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Ditmer A, Zhang B, Shujaat T, Pavlina A, Luibrand N, Gaskill-Shipley M, Vagal A. Diagnostic accuracy of MRI texture analysis for grading gliomas. J Neurooncol 2018; 140:583-589. [PMID: 30145731 DOI: 10.1007/s11060-018-2984-4] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 08/15/2018] [Indexed: 01/14/2023]
Abstract
PURPOSE Texture analysis (TA) can quantify variations in surface intensity or patterns, including some that are imperceptible to the human visual system. The purpose of this study was to determine the diagnostic accuracy of radiomic based filtration-histogram TA to differentiate high-grade from low-grade gliomas by assessing tumor heterogeneity. METHODS Patients with a histopathological diagnosis of glioma and preoperative 3T MRI imaging were included in this retrospective study. A region of interest was manually delineated on post-contrast T1 images. TA was performed using commercially available research software. The histogram parameters including mean, standard deviation, entropy, mean of the positive pixels, skewness, and kurtosis were analyzed at spatial scaling factors ranging from 0 to 6 mm. The parameters were correlated with WHO glioma grade using Spearman correlation. Areas under the curve (AUC) were calculated using ROC curve analysis to distinguish tumor grades. RESULTS Of a total of 94 patients, 14 had low-grade gliomas and 80 had high-grade gliomas. Mean, SD, MPP, entropy and kurtosis each showed significant differences between glioma grades for different spatial scaling filters. Low and high-grade gliomas were best-discriminated using mean of 2 mm fine texture scale, with a sensitivity and specificity of 93% and 86% (AUC of 0.90). CONCLUSIONS Quantitative measurement of heterogeneity using TA can discriminate high versus low-grade gliomas. Radiomic data of texture features can provide complementary diagnostic information for gliomas.
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Affiliation(s)
- Austin Ditmer
- University of Cincinnati Medical Center, 234 Goodman Street, Cincinnati, OH, 45267, USA.
| | - Bin Zhang
- UC Department of Pediatrics, Cincinnati Children's Hospital and Medical Center, 333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Taimur Shujaat
- Department of Radiology, Ohio State University, Room 460, 395 W. 12th Avenue, Columbus, OH, 43210, USA
| | - Andrew Pavlina
- University of Cincinnati Medical Center, 234 Goodman Street, Cincinnati, OH, 45267, USA
| | - Nicholas Luibrand
- University of Cincinnati Medical Center, 234 Goodman Street, Cincinnati, OH, 45267, USA
| | - Mary Gaskill-Shipley
- University of Cincinnati Medical Center, 234 Goodman Street, Cincinnati, OH, 45267, USA
| | - Achala Vagal
- University of Cincinnati Medical Center, 234 Goodman Street, Cincinnati, OH, 45267, USA
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Single extracorporeal shock-wave lithotripsy for proximal ureter stones: Can CT texture analysis technique help predict the therapeutic effect? Eur J Radiol 2018; 107:84-89. [PMID: 30292278 DOI: 10.1016/j.ejrad.2018.08.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Revised: 08/13/2018] [Accepted: 08/21/2018] [Indexed: 01/08/2023]
Abstract
PURPOSE To explore whether the computed tomography texture analysis (CTTA) technique can help predict the curative effects of a single extracorporeal shock-wave lithotripsy (ESWL) for proximal ureteral stones. MATERIALS AND METHODS In all, 100 patients with proximal ureteral stone underwent non-enhanced multi-detector computed tomography (MDCT) before ESWL. The patients were divided into success and failure groups. Success of ESWL was defined as the patients being stone-free or having residual stone fragments of ≤2 mm. Traditional characteristics, such as stone size, body mass index (BMI), and skin-to-stone distance (SSD), and CTTA metrics, such as the mean Hounsfield unit (HU) density, entropy, kurtosis, and skewness, were analyzed and compared between two groups by univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC) curves were generated to determine Youden index-based cutoff values. RESULT Failure of stone removal was observed in 36 patients (36%). Stone height, stone cross-sectional diameter, largest cross-sectional area, stone volume, stone density (mean HU), and CTTA metrics (kurtosis and entropy) were the significant independent predictors of ESWL success on univariate analysis (p < 0.05). On multivariate analysis, mean HU, skewness, and kurtosis were shown to be significant predictors of ESWL success (p < 0.05). In subgroup analysis based on the cutoff value of mean stone density (HU = 857), the only significant independent factor associated with both subgroups was kurtosis (p < 0.05). CONCLUSIONS As a quantitative analysis method, CTTA may be helpful in selecting appropriate ESWL patients. High kurtosis and low mean HU values simultaneously indicate a relatively higher ESWL success rate.
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203
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Sandrasegaran K, Lin Y, Asare-Sawiri M, Taiyini T, Tann M. CT texture analysis of pancreatic cancer. Eur Radiol 2018; 29:1067-1073. [PMID: 30116961 DOI: 10.1007/s00330-018-5662-1] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 06/15/2018] [Accepted: 07/13/2018] [Indexed: 02/07/2023]
Abstract
OBJECTIVES We investigated the value of CT texture analysis (CTTA) in predicting prognosis of unresectable pancreatic cancer. METHODS Sixty patients with unresectable pancreatic cancers at presentation were enrolled for post-processing with CTTA using commercially available software (TexRAD Ltd, Cambridge, UK). The largest cross-section of the tumour on axial CT was chosen to draw a region-of-interest. CTTA parameters (mean value of positive pixels (MPP), kurtosis, entropy, skewness), arterial and venous invasion, metastatic disease and tumour size were correlated with overall and progression-free survivals. RESULTS The median overall and progression-free survivals of cohort were 13.3 and 7.8 months, respectively. On multivariate Cox proportional hazard regression analysis, presence of metastatic disease at presentation had the highest association with overall survival (p = 0.003-0.05) and progression-free survival (p < 0.001 to p = 0.004). MPP at medium spatial filter was significantly associated with poor overall survival (p = 0.04). On Kaplan-Meier survival analysis of CTTA parameters at medium spatial filter, MPP of more than 31.625 and kurtosis of more than 0.565 had significantly worse overall survival (p = 0.036 and 0.028, respectively). CONCLUSIONS CTTA features were significantly associated with overall survival in pancreas cancer, particularly in patients with non-metastatic, locally advanced disease. KEY POINTS • CT texture analysis is easy to perform on contrast-enhanced CT. • CT texture analysis can determine prognosis in patients with unresectable pancreas cancer. • The best predictors of poor prognosis were high kurtosis and MPP.
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Affiliation(s)
- Kumar Sandrasegaran
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA.
| | - Yuning Lin
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA.,Department of Medical Imaging, Fuzhou General Hospital, Fuzhou, China
| | - Michael Asare-Sawiri
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA.,Hope Radiation Cancer, Panama City, FL, USA
| | - Tai Taiyini
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA
| | - Mark Tann
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA
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204
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Saad M, Choi TS. Deciphering unclassified tumors of non-small-cell lung cancer through radiomics. Comput Biol Med 2018; 91:222-230. [PMID: 29100116 DOI: 10.1016/j.compbiomed.2017.10.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 10/24/2017] [Accepted: 10/24/2017] [Indexed: 01/14/2023]
Abstract
BACKGROUND Tumors are highly heterogeneous at the phenotypic, physiologic, and genomic levels. They are categorized in terms of a differentiated appearance under a microscope. Non-small-cell lung cancer tumors are categorized into three main subgroups: adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. In approximately 20% of pathology reports, they are returned unclassified or classified as not-otherwise-specified (NOS) owing to scant materials or poor tumor differentiation. METHOD We present a radiomic interrogation of molecular spatial variations to decode unclassified NOS tumor architecture quantitatively. Twelve spatial descriptors with various displacements and directions were extracted and profiled with respect to the subgroups. The profiled descriptors were used to decipher the NOS tumor morphologic clues from the imaging phenotype perspective. This profiler was built as an extended version of a conventional support-vector-machine classifier, wherein a genetic algorithm and correlation analysis were embedded to define the molecular signatures of poorly differentiated tumors using well-differentiated-tumor information. RESULTS Sixteen multi-class classifier models with an 81% average accuracy and descriptor subset size ranging from 12 to 144 were reported. The average area under the curve was 86.3% at a 95% confidence interval and a 0.03-0.08 standard error. Correlation analysis returned an unclassified NOS membership matrix with respect to the cell-architecture similarity score for the subgroups. The best model demonstrated 53% NOS reduction. CONCLUSION The membership matrix is expected to assist pathologists and oncologists in cases of unresectable tumors or scant biopsy materials for histological subtyping and cancer therapy.
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Affiliation(s)
- Maliazurina Saad
- School of Mechatronics, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, Oryong-Dong, Buk-gu, Gwangju 61005, South Korea.
| | - Tae-Sun Choi
- School of Mechatronics, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, Oryong-Dong, Buk-gu, Gwangju 61005, South Korea.
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205
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Computer-assisted subtyping and prognosis for non-small cell lung cancer patients with unresectable tumor. Comput Med Imaging Graph 2018; 67:1-8. [DOI: 10.1016/j.compmedimag.2018.04.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 03/10/2018] [Accepted: 04/02/2018] [Indexed: 11/21/2022]
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206
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Varghese BA, Chen F, Hwang DH, Cen SY, Gill IS, Duddalwar VA. Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT. Br J Radiol 2018; 91:20170789. [PMID: 29888982 DOI: 10.1259/bjr.20170789] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE To test the feasibility of two-dimensional fast Fourier transforms (FFT)-based imaging metrics in differentiating solid, non-macroscopic fat containing, enhancing renal masses using contrast-enhanced CT images. We quantify image-based intratumoral textural variations (indicator of tumor heterogeneity) using frequency-based (FFT) imaging metrics. METHODS In this Institutional Review Board approved, Health Insurance Portability and Accountability Act -compliant, retrospective case-control study, we evaluated 156 patients with predominantly solid, non-macroscopic fat containing, enhancing renal masses identified between June 2009 and June 2016. 110 cases (70%) were malignant RCC, including clear cell, papillary and chromophobe subtypes and, 46 cases (30%) were benign renal masses: oncocytoma and lipid-poor angiomyolipoma. Whole lesions were manually segmented using Synapse 3D (Fujifilm, CT) and co-registered from the multiphase CT acquisitions for each tumor. Pathological diagnosis of all tumors was obtained following surgical resection. Matlab function, FFT2 was used to perform the image to frequency transformation. RESULTS A Wilcoxon rank sum test showed that FFT-based metrics were significantly (p < 0.005) different between 1. benign vs malignant renal masses, 2. oncocytoma vs clear cell renal cell carcinoma and 3. oncocytoma vs lipid-poor angiomyolipoma. Receiver operator characteristics analysis revealed reasonable discrimination (area under the curve >0.7, p < 0.05) within these three groups of comparisons. CONCLUSION In combination with other metrics, FFT-metrics may improve patient management and potentially help differentiate other renal tumors. Advances in knowledge: We report for the first time that FFT-based metrics can differentiate between some solid, non-macroscopic fat containing, enhancing renal masses using their contrast-enhanced CT data.
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Affiliation(s)
- Bino A Varghese
- 1 Department of Radiology, University of Southern California , Los Angeles, CA , USA
| | - Frank Chen
- 1 Department of Radiology, University of Southern California , Los Angeles, CA , USA
| | - Darryl H Hwang
- 1 Department of Radiology, University of Southern California , Los Angeles, CA , USA
| | - Steven Y Cen
- 1 Department of Radiology, University of Southern California , Los Angeles, CA , USA
| | - Inderbir S Gill
- 2 Institute of Urology, University of Southern California , Los Angeles, CA , USA
| | - Vinay A Duddalwar
- 1 Department of Radiology, University of Southern California , Los Angeles, CA , USA.,2 Institute of Urology, University of Southern California , Los Angeles, CA , USA
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Reimer RP, Reimer P, Mahnken AH. Assessment of Therapy Response to Transarterial Radioembolization for Liver Metastases by Means of Post-treatment MRI-Based Texture Analysis. Cardiovasc Intervent Radiol 2018; 41:1545-1556. [PMID: 29881933 DOI: 10.1007/s00270-018-2004-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 05/28/2018] [Indexed: 12/13/2022]
Abstract
INTRODUCTION To determine whether post-treatment magnetic resonance imaging (MRI)-based texture analysis of liver metastases (LM) may be suited predicting therapy response to transarterial radioembolization (TARE) during follow-up. MATERIALS AND METHODS Thirty-seven patients with LM treated by TARE (mean age 63.4 years) between January 2006 and December 2014 were identified in this retrospective feasibility study. They underwent dynamic contrast-enhanced and hepatocellular phase MRI after TARE (mean 2.2 days). Response was evaluated on follow-up imaging scheduled in intervals of 3 months (median follow-up, 7.3 months) based on response evaluation criteria in solid tumors 1.1 (RECIST 1.1). Results of texture analysis [mean, standard deviation, skewness (s), kurtosis (k), entropy and uniformity] were compared between patients with progressive disease (PD) and patients with stable disease (SD), partial or complete response (PR/CR). Receiver operating characteristics including the area under the curve (AUC) and cutoff values including the sensitivity and specificity were calculated. RESULTS According to RECIST 1.1, 24 patients (64.9%) had PD, 8 SD (21.6%) and 5 PR (13.5%). MRI-based texture analysis showed an earlier differentiation between patients with and without PD when compared with RECIST 1.1. Median k (2.88 vs. 2.35) in arterial phase MRI and median s (0.48 vs. 0.25) and k (2.85 vs. 2.25) in venous phase MRI were significantly different (p < 0.05). The AUC for k derived from arterial phase MRI was 0.73 (cutoff = 2.55, sensitivity = 0.83, specificity = 0.62) (p < 0.05). The AUC for s and k in venous phase MRI was 0.76 (cutoff = 0.35, sensitivity = 0.71, specificity = 0.85) (p > 0.05) and 0.83 (cutoff = 2.50, sensitivity = 0.75, specificity = 0.85) (p < 0.05). CONCLUSION This study indicates the potential of MRI-based texture analysis at arterial and venous phase MRI for the early prediction of PD after TARE. LEVEL OF EVIDENCE IV.
