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https://www.readbyqxmd.com/read/30328048/multivariate-machine-learning-models-for-prediction-of-pathologic-response-to-neoadjuvant-therapy-in-breast-cancer-using-mri-features-a-study-using-an-independent-validation-set
#1
Elizabeth Hope Cain, Ashirbani Saha, Michael R Harowicz, Jeffrey R Marks, P Kelly Marcom, Maciej A Mazurowski
PURPOSE: To determine whether a multivariate machine learning-based model using computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer patients. METHODS: Institutional review board approval was obtained for this retrospective study of 288 breast cancer patients at our institution who received NAT and had a pre-treatment breast MRI...
October 16, 2018: Breast Cancer Research and Treatment
https://www.readbyqxmd.com/read/30325283/biliary-tract-cancer-at-ct-a-radiomics-based-model-to-predict-lymph-node-metastasis-and-survival-outcomes
#2
Gu-Wei Ji, Yu-Dong Zhang, Hui Zhang, Fei-Peng Zhu, Ke Wang, Yong-Xiang Xia, Yao-Dong Zhang, Wang-Jie Jiang, Xiang-Cheng Li, Xue-Hao Wang
Purpose To evaluate a radiomics model for predicting lymph node (LN) metastasis in biliary tract cancers (BTCs) and to determine its prognostic value for disease-specific and recurrence-free survival. Materials and Methods For this retrospective study, a radiomics model was developed on the basis of a primary cohort of 177 patients with BTC who underwent resection and LN dissection between June 2010 and December 2016. Radiomic features were extracted from portal venous CT scans. A radiomics signature was built on the basis of reproducible features by using the least absolute shrinkage and selection operator method...
October 16, 2018: Radiology
https://www.readbyqxmd.com/read/30325279/ct-based-radiomics-for-biliary-tract-cancer-a-possible-solution-for-predicting-lymph-node-metastases
#3
Andrea Laghi, Cecilia Voena
See also the article by Ji et al in this issue.
October 16, 2018: Radiology
https://www.readbyqxmd.com/read/30319179/effects-of-mri-scanner-parameters-on-breast-cancer-radiomics
#4
Ashirbani Saha, Xiaozhi Yu, Dushyant Sahoo, Maciej A Mazurowski
Purpose: To assess the impact of varying magnetic resonance imaging (MRI) scanner parameters on the extraction of algorithmic features in breast MRI radiomics studies. Methods: In this retrospective study, breast imaging data for 272 patients were analyzed with magnetic resonance (MR) images. From the MR images, we assembled and implemented 529 algorithmic features of breast tumors and fibrograndular tissue (FGT). We divided the features into 10 groups based on the type of data used for the feature extraction and the nature of the extracted information...
November 30, 2017: Expert Systems with Applications
https://www.readbyqxmd.com/read/30305102/a-biomarker-basing-on-radiomics-for-the-prediction-of-overall-survival-in-non-small-cell-lung-cancer-patients
#5
Bo He, Wei Zhao, Jiang-Yuan Pi, Dan Han, Yuan-Ming Jiang, Zhen-Guang Zhang, Wei Zhao
BACKGROUND: This study aimed at predicting the survival status on non-small cell lung cancer patients with the phenotypic radiomics features obtained from the CT images. METHODS: A total of 186 patients' CT images were used for feature extraction via Pyradiomics. The minority group was balanced via SMOTE method. The final dataset was randomized into training set (n = 223) and validation set (n = 75) with the ratio of 3:1. Multiple random forest models were trained applying hyperparameters grid search with 10-fold cross-validation using precision or recall as evaluation standard...
October 10, 2018: Respiratory Research
https://www.readbyqxmd.com/read/30302536/advances-in-imaging-and-automated-quantification-of-malignant-pulmonary-diseases-a-state-of-the-art-review
#6
REVIEW
Bruno Hochhegger, Matheus Zanon, Stephan Altmayer, Gabriel S Pacini, Fernanda Balbinot, Martina Z Francisco, Ruhana Dalla Costa, Guilherme Watte, Marcel Koenigkam Santos, Marcelo C Barros, Diana Penha, Klaus Irion, Edson Marchiori
Quantitative imaging in lung cancer is a rapidly evolving modality in radiology that is changing clinical practice from a qualitative analysis of imaging features to a more dynamic, spatial, and phenotypical characterization of suspected lesions. Some quantitative parameters, such as the use of 18F-FDG PET/CT-derived standard uptake values (SUV), have already been incorporated into current practice as it provides important information for diagnosis, staging, and treatment response of patients with lung cancer...
