small size or reactive nodes. Radiomics and deep learning are the two preferred imaging-based strategies for node malignancy prediction. Radiomics models are built based on handcrafted features, and deep learning can learn the features automatically...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Remy Klaassen, Ruben T H M Larue, Banafsche Mearadji, Stephanie O van der Woude, Jaap Stoker, Philippe Lambin, Hanneke W M van Laarhoven
In this study we investigate a CT radiomics approach to predict response to chemotherapy of individual liver metastases in patients with esophagogastric cancer (EGC). In eighteen patients with metastatic EGC treated with chemotherapy, all liver metastases were manually delineated in 3D on the pre-treatment and evaluation CT. From the pre-treatment CT scans 370 radiomics features were extracted per lesion. Random forest (RF) models were generated to discriminate partial responding (PR, >65% volume decrease, including 100% volume decrease), and complete remission (CR, only 100% volume decrease) lesions from other lesions...
2018: PloS One
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