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Affiliation(s)
- Robert P Reimer
- Department of Radiology, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany. .,Department of Diagnostic and Interventional Radiology, Marburg University Hospital, Philipps-University, Baldingerstrasse, 35043, Marburg, Germany.
| | - Peter Reimer
- Institute of Diagnostic and Interventional Radiology, Klinikum Karlsruhe, Academic Teaching Hospital of the University of Freiburg, 76133, Karlsruhe, Germany
| | - Andreas H Mahnken
- Department of Diagnostic and Interventional Radiology, Marburg University Hospital, Philipps-University, Baldingerstrasse, 35043, Marburg, Germany
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208
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Kandathil A, Kay F, Batra K, Saboo SS, Rajiah P. Advances in Computed Tomography in Thoracic Imaging. Semin Roentgenol 2018; 53:157-170. [PMID: 29861007 DOI: 10.1053/j.ro.2018.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Asha Kandathil
- Cardiothoracic Imaging, Radiology Department, UT Southwestern Medical Center, Dallas, TX
| | - Fernando Kay
- Cardiothoracic Imaging, Radiology Department, UT Southwestern Medical Center, Dallas, TX
| | - Kiran Batra
- Cardiothoracic Imaging, Radiology Department, UT Southwestern Medical Center, Dallas, TX
| | - Sachin S Saboo
- Cardiothoracic Imaging, Radiology Department, UT Southwestern Medical Center, Dallas, TX
| | - Prabhakar Rajiah
- Cardiothoracic Imaging, Radiology Department, UT Southwestern Medical Center, Dallas, TX.
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209
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Rastogi A, Maheshwari S, Shinagare AB, Baheti AD. Computed Tomography Advances in Oncoimaging. Semin Roentgenol 2018; 53:147-156. [PMID: 29861006 DOI: 10.1053/j.ro.2018.02.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Ashita Rastogi
- Department of Radiodiagnosis, Tata Memorial Centre, Mumbai, India
| | - Sharad Maheshwari
- Department of Radiology, Kokilaben Dhirubhai Ambani Hospital, Mumbai, India
| | - Atul B Shinagare
- Department of Radiology, Harvard Medical School, Dana-Farber Cancer Institute, Boston, MA
| | - Akshay D Baheti
- Department of Radiodiagnosis, Tata Memorial Centre, Mumbai, India.
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210
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Liu Y, Zhang Y, Cheng R, Liu S, Qu F, Yin X, Wang Q, Xiao B, Ye Z. Radiomics analysis of apparent diffusion coefficient in cervical cancer: A preliminary study on histological grade evaluation. J Magn Reson Imaging 2018; 49:280-290. [PMID: 29761595 DOI: 10.1002/jmri.26192] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 04/26/2018] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The role of apparent diffusion coefficient (ADC)-based radiomics features in evaluating histopathological grade of cervical cancer is unresolved. PURPOSE To determine if there is a difference between radiomics features derived from center-slice 2D versus whole-tumor volumetric 3D for ADC measurements in patients with cervical cancer regarding tumor histopathological grade, and systematically assess the impact of the b value on radiomics analysis in ADC quantifications. STUDY TYPE Prospective. SUBJECTS In all, 160 patients with histopathologically confirmed squamous cell carcinoma of uterine cervix. FIELD STRENGTH/SEQUENCE Conventional and diffusion-weighted MR images (b values = 0, 800, 1000 s/mm2 ) were acquired on a 3.0T MR scanner. ASSESSMENT Regions of interest (ROIs) were drawn manually along the margin of tumor on each slice, and then the center slice of the tumor was selected with naked eyes in the course of whole-tumor segmentation. A total of 624 radiomics features were derived from T2 -weighted images and ADC maps. We randomly selected 50 cases and did the reproducibility analysis. STATISTICAL TESTS Parameters were compared using Wilcoxon signed rank test, Bland-Altman analysis, t-test, and least absolute shrinkage and selection operator (LASSO) regression with crossvalidation. RESULTS In all, 95 radiomics features were insensitive to ROI variation among T2 images, ADC map of b800, and ADC map of b1000 (P > 0.0002). There was a significant statistical difference between the performances of 2D center-slice and 3D whole-tumor radiomics models in both ADC feature sets of b800 and b1000 (P < 0.0001, P < 0.0001). Compared with ADC features of b800 (0.3758 ± 0.0118), the model of b1000 ADC features appeared to be slightly lower in overall misclassification error (0.3642 ± 0.0162) (P = 0.0076). DATA CONCLUSION Several radiomics features extracted from T2 images and ADC maps were highly reproducible. Whole-tumor volumetric 3D radiomics analysis had a better performance than using the 2D center-slice of tumor in stratifying the histological grade of cervical cancer. A b value of 1000 s/mm2 is suggested as the optimal parameter in pelvic DWI scans. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:280-290.
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Affiliation(s)
- Ying Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Yuwei Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China.,School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Runfen Cheng
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Shichang Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Fangyuan Qu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Xiaoyu Yin
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Qin Wang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Bohan Xiao
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
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211
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Yun G, Kim YH, Lee YJ, Kim B, Hwang JH, Choi DJ. Tumor heterogeneity of pancreas head cancer assessed by CT texture analysis: association with survival outcomes after curative resection. Sci Rep 2018; 8:7226. [PMID: 29740111 PMCID: PMC5940761 DOI: 10.1038/s41598-018-25627-x] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 04/25/2018] [Indexed: 12/12/2022] Open
Abstract
The value of image based texture features as a powerful method to predict prognosis and assist clinical management in cancer patients has been established recently. However, texture analysis using histograms and grey-level co-occurrence matrix in pancreas cancer patients has rarely been reported. We aimed to analyze the association of survival outcomes with texture features in pancreas head cancer patients. Eighty-eight pancreas head cancer patients who underwent preoperative CT images followed by curative resection were included. Texture features using different filter values were obtained. The texture features of average, contrast, correlation, and standard deviation with no filter, and fine to medium filter values as well as the presence of nodal metastasis were significantly different between the recurred (n = 70, 79.5%) and non-recurred group (n = 18, 20.5%). In the multivariate Cox regression analysis, lower standard deviation and contrast and higher correlation with lower average value representing homogenous texture were significantly associated with poorer DFS (disease free survival), along with the presence of lymph node metastasis. Texture parameters from routinely performed pre-operative CT images could be used as an independent imaging tool for predicting the prognosis in pancreas head cancer patients who underwent curative resection.
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Affiliation(s)
- Gabin Yun
- Seoul National University Bundang Hospital, Department of Radiology, Seongnam, 13620, Korea
| | - Young Hoon Kim
- Seoul National University Bundang Hospital, Department of Radiology, Seongnam, 13620, Korea.
| | - Yoon Jin Lee
- Seoul National University Bundang Hospital, Department of Radiology, Seongnam, 13620, Korea
| | - Bohyoung Kim
- Seoul National University Bundang Hospital, Department of Radiology, Seongnam, 13620, Korea.,Hankuk University of Foreign Studies, Division of Biomedical Engineering, Yongin, 17035, Korea
| | - Jin-Hyeok Hwang
- Seoul National University Bundang Hospital, Department of Internal Medicine, Seongnam, 13620, Korea
| | - Dong Joon Choi
- Seoul National University Bundang Hospital, Department of Radiology, Seongnam, 13620, Korea
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Beckers R, Trebeschi S, Maas M, Schnerr R, Sijmons J, Beets G, Houwers J, Beets-Tan R, Lambregts D. CT texture analysis in colorectal liver metastases and the surrounding liver parenchyma and its potential as an imaging biomarker of disease aggressiveness, response and survival. Eur J Radiol 2018; 102:15-21. [DOI: 10.1016/j.ejrad.2018.02.031] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 01/10/2018] [Accepted: 02/26/2018] [Indexed: 12/20/2022]
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213
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Bier G, Bier S, Bongers MN, Othman A, Ernemann U, Hempel JM. Value of computed tomography texture analysis for prediction of perioperative complications during laparoscopic partial nephrectomy in patients with renal cell carcinoma. PLoS One 2018; 13:e0195270. [PMID: 29668695 PMCID: PMC5905959 DOI: 10.1371/journal.pone.0195270] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Accepted: 03/19/2018] [Indexed: 01/20/2023] Open
Abstract
Purpose Tumorous texture is a marker for tumor tissue inhomogeneity. Based on this assumption, this study aims to evaluate the value of computed tomography texture analysis for imaging-based prediction of perioperative complications during laparoscopic partial tumor nephrectomy. Methods A total of 106 patients with histologically confirmed renal cell carcinoma and pre-operative CT were included and volumetric texture analysis of the tumors was performed by two readers. Texture analysis parameter ratios and differences were calculated using the kidney parenchyma as reference (“reference-corrected”). Regression analysis was performed, regarding the value of the texture analysis parameters, for assessment of the tumor nuclear grade and the prediction of peri- and postoperative complications and approximated blood loss. Moreover, the inter-rater agreement in terms of the intra-class correlation coefficient (ICC) was calculated. Results Regarding the reference-corrected values, the predictive value of texture analysis parameters for severe perioperative complications was highest for the standard deviation of the mean attenuation (Area under curve/AUC, .615; sensitivity, 93.8%, specificity, 30.0%), followed by the uniformity (AUC, .599; sensitivity, 62.5%, specificity, 60.0%), and the uniformity of distribution of positive pixels (AUC, .597; sensitivity, 62.5%; specificity, 61.1%). Regarding the blood loss, the uniformity of positive pixel values (UPP; AUC, 0.638), uniformity (AUC, 0.635), and entropy (AUC, 0.633) yielded the best predictive values, whilst the tumor grade was a weaker predictor (AUC, 0.574). The applied texture analysis parameters did not correlate with the time of surgery or the warm ischemic time. All measured parameters were better predictors for complications than the tumor diameter alone. The inter-rater agreement was almost perfect (ICC, .982). Conclusion CT and CT texture analysis parameters are valuable for prediction of perioperative outcome before laparoscopic partial nephrectomy in patients with renal cell carcinoma.
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Affiliation(s)
- Georg Bier
- Department of Neuroradiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany
- * E-mail:
| | - Simone Bier
- Department of Urology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany
| | - Malte Niklas Bongers
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany
| | - Ahmed Othman
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany
| | - Ulrike Ernemann
- Department of Neuroradiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany
| | - Johann-Martin Hempel
- Department of Neuroradiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany
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Choi TW, Kim JH, Yu MH, Park SJ, Han JK. Pancreatic neuroendocrine tumor: prediction of the tumor grade using CT findings and computerized texture analysis. Acta Radiol 2018; 59:383-392. [PMID: 28766979 DOI: 10.1177/0284185117725367] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background Pancreatic neuroendocrine tumors (PNET) include heterogeneous tumors with a variable degree of inherent biologic aggressiveness represented by the histopathologic grade. Although several studies investigated the computed tomography (CT) characteristics which can predict the histopathologic grade of PNET, accurate prediction of the PNET grade by CT examination alone is still limited. Purpose To investigate the important CT findings and CT texture variables for prediction of grade of PNET. Material and Methods Sixty-six patients with pathologically confirmed PNETs (grade 1 = 45, grades 2/3 = 21) underwent preoperative contrast-enhanced CT. Two reviewers determined the presence of predefined CT findings. CT texture was also analyzed on arterial and portal phase using both two-dimensional (2D) and three-dimensional (3D) analysis. Multivariate logistic regression analysis was performed in order to identify significant predictors for tumor grade. Results Among CT findings and CT texture variables, the significant predictors for grade 2/3 tumors were an ill-defined margin (odds ratio [OR] = 7.273), lower sphericity (OR = 0.409) on arterial 2D analysis, higher skewness (OR = 1.972) and lower sphericity (OR = 0.408) on arterial 3D analysis, lower kurtosis (OR = 0.436) and lower sphericity (OR = 0.420) on portal 2D analysis, and a larger surface area (OR = 2.007) and lower sphericity (OR = 0.503) on portal 3D analysis ( P < 0.05). Diagnostic performance of texture analysis was superior to CT findings (AUC = 0.774 vs. 0.683). Conclusion CT is useful for predicting grade 2/3 PNET using not only the imaging findings including an ill-defined margin, but also the CT texture variables such as lower sphericity, higher skewness, and lower kurtosis.
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Affiliation(s)
- Tae Won Choi
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jung Hoon Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Mi Hye Yu
- Department of Radiology, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Sang Joon Park
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Cancer Research Institute, Seoul National University, Seoul, Republic of Korea
| | - Joon Koo Han
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
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Talwar A, Willaime J, Pickup L, Enescu M, Boukerroui D, Hickes W, Rahman N, Gooding M, Kadir T, Gleeson F. Pulmonary nodules: Assessing the imaging biomarkers of malignancy in a “coffee-break”. Eur J Radiol 2018; 101:82-86. [DOI: 10.1016/j.ejrad.2018.02.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Revised: 01/03/2018] [Accepted: 02/06/2018] [Indexed: 11/16/2022]
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Mulé S, Thiefin G, Costentin C, Durot C, Rahmouni A, Luciani A, Hoeffel C. Advanced Hepatocellular Carcinoma: Pretreatment Contrast-enhanced CT Texture Parameters as Predictive Biomarkers of Survival in Patients Treated with Sorafenib. Radiology 2018; 288:445-455. [PMID: 29584597 DOI: 10.1148/radiol.2018171320] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Purpose To determine whether texture features on pretreatment contrast material-enhanced computed tomographic (CT) images can help predict overall survival (OS) and time to progression (TTP) in patients with advanced hepatocellular carcinoma (HCC) treated with sorafenib. Materials and Methods This retrospective study included 92 patients with advanced HCC treated with sorafenib between January 2009 and April 2015 at two independent university hospitals. Sixty-four of the 92 patients (70%) (six women, 58 men; median age, 66 years) were included from institution 1 and constituted a training cohort; 28 patients (30%) (five women, 23 men; median age, 64 years) were included from institution 2 and constituted a validation cohort. Pretreatment CT texture analysis was performed on late arterial and portal venous phase HCC images. Mean gray-level intensity, entropy, kurtosis, skewness, and standard deviation values were derived from the pixel distribution histogram before and after spatial filtration at different anatomic scales ranging from fine to coarse texture. Lesion heterogeneity was also visually graded on a 4-point scale. Correlations between visual analysis and texture parameters were assessed with the Spearman rank correlation. Univariate Kaplan-Meier and multivariate Cox proportional hazards regression analyses were performed in the training cohort to identify independent predictors of OS and TTP. Their predictive capacity was tested on the validation cohort by using Kaplan-Meier analysis. Results Visual analysis of tumor heterogeneity correlated with entropy at both arterial (P = .012) and portal venous (P = .038) phases. Portal phase-derived entropy at fine (hazard ratio [HR], 5.08; P = .0033), medium (HR, 2.23; P = .019), and coarse (HR, 2.26; P = .0032) texture scales was identified as an independent predictor of OS and confirmed in the validation cohort (P < .05). The difference in median survival between patients in the validation cohort with entropy values below and above the identified threshold was 272 days (with fine texture) and 741 days (with medium and coarse textures). Arterial phase-derived texture parameters (P > .085) and visual analysis (P > .11) were not associated with changes in survival. Conclusion Pretreatment portal venous phase-derived tumor entropy may be a predictor of survival in patients with advanced HCC treated with sorafenib.