October 9, 2018: Lung
https://www.readbyqxmd.com/read/30292538/towards-a-modular-decision-support-system-for-radiomics-a-case-study-on-rectal-cancer
#7
Roberto Gatta, Mauro Vallati, Nicola Dinapoli, Carlotta Masciocchi, Jacopo Lenkowicz, Davide Cusumano, Calogero Casá, Alessandra Farchione, Andrea Damiani, Johan van Soest, Andre Dekker, Vincenzo Valentini
Following the personalized medicine paradigm, there is a growing interest in medical agents capable of predicting the effect of therapies on patients, by exploiting the amount of data that is now available for each patient. In disciplines like oncology, where images and scans are available, the exploitation of medical images can provide an additional source of potentially useful information. The study and analysis of features extracted by medical images, exploited for predictive purposes, is termed radiomics...
October 3, 2018: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/30291656/pet-mri-in-breast-cancer
#8
Akshat C Pujara, Eric Kim, Deborah Axelrod, Amy N Melsaether
Positron emission tomography / magnetic resonance imaging (PET/MRI) is an emerging imaging technology that allows for the acquisition of multiple MRI parameters simultaneously with PET data. In this review, we address the technical requirements of PET/MRI including protocols and tracers, the potential of integrated localized breast PET/MRI exams, and possible applications of whole-body PET/MRI in breast cancer patients. Currently, PET/MRI can be performed on sequential and integrated PET/MRI scanners but, as not all practices can access these dedicated machines, several studies look at PET and MRI exams that are performed separately on separate scanners within a short time frame...
October 6, 2018: Journal of Magnetic Resonance Imaging: JMRI
https://www.readbyqxmd.com/read/30290849/effect-of-machine-learning-methods-on-predicting-nsclc-overall-survival-time-based-on-radiomics-analysis
#9
Wenzheng Sun, Mingyan Jiang, Jun Dang, Panchun Chang, Fang-Fang Yin
BACKGROUND: To investigate the effect of machine learning methods on predicting the Overall Survival (OS) for non-small cell lung cancer based on radiomics features analysis. METHODS: A total of 339 radiomic features were extracted from the segmented tumor volumes of pretreatment computed tomography (CT) images. These radiomic features quantify the tumor phenotypic characteristics on the medical images using tumor shape and size, the intensity statistics and the textures...
October 5, 2018: Radiation Oncology
https://www.readbyqxmd.com/read/30286184/lung-tumor-segmentation-methods-impact-on-the-uncertainty-of-radiomics-features-for-non-small-cell-lung-cancer
#10
Constance A Owens, Christine B Peterson, Chad Tang, Eugene J Koay, Wen Yu, Dennis S Mackin, Jing Li, Mohammad R Salehpour, David T Fuentes, Laurence E Court, Jinzhong Yang
PURPOSE: To evaluate the uncertainty of radiomics features from contrast-enhanced breath-hold helical CT scans of non-small cell lung cancer for both manual and semi-automatic segmentation due to intra-observer, inter-observer, and inter-software reliability. METHODS: Three radiation oncologists manually delineated lung tumors twice from 10 CT scans using two software tools (3D-Slicer and MIM Maestro). Additionally, three observers without formal clinical training were instructed to use two semi-automatic segmentation tools, Lesion Sizing Toolkit (LSTK) and GrowCut, to delineate the same tumor volumes...
