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Machine learning algorithm for lung cancer prediction

Aymen A Elfiky, Maximilian J Pany, Ravi B Parikh, Ziad Obermeyer
Importance: Patients with cancer who die soon after starting chemotherapy incur costs of treatment without the benefits. Accurately predicting mortality risk before administering chemotherapy is important, but few patient data-driven tools exist. Objective: To create and validate a machine learning model that predicts mortality in a general oncology cohort starting new chemotherapy, using only data available before the first day of treatment. Design, Setting, and Participants: This retrospective cohort study of patients at a large academic cancer center from January 1, 2004, through December 31, 2014, determined date of death by linkage to Social Security data...
July 6, 2018: JAMA network open
Yajun Li, Lin Lu, Manjun Xiao, Laurent Dercle, Yue Huang, Zishu Zhang, Lawrence H Schwartz, Daiqiang Li, Binsheng Zhao
We evaluated whether the optimal selection of CT reconstruction settings enables the construction of a radiomics model to predict epidermal growth factor receptor (EGFR) mutation status in primary lung adenocarcinoma (LAC) using standard of care CT images. Fifty-one patients (EGFR:wildtype = 23:28) with LACs of clinical stage I/II/IIIA were included in the analysis. The LACs were segmented in four conditions, two slice thicknesses (Thin: 1 mm; Thick: 5 mm) and two convolution kernels (Sharp: B70f/B70s; Smooth: B30f/B31f/B31s), which constituted four groups: (1) Thin-Sharp, (2) Thin-Smooth, (3) Thick-Sharp, and (4) Thick-Smooth...
December 17, 2018: Scientific Reports
Helge C Kniep, Frederic Madesta, Tanja Schneider, Uta Hanning, Michael H Schönfeld, Gerhard Schön, Jens Fiehler, Tobias Gauer, René Werner, Susanne Gellissen
Purpose To investigate the feasibility of tumor type prediction with MRI radiomic image features of different brain metastases in a multiclass machine learning approach for patients with unknown primary lesion at the time of diagnosis. Materials and methods This single-center retrospective analysis included radiomic features of 658 brain metastases from T1-weighted contrast material-enhanced, T1-weighted nonenhanced, and fluid-attenuated inversion recovery (FLAIR) images in 189 patients (101 women, 88 men; mean age, 61 years; age range, 32-85 years)...
December 11, 2018: Radiology
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
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...
December 2018: Medical Image Analysis
Likai Wang, Yanpeng Xi, Sibum Sung, Hong Qiao
BACKGROUND: Although different quality controls have been applied at different stages of the sample preparation and data analysis to ensure both reproducibility and reliability of RNA-seq results, there are still limitations and bias on the detectability for certain differentially expressed genes (DEGs). Whether the transcriptional dynamics of a gene can be captured accurately depends on experimental design/operation and the following data analysis processes. The workflow of subsequent data processing, such as reads alignment, transcript quantification, normalization, and statistical methods for ultimate identification of DEGs can influence the accuracy and sensitivity of DEGs analysis, producing a certain number of false-positivity or false-negativity...
July 20, 2018: BMC Genomics
Hann-Hsiang Chao, Gilmer Valdes, Jose M Luna, Marina Heskel, Abigail T Berman, Timothy D Solberg, Charles B Simone
BACKGROUND AND PURPOSE: Chest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We utilize machine learning algorithms to identify toxicity predictors to develop dose-volume constraints. MATERIALS AND METHODS: Twenty-five patient, tumor, and dosimetric features were recorded for 197 consecutive patients with Stage I NSCLC treated with SBRT, 11 of whom (5.6%) developed CTCAEv4 grade ≥2 chest wall pain...
September 2018: Journal of Applied Clinical Medical Physics
Turki Turki, Zhi Wei, Jason T L Wang
Transfer learning (TL) algorithms aim to improve the prediction performance in a target task (e.g. the prediction of cisplatin sensitivity in triple-negative breast cancer patients) via transferring knowledge from auxiliary data of a related task (e.g. the prediction of docetaxel sensitivity in breast cancer patients), where the distribution and even the feature space of the data pertaining to the tasks can be different. In real-world applications, we sometimes have a limited training set in a target task while we have auxiliary data from a related task...