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Affiliation(s)
- Sébastien Mulé
- From the Service d'Imagerie Médicale (S.M., C.D., C.H.) and Service d'Hépato-Gastro-Entérologie et Cancérologie Digestive (G.T.), CHU Reims, 45 Rue Cognacq Jay, 51092 Reims, France; Service d'Hépatologie (C.C.) and Service d'Imagerie Médicale (A.R., A.L.), AP-HP, Hôpitaux Universitaires Henri Mondor, 51 Avenue du Maréchal de Lattre de Tassigny, 94010 Créteil Cedex, France; Faculté de Médecine, Université Paris Est Créteil, Créteil, France (A.R., A.L.); and CRESTIC, Université de Reims Champagne-Ardenne, Reims, France (C.H.)
| | - Gérard Thiefin
- From the Service d'Imagerie Médicale (S.M., C.D., C.H.) and Service d'Hépato-Gastro-Entérologie et Cancérologie Digestive (G.T.), CHU Reims, 45 Rue Cognacq Jay, 51092 Reims, France; Service d'Hépatologie (C.C.) and Service d'Imagerie Médicale (A.R., A.L.), AP-HP, Hôpitaux Universitaires Henri Mondor, 51 Avenue du Maréchal de Lattre de Tassigny, 94010 Créteil Cedex, France; Faculté de Médecine, Université Paris Est Créteil, Créteil, France (A.R., A.L.); and CRESTIC, Université de Reims Champagne-Ardenne, Reims, France (C.H.)
| | - Charlotte Costentin
- From the Service d'Imagerie Médicale (S.M., C.D., C.H.) and Service d'Hépato-Gastro-Entérologie et Cancérologie Digestive (G.T.), CHU Reims, 45 Rue Cognacq Jay, 51092 Reims, France; Service d'Hépatologie (C.C.) and Service d'Imagerie Médicale (A.R., A.L.), AP-HP, Hôpitaux Universitaires Henri Mondor, 51 Avenue du Maréchal de Lattre de Tassigny, 94010 Créteil Cedex, France; Faculté de Médecine, Université Paris Est Créteil, Créteil, France (A.R., A.L.); and CRESTIC, Université de Reims Champagne-Ardenne, Reims, France (C.H.)
| | - Carole Durot
- From the Service d'Imagerie Médicale (S.M., C.D., C.H.) and Service d'Hépato-Gastro-Entérologie et Cancérologie Digestive (G.T.), CHU Reims, 45 Rue Cognacq Jay, 51092 Reims, France; Service d'Hépatologie (C.C.) and Service d'Imagerie Médicale (A.R., A.L.), AP-HP, Hôpitaux Universitaires Henri Mondor, 51 Avenue du Maréchal de Lattre de Tassigny, 94010 Créteil Cedex, France; Faculté de Médecine, Université Paris Est Créteil, Créteil, France (A.R., A.L.); and CRESTIC, Université de Reims Champagne-Ardenne, Reims, France (C.H.)
| | - Alain Rahmouni
- From the Service d'Imagerie Médicale (S.M., C.D., C.H.) and Service d'Hépato-Gastro-Entérologie et Cancérologie Digestive (G.T.), CHU Reims, 45 Rue Cognacq Jay, 51092 Reims, France; Service d'Hépatologie (C.C.) and Service d'Imagerie Médicale (A.R., A.L.), AP-HP, Hôpitaux Universitaires Henri Mondor, 51 Avenue du Maréchal de Lattre de Tassigny, 94010 Créteil Cedex, France; Faculté de Médecine, Université Paris Est Créteil, Créteil, France (A.R., A.L.); and CRESTIC, Université de Reims Champagne-Ardenne, Reims, France (C.H.)
| | - Alain Luciani
- From the Service d'Imagerie Médicale (S.M., C.D., C.H.) and Service d'Hépato-Gastro-Entérologie et Cancérologie Digestive (G.T.), CHU Reims, 45 Rue Cognacq Jay, 51092 Reims, France; Service d'Hépatologie (C.C.) and Service d'Imagerie Médicale (A.R., A.L.), AP-HP, Hôpitaux Universitaires Henri Mondor, 51 Avenue du Maréchal de Lattre de Tassigny, 94010 Créteil Cedex, France; Faculté de Médecine, Université Paris Est Créteil, Créteil, France (A.R., A.L.); and CRESTIC, Université de Reims Champagne-Ardenne, Reims, France (C.H.)
| | - Christine Hoeffel
- From the Service d'Imagerie Médicale (S.M., C.D., C.H.) and Service d'Hépato-Gastro-Entérologie et Cancérologie Digestive (G.T.), CHU Reims, 45 Rue Cognacq Jay, 51092 Reims, France; Service d'Hépatologie (C.C.) and Service d'Imagerie Médicale (A.R., A.L.), AP-HP, Hôpitaux Universitaires Henri Mondor, 51 Avenue du Maréchal de Lattre de Tassigny, 94010 Créteil Cedex, France; Faculté de Médecine, Université Paris Est Créteil, Créteil, France (A.R., A.L.); and CRESTIC, Université de Reims Champagne-Ardenne, Reims, France (C.H.)
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Tu SJ, Wang CW, Pan KT, Wu YC, Wu CT. Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening. Phys Med Biol 2018; 63:065005. [PMID: 29446758 DOI: 10.1088/1361-6560/aaafab] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clinical trials of lung cancer screening have shown that the false-positive rate is high and positive predictive value is low. To address these problems, a technical approach is greatly needed for accurate malignancy differentiation among these early-detected nodules. We studied the clinical feasibility of an additional protocol of localized thin-section CT for further assessment on recalled patients from lung cancer screening tests. Our approach of localized thin-section CT was integrated with radiomics features extraction and machine learning classification which was supervised by pathological diagnosis. Localized thin-section CT images of 122 nodules were retrospectively reviewed and 374 radiomics features were extracted. In this study, 48 nodules were benign and 74 malignant. There were nine patients with multiple nodules and four with synchronous multiple malignant nodules. Different machine learning classifiers with a stratified ten-fold cross-validation were used and repeated 100 times to evaluate classification accuracy. Of the image features extracted from the thin-section CT images, 238 (64%) were useful in differentiating between benign and malignant nodules. These useful features include CT density (p = 0.002 518), sigma (p = 0.002 781), uniformity (p = 0.032 41), and entropy (p = 0.006 685). The highest classification accuracy was 79% by the logistic classifier. The performance metrics of this logistic classification model was 0.80 for the positive predictive value, 0.36 for the false-positive rate, and 0.80 for the area under the receiver operating characteristic curve. Our approach of direct risk classification supervised by the pathological diagnosis with localized thin-section CT and radiomics feature extraction may support clinical physicians in determining truly malignant nodules and therefore reduce problems in lung cancer screening.
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Affiliation(s)
- Shu-Ju Tu
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Tao-Yuan, Taiwan. Department of Medical Imaging and Intervention, Linkuo Chang Gung Memorial Hospital, Tao-Yuan, Taiwan
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Cai WL, Hong GB. Quantitative image analysis for evaluation of tumor response in clinical oncology. Chronic Dis Transl Med 2018; 4:18-28. [PMID: 29756120 PMCID: PMC5938243 DOI: 10.1016/j.cdtm.2018.01.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Indexed: 12/13/2022] Open
Abstract
The objective, accurate, and standardized evaluation of tumor response to treatment is an indispensable procedure in clinical oncology. Compared to manual measurement, computer-assisted linear measurement can significantly improve the accuracy and reproducibility of tumor burden quantification. For irregular-shaped and infiltrating or diffuse tumors, which are difficult to quantify by linear measurement, computer-assisted volumetric measurement may provide a more objective and sensitive quantification to evaluate tumor response to treatment than linear measurement does. In the evaluation of tumor response to novel oncologic treatments such as targeted therapy, changes in overall tumor size do not necessarily reflect tumor response to therapy due to the presence of internal necrosis or hemorrhages. This leads to a new generation of imaging biomarkers to evaluate tumor response by using texture analysis methods, also called radiomics. Computer-assisted texture analysis technology offers a more comprehensive and in-depth imaging biomarker to evaluate tumor response. The application of computer-assisted quantitative imaging analysis techniques not only reduces the inaccuracy and improves the reliability in tumor burden quantification, but facilitates the development of more comprehensive and intelligent approaches to evaluate treatment response, and hence promotes precision imaging in the evaluation of tumor response in clinical oncology. This article summarizes the state-of-the-art technical developments and clinical applications of quantitative imaging analysis in evaluation of tumor response in clinical oncology.
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Affiliation(s)
- Wen-Li Cai
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Guo-Bin Hong
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong 519000, China
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219
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Quantitative CT density histogram values and standardized uptake values of FDG-PET/CT with respiratory gating can distinguish solid adenocarcinomas from squamous cell carcinomas of the lung. Eur J Radiol 2018; 100:108-115. [DOI: 10.1016/j.ejrad.2018.01.021] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2017] [Revised: 01/14/2018] [Accepted: 01/18/2018] [Indexed: 01/22/2023]
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220
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Cohen JG, Reymond E, Medici M, Lederlin M, Lantuejoul S, Laurent F, Toffart AC, Moreau-Gaudry A, Jankowski A, Ferretti GR. CT-texture analysis of subsolid nodules for differentiating invasive from in-situ and minimally invasive lung adenocarcinoma subtypes. Diagn Interv Imaging 2018; 99:291-299. [PMID: 29477490 DOI: 10.1016/j.diii.2017.12.013] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Revised: 12/11/2017] [Accepted: 12/21/2017] [Indexed: 12/17/2022]
Abstract
PURPOSE The purpose of this study was to evaluate the usefulness of computed tomography-texture analysis (CTTA) in differentiating between in-situ and minimally-invasive from invasive adenocarcinomas in subsolid lung nodules (SSLNs). MATERIAL AND METHODS Two radiologists retrospectively reviewed 49 SSLNs in 44 patients. There were 27 men and 17 women with a mean age of 63±7 (SD) years (range: 47-78years). For each SSLN, type (pure ground-glass or part-solid) was assessed by consensus and CTTA was conducted independently by each observer using a filtration-histogram technique. Different filters were used before histogram quantification: no filtration, fine, medium and coarse, followed by histogram quantification using mean intensity, standard deviation (SD), entropy, mean positive pixels (MPP), skewness and kurtosis. RESULTS We analyzed 13 pure ground-glass and 36 part-solid nodules corresponding to 16 adenocarcinomas in-situ (AIS), 5 minimally invasive adenocarcinomas (MIA) and 28 invasive adenocarcinomas (IVA). At uni- and multivariate analysis CTTA allowed discriminating between IVAs and AIS/MIA (P<0.05 and P=0.025, respectively) with the following histogram parameters: skewness using fine textures and kurtosis using coarse filtration for pure ground-glass nodules, and SD without filtration for part-solid nodules. CONCLUSION CTTA has the potential to differentiate AIS and MIA from IVA among SSLNs. However, our results require further validation on a larger cohort.
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Affiliation(s)
- J G Cohen
- Radiology department, Grenoble Alpes University Teaching Hospital, CS 10217, 38043 Grenoble cedex 9, France.
| | - E Reymond
- Radiology department, Grenoble Alpes University Teaching Hospital, CS 10217, 38043 Grenoble cedex 9, France
| | - M Medici
- Clinical Investigation Center for Innovative Technology (CICIT), Grenoble Alpes University Teaching Hospital, CS 10217, 38043 Grenoble cedex 9, France
| | - M Lederlin
- Department of Medical Imaging, Haut-Lévêque Teaching Hospital, 33000 Bordeaux, France
| | - S Lantuejoul
- Pathology department, Grenoble Alpes University Teaching Hospital, CS 10217, 38043 Grenoble cedex 9, France; INSERM research unit 823, Albert Bonniot Institute, 38700 La Tronche, France
| | - F Laurent
- Department of Medical Imaging, Haut-Lévêque Teaching Hospital, 33000 Bordeaux, France
| | - A C Toffart
- INSERM research unit 823, Albert Bonniot Institute, 38700 La Tronche, France; Pneumology department, Grenoble Alpes University Teaching Hospital, CS 10217, 38043 Grenoble cedex 9, France
| | - A Moreau-Gaudry
- Clinical Investigation Center for Innovative Technology (CICIT), Grenoble Alpes University Teaching Hospital, CS 10217, 38043 Grenoble cedex 9, France
| | - A Jankowski
- Radiology department, Grenoble Alpes University Teaching Hospital, CS 10217, 38043 Grenoble cedex 9, France
| | - G R Ferretti
- Radiology department, Grenoble Alpes University Teaching Hospital, CS 10217, 38043 Grenoble cedex 9, France; Pneumology department, Grenoble Alpes University Teaching Hospital, CS 10217, 38043 Grenoble cedex 9, France
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Pak LM, Chakraborty J, Gonen M, Chapman WC, Do RKG, Groot Koerkamp B, Verhoef K, Lee SY, Massani M, van der Stok EP, Simpson AL. Quantitative Imaging Features and Postoperative Hepatic Insufficiency: A Multi-Institutional Expanded Cohort. J Am Coll Surg 2018; 226:835-843. [PMID: 29454098 DOI: 10.1016/j.jamcollsurg.2018.02.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 11/22/2017] [Accepted: 02/06/2018] [Indexed: 02/08/2023]
Abstract
BACKGROUND Post-hepatectomy liver insufficiency (PHLI) is a significant cause of morbidity and mortality after liver resection. Quantitative imaging analysis using CT scans measures variations in pixel intensity related to perfusion. A preliminary study demonstrated a correlation between quantitative imaging features of the future liver remnant (FLR) parenchyma from preoperative CT scans and PHLI. The objective of this study was to explore the potential application of quantitative imaging analysis in PHLI in an expanded, multi-institutional cohort. STUDY DESIGN We retrospectively identified patients from 5 high-volume academic centers who developed PHLI after major hepatectomy, and matched them to control patients without PHLI (by extent of resection, preoperative chemotherapy treatment, age [±5 years], and sex). Quantitative imaging features were extracted from the FLR in the preoperative CT scan, and the most discriminatory features were identified using conditional logistic regression. Percent remnant liver volume (RLV) was defined as follows: (FLR volume)/(total liver volume) × 100. Significant clinical and imaging features were combined in a multivariate analysis using conditional logistic regression. RESULTS From 2000 to 2015, 74 patients with PHLI and 74 matched controls were identified. The most common indications for surgery were colorectal liver metastases (53%), hepatocellular carcinoma (37%), and cholangiocarcinoma (9%). Two CT imaging features (FD1_4: image complexity; ACM1_10: spatial distribution of pixel intensity) were strongly associated with PHLI and remained associated with PHLI on multivariate analysis (p = 0.018 and p = 0.023, respectively), independent of clinical variables, including preoperative bilirubin and %RLV. CONCLUSIONS Quantitative imaging features are independently associated with PHLI and are a promising preoperative risk stratification tool.