2018: PloS One
https://www.readbyqxmd.com/read/30279049/quantitative-radiomics-validating-image-textural-features-for-oncological-pet-in-lung-cancer
#11
Fei Yang, Lori A Young, Perry B Johnson
BACKGROUND AND PURPOSE: Radiomics textural features derived from PET imaging are of broad and current interest due to recent evidence of their prognostic value during cancer management. An inherent assumption is the link between these imaging features and the underlying tumoral phenotypic spatial heterogeneity. The purpose of this work was to validate this assumption for tumors within the lung through a comparison of image based textural features and the ground truth activity distribution from which the images were created...
September 29, 2018: Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology
https://www.readbyqxmd.com/read/30272571/spatial-temporal-variability-of-radiomic-features-and-its-effect-on-the-classification-of-lung-cancer-histology
#12
Kyle Lafata, Jing Cai, Chunhao Wang, Julian Hong, Christopher R Kelsey, Fang-Fang Yin
The purpose of this research was to study the sensitivity of Computed Tomography (CT) radiomic features to motion blurring and signal-to-noise ratios (SNR), and investigate its downstream effect regarding the classification of non-small cell lung cancer (NSCLC) histology.
 
 Methods: Forty-three radiomic features were considered and classified into one of four categories: Morphological, Intensity, Fine Texture, and Coarse Texture. First, a series of simulations were used to study feature-sensitivity to changes in spatial-temporal resolution...
October 1, 2018: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/30268481/applicability-of-a-prognostic-ct-based-radiomic-signature-model-trained-on-stage-i-iii-non-small-cell-lung-cancer-in-stage-iv-non-small-cell-lung-cancer
#13
Evelyn E C de Jong, Wouter van Elmpt, Stefania Rizzo, Anna Colarieti, Gianluca Spitaleri, Ralph T H Leijenaar, Arthur Jochems, Lizza E L Hendriks, Esther G C Troost, Bart Reymen, Anne-Marie C Dingemans, Philippe Lambin
OBJECTIVES: Recently it has been shown that radiomic features of computed tomography (CT) have prognostic information in stage I-III non-small cell lung cancer (NSCLC) patients. We aim to validate this prognostic radiomic signature in stage IV adenocarcinoma patients undergoing chemotherapy. MATERIALS AND METHODS: Two datasets of chemo-naive stage IV adenocarcinoma patients were investigated, dataset 1: 285 patients with CTs performed in a single center; dataset 2: 223 patients included in a multicenter clinical trial...
October 2018: Lung Cancer: Journal of the International Association for the Study of Lung Cancer
https://www.readbyqxmd.com/read/30268005/practical-guidelines-for-handling-head-and-neck-computed-tomography-artifacts-for-quantitative-image-analysis
#14
Rachel B Ger, Daniel F Craft, Dennis S Mackin, Shouhao Zhou, Rick R Layman, A Kyle Jones, Hesham Elhalawani, Clifton D Fuller, Rebecca M Howell, Heng Li, R Jason Stafford, Laurence E Court
Radiomics studies have demonstrated the potential use of quantitative image features to improve prognostic stratification of patients with head and neck cancer. Imaging protocol parameters that can affect radiomics feature values have been investigated, but the effects of artifacts caused by intrinsic patient factors have not. Two such artifacts that are common in patients with head and neck cancer are streak artifacts caused by dental fillings and beam-hardening artifacts caused by bone. The purpose of this study was to test the impact of these artifacts and if needed, methods for compensating for these artifacts in head and neck radiomics studies...
November 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30266009/a-pilot-study-using-kernelled-support-tensor-machine-for-distant-failure-prediction-in-lung-sbrt
#15
Shulong Li, Ning Yang, Bin Li, Zhiguo Zhou, Hongxia Hao, Michael R Folkert, Puneeth Iyengar, Kenneth Westover, Hak Choy, Robert Timmerman, Steve Jiang, Jing Wang
We developed a kernelled support tensor machine (KSTM)-based model with tumor tensors derived from pre-treatment PET and CT imaging as input to predict distant failure in early stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT). The patient cohort included 110 early stage NSCLC patients treated with SBRT, 25 of whom experienced failure at distant sites. Three-dimensional tumor tensors were constructed and used as input for the KSTM-based classifier. A KSTM iterative algorithm with a convergent proof was developed to train the weight vectors for every mode of the tensor for the classifier...
