101
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Du Q, Baine M, Bavitz K, McAllister J, Liang X, Yu H, Ryckman J, Yu L, Jiang H, Zhou S, Zhang C, Zheng D. Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction. PLoS One 2019; 14:e0216480. [PMID: 31063500 PMCID: PMC6504105 DOI: 10.1371/journal.pone.0216480] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 04/22/2019] [Indexed: 11/25/2022] Open
Abstract
Radiomic analysis has recently demonstrated versatile uses in improving diagnostic and prognostic prediction accuracy for lung cancer. However, since lung tumors are subject to substantial motion due to respiration, the stability of radiomic features over the respiratory cycle of the patient needs to be investigated to better evaluate the robustness of the inter-patient feature variability for clinical applications, and its impact in such applications needs to be assessed. A full panel of 841 radiomic features, including tumor intensity, shape, texture, and wavelet features, were extracted from individual phases of a four-dimensional (4D) computed tomography on 20 early-stage non-small-cell lung cancer (NSCLC) patients. The stability of each radiomic feature was assessed across different phase images of the same patient using the coefficient of variation (COV). The relationship between individual COVs and tumor motion magnitude was inspected. Population COVs, the mean COVs of all 20 patients, were used to evaluate feature motion stability and categorize the radiomic features into 4 different groups. The two extremes, the Very Small group (COV≤5%) and the Large group (COV>20%), each accounted for about a quarter of the features. Shape features were the most stable, with COV≤10% for all features. A clinical study was subsequently conducted using 140 early-stage NSCLC patients. Radiomic features were employed to predict the overall survival with a 500-round bootstrapping. Identical multiple regression model development process was applied, and the model performance was compared between models with and without a feature pre-selection step based on 4D COV to pre-exclude unstable features. Among the systematically tested cutoff values, feature pre-selection with 4D COV≤5% achieved the optimal model performance. The resulting 3-feature radiomic model significantly outperformed its counterpart with no 4D COV pre-selection, with P = 2.16x10-27 in the one-tailed t-test comparing the prediction performances of the two models.
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Affiliation(s)
- Qian Du
- Biological Sciences, University of Nebraska Lincoln, Lincoln, NE, United States of America
| | - Michael Baine
- Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, United States of America
| | - Kyle Bavitz
- Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, United States of America
| | - Josiah McAllister
- Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, United States of America
| | - Xiaoying Liang
- University of Florida Proton Therapy Institute, Jacksonville, FL, United States of America
| | - Hongfeng Yu
- Computer Science, University of Nebraska Lincoln, Lincoln, NE, United States of America
| | - Jeffrey Ryckman
- Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, United States of America
| | - Lina Yu
- Computer Science, University of Nebraska Lincoln, Lincoln, NE, United States of America
| | - Hengle Jiang
- Computer Science, University of Nebraska Lincoln, Lincoln, NE, United States of America
| | - Sumin Zhou
- Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, United States of America
| | - Chi Zhang
- Biological Sciences, University of Nebraska Lincoln, Lincoln, NE, United States of America
| | - Dandan Zheng
- Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, United States of America
- * E-mail:
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102
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Hoye J, Solomon J, Sauer TJ, Robins M, Samei E. Systematic analysis of bias and variability of morphologic features for lung lesions in computed tomography. J Med Imaging (Bellingham) 2019; 6:013504. [PMID: 30944842 DOI: 10.1117/1.jmi.6.1.013504] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 03/01/2019] [Indexed: 11/14/2022] Open
Abstract
We propose to characterize the bias and variability of quantitative morphology features of lung lesions across a range of computed tomography (CT) imaging conditions. A total of 15 lung lesions were simulated (five in each of three spiculation classes: low, medium, and high). For each lesion, a series of simulated CT images representing different imaging conditions were synthesized by applying three-dimensional blur and adding correlated noise based on the measured noise and resolution properties of five commercial multislice CT systems, representing three dose levels ( CTDI vol of 1.90, 3.75, 7.50 mGy), three slice thicknesses (0.625, 1.25, 2.5 mm), and 33 clinical reconstruction kernels from five clinical scanners. The images were segmented using three segmentation algorithms and each algorithm was evaluated by computing a Sørensen-Dice coefficient between the ground truth and the segmentation. A series of 21 shape-based morphology features were extracted from both "ground truth" (i.e., preblur without noise) and "image rendered" lesions (i.e., postblur and with noise). For each morphology feature, the bias was quantified by comparing the percentage relative error in the morphology metric between the imaged lesions and the ground-truth lesions. The variability was characterized by calculating the average coefficient of variation averaged across repeats and imaging conditions. The active contour segmentation had the highest average Dice coefficient of 0.80 followed by 0.63 for threshold, and 0.39 for fuzzy c-means. The bias of the features was segmentation algorithm and feature-dependent, with sharper kernels being less biased and smoother kernels being more biased in general. The feature variability from simulated images ranged from 0.30% to 10% for repeats of the same condition and from 0.74% to 25.3% for different lesions in the same spiculation class. In conclusion, the bias of morphology features is dependent on the acquisition protocol in combination with the segmentation algorithm used and the variability is primarily dependent on the segmentation algorithm.
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Affiliation(s)
- Jocelyn Hoye
- Duke University, Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina, United States.,Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States.,Duke University Medical Center, Department of Radiology, Durham, North Carolina, United States.,Duke University Medical Center, Durham, North Carolina, United States
| | - Justin Solomon
- Duke University, Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina, United States.,Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States.,Duke University Medical Center, Department of Radiology, Durham, North Carolina, United States.,Duke University Medical Center, Durham, North Carolina, United States
| | - Thomas J Sauer
- Duke University, Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina, United States.,Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States.,Duke University Medical Center, Department of Radiology, Durham, North Carolina, United States.,Duke University Medical Center, Durham, North Carolina, United States
| | - Marthony Robins
- Duke University, Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina, United States.,Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States.,Duke University Medical Center, Department of Radiology, Durham, North Carolina, United States.,Duke University Medical Center, Durham, North Carolina, United States
| | - Ehsan Samei
- Duke University, Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina, United States.,Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States.,Duke University Medical Center, Department of Radiology, Durham, North Carolina, United States.,Duke University Medical Center, Durham, North Carolina, United States
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103
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Gardin I, Grégoire V, Gibon D, Kirisli H, Pasquier D, Thariat J, Vera P. Radiomics: Principles and radiotherapy applications. Crit Rev Oncol Hematol 2019; 138:44-50. [PMID: 31092384 DOI: 10.1016/j.critrevonc.2019.03.015] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 12/26/2018] [Accepted: 03/26/2019] [Indexed: 12/14/2022] Open
Abstract
Radiomics is defined as the extraction of a large quantity of quantitative image features. The different radiomic indexes that have been proposed in the literature are described as well as the various factors that have an impact on the robustness of these indexes. We will see that several hundred quantitative features can be extracted per lesion and imaging modality. The ever-growing number of features studied raises the question of the statistical method of analysis used. This review addresses the research supporting the clinical use of radiomics in oncology in the staging of disease, discrimination between healthy and pathological tissues, the identification of genetic features, the prediction of patient survival, the response to treatment, the recurrence after radiotherapy and chemoradiotherapy and the side effects. Based on the existing literature, it remains difficult to identify features that should be used for current clinical practice.
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Affiliation(s)
- I Gardin
- Department of Nuclear Medicine, Centre Henri-Becquerel, France; LITIS EA4108, Normandie University, Rouen, France.
| | - V Grégoire
- Department of Radiation Oncology, Centre Léon Bérard, France
| | - D Gibon
- Research and Innovation Department, AQUILAB, Loos Les Lille, France
| | - H Kirisli
- Research and Innovation Department, AQUILAB, Loos Les Lille, France
| | - D Pasquier
- Department of Radiation Oncology, Centre Oscar Lambret, CRIStAL UMR CNRS 9189, Lille University, Lille, France
| | - J Thariat
- Radiotherapy Department, Centre François Baclesse, Caen, France
| | - P Vera
- Department of Nuclear Medicine, Centre Henri-Becquerel, France; LITIS EA4108, Normandie University, Rouen, France
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104
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Boldrini L, Cusumano D, Chiloiro G, Casà C, Masciocchi C, Lenkowicz J, Cellini F, Dinapoli N, Azario L, Teodoli S, Gambacorta MA, De Spirito M, Valentini V. Delta radiomics for rectal cancer response prediction with hybrid 0.35 T magnetic resonance-guided radiotherapy (MRgRT): a hypothesis-generating study for an innovative personalized medicine approach. LA RADIOLOGIA MEDICA 2019; 124:145-153. [PMID: 30374650 PMCID: PMC6373341 DOI: 10.1007/s11547-018-0951-y] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 10/15/2018] [Indexed: 02/07/2023]
Abstract
The aim of this study was to evaluate the variation of radiomics features, defined as "delta radiomics", in patients undergoing neoadjuvant radiochemotherapy (RCT) for rectal cancer treated with hybrid magnetic resonance (MR)-guided radiotherapy (MRgRT). The delta radiomics features were then correlated with clinical complete response (cCR) outcome, to investigate their predictive power. A total of 16 patients were enrolled, and 5 patients (31%) showed cCR at restaging examinations. T2*/T1 MR images acquired with a hybrid 0.35 T MRgRT unit were considered for this analysis. An imaging acquisition protocol of 6 MR scans per patient was performed: the first MR was acquired at first simulation (t0) and the remaining ones at fractions 5, 10, 15, 20 and 25. Radiomics features were extracted from the gross tumour volume (GTV), and each feature was correlated with the corresponding delivered dose. The variations of each feature during treatment were quantified, and the ratio between the values calculated at different dose levels and the one extracted at t0 was calculated too. The Wilcoxon-Mann-Whitney test was performed to identify the features whose variation can be predictive of cCR, assessed with a MR acquired 6 weeks after RCT and digital examination. The most predictive feature ratios in cCR prediction were the L_least and glnu ones, calculated at the second week of treatment (22 Gy) with a p value = 0.001. Delta radiomics approach showed promising results and the quantitative analysis of images throughout MRgRT treatment can successfully predict cCR offering an innovative personalized medicine approach to rectal cancer treatment.
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Affiliation(s)
- Luca Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Istituto di Radiologia, Fondazione Policlinico A. Gemelli IRCCS - Università Cattolica Sacro Cuore, Largo A. Gemelli, 8, 00168, Rome, Italy
| | - Davide Cusumano
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Istituto di Radiologia, Fondazione Policlinico A. Gemelli IRCCS - Università Cattolica Sacro Cuore, Largo A. Gemelli, 8, 00168, Rome, Italy.
| | - Giuditta Chiloiro
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Istituto di Radiologia, Fondazione Policlinico A. Gemelli IRCCS - Università Cattolica Sacro Cuore, Largo A. Gemelli, 8, 00168, Rome, Italy
| | - Calogero Casà
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Istituto di Radiologia, Fondazione Policlinico A. Gemelli IRCCS - Università Cattolica Sacro Cuore, Largo A. Gemelli, 8, 00168, Rome, Italy
| | - Carlotta Masciocchi
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Istituto di Radiologia, Fondazione Policlinico A. Gemelli IRCCS - Università Cattolica Sacro Cuore, Largo A. Gemelli, 8, 00168, Rome, Italy
| | - Jacopo Lenkowicz
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Istituto di Radiologia, Fondazione Policlinico A. Gemelli IRCCS - Università Cattolica Sacro Cuore, Largo A. Gemelli, 8, 00168, Rome, Italy
| | - Francesco Cellini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Nicola Dinapoli
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Luigi Azario
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Istituto di Fisica, Fondazione Policlinico A. Gemelli IRCCS - Università Cattolica Sacro Cuore, Rome, Italy
| | - Stefania Teodoli
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Maria Antonietta Gambacorta
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Istituto di Radiologia, Fondazione Policlinico A. Gemelli IRCCS - Università Cattolica Sacro Cuore, Largo A. Gemelli, 8, 00168, Rome, Italy
| | - Marco De Spirito
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Istituto di Fisica, Fondazione Policlinico A. Gemelli IRCCS - Università Cattolica Sacro Cuore, Rome, Italy
| | - Vincenzo Valentini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Istituto di Radiologia, Fondazione Policlinico A. Gemelli IRCCS - Università Cattolica Sacro Cuore, Largo A. Gemelli, 8, 00168, Rome, Italy
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105
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Abdollahi H, Tanha K, Mofid B, Razzaghdoust A, Saadipoor A, Khalafi L, Bakhshandeh M, Mahdavi SR. MRI Radiomic Analysis of IMRT-Induced Bladder Wall Changes in Prostate Cancer Patients: A Relationship with Radiation Dose and Toxicity. J Med Imaging Radiat Sci 2019; 50:252-260. [PMID: 31176433 DOI: 10.1016/j.jmir.2018.12.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Revised: 11/18/2018] [Accepted: 12/14/2018] [Indexed: 01/10/2023]
Abstract
BACKGROUND The main purpose of this study was to assess the structural changes in the bladder wall of prostate cancer patients treated with intensity-modulated radiation therapy using magnetic resonance imaging texture features analysis and to correlate image texture changes with radiation dose and urinary toxicity. METHODS Ethical clearance was granted to enroll 33 patients into this study who were treated with intensity-modulated radiation therapy for prostate cancer. All patients underwent two magnetic resonance imagings before and after radiation therapy (RT). A total of 274 radiomic features were extracted from MR-T2W-weighted images. Wilcoxon singed rank-test was performed to assess significance of the change in mean radiomic features post-RT relative to pre-RT values. The relationship between radiation dose and feature changes was assessed and depicted. Cystitis was recorded as urinary toxicity. Area under receiver operating characteristic curve of a logistic regression-based classifier was used to find correlation between radiomic features with significant changes and radiation toxicity. RESULTS Thirty-three bladder walls were analyzed, with 11 patients developing grade ≥2 urinary toxicity. We showed that radiomic features may predict radiation toxicity and features including S5.0SumVarnc, S2.2SumVarnc, S1.0AngScMom, S0.4SumAverg, and S5. _5InvDfMom with area under receiver operating characteristic curve 0.75, 0.69, 0.65, 0.63, and 0.62 had highest correlation with toxicity, respectively. The results showed that most of the radiomic features were changed with radiation dose. CONCLUSION Feature changes have a good correlation with radiation dose and radiation-induced urinary toxicity. These radiomic features can be identified as being potentially important imaging biomarkers and also assessing mechanisms of radiation-induced bladder injuries.
