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Hubert S Gabryś, Florian Buettner, Florian Sterzing, Henrik Hauswald, Mark Bangert
Purpose: The purpose of this study is to investigate whether machine learning with dosiomic, radiomic, and demographic features allows for xerostomia risk assessment more precise than normal tissue complication probability (NTCP) models based on the mean radiation dose to parotid glands. Material and methods: A cohort of 153 head-and-neck cancer patients was used to model xerostomia at 0-6 months (early), 6-15 months (late), 15-24 months (long-term), and at any time (a longitudinal model) after radiotherapy...
2018: Frontiers in Oncology
Harini Veeraraghavan, Brittany Z Dashevsky, Natsuko Onishi, Meredith Sadinski, Elizabeth Morris, Joseph O Deasy, Elizabeth J Sutton
We present a segmentation approach that combines GrowCut (GC) with cancer-specific multi-parametric Gaussian Mixture Model (GCGMM) to produce accurate and reproducible segmentations. We evaluated GCGMM using a retrospectively collected 75 invasive ductal carcinoma with ERPR+ HER2- (n = 15), triple negative (TN) (n = 9), and ER-HER2+ (n = 57) cancers with variable presentation (mass and non-mass enhancement) and background parenchymal enhancement (mild and marked). Expert delineated manual contours were used to assess the segmentation performance using Dice coefficient (DSC), mean surface distance (mSD), Hausdorff distance, and volume ratio (VR)...
March 19, 2018: Scientific Reports
Mei Yuan, Jin-Yuan Liu, Teng Zhang, Yu-Dong Zhang, Hai Li, Tong-Fu Yu
Visceral pleural invasion (VPI) in stageI lung adenocarcinoma is an independent negative prognostic factor. However, no studies proved any morphologic pattern could be referred to as a prognostic factor. Thus, we aim to investigate the potential prognostic impact of VPI by extracting high-dimensional radiomics features on thin-section computed tomography (CT). A total of 327 surgically resected pathological-N0M0 lung adenocarcinoma 3 cm or less in size were evaluated. Radiomics signature was generated by calculating the contribution weight of each feature and validated using repeated leaving-one-out ten-fold cross-validation approach...
March 16, 2018: Scientific Reports
Philipp Kickingereder, Ovidiu Cristian Andronesi
Magnetic resonance imaging plays a key role in diagnosis and treatment monitoring of brain tumors. Novel imaging techniques that specifically interrogate aspects of underlying tumor biology and biochemical pathways have great potential in neuro-oncology. This review focuses on the emerging role of 2-hydroxyglutarate-targeted magnetic resonance spectroscopy, as well as radiomics and radiogenomics in establishing diagnosis for isocitrate dehydrogenase mutant gliomas, and for monitoring treatment response and predicting prognosis of this group of brain tumor patients...
February 2018: Seminars in Neurology
Jean-Philippe Foy, Catherine Durdux, Philippe Giraud, Jean-Emmanuel Bibault
No abstract text is available yet for this article.
March 12, 2018: Journal of the National Cancer Institute
Yanqi Huang, Lan He, Di Dong, Caiyun Yang, Cuishan Liang, Xin Chen, Zelan Ma, Xiaomei Huang, Su Yao, Changhong Liang, Jie Tian, Zaiyi Liu
Objective: To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). Methods: After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]...
February 2018: Chinese Journal of Cancer Research, Chung-kuo Yen Cheng Yen Chiu
Zijian Zhang, Jinzhong Yang, Angela Ho, Wen Jiang, Jennifer Logan, Xin Wang, Paul D Brown, Susan L McGovern, Nandita Guha-Thakurta, Sherise D Ferguson, Xenia Fave, Lifei Zhang, Dennis Mackin, Laurence E Court, Jing Li
The original version of this article, published on 24 November 2017, unfortunately contained a mistake.
March 14, 2018: European Radiology
Antonio Esposito, Anna Palmisano, Sofia Antunes, Caterina Colantoni, Paola Maria Vittoria Rancoita, Davide Vignale, Francesca Baratto, Paolo Della Bella, Alessandro Del Maschio, Francesco De Cobelli
PURPOSE: Diffuse remodeling of myocardial extra-cellular matrix is largely responsible for left ventricle (LV) dysfunction and arrhythmias. Our hypothesis is that the texture analysis of late iodine enhancement (LIE) cardiac computed tomography (cCT) images may improve characterization of the diffuse extra-cellular matrix changes. Our aim was to extract volumetric extracellular volume (ECV) and LIE texture features of non-scarred (remote) myocardium from cCT of patients with recurrent ventricular tachycardia (rVT), and to compare these radiomic features with LV-function, LV-remodeling, and underlying cardiac disease...