September 15, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/30252978/quantitative-identification-of-nonmuscle-invasive-and-muscle-invasive-bladder-carcinomas-a-multiparametric-mri-radiomics-analysis
#16
Xiaopan Xu, Xi Zhang, Qiang Tian, Huanjun Wang, Long-Biao Cui, Shurong Li, Xing Tang, Baojuan Li, Jose Dolz, Ismail Ben Ayed, Zhengrong Liang, Jing Yuan, Peng Du, Hongbing Lu, Yang Liu
BACKGROUND: Preoperative discrimination between nonmuscle-invasive bladder carcinomas (NMIBC) and the muscle-invasive ones (MIBC) is very crucial in the management of patients with bladder cancer (BC). PURPOSE: To evaluate the discriminative performance of multiparametric MRI radiomics features for precise differentiation of NMIBC from MIBC, preoperatively. STUDY TYPE: Retrospective, radiomics. POPULATION: Fifty-four patients with postoperative pathologically proven BC lesions (24 in NMIBC and 30 in MIBC groups) were included...
September 25, 2018: Journal of Magnetic Resonance Imaging: JMRI
https://www.readbyqxmd.com/read/30250576/correlation-between-dce-mri-radiomics-features-and-ki-67-expression-in-invasive-breast-cancer
#17
Ma-Wen Juan, Ji Yu, Guo-Xin Peng, Liu-Jun Jun, Sun-Peng Feng, Liu-Pei Fang
The aim of the present study was to investigate the association between Ki-67 expression and radiomics features of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with invasive breast cancer. A total of 53 cases with low-Ki-67 expression (Ki-67 proliferation index <14%) and 106 cases with high-Ki-67 expression (Ki-67 proliferation index >14%) were investigated. A systematic approach was applied that focused on the automated segmentation of lesions and extraction of radiomics features...
October 2018: Oncology Letters
https://www.readbyqxmd.com/read/30247662/radiomics-with-artificial-intelligence-for-precision-medicine-in-radiation-therapy
#18
Hidetaka Arimura, Mazen Soufi, Kamezawa, Kenta Ninomiya, Masahiro Yamada
Recently, the concept of radiomics has emerged from radiation oncology. It is a novel approach for solving the issues of precision medicine and how it can be performed, based on multimodality medical images that are non-invasive, fast and low in cost. Radiomics is the comprehensive analysis of massive numbers of medical images in order to extract a large number of phenotypic features (radiomic biomarkers) reflecting cancer traits, and it explores the associations between the features and patients' prognoses in order to improve decision-making in precision medicine...
September 22, 2018: Journal of Radiation Research
https://www.readbyqxmd.com/read/30231314/novel-quantitative-imaging-for-predicting-response-to-therapy-techniques-and-clinical-applications
#19
Kaustav Bera, Vamsidhar Velcheti, Anant Madabhushi
The current standard of Response Evaluation Criteria in Solid Tumors (RECIST)-based tumor response evaluation is limited in its ability to accurately monitor treatment response. Radiomics, an approach involving computerized extraction of several quantitative imaging features, has shown promise in predicting as well as monitoring response to therapy. In this article, we provide a brief overview of radiomic approaches and the various analytical methods and techniques, specifically in the context of predicting and monitoring treatment response for non-small cell lung cancer (NSCLC)...
May 23, 2018: American Society of Clinical Oncology Educational Book
https://www.readbyqxmd.com/read/30230556/identification-of-optimal-mother-wavelets-in-survival-prediction-of-lung-cancer-patients-using-wavelet-decomposition-based-radiomic-features
#20
Mazen Soufi, Hidetaka Arimura, Noriyuki Nagami
PURPOSE: To identify the optimal mother wavelets in survival prediction of lung cancer patients using wavelet-decomposition-based (WDB) radiomic features in CT images. MATERIALS AND METHODS: CT images of patients with histologically confirmed non-small cell lung carcinomas (NSCLCs) in Training (Dataset T; n = 162) and validation (Dataset V; n = 143) datasets were analyzed for this study. The optimal mother wavelets were identified based on the impacts of the WDB radiomic features on the patient survival times...
September 19, 2018: Medical Physics
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