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Affiliation(s)
- Hamid Abdollahi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
| | - Kiarash Tanha
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
| | - Bahram Mofid
- Department of Radiotherapy, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Abolfazl Razzaghdoust
- Urology and Nephrology Research Center, Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Afshin Saadipoor
- Department of Radiotherapy, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Leila Khalafi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Mohsen Bakhshandeh
- Department of Radiology Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seied Rabi Mahdavi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran.
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106
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Wang L, Dong T, Xin B, Xu C, Guo M, Zhang H, Feng D, Wang X, Yu J. Integrative nomogram of CT imaging, clinical, and hematological features for survival prediction of patients with locally advanced non-small cell lung cancer. Eur Radiol 2019; 29:2958-2967. [PMID: 30643940 DOI: 10.1007/s00330-018-5949-2] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 11/07/2018] [Accepted: 12/04/2018] [Indexed: 12/20/2022]
Abstract
OBJECTIVES To determine the integrative value of clinical, hematological, and computed tomography (CT) radiomic features in survival prediction for locally advanced non-small cell lung cancer (LA-NSCLC) patients. METHODS Radiomic features and clinical and hematological features of 118 LA-NSCLC cases were firstly extracted and analyzed in this study. Then, stable and prognostic radiomic features were automatically selected using the consensus clustering method with either Cox proportional hazard (CPH) model or random survival forest (RSF) analysis. Predictive radiomic, clinical, and hematological parameters were subsequently fitted into a final prognostic model using both the CPH model and the RSF model. A multimodality nomogram was then established from the fitting model and was cross-validated. Finally, calibration curves were generated with the predicted versus actual survival status. RESULTS Radiomic features selected by clustering combined with CPH were found to be more predictive, with a C-index of 0.699 in comparison to 0.648 by clustering combined with RSF. Based on multivariate CPH model, our integrative nomogram achieved a C-index of 0.792 and retained 0.743 in the cross-validation analysis, outperforming radiomic, clinical, or hematological model alone. The calibration curve showed agreement between predicted and actual values for the 1-year and 2-year survival prediction. Interestingly, the selected important radiomic features were significantly correlated with levels of platelet, platelet/lymphocyte ratio (PLR), and lymphocyte/monocyte ratio (LMR) (p values all < 0.05). CONCLUSIONS The integrative nomogram incorporated CT radiomic, clinical, and hematological features improved survival prediction in LA-NSCLC patients, which would offer a feasible and practical reference for individualized management of these patients. KEY POINTS • An integrative nomogram incorporated CT radiomic, clinical, and hematological features was constructed and cross-validated to predict prognosis of LA-NSCLC patients. • The integrative nomogram outperformed radiomic, clinical, or hematological model alone. • This nomogram has value to permit non-invasive, comprehensive, and dynamical evaluation of the phenotypes of LA-NSCLC and can provide a feasible and practical reference for individualized management of LA-NSCLC patients.
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Affiliation(s)
- Linlin Wang
- Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Science, No. 440, Ji Yan Road, Jinan, 250017, China
| | - Taotao Dong
- Department of Gynecology and Obstetrics, Qilu Hospital of Shandong University, Jinan, China
| | - Bowen Xin
- School of Information Technologies, the University of Sydney, Building J12, Sydney, NSW, 2006, Australia
| | - Chongrui Xu
- School of Information Technologies, the University of Sydney, Building J12, Sydney, NSW, 2006, Australia
| | - Meiying Guo
- Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Science, No. 440, Ji Yan Road, Jinan, 250017, China
- Medical College of Shandong University, Jinan, China
| | - Huaqi Zhang
- Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Science, No. 440, Ji Yan Road, Jinan, 250017, China
- Tianjin Medical University, Tianjin, China
| | - Dagan Feng
- School of Information Technologies, the University of Sydney, Building J12, Sydney, NSW, 2006, Australia
| | - Xiuying Wang
- School of Information Technologies, the University of Sydney, Building J12, Sydney, NSW, 2006, Australia.
| | - Jinming Yu
- Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Science, No. 440, Ji Yan Road, Jinan, 250017, China.
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107
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Abdollahi H, Mofid B, Shiri I, Razzaghdoust A, Saadipoor A, Mahdavi A, Galandooz HM, Mahdavi SR. Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer. Radiol Med 2019; 124:555-567. [PMID: 30607868 DOI: 10.1007/s11547-018-0966-4] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 12/04/2018] [Indexed: 01/01/2023]
Abstract
OBJECTIVE To develop different radiomic models based on the magnetic resonance imaging (MRI) radiomic features and machine learning methods to predict early intensity-modulated radiation therapy (IMRT) response, Gleason scores (GS) and prostate cancer (Pca) stages. METHODS Thirty-three Pca patients were included. All patients underwent pre- and post-IMRT T2-weighted (T2 W) and apparent diffusing coefficient (ADC) MRI. IMRT response was calculated in terms of changes in the ADC value, and patients were divided as responders and non-responders. A wide range of radiomic features from different feature sets were extracted from all T2 W and ADC images. Univariate radiomic analysis was performed to find highly correlated radiomic features with IMRT response, and a paired t test was used to find significant features between responders and non-responders. To find high predictive radiomic models, tenfold cross-validation as the criterion for feature selection and classification was applied on the pre-, post- and delta IMRT radiomic features, and area under the curve (AUC) of receiver operating characteristics was calculated as model performance value. RESULTS Of 33 patients, 15 patients (45%) were found as responders. Univariate analysis showed 20 highly correlated radiomic features with IMRT response (20 ADC and 20 T2). Two and fifteen T2 and ADC radiomic features were found as significant (P-value ≤ 0.05) features between responders and non-responders, respectively. Several cross-combined predictive radiomic models were obtained, and post-T2 radiomic models were found as high predictive models (AUC 0.632) followed by pre-ADC (AUC 0.626) and pre-T2 (AUC 0.61). For GS prediction, T2 W radiomic models were found as more predictive (mean AUC 0.739) rather than ADC models (mean AUC 0.70), while for stage prediction, ADC models had higher prediction performance (mean AUC 0.675). CONCLUSIONS Radiomic models developed by MR image features and machine learning approaches are noninvasive and easy methods for personalized prostate cancer diagnosis and therapy.
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Affiliation(s)
- Hamid Abdollahi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Bahram Mofid
- Shohada-e-Tajrish Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Biomedical and Health Informatics, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.,Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Abolfazl Razzaghdoust
- Urology and Nephrology Research Center, Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Afshin Saadipoor
- Shohada-e-Tajrish Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Arash Mahdavi
- Department of Radiology, Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hassan Maleki Galandooz
- Faculty of Computer Science and Engineering, Image Processing and Distributed System Lab, Shahid Beheshti University, Tehran, Iran
| | - Seied Rabi Mahdavi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. .,Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran.
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108
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Arimura H, Soufi M, Kamezawa H, Ninomiya K, Yamada M. Radiomics with artificial intelligence for precision medicine in radiation therapy. JOURNAL OF RADIATION RESEARCH 2019; 60:150-157. [PMID: 30247662 PMCID: PMC6373667 DOI: 10.1093/jrr/rry077] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 07/21/2018] [Indexed: 05/27/2023]
Abstract
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. Individual patients can be stratified into subtypes based on radiomic biomarkers that contain information about cancer traits that determine the patient's prognosis. Machine-learning algorithms of AI are boosting the powers of radiomics for prediction of prognoses or factors associated with treatment strategies, such as survival time, recurrence, adverse events, and subtypes. Therefore, radiomic approaches, in combination with AI, may potentially enable practical use of precision medicine in radiation therapy by predicting outcomes and toxicity for individual patients.
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Affiliation(s)
- Hidetaka Arimura
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, Japan
| | - Mazen Soufi
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5, Takayama-cho, Ikoma, Nara, Japan
| | - Hidemi Kamezawa
- Department of Radiological Technology, Faculty of Fukuoka Medical Technology, Teikyo University, 6-22, Misaki-machi, Omuta, Fukuoka, Japan
| | - Kenta Ninomiya
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, Japan
| | - Masahiro Yamada
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, Japan
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109
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Gu J, Zhu J, Qiu Q, Wang Y, Bai T, Duan J, Yin Y. The Feasibility Study of Megavoltage Computed Tomographic (MVCT) Image for Texture Feature Analysis. Front Oncol 2018; 8:586. [PMID: 30568918 PMCID: PMC6290333 DOI: 10.3389/fonc.2018.00586] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 11/21/2018] [Indexed: 11/13/2022] Open
Abstract
Purpose: To determine whether radiomics texture features can be reproducibly obtained from megavoltage computed tomographic (MVCT) images acquired by Helical TomoTherapy (HT) with different imaging conditions. Methods: For each of the 195 textures enrolled, the mean intrapatient difference, which is considered to be the benchmark for reproducibility, was calculated from the MVCT images of 22 patients with early-stage non-small-cell lung cancer. Test–retest MVCT images of an in-house designed phantom were acquired to determine the concordance correlation coefficient (CCC) for these 195 texture features. Features with high reproducibility (CCC > 0.9) in the phantom test–retest set were investigated for sensitivities to different imaging protocols, scatter levels, and motion frequencies using a wood phantom and in-vitro animal tissues. Results: Of the 195 features, 165 (85%) features had CCC > 0.9. For the wood phantom, 124 features were reproducible in two kinds of scatter materials, and further investigations were performed on these features. For animal tissues, 108 features passed the criteria for reproducibility when one layer of scatter was covered, while 106 and 108 features of in-vitro liver and bone passed with two layers of scatter, respectively. Considering the effect of differing acquisition pitch (AcP), 97 features extracted from wood passed, while 103 and 59 features extracted from in-vitro liver and bone passed, respectively. Different reconstruction intervals (RI) had a small effect on the stability of the feature value. When AcP and RI were held consistent without motion, all 124 features calculated from wood passed, and a majority (122 of 124) of the features passed when imaging with a “fine” AcP with different RIs. However, only 55 and 40 features passed with motion frequencies of 20 and 25 beats per minute, respectively. Conclusion: Motion frequency has a significant impact on MVCT texture features, and features from MVCT were more reproducibility in different scatter conditions than those from CBCT. Considering the effects of AcP and RI, the scanning protocols should be kept consistent when MVCT images are used for feature analysis. Some radiomics features from HT MVCT images are reproducible and could be used for creating clinical prediction models in the future.