March 13, 2018: Molecular Imaging and Biology: MIB: the Official Publication of the Academy of Molecular Imaging
Roelof J Beukinga, Jan Binne Hulshoff, Véronique E M Mul, Walter Noordzij, Gursah Kats-Ugurlu, Riemer H J A Slart, John T M Plukker
Purpose To assess the value of baseline and restaging fluorine 18 (18 F) fluorodeoxyglucose (FDG) positron emission tomography (PET) radiomics in predicting pathologic complete response to neoadjuvant chemotherapy and radiation therapy (NCRT) in patients with locally advanced esophageal cancer. Materials and Methods In this retrospective study, 73 patients with histologic analysis-confirmed T1/N1-3/M0 or T2-4a/N0-3/M0 esophageal cancer were treated with NCRT followed by surgery (Chemoradiotherapy for Esophageal Cancer followed by Surgery Study regimen) between October 2014 and August 2017...
March 14, 2018: Radiology
Saima Rathore, Hamed Akbari, Jimit Doshi, Gaurav Shukla, Martin Rozycki, Michel Bilello, Robert Lustig, Christos Davatzikos
Standard surgical resection of glioblastoma, mainly guided by the enhancement on postcontrast T1-weighted magnetic resonance imaging (MRI), disregards infiltrating tumor within the peritumoral edema region (ED). Subsequent radiotherapy typically delivers uniform radiation to peritumoral FLAIR-hyperintense regions, without attempting to target areas likely to be infiltrated more heavily. Noninvasive in vivo delineation of the areas of tumor infiltration and prediction of early recurrence in peritumoral ED could assist in targeted intensification of local therapies, thereby potentially delaying recurrence and prolonging survival...
April 2018: Journal of Medical Imaging
Yiming Li, Zenghui Qian, Kaibin Xu, Kai Wang, Xing Fan, Shaowu Li, Tao Jiang, Xing Liu, Yinyan Wang
Background: P53 mutation status is a pivotal biomarker for gliomas. Here, we developed a machine-learning model to predict p53 status in lower-grade gliomas based on radiomic features extracted from conventional magnetic resonance (MR) images. Methods: Preoperative MR images were retrospectively obtained from 272 patients with primary grade II/III gliomas. The patients were randomly allocated in a 2:1 ratio to a training ( n  = 180) or validation ( n  = 92) set...
2018: NeuroImage: Clinical
Wenjuan Ma, Yumei Zhao, Yu Ji, Xinpeng Guo, Xiqi Jian, Peifang Liu, Shandong Wu
RATIONALE AND OBJECTIVES: This study aimed to investigate whether quantitative radiomic features extracted from digital mammogram images are associated with molecular subtypes of breast cancer. MATERIALS AND METHODS: In this institutional review board-approved retrospective study, we collected 331 Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 29 triple-negative, 45 human epidermal growth factor receptor 2 (HER2)-enriched, 36 luminal A, and 221 luminal B lesions...
March 8, 2018: Academic Radiology
Wenbing Lv, Qingyu Yuan, Quanshi Wang, Jianhua Ma, Jun Jiang, Wei Yang, Qianjin Feng, Wufan Chen, Arman Rahmim, Lijun Lu
OBJECTIVES: To investigate the impact of parameter settings as used for the generation of radiomics features on their robustness and disease differentiation (nasopharyngeal carcinoma (NPC) versus chronic nasopharyngitis (CN) in FDG PET/CT imaging). METHODS: We studied 106 patients (69/37 NPC/CN, pathology confirmed), and extracted 57 radiomics features under different parameter settings. Robustness was assessed by the intra-class correlation coefficient (ICC). Logistic regression with leave-one-out cross validation was used to generate classification probabilities, and diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC)...
March 8, 2018: European Radiology
Mazen Soufi, Hidetaka Arimura, Takahiro Nakamoto, Taka-Aki Hirose, Saiji Ohga, Yoshiyuki Umezu, Hiroshi Honda, Tomonari Sasaki
PURPOSE: We aimed to explore the temporal stability of radiomic features in the presence of tumor motion and the prognostic powers of temporally stable features. METHODS: We selected single fraction dynamic electronic portal imaging device (EPID) (n = 275 frames) and static digitally reconstructed radiographs (DRRs) of 11 lung cancer patients, who received stereotactic body radiation therapy (SBRT) under free breathing. Forty-seven statistical radiomic features, which consisted of 14 histogram-based features and 33 texture features derived from the graylevel co-occurrence and graylevel run-length matrices, were computed...
February 2018: Physica Medica: PM
Miaolong Lu, Xianquan Zhan
Cancer with heavily economic and social burden is the hot point in the field of medical research. Some remarkable achievements have been made; however, the exact mechanisms of tumor initiation and development remain unclear. Cancer is a complex, whole-body disease that involves multiple abnormalities in the levels of DNA, RNA, protein, metabolite and medical imaging. Biological omics including genomics, transcriptomics, proteomics, metabolomics and radiomics aims to systematically understand carcinogenesis in different biological levels, which is driving the shift of cancer research paradigm from single parameter model to multi-parameter systematical model...