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Affiliation(s)
- Linda M Pak
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Mithat Gonen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - William C Chapman
- Department of Surgery, Washington University in St Louis, St Louis, MO
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Kees Verhoef
- Department of Surgery, Erasmus Medical Center, Rotterdam, Netherlands
| | - Ser Yee Lee
- Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital, Singapore; Duke-National University of Singapore Medical School, Singapore
| | - Marco Massani
- Regional Center for HPB Surgery, Regional Hospital of Treviso, Treviso, Italy
| | | | - Amber L Simpson
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY.
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Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis. Eur Radiol 2018; 28:3050-3058. [PMID: 29404772 DOI: 10.1007/s00330-017-5270-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 12/10/2017] [Accepted: 12/20/2017] [Indexed: 12/20/2022]
Abstract
OBJECTIVES To determine if texture analysis of non-contrast-enhanced CT (NECT) images is able to predict nonalcoholic steatohepatitis (NASH). METHODS NECT images from 88 patients who underwent a liver biopsy for the diagnosis of suspected NASH were assessed and texture feature parameters were obtained without and with filtration. The patient population was divided into a predictive learning dataset and a validation dataset, and further divided into groups according to the prediction of liver fibrosis as assessed by hyaluronic acid levels. The reference standard was the histological result of a liver biopsy. A predictive model for NASH was developed using parameters derived from the learning dataset that demonstrated areas under the receiver operating characteristic curve (AUC) of >0.65. The resulting model was then applied to the validation dataset. RESULTS In patients without suspected fibrosis, the texture parameter mean without filter and skewness with a 2-mm filter were selected for the NASH prediction model. The AUC of the predictive model for the validation dataset was 0.94 and the accuracy was 94%. In patients with suspicion of fibrosis, the mean without filtration and kurtosis with a 4-mm filter were selected for the NASH prediction model. The AUC for the validation dataset was 0.60 and the accuracy was 42%. CONCLUSIONS In patients without suspicion of fibrosis, NECT texture analysis effectively predicted NASH. KEY POINTS • In patients without suspicion of fibrosis, NECT texture analysis effectively predicted NASH. • The mean without filtration and skewness with a 2-mm filter were modest predictors of NASH in patients without suspicion of liver fibrosis. • Hepatic fibrosis masks the characteristic texture features of NASH.
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Silva M, Milanese G, Seletti V, Ariani A, Sverzellati N. Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications. Br J Radiol 2018; 91:20170644. [PMID: 29172671 PMCID: PMC5965469 DOI: 10.1259/bjr.20170644] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Revised: 11/14/2017] [Accepted: 11/23/2017] [Indexed: 12/14/2022] Open
Abstract
The frenetic development of imaging technology-both hardware and software-provides exceptional potential for investigation of the lung. In the last two decades, CT was exploited for detailed characterization of pulmonary structures and description of respiratory disease. The introduction of volumetric acquisition allowed increasingly sophisticated analysis of CT data by means of computerized algorithm, namely quantitative CT (QCT). Hundreds of thousands of CTs have been analysed for characterization of focal and diffuse disease of the lung. Several QCT metrics were developed and tested against clinical, functional and prognostic descriptors. Computer-aided detection of nodules, textural analysis of focal lesions, densitometric analysis and airway segmentation in obstructive pulmonary disease and textural analysis in interstitial lung disease are the major chapters of this discipline. The validation of QCT metrics for specific clinical and investigational needs prompted the translation of such metrics from research field to patient care. The present review summarizes the state of the art of QCT in both focal and diffuse lung disease, including a dedicated discussion about application of QCT metrics as parameters for clinical care and outcomes in clinical trials.
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Affiliation(s)
- Mario Silva
- Department of Medicine and Surgery (DiMeC), Section of Radiology, Unit of Surgical Sciences, University of Parma, Parma, Italy
| | - Gianluca Milanese
- Department of Medicine and Surgery (DiMeC), Section of Radiology, Unit of Surgical Sciences, University of Parma, Parma, Italy
| | - Valeria Seletti
- Department of Medicine and Surgery (DiMeC), Section of Radiology, Unit of Surgical Sciences, University of Parma, Parma, Italy
| | - Alarico Ariani
- Department of Medicine, Internal Medicine and Rheumatology Unit, University Hospital of Parma, Parma, Italy
| | - Nicola Sverzellati
- Department of Medicine and Surgery (DiMeC), Section of Radiology, Unit of Surgical Sciences, University of Parma, Parma, Italy
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Abstract
Liver fibrosis is an important pathological precondition for hepatocellular carcinoma. The degree of hepatic fibrosis is positively correlated with liver cancer. Liver fibrosis is a series of pathological and physiological process related to liver cell necrosis and degeneration after chronic liver injury, which finally leads to extracellular matrix and collagen deposition. The early detection and precise staging of fibrosis and cirrhosis are very important for early diagnosis and timely initiation of appropriate therapeutic regimens. The risk of severe liver fibrosis finally progressing to liver carcinoma is >50%. It is known that biopsy is the gold standard for the diagnosis and staging of liver fibrosis. However, this method has some limitations, such as the potential for pain, sampling variability, and low patient acceptance. Furthermore, the necessity of obtaining a tissue diagnosis of liver fibrosis still remains controversial. An increasing number of reliable non-invasive approaches are now available that are widely applied in clinical practice, mostly in cases of viral hepatitis, resulting in a significantly decreased need for liver biopsy. In fact, the non-invasive detection and evaluation of liver cirrhosis now has good accuracy due to current serum markers, ultrasound imaging, and magnetic resonance imaging quantification techniques. A prominent advantage of the non-invasive detection and assessment of liver fibrosis is that liver fibrosis can be monitored repeatedly and easily in the same patient. Serum biomarkers have the advantages of high applicability (>95%) and good reproducibility. However, their results can be influenced by different patient conditions because none of these markers are liver-specific. The most promising techniques appear to be transient elastography and magnetic resonance elastography because they provide reliable results for the detection of fibrosis in the advanced stages, and future developments promise to increase the reliability and accuracy of the staging of hepatic fibrosis. This article aims to describe the recent progress in the development of non-invasive assessment methods for the staging of liver fibrosis, with a special emphasize on computer-aided quantitative and deep learning methods.
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Affiliation(s)
- Chengxi Li
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Rentao Li
- Department of Hepatobiliary Surgery, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Wei Zhang
- Department of Hepatobiliary Surgery, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
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Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. Radiographics 2017; 37:1483-1503. [PMID: 28898189 DOI: 10.1148/rg.2017170056] [Citation(s) in RCA: 531] [Impact Index Per Article: 75.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This review discusses potential oncologic and nononcologic applications of CT texture analysis ( CTTA CT texture analysis ), an emerging area of "radiomics" that extracts, analyzes, and interprets quantitative imaging features. CTTA CT texture analysis allows objective assessment of lesion and organ heterogeneity beyond what is possible with subjective visual interpretation and may reflect information about the tissue microenvironment. CTTA CT texture analysis has shown promise in lesion characterization, such as differentiating benign from malignant or more biologically aggressive lesions. Pretreatment CT texture features are associated with histopathologic correlates such as tumor grade, tumor cellular processes such as hypoxia or angiogenesis, and genetic features such as KRAS or epidermal growth factor receptor (EGFR) mutation status. In addition, and likely as a result, these CT texture features have been linked to prognosis and clinical outcomes in some tumor types. CTTA CT texture analysis has also been used to assess response to therapy, with decreases in tumor heterogeneity generally associated with pathologic response and improved outcomes. A variety of nononcologic applications of CTTA CT texture analysis are emerging, particularly quantifying fibrosis in the liver and lung. Although CTTA CT texture analysis seems to be a promising imaging biomarker, there is marked variability in methods, parameters reported, and strength of associations with biologic correlates. Before CTTA CT texture analysis can be considered for widespread clinical implementation, standardization of tumor segmentation and measurement techniques, image filtration and postprocessing techniques, and methods for mathematically handling multiple tumors and time points is needed, in addition to identification of key texture parameters among hundreds of potential candidates, continued investigation and external validation of histopathologic correlates, and structured reporting of findings. ©RSNA, 2017.
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Affiliation(s)
- Meghan G Lubner
- From the Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Sciences Center, 600 Highland Ave, Madison, WI 35792 (M.G.L., P.J.P.); Department of Radiology, University of Mississippi Medical Center, Jackson, Miss (A.D.S.); Department of Radiology, Indiana University School of Medicine, Indianapolis, Ind (K.S.); and Department of Radiology, Harvard Medical School, Boston, Mass (D.V.S.)
| | - Andrew D Smith
- From the Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Sciences Center, 600 Highland Ave, Madison, WI 35792 (M.G.L., P.J.P.); Department of Radiology, University of Mississippi Medical Center, Jackson, Miss (A.D.S.); Department of Radiology, Indiana University School of Medicine, Indianapolis, Ind (K.S.); and Department of Radiology, Harvard Medical School, Boston, Mass (D.V.S.)
| | - Kumar Sandrasegaran
- From the Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Sciences Center, 600 Highland Ave, Madison, WI 35792 (M.G.L., P.J.P.); Department of Radiology, University of Mississippi Medical Center, Jackson, Miss (A.D.S.); Department of Radiology, Indiana University School of Medicine, Indianapolis, Ind (K.S.); and Department of Radiology, Harvard Medical School, Boston, Mass (D.V.S.)
| | - Dushyant V Sahani
- From the Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Sciences Center, 600 Highland Ave, Madison, WI 35792 (M.G.L., P.J.P.); Department of Radiology, University of Mississippi Medical Center, Jackson, Miss (A.D.S.); Department of Radiology, Indiana University School of Medicine, Indianapolis, Ind (K.S.); and Department of Radiology, Harvard Medical School, Boston, Mass (D.V.S.)
| | - Perry J Pickhardt
- From the Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Sciences Center, 600 Highland Ave, Madison, WI 35792 (M.G.L., P.J.P.); Department of Radiology, University of Mississippi Medical Center, Jackson, Miss (A.D.S.); Department of Radiology, Indiana University School of Medicine, Indianapolis, Ind (K.S.); and Department of Radiology, Harvard Medical School, Boston, Mass (D.V.S.)
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Kloth C, Blum AC, Thaiss WM, Preibsch H, Ditt H, Grimmer R, Fritz J, Nikolaou K, Bösmüller H, Horger M. Differences in Texture Analysis Parameters Between Active Alveolitis and Lung Fibrosis in Chest CT of Patients with Systemic Sclerosis: A Feasibility Study. Acad Radiol 2017; 24:1596-1603. [PMID: 28807589 DOI: 10.1016/j.acra.2017.07.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Revised: 07/04/2017] [Accepted: 07/05/2017] [Indexed: 01/13/2023]
Abstract
RATIONALE AND OBJECTIVES This study aimed to determine the diagnostic aid of computed tomography (CT) features for the differentiation of active alveolitis and fibrosis using a CT texture analysis (CTTA) prototype and CT densitometry in patients with systemic sclerosis (SSc) using ancillary high-resolution computed tomography (HRCT) features and their longitudinal course as standard of reference. MATERIALS AND METHODS We retrospectively analyzed thin-slice noncontrast chest CT image data of 43 patients with SSc (18 men, mean age 51.55 ± 15.52 years; range 23-71 years). All of them had repeated noncontrast enhanced HRCT of the lung. Classification into active alveolitis or fibrosis was done on HRCT based on classical HRCT findings (active alveolitis [19; 44.2%] and fibrosis [24; 55.8%]) and their course at midterm. Results were compared to pulmonary functional tests and were followed up by CT. Ground glass opacity was considered suggestive of alveolitis, whereas coarse reticulation with parenchymal distortion, traction bronchiectasis, and honeycombing were assigned to fibrosis. RESULTS Statistically significant differences in CTTA were found for first-order textural features (mean intensity, average, deviation, skewness) and second-order statistics (entropy of co-occurrence matrix, mean number of nonuniformity (NGLDM), entropy of NGLDM, entropy of heterogeneity, intensity, and average). Cut-off value for the prediction of fibrosis at baseline was significant for entropy of intensity (P value < .001) and for mean deviation (P value < .001), and for prediction of alveolitis was significant for uniformity of intensity (P value < .001) and for NGLDM (P value < .001). At pulmonary functional tests, forced expiratory volume in 1 second and single-breath diffusion capacity for carbon monoxide were significantly lower in fibrosis than in alveolitis 2.03 ± 0.78 vs. 2.61 ± 0.83, P < .016 and 4.51 ± 1.61 vs. 6.04 ± 1.75, P < .009, respectively. Differences in CT densitometry between alveolitis and fibrosis were not significant. CONCLUSIONS CTTA parameters are significantly different in active alveolitis vs. fibrosis in patients with SSc and may be helpful for differentiation of these two entities.