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Affiliation(s)
- Jiabing Gu
- School of Medicine and Life Sciences, University of Jinan-Shandong Academy of Medical Sciences, Jinan, China.,Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, China
| | - Jian Zhu
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, China
| | - Qingtao Qiu
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, China
| | - Yungang Wang
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, China
| | - Tong Bai
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, China
| | - Jinghao Duan
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, China
| | - Yong Yin
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, China
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110
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Foy JJ, Robinson KR, Li H, Giger ML, Al-Hallaq H, Armato SG. Variation in algorithm implementation across radiomics software. J Med Imaging (Bellingham) 2018; 5:044505. [PMID: 30840747 DOI: 10.1117/1.jmi.5.4.044505] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 10/30/2018] [Indexed: 01/09/2023] Open
Abstract
Given the increased need for consistent quantitative image analysis, variations in radiomics feature calculations due to differences in radiomics software were investigated. Two in-house radiomics packages and two freely available radiomics packages, MaZda and IBEX, were utilized. Forty 256 × 256 - pixel regions of interest (ROIs) from 40 digital mammograms were studied along with 39 manually delineated ROIs from the head and neck (HN) computed tomography (CT) scans of 39 patients. Each package was used to calculate first-order histogram and second-order gray-level co-occurrence matrix (GLCM) features. Friedman tests determined differences in feature values across packages, whereas intraclass-correlation coefficients (ICC) quantified agreement. All first-order features computed from both mammography and HN cases (except skewness in mammography) showed significant differences across all packages due to systematic biases introduced by each package; however, based on ICC values, all but one first-order feature calculated on mammography ROIs and all but two first-order features calculated on HN CT ROIs showed excellent agreement, indicating the observed differences were small relative to the feature values but the bias was systematic. All second-order features computed from the two databases both differed significantly and showed poor agreement among packages, due largely to discrepancies in package-specific default GLCM parameters. Additional differences in radiomics features were traced to variations in image preprocessing, algorithm implementation, and naming conventions. Large variations in features among software packages indicate that increased efforts to standardize radiomics processes must be conducted.
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Affiliation(s)
- Joseph J Foy
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Kayla R Robinson
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Hui Li
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Maryellen L Giger
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Hania Al-Hallaq
- University of Chicago, Department of Radiation and Cellular Oncology, Chicago, Illinois, United States
| | - Samuel G Armato
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
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111
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Veiga C, McClelland JR, Landau D. Response to Oymak et al. Radiother Oncol 2018; 129:613-614. [PMID: 30041819 DOI: 10.1016/j.radonc.2018.06.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 06/26/2018] [Accepted: 06/27/2018] [Indexed: 11/15/2022]
Affiliation(s)
- Catarina Veiga
- Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering, University College London, UK.
| | - Jamie R McClelland
- Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering, University College London, UK
| | - David Landau
- Oncology, Guy's & St. Thomas' NHS Trust, London, UK; Division of Imaging Sciences, King's College London, UK
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112
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Alahmari SS, Cherezov D, Goldgof D, Hall L, Gillies RJ, Schabath MB. Delta Radiomics Improves Pulmonary Nodule Malignancy Prediction in Lung Cancer Screening. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2018; 6:77796-77806. [PMID: 30607311 PMCID: PMC6312194 DOI: 10.1109/access.2018.2884126] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Low-dose computed tomography (LDCT) plays a critical role in the early detection of lung cancer. Despite the life-saving benefit of early detection by LDCT, there are many limitations of this imaging modality including high rates of detection of indeterminate pulmonary nodules. Radiomics is the process of extracting and analyzing image-based, quantitative features from a region-of-interest which then can be analyzed to develop decision support tools that can improve lung cancer screening. Although prior published research has shown that delta radiomics (i.e., changes in features over time) have utility in predicting treatment response, limited work has been conducted using delta radiomics in lung cancer screening. As such, we conducted analyses to assess the performance of incorporating delta with conventional (non delta) features using machine learning to predict lung nodule malignancy. We found the best improved area under the receiver operating characteristic curve (AUC) was 0.822 when delta features were combined with conventional features versus an AUC 0.773 for conventional features only. Overall, this study demonstrated the important utility of combining delta radiomics features with conventional radiomics features to improve performance of models in the lung cancer screening setting.
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Affiliation(s)
- Saeed S Alahmari
- Department of Computer Sciences and Engineering, University of South Florida, Tampa, Florida
| | - Dmitry Cherezov
- Department of Computer Sciences and Engineering, University of South Florida, Tampa, Florida
| | - Dmitry Goldgof
- Department of Computer Sciences and Engineering, University of South Florida, Tampa, Florida
| | - Lawrence Hall
- Department of Computer Sciences and Engineering, University of South Florida, Tampa, Florida
| | - Robert J Gillies
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Matthew B Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
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113
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Neri E, Del Re M, Paiar F, Erba P, Cocuzza P, Regge D, Danesi R. Radiomics and liquid biopsy in oncology: the holons of systems medicine. Insights Imaging 2018; 9:915-924. [PMID: 30430428 PMCID: PMC6269342 DOI: 10.1007/s13244-018-0657-7] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 08/10/2018] [Accepted: 08/28/2018] [Indexed: 12/15/2022] Open
Abstract
Abstract Radiomics is a process of extraction and analysis of quantitative features from diagnostic images. Liquid biopsy is a test done on a sample of blood to look for cancer cells or for pieces of tumourigenic DNA circulating in the blood. Radiomics and liquid biopsy have great potential in oncology, since both are minimally invasive, easy to perform, and can be repeated in patient follow-up visits, enabling the extraction of valuable information regarding tumour type, aggressiveness, progression, and response to treatment. Both methods are in their infancy, with major evidence of application in lung and gastrointestinal cancer, while still undergoing evaluation in other cancer types. In this paper, the main oncologic applications of radiomics and liquid biopsy are reviewed, and a synergistic approach incorporating both tests for cancer diagnosis and follow-up is discussed within the context of systems medicine. Teaching Points • Radiomics is a process of extraction and analysis of quantitative features from diagnostic images. • Most clinical applications of radiomics are in the field of oncologic imaging. • Radiomics applies to all imaging modalities. • A cluster of radiomic features is a “radiomic signature”. • Machine learning may improve the efficacy of radiomics analysis.
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Affiliation(s)
- Emanuele Neri
- Diagnostic and Interventional Radiology, Department of Translational Research, University of Pisa, Pisa, Italy.
| | - Marzia Del Re
- Clinical Pharmacology and Pharmacogenetics Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Fabiola Paiar
- Radiation Oncology Unit, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Paola Erba
- Nuclear Medicine Unit, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Paola Cocuzza
- Radiation Oncology Unit, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Daniele Regge
- Radiology Unit, Candiolo Cancer Institute - FPO, IRCCS, Candiolo, Turin, Italy
| | - Romano Danesi
- Clinical Pharmacology and Pharmacogenetics Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
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114
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Dou TH, Coroller TP, van Griethuysen JJM, Mak RH, Aerts HJWL. Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC. PLoS One 2018; 13:e0206108. [PMID: 30388114 PMCID: PMC6214508 DOI: 10.1371/journal.pone.0206108] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 10/05/2018] [Indexed: 12/23/2022] Open
Abstract
Purpose Radiomics provides quantitative tissue heterogeneity profiling and is an exciting approach to developing imaging biomarkers in the context of precision medicine. Normal-appearing parenchymal tissues surrounding primary tumors can harbor microscopic disease that leads to increased risk of distant metastasis (DM). This study assesses whether computed-tomography (CT) imaging features of such peritumoral tissues can predict DM in locally advanced non-small cell lung cancer (NSCLC). Material and methods 200 NSCLC patients of histological adenocarcinoma were included in this study. The investigated lung tissues were tumor rim, defined to be 3mm of tumor and parenchymal tissue on either side of the tumor border and the exterior region extended from 3 to 9mm outside of the tumor. Fifteen stable radiomic features were extracted and evaluated from each of these regions on pre-treatment CT images. For comparison, features from expert-delineated tumor contours were similarly prepared. The patient cohort was separated into training and validation datasets for prognostic power evaluation. Both univariable and multivariable analyses were performed for each region using concordance index (CI). Results Univariable analysis reveals that six out of fifteen tumor rim features were significantly prognostic of DM (p-value < 0.05), as were ten features from the visible tumor, and only one of the exterior features was. Multivariablely, a rim radiomic signature achieved the highest prognostic performance in the independent validation sub-cohort (CI = 0.64, p-value = 2.4×10−5) significantly over a multivariable clinical model (CI = 0.53), a visible tumor radiomics model (CI = 0.59), or an exterior tissue model (CI = 0.55). Furthermore, patient stratification by the combined rim signature and clinical predictor led to a significant improvement on the clinical predictor alone and also outperformed stratification using the combined tumor signature and clinical predictor. Conclusions We identified peritumoral rim radiomic features significantly associated with DM. This study demonstrated that peritumoral imaging characteristics may provide additional valuable information over the visible tumor features for patient risk stratification due to cancer metastasis.
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Affiliation(s)
- Tai H. Dou
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
- * E-mail:
| | - Thibaud P. Coroller
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Joost J. M. van Griethuysen
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
- Netherlands Cancer Institute (NKI), Amsterdam, the Netherlands
| | - Raymond H. Mak
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Hugo J. W. L. Aerts
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
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115
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Keek SA, Leijenaar RTH, Jochems A, Woodruff HC. A review on radiomics and the future of theranostics for patient selection in precision medicine. Br J Radiol 2018; 91:20170926. [PMID: 29947266 PMCID: PMC6475933 DOI: 10.1259/bjr.20170926] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 05/17/2018] [Accepted: 06/20/2018] [Indexed: 02/07/2023] Open
Abstract
The growing complexity and volume of clinical data and the associated decision-making processes in oncology promote the advent of precision medicine. Precision (or personalised) medicine describes preventive and/or treatment procedures that take individual patient variability into account when proscribing treatment, and has been hindered in the past by the strict requirements of accurate, robust, repeatable and preferably non-invasive biomarkers to stratify both the patient and the disease. In oncology, tumour subtypes are traditionally measured through repeated invasive biopsies, which are taxing for the patient and are cost and labour intensive. Quantitative analysis of routine clinical imaging provides an opportunity to capture tumour heterogeneity non-invasively, cost-effectively and on large scale. In current clinical practice radiological images are qualitatively analysed by expert radiologists whose interpretation is known to suffer from inter- and intra-operator variability. Radiomics, the high-throughput mining of image features from medical images, provides a quantitative and robust method to assess tumour heterogeneity, and radiomics-based signatures provide a powerful tool for precision medicine in cancer treatment. This study aims to provide an overview of the current state of radiomics as a precision medicine decision support tool. We first provide an overview of the requirements and challenges radiomics currently faces in being incorporated as a tool for precision medicine, followed by an outline of radiomics' current applications in the treatment of various types of cancer. We finish with a discussion of possible future advances that can further develop radiomics as a precision medicine tool.
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Affiliation(s)
- Simon A Keek
- The D-Lab: Decision Support for Precision Medicine GROW - School for Oncology and Developmental Biology & MCCC , Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ralph TH Leijenaar
- The D-Lab: Decision Support for Precision Medicine GROW - School for Oncology and Developmental Biology & MCCC , Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Arthur Jochems
- The D-Lab: Decision Support for Precision Medicine GROW - School for Oncology and Developmental Biology & MCCC , Maastricht University Medical Centre+, Maastricht, The Netherlands
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116
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Krafft SP, Rao A, Stingo F, Briere TM, Court LE, Liao Z, Martel MK. The utility of quantitative CT radiomics features for improved prediction of radiation pneumonitis. Med Phys 2018; 45:5317-5324. [PMID: 30133809 DOI: 10.1002/mp.13150] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 07/05/2018] [Accepted: 07/23/2018] [Indexed: 12/25/2022] Open
Abstract
PURPOSE The purpose of this study was to explore gains in predictive model performance for radiation pneumonitis (RP) using pretreatment CT radiomics features extracted from the normal lung volume. METHODS A total of 192 patients treated for nonsmall cell lung cancer with definitive radiotherapy were considered in the current study. In addition to clinical and dosimetric data, CT radiomics features were extracted from the total lung volume defined using the treatment planning scan. A total of 6851 features (15 clinical, 298 total lung and heart dosimetric, and 6538 image features) were gathered and considered candidate predictors for modeling of RP grade ≥3. Models were built with the least absolute shrinkage and selection operator (LASSO) logistic regression and applied to the set of candidate predictors with 50 iterations of tenfold nested cross-validation. RESULTS In the current cohort, 30 of 192 patients (15.6%) presented with RP grade ≥3. Average cross-validated AUC (CV-AUC) using only the clinical and dosimetric parameters was 0.51. CV-AUC was 0.68 when total lung CT radiomics features were added. Analysis with the entire set of available predictors revealed seven different image features selected in at least 40% of the model fits. CONCLUSIONS We have successfully incorporated CT radiomics features into a framework for building predictive RP models via LASSO logistic regression. Addition of normal lung image features produced superior model performance relative to traditional dosimetric and clinical predictors of RP, suggesting that pretreatment CT radiomics features should be considered in the context of RP prediction.
