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Radiomics, cancer

Haonan Lu, Mubarik Arshad, Andrew Thornton, Giacomo Avesani, Paula Cunnea, Ed Curry, Fahdi Kanavati, Jack Liang, Katherine Nixon, Sophie T Williams, Mona Ali Hassan, David D L Bowtell, Hani Gabra, Christina Fotopoulou, Andrea Rockall, Eric O Aboagye
The five-year survival rate of epithelial ovarian cancer (EOC) is approximately 35-40% despite maximal treatment efforts, highlighting a need for stratification biomarkers for personalized treatment. Here we extract 657 quantitative mathematical descriptors from the preoperative CT images of 364 EOC patients at their initial presentation. Using machine learning, we derive a non-invasive summary-statistic of the primary ovarian tumor based on 4 descriptors, which we name "Radiomic Prognostic Vector" (RPV)...
February 15, 2019: Nature Communications
Yu Sun, Hayley M Reynolds, Bimal Parameswaran, Darren Wraith, Mary E Finnegan, Scott Williams, Annette Haworth
Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging with one or more functional MRI sequences. It has become a versatile tool for detecting and characterising prostate cancer (PCa). The traditional role of mpMRI was confined to PCa staging, but due to the advanced imaging techniques, its role has expanded to various stages in clinical practises including tumour detection, disease monitor during active surveillance and sequential imaging for patient follow-up. Meanwhile, with the growing speed of data generation and the increasing volume of imaging data, it is highly demanded to apply computerised methods to process mpMRI data and extract useful information...
February 14, 2019: Australasian Physical & Engineering Sciences in Medicine
Hui Li, Kayla R Mendel, Li Lan, Deepa Sheth, Maryellen L Giger
Background Previous studies have suggested that breast parenchymal texture features may reflect the biologic risk factors associated with breast cancer development. Therefore, combining the characteristics of normal parenchyma from the contralateral breast with radiomic features of breast tumors may improve the accuracy of digital mammography in the diagnosis of breast cancer. Purpose To determine whether the addition of radiomic analysis of contralateral breast parenchyma to the characterization of breast lesions with digital mammography improves lesion classification over that with radiomic tumor features alone...
February 12, 2019: Radiology
Bino Varghese, Frank Chen, Darryl Hwang, Suzanne L Palmer, Andre Luis De Castro Abreu, Osamu Ukimura, Monish Aron, Manju Aron, Inderbir Gill, Vinay Duddalwar, Gaurav Pandey
Multiparametric magnetic resonance imaging (mpMRI) has become increasingly important for the clinical assessment of prostate cancer (PCa), but its interpretation is generally variable due to its relatively subjective nature. Radiomics and classification methods have shown potential for improving the accuracy and objectivity of mpMRI-based PCa assessment. However, these studies are limited to a small number of classification methods, evaluation using the AUC score only, and a non-rigorous assessment of all possible combinations of radiomics and classification methods...
February 7, 2019: Scientific Reports
Qiu-Xia Feng, Chang Liu, Liang Qi, Shu-Wen Sun, Yang Song, Guang Yang, Yu-Dong Zhang, Xi-Sheng Liu
PURPOSE: The aim of this study was to develop and validate a computational clinical decision support system (DSS) on the basis of CT radiomics features for the prediction of lymph node (LN) metastasis in gastric cancer (GC) using machine learning-based analysis. METHODS: Clinicopathologic and CT imaging data were retrospectively collected from 490 patients who were diagnosed with GC between January 2002 and December 2016. Radiomics features were extracted from venous-phase CT images...
February 4, 2019: Journal of the American College of Radiology: JACR
Sarah A Milgrom, Hesham Elhalawani, Joonsang Lee, Qianghu Wang, Abdallah S R Mohamed, Bouthaina S Dabaja, Chelsea C Pinnix, Jillian R Gunther, Laurence Court, Arvind Rao, Clifton D Fuller, Mani Akhtari, Michalis Aristophanous, Osama Mawlawi, Hubert H Chuang, Erik P Sulman, Hun J Lee, Frederick B Hagemeister, Yasuhiro Oki, Michelle Fanale, Grace L Smith
First-order radiomic features, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), are associated with disease progression in early-stage classical Hodgkin lymphoma (HL). We hypothesized that a model incorporating first- and second-order radiomic features would more accurately predict outcome than MTV or TLG alone. We assessed whether radiomic features extracted from baseline PET scans predicted relapsed or refractory disease status in a cohort of 251 patients with stage I-II HL who were managed at a tertiary cancer center...
February 4, 2019: Scientific Reports
Sara Lewis, Steven Peti, Stefanie J Hectors, Michael King, Ally Rosen, Amita Kamath, Juan Putra, Swan Thung, Bachir Taouli
OBJECTIVE: To evaluate the ability of volumetric quantitative apparent diffusion coefficient (ADC) histogram parameters and LI-RADS categorization to distinguish hepatocellular carcinoma (HCC) from other primary liver cancers [intrahepatic cholangiocarcinoma (ICC) and combined HCC-ICC]. METHODS: Sixty-three consecutive patients (44 M/19F; mean age 62 years) with primary liver cancers and pre-treatment MRI including diffusion-weighted imaging (DWI) were included in this IRB-approved single-center retrospective study...