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Affiliation(s)
- Christopher Kloth
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076 Tuebingen, Germany.
| | - Anya C Blum
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076 Tuebingen, Germany
| | - Wolfgang M Thaiss
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076 Tuebingen, Germany
| | - Heike Preibsch
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076 Tuebingen, Germany
| | - Hendrik Ditt
- Siemens Healthcare GmbH, Diagnostic Imaging, Forchheim, Germany
| | - Rainer Grimmer
- Siemens Healthcare GmbH, Diagnostic Imaging, Forchheim, Germany
| | - Jan Fritz
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076 Tuebingen, Germany
| | - Hans Bösmüller
- Institute of Pathology, Eberhard-Kales-University Tuebingen, Tuebingen, Germany
| | - Marius Horger
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076 Tuebingen, Germany
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Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis. AJR Am J Roentgenol 2017; 210:341-346. [PMID: 29140113 DOI: 10.2214/ajr.17.18417] [Citation(s) in RCA: 113] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
OBJECTIVE The purposes of this study were to assess whether CT texture analysis and CT features are predictive of pancreatic neuroendocrine tumor (PNET) grade based on the World Health Organization (WHO) classification and to identify features related to disease progression after surgery. MATERIALS AND METHODS Preoperative contrast-enhanced CT images of 101 patients with PNETs were assessed. The images were evaluated for tumor location, tumor size, tumor pattern, predominantly solid or cystic composition, presence of calcification, presence of heterogeneous enhancement on contrast-enhanced images, presence of pancreatic duct dilatation, presence of pancreatic atrophy, presence of vascular involvement by the tumor, and presence of lymphadenopathy. Texture features were also extracted from CT images. Surgically verified tumors were graded according to the WHO classification, and patients underwent CT or MRI follow-up after surgical resection. Data were analyzed with chi-square tests, kappa statistics, logistic regression analysis, and Kaplan-Meier curves. RESULTS The CT features predictive of a more aggressive tumor (grades 2 and 3) were size larger than 2.0 cm (odds ratio [OR], 3.3; p = 0.014), presence of vascular involvement (OR, 25.2; p = 0.003), presence of pancreatic ductal dilatation (OR, 6.0; p = 0.002), and presence of lymphadenopathy (OR, 6.8; p = 0.002). The texture parameter entropy (OR, 3.7; p = 0.008) was also predictive of more aggressive tumors. Differences in progression-free survival distribution were found for grade 1 versus grades 2 and 3 tumors (χ2 [df, 1] = 21.6; p < 0.001); for PNETs with vascular involvement (χ2 [df, 1] = 20.8; p < 0.001); and for tumors with entropy (spatial scale filter 2) values greater than 4.65 (χ2 (df, 1) = 4.4; p = 0.037). CONCLUSION CT texture analysis and CT features are predictive of PNET aggressiveness and can be used to identify patients at risk of early disease progression after surgical resection.
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Karakas Y, Lacin S, Yalcin S. Recent advances in the management of pancreatic adenocarcinoma. Expert Rev Anticancer Ther 2017; 18:51-62. [DOI: 10.1080/14737140.2018.1403319] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Yusuf Karakas
- Department of Medical Oncology, Cancer Institute, Hacettepe University, Ankara, Turkey
| | - Sahin Lacin
- Department of Medical Oncology, Cancer Institute, Hacettepe University, Ankara, Turkey
| | - Suayib Yalcin
- Department of Medical Oncology, Cancer Institute, Hacettepe University, Ankara, Turkey
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Virginia BM, Laura F, Silvia R, Roberto F, Francesco F, Eva H, Charles F, Samy A, Stefan M, Jean-Charles S, Caroline C, Benjamin B. Prognostic value of histogram analysis in advanced non-small cell lung cancer: a radiomic study. Oncotarget 2017; 9:1906-1914. [PMID: 29416740 PMCID: PMC5788608 DOI: 10.18632/oncotarget.22316] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 06/02/2017] [Indexed: 12/21/2022] Open
Abstract
Introduction Quantitative assessment of heterogeneity by histogram analysis (HA) of tumor images can potentially provide a non-invasive prognostic biomarker. We assessed the prognostic value of HA and evaluated a correlation with molecular signature. Results CT scans performed between July 2009 and January 2015 from 692 patients were reviewed. HA was performed on scans from 313 patients in the training dataset and 108 in the validation dataset. Median follow-up were 33.7 months [range: 1.7 - 65.5] and 29 months [range: 1.1 - 35.6] with a median overall survival (OS) of 11.7 months [95%CI: 10.7 - 13.1] and 9.5 months [95%CI: 7.9 - 12.7] respectively. Primary mass entropy in coarse texture with spatial filter 3.3 was prognostic for OS in a multivariate Cox analysis (HR: 1.3 [95%CI: 1.1 - 1.5], p=0.001). Results were not reproduced in our validation set and no correlation with molecular signature was identified. Materials and Methods HA using filtration-histogram method was applied to the region of interest on the primary tumor in enhanced-CT acquired as diagnostic/staging routine, from a cohort of patients with advanced non-small cell lung cancer (NSCLC) treated with platinum-based chemotherapy. The resultants parameters were prospectively applied to a validation dataset. CT scans, clinical and molecular data were retrospectively collected. Cox proportional hazard models were used for survival analysis and Wilcoxon test for correlations. Conclusion Primary mass entropy was significantly associated with survival in the training set but was not validated in the validation cohort, raising doubt over the reliability of published data from small cohorts.
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Affiliation(s)
| | - Faivre Laura
- Department of Biostatistics and Epidemiology, Gustave Roussy Cancer Center, 94805 Villejuif, France
| | - Rosellini Silvia
- Department of Biostatistics and Epidemiology, Gustave Roussy Cancer Center, 94805 Villejuif, France
| | - Ferrara Roberto
- Department of Cancer Medicine, Gustave Roussy Cancer Center, 94805 Villejuif, France
| | - Facchinetti Francesco
- Department of Cancer Medicine, Gustave Roussy Cancer Center, 94805 Villejuif, France
| | - Haspinger Eva
- Department of Cancer Medicine, Gustave Roussy Cancer Center, 94805 Villejuif, France
| | - Ferte Charles
- Department of Cancer Medicine, Gustave Roussy Cancer Center, 94805 Villejuif, France
| | - Ammari Samy
- Department of Radiology, Gustave Roussy Cancer Center, 94805 Villejuif, France
| | - Michiels Stefan
- Department of Biostatistics and Epidemiology, Gustave Roussy Cancer Center, 94805 Villejuif, France.,INSERM U1018, CESP, Université Paris-Sud, Université Paris-Saclay, Villejuif, France.,Université Paris-Sud, 91400 Orsay, France
| | - Soria Jean-Charles
- Department of Cancer Medicine, Gustave Roussy Cancer Center, 94805 Villejuif, France.,Université Paris-Sud, 91400 Orsay, France
| | - Caramella Caroline
- Department of Radiology, Gustave Roussy Cancer Center, 94805 Villejuif, France.,Université Paris-Sud, 91400 Orsay, France
| | - Besse Benjamin
- Department of Cancer Medicine, Gustave Roussy Cancer Center, 94805 Villejuif, France.,Université Paris-Sud, 91400 Orsay, France
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Kloth C, Thaiss WM, Kärgel R, Grimmer R, Fritz J, Ioanoviciu SD, Ketelsen D, Nikolaou K, Horger M. Evaluation of Texture Analysis Parameter for Response Prediction in Patients with Hepatocellular Carcinoma Undergoing Drug-eluting Bead Transarterial Chemoembolization (DEB-TACE) Using Biphasic Contrast-enhanced CT Image Data: Correlation with Liver Perfusion CT. Acad Radiol 2017; 24:1352-1363. [PMID: 28652049 DOI: 10.1016/j.acra.2017.05.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 05/16/2017] [Accepted: 05/19/2017] [Indexed: 02/07/2023]
Abstract
RATIONALE AND OBJECTIVES This study aimed to evaluate the potential role of computed tomography texture analysis (CTTA) of arterial and portal-venous enhancement phase image data for prediction and accurate assessment of response of hepatocellular carcinoma undergoing drug-eluting bead transarterial chemoembolization (TACE) by comparison to liver perfusion CT (PCT). MATERIALS AND METHODS Twenty-eight patients (27 male; mean age 67.2 ± 10.4) with 56 hepatocellular carcinoma-typical liver lesions were included. Arterial and portal-venous phase CT data obtained before and after TACE with a mean time of 39.93 ± 62.21 days between examinations were analyzed. TACE was performed within 48 hours after first contrast-enhanced CT. CTTA software was a prototype. CTTA analysis was performed blinded (for results) by two observers separately. Combined results of modified Response Evaluation Criteria In Solid Tumors (mRECIST) and PCT of the liver were used as the standard of reference. Time to progression was additionally assessed for all patients. CTTA parameters included heterogeneity, intensity, average, deviation, skewness, and entropy of co-occurrence. Each parameter was compared to those of PCT (blood flow [BF], blood volume, arterial liver perfusion [ALP], portal-venous perfusion, and hepatic perfusion index) measured before and after TACE. RESULTS mRECIST + PCT yielded 28.6% complete response (CR), 42.8% partial response, and 28.6% stable disease. Significant correlations were registered in the arterial phase in CR between changes in mean heterogeneity and BF (P = .004, r = -0.815), blood volume (P = .002, r = -0.851), and ALP (P = .002, r = -0.851), respectively. In the partial response group, changes in mean heterogeneity correlated with changes in ALP (P = .003) and to a lesser degree with hepatic perfusion index (P = .027) in the arterial phase. In the stable disease group, BF correlated with entropy of nonuniformity (P = .010). In the portal-venous phase, no statistically significant correlations were registered in all groups. Receiver operating characteristic analysis of CTTA parameters yielded predictive cutoff values for CR in the arterial contrast-enhanced CT phase for uniformity of skewness (sensitivity: 90.0%; specificity: 45.8%), and in the portal-venous phase for uniformity of heterogeneity (sensitivity: 92.3%; specificity: 81.8%). CONCLUSIONS Significant correlations exist between CTTA parameters and those derived from PCT both in the pre- and the post-TACE settings, and some of them have predictive value for TACE midterm outcome.
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231
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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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232
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The Attenuation Distribution Across the Long Axis of Breast Cancer Liver Metastases at CT: A Quantitative Biomarker for Predicting Overall Survival. AJR Am J Roentgenol 2017; 210:W1-W7. [PMID: 29064750 DOI: 10.2214/ajr.17.18249] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE The objective of our study was to compare attenuation distribution across the long-axis (ADLA) measurements, Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1, and Choi criteria for predicting overall survival (OS) in patients with metastatic breast cancer treated with bevacizumab. MATERIALS AND METHODS We obtained HIPAA-compliant data from a prospective, multisite, phase 3 trial of bevacizumab for the treatment of metastatic breast cancer. For patients with one or more liver metastases measuring 15 mm or larger at baseline, we evaluated up to two target liver lesions using RECIST, Choi criteria, and ADLA measurements, with the latter defined as the SD of the CT attenuation values of each pixel along the tumor long-axis diameter. The optimal percentage change threshold for defining an ADLA response was computed by cross-validation analysis in a Cox model. The log-rank test was applied to evaluate RECIST, Choi criteria, and ADLA for discriminating patients with superior OS. The predictive accuracies of all three techniques were compared using Brier scores and areas under the ROC curve (AUC). All analyses were performed separately using best overall response (BOR) and response at the first follow-up time point (FU1). RESULTS One hundred sixty-four patients met the inclusion criteria. A 25% decrease in the ADLA measurement from baseline was the optimal ADLA response threshold for BOR and FU1. RECIST, Choi criteria, and ADLA successfully identified patients with superior OS when using BOR (RECIST, p = 0.02; Choi and ADLA, p < 0.001), but only Choi criteria and ADLA measurements were successful when using FU1 (RECIST, p = 0.43; Choi and ADLA, p < 0.001). In a direct comparison, ADLA measurements outperformed both RECIST and Choi criteria using BOR (95% CI for Brier score differences, ADLA-RECIST [-0.58 to -0.08] and ADLA-Choi [-0.55 to -0.06]; 95% CI for AUC differences, ADLA-RECIST [0.16-0.33] and ADLA-Choi [0.17-0.36]) as well as using FU1 (95% CI for Brier score differences, ADLA-RECIST [-0.77 to -0.08] and ADLA-Choi [-0.58 to -0.03]; 95% CI for AUC differences, ADLA-RECIST [0.22-0.39] and ADLA-Choi [0.01-0.22]). CONCLUSION ADLA measurements may be a useful noninvasive indicator of cancer treatment response. Because ADLA measurements may be extracted relatively easily using existing radiologist workflows, further investigation of the ADLA technique is warranted.
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Kiryu S, Akai H, Nojima M, Hasegawa K, Shinkawa H, Kokudo N, Yasaka K, Ohtomo K. Impact of hepatocellular carcinoma heterogeneity on computed tomography as a prognostic indicator. Sci Rep 2017; 7:12689. [PMID: 28978930 PMCID: PMC5627280 DOI: 10.1038/s41598-017-12688-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Accepted: 09/13/2017] [Indexed: 12/12/2022] Open
Abstract
We assessed the relationship between the heterogeneity of HCC on preoperative non-contrast-enhanced CT and patient prognosis. The heterogeneity of CT images from 122 patients was assessed and texture feature parameters such as mean, standard deviation (SD), entropy, mean of the positive pixels (MPP), skewness, and kurtosis were obtained using filtration. The relationship between CT texture features and 5-year overall survival (OS) or disease-free survival (DFS) was assessed. Multivariate regression analysis was performed to evaluate the independence of texture feature from clinical or pathological parameters. The Kaplan-Meier curves for OS or DFS was significantly different between patient groups dichotomized by cut-off values for all CT texture parameters with filtration at at least one filter level. Multivariate regression analysis showed the independence of most CT texture parameters on clinical and pathological parameters for OS with filtration at at least one filter level and without filtration except kurtosis. SD, entropy, and MPP with coarse filter, and skewness without filtration showed a significant correlation for DFS. CT texture features of non-contrast-enhanced CT images showed a relationship with HCC prognosis. Multivariate regression analysis showed the possibility of CT texture feature increase the prognostic prediction of HCC by clinical and pathological information.