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Affiliation(s)
- Shane P Krafft
- Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA.,The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX, USA
| | - Arvind Rao
- Department of Bioinformatics and Computational Biology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Francesco Stingo
- Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Tina Marie Briere
- Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Zhongxing Liao
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Mary K Martel
- Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
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Morin O, Vallières M, Jochems A, Woodruff HC, Valdes G, Braunstein SE, Wildberger JE, Villanueva-Meyer JE, Kearney V, Yom SS, Solberg TD, Lambin P. A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change. Int J Radiat Oncol Biol Phys 2018; 102:1074-1082. [PMID: 30170101 DOI: 10.1016/j.ijrobp.2018.08.032] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 08/21/2018] [Accepted: 08/21/2018] [Indexed: 12/13/2022]
Abstract
The adoption of enterprise digital imaging, along with the development of quantitative imaging methods and the re-emergence of statistical learning, has opened the opportunity for more personalized cancer treatments through transformative data science research. In the last 5 years, accumulating evidence has indicated that noninvasive advanced imaging analytics (i.e., radiomics) can reveal key components of tumor phenotype for multiple lesions at multiple time points over the course of treatment. Many groups using homegrown software have extracted engineered and deep quantitative features on 3-dimensional medical images for better spatial and longitudinal understanding of tumor biology and for the prediction of diverse outcomes. These developments could augment patient stratification and prognostication, buttressing emerging targeted therapeutic approaches. Unfortunately, the rapid growth in popularity of this immature scientific discipline has resulted in many early publications that miss key information or use underpowered patient data sets, without production of generalizable results. Quantitative imaging research is complex, and key principles should be followed to realize its full potential. The fields of quantitative imaging and radiomics in particular require a renewed focus on optimal study design and reporting practices, standardization, interpretability, data sharing, and clinical trials. Standardization of image acquisition, feature calculation, and statistical analysis (i.e., machine learning) are required for the field to move forward. A new data-sharing paradigm enacted among open and diverse participants (medical institutions, vendors and associations) should be embraced for faster development and comprehensive clinical validation of imaging biomarkers. In this review and critique of the field, we propose working principles and fundamental changes to the current scientific approach, with the goal of high-impact research and development of actionable prediction models that will yield more meaningful applications of precision cancer medicine.
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Affiliation(s)
- Olivier Morin
- Department of Radiation Oncology, University of California San Francisco, San Francisco, California.
| | | | - Arthur Jochems
- The D-Lab, Grow Research Institute for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Henry C Woodruff
- The D-Lab, Grow Research Institute for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Gilmer Valdes
- Department of Radiation Oncology, University of California San Francisco, San Francisco, California
| | - Steve E Braunstein
- Department of Radiation Oncology, University of California San Francisco, San Francisco, California
| | - Joachim E Wildberger
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | | | - Vasant Kearney
- Department of Radiation Oncology, University of California San Francisco, San Francisco, California
| | - Sue S Yom
- Department of Radiation Oncology, University of California San Francisco, San Francisco, California
| | - Timothy D Solberg
- Department of Radiation Oncology, University of California San Francisco, San Francisco, California
| | - Philippe Lambin
- The D-Lab, Grow Research Institute for Oncology, Maastricht University, Maastricht, The Netherlands
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Choi W, Riyahi S, Kligerman SJ, Liu CJ, Mechalakos JG, Lu W. Technical Note: Identification of CT Texture Features Robust to Tumor Size Variations for Normal Lung Texture Analysis. ACTA ACUST UNITED AC 2018; 7:330-338. [PMID: 31131158 DOI: 10.4236/ijmpcero.2018.73027] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (RILD). For these features to be clinically useful, they should be robust to tumor size variations and not correlated with the normal lung volume of interest, i.e., the volume of the peri-tumoral region (PTR). CT images of 14 lung cancer patients were studied. Different sizes of gross tumor volumes (GTVs) were simulated and placed in the lung contralateral to the tumor. 27 texture features [nine from intensity histogram, eight from the gray-level co-occurrence matrix (GLCM) and ten from the gray-level run-length matrix (GLRM)] were extracted from the PTR. The Bland-Altman analysis was applied to measure the normalized range of agreement (nRoA) for each feature when GTV size varied. A feature was considered as robust when its nRoA was less than the threshold (100%). Sixteen texture features were identified as robust. None of the robust features was correlated with the volume of the PTR. No feature showed statistically significant differences (P<0.05) on GTV locations. We identified 16 robust normal lung CT texture features that can be further examined for the prediction of RILD.
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Affiliation(s)
- Wookjin Choi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Sadegh Riyahi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Seth J Kligerman
- Department of Radiology, University of California at San Diego, San Diego, CA 92103
| | - Chia-Ju Liu
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - James G Mechalakos
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Wei Lu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065
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119
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Leger S, Zwanenburg A, Pilz K, Zschaeck S, Zöphel K, Kotzerke J, Schreiber A, Zips D, Krause M, Baumann M, Troost EGC, Richter C, Löck S. CT imaging during treatment improves radiomic models for patients with locally advanced head and neck cancer. Radiother Oncol 2018; 130:10-17. [PMID: 30087056 DOI: 10.1016/j.radonc.2018.07.020] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Revised: 06/27/2018] [Accepted: 07/24/2018] [Indexed: 12/27/2022]
Abstract
BACKGROUND AND PURPOSE The development of radiomic risk models to predict clinical outcome is usually based on pre-treatment imaging, such as computed tomography (CT) scans used for radiation treatment planning. Imaging data acquired during the course of treatment may improve their prognostic performance. We compared the performance of radiomic risk models based on the pre-treatment CT and CT scans acquired in the second week of therapy. MATERIAL AND METHODS Treatment planning and second week CT scans of 78 head and neck squamous cell carcinoma patients treated with primary radiochemotherapy were collected. 1538 image features were extracted from each image. Prognostic models for loco-regional tumour control (LRC) and overall survival (OS) were built using 6 feature selection methods and 6 machine learning algorithms. Prognostic performance was assessed using the concordance index (C-Index). Furthermore, patients were stratified into risk groups and differences in LRC and OS were evaluated by log-rank tests. RESULTS The performance of radiomic risk model in predicting LRC was improved using the second week CT scans (C-Index: 0.79), in comparison to the pre-treatment CT scans (C-Index: 0.65). This was confirmed by Kaplan-Meier analyses, in which risk stratification based on the second week CT could be improved for LRC (p = 0.002) compared to pre-treatment CT (p = 0.063). CONCLUSION Incorporation of imaging during treatment may be a promising way to improve radiomic risk models for clinical treatment adaption, i.e., to select patients that may benefit from dose modification.
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Affiliation(s)
- Stefan Leger
- OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) partner site Dresden, Dresden, Germany; National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany German Cancer Research Center (DKFZ), Heidelberg, Germany, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and Helmholtz Association / Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany.
| | - Alex Zwanenburg
- OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) partner site Dresden, Dresden, Germany; National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany German Cancer Research Center (DKFZ), Heidelberg, Germany, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and Helmholtz Association / Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
| | - Karoline Pilz
- OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) partner site Dresden, Dresden, Germany; National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany German Cancer Research Center (DKFZ), Heidelberg, Germany, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and Helmholtz Association / Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany; Department of Radiotherapy, Hospital Dresden-Friedrichstadt, Germany
| | - Sebastian Zschaeck
- OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) partner site Dresden, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; Charité Universitätsmedizin Berlin, Department of Radiation Oncology, Germany
| | - Klaus Zöphel
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf, PET Center, Institute of Radiopharmaceutical Cancer Research, Germany
| | - Jörg Kotzerke
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf, PET Center, Institute of Radiopharmaceutical Cancer Research, Germany
| | - Andreas Schreiber
- Department of Radiotherapy, Hospital Dresden-Friedrichstadt, Germany
| | - Daniel Zips
- Department of Radiation Oncology, Faculty of Medicine and University Hospital Tübingen, Eberhard Karls Universität Tübingen, Germany
| | - Mechthild Krause
- OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) partner site Dresden, Dresden, Germany; National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany German Cancer Research Center (DKFZ), Heidelberg, Germany, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and Helmholtz Association / Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology OncoRay, Germany
| | - Michael Baumann
- OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) partner site Dresden, Dresden, Germany; National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany German Cancer Research Center (DKFZ), Heidelberg, Germany, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and Helmholtz Association / Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology OncoRay, Germany
| | - Esther G C Troost
- OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) partner site Dresden, Dresden, Germany; National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany German Cancer Research Center (DKFZ), Heidelberg, Germany, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and Helmholtz Association / Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology OncoRay, Germany
| | - Christian Richter
- OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) partner site Dresden, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology OncoRay, Germany
| | - Steffen Löck
- OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) partner site Dresden, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany
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The Role of PET-Based Radiomic Features in Predicting Local Control of Esophageal Cancer Treated with Concurrent Chemoradiotherapy. Sci Rep 2018; 8:9902. [PMID: 29967326 PMCID: PMC6028651 DOI: 10.1038/s41598-018-28243-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 06/19/2018] [Indexed: 01/09/2023] Open
Abstract
This study was designed to evaluate the predictive performance of 18F-fluorodeoxyglucose positron emission tomography (PET)-based radiomic features for local control of esophageal cancer treated with concurrent chemoradiotherapy (CRT). For each of the 30 patients enrolled, 440 radiomic features were extracted from both pre-CRT and mid-CRT PET images. The top 25 features with the highest areas under the receiver operating characteristic curve for identifying local control status were selected as discriminative features. Four machine-learning methods, random forest (RF), support vector machine, logistic regression, and extreme learning machine, were used to build predictive models with clinical features, radiomic features or a combination of both. An RF model incorporating both clinical and radiomic features achieved the best predictive performance, with an accuracy of 93.3%, a specificity of 95.7%, and a sensitivity of 85.7%. Based on risk scores of local failure predicted by this model, the 2-year local control rate and PFS rate were 100.0% (95% CI 100.0–100.0%) and 52.2% (31.8–72.6%) in the low-risk group and 14.3% (0.0–40.2%) and 0.0% (0.0–40.2%) in the high-risk group, respectively. This model may have the potential to stratify patients with different risks of local failure after CRT for esophageal cancer, which may facilitate the delivery of personalized treatment.
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Yang F, Ford JC, Dogan N, Padgett KR, Breto AL, Abramowitz MC, Dal Pra A, Pollack A, Stoyanova R. Magnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapy. Transl Androl Urol 2018; 7:445-458. [PMID: 30050803 PMCID: PMC6043736 DOI: 10.21037/tau.2018.06.05] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2018] [Accepted: 06/05/2018] [Indexed: 11/25/2022] Open
Abstract
In radiotherapy (RT) of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate tumor habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated. Other issues in the treatment of the RT patient include the choice of the RT technique (hypo- or standard fractionation) and the use and length of concurrent/adjuvant androgen deprivation therapy (ADT). Up to 50% of high-risk men demonstrate biochemical failure suggesting that additional strategies for defining and treating patients based on improved risk stratification are required. The use of multiparametric MRI (mpMRI) is rapidly gaining momentum in the management of prostate cancer because of its improved diagnostic potential and its ability to combine functional and anatomical information. Currently, the Prostate Imaging, Reporting and Diagnosis System (PIRADS) is the standard of care for region of interest (ROI) identification and risk classification. However, PIRADS was not designed for 3D tumor volume delineation; there is a large degree of subjectivity and PIRADS does not accurately and reproducibly elucidate inter- and intra-lesional spatial heterogeneity. "Radiomics", as it refers to the extraction and analysis of large number of advanced quantitative radiological features from medical images using high throughput methods, is perfectly suited as an engine to effectively sift through the multiple series of prostate mpMRI sequences and quantify regions of interest. The radiomic efforts can be summarized in two main areas: (I) detection/segmentation of the suspicious lesion; and (II) assessment of the aggressiveness of prostate cancer. As related to RT, the goal of the latter is in particular to identify patients at high risk for metastatic disease; and the aim of the former is to identify and segment cancerous lesions and thus provide targets for radiation boost. The article is structured as follows: first, we describe the radiomic approach; and second, we discuss the radiomic pipeline as tailored for RT of prostate cancer. In this process we summarize the current efforts and progress in integrating mpMRI radiomics into the radiotherapeutic management of prostate cancer with emphasis placed on its role in treatment target definition, treatment plan strategizing, and prognostic assessment. The described concepts, methods and tools are not currently applicable to the radiation oncology practice outside of the research setting. More data are required in the form of clinical trials to assess the robustness of radiomics-based predictive models, and to maximize the efficacy of these models.