February 2, 2019: Abdominal Radiology
Meng Liang, Zhengting Cai, Hongmei Zhang, Chencui Huang, Yankai Meng, Li Zhao, Dengfeng Li, Xiaohong Ma, Xinming Zhao
RATIONALE AND OBJECTIVES: To use machine learning-based magnetic resonance imaging radiomics to predict metachronous liver metastases (MLM) in patients with rectal cancer. MATERIALS AND METHODS: This study retrospectively analyzed 108 patients with rectal cancer (54 in MLM group and 54 in nonmetastases group). Feature selection were performed in the radiomic feature sets extracted from images of T2-weighted image (T2WI) and venous phase (VP) sequence respectively, and the combining feature set with 2058 radiomic features incorporating two sequences with the least absolute shrinkage and selection operator method...
January 30, 2019: Academic Radiology
Lu Han, Yongbei Zhu, Zhenyu Liu, Tao Yu, Cuiju He, Wenyan Jiang, Yangyang Kan, Di Dong, Jie Tian, Yahong Luo
OBJECTIVE: To develop a radiomic nomogram for preoperative prediction of axillary lymph node (LN) metastasis in breast cancer patients. METHODS: Preoperative magnetic resonance imaging data from 411 breast cancer patients was studied. Patients were assigned to either a training cohort (n = 279) or a validation cohort (n = 132). Eight hundred eight radiomic features were extracted from the first phase of T1-DCE images. A support vector machine was used to develop a radiomic signature, and logistic regression was used to develop a nomogram...
January 30, 2019: European Radiology
Rossano Girometti, Lorenzo Cereser, Filippo Bonato, Chiara Zuiani
Multiparametric magnetic resonance imaging (mpMRI) has become the standard of care to achieve accurate and reproducible diagnosis of prostate cancer. However, mpMRI is quite demanding in terms of technical rigour, patient's tolerability and safety, expertise in interpretation, and costs. This paper reviews the main technical strategies proposed as less-is-better solutions for clinical practice (non-contrast biparametric MRI, reduction of acquisition time, abbreviated protocols, computer-aided diagnosis systems), discussing them in the light of the available evidence and of the concurrent evolution of Prostate Imaging Reporting and Data System (PI-RADS)...
January 28, 2019: European radiology experimental
D Dong, L Tang, Z-Y Li, M-J Fang, J-B Gao, X-H Shan, X-J Ying, Y-S Sun, J Fu, X-X Wang, L-M Li, Z-H Li, D-F Zhang, Y Zhang, Z-M Li, F Shan, Z-D Bu, J Tian, J-F Ji
Background: Occult peritoneal metastasis (PM) in advanced gastric cancer (AGC) patients is highly possible to be missed on CT images. Patients with occult PMs are subject to late detection or even improper surgical treatment. We therefore aimed to develop a radiomic nomogram to preoperatively identify occult PMs in AGC patients. Patients and methods: 554 AGC patients from four centers were divided into one training, one internal validation, and two external validation cohorts...
January 23, 2019: Annals of Oncology: Official Journal of the European Society for Medical Oncology
Shi-Ting Feng, Yingmei Jia, Bing Liao, Bingsheng Huang, Qian Zhou, Xin Li, Kaikai Wei, Lili Chen, Bin Li, Wei Wang, Shuling Chen, Xiaofang He, Haibo Wang, Sui Peng, Ze-Bin Chen, Mimi Tang, Zhihang Chen, Yang Hou, Zhenwei Peng, Ming Kuang
OBJECTIVES: Preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular cancer (HCC) is important for surgery strategy making. We aimed to develop and validate a combined intratumoural and peritumoural radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for preoperative prediction of MVI in primary HCC patients. METHODS: This study included a training cohort of 110 HCC patients and a validating cohort of 50 HCC patients...
January 28, 2019: European Radiology
Alex Zwanenburg, Stefan Leger, Linda Agolli, Karoline Pilz, Esther G C Troost, Christian Richter, Steffen Löck
Image features need to be robust against differences in positioning, acquisition and segmentation to ensure reproducibility. Radiomic models that only include robust features can be used to analyse new images, whereas models with non-robust features may fail to predict the outcome of interest accurately. Test-retest imaging is recommended to assess robustness, but may not be available for the phenotype of interest. We therefore investigated 18 combinations of image perturbations to determine feature robustness, based on noise addition (N), translation (T), rotation (R), volume growth/shrinkage (V) and supervoxel-based contour randomisation (C)...