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Affiliation(s)
- Shigeru Kiryu
- Department of Radiology, International University of Health and Welfare Hospital, Nasushiobara, Tochigi, Japan.
| | - Hiroyuki Akai
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Masanori Nojima
- Division of Advanced Medicine Promotion, The Advanced Clinical Research Center, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Kiyoshi Hasegawa
- Division of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Hiroji Shinkawa
- Division of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Norihiro Kokudo
- Division of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Koichiro Yasaka
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Kuni Ohtomo
- International University of Health and Welfare, Otawara, Tochigi, Japan
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Abstract
PURPOSE To evaluate CT texture analysis (CTTA) for staging of hepatic fibrosis (stages F0-F4) METHODS: Quantitative texture analysis (QTA) of the liver was performed on abdominal MDCT scans using commercially available software (TexRAD), which uses a filtration-histogram statistic-based technique. Single-slice ROI measurements of the total liver, Couinaud segments IV-VIII, and segments I-III were obtained. CTTA parameters were correlated against fibrosis stage (F0-F4), with biopsy performed within one year for all cases with intermediate fibrosis (F1-F3). RESULTS The study cohort consisted of 289 adults (158M/131W; mean age, 51 years), including healthy controls (F0, n = 77), and patients with increasing stages of fibrosis (F1, n = 42; F2 n = 37; F3 n = 53; F4 n = 80). Mean gray-level intensity increased with fibrosis stage, demonstrating an ROC AUC of 0.78 at medium filtration for F0 vs F1-4, with sensitivity and specificity of 74% and 74% at cutoff 0.18. For significant fibrosis (≥F2), mean showed AUCs ranging from 0.71-0.73 across medium- and coarse- filtered textures with sensitivity and specificity of 71% and 68% at cutoff of 0.3, with similar performance also observed for advanced fibrosis (≥F3). Entropy showed a similar trend. Conversely, kurtosis and skewness decreased with increasing fibrosis, particularly in cirrhotic patients. For cirrhosis (≥F4), kurtosis and skewness showed AUCs of 0.86 and 0.87, respectively, at coarse-filtered scale, with skewness showing a sensitivity and specificity of 84% and 75% at cutoff of 1.3. CONCLUSION CTTA may be helpful in detecting the presence of hepatic fibrosis and discriminating between stages of fibrosis, particularly at advanced levels.
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Burrowes D, Fangusaro JR, Nelson PC, Zhang B, Wadhwani NR, Rozenfeld MJ, Deng J. Extended diffusion weighted magnetic resonance imaging with two-compartment and anomalous diffusion models for differentiation of low-grade and high-grade brain tumors in pediatric patients. Neuroradiology 2017; 59:803-811. [DOI: 10.1007/s00234-017-1865-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 06/13/2017] [Indexed: 10/19/2022]
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Choi TW, Kim JH, Park SJ, Ahn SJ, Joo I, Han JK. Risk stratification of gallbladder polyps larger than 10 mm using high-resolution ultrasonography and texture analysis. Eur Radiol 2017; 28:196-205. [PMID: 28687913 DOI: 10.1007/s00330-017-4954-1] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Revised: 05/23/2017] [Accepted: 06/20/2017] [Indexed: 12/20/2022]
Abstract
OBJECTIVES To assess important features for risk stratification of gallbladder (GB) polyps >10 mm using high-resolution ultrasonography (HRUS) and texture analysis. METHODS We included 136 patients with GB polyps (>10 mm) who underwent both HRUS and cholecystectomy (non-neoplastic, n = 58; adenomatous, n = 32; and carcinoma, n = 46). Two radiologists retrospectively assessed HRUS findings and texture analysis. Multivariate analysis was performed to identify significant predictors for neoplastic polyps and carcinomas. RESULTS Single polyp (OR, 3.680-3.856) and larger size (OR, 1.450-1.477) were independently associated with neoplastic polyps (p < 0.05). In a single or polyp >14 mm, sensitivity for differentiating neoplastic from non-neoplastic polyps was 92.3%. To differentiate carcinoma from adenoma, sessile shape (OR, 9.485-41.257), larger size (OR, 1.267-1.303), higher skewness (OR, 6.382) and lower grey-level co-occurrence matrices (GLCM) contrast (OR, 0.963) were significant predictors (p < 0.05). In a polyp >22 mm or sessile, sensitivity for differentiating carcinomas from adenomas was 93.5-95.7%. If a polyp demonstrated at least one HRUS finding and at least one texture feature, the specificity for diagnosing carcinoma was increased to 90.6-93.8%. CONCLUSION In a GB polyp >10 mm, single and diameter >14 mm were useful for predicting neoplastic polyps. In neoplastic polyps, sessile shape, diameter >22 mm, higher skewness and lower GLCM contrast were useful for predicting carcinoma. KEY POINTS • Risk of neoplastic polyp is low in <14 mm and multiple polyps • A sessile polyp or >22 mm has increased risk for GB carcinomas • Higher skewness and lower GLCM contrast are predictors of GB carcinoma • HRUS is useful for risk stratification of GB polyps >1 cm.
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Affiliation(s)
- Tae Won Choi
- Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, 110-744, Republic of Korea
| | - Jung Hoon Kim
- Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, 110-744, Republic of Korea.
- Institute of Radiation Medicine, Seoul National University College of Medicine, Seoul, Korea.
| | - Sang Joon Park
- Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, 110-744, Republic of Korea
| | - Su Joa Ahn
- Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, 110-744, Republic of Korea
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, 110-744, Republic of Korea
| | - Joon Koo Han
- Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, 110-744, Republic of Korea
- Institute of Radiation Medicine, Seoul National University College of Medicine, Seoul, Korea
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237
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Yip SSF, Liu Y, Parmar C, Li Q, Liu S, Qu F, Ye Z, Gillies RJ, Aerts HJWL. Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer. Sci Rep 2017; 7:3519. [PMID: 28615677 PMCID: PMC5471260 DOI: 10.1038/s41598-017-02425-5] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 04/11/2017] [Indexed: 12/26/2022] Open
Abstract
Tumor phenotypes captured in computed tomography (CT) images can be described qualitatively and quantitatively using radiologist-defined "semantic" and computer-derived "radiomic" features, respectively. While both types of features have shown to be promising predictors of prognosis, the association between these groups of features remains unclear. We investigated the associations between semantic and radiomic features in CT images of 258 non-small cell lung adenocarcinomas. The tumor imaging phenotypes were described using 9 qualitative semantic features that were scored by radiologists, and 57 quantitative radiomic features that were automatically calculated using mathematical algorithms. Of the 9 semantic features, 3 were rated on a binary scale (cavitation, air bronchogram, and calcification) and 6 were rated on a categorical scale (texture, border definition, contour, lobulation, spiculation, and concavity). 32-41 radiomic features were associated with the binary semantic features (AUC = 0.56-0.76). The relationship between all radiomic features and the categorical semantic features ranged from weak to moderate (|Spearmen's correlation| = 0.002-0.65). There are associations between semantic and radiomic features, however the associations were not strong despite being significant. Our results indicate that radiomic features may capture distinct tumor phenotypes that fail to be perceived by naked eye that semantic features do not describe and vice versa.
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Affiliation(s)
- Stephen S F Yip
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, 02115, USA.
| | - Ying Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin, PR China
| | - Chintan Parmar
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, 02115, USA
| | - Qian Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin, PR China
| | - Shichang Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin, PR China
| | - Fangyuan Qu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin, PR China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin, PR China
| | - Robert J Gillies
- Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Hugo J W L Aerts
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, 02115, USA
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
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238
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Sidhu HS, Benigno S, Ganeshan B, Dikaios N, Johnston EW, Allen C, Kirkham A, Groves AM, Ahmed HU, Emberton M, Taylor SA, Halligan S, Punwani S. "Textural analysis of multiparametric MRI detects transition zone prostate cancer". Eur Radiol 2017; 27:2348-2358. [PMID: 27620864 PMCID: PMC5408048 DOI: 10.1007/s00330-016-4579-9] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Revised: 08/10/2016] [Accepted: 08/22/2016] [Indexed: 01/03/2023]
Abstract
OBJECTIVES To evaluate multiparametric-MRI (mpMRI) derived histogram textural-analysis parameters for detection of transition zone (TZ) prostatic tumour. METHODS Sixty-seven consecutive men with suspected prostate cancer underwent 1.5T mpMRI prior to template-mapping-biopsy (TPM). Twenty-six men had 'significant' TZ tumour. Two radiologists in consensus matched TPM to the single axial slice best depicting tumour, or largest TZ diameter for those with benign histology, to define single-slice whole TZ-regions-of-interest (ROIs). Textural-parameter differences between single-slice whole TZ-ROI containing significant tumour versus benign/insignificant tumour were analysed using Mann Whitney U test. Diagnostic accuracy was assessed by receiver operating characteristic area under curve (ROC-AUC) analysis cross-validated with leave-one-out (LOO) analysis. RESULTS ADC kurtosis was significantly lower (p < 0.001) in TZ containing significant tumour with ROC-AUC 0.80 (LOO-AUC 0.78); the difference became non-significant following exclusion of significant tumour from single-slice whole TZ-ROI (p = 0.23). T1-entropy was significantly lower (p = 0.004) in TZ containing significant tumour with ROC-AUC 0.70 (LOO-AUC 0.66) and was unaffected by excluding significant tumour from TZ-ROI (p = 0.004). Combining these parameters yielded ROC-AUC 0.86 (LOO-AUC 0.83). CONCLUSION Textural features of the whole prostate TZ can discriminate significant prostatic cancer through reduced kurtosis of the ADC-histogram where significant tumour is included in TZ-ROI and reduced T1 entropy independent of tumour inclusion. KEY POINTS • MR textural features of prostate transition zone may discriminate significant prostatic cancer. • Transition zone (TZ) containing significant tumour demonstrates a less peaked ADC histogram. • TZ containing significant tumour reveals higher post-contrast T1-weighted homogeneity. • The utility of MR texture analysis in prostate cancer merits further investigation.
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Affiliation(s)
- Harbir S Sidhu
- Centre for Medical Imaging, University College London, 3rd Floor East, 250 Euston Road, London, NW1 2BU, UK
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
| | - Salvatore Benigno
- Centre for Medical Imaging, University College London, 3rd Floor East, 250 Euston Road, London, NW1 2BU, UK
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College London, University College Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Nikos Dikaios
- Centre for Medical Imaging, University College London, 3rd Floor East, 250 Euston Road, London, NW1 2BU, UK
| | - Edward W Johnston
- Centre for Medical Imaging, University College London, 3rd Floor East, 250 Euston Road, London, NW1 2BU, UK
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
| | - Clare Allen
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
| | - Alex Kirkham
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
| | - Ashley M Groves
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
- Institute of Nuclear Medicine, University College London, University College Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Hashim U Ahmed
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
- Research Department of Urology, University College London, 3rd Floor, Charles Bell House 67 Riding House Street, London, W1P 7NN, UK
| | - Mark Emberton
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
- Research Department of Urology, University College London, 3rd Floor, Charles Bell House 67 Riding House Street, London, W1P 7NN, UK
| | - Stuart A Taylor
- Centre for Medical Imaging, University College London, 3rd Floor East, 250 Euston Road, London, NW1 2BU, UK
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
| | - Steve Halligan
- Centre for Medical Imaging, University College London, 3rd Floor East, 250 Euston Road, London, NW1 2BU, UK
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, 3rd Floor East, 250 Euston Road, London, NW1 2BU, UK.
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK.
- Centre for Medical Imaging, University College London and University College London Hospitals NIHR Biomedical Research Centre, 250 Euston Road, London, NW1 2BU, UK.
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239
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Yasaka K, Akai H, Mackin D, Court L, Moros E, Ohtomo K, Kiryu S. Precision of quantitative computed tomography texture analysis using image filtering: A phantom study for scanner variability. Medicine (Baltimore) 2017; 96:e6993. [PMID: 28538408 PMCID: PMC5457888 DOI: 10.1097/md.0000000000006993] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Quantitative computed tomography (CT) texture analyses for images with and without filtration are gaining attention to capture the heterogeneity of tumors. The aim of this study was to investigate how quantitative texture parameters using image filtering vary among different computed tomography (CT) scanners using a phantom developed for radiomics studies.A phantom, consisting of 10 different cartridges with various textures, was scanned under 6 different scanning protocols using four CT scanners from four different vendors. CT texture analyses were performed for both unfiltered images and filtered images (using a Laplacian of Gaussian spatial band-pass filter) featuring fine, medium, and coarse textures. Forty-five regions of interest were placed for each cartridge (x) in a specific scan image set (y), and the average of the texture values (T(x,y)) was calculated. The interquartile range (IQR) of T(x,y) among the 6 scans was calculated for a specific cartridge (IQR(x)), while the IQR of T(x,y) among the 10 cartridges was calculated for a specific scan (IQR(y)), and the median IQR(y) was then calculated for the 6 scans (as the control IQR, IQRc). The median of their quotient (IQR(x)/IQRc) among the 10 cartridges was defined as the variability index (VI).The VI was relatively small for the mean in unfiltered images (0.011) and for standard deviation (0.020-0.044) and entropy (0.040-0.044) in filtered images. Skewness and kurtosis in filtered images featuring medium and coarse textures were relatively variable across different CT scanners, with VIs of 0.638-0.692 and 0.430-0.437, respectively.Various quantitative CT texture parameters are robust and variable among different scanners, and the behavior of these parameters should be taken into consideration.