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Affiliation(s)
- Fei Yang
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - John C. Ford
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Nesrin Dogan
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Kyle R. Padgett
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Department of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Adrian L. Breto
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Matthew C. Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
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Smith WP, Richard PJ, Zeng J, Apisarnthanarax S, Rengan R, Phillips MH. Decision analytic modeling for the economic analysis of proton radiotherapy for non-small cell lung cancer. Transl Lung Cancer Res 2018; 7:122-133. [PMID: 29876311 DOI: 10.21037/tlcr.2018.03.27] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background Although proton radiation treatments are more costly than photon/X-ray therapy, they may lower overall treatment costs through reducing rates of severe toxicities and the costly management of those toxicities. To study this issue, we created a decision-model comparing proton vs. X-ray radiotherapy for locally advanced non-small cell lung cancer patients. Methods An influence diagram was created to model for radiation delivery, associated 6-month pneumonitis/esophagitis rates, and overall costs (radiation plus toxicity costs). Pneumonitis (age, chemo type, V20, MLD) and esophagitis (V60) predictors were modeled to impact toxicity rates. We performed toxicity-adjusted, rate-adjusted, risk group-adjusted, and radiosensitivity analyses. Results Upfront proton treatment costs exceeded that of photons [$16,730.37 (3DCRT), $23,893.83 (IMRT), $41,061.80 (protons)]. Based upon expected population pneumonitis and esophagitis rates for each modality, protons would be expected to recover $1,065.62 and $1,139.63 of the cost difference compared to 3DCRT or IMRT. For patients treated with IMRT experiencing grade 4 pneumonitis or grade 4 esophagitis, costs exceeded patients treated with protons without this toxicity. 3DCRT patients with grade 4 esophagitis had higher costs than proton patients without this toxicity. For the risk group analysis, high risk patients (age >65, carboplatin/paclitaxel) benefited more from proton therapy. A biomarker may allow patient selection for proton therapy, although the AUC alone is not sufficient to determine if the biomarker is clinically useful. Conclusions The comparison between proton and photon/X-ray radiation therapy for NSCLC needs to consider both the up-front cost of treatment and the possible long term cost of complications. In our analysis, current costs favor X-ray therapy. However, relatively small reductions in the cost of proton therapy may result in a shift to the preference for proton therapy.
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Affiliation(s)
- Wade P Smith
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Patrick J Richard
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Jing Zeng
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Smith Apisarnthanarax
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Ramesh Rengan
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Mark H Phillips
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
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Peeken JC, Bernhofer M, Wiestler B, Goldberg T, Cremers D, Rost B, Wilkens JJ, Combs SE, Nüsslin F. Radiomics in radiooncology - Challenging the medical physicist. Phys Med 2018; 48:27-36. [PMID: 29728226 DOI: 10.1016/j.ejmp.2018.03.012] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 03/07/2018] [Accepted: 03/20/2018] [Indexed: 02/06/2023] Open
Abstract
PURPOSE Noticing the fast growing translation of artificial intelligence (AI) technologies to medical image analysis this paper emphasizes the future role of the medical physicist in this evolving field. Specific challenges are addressed when implementing big data concepts with high-throughput image data processing like radiomics and machine learning in a radiooncology environment to support clinical decisions. METHODS Based on the experience of our interdisciplinary radiomics working group, techniques for processing minable data, extracting radiomics features and associating this information with clinical, physical and biological data for the development of prediction models are described. A special emphasis was placed on the potential clinical significance of such an approach. RESULTS Clinical studies demonstrate the role of radiomics analysis as an additional independent source of information with the potential to influence the radiooncology practice, i.e. to predict patient prognosis, treatment response and underlying genetic changes. Extending the radiomics approach to integrate imaging, clinical, genetic and dosimetric data ('panomics') challenges the medical physicist as member of the radiooncology team. CONCLUSIONS The new field of big data processing in radiooncology offers opportunities to support clinical decisions, to improve predicting treatment outcome and to stimulate fundamental research on radiation response both of tumor and normal tissue. The integration of physical data (e.g. treatment planning, dosimetric, image guidance data) demands an involvement of the medical physicist in the radiomics approach of radiooncology. To cope with this challenge national and international organizations for medical physics should organize more training opportunities in artificial intelligence technologies in radiooncology.
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Affiliation(s)
- Jan C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany
| | - Michael Bernhofer
- Department of Informatics, Technical University of Munich (TUM), Boltzmannstraße 3, 85748 Garching, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany
| | | | - Daniel Cremers
- Department of Informatics, Technical University of Munich (TUM), Boltzmannstraße 3, 85748 Garching, Germany
| | - Burkhard Rost
- Department of Informatics, Technical University of Munich (TUM), Boltzmannstraße 3, 85748 Garching, Germany
| | - Jan J Wilkens
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; Institute of Innovative Radiotherapy (iRT), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Ingolstaedter Landstrasse 1, 85764 Neuherberg, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany
| | - Fridtjof Nüsslin
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany.
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Monti S, Pacelli R, Cella L, Palma G. Inter-patient image registration algorithms to disentangle regional dose bioeffects. Sci Rep 2018; 8:4915. [PMID: 29559687 PMCID: PMC5861107 DOI: 10.1038/s41598-018-23327-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 03/06/2018] [Indexed: 12/25/2022] Open
Abstract
Radiation therapy (RT) technological advances call for a comprehensive reconsideration of the definition of dose features leading to radiation induced morbidity (RIM). In this context, the voxel-based approach (VBA) to dose distribution analysis in RT offers a radically new philosophy to evaluate local dose response patterns, as an alternative to dose-volume-histograms for identifying dose sensitive regions of normal tissue. The VBA relies on mapping patient dose distributions into a single reference case anatomy which serves as anchor for local dosimetric evaluations. The inter-patient elastic image registrations (EIRs) of the planning CTs provide the deformation fields necessary for the actual warp of dose distributions. In this study we assessed the impact of EIR on the VBA results in thoracic patients by identifying two state-of-the-art EIR algorithms (Demons and B-Spline). Our analysis demonstrated that both the EIR algorithms may be successfully used to highlight subregions with dose differences associated with RIM that substantially overlap. Furthermore, the inclusion for the first time of covariates within a dosimetric statistical model that faces the multiple comparison problem expands the potential of VBA, thus paving the way to a reliable voxel-based analysis of RIM in datasets with strong correlation of the outcome with non-dosimetric variables.
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Affiliation(s)
| | - Roberto Pacelli
- Department of Advanced Biomedical Sciences, "Federico II" University School of Medicine, Napoli, Italy
| | - Laura Cella
- Institute of Biostructures and Bioimaging, National Research Council, Napoli, Italy.
| | - Giuseppe Palma
- Institute of Biostructures and Bioimaging, National Research Council, Napoli, Italy
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Lu M, Zhan X. The crucial role of multiomic approach in cancer research and clinically relevant outcomes. EPMA J 2018; 9:77-102. [PMID: 29515689 PMCID: PMC5833337 DOI: 10.1007/s13167-018-0128-8] [Citation(s) in RCA: 141] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 01/29/2018] [Indexed: 02/06/2023]
Abstract
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. The rapid development of various omics technologies is driving one to conveniently get multi-omics data, which accelerates predictive, preventive and personalized medicine (PPPM) practice allowing prediction of response with substantially increased accuracy, stratification of particular patients and eventual personalization of medicine. This review article describes the methodology, advances, and clinically relevant outcomes of different "omics" technologies in cancer research, and especially emphasizes the importance and scientific merit of integrating multi-omics in cancer research and clinically relevant outcomes.
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Affiliation(s)
- Miaolong Lu
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
| | - Xianquan Zhan
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- The State Key Laboratory of Medical Genetics, Central South University, 88 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
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Veiga C, Landau D, McClelland JR, Ledermann JA, Hawkes D, Janes SM, Devaraj A. Long term radiological features of radiation-induced lung damage. Radiother Oncol 2018; 126:300-306. [PMID: 29191458 DOI: 10.1016/j.radonc.2017.11.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Revised: 11/01/2017] [Accepted: 11/09/2017] [Indexed: 12/25/2022]
Abstract
PURPOSE To describe the radiological findings of radiation-induced lung damage (RILD) present on CT imaging of lung cancer patients 12 months after radical chemoradiation. MATERIAL AND METHODS Baseline and 12-month CT scans of 33 patients were reviewed from a phase I/II clinical trial of isotoxic chemoradiation (IDEAL CRT). CT findings were scored in three categories derived from eleven sub-categories: (1) parenchymal change, defined as the presence of consolidation, ground-glass opacities (GGOs), traction bronchiectasis and/or reticulation; (2) lung volume reduction, identified through reduction in lung height and/or distortions in fissures, diaphragm, anterior junction line and major airways anatomy, and (3) pleural changes, either thickening and/or effusion. RESULTS Six patients were excluded from the analysis due to anatomical changes caused by partial lung collapse and abscess. All remaining 27 patients had radiological evidence of lung damage. The three categories, parenchymal change, shrinkage and pleural change were present in 100%, 96% and 82% respectively. All patients had at least two categories of change present and 72% all three. GGOs, reticulation and traction bronchiectasis were present in 44%, 52% and 37% of patients. CONCLUSIONS Parenchymal change, lung shrinkage and pleural change are present in a high proportion of patients and are frequently identified in RILD. GGOs, reticulation and traction bronchiectasis are common at 12 months but not diagnostic.
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Affiliation(s)
- Catarina Veiga
- Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering, University College London, London, UK.
| | - David Landau
- Department of Oncology, Guy's & St. Thomas' NHS Trust, London, UK; Department of Oncology, University College London Hospital, London, UK
| | - Jamie R McClelland
- Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering, University College London, London, UK
| | - Jonathan A Ledermann
- Cancer Research UK and UCL Cancer Trials Centre, UCL Cancer Institute, London, UK
| | - David Hawkes
- Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering, University College London, London, UK
| | - Sam M Janes
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Anand Devaraj
- Department of Radiology, Royal Brompton Hospital, London, UK
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Acharya UR, Hagiwara Y, Sudarshan VK, Chan WY, Ng KH. Towards precision medicine: from quantitative imaging to radiomics. J Zhejiang Univ Sci B 2018; 19:6-24. [PMID: 29308604 PMCID: PMC5802973 DOI: 10.1631/jzus.b1700260] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Accepted: 08/16/2017] [Indexed: 12/12/2022]
Abstract
Radiology (imaging) and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations (phenotypes) to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype (to obtain genomic information) of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET), the challenges we are facing, and the potential contribution of radiomics towards precision medicine.
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Affiliation(s)
- U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Vidya K. Sudarshan
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Wai Yee Chan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia
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Shi L, He Y, Yuan Z, Benedict S, Valicenti R, Qiu J, Rong Y. Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer. Technol Cancer Res Treat 2018; 17:1533033818782788. [PMID: 29940810 PMCID: PMC6048673 DOI: 10.1177/1533033818782788] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 03/09/2018] [Accepted: 05/16/2018] [Indexed: 12/24/2022] Open
Abstract
Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients' medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature.
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Affiliation(s)
- Liting Shi
- Department of Radiology, Taishan Medical University, Tai’an, China
| | - Yaoyao He
- Department of Radiology, Taishan Medical University, Tai’an, China
| | - Zilong Yuan
- Department of Radiology, Hubei Cancer Hospital, Wuhan, China
| | - Stanley Benedict
- Department of Radiation Oncology, University of California Davis
Comprehensive Cancer Center, Sacramento, CA, USA
| | - Richard Valicenti
- Department of Radiation Oncology, University of California Davis
Comprehensive Cancer Center, Sacramento, CA, USA
| | - Jianfeng Qiu
- Department of Radiology, Taishan Medical University, Tai’an, China
| | - Yi Rong
- Department of Radiation Oncology, University of California Davis
Comprehensive Cancer Center, Sacramento, CA, USA
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129
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Jain A, Shannon VR, Sheshadri A. Immune-Related Adverse Events: Pneumonitis. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 995:131-149. [PMID: 30539509 DOI: 10.1007/978-3-030-02505-2_6] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Checkpoint inhibitors are part of the family of immunotherapies and are increasingly being used in a wide variety of cancers. Immune-related adverse events pose a major challenge in the treatment of cancer patients. Pneumonitis is a rare immune-related adverse event that presents in distinct patterns. The goal of this chapter is to instruct readers on the incidence and clinical manifestations of pneumonitis and to offer guidance in the evaluation and treatment of patients with pneumonitis.
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Affiliation(s)
- Akash Jain
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vickie R Shannon
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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130
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On the Fuzziness of Machine Learning, Neural Networks, and Artificial Intelligence in Radiation Oncology. Int J Radiat Oncol Biol Phys 2018; 100:1-4. [DOI: 10.1016/j.ijrobp.2017.06.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 06/08/2017] [Indexed: 12/25/2022]
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131
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MacManus M, Everitt S, Schimek-Jasch T, Li XA, Nestle U, Kong FMS. Anatomic, functional and molecular imaging in lung cancer precision radiation therapy: treatment response assessment and radiation therapy personalization. Transl Lung Cancer Res 2017; 6:670-688. [PMID: 29218270 DOI: 10.21037/tlcr.2017.09.05] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
This article reviews key imaging modalities for lung cancer patients treated with radiation therapy (RT) and considers their actual or potential contributions to critical decision-making. An international group of researchers with expertise in imaging in lung cancer patients treated with RT considered the relevant literature on modalities, including computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET). These perspectives were coordinated to summarize the current status of imaging in lung cancer and flag developments with future implications. Although there are no useful randomized trials of different imaging modalities in lung cancer, multiple prospective studies indicate that management decisions are frequently impacted by the use of complementary imaging modalities, leading both to more appropriate treatments and better outcomes. This is especially true of 18F-fluoro-deoxyglucose (FDG)-PET/CT which is widely accepted to be the standard imaging modality for staging of lung cancer patients, for selection for potentially curative RT and for treatment planning. PET is also more accurate than CT for predicting survival after RT. PET imaging during RT is also correlated with survival and makes response-adapted therapies possible. PET tracers other than FDG have potential for imaging important biological process in tumors, including hypoxia and proliferation. MRI has superior accuracy in soft tissue imaging and the MRI Linac is a rapidly developing technology with great potential for online monitoring and modification of treatment. The role of imaging in RT-treated lung cancer patients is evolving rapidly and will allow increasing personalization of therapy according to the biology of both the tumor and dose limiting normal tissues.