June 2018: Journal of Bioinformatics and Computational Biology
Timo M Deist, Frank J W M Dankers, Gilmer Valdes, Robin Wijsman, I-Chow Hsu, Cary Oberije, Tim Lustberg, Johan van Soest, Frank Hoebers, Arthur Jochems, Issam El Naqa, Leonard Wee, Olivier Morin, David R Raleigh, Wouter Bots, Johannes H Kaanders, José Belderbos, Margriet Kwint, Timothy Solberg, René Monshouwer, Johan Bussink, Andre Dekker, Philippe Lambin
PURPOSE: Machine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boosting) in terms of classification performance. The purpose of this study is to compare such classifiers specifically for (chemo)radiotherapy datasets and to estimate their average discriminative performance for radiation treatment outcome prediction...
July 2018: Medical Physics
Timo M Deist, A Jochems, Johan van Soest, Georgi Nalbantov, Cary Oberije, Seán Walsh, Michael Eble, Paul Bulens, Philippe Coucke, Wim Dries, Andre Dekker, Philippe Lambin
Machine learning applications for personalized medicine are highly dependent on access to sufficient data. For personalized radiation oncology, datasets representing the variation in the entire cancer patient population need to be acquired and used to learn prediction models. Ethical and legal boundaries to ensure data privacy hamper collaboration between research institutes. We hypothesize that data sharing is possible without identifiable patient data leaving the radiation clinics and that building machine learning applications on distributed datasets is feasible...
June 2017: Clinical and Translational Radiation Oncology
Christos Davatzikos, Saima Rathore, Spyridon Bakas, Sarthak Pati, Mark Bergman, Ratheesh Kalarot, Patmaa Sridharan, Aimilia Gastounioti, Nariman Jahani, Eric Cohen, Hamed Akbari, Birkan Tunc, Jimit Doshi, Drew Parker, Michael Hsieh, Aristeidis Sotiras, Hongming Li, Yangming Ou, Robert K Doot, Michel Bilello, Yong Fan, Russell T Shinohara, Paul Yushkevich, Ragini Verma, Despina Kontos
The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer...
January 2018: Journal of Medical Imaging
Shuangtao Zhao, Jiangyong Yu, Luhua Wang
OBJECTIVES: MicroRNAs (miRNAs) play a key role in governing posttranscriptional regulation through binding to the mRNAs of target genes. This study is to assess miRNAs expression profiles for identifying brain metastasis-related miRNAs to develop the predictive model by microarray in tumor tissues. METHODS: For this study, we screened the significant brain metastasis-related miRNAs from 77 lung adenocarcinoma (LUAD) patients with brain metastasis (BM+) or non-brain metastasis (BM-)...
February 2018: Translational Oncology
Eunsun Oh, Sung Wook Seo, Young Cheol Yoon, Dong Wook Kim, Sunyoung Kwon, Sungroh Yoon
PURPOSE: The purpose of this article is to compare the predictive power of two models trained with computed tomography (CT)-based radiological features and both CT-based radiological and clinical features for pathologic femoral fractures in patients with lung cancer using machine learning algorithms. METHODS: Between January 2010 and December 2014, 315 lung cancer patients with metastasis to the femur were included. Among them, 84 patients who underwent CT scan and were followed up for more than 3 months were enrolled...
May 2017: Journal of Orthopaedic Surgery
Nuno Rui Paulino Pereira, Stein J Janssen, Eva van Dijk, Mitchel B Harris, Francis J Hornicek, Marco L Ferrone, Joseph H Schwab
BACKGROUND: Current prognostication models for survival estimation in patients with metastatic spine disease lack accuracy. Identifying new risk factors could improve existing models. We assessed factors associated with survival in patients surgically treated for spine metastases, created a classic scoring algorithm, nomogram, and boosting algorithm, and tested the predictive accuracy of the three created algorithms at estimating survival. METHODS: We included 649 patients from two tertiary care referral centers in this retrospective study (2002 to 2014)...