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Affiliation(s)
- Koichiro Yasaka
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Hiroyuki Akai
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Dennis Mackin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Laurence Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Eduardo Moros
- Departments of Radiation Oncology, Diagnostic Imaging, and Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Kuni Ohtomo
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shigeru Kiryu
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
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240
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Beckers RCJ, Lambregts DMJ, Schnerr RS, Maas M, Rao SX, Kessels AGH, Thywissen T, Beets GL, Trebeschi S, Houwers JB, Dejong CH, Verhoef C, Beets-Tan RGH. Whole liver CT texture analysis to predict the development of colorectal liver metastases-A multicentre study. Eur J Radiol 2017. [PMID: 28624022 DOI: 10.1016/j.ejrad.2017.04.019] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVES CT texture analysis has shown promise to differentiate colorectal cancer patients with/without hepatic metastases. AIM To investigate whether whole-liver CT texture analysis can also predict the development of colorectal liver metastases. MATERIAL AND METHODS Retrospective multicentre study (n=165). Three subgroups were assessed: patients [A] without metastases (n=57), [B] with synchronous metastases (n=54) and [C] who developed metastases within ≤24 months (n=54). Whole-liver texture analysis was performed on primary staging CT. Mean grey-level intensity, entropy and uniformity were derived with different filters (σ0.5-2.5). Univariable logistic regression (group A vs. B) identified potentially predictive parameters, which were tested in multivariable analyses to predict development of metastases (group A vs. C), including subgroup analyses for early (≤6 months), intermediate (7-12 months) and late (13-24 months) metastases. RESULTS Univariable analysis identified uniformity (σ0.5), sex, tumour site, nodal stage and carcinoembryonic antigen as potential predictors. Uniformity remained a significant predictor in multivariable analysis to predict early metastases (OR 0.56). None of the parameters could predict intermediate/late metastases. CONCLUSIONS Whole-liver CT-texture analysis has potential to predict patients at risk of developing early liver metastases ≤6 months, but is not robust enough to identify patients at risk of developing metastases at later stage.
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Affiliation(s)
- Rianne C J Beckers
- GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD, The Netherlands; Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands; Department of Radiology, Maastricht University Medical Center, P.O. Box 6200, 6202 AZ Maastricht, The Netherlands; Department of Surgery, Maastricht University Medical Center, P.O. Box 6200, 6202 AZ Maastricht, The Netherlands
| | - Doenja M J Lambregts
- Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands.
| | - Roald S Schnerr
- Department of Radiology, Maastricht University Medical Center, P.O. Box 6200, 6202 AZ Maastricht, The Netherlands
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands
| | - Sheng-Xiang Rao
- Department of Radiology, Zhongshan Hospital, Fudan University,180 Fenglin Road Shangai 200032, China
| | - Alfons G H Kessels
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University, P.O. Box 6200, 6202 AZ Maastricht, , The Netherlands
| | - Thomas Thywissen
- Department of Radiology, Maastricht University Medical Center, P.O. Box 6200, 6202 AZ Maastricht, The Netherlands
| | - Geerard L Beets
- GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD, The Netherlands; Department of Surgery, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands
| | - Stefano Trebeschi
- GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD, The Netherlands; Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands
| | - Janneke B Houwers
- GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD, The Netherlands; Department of Radiology, Maastricht University Medical Center, P.O. Box 6200, 6202 AZ Maastricht, The Netherlands
| | - Cornelis H Dejong
- Department of Surgery, Maastricht University Medical Center, P.O. Box 6200, 6202 AZ Maastricht, The Netherlands; NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, P.O. Box 616, 6200 MD, The Netherlands; Department of Surgery, RWTH Universitätsklinikum Aachen, Pauwelsstraße 30, 52074 Aachen, Germany
| | - Cornelis Verhoef
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Groene Hilledijk 301, 3075 EA, Rotterdam, The Netherlands
| | - Regina G H Beets-Tan
- GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD, The Netherlands; Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands
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241
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Yasaka K, Akai H, Nojima M, Shinozaki-Ushiku A, Fukayama M, Nakajima J, Ohtomo K, Kiryu S. Quantitative computed tomography texture analysis for estimating histological subtypes of thymic epithelial tumors. Eur J Radiol 2017. [PMID: 28624025 DOI: 10.1016/j.ejrad.2017.04.017] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
OBJECTIVES To investigate whether high-risk thymic epithelial tumor (TET) (HTET) can be differentiated from low-risk TET (LTET) using computed tomography (CT) quantitative texture analysis. MATERIALS AND METHODS The data of 39 patients (mean age, 58.6±14.1 years) (39 unenhanced CT (UECT) and 33 contrast-enhanced CT (CECT)) who underwent thymectomy for TET were retrospectively analyzed. A region of interest was placed to include the entire TET within the slice at its maximum diameter. Texture analysis was performed for images with or without a Laplacian of Gaussian filter (with various spatial scaling factors [SSFs]). Two radiologists evaluated the visual heterogeneity of TET using a 3-point scale. RESULTS The mean in the unfiltered image (mean0u) and entropy in the filtered image (SSF: 6mm) (entropy6u) for UECT, and the mean in the unfiltered image (mean0c) for CECT were significant parameters for differentiating between HTET and LTET as determined by logistic regression analysis. The area under the receiver operating characteristics curve (AUC) for differentiating HTET from LTET using mean0u, entropy6u, and mean0c was 0.75, 0.76, and 0.89, respectively. And the combination of mean0u and entropy6u allowed AUC of 0.87. Entropy6u provided a higher diagnostic performance compared with visual heterogeneity analysis (p≤0.018). CONCLUSION Using CT quantitative texture analysis, HTET can be differentiated from LTET with a high diagnostic performance.
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Affiliation(s)
- Koichiro Yasaka
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
| | - Hiroyuki Akai
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
| | - Masanori Nojima
- Center for Translational Research, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
| | - Aya Shinozaki-Ushiku
- Department of Pathology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Masashi Fukayama
- Department of Pathology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Jun Nakajima
- Department of Thoracic Surgery, Graduate School of Medicine, The University of Tokyok, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Kuni Ohtomo
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Shigeru Kiryu
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.
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242
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Alam N, Choi M, Ghammraoui B, Dahal E, Badano A. Small-angle x-ray scattering cross-section measurements of imaging materials. Biomed Phys Eng Express 2017. [DOI: 10.1088/2057-1976/aa6720] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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243
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Jalil O, Afaq A, Ganeshan B, Patel UB, Boone D, Endozo R, Groves A, Sizer B, Arulampalam T. Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long-term survival in locally advanced rectal cancer treated by chemoradiotherapy. Colorectal Dis 2017; 19:349-362. [PMID: 27538267 DOI: 10.1111/codi.13496] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 06/08/2016] [Indexed: 12/12/2022]
Abstract
AIM The study aimed to investigate whether textural features of rectal cancer on MRI can predict long-term survival in patients treated with long-course chemoradiotherapy. METHOD Textural analysis (TA) using a filtration-histogram technique of T2-weighted pre- and 6-week post-chemoradiotherapy MRI was undertaken using TexRAD, a proprietary software algorithm. Regions of interest enclosing the largest cross-sectional area of the tumour were manually delineated on the axial images and the filtration step extracted features at different anatomical scales (fine, medium and coarse) followed by quantification of statistical features [mean intensity, standard deviation, entropy, skewness, kurtosis and mean of positive pixels (MPP)] using histogram analysis. Cox multiple regression analysis determined which univariate features including textural, radiological and histological independently predicted overall survival (OS), disease-free survival (DFS) and recurrence-free survival (RFS). RESULTS MPP [fine texture, hazard ratio (HR) 6.9, 95% CI: 2.43-19.55, P < 0.001], mean (medium texture, HR 5.6, 95% CI: 1.4-21.7, P = 0.007) and extramural venous invasion (EMVI) on MRI (HR 2.96, 95% CI: 1.04-8.37, P = 0.041) independently predicted OS while mean (medium texture, HR 4.53, 95% CI: 1.58-12.94, P = 0.003), MPP (fine texture, HR 3.36, 95% CI: 1.36-8.31, P = 0.008) and threatened circumferential resection margin (CRM) on MRI (HR 3.1, 95% CI: 1.01-9.46, P = 0.046) predicted DFS. For OS, EMVI on MRI (HR 4.23, 95% CI: 1.41-12.69, P = 0.01) and for DFS kurtosis (medium texture, HR 3.97, 95% CI: 1.44-10.94, P = 0.007) and CRM involvement on MRI (HR 3.36, 95% CI: 1.21-9.32, P = 0.02) were the independent post-treatment factors. Only TA independently predicted RFS on pre- or post-treatment analyses. CONCLUSION MR based TA of rectal cancers can predict outcome before undergoing surgery and could potentially select patients for individualized therapy.
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Affiliation(s)
- O Jalil
- Colchester University Hospital, Colchester, UK
| | - A Afaq
- Institute of Nuclear Medicine, University College London, London, UK
| | - B Ganeshan
- Institute of Nuclear Medicine, University College London, London, UK
| | - U B Patel
- London North-West NHS Trust, London, UK
| | - D Boone
- Colchester University Hospital, Colchester, UK
| | - R Endozo
- Institute of Nuclear Medicine, University College London, London, UK
| | - A Groves
- Institute of Nuclear Medicine, University College London, London, UK
| | - B Sizer
- Colchester University Hospital, Colchester, UK
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244
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Shafiq-Ul-Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, Abdalah MA, Schabath MB, Goldgof DG, Mackin D, Court LE, Gillies RJ, Moros EG. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys 2017; 44:1050-1062. [PMID: 28112418 DOI: 10.1002/mp.12123] [Citation(s) in RCA: 388] [Impact Index Per Article: 55.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 12/23/2016] [Accepted: 01/16/2017] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Many radiomics features were originally developed for non-medical imaging applications and therefore original assumptions may need to be reexamined. In this study, we investigated the impact of slice thickness and pixel spacing (or pixel size) on radiomics features extracted from Computed Tomography (CT) phantom images acquired with different scanners as well as different acquisition and reconstruction parameters. The dependence of CT texture features on gray-level discretization was also evaluated. METHODS AND MATERIALS A texture phantom composed of 10 different cartridges of different materials was scanned on eight different CT scanners from three different manufacturers. The images were reconstructed for various slice thicknesses. For each slice thickness, the reconstruction Field Of View (FOV) was varied to render pixel sizes ranging from 0.39 to 0.98 mm. A fixed spherical region of interest (ROI) was contoured on the images of the shredded rubber cartridge and the 3D printed, 20% fill, acrylonitrile butadiene styrene plastic cartridge (ABS20) for all phantom imaging sets. Radiomic features were extracted from the ROIs using an in-house program. Features categories were: shape (10), intensity (16), GLCM (24), GLZSM (11), GLRLM (11), and NGTDM (5), fractal dimensions (8) and first-order wavelets (128), for a total of 213 features. Voxel-size resampling was performed to investigate the usefulness of extracting features using a suitably chosen voxel size. Acquired phantom image sets were resampled to a voxel size of 1 × 1 × 2 mm3 using linear interpolation. Image features were therefore extracted from resampled and original datasets and the absolute value of the percent coefficient of variation (%COV) for each feature was calculated. Based on the %COV values, features were classified in 3 groups: (1) features with large variations before and after resampling (%COV >50); (2) features with diminished variation (%COV <30) after resampling; and (3) features that had originally moderate variation (%COV <50%) and were negligibly affected by resampling. Group 2 features were further studied by modifying feature definitions to include voxel size. Original and voxel-size normalized features were used for interscanner comparisons. A subsequent analysis investigated feature dependency on gray-level discretization by extracting 51 texture features from ROIs from each of the 10 different phantom cartridges using 16, 32, 64, 128, and 256 gray levels. RESULTS Out of the 213 features extracted, 150 were reproducible across voxel sizes, 42 improved significantly (%COV <30, Group 2) after resampling, and 21 had large variations before and after resampling (Group 1). Ten features improved significantly after definition modification effectively removed their voxel-size dependency. Interscanner comparison indicated that feature variability among scanners nearly vanished for 8 of these 10 features. Furthermore, 17 out of 51 texture features were found to be dependent on the number of gray levels. These features were redefined to include the number of gray levels which greatly reduced this dependency. CONCLUSION Voxel-size resampling is an appropriate pre-processing step for image datasets acquired with variable voxel sizes to obtain more reproducible CT features. We found that some of the radiomics features were voxel size and gray-level discretization-dependent. The introduction of normalizing factors in their definitions greatly reduced or removed these dependencies.
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Affiliation(s)
- Muhammad Shafiq-Ul-Hassan
- Department of Physics, University of South Florida, Tampa, FL, 33620, USA.,H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Geoffrey G Zhang
- Department of Physics, University of South Florida, Tampa, FL, 33620, USA.,H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Kujtim Latifi
- Department of Physics, University of South Florida, Tampa, FL, 33620, USA.,H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Ghanim Ullah
- Department of Physics, University of South Florida, Tampa, FL, 33620, USA
| | - Dylan C Hunt
- H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | | | | | - Matthew B Schabath
- H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Dmitry G Goldgof
- Department of Computer Science and Engineering, University of South Florida, Tampa, FL, 33620, USA
| | - Dennis Mackin
- Department of Radiation Physics, University of Texas, MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Laurence Edward Court
- Department of Radiation Physics, University of Texas, MD Anderson Cancer Center, Houston, TX, 77030, USA
| | | | - Eduardo Gerardo Moros
- Department of Physics, University of South Florida, Tampa, FL, 33620, USA.,H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
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Buch K, Li B, Qureshi MM, Kuno H, Anderson SW, Sakai O. Quantitative Assessment of Variation in CT Parameters on Texture Features: Pilot Study Using a Nonanatomic Phantom. AJNR Am J Neuroradiol 2017; 38:981-985. [PMID: 28341714 DOI: 10.3174/ajnr.a5139] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Accepted: 01/09/2017] [Indexed: 12/23/2022]
Abstract
Our aim was to evaluate changes in texture features based on variations in CT parameters on a phantom. Scans were performed with varying milliampere, kilovolt, section thickness, pitch, and acquisition mode. Forty-two texture features were extracted by using an in-house-developed Matlab program. Two-tailed t tests and false-detection analyses were performed with significant differences in texture features based on detector array configurations (Q values = 0.001-0.006), section thickness (Q values = 0.0002-0.001), and acquisition mode (Q values = 0.003-0.006). Variations in milliampere and kilovolt had no significant effect.