January 24, 2019: Scientific Reports
Jeffrey D Rudie, Andreas M Rauschecker, R Nick Bryan, Christos Davatzikos, Suyash Mohan
Due to the exponential growth of computational algorithms, artificial intelligence (AI) methods are poised to improve the precision of diagnostic and therapeutic methods in medicine. The field of radiomics in neuro-oncology has been and will likely continue to be at the forefront of this revolution. A variety of AI methods applied to conventional and advanced neuro-oncology MRI data can already delineate infiltrating margins of diffuse gliomas, differentiate pseudoprogression from true progression, and predict recurrence and survival better than methods used in daily clinical practice...
January 22, 2019: Radiology
Shuling Chen, Shiting Feng, Jingwei Wei, Fei Liu, Bin Li, Xin Li, Yang Hou, Dongsheng Gu, Mimi Tang, Han Xiao, Yingmei Jia, Sui Peng, Jie Tian, Ming Kuang
OBJECTIVES: Immunoscore evaluates the density of CD3+ and CD8+ T cells in both the tumor core and invasive margin. Pretreatment prediction of immunoscore in hepatocellular cancer (HCC) is important for precision immunotherapy. We aimed to develop a radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced MRI for pretreatment prediction of immunoscore (0-2 vs. 3-4) in HCC. MATERIALS AND METHODS: The study included 207 (training cohort: n = 150; validation cohort: n = 57) HCC patients with hepatectomy who underwent preoperative Gd-EOB-DTPA-enhanced MRI...
January 21, 2019: European Radiology
Linning E, Lin Lu, Li Li, Hao Yang, Lawrence H Schwartz, Binsheng Zhao
OBJECTIVES: The aim of this study was to evaluate the performance of the radiomics method in classifying lung cancer histological subtypes based on multiphasic contrast-enhanced computed tomography (CT) images. METHODS: A total of 229 patients with pathologically confirmed lung cancer were retrospectively recruited. All recruited patients underwent nonenhanced and dual-phase chest contrast-enhanced CT; 1160 quantitative radiomics features were calculated to build a radiomics classification model...
January 17, 2019: Journal of Computer Assisted Tomography
Geewon Lee, So Hyeon Bak, Ho Yun Lee, Joon Young Choi, Hyunjin Park, Seung-Hak Lee, Yoshiharu Ohno, Mizuki Nishino, Edwin J R van Beek, Kyung Soo Lee
Multimodality imaging measurements of treatment response are critical for clinical practice, oncology trials, and the evaluation of new treatment modalities. The current standard for determining treatment response in non-small cell lung cancer (NSCLC) is based on tumor size using the RECIST criteria. Molecular targeted agents and immunotherapies often cause morphological change without reduction of tumor size. Therefore, it is difficult to evaluate therapeutic response by conventional methods. Radiomics is the study of cancer imaging features that are extracted using machine learning and other semantic features...
January 17, 2019: Journal of Thoracic Imaging
Chiharu Kai, Mako Ishimaru, Yoshikazu Uchiyama, Junji Shiraishi, Norimitsu Shinohara, Hiroshi Fujita
Subtype classification of breast cancer by analyzing the gene expression profile of cancer cells is becoming a standard procedure. Breast cancer subtype classification is more useful than the conventional method because the characteristics of subtype classification is directly connected with the treatment method. However, genetic testing is invasive, and a part of cancer cells may not represent the overall nature of the cancer. In the computer-aided diagnosis (CAD) scheme for differentiation of triple-negative breast cancer (TNBC) by estimating the genetic properties of cancer based on Radiogenomics, principal component analysis (PCA) and least absolute shrinkage and selection operator (Lasso) were used for reducing the dimension of radiomic features, and we compared usefulness of both...
2019: Nihon Hoshasen Gijutsu Gakkai Zasshi
Xin Chen, Mengjie Fang, Di Dong, Lingling Liu, Xiangdong Xu, Xinhua Wei, Xinqing Jiang, Lei Qin, Zaiyi Liu
RATIONALE AND OBJECTIVES: Glioblastoma multiforme (GBM) is the most common and deadly type of primary malignant tumor of the central nervous system. Accurate risk stratification is vital for a more personalized approach in GBM management. The purpose of this study is to develop and validate a MRI-based prognostic quantitative radiomics classifier in patients with newly diagnosed GBM and to evaluate whether the classifier allows stratification with improved accuracy over the clinical and qualitative imaging features risk models...
January 16, 2019: Academic Radiology
Elisa Dama, Valentina Melocchi, Tommaso Colangelo, Roberto Cuttano, Fabrizio Bianchi
Recent advances in radiological imaging and genomic analysis are profoundly changing the way to manage lung cancer patients. Screening programs which couple lung cancer risk prediction models and low-dose computed tomography (LDCT) recently showed their effectiveness in the early diagnosis of lung tumors. In addition, the emerging field of radiomics is revolutionizing the approach to handle medical images, i.e., from a "simple" visual inspection to a high-throughput analysis of hundreds of quantitative features of images which can predict prognosis and therapy response...
January 17, 2019: Journal of Clinical Medicine
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