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Affiliation(s)
- Michael MacManus
- Department of Radiation Oncology, Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Australia.,The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Australia
| | - Sarah Everitt
- Department of Radiation Oncology, Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Australia.,The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Australia
| | - Tanja Schimek-Jasch
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, WI, USA
| | - Ursula Nestle
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Radiation Oncology, Kliniken Maria Hilf, Moenchengladbach, Germany
| | - Feng-Ming Spring Kong
- Indiana University Simon Cancer Center, Indiana University School of Medicine, Indianapolis, IN, USA
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132
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Radiomic analysis in contrast-enhanced CT: predict treatment response to chemoradiotherapy in esophageal carcinoma. Oncotarget 2017; 8:104444-104454. [PMID: 29262652 PMCID: PMC5732818 DOI: 10.18632/oncotarget.22304] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 10/05/2017] [Indexed: 01/04/2023] Open
Abstract
Objectives To investigate the capability of computed-tomography (CT) radiomic features to predict the therapeutic response of Esophageal Carcinoma (EC) to chemoradiotherapy (CRT). Methods Pretreatment contrast-enhanced CT images of 49 EC patients (33 responders, 16 nonresponders) who received with CRT were retrospectively analyzed. The region of tumor was contoured by two radiologists. A total of 214 features were extracted from the tumor region. Kruskal-Wallis test and receiver operating characteristic (ROC) analysis were performed to evaluate the capability of each feature on treatment response classification. Support vector machine (SVM) and artificial neural network (ANN) algorithms were used to build models for prediction of the treatment response. The statistical difference between the performances of the models was assessed using McNemar's test. Results Radiomic-based classification showed significance in differentiating responders from nonresponders. Five features were found to discriminate nonresponders from responders (AUCs from 0.686 to 0.727). Considering these features, two features (Histogram2D_skewness: P = 0.015. Histogram2D_kurtosis: P = 0.039) were significant for differentiating SDs (stable disease) from PRs (partial response) and one feature (Histogram2D_skewness: P = 0.027) for differentiating SDs from CRs (complete response). Both classifiers showed potential in predicting the treatment response with higher accuracy (ANN: 0.972, SVM: 0.891). No statistically significant difference was observed in the performance of the two classifiers (P = 0.250). Conclusions CT-based radiomic features can be used as imaging biomarkers to predict tumor response to CRT in EC patients.
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Jaffee EM, Dang CV, Agus DB, Alexander BM, Anderson KC, Ashworth A, Barker AD, Bastani R, Bhatia S, Bluestone JA, Brawley O, Butte AJ, Coit DG, Davidson NE, Davis M, DePinho RA, Diasio RB, Draetta G, Frazier AL, Futreal A, Gambhir SS, Ganz PA, Garraway L, Gerson S, Gupta S, Heath J, Hoffman RI, Hudis C, Hughes-Halbert C, Ibrahim R, Jadvar H, Kavanagh B, Kittles R, Le QT, Lippman SM, Mankoff D, Mardis ER, Mayer DK, McMasters K, Meropol NJ, Mitchell B, Naredi P, Ornish D, Pawlik TM, Peppercorn J, Pomper MG, Raghavan D, Ritchie C, Schwarz SW, Sullivan R, Wahl R, Wolchok JD, Wong SL, Yung A. Future cancer research priorities in the USA: a Lancet Oncology Commission. Lancet Oncol 2017; 18:e653-e706. [PMID: 29208398 PMCID: PMC6178838 DOI: 10.1016/s1470-2045(17)30698-8] [Citation(s) in RCA: 130] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 08/23/2017] [Accepted: 08/23/2017] [Indexed: 12/12/2022]
Abstract
We are in the midst of a technological revolution that is providing new insights into human biology and cancer. In this era of big data, we are amassing large amounts of information that is transforming how we approach cancer treatment and prevention. Enactment of the Cancer Moonshot within the 21st Century Cures Act in the USA arrived at a propitious moment in the advancement of knowledge, providing nearly US$2 billion of funding for cancer research and precision medicine. In 2016, the Blue Ribbon Panel (BRP) set out a roadmap of recommendations designed to exploit new advances in cancer diagnosis, prevention, and treatment. Those recommendations provided a high-level view of how to accelerate the conversion of new scientific discoveries into effective treatments and prevention for cancer. The US National Cancer Institute is already implementing some of those recommendations. As experts in the priority areas identified by the BRP, we bolster those recommendations to implement this important scientific roadmap. In this Commission, we examine the BRP recommendations in greater detail and expand the discussion to include additional priority areas, including surgical oncology, radiation oncology, imaging, health systems and health disparities, regulation and financing, population science, and oncopolicy. We prioritise areas of research in the USA that we believe would accelerate efforts to benefit patients with cancer. Finally, we hope the recommendations in this report will facilitate new international collaborations to further enhance global efforts in cancer control.
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Affiliation(s)
| | - Chi Van Dang
- Ludwig Institute for Cancer Research New York, NY; Wistar Institute, Philadelphia, PA, USA.
| | - David B Agus
- University of Southern California, Beverly Hills, CA, USA
| | - Brian M Alexander
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | | | - Alan Ashworth
- University of California San Francisco, San Francisco, CA, USA
| | | | - Roshan Bastani
- Fielding School of Public Health and the Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
| | - Sangeeta Bhatia
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jeffrey A Bluestone
- University of California San Francisco, San Francisco, CA, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | | | - Atul J Butte
- University of California San Francisco, San Francisco, CA, USA
| | - Daniel G Coit
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Nancy E Davidson
- Fred Hutchinson Cancer Research Center and University of Washington, Seattle, WA, USA
| | - Mark Davis
- California Institute for Technology, Pasadena, CA, USA
| | | | | | - Giulio Draetta
- University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - A Lindsay Frazier
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Andrew Futreal
- University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Patricia A Ganz
- Fielding School of Public Health and the Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
| | - Levi Garraway
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; The Broad Institute, Cambridge, MA, USA; Eli Lilly and Company, Boston, MA, USA
| | | | - Sumit Gupta
- Division of Haematology/Oncology, Hospital for Sick Children, Faculty of Medicine and IHPME, University of Toronto, Toronto, Canada
| | - James Heath
- California Institute for Technology, Pasadena, CA, USA
| | - Ruth I Hoffman
- American Childhood Cancer Organization, Beltsville, MD, USA
| | - Cliff Hudis
- Breast Cancer Medicine Service, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Chanita Hughes-Halbert
- Medical University of South Carolina and the Hollings Cancer Center, Charleston, SC, USA
| | - Ramy Ibrahim
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | - Hossein Jadvar
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Brian Kavanagh
- Department of Radiation Oncology, University of Colorado, Denver, CO, USA
| | - Rick Kittles
- College of Medicine, University of Arizona, Tucson, AZ, USA; University of Arizona Cancer Center, University of Arizona, Tucson, AZ, USA
| | | | - Scott M Lippman
- University of California San Diego Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
| | - David Mankoff
- Department of Radiology and Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elaine R Mardis
- The Institute for Genomic Medicine at Nationwide Children's Hospital Columbus, OH, USA; College of Medicine, Ohio State University, Columbus, OH, USA
| | - Deborah K Mayer
- University of North Carolina Lineberger Cancer Center, Chapel Hill, NC, USA
| | - Kelly McMasters
- The Hiram C Polk Jr MD Department of Surgery, University of Louisville School of Medicine, Louisville, KY, USA
| | | | | | - Peter Naredi
- Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Dean Ornish
- University of California San Francisco, San Francisco, CA, USA
| | - Timothy M Pawlik
- Department of Surgery, Wexner Medical Center, Ohio State University, Columbus, OH, USA
| | | | - Martin G Pomper
- The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Derek Raghavan
- Levine Cancer Institute, Carolinas HealthCare, Charlotte, NC, USA
| | | | - Sally W Schwarz
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | | | - Richard Wahl
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Jedd D Wolchok
- Ludwig Center for Cancer Immunotherapy, Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | - Sandra L Wong
- Department of Surgery, The Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Alfred Yung
- University of Texas MD Anderson Cancer Center, Houston, TX, USA
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134
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Colen RR, Fujii T, Bilen MA, Kotrotsou A, Abrol S, Hess KR, Hajjar J, Suarez-Almazor ME, Alshawa A, Hong DS, Giniebra-Camejo D, Stephen B, Subbiah V, Sheshadri A, Mendoza T, Fu S, Sharma P, Meric-Bernstam F, Naing A. Radiomics to predict immunotherapy-induced pneumonitis: proof of concept. Invest New Drugs 2017; 36:601-607. [PMID: 29075985 DOI: 10.1007/s10637-017-0524-2] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 10/11/2017] [Indexed: 01/04/2023]
Abstract
We present the first reported work that explores the potential of radiomics to predict patients who are at risk for developing immunotherapy-induced pneumonitis. Despite promising results with immunotherapies, immune-related adverse events (irAEs) are challenging. Although less common, pneumonitis is a potentially fatal irAE. Thus, early detection is critical for improving treatment outcomes; an urgent need to identify biomarkers that predict patients at risk for pneumonitis exists. Radiomics, an emerging field, is the automated extraction of high fidelity, high-dimensional imaging features from standard medical images and allows for comprehensive visualization and characterization of the tissue of interest and corresponding microenvironment. In this pilot study, we sought to determine whether radiomics has the potential to predict development of pneumonitis. We performed radiomic analyses using baseline chest computed tomography images of patients who did (N = 2) and did not (N = 30) develop immunotherapy-induced pneumonitis. We extracted 1860 radiomic features in each patient. Maximum relevance and minimum redundancy feature selection method, anomaly detection algorithm, and leave-one-out cross-validation identified radiomic features that were significantly different and predicted subsequent immunotherapy-induced pneumonitis (accuracy, 100% [p = 0.0033]). This study suggests that radiomic features can classify and predict those patients at baseline who will subsequently develop immunotherapy-induced pneumonitis, further enabling risk-stratification that will ultimately lead to better treatment outcomes.
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Affiliation(s)
- Rivka R Colen
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. .,Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
| | - Takeo Fujii
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030-4004, USA
| | - Mehmet Asim Bilen
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Aikaterini Kotrotsou
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Srishti Abrol
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kenneth R Hess
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Joud Hajjar
- Department of Immunology, Allergy, and Rheumatology, Baylor College of Medicine, Houston, TX, USA
| | - Maria E Suarez-Almazor
- Department of General Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anas Alshawa
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030-4004, USA
| | - David S Hong
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030-4004, USA
| | - Dunia Giniebra-Camejo
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Bettzy Stephen
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030-4004, USA
| | - Vivek Subbiah
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030-4004, USA
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tito Mendoza
- Department of Symptom Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Siqing Fu
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030-4004, USA
| | - Padmanee Sharma
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Funda Meric-Bernstam
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030-4004, USA
| | - Aung Naing
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030-4004, USA.
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Phillips I, Ajaz M, Ezhil V, Prakash V, Alobaidli S, McQuaid SJ, South C, Scuffham J, Nisbet A, Evans P. Clinical applications of textural analysis in non-small cell lung cancer. Br J Radiol 2017; 91:20170267. [PMID: 28869399 DOI: 10.1259/bjr.20170267] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Lung cancer is the leading cause of cancer mortality worldwide. Treatment pathways include regular cross-sectional imaging, generating large data sets which present intriguing possibilities for exploitation beyond standard visual interpretation. This additional data mining has been termed "radiomics" and includes semantic and agnostic approaches. Textural analysis (TA) is an example of the latter, and uses a range of mathematically derived features to describe an image or region of an image. Often TA is used to describe a suspected or known tumour. TA is an attractive tool as large existing image sets can be submitted to diverse techniques for data processing, presentation, interpretation and hypothesis testing with annotated clinical outcomes. There is a growing anthology of published data using different TA techniques to differentiate between benign and malignant lung nodules, differentiate tissue subtypes of lung cancer, prognosticate and predict outcome and treatment response, as well as predict treatment side effects and potentially aid radiotherapy planning. The aim of this systematic review is to summarize the current published data and understand the potential future role of TA in managing lung cancer.