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Affiliation(s)
- K Buch
- From the Departments of Radiology (K.B., B.L., M.M.Q., H.K., S.W.A., O.S.)
| | - B Li
- From the Departments of Radiology (K.B., B.L., M.M.Q., H.K., S.W.A., O.S.)
| | - M M Qureshi
- From the Departments of Radiology (K.B., B.L., M.M.Q., H.K., S.W.A., O.S.).,Radiation Oncology (M.M.Q., O.S.)
| | - H Kuno
- From the Departments of Radiology (K.B., B.L., M.M.Q., H.K., S.W.A., O.S.)
| | - S W Anderson
- From the Departments of Radiology (K.B., B.L., M.M.Q., H.K., S.W.A., O.S.)
| | - O Sakai
- From the Departments of Radiology (K.B., B.L., M.M.Q., H.K., S.W.A., O.S.) .,Radiation Oncology (M.M.Q., O.S.).,Otolaryngology-Head and Neck Surgery (O.S.), Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
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246
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Giganti F, Marra P, Ambrosi A, Salerno A, Antunes S, Chiari D, Orsenigo E, Esposito A, Mazza E, Albarello L, Nicoletti R, Staudacher C, Del Maschio A, De Cobelli F. Pre-treatment MDCT-based texture analysis for therapy response prediction in gastric cancer: Comparison with tumour regression grade at final histology. Eur J Radiol 2017; 90:129-137. [PMID: 28583623 DOI: 10.1016/j.ejrad.2017.02.043] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Revised: 02/25/2017] [Accepted: 02/27/2017] [Indexed: 02/07/2023]
Abstract
PURPOSE An accurate prediction of tumour response to therapy is fundamental in oncology, so as to prompt personalised treatment options if needed. The aim of this study was to investigate the ability of preoperative texture analysis from multi-detector computed tomography (MDCT) in the prediction of the response rate to neo-adjuvant therapy in patients with gastric cancer. MATERIAL AND METHODS Thirty-four patients with biopsy-proven gastric cancer were examined by MDCT before neo-adjuvant therapy, and treated with radical surgery after treatment completion. Tumour regression grade (TRG) at final histology was also assessed. Image features from texture analysis were quantified, with and without filters for fine to coarse textures. Patients with TRG 1-3 were considered responders while TRG 4-5 as non- responders. The response rate to neo-adjuvant therapy was assessed both at univariate and multivariate analysis. RESULTS Fourteen parameters were significantly different between the two subgroups at univariate analysis; in particular, entropy and compactness (higher in responders) and uniformity (lower in responders). According to our model, the following parameters could identify non-responders at multivariate analysis: entropy (≤6.86 with a logarithm of Odds Ratio - Log OR -: 4.11; p=0.003); range (>158.72; Log OR: 3.67; p=0.010) and root mean square (≤3.71; Log OR: 4.57; p=0.005). Entropy and three-dimensional volume were not significantly correlated (r=0.06; p=0.735). CONCLUSION Pre-treatment texture analysis can potentially provide important information regarding the response rate to neo-adjuvant therapy for gastric cancer, improving risk stratification.
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Affiliation(s)
- Francesco Giganti
- Department of Radiology and Experimental Imaging Centre, San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy.
| | - Paolo Marra
- Department of Radiology and Experimental Imaging Centre, San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | | | - Annalaura Salerno
- Department of Radiology and Experimental Imaging Centre, San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Sofia Antunes
- Experimental Imaging Centre, San Raffaele Scientific Institute, Milan, Italy
| | - Damiano Chiari
- Vita-Salute San Raffaele University, Milan, Italy; Department of Surgery, San Raffaele Scientific Institute, Milan, Italy
| | - Elena Orsenigo
- Department of Surgery, San Raffaele Scientific Institute, Milan, Italy
| | - Antonio Esposito
- Department of Radiology and Experimental Imaging Centre, San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Elena Mazza
- Department of Oncology, San Raffaele Scientific Institute, Milan, Italy
| | - Luca Albarello
- Pathology Unit, San Raffaele Scientific Institute, Milan, Italy
| | - Roberto Nicoletti
- Department of Radiology and Experimental Imaging Centre, San Raffaele Scientific Institute, Milan, Italy
| | - Carlo Staudacher
- Vita-Salute San Raffaele University, Milan, Italy; Department of Surgery, San Raffaele Scientific Institute, Milan, Italy
| | - Alessandro Del Maschio
- Department of Radiology and Experimental Imaging Centre, San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Francesco De Cobelli
- Department of Radiology and Experimental Imaging Centre, San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
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247
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Ganeshan B, Miles KA, Babikir S, Shortman R, Afaq A, Ardeshna KM, Groves AM, Kayani I. CT-based texture analysis potentially provides prognostic information complementary to interim fdg-pet for patients with hodgkin's and aggressive non-hodgkin's lymphomas. Eur Radiol 2017; 27:1012-1020. [PMID: 27380902 PMCID: PMC5306313 DOI: 10.1007/s00330-016-4470-8] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Revised: 06/03/2016] [Accepted: 06/07/2016] [Indexed: 12/21/2022]
Abstract
OBJECTIVES The purpose of this study was to investigate the ability of computed tomography texture analysis (CTTA) to provide additional prognostic information in patients with Hodgkin's lymphoma (HL) and high-grade non-Hodgkin's lymphoma (NHL). METHODS This retrospective, pilot-study approved by the IRB comprised 45 lymphoma patients undergoing routine 18F-FDG-PET-CT. Progression-free survival (PFS) was determined from clinical follow-up (mean-duration: 40 months; range: 10-62 months). Non-contrast-enhanced low-dose CT images were submitted to CTTA comprising image filtration to highlight features of different sizes followed by histogram-analysis using kurtosis. Prognostic value of CTTA was compared to PET FDG-uptake value, tumour-stage, tumour-bulk, lymphoma-type, treatment-regime, and interim FDG-PET (iPET) status using Kaplan-Meier analysis. Cox regression analysis determined the independence of significantly prognostic imaging and clinical features. RESULTS A total of 27 patients had aggressive NHL and 18 had HL. Mean PFS was 48.5 months. There was no significant difference in pre-treatment CTTA between the lymphoma sub-types. Kaplan-Meier analysis found pre-treatment CTTA (medium feature scale, p=0.010) and iPET status (p<0.001) to be significant predictors of PFS. Cox analysis revealed that an interaction between pre-treatment CTTA and iPET status was the only independent predictor of PFS (HR: 25.5, 95% CI: 5.4-120, p<0.001). Specifically, pre-treatment CTTA risk stratified patients with negative iPET. CONCLUSION CTTA can potentially provide prognostic information complementary to iPET for patients with HL and aggressive NHL. KEY POINTS • CT texture-analysis (CTTA) provides prognostic information complementary to interim FDG-PET in Lymphoma. • Pre-treatment CTTA and interim PET status were significant predictors of progression-free survival. • Patients with negative interim PET could be further stratified by pre-treatment CTTA. • Provide precision surveillance where additional imaging reserved for patients at greatest recurrence-risk. • Assists in risk-adapted treatment strategy based on interim PET and CTTA.
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Affiliation(s)
- B Ganeshan
- Institute of Nuclear Medicine, University College London, Euston Rd, London, UK.
| | - K A Miles
- Institute of Nuclear Medicine, University College London, Euston Rd, London, UK
| | - S Babikir
- Human Health Division, Nuclear Medicine and Diagnostic Imaging Section, International Atomic Energy Agency (IAEA), Vienna, Austria
| | - R Shortman
- Institute of Nuclear Medicine, University College London, Euston Rd, London, UK
| | - A Afaq
- Institute of Nuclear Medicine, University College London, Euston Rd, London, UK
| | - K M Ardeshna
- Institute of Nuclear Medicine, University College London, Euston Rd, London, UK
| | - A M Groves
- Institute of Nuclear Medicine, University College London, Euston Rd, London, UK
| | - I Kayani
- Institute of Nuclear Medicine, University College London, Euston Rd, London, UK
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Cassinotto C, Chong J, Zogopoulos G, Reinhold C, Chiche L, Lafourcade JP, Cuggia A, Terrebonne E, Dohan A, Gallix B. Resectable pancreatic adenocarcinoma: Role of CT quantitative imaging biomarkers for predicting pathology and patient outcomes. Eur J Radiol 2017; 90:152-158. [PMID: 28583627 DOI: 10.1016/j.ejrad.2017.02.033] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Revised: 02/18/2017] [Accepted: 02/20/2017] [Indexed: 02/08/2023]
Abstract
BACKGROUNDS Patients with a pancreatic cancer amenable to surgery still have a poor prognosis and high risk of post-operative recurrence. We aimed to assess the value of quantitative imaging biomarkers using computed-tomography (CT) texture analysis to evaluate the pathologic tumor aggressiveness and predict disease-free survival (DFS) in patients with resectable pancreatic adenocarcinoma. METHODS We retrospectively performed attenuation measurements and texture analysis on the portal-venous phase of the pre-operative CT scan of 99 patients that underwent resection of a pancreatic ductal adenocarcinoma in two university hospitals. Tumor attenuation parameters included: mean attenuation value of the whole tumor (WHOLE-AV), and of the most hypoattenuating area within the tumor (CENTRAL-AV). Tumor heterogeneity parameters included: standard deviation, entropy, skewness, and kurtosis. RESULTS Tumor attenuation parameters showed significant association with the tumor differentiation grade (CENTRAL-AV, Odds ratio (OR) 0.968, 95% confidence interval (CI) 0.94-0.998) and lymph node invasion (WHOLE-AV, OR 0.886, CI 0.823-0.955). Variables associated with early-recurrence were: lymph node ratio (R2=0.15), kurtosis (R2=0.08), and CENTRAL-AV (R2=0.04). Lymph node ratio (Hazard ratio (HR) 1.02), and CENTRAL-AV (HR 0.98) were independently associated with shorter DFS. Patients with CENTRAL-AV<62 Hounsfield units had a shorter 1-year DFS (35% versus 68%, p=0.004). CONCLUSION Tumors that are more hypoattenuating on the portal-venous phase on CT scan are potentially more aggressive with higher tumor grade, greater lymph node invasion, and shorter DFS.
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Affiliation(s)
- Christophe Cassinotto
- Department of Radiology, Royal Victoria Hospital, McGill University Health Center, 1001 Boulevard Decarie, Montreal, QC H4A 3J1, Canada.
| | - Jaron Chong
- Department of Radiology, Royal Victoria Hospital, McGill University Health Center, 1001 Boulevard Decarie, Montreal, QC H4A 3J1, Canada.
| | - George Zogopoulos
- Department of Visceral Surgery, Royal Victoria Hospital, McGill University Health Center, 1001 Boulevard Decarie, Montreal, QC H4A 3J1, Canada.
| | - Caroline Reinhold
- Department of Radiology, Royal Victoria Hospital, McGill University Health Center, 1001 Boulevard Decarie, Montreal, QC H4A 3J1, Canada.
| | - Laurence Chiche
- Department of Visceral Surgery, Hôpital Haut-Lévêque, University Hospital of Bordeaux, 1 Avenue de Magellan, 33604 Pessac, France.
| | - Jean-Pierre Lafourcade
- Department of Diagnostic and Interventional Radiology, Hôpital Haut-Lévêque, University Hospital of Bordeaux, 1 avenue de Magellan, 33604 Pessac, France.
| | - Adeline Cuggia
- Department of Visceral Surgery, Royal Victoria Hospital, McGill University Health Center, 1001 Boulevard Decarie, Montreal, QC H4A 3J1, Canada.
| | - Eric Terrebonne
- Department of Digestive Oncology, Hôpital Haut-Lévêque, University Hospital of Bordeaux, 1 Avenue de Magellan, 33604 Pessac, France.
| | - Anthony Dohan
- Department of Radiology, Royal Victoria Hospital, McGill University Health Center, 1001 Boulevard Decarie, Montreal, QC H4A 3J1, Canada.
| | - Benoît Gallix
- Department of Radiology, Royal Victoria Hospital, McGill University Health Center, 1001 Boulevard Decarie, Montreal, QC H4A 3J1, Canada.
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Wagner F, Hakami YA, Warnock G, Fischer G, Huellner MW, Veit-Haibach P. Comparison of Contrast-Enhanced CT and [18F]FDG PET/CT Analysis Using Kurtosis and Skewness in Patients with Primary Colorectal Cancer. Mol Imaging Biol 2017; 19:795-803. [DOI: 10.1007/s11307-017-1066-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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250
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Liu Y, Liu S, Qu F, Li Q, Cheng R, Ye Z. Tumor heterogeneity assessed by texture analysis on contrast-enhanced CT in lung adenocarcinoma: association with pathologic grade. Oncotarget 2017; 8:53664-53674. [PMID: 28881840 PMCID: PMC5581139 DOI: 10.18632/oncotarget.15399] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Accepted: 01/31/2017] [Indexed: 12/21/2022] Open
Abstract
Objectives To investigate whether texture features on contrast-enhanced computed tomography (CECT) images of lung adenocarcinoma have association with pathologic grade. Methods A cohort of 148 patients with surgically operated adenocarcinoma was retrospectively reviewed. Fifty-four CT features of the primary lung tumor were extracted from CECT images using open-source 3D Slicer software; meanwhile, enhancement homogeneity was evaluated by two radiologists using visual assessment. Multivariate logistic regression analysis was performed to determine significant image indicator of pathologic grade. Results Tumors of intermediate grade were more likely to be never smokers (P=0.020). Enhancement heterogeneity by visual assessment showed no statistical difference between intermediate grade and high grade (P=0.671). Among those 54 features, 29 of them were significantly associated with pathologic grade. Multivariate logistic regression analyses identified F33 (Homogeneity 1) (P=0.005) and F38 (Inverse Variance) (P=0.032) as unique independent image indicators of pathologic grade, and the AUC calculated from this model (AUC=0.834) was higher than clinical model (AUC=0.615) (P=0.0001). Conclusions Our study revealed that texture analysis on CECT images could be helpful in predicting pathologic grade of lung adenocarcinoma.
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Affiliation(s)
- Ying Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Shichang Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Fangyuan Qu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Qian Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Runfen Cheng
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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