November 2, 2016: Journal of Bone and Joint Surgery. American Volume
Charles J Labuzzetta, Margaret L Antonio, Patricia M Watson, Robert C Wilson, Lauren A Laboissonniere, Jeffrey M Trimarchi, Baris Genc, P Hande Ozdinler, Dennis K Watson, Paul E Anderson
MOTIVATION: A central task of bioinformatics is to develop sensitive and specific means of providing medical prognoses from biomarker patterns. Common methods to predict phenotypes in RNA-Seq datasets utilize machine learning algorithms trained via gene expression. Isoforms, however, generated from alternative splicing, may provide a novel and complementary set of transcripts for phenotype prediction. In contrast to gene expression, the number of isoforms increases significantly due to numerous alternative splicing patterns, resulting in a prioritization problem for many machine learning algorithms...
September 1, 2016: Bioinformatics
Gilmer Valdes, Timothy D Solberg, Marina Heskel, Lyle Ungar, Charles B Simone
To develop a patient-specific 'big data' clinical decision tool to predict pneumonitis in stage I non-small cell lung cancer (NSCLC) patients after stereotactic body radiation therapy (SBRT). 61 features were recorded for 201 consecutive patients with stage I NSCLC treated with SBRT, in whom 8 (4.0%) developed radiation pneumonitis. Pneumonitis thresholds were found for each feature individually using decision stumps. The performance of three different algorithms (Decision Trees, Random Forests, RUSBoost) was evaluated...
August 21, 2016: Physics in Medicine and Biology
Zhiguo Zhou, Michael Folkert, Nathan Cannon, Puneeth Iyengar, Kenneth Westover, Yuanyuan Zhang, Hak Choy, Robert Timmerman, Jingsheng Yan, Xian-J Xie, Steve Jiang, Jing Wang
PURPOSE/OBJECTIVE: The aim of this study is to predict early distant failure in early stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT) using clinical parameters by machine learning algorithms. MATERIALS/METHODS: The dataset used in this work includes 81 early stage NSCLC patients with at least 6months of follow-up who underwent SBRT between 2006 and 2012 at a single institution. The clinical parameters (n=18) for each patient include demographic parameters, tumor characteristics, treatment fraction schemes, and pretreatment medications...
June 2016: Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology
Weimiao Wu, Chintan Parmar, Patrick Grossmann, John Quackenbush, Philippe Lambin, Johan Bussink, Raymond Mak, Hugo J W L Aerts
BACKGROUND: Radiomics can quantify tumor phenotypic characteristics non-invasively by applying feature algorithms to medical imaging data. In this study of lung cancer patients, we investigated the association between radiomic features and the tumor histologic subtypes (adenocarcinoma and squamous cell carcinoma). Furthermore, in order to predict histologic subtypes, we employed machine-learning methods and independently evaluated their prediction performance. METHODS: Two independent radiomic cohorts with a combined size of 350 patients were included in our analysis...
2016: Frontiers in Oncology
Matt R Paul, Nicholas P Levitt, David E Moore, Patricia M Watson, Robert C Wilson, Chadrick E Denlinger, Dennis K Watson, Paul E Anderson
BACKGROUND: It has recently been shown that significant and accurate single nucleotide variants (SNVs) can be reliably called from RNA-Seq data. These may provide another source of features for multivariate predictive modeling of disease phenotype for the prioritization of candidate biomarkers. The continuous nature of SNV allele fraction features allows the concurrent investigation of several genomic phenomena, including allele specific expression, clonal expansion and/or deletion, and copy number variation...
March 31, 2016: BMC Genomics
Mingguang Shi, Jianmin He
Adjuvant chemotherapy (CTX) should be individualized to provide potential survival benefit and avoid potential harm to cancer patients. Our goal was to establish a computational approach for making personalized estimates of the survival benefit from adjuvant CTX. We developed Sub-Network based Random Forest classifier for predicting Chemotherapy Benefit (SNRFCB) based gene expression datasets of lung cancer. The SNRFCB approach was then validated in independent test cohorts for identifying chemotherapy responder cohorts and chemotherapy non-responder cohorts...
April 2016: Molecular BioSystems
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