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Affiliation(s)
- Iain Phillips
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK
| | - Mazhar Ajaz
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK.,2 Surrey Clinical Research Centre, University of Surrey, Guildford, UK
| | - Veni Ezhil
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK
| | - Vineet Prakash
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK
| | - Sheaka Alobaidli
- 3 Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK
| | | | | | - James Scuffham
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK
| | - Andrew Nisbet
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK
| | - Philip Evans
- 3 Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK
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136
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Abstract
Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.
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137
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Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning. Sci Rep 2017; 7:8738. [PMID: 28821822 PMCID: PMC5562694 DOI: 10.1038/s41598-017-09315-w] [Citation(s) in RCA: 109] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 07/18/2017] [Indexed: 02/06/2023] Open
Abstract
Cross-sectional X-ray imaging has become the standard for staging most solid organ malignancies. However, for some malignancies such as urinary bladder cancer, the ability to accurately assess local extent of the disease and understand response to systemic chemotherapy is limited with current imaging approaches. In this study, we explored the feasibility that radiomics-based predictive models using pre- and post-treatment computed tomography (CT) images might be able to distinguish between bladder cancers with and without complete chemotherapy responses. We assessed three unique radiomics-based predictive models, each of which employed different fundamental design principles ranging from a pattern recognition method via deep-learning convolution neural network (DL-CNN), to a more deterministic radiomics feature-based approach and then a bridging method between the two, utilizing a system which extracts radiomics features from the image patterns. Our study indicates that the computerized assessment using radiomics information from the pre- and post-treatment CT of bladder cancer patients has the potential to assist in assessment of treatment response.
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138
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Peeken JC, Nüsslin F, Combs SE. "Radio-oncomics" : The potential of radiomics in radiation oncology. Strahlenther Onkol 2017; 193:767-779. [PMID: 28687979 DOI: 10.1007/s00066-017-1175-0] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 06/19/2017] [Indexed: 12/23/2022]
Abstract
INTRODUCTION Radiomics, a recently introduced concept, describes quantitative computerized algorithm-based feature extraction from imaging data including computer tomography (CT), magnetic resonance imaging (MRT), or positron-emission tomography (PET) images. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and follow-up workflow. METHODS After image acquisition, image preprocessing, and defining regions of interest by structure segmentation, algorithms are applied to calculate shape, intensity, texture, and multiscale filter features. By combining multiple features and correlating them with clinical outcome, prognostic models can be created. RESULTS Retrospective studies have proposed radiomics classifiers predicting, e. g., overall survival, radiation treatment response, distant metastases, or radiation-related toxicity. Besides, radiomics features can be correlated with genomic information ("radiogenomics") and could be used for tumor characterization. DISCUSSION Distinct patterns based on data-based as well as genomics-based features will influence radiation oncology in the future. Individualized treatments in terms of dose level adaption and target volume definition, as well as other outcome-related parameters will depend on radiomics and radiogenomics. By integration of various datasets, the prognostic power can be increased making radiomics a valuable part of future precision medicine approaches. CONCLUSION This perspective demonstrates the evidence for the radiomics concept in radiation oncology. The necessity of further studies to integrate radiomics classifiers into clinical decision-making and the radiation therapy workflow is emphasized.
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Affiliation(s)
- Jan Caspar Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technische Universität München (TUM), Ismaninger Straße 22, 81675, München, Germany.
| | - Fridtjof Nüsslin
- Department of Radiation Oncology, Klinikum rechts der Isar, Technische Universität München (TUM), Ismaninger Straße 22, 81675, München, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technische Universität München (TUM), Ismaninger Straße 22, 81675, München, Germany
- Institute of Innovative Radiotherapy (iRT), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
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139
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Abstract
The domain of investigation of radiomics consists of large-scale radiological image analysis and association with biological or clinical endpoints. The purpose of the present study is to provide a recent update on the status of this rapidly emerging field by performing a systematic review of the literature on radiomics, with a primary focus on oncologic applications. The systematic literature search, performed in Pubmed using the keywords: "radiomics OR radiomic" provided 97 research papers. Based on the results of this search, we describe the methods used for building a model of prognostic value from quantitative analysis of patient images. Then, we provide an up-to-date overview of the results achieved in this field, and discuss the current challenges and future developments of radiomics for oncology.
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140
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van Timmeren JE, Leijenaar RT, van Elmpt W, Reymen B, Oberije C, Monshouwer R, Bussink J, Brink C, Hansen O, Lambin P. Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images. Radiother Oncol 2017; 123:363-369. [DOI: 10.1016/j.radonc.2017.04.016] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Revised: 03/20/2017] [Accepted: 04/17/2017] [Indexed: 01/20/2023]
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141
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Moran A, Daly ME, Yip SSF, Yamamoto T. Radiomics-based Assessment of Radiation-induced Lung Injury After Stereotactic Body Radiotherapy. Clin Lung Cancer 2017. [PMID: 28623121 DOI: 10.1016/j.cllc.2017.05.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Over 50% of patients who receive stereotactic body radiotherapy (SBRT) develop radiographic evidence of radiation-induced lung injury. Radiomics is an emerging approach that extracts quantitative features from image data, which may provide greater value and a better understanding of pulmonary toxicity than conventional approaches. We aimed to investigate the potential of computed tomography-based radiomics in characterizing post-SBRT lung injury. METHODS A total of 48 diagnostic thoracic computed tomography scans (acquired prior to SBRT and at 3, 6, and 9 months post-SBRT) from 14 patients were analyzed. Nine radiomic features (ie, 7 gray level co-occurrence matrix [GLCM] texture features and 2 first-order features) were investigated. The ability of radiomic features to distinguish radiation oncologist-defined moderate/severe lung injury from none/mild lung injury was assessed using logistic regression and area under the receiver operating characteristic curve (AUC). Moreover, dose-response curves (DRCs) for radiomic feature changes were determined as a function of time to investigate whether there was a significant dose-response relationship. RESULTS The GLCM features (logistic regression P-value range, 0.012-0.262; AUC range, 0.643-0.750) outperformed the first-order features (P-value range, 0.100-0.990; AUC range, 0.543-0.661) in distinguishing lung injury severity levels. Eight of 9 radiomic features demonstrated a significant dose-response relationship at 3, 6, and 9 months post-SBRT. Although not statistically significant, the GLCM features showed clear separations between the 3- or 6-month DRC and the 9-month DRC. CONCLUSION Radiomic features significantly correlated with radiation oncologist-scored post-SBRT lung injury and showed a significant dose-response relationship, suggesting the potential for radiomics to provide a quantitative, objective measurement of post-SBRT lung injury.
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Affiliation(s)
- Angel Moran
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, CA
| | - Megan E Daly
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, CA
| | - Stephen S F Yip
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA
| | - Tokihiro Yamamoto
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, CA.
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142
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Verma V, Simone CB, Krishnan S, Lin SH, Yang J, Hahn SM. The Rise of Radiomics and Implications for Oncologic Management. J Natl Cancer Inst 2017; 109:3573848. [PMID: 28423406 DOI: 10.1093/jnci/djx055] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 03/07/2017] [Indexed: 12/28/2022] Open
Abstract
Clinical medicine, particularly oncology, is progressing toward personalized care. Whereas the terms genomics, proteomics, transcriptomics, and metabolomics have dominated personalized medicine for the past couple decades, the concept of radiomics was first described in 2012. This nascent concept has major implications for personalized cancer care and involves extracting hundreds of standardized and quantifiable imaging characteristics from diagnostic computed tomography/magnetic resonance imaging images. The central hypothesis of radiomics is that these libraries of quantitative individual voxel-based variables are more sensitively associated with various clinical endpoints compared with the more qualitative radiologic, histopathologic, and clinical data more commonly utilized today. Because radiomics offers immense potential but has not reached a mainstream oncologic audience, the authors discuss herein the role of radiomics in cancer care in the future.
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Affiliation(s)
- Vivek Verma
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Charles B Simone
- Department of Radiation Oncology, University of Maryland Medical Center, Baltimore, MD, USA
| | - Sunil Krishnan
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Steven H Lin
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jinzhong Yang
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Stephen M Hahn
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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143
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Anthony GJ, Cunliffe A, Castillo R, Pham N, Guerrero T, Armato SG, Al-Hallaq HA. Incorporation of pre-therapy 18 F-FDG uptake data with CT texture features into a radiomics model for radiation pneumonitis diagnosis. Med Phys 2017; 44:3686-3694. [PMID: 28422299 DOI: 10.1002/mp.12282] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Revised: 03/29/2017] [Accepted: 04/11/2017] [Indexed: 12/25/2022] Open
Abstract
PURPOSE To determine whether the addition of standardized uptake value (SUV) from PET scans to CT lung texture features could improve a radiomics-based model of radiation pneumonitis (RP) diagnosis in patients undergoing radiotherapy. METHODS AND MATERIALS Anonymized data from 96 esophageal cancer patients (18 RP-positive cases of Grade ≥ 2) were collected including pre-therapy PET/CT scans, pre-/post-therapy diagnostic CT scans and RP status. Twenty texture features (first-order, fractal, Laws' filter and gray-level co-occurrence matrix) were calculated from diagnostic CT scans and compared in anatomically matched regions of the lung. Classifier performance (texture, SUV, or combination) was assessed by calculating the area under the receiver operating characteristic curve (AUC). For each texture feature, logistic regression classifiers consisting of the average change in texture feature value and the pre-therapy SUV standard deviation (SUVSD ) were created and compared with the texture feature as a lone classifier using ANOVA with correction for multiple comparisons (P < 0.0025). RESULTS While clinical parameters (mean lung dose, smoking history, tumor location) were not significantly different among patients with and without symptomatic RP, SUV and texture parameters were significantly associated with RP status. AUC for single-texture feature classifiers alone ranged from 0.58 to 0.81 and 0.53 to 0.71 in high-dose (≥ 30 Gy) and low-dose (< 10 Gy) regions of the lungs, respectively. AUC for SUVSD alone was 0.69 (95% confidence interval: 0.54-0.83). Adding SUVSD into a logistic regression model significantly improved model fit for 18, 14 and 11 texture features and increased the mean AUC across features by 0.08, 0.06, and 0.04 in the low-, medium-, and high-dose regions, respectively. CONCLUSIONS Addition of SUVSD to a single-texture feature improves classifier performance on average, but the improvement is smaller in magnitude when SUVSD is added to an already effective classifier using texture alone. These findings demonstrate the potential for more accurate assessment of RP using information from multiple imaging modalities.
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Affiliation(s)
- Gregory J Anthony
- Department of Radiology, The University of Chicago, Chicago, IL, USA
| | | | - Richard Castillo
- Department of Radiation Oncology, The University of Texas Medical Branch, Galveston, TX, USA
| | - Ngoc Pham
- Baylor College of Medicine, Houston, TX, USA
| | - Thomas Guerrero
- Department of Radiation Oncology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA
| | - Samuel G Armato
- Department of Radiology, The University of Chicago, Chicago, IL, USA
| | - Hania A Al-Hallaq
- Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL, USA
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144
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Bagher-Ebadian H, Siddiqui F, Liu C, Movsas B, Chetty IJ. On the impact of smoothing and noise on robustness of CT and CBCT radiomics features for patients with head and neck cancers. Med Phys 2017; 44:1755-1770. [PMID: 28261818 DOI: 10.1002/mp.12188] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Revised: 02/13/2017] [Accepted: 02/17/2017] [Indexed: 01/09/2023] Open
Abstract
PURPOSE We investigated the characteristics of radiomics features extracted from planning CT (pCT) and cone beam CT (CBCT) image datasets acquired for 18 oropharyngeal cancer patients treated with fractionated radiation therapy. Images were subjected to smoothing, sharpening, and noise to evaluate changes in features relative to baseline datasets. METHODS Textural features were extracted from tumor volumes, contoured on pCT and CBCT images, according to the following eight different classes: intensity based histogram features (IBHF), gray level run length (GLRL), law's textural information (LAWS), discrete orthonormal stockwell transform (DOST), local binary pattern (LBP), two-dimensional wavelet transform (2DWT), Two dimensional Gabor filter (2DGF), and gray level co-occurrence matrix (GLCM). A total of 165 radiomics features were extracted. Images were post-processed prior to feature extraction using a Gaussian noise model with different signal-to-noise-ratios (SNR = 5, 10, 15, 20, 25, 35, 50, 75, 100, and 150). Gaussian filters with different cut off frequencies (varied discreetly from 0.0458 to 0.7321 cycles-mm-1 ) were applied to image datasets. Effect of noise and smoothing on each extracted feature was quantified using mean absolute percent change (MAPC) between the respective values on post-processed and baseline images. The Fisher method for combining Welch P-values was used for tests of significance. Three comparisons were investigated: (a) Baseline pCT versus modified pCT (with given filter applied); (b) Baseline CBCT versus modified CBCT, and (c) Baseline and modified pCT versus baseline and modified CBCT. RESULTS Features extracted from CT and CBCT image datasets were robust to low-pass filtering (MAPC = 17.5%, pvalFisher¯ = 0.93 for CBCT and MAPC = 7.5%, pvalFisher¯ = 0.98 for pCT) and noise (MAPC = 27.1%, pvalFisher¯ = 0.89 for CBCT, and MAPC = 34.6%, pvalFisher¯ = 0.61 for pCT). Extracted features were significantly impacted (MAPC=187.7%, pvalFisher¯ < 0.0001 for CBCT, and MAPC = 180.6%, pvalFisher¯ < 0.01 for pCT) by LOG which is classified as a high-pass filter. Features most impacted by low pass filtering were LAWS (MAPC = 11.2%, pvalFisher¯ = 0.44), GLRL (MAPC = 9.7%, pvalFisher¯ = 0.70) and IBHF (MAPC = 21.7%, pvalFisher¯ = 0.83), for the pCT datasets, and LAWS (MAPC = 20.2%, pvalFisher¯ = 0.24), GLRL (MAPC = 14.5%, pvalFisher¯ = 0.44), and 2DGF (MAPC=16.3%, pvalFisher¯ = 0.52), for CBCT image datasets. For pCT datasets, features most impacted by noise were GLRL (MAPC = 29.7%, pvalFisher¯ = 0.06), LAWS (MAPC = 96.6%, pvalFisher¯ = 0.42), and GLCM (MAPC = 36.2%, pvalFisher¯ = 0.48), while the LBPF (MAPC = 5.2%, pvalFisher¯ = 0.99) was found to be relatively insensitive to noise. For CBCT datasets, GLRL (MAPC = 8.9%, pvalFisher¯ = 0.80) and LAWS (MAPC = 89.3%, pvalFisher¯ = 0.81) features were impacted by noise, while the LBPF (MAPC = 2.2%, pvalFisher¯ = 0.99) and DOST (MAPC = 13.7%, pvalFisher¯ = 0.98) features were noise insensitive. Apart from 15 features, no significant differences were observed for the remaining 150 textural features extracted from baseline pCT and CBCT image datasets (MAPC = 90.1%, pvalFisher¯ = 0.26). CONCLUSIONS Radiomics features extracted from planning CT and daily CBCT image datasets for head/neck cancer patients were robust to low-power Gaussian noise and low-pass filtering, but were impacted by high-pass filtering. Textural features extracted from CBCT and pCT image datasets were similar, suggesting interchangeability of pCT and CBCT for investigating radiomics features as possible biomarkers for outcome.
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Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, MI, USA.,Department of Physics, Oakland University, Rochester, MI, USA
| | - Farzan Siddiqui
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, MI, USA
| | - Chang Liu
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, MI, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, MI, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, MI, USA
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145
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Fave X, Zhang L, Yang J, Mackin D, Balter P, Gomez D, Followill D, Jones AK, Stingo F, Liao Z, Mohan R, Court L. Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci Rep 2017; 7:588. [PMID: 28373718 PMCID: PMC5428827 DOI: 10.1038/s41598-017-00665-z] [Citation(s) in RCA: 231] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Accepted: 03/07/2017] [Indexed: 02/06/2023] Open
Abstract
Radiomics is the use of quantitative imaging features extracted from medical images to characterize tumor pathology or heterogeneity. Features measured at pretreatment have successfully predicted patient outcomes in numerous cancer sites. This project was designed to determine whether radiomics features measured from non–small cell lung cancer (NSCLC) change during therapy and whether those features (delta-radiomics features) can improve prognostic models. Features were calculated from pretreatment and weekly intra-treatment computed tomography images for 107 patients with stage III NSCLC. Pretreatment images were used to determine feature-specific image preprocessing. Linear mixed-effects models were used to identify features that changed significantly with dose-fraction. Multivariate models were built for overall survival, distant metastases, and local recurrence using only clinical factors, clinical factors and pretreatment radiomics features, and clinical factors, pretreatment radiomics features, and delta-radiomics features. All of the radiomics features changed significantly during radiation therapy. For overall survival and distant metastases, pretreatment compactness improved the c-index. For local recurrence, pretreatment imaging features were not prognostic, while texture-strength measured at the end of treatment significantly stratified high- and low-risk patients. These results suggest radiomics features change due to radiation therapy and their values at the end of treatment may be indicators of tumor response.
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Affiliation(s)
- Xenia Fave
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA. .,The University of Texas Graduate School of Biomedical Sciences at Houston, 6767 Bertner Ave, Houston, TX, 77030, USA.
| | - Lifei Zhang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Dennis Mackin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Peter Balter
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Daniel Gomez
- Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - David Followill
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Aaron Kyle Jones
- Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Francesco Stingo
- Dipartimento Di Statistica, Informatica, Applicazioni, University of Florence, Viale Morgagni, 59, Florence, 50134, Italy
| | - Zhongxing Liao
- Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Radhe Mohan
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Laurence Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA.,The University of Texas Graduate School of Biomedical Sciences at Houston, 6767 Bertner Ave, Houston, TX, 77030, USA
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146
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Lee G, Lee HY, Ko ES, Jeong WK. Radiomics and imaging genomics in precision medicine. PRECISION AND FUTURE MEDICINE 2017. [DOI: 10.23838/pfm.2017.00101] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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147
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Yue Y, Osipov A, Fraass B, Sandler H, Zhang X, Nissen N, Hendifar A, Tuli R. Identifying prognostic intratumor heterogeneity using pre- and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients. J Gastrointest Oncol 2017; 8:127-138. [PMID: 28280617 DOI: 10.21037/jgo.2016.12.04] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND To stratify risks of pancreatic adenocarcinoma (PA) patients using pre- and post-radiotherapy (RT) PET/CT images, and to assess the prognostic value of texture variations in predicting therapy response of patients. METHODS Twenty-six PA patients treated with RT from 2011-2013 with pre- and post-treatment 18F-FDG-PET/CT scans were identified. Tumor locoregional texture was calculated using 3D kernel-based approach, and texture variations were identified by fitting discrepancies of texture maps of pre- and post-treatment images. A total of 48 texture and clinical variables were identified and evaluated for association with overall survival (OS). The prognostic heterogeneity features were selected using lasso/elastic net regression, and further were evaluated by multivariate Cox analysis. RESULTS Median age was 69 y (range, 46-86 y). The texture map and temporal variations between pre- and post-treatment were well characterized by histograms and statistical fitting. The lasso analysis identified seven predictors (age, node stage, post-RT SUVmax, variations of homogeneity, variance, sum mean, and cluster tendency). The multivariate Cox analysis identified five significant variables: age, node stage, variations of homogeneity, variance, and cluster tendency (with P=0.020, 0.040, 0.065, 0.078, and 0.081, respectively). The patients were stratified into two groups based on the risk score of multivariate analysis with log-rank P=0.001: a low risk group (n=11) with a longer mean OS (29.3 months) and higher texture variation (>30%), and a high risk group (n=15) with a shorter mean OS (17.7 months) and lower texture variation (<15%). CONCLUSIONS Locoregional metabolic texture response provides a feasible approach for evaluating and predicting clinical outcomes following treatment of PA with RT. The proposed method can be used to stratify patient risk and help select appropriate treatment strategies for individual patients toward implementing response-driven adaptive RT.
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Affiliation(s)
- Yong Yue
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Arsen Osipov
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Benedick Fraass
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Howard Sandler
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Xiao Zhang
- Department of Biostatistics and Bioinformatics, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Nicholas Nissen
- Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Andrew Hendifar
- Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Richard Tuli
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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148
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Scalco E, Rizzo G. Texture analysis of medical images for radiotherapy applications. Br J Radiol 2017; 90:20160642. [PMID: 27885836 PMCID: PMC5685100 DOI: 10.1259/bjr.20160642] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Revised: 10/27/2016] [Accepted: 11/16/2016] [Indexed: 12/29/2022] Open
Abstract
The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.
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Affiliation(s)
- Elisa Scalco
- Institute of Molecular Bioimaging and Physiology (IBFM), Italian
National Research Council (CNR), Milan, Italy
| | - Giovanna Rizzo
- Institute of Molecular Bioimaging and Physiology (IBFM), Italian
National Research Council (CNR), Milan, Italy
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149
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Huynh E, Coroller TP, Narayan V, Agrawal V, Romano J, Franco I, Parmar C, Hou Y, Mak RH, Aerts HJWL. Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT. PLoS One 2017; 12:e0169172. [PMID: 28046060 PMCID: PMC5207741 DOI: 10.1371/journal.pone.0169172] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2016] [Accepted: 12/13/2016] [Indexed: 02/06/2023] Open
Abstract
Radiomics aims to quantitatively capture the complex tumor phenotype contained in medical images to associate them with clinical outcomes. This study investigates the impact of different types of computed tomography (CT) images on the prognostic performance of radiomic features for disease recurrence in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT). 112 early stage NSCLC patients treated with SBRT that had static free breathing (FB) and average intensity projection (AIP) images were analyzed. Nineteen radiomic features were selected from each image type (FB or AIP) for analysis based on stability and variance. The selected FB and AIP radiomic feature sets had 6 common radiomic features between both image types and 13 unique features. The prognostic performances of the features for distant metastasis (DM) and locoregional recurrence (LRR) were evaluated using the concordance index (CI) and compared with two conventional features (tumor volume and maximum diameter). P-values were corrected for multiple testing using the false discovery rate procedure. None of the FB radiomic features were associated with DM, however, seven AIP radiomic features, that described tumor shape and heterogeneity, were (CI range: 0.638-0.676). Conventional features from FB images were not associated with DM, however, AIP conventional features were (CI range: 0.643-0.658). Radiomic and conventional multivariate models were compared between FB and AIP images using cross validation. The differences between the models were assessed using a permutation test. AIP radiomic multivariate models (median CI = 0.667) outperformed all other models (median CI range: 0.601-0.630) in predicting DM. None of the imaging features were prognostic of LRR. Therefore, image type impacts the performance of radiomic models in their association with disease recurrence. AIP images contained more information than FB images that were associated with disease recurrence in early stage NSCLC patients treated with SBRT, which suggests that AIP images may potentially be more optimal for the development of an imaging biomarker.
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Affiliation(s)
- Elizabeth Huynh
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
- * E-mail:
| | - Thibaud P. Coroller
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
| | - Vivek Narayan
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
| | - Vishesh Agrawal
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
| | - John Romano
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
| | - Idalid Franco
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
| | - Chintan Parmar
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
| | - Ying Hou
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
| | - Raymond H. Mak
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
| | - Hugo J. W. L. Aerts
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
- Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
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150
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Soliman A, Khalifa F, Elnakib A, Abou El-Ghar M, Dunlap N, Wang B, Gimel'farb G, Keynton R, El-Baz A. Accurate Lungs Segmentation on CT Chest Images by Adaptive Appearance-Guided Shape Modeling. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:263-276. [PMID: 27705854 DOI: 10.1109/tmi.2016.2606370] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
To accurately segment pathological and healthy lungs for reliable computer-aided disease diagnostics, a stack of chest CT scans is modeled as a sample of a spatially inhomogeneous joint 3D Markov-Gibbs random field (MGRF) of voxel-wise lung and chest CT image signals (intensities). The proposed learnable MGRF integrates two visual appearance sub-models with an adaptive lung shape submodel. The first-order appearance submodel accounts for both the original CT image and its Gaussian scale space (GSS) filtered version to specify local and global signal properties, respectively. Each empirical marginal probability distribution of signals is closely approximated with a linear combination of discrete Gaussians (LCDG), containing two positive dominant and multiple sign-alternate subordinate DGs. The approximation is separated into two LCDGs to describe individually the lungs and their background, i.e., all other chest tissues. The second-order appearance submodel quantifies conditional pairwise intensity dependencies in the nearest voxel 26-neighborhood in both the original and GSS-filtered images. The shape submodel is built for a set of training data and is adapted during segmentation using both the lung and chest appearances. The accuracy of the proposed segmentation framework is quantitatively assessed using two public databases (ISBI VESSEL12 challenge and MICCAI LOLA11 challenge) and our own database with, respectively, 20, 55, and 30 CT images of various lung pathologies acquired with different scanners and protocols. Quantitative assessment of our framework in terms of Dice similarity coefficients, 95-percentile bidirectional Hausdorff distances, and percentage volume differences confirms the high accuracy of our model on both our database (98.4±1.0%, 2.2±1.0 mm, 0.42±0.10%) and the VESSEL12 database (99.0±0.5%, 2.1±1.6 mm, 0.39±0.20%), respectively. Similarly, the accuracy of our approach is further verified via a blind evaluation by the organizers of the LOLA11 competition, where an average overlap of 98.0% with the expert's segmentation is yielded on all 55 subjects with our framework being ranked first among all the state-of-the-art techniques compared.
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