March 2018: EPMA Journal
Natally Horvat, Harini Veeraraghavan, Monika Khan, Ivana Blazic, Junting Zheng, Marinela Capanu, Evis Sala, Julio Garcia-Aguilar, Marc J Gollub, Iva Petkovska
Purpose To investigate the value of T2-weighted-based radiomics compared with qualitative assessment at T2-weighted imaging and diffusion-weighted (DW) imaging for diagnosis of clinical complete response in patients with rectal cancer after neoadjuvant chemotherapy-radiation therapy (CRT). Materials and Methods This retrospective study included 114 patients with rectal cancer who underwent magnetic resonance (MR) imaging after CRT between March 2012 and February 2016. Median age among women (47 of 114, 41%) was 55...
March 7, 2018: Radiology
Matea Pavic, Marta Bogowicz, Xaver Würms, Stefan Glatz, Tobias Finazzi, Oliver Riesterer, Johannes Roesch, Leonie Rudofsky, Martina Friess, Patrick Veit-Haibach, Martin Huellner, Isabelle Opitz, Walter Weder, Thomas Frauenfelder, Matthias Guckenberger, Stephanie Tanadini-Lang
BACKGROUND: Radiomics is a promising methodology for quantitative analysis and description of radiological images using advanced mathematics and statistics. Tumor delineation, which is still often done manually, is an essential step in radiomics, however, inter-observer variability is a well-known uncertainty in radiation oncology. This study investigated the impact of inter-observer variability (IOV) in manual tumor delineation on the reliability of radiomic features (RF). METHODS: Three different tumor types (head and neck squamous cell carcinoma (HNSCC), malignant pleural mesothelioma (MPM) and non-small cell lung cancer (NSCLC)) were included...
March 7, 2018: Acta Oncologica
Anastasia Oikonomou, Farzad Khalvati, Pascal N Tyrrell, Masoom A Haider, Usman Tarique, Laura Jimenez-Juan, Michael C Tjong, Ian Poon, Armin Eilaghi, Lisa Ehrlich, Patrick Cheung
We sought to quantify contribution of radiomics and SUVmax at PET/CT to predict clinical outcome in lung cancer patients treated with stereotactic body radiotherapy (SBRT). 150 patients with 172 lung cancers, who underwent SBRT were retrospectively included. Radiomics were applied on PET/CT. Principal components (PC) for 42 CT and PET-derived features were examined to determine which ones accounted for most of variability. Survival analysis quantified ability of radiomics and SUVmax to predict outcome. PCs including homogeneity, size, maximum intensity, mean and median gray level, standard deviation, entropy, kurtosis, skewness, morphology and asymmetry were included in prediction models for regional control (RC) [PC4-HR:0...
March 5, 2018: Scientific Reports
Jennifer Yin Yee Kwan, Jie Su, Shao Hui Huang, Laleh S Ghoraie, Wei Xu, Biu Chan, Kenneth W Yip, Meredith Giuliani, Andrew Bayley, John Kim, Andrew J Hope, Jolie Ringash, John Cho, Andrea McNiven, Aaron Hansen, David Goldstein, John R de Almeida, Hugo J Aerts, John N Waldron, Benjamin Haibe-Kains, Brian O'Sullivan, Scott V Bratman, Fei-Fei Liu
PURPOSE: Distant metastasis (DM) is the main cause of death for patients with human papillomavirus (HPV)-related oropharyngeal cancers (OPCs); yet, there are few reliable predictors of DM in this disease. The role of quantitative imaging (ie, radiomic) analysis was examined to determine whether there are primary tumor features discernible on imaging studies that are associated with a higher risk of DM developing. METHODS AND MATERIALS: Radiation therapy planning computed tomography scans were retrieved for all nonmetastatic p16-positive OPC patients treated with radiation therapy or chemoradiation therapy at a single institution between 2005 and 2010...
February 1, 2018: International Journal of Radiation Oncology, Biology, Physics
Xiaomei Huang, Zixuan Cheng, Yanqi Huang, Cuishan Liang, Lan He, Zelan Ma, Xin Chen, Xiaomei Wu, Yexing Li, Changhong Liang, Zaiyi Liu
RATIONALE AND OBJECTIVES: To develop and validate a computed tomography-based radiomics signature for preoperatively discriminating high-grade from low-grade colorectal adenocarcinoma (CRAC). MATERIALS AND METHODS: This retrospective study was approved by our institutional review board, and the informed consent requirement was waived. This study enrolled 366 patients with CRAC (training dataset: n = 222, validation dataset: n = 144) from January 2008 to August 2015...
March 1, 2018: Academic